Hold the Phone! Factors affecting call centre customer and staff experience, and evaluation of an on-hold intervention to increase customer satisfaction and reduce negative affect.

Student: Isaac Malpass. Supervisor: Dr. L. S. Leland Jr.

Thesis submitted for completion of the degree of Master of Science (Psychology). Executive Summary Hold the Phone! Factors affecting call centre customer and staff experience, and evaluation of an on-hold intervention to increase customer satisfaction and reduce negative affect.

This thesis was written in an attempt to examine some of the issues facing New Zealand contact centres, and how they could be ameliorated through what customers listen to when they call through to the contact centre. The research was made up of four main parts:

Study 1: In the first study we surveyed opinions of customer service representatives from two call centres. We asked these frontline workers what their likes and dislikes of the job were, whether angry or abusive callers caused them problems, and how they thought the number of angry or abusive callers could be reduced.

Study 2: In the second study we set up a fake call centre experience and played different listening alternatives to participants at a time during which they were lead to believe that they were on-hold, waiting for service. We primarily examined whether these different listening alternatives had any effect on the participants’ mood or level of satisfaction during the wait.

Study 3 and 3a: This part of the research examined in further depth the more effective listing alternatives from the first study and, in addition, whether the accents of commonly used offshore outsourcing destinations affected participant mood, satisfaction with the call, and their appraisal of the operators.

Study 4: For those companies that may not have the technical capability to play anything other than simplistic on-hold music, this study was carried out to see what music a sample of undergraduate university students would prefer on-hold, compared to in a more informal public social setting.

i Key Findings

Study 1: Results showed that helping people is what the customer service representatives (CSRs) surveyed most like about their jobs. Dislikes included restrictions/limitations/strict guidelines, internal staff and equipment problems, relentless calls/time pressure, unreasonable/angry clients, roster/hours/pay and feeling dehumanised. Call centre staff believed that angry callers lead to longer call times (which can increase time pressure). One of the common suggestions to reduce the number of angry callers was to remove organizational messages or change music – altering the on-hold listening environment.

Study 2: Results of Study 2 indicated that there were no differences in mood for any of our eight trialled on-hold listening alternatives. There was a difference in satisfaction found. Those in a Straight-through group, who were connected after a very short delay, were more satisfied than those listening to any of the other conditions except a group that had a choice of options to listen to, and a group that listened to comedy. What makes this result astounding is that both the Choice and the Comedy group were waiting for five minutes compared to the ten seconds that the Straight-through group waited.

Study 3 and 3a: The results of Study 3 again showed no difference in mood among the top listening alternatives. The only difference in satisfaction that we saw in this study was that, given a closer look, the Straight-through condition gave a higher level of satisfaction than Comedy, Choice and a Pop music condition. The accents of the different speakers in Study 3 did not affect participant mood or call satisfaction. Study 3 and 3a results in combination showed that non-native English speakers were rated lower in competence than an America speaker, and lower in likeability than a New Zealand speaker

Study 4: This study showed that there were differences in what music was preferred on-hold compared to a more social setting. People reported they were willing to listen to Pop, Country, Sixties and Piano styles of music for the longest while on-hold. Follow up questions also indicated a preference for Pop music on-hold for the age group.

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Conclusions CSR suggestions and opinions appear to lend credence to the idea of using the on-hold period to reduce angry or unreasonable clients, one of the dislikes of the job that CSRs have, and one that they see as leading to longer call times (and possibly more time pressure). What is played on-hold was found to influence caller satisfaction in our experiments. Two promising on-hold listening alternatives in the form of a choice of listening options, and a comedy routine, were found. We believe that, for those that have the technical capability, a choice option may be the most practical. For those that do not have the technical capability to provide a choice, comedy routines (if practical) followed by pop style music played on-hold appear to be the next best bets for the age group we examined. Companies outsourcing to offshore countries may not need to be concerned that customer satisfaction or mood is negatively affected by the accent of the overseas operators. However, non-native English speakers may not be treated as seriously, or as amicably compared to native English speakers. We suggest training in either adopting a neutral English accent (as per Cowie, 2007), or that of the nation that is being serviced.

iii Acknowledgements

Firstly, I would like to give many thanks to Dr Louis Leland Jr for his help in the planning and analysis involved in the project, and overseeing the project through to completion. I would also like to thank Mr Brian Niven, Statistical Consultant, for several long consultations about the appropriate statistical analyses to use for various parts of the research. Special thanks go to Lynn Shawcroft, partner of the late Mitch Hedberg, for her blessing to use some of Mitch’s comedy in the studies. I would also like to thank Skye Hignett for her collaboration on the accent research project, and Sabrina Goh for her advice in the planning of that project. I would also like to acknowledge Andrew Mitchell as a sounding board for ideas. Many thanks to Rebekah Everdon, Mike Breicker, Sweta Arya, and Sigrid Lorraine Labidon for being voice actors at various stages of the research. Also, thanks to William van der Vliet and Hadyn Youens for their assistance in programming and setting up computer based testing. Thanks to Meric Hoffman, Richard Hamelink, Jeremy Anderson and the other technical staff of the Otago University psychology department for the creation of our telephone devices and behavioural testing apparatus. I would also like to thank Dale Watts from the Physics Department for lending the known weights used testing the behavioural apparatus in Study 3. In addition, I would like to acknowledge the help of the On-hold Messaging Association, Stressbusting.co.uk, Business Voice and On-hold Marketing New Zealand for responding to research inquiries and offering assistance. I would like to thank the management and staff of the Dunedin Fisher & Paykel global contact centre for their support; particularly Keith Campbell for his information and co-operation. I would also like to acknowledge all of those who made it a pleasure to work in the Leland lab and all those who took the time to participate in the studies. Finally I would like to thank my family and friends for their support throughout the process of researching and writing this thesis. Thank you.

iv Abstract The current research examined a number of issues relating to New Zealand call centres. We specifically focused on how the aural conditions that occur during a wait on-hold for telephone service could alter the affect and satisfaction of a caller. Four main studies were carried out. The first was a survey of customer service representatives (CSRs) to examine their opinions about the best and worst parts of their roles, and how they would reduce angry or abusive callers. The second was a trial of a number of different listening conditions, in a fictional call centre scenario, to examine their effects on participant mood and satisfaction. The third study further focussed on the most promising on-hold conditions from Study 2, and examined the effect that speakers of different accents might have on appraisals of the call, and the operator. Finally the fourth main study examined music preference on-hold compared to preference in more social situations. From Study 1 we found that some CSRs suggested altering the on-hold environment to reduce the incidence of angry callers. We also found that, although we did not alter mood significantly in any of our studies, the most promising on-hold listening condition was offering participants a choice of listening options for the effect it had on satisfaction. In Study 2 there was no significant difference in satisfaction between a choice listening group and a group that was put straight through to service. It is our hope that future research may use the promising on-hold listening conditions from the current research, put them in place in an applied setting, and monitor whether there is any evidence of a reduction in turnover or absenteeism in call centre staff. A further finding, that non-native English speakers are generally rated less favourably than native English speakers, and the preferences that undergraduates show for on-hold music are also reported and discussed.

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Table of Contents Executive Summary (lemon pastel pages) ...... i Acknowledgements ...... iv Abstract ...... v Figures ...... 4 Tables ...... 5 Hold the Phone! Factors affecting call centre customer and staff experience, and evaluation of an on-hold intervention to increase customer satisfaction and reduce negative affect...... 7 1. The Issues within Call Centres ...... 11 1.1 Turnover and Absenteeism ...... 11 1.1.1 Emotional Dissonance ...... 12 1.2. Procedures for Dealing with Angry Callers ...... 13 1.3. Aggressive Encounters ...... 15 2. On-hold Messaging ...... 18 3. Outsourcing/Offshoring ...... 21 4. Customer Satisfaction ...... 22 5. Intervention Techniques ...... 23 5.1. Reducing perceived wait time as a method of reducing negative affect/dissatisfaction ...... 24 5.2. Providing choice and increasing perceived control as a method for reducing negative affect and dissatisfaction ...... 28 5.3. Affect/mood regulation as a possible method to reduce customer negative affect and dissatisfaction...... 31 5.3.1. Music ...... 33 5.3.2. Humour and comedy ...... 34 6. The current work ...... 35 Study 1: Working in the call centre: Issues from a Customer Service Representative point of view...... 39 Methods ...... 42 Participants ...... 42 Apparatus ...... 42 Design ...... 43 Procedure ...... 43 Results ...... 44 Data collection ...... 44 Reliability ...... 45 Data analysis ...... 45 Data presentation ...... 46 Discussion ...... 54 Implications for research ...... 55 Limitations ...... 56 Reasons for the differences in responding between the call centres ...... 56 Summary ...... 57 Study 2: Reducing customer negativity and increasing customer satisfaction with a telephone service, through the use of an on-hold intervention...... 59 Methods ...... 62 Participants ...... 62 Apparatus ...... 63

1 Procedure ...... 65 Results ...... 70 Exclusion Criteria for group difference analyses ...... 70 Analyses ...... 70 Differences in satisfaction amongst listening conditions ...... 70 Differences in perceived control amongst listening conditions ...... 71 Group differences in Enjoyment/Dislike ...... 72 Group differences in mood ...... 73 Group differences in perceived wait ...... 74 Correlations of measured variables ...... 75 What may be the reasons that people become dissatisfied? ...... 77 Hang-up data ...... 80 Analysis ...... 81 Discussion ...... 82 Study 3: The effect of accent on appraisals of a call centre experience...... 89 Methods ...... 92 Participants ...... 92 Apparatus ...... 93 Design ...... 94 Procedure ...... 94 Results ...... 99 Self report ...... 99 Accent Likert type scales ...... 99 Profile of Mood States for accent ...... 101 Listening condition Likert type scales ...... 102 Profile of Mood States for listening condition ...... 104 Perceived wait (for accent and listening condition) ...... 105 Correlations ...... 105 Behavioural measure (button press) ...... 108 Apparatus test-retest reliability ...... 109 Choice analyses for behavioural apparatus ...... 109 Discussion ...... 111 Accent ...... 111 Listening condition ...... 113 Limitations ...... 114 Study 3a: Accent Appraisal ...... 116 Methods ...... 116 Participants ...... 116 Apparatus ...... 116 Procedure ...... 117 Results ...... 117 Discussion ...... 123 Study 4: Undergraduates self reported music preferences in an on-hold situation vs. a public social situation ...... 127 Methods ...... 129 Participants ...... 129 Apparatus ...... 130 Setting ...... 130 Design ...... 130 Procedure ...... 131 2 Results ...... 132 Exclusions ...... 132 Data Analysis and Presentation ...... 133 Discussion ...... 143 Length of time participants were prepared to listen ...... 143 Responses to questions ...... 144 Summary ...... 145 Overall Discussion ...... 149 Summary of research ...... 149 Turnover and Absenteeism ...... 151 Emotional Dissonance ...... 151 Angry Callers ...... 152 On-hold Messaging ...... 153 Outsourcing/Offshoring ...... 154 Customer satisfaction ...... 155 Intervention Techniques ...... 155 Reducing perceived wait time as a method of reducing negative affect/dissatisfaction ...... 155 Providing choice and increasing perceived control as a method for reducing negative affect and dissatisfaction ...... 156 Affect/mood regulation as a possible method to reduce customer negative affect and dissatisfaction ...... 157 Music ...... 157 Humour and comedy ...... 158 Limitations ...... 158 Major methodological limitations ...... 158 Research limitations ...... 159 Conclusions/Implications ...... 159 Best on-hold alternatives we examined...... 160 Mechanisms by which the listening conditions affect satisfaction ...... 160 Implications of the findings ...... 160 Future research ...... 162 Conclusion ...... 162 References ...... 165 Table of Appendices ...... 178

3 Figures Figure 1 : Categorical breakdown of respondents’ favourite part of being a CSR. Call Centre 1 is referred to as “Centre 1”, and Call Centre 2 as “Centre 2”...... 47 Figure 2 : Categorical breakdown of respondents’ greatest dislikes of the CSR position. Call Centre 1 is referred to as “Centre 1”, and Call Centre 2 as “Centre 2”. 48 Figure 3 : Categorical breakdown of how respondents deal with angry callers. Call Centre 1 is referred to as “Centre 1”, and Call Centre 2 as “Centre 2”...... 49 Figure 4: Categorical breakdown of how respondents deal with abusive callers. Call Centre 1 is referred to as “Centre 1”, and Call Centre 2 as “Centre 2”...... 50 Figure 5 : Categorical breakdown of whether respondents had ever had callers say they were angry or abusive due to what they have listened to while waiting for service. Call Centre 1 is referred to as “Centre 1”, and Call Centre 2 as “Centre 2”. ..51 Figure 6 : Categorical breakdown of whether respondents had ever had callers say they were angry or abusive due to what they have listened to while waiting for service. Call Centre 1 is referred to as “Centre 1”, and Call Centre 2 as “Centre 2”. ..52 Figure 7 : Categorical breakdown of what suggestions respondents had to reduce the number of callers becoming angry and aggressive as they wait for assistance. Call Centre 1 is referred to as “Centre 1”, and Call Centre 2 as “Centre 2”...... 53 Figure 8 : Flow chart of the time spent in different parts of a call for each condition .69 Figure 9 : Mean satisfaction ratings (+SE) for the different on-hold listening conditions...... 71 Figure 10 : Mean ratings of perceived control (+SE) for the different on-hold listening conditions...... 72 Figure 11 : Mean rating of Dislike (+SE) for the different on-hold listening conditions...... 73 Figure 12 : A percentage breakdown of the choices made by those allocated to the choice on-hold listening condition...... 79 Figure 13 : Percentage breakdown of the music which those who chose music listened...... 80 Figure 14 : Flow chart of calls in different listening conditions. This includes time to answer/connection to CSR (actual wait), and the total time it took for the call to complete, provided there were no hang ups (Total time). The accents were matched exactly for the time each statement took...... 97 Figure 15 : Mean Likert type ratings (+SE) of speaker competence across accent conditions...... 100 Figure 16 : Mean Likert type ratings (+SE) of dislike across accent conditions...... 100 Figure 17 : Mean Likert type rating (+SE) of speaker likeability across accent conditions...... 101 Figure 18 : Mean Likert type ratings (+SE) of satisfaction with the Psychnet telephone service...... 103 Figure 19 : Mean Likert type rating (+SE) of dislike of what was listened to on-hold, across listening conditions...... 104 Figure 20 : Mean competence ratings (+SE) for speakers with different accents...... 118 Figure 21 : Mean accent dislike ratings (+SE) for speakers with different accents. ..118 Figure 22 : Mean intelligibility ratings (+SE) for speakers with different accents. ...119 Figure 23 : Mean age estimates (+SE) for the different speakers. Actual ages are included for comparison...... 120 Figure 24 : Mean durations that undergraduates reported they would be willing to listen to different musical styles (+SE)...... 134

4 Figure 25 : Mean durations that participants report they would be prepared to listen to various musical styles when on-hold compared to in a public area (+SE)...... 135 Figure 26 : Mean durations participants of each gender reported that they were willing to listen to music in an on-hold situation compared to in a public area (+SE)...... 136 Figure 27 : Genres/styles that participants say they would like to listen to while placed on-hold. The “Expected Percentage” line shows what percentage a theoretical distribution of equal responding across categories would give...... 138 Figure 28 : Styles/Genre that participants reported would encourage them to hang up when placed on-hold. The “Expected Percentage” line shows what percentage a theoretical distribution of equal responding across categories would give...... 140 Figure 29 : Participants’ favourite genres/styles of music to listen to around friends. The “Expected Percentage” line shows what percentage a theoretical distribution of equal responding across categories would give...... 141 Figure 30 : Genres/styles of music that participants report they would never be seen listening to around friends. The “Expected Percentage” line shows what percentage a theoretical distribution of equal responding across categories would give...... 142

Tables Table 1 : Correlations of Measured variables, Age and Actual wait time...... 76 Table 2 : Parameter Estimates for Ordinal Regression of Satisfaction...... 77 Table 3: Abridged correlation matrix for self report measures and actual wait in Study 3. Continued on next page, with key...... 106 Table 4 : Crosstab examination of Questions 2 and 3...... 110 Table 5 : Correlations of the dependent measures in Study 3a...... 121 Table 6 : Means for correct vs. incorrect identifications of Country of origin on measures of speaker competence, likeability, intelligibility and accent dislike...... 122

5 6 Hold the Phone! Factors affecting call centre customer and staff experience, and evaluation of an on-hold intervention to increase customer satisfaction and reduce negative affect. Most of us will have to do it at some stage…..telephone through to an organisation’s call centre in order to achieve some form of goal, and end up waiting on-hold for an operator to come available to help us. While waiting do you find that you are getting angry or frustrated? You are not alone. Waiting for a service can be dissatisfying (Clemmer & Schneider, 1989; Davis & Heineke, 1998; Unzicker, 1999), and long waits can lead to feelings of anger and uncertainty (Taylor, 1994). The current research examines some of the issues the call centre industry faces, and our attempts to reduce the negative affect and dissatisfaction that may arise when a customer is placed on-hold. The second aspect of the research is relevant to businesses for two main reasons 1) Lowering negative affect may lead to greater satisfaction with a company, which in turn could lead to fewer customers switching service providers; 2) Lowering negative affect may decrease emotional dissonance and increase streamlining of calls for customer service representatives, possibly leading to lower stress and absenteeism levels. It is of interest to the general public for one main reason – they could have a more pleasant telephone/service experience! However, lowering negative affect for a caller may also be advantageous for the health of the caller. In a review of studies linking coronary heart disease and negative emotion between 1980 and 1988, Kubzansky and Kawachi (2000), saw a relationship between anxiety and onset of coronary heart disease. The authors also saw a trend linking anger and onset of coronary heart disease, but the studies reviewed did not show a clear relationship when hostility was controlled for. In a more recent review of studies, Buerki and Adler (2005) reported that a number of negative affective states including depression, hopelessness and vital exhaustion have been evidenced as independent risk factors for cardiovascular disorders. It may seem like an overstatement to propose a possible health risk to callers when the anger or anxiety created on-hold is a transient change in affect. However, if estimates that executives spend 17 minutes (USA Today, 1999) a day on-hold are accurate, then a negative state could be a daily occurrence due to being placed on-hold. On the internet, a CNN Survey is credited with finding that the average caller is commonly purported to spend 60 hours per year on-hold (On-hold Marketing Services Inc., 2010; Program Software

7 (voice call central), 2008)! If this correct, then the general public is also spending a great deal of time each year exposing themselves to potentially stressful experiences. At the very least, if not affecting the health of callers, a successful on-hold intervention may stop some in the wider community being in a bad mood! In the current work, feeling, emotion, and mood are included under the umbrella term of affect. In the APA Dictionary of Psychology affect is defined in part as: “ n. any experience of feeling or emotion, ranging from suffering to elation, from the simplest to the most complex sensations of feeling, and from the most normal to the most pathological of emotional reactions…” (“Affect”, 2007, p. 26) . Mood has in turn been defined as “ n.1.any short-lived emotional state, usually of low intensity (e.g., a cheerful mood, an irritable mood).” (“Mood”, 2007, p. 590). This means, like emotion and feeling, mood also can be thought of as an aspect of affect. In the current work, mood (which is measured in two of the studies) is seen as a transient affective state, and the person involved may not be able to verbalise the cause. In the field of marketing, there has been much research carried out into waiting for “face to face” interactions, with surprisingly little research in the area of telephone or “voice to voice” interactions (Unzicker, 1991; Whiting & Donthu, 2006). How customers feel, while physically waiting for a service in the form of interpersonal interaction, and the effect that the surrounding environment has on these feelings, has usually been examined. For example, research has been carried out into waiting conditions in lines at banks (e.g. Clemmer & Schneider 1989; Hui & Bateson, 1991; Katz, Larson & Larson, 1991), for flights at airports (e.g. Folkes, Koletsky & Graham, 1987; Taylor, 1994) and even in hospital waiting rooms (e.g. Pruyn & Smidts, 1998). In the fast paced world of today, call centre contact is often the first, and sometimes the major, source of interaction that a client has with an organisation (Gans, Koole & Mandelbaum, 2003; Unzicker, 1999). Anton (2000) proposes that where call centres used to be a secondary form of contact for a company, they are moving to the forefront as companies’ initial contact with clients. Both private companies and government agencies may use one, or several, call centres for their business. These may be managed locally or outsourced to other parts of a country or even overseas (Gans et al., 2003). In any given private company or government agency large enough, there may be several call centres dealing with

8 different aspects of the business e.g. general inquiries, accounts, and complaints; any of which could be the first interpersonal contact that a client has with a member of the organisation. At a glance, complaints or accounts may not seem like initial interpersonal contact points, but this is not always the case. For example, the first time a client makes contact with a manufacturer can be, following a purchase of some faulty equipment of their brand in a department store, with a call to their complaints line. Likewise, an initial call could be made to an accounts line e.g. a customer may be browsing telecommunications packages online and call through to a specific line for the type of account or package they wish to activate. It is important that telephone services (e.g. call centres) make a good first impression from a business perspective, as studies have shown that clients may switch service providers if they are dissatisfied due to the wait (Anton, 2000; Davis & Heineke, 1998; Tom, Burns & Zeng, 1997). There has also been a finding that it is wise to make a good impression in these encounters, not only because a customer may switch provider due to dissatisfaction, but because they may also be angered due to prolonged waiting and this may be a reason for an aggressive encounter to occur (Yagil, 2008). Although call centres are becoming a more likely point of first contact, they are not always going to be the first experience that a client has with a member of a company, especially with online communication (i.e. email) becoming so prevalent. However, if a call centre is acting as a secondary point of contact they may play a vital support role that can be instrumental in shaping client satisfaction (Feinberg, Kim, Hokama, de Ruyter & Keen, 2000). The aspect of having a support person that one can call (in some instances 24 hours a day) may also help create an impression that a company cares (Feinberg et al., 2000). In service encounters where there is a wait, this wait can be before service, while a service is occurring or in some cases following a service. These are termed pre-process, in-process and post-process waits (Maister, 1985). These have been described, in the past, in terms of face to face interactions. Taylor (1994) describes the scenario of customers of a restaurant waiting to be seated (pre-process), for their orders to be taken by a waiter (in-process) and following the meal waiting to pay (post-process). In call centres these same types of waits can also be seen. Let us take for example a ticketing call centre. To start, you could be waiting in queue for an

9 operator initially (pre-process). After you have spoken to an operator, they may place you on-hold while they find pricing and seating information on the computer (in- process). After attaining your goal (booking the tickets) you may find yourself waiting for confirmation that your credit card or booking has been accepted into the computer system following a purchase (post-process). In a study by Dube, Schmitt and Leclerc (1991), high school students in a classroom setting were examined to see their affective responses to having a wait during these different phases of service delivery, i.e. their teaching being interrupted, for 8 minutes. These interruptions occurred for different groups prior to teaching really beginning, midway through a lesson or at the end of a lesson (but 15 minutes prior to the lesson finishing). There was also a control group which had no interruption. Questionnaires were filled out part way through a class. Questionnaires were administered at the end of eight minutes of wait time, or equivalent period, asking participants to rate from one to nine how four positive affect states and four negative affect states applied to them at that time. Composite negative affect scores were calculated by adding the ratings of the negative affect states. This was also done for the positive affect ratings. The authors found that there was an increase in composite negative affect in the pre-process condition wait (16.10), and post-process wait (19.92), but none for in- process waits (11.13) compared to the control group (10.72). Evidence from Dube et al. (1991) suggests that there should have been negative affect as a consequence of this wait. However, the classroom environment of this study may not be very generalizable to other environments. High school students may wish to have a break more than other populations – this could in fact explain why negative affect did not increase in the in-process waits, as they were glad to be interrupted during class. The current study examines the effects of altering aural stimuli during a pre- process wait, as this is seen as an important period where unnecessary negative mood or affect may be reduced. Not only did Dube et al. (1991) report that the pre-process waits lead to negative affect, but Maister (1985) also proposed that “pre-process waits feel longer than in-process waits” (p.116) and cause greater impatience (relating to perceived wait).

10 1. The Issues within Call Centres Working in a call centre is a unique experience with unique pressures and issues facing employees. All call centres are not created equal. There are different types of call centres, which deal with different service requirements. An outbound (sales based) call centre, for example, involves predominantly calling prospective customers. An inbound call centre, in comparison, is a service group which receives calls but does not make outbound calls (Zapf, Isic, Bechtoldt & Blau, 2003). If a large number of callers call through to an inbound call centre at any one time, there are only a finite number of customer service representatives to answer these calls, and the caller may find themselves on-hold, waiting for service, and developing a negative mood. In the current work Studies 2 and 3 replicated an inbound call centre situation experimentally for this reason.

1.1 Turnover and Absenteeism Call centres are reported to have ‘high’ employee turnover rates. For example, in a recent global call centre report Holman, Batt and Holtgrewe (2007) found that in a comprehensive survey, which saw 40 researchers from 17 countries contributing to the report, the median turnover rate for a sample of almost 2,500 call centres was 20% a year. Other estimates place call centres commonly above 30% in worker turnover (Taylor & Bain, 1999). However, there is a great deal of variation in call centre turnover among countries, and organisations. Holman et al. (2007) noted that turnover ranged from 4% to 40% in the various call centres they examined. In New Zealand the overall average worker turnover rate, as of 2008, was 17.2% (Statistics New Zealand, 2008, p. 2). This figure was arrived at by creating a ratio of the average number of accessions (employees joining employment since the previous quarter) and separations (employees leaving employment since the previous quarter) and the average number of jobs in the current and previous quarter. As a rough comparison, Boyte (2009) found a turnover rate of 21.66% in a survey of 20 New Zealand call centres. Further, the New Zealand Contact Centre Industry Benchmarking Report (2007, as cited in Boyte, 2009) found a turnover rate of 24% in New Zealand contact centres. The calculation used to find out the turnover rate could well be different to that used by Statistics New Zealand, so it is hard to compare to the national cross-industry turnover average, but it does indicate that New Zealand is

11 close to the global median call centre turnover. Boyte (2009) estimated that the cost of call centre turnover in New Zealand call centres surveyed in her study at over $NZ67 million yearly. Absenteeism is also said to be very high in call centres (Lewig & Dollard, 2003). In their global report, Holman et al. (2007) found that, across the nations they examined, there was a median number of 6 days taken as sick leave by each call centre worker each year. Studies carried out in Australia (Direct Health Solutions, 2008), recently showed that call centres there had a higher absenteeism rate than average absenteeism rates for other professions. In New Zealand, CSRs average 8 days sick leave each year (New Zealand Contact Centre Industry Benchmarking Report, 2007; as cited in Boyte, 2009). Boyte (2009) estimated that the absenteeism (at 7%) costs the contact centre industry in New Zealand over $57 million a year. Statistics New Zealand does not collect data relating to sick leave specifically (Mike Moore, Respondent advocate, Statistics New Zealand, Personal Communication, Sep. 4, 2009).

1.1.1 Emotional Dissonance Because of the suggested high personnel turnover and absenteeism that occurs within call centres, several studies have examined the working conditions of call centres to look at possible reasons for these trends. It appears, call centre workers often have to engage in some form of emotional regulation (Holman, Chissick & Totterdell, 2002; Totterdell & Holman, 2003; Zapf et al., 2003). This involves showing a certain demeanour (in call centres usually empathy and positive affect) at all times as a part of their position, regardless of the emotion of a caller, or their own emotions (e.g. angry, annoyed or upset). Workers themselves may be frustrated and angry, but have to project opposing emotions (Grandey, 2000). Emotional regulation can be ‘deep’ or ‘surface’ acting. Surface acting regulation is modifying observable expression of emotion; deep acting is modifying one’s actual internal emotions. Surface acting has been found to have a negative impact on workers wellbeing (e.g. Holman et al., 2002; Totterdell & Holman, 2003). The projection of outward emotion that differs from one’s true emotion, seen in surface acting, causes a state known as emotional dissonance, which has been shown

12 to uniquely contribute to the prediction of irritable responses and psychosomatic issues within call centre workers, i.e. CSRs (Grebner, et al., 2003). Emotional dissonance has also shown positive associations with intentions to resign, an “inability to switch off” and negative associations with job satisfaction and an affective commitment to an organisation (Grebner et al. 2003, p.355). Lewig and Dollard (2003) examined effects on emotional exhaustion that a number of emotional labour factors had. These included the frequency and duration that sensitivity (or empathy) was required, the ability to control the length of an interaction, and emotional dissonance. Of these factors, emotional dissonance accounted for the most variance in predicting emotional exhaustion and job dissatisfaction. Emotional dissonance also accounted for the same amount of variance in predicting these outcomes as psychosocial stress factors, one of the most researched contributing factors to work stress and job satisfaction (Lewig & Dollard, 2003). Furthermore, when psychosocial stress factors were high, emotional dissonance was found to intensify the level of emotional exhaustion. Lewig and Dollard also found that 21% of call centre workers surveyed revealed that they feel emotional dissonance a number of times every hour. In an unpublished piece of research, Halik, Dollard and de Jonge, 2003, cited in Lewig & Dollard (2003) found that there was a significant correlation (r = .24, p<.05) between emotional exhaustion and absenteeism. Grandey, Dickter & Sin (2004) have also reported a similar trend. Angry or aggressive encounters may hurt both the company and the client. A client may feel vindicated after giving a phone operator a verbal dressing down, and there is evidence to suggest that making complaints can be beneficial to a client and is useful for a company’s feedback (Nyer, 2000). A complaint may even result in the company fixing a service breakdown (Bougie, Pieters & Zeelenberg, 2003). This said, it may actually take an angry, complaining, customer longer to get to their end goal of service, and service could even be withdrawn if comments made are aggressive or insulting enough (Matthew Lee, Customer Service Representative, Personal Communication, Jun. 12, 2009).

1.2. Procedures for Dealing with Angry Callers These customer complaints are sometimes about on-hold music or messages, and can become quite irate. In at least one large call centre in New Zealand it is part

13 of common practice to terminate a call if a customer becomes personally abusive to a call handler, whatever the reason may be (Inbound call centre team manager, Personal Communication, Jun. 17, 2009). In the call centre, if on-hold for a period of time, callers have complained simply because of what they were listening to on-hold, before a customer representative can even ascertain the purpose of the call (Matthew Lee, Customer Service Representative, Personal Communication, Jun. 12, 2009). A number of callers have asked to have calls escalated through to management for the same reason (Inbound call centre team manager, Personal Communication, Jun. 17, 2009). Through a simple online search, one can see that call centre management advice on dealing with angry callers is often to let them speak and give them time to share frustration, before trying to help with service (e.g. Filek, 2006; Gunelius, n. d.; Thompson, n. d). Filek (2006) gives a good example of this type of technique, describing a six step process to deal with angry callers. Letting customers vent, empathizing, asking questions to ensure that their concerns are recognised, paraphrasing what the issues are, offering alternative solutions, then finally summarising and following through with any promises made, are the steps suggested by Filek (2006). This technique may be a good way to deal with the angry caller, but letting a customer vent may increase call time. This presents an issue, as not only could there be emotional reaction to the nature of the angry call for a call centre employee (Yagil, 2008), and emotional dissonance (Grebner et al., 2003; Lewig & Dollard, 2003), but also there is a stressor created due to time pressure (Grebner et al., 2003). Employee job performance in some inbound call centres is measured, in part, by whether employees can keep to strict criteria that are monitored electronically (Zapf et al., 2003) that can include keeping call times short. The trade off between calming/satisfying a customer and continuing to take a high number of calls daily is a common tension within many call centres (Russell, 2008). This trade off between a high rate of call completion and high quality of service has been described as the “quantity/quality dilemma” (Taylor & Bain, 1991, p. 111).

14 1.3. Aggressive Encounters Physical aggression is not possible in a voice to voice interaction; however, a client is able to be verbally aggressive or abusive. Even one team manager in an inbound call centre I spoke to, who has worked as a customer service representative and has experienced the abuse that can come from angered callers, admitted to being less compassionate when speaking to call centre workers than if she were speaking to someone face to face. She is amazed at how ready people are to be verbally aggressive communicating via telephone (Inbound call centre team manager, Personal Communication, Jun. 17, 2009). She stated that she thought the relative anonymity and distance in the interaction was what may cause this. Related to this idea is some of the work done by Milgram (1974) in his famous obedience experiments; in which participants were instructed to shock an actor, up to what appeared to be a dangerous level, using a series of switches with incrementally higher voltages. As the experiment progressed, the actor screamed and hollered for them to stop (though no real shocks were given). The proximity of the actor was altered in a series of these experiments. In the original remote experiment feedback from the actor was via speaker. In a voice feedback experiment, the actor could be heard screaming through the wall. In the proximity condition, the actor could be heard and seen a few feet away. In the final experiment, the participant was instructed to hold the actor’s hand to a shock plate to administer the shocks. As the level of proximity increased, so too did the number of participants who refused to see the experiment through to completion. From 35% in the remote experiment, to 37.5% for direct voice feedback; 60% for proximity and 70% for touch proximity. Milgram (1974) lists several possible explanations for the proximity effect, some of which would hold true in a call centre situation to explain an aggressive caller. Those particularly relevant in the call centre situation are as follows: 1. a lack of empathic cues, as a result of being unable to visually observe the victim. 2. Denial of inflicting harm and narrowing of the cognitive field, due to only sporadic verbal feedback from the victim. 3. Incipient group-formation. This sees the call centre worker as a member of the out-group and therefore discriminated against. In the Milgram experiments the experimenter and the participant were proposed to form a group which makes the victim an outsider, unless they were proximal enough to form a group with the participant. There is no experimenter in the call centre scenario, but

15 the call centre staff could well be considered a part of the out-group, as they are not a customer/client, but are a part of a ‘company’ group. In call centre situations the phrase “you people” used in place of a worker’s name seems prevalent, and the worker falls into this category automatically, simply due to working for a company (Simon Humphries, former Customer Service Representative, Personal Communication, Sept. 22, 2009). 4. Acquired behaviour dispositions. People have acquired behaviour through learning that if they are to strike someone, and that person is in close proximity, there could well be retaliation. However, due to distance (and policy) in call centres these acquired dispositions can be ignored. A possible cause for the aggressive interactions that can occur in call centre interactions is a modified version of the Frustration-Aggression hypothesis first postulated by Dollard, Miller, Doob, Mowrer & Sears (1939). Berkowitz (1989) proposes in his slightly modified relationship between aggression and frustration, that frustration is basically an impediment to reaching the end goal of an exercise. In the case of the current research, that would mean having to wait on-hold before getting through to an operator for service (the end goal). Berkowitz (1989) presents the argument that frustration causes such an aggressive tendency in a person that it leads to a general negative affect. He argues against the idea that aggression arises from frustration only when there is a personal attack felt, or when it would be useful for some reason. In the call centre interaction, it may indeed be true that this is the case. It is not a personal attack on the client that they have to wait (as everyone does if the lines are busy) nor is it often useful to be aggressive when they get through to an operator, as this may mean that a call may be disconnected, or could see the client take longer to get to an end goal of service because of vocalising their aggression (Matthew Lee, Customer Service Representative, Personal Communication, Jun. 12, 2009). However, clients may not take into consideration that others are also having to wait, and using this egocentric view, may feel that they are suffering some form of personal attack from the organisation that they are on-hold with, as waiting for a reasonable time is seen as an element of trust (Burgers, de Ruyter, Keen & Streukens, 2000). Berkowitz (1999) puts forth a summary of a cognitive-neoassociationistic approach to the formulation of anger that may occur. An aversive event leads to general negative affect. This causes a primitive form of processing to occur and basic

16 cognitive associations to be made. These processes lead to a fight or flight type of physiological response. The aggression related tendencies associated with a fight response lead to rudimentary feelings of anger. Higher order processing (cognitive reasoning) then occurs and anger, irritation, or annoyance may be manifested, and directed toward a convenient target following this reasoning. In the long term, customer service representatives who experience a high rate of unpleasant and aggressive calls can become stressed. If they do not have appropriate coping mechanisms in place, they have been found to take more time off work than those who experience fewer aggressive calls, and are also more likely to experience emotional exhaustion (Grandey et al., 2004). Grandey et al. also examined completed surveys which asked 198 call centre workers from two different organisations how many hostile or aggressive encounters they had during a day. The spread was between zero and 50, with a mean of 7.02 encounters per operator per day and a mode of 10. Customer service providers, who experience aggression or sexual harassment on such a regular basis, may become intimidated, depressed and stressed (Yagil, 2008). Because customer service representatives have a primary role of serving the customer and trying to provide customer satisfaction, they may experience an overwhelming sense of failure caused by interaction with overly angry and hostile customers (as well as emotional dissonance). They may even have long term effects of anxiety when aggression has been severe (Harris & Reynolds, 2003; Yagil, 2008). Ultimately, a business may suffer due to absenteeism of staff and other workers may become stressed trying to fill the void left by the absent worker (in a call centre, increased numbers of calls for other operators to answer). The customers may also suffer, as fewer operators can mean longer wait times that could lead to a greater incidence of customer frustration and anger. One area which may be modified to help alleviate anger in customers, and therefore emotional dissonance and emotional exhaustion in call centre workers, is the period which customers spend on-hold. If something may be done to reduce negative affect or negative mood as much as possible when a customer is on-hold, this may lead to fewer hostile interactions and instances of emotional dissonance in the call handlers. This period may also be used to try and create satisfaction with service for the customer.

17

2. On-hold Messaging There are a number of companies that offer the service of producing on-hold messaging and music for businesses. Messages may be used for a host of reasons including entertainment, information and marketing (Business Voice, 2009). The logical reasons for using on-hold messaging for marketing purposes were well summarized by the director of one of these companies. its [sic] inexpensive (less than a cup of coffee a day), can be quickly & easily updated and guaranteed to reach your target audience (they wouldn’t be calling if they weren’t interested in what you had to offer) with 100% of their attention (it’s a one on one medium) unlike other traditional advertising mediums (Suzie Jones, Managing Director, On-hold Marketing Limited, Personal Communication, Nov. 4, 2009). These on-hold messaging companies tend to have some amazing statistics on their websites, which, unfortunately, often do not have very good source references. For example, "Executives spend 15 minutes a day or 68 hours a year on-hold." - USA Today (On-Hold Marketing Ltd, 2004; On-hold Messaging Association, 2009). Messages on-hold (2005), give this same statistic, but cite the source as CNN news. A search of the USA Today publication from 1988 to the present using the key words ‘on-hold’ and ‘executive’ found a single article stating that “the average executive spends 17 minutes on-hold each day” (“Please Hold” Makes callers fume, USA Today, 1999, p.6). If this is the article referred to originally, then the quote is incorrect. Another example of a great statistic is “On average, seven out of ten callers are placed on-hold.” - Inbound/Outbound (On-Hold Marketing Ltd, 2004; On-hold Messaging Association, 2009; Messages On-hold, 2005). Though this is given in quotation marks on several of the websites, this ‘quote’ was not found when entered in the database of the Inbound/Outbound magazine past issues (now Call Center Magazine). Neither was an article that contained the information when the sentence keywords are searched. Remember the statistic I spoke of earlier that the average caller spends 60 hours a year on-hold? This was reported to come from a CNN Survey (On-hold Marketing Services Inc., 2010; Program Software (voice call central), 2008). Searching CNN for any mention of this was an exercise in futility. Even this astounding statistic may be misrepresented. If it does exist, who knows how out of date it may be?

18 In an attempt to find where some of the statistics came from, I contacted a number of the companies listing these statistics, though few replied to me to explain where they sourced their information. The On-hold Messaging Association (On-hold Messaging Association, 2009) was helpful in suggesting places to look but could not point me to specific literature. On-hold Marketing New Zealand (On-Hold Marketing, 2004) also tried to help, and advised that many of their statistics came from the United States (Suzie Jones, Managing Director, On-Hold Marketing Limited, Personal Communication, Jul. 29). In a response by Business Voice (Business Voice, 2009), I was told that some of the older statistics had been simply recycled around companies on the internet, and errors in statements and sources had crept in due to this (Jerry Brown, President, Business Voice, Personal Communication, Oct 31, 2009). I also contacted organisations that were cited as sources for these statistics. One organization replied, saying they had not carried out a study which one such website (www.sayitonhold.com), suggested they had. Say it On-hold (Audio Advantage, 2008) claimed the North American Telecommunications Association conducted studies showing “callers hang-up the fastest during silence on-hold; callers will hold approximately 30 seconds longer hearing music; callers will hold up to 3 minutes longer hearing music with information”. When contacted, the North American Association of Telecommunications Dealers (sometimes known as the North American Telecommunications Association) advised they had done no such study (Customer Services, North American Association of Telecommunications Dealers, Personal Communication, Jul. 20, 2009). It is possible that a group with a similar name carried out the study though. On a related matter, an Australian company makes the statements: A national study published by leading telecommunications associations conclude; Callers with SILENCE-ON-HOLD will abandon their calls in less than one minute, 90% hang-up within 40 seconds. Callers with MUSIC-ON-HOLD will stay on the line 30 seconds longer than silence. Callers with INFORMATION-ON-HOLD will stay on the line for up to 3 minutes longer (Evolved Sound, n. d.). It seems eerily similar to the information that is meant to have come from a North American Telecommunications Association, yet is being reported as a national study published in Australia. In one claim, British airways’ in flight magazine, ‘Business Life’ was said to report “As many as 30 million calls get abandoned every year in the financial services

19 sector alone, by callers irritated by (amongst other things) uninformative queuing systems and music on-hold” (On-Hold Marketing New Zealand Ltd, 2004). British airways was eager to help in supplying articles from the magazine if I could provide the volume number the information was from, but unfortunately, this was never found. Certainly most of these findings appear not to be from academic and peer reviewed literature, but from company surveys, many which are now, seemingly, unavailable. Refreshingly, Business Voice was able to inform me of a number of statistics from case studies they themselves had done. For example, “Pilkington AGR reported that sales of their non-glass products increased 100% 30 days after they implemented a national On-hold Marketing program from Business Voice” (Business Voice, 2009). A number of statistics cited on these websites come from magazine publications where the sources of statistics are just as mysterious. For example, even in the USA today article, no reference was given as to where the estimate of time executives spend on-hold came from. As such, the statistics given in support of on- hold messaging by some of these production companies may be considered contentious. The studies described below, undertaken as a part of the current work, should give some guidance to businesses as to whether they should believe the hype surrounding these production companies, or if one is possibly better off without on- hold messaging or music. Alternatively, this paper could become a resource for the production companies that sell this type of product. I find this area of great interest because personal communication has often pointed in the opposite direction, with people disliking on-hold messaging (e.g. Kelly Lindsay, Customer Service Representative, Personal Communication, Jun. 12, 2009; Callumn James, Customer Service Representative, Personal Communication, Jun. 12, 2009). The production of on-hold messaging varies among companies. Some overlay music and information (Messages on-hold, 2005), and others report that this should definitely not be done (Will, n. d.). This variation may set on-hold messages apart from each other in efficacy of holding on to customers. Also, one of the main issues people have with this messaging is the repetitive nature, and the inappropriate nature, of some of the messaging e.g. trying to advertise products on a complaint or repair line (Callumn James, Customer Service Representative, Personal Communication,

20 Jun. 12, 2009). These frustrations are less to do with the messaging itself, but the potential misuse of this messaging by some companies. Not only personal communications attest to the fact that customers may not like having advertising information playing while on-hold. An Associated Press-Ipsos poll found that half of their sample would lose patience with waiting for service after five minutes, and three quarters of their sample did not want to hear advertisements during this wait. Customers in this poll did want to hear estimates of wait time (Herring, 2006). A study that lends credibility to this suggestion was carried out by Munichor and Rafaeli (2007), and examined whether customers placed on-hold were more satisfied following music, music with apologies, or music with information regarding their place in queue while they waited. Music with apologies lead to significantly lower satisfaction ratings than music with feedback information. There was no difference between the music and the music-apology condition. However, an Aspect software survey found that customers did not appreciate having an automated estimate of wait time. Most companies do not use these either, though they are a standard feature in call distribution technology these days, as they do not wish people to know how long the wait is (Stockford, 2006).

3. Outsourcing/Offshoring Another factor that is of interest is the effect of outsourcing on customer perceptions of a call centre experience. Outsourcing and offshoring are similar terms, and used somewhat interchangeably in the literature. “Outsource” is defined by Merriam-Webster as “to procure (as some goods or services needed by a business or organization) under contract with an outside supplier” (“Outsource”, 2010). This does not necessarily mean that the outsourcing will occur offshore (though it may). Offshoring is defined in the Cambridge Advanced Learner’s Dictionary as “the practice of paying someone in another Country to do part of a company's work” (“Offshoring”, 2010), thus is simply outsourcing to another nation. Sometimes the term “global outsourcing” is used rather than “offshoring” (e.g. Elmuti & Kathawala, 2000). Some of the major reasons for offshore outsourcing include decreasing costs (Elmuti & Kathawala, 2000; Ferguson, McCracken, Kussmaul & Robbert, 2004; Khan, Currie, Weerakkody & Desai, 2003), increasing service quality (Elmuti &

21 Kathawala, 2000), increasing freedom to recruit/terminate staff as required, accessing expertise in a certain area, and in the United States, at least, allowing for service 24 hours a day (Ferguson et al., 2004). By having an offshore call centre, overseas workers can serve clients while those in their home county sleep. There are, however, other factors for business to consider prior to deciding on outsourcing a call centre service to another nation. Something as simple as the accent of the operator in an offshore call centre may prejudice a caller to such an extent that their perceptions of a call are more negative. Past research and reviews have shown accents can affect customer satisfaction (Stringfellow, Teagarden & Nie, 2008), affective response (Bresnahan, Ohashi, Nebashi, Liu & Shearman, 2002), brand perception (Bennett & Loken, 2008), and purchasing intentions (DeShields & de los Santos, 2000). This is an important issue in New Zealand as, in the past few years, Yellow Pages (NZPA, 2009), Telecom (Campbell, 2009), Vodafone (Drinnan, 2009), and TelstraClear (Speedy, 2009), have all outsourced to the Philippines. Public backlash was so great from Australian customers to a Telstra Manila call centre, that they closed it after a period of around six months (Keall, 2009). The backlash towards offshore call centres may be influenced, in part, by accent. Studies 3 and 3a of the current work examine the effects that accent may have on appraisal of a call centre experience, and the theory underlying these effects.

4. Customer Satisfaction Aside from the issues facing frontline call centre staff, what occurs on-hold may be important due to the impact it has on customer satisfaction. Customers may switch providers if dissatisfied, as mentioned earlier, meaning a loss of potential business for a company. A very simple general relationship between customer satisfaction and wait time has been shown by several studies that suggest longer actual wait time leads to decreased customer satisfaction (Davis & Heineke, 1998; Davis & Vollman, 1990; Clemmer & Schneider, 1989; Tom et al., 1997). However, what occurs during a wait may influence a client’s mood (e.g. Cameron, Baker, Peterson & Braunsberger, 2003), perception of wait time (e.g. Whiting & Donthu, 2006), and also their satisfaction with their interaction with a company, or service (e.g. Davis & Heineke, 1998; Pruyn

22 & Smidts, 1998; Tom et al., 1997). Therefore, if a long wait is unavoidable, the best a company may be able to do to make a good first impression is to ameliorate the negative effects of the waiting experience as much as possible by changing what occurs during the wait. Maister (1985) critically proposes that satisfaction during a wait does not just depend on the actual amount of time that is waited, but also on the experience of the wait. Therefore satisfaction does not only depend on how short a wait is, but when it occurs, what one is waiting for, and what occurs during the wait. He had little experimental evidence as a basis for his statements, but turned to anecdotal evidence to create eight different propositions regarding the psychology underlying waiting in line. These are as follows: 1) Unoccupied time feels longer than occupied time; 2) Pre-Process waits feel longer than In-process waits; 3) Anxiety makes waits seem longer; 4) Uncertain waits are longer than known, finite waits; 5) Unexplained waits are longer than explained waits; 6) Unfair waits are longer than equitable waits; 7) The more valuable the service, the longer the customer will wait; 8) Solo waits feel longer than group waits. Though these were originally just propositions, work has subsequently been done that examines whether these propositions hold true, some of which will be discussed in the following section.

5. Intervention Techniques Several researchers have examined how waiting under various conditions can influence customer satisfaction with, or evaluation of, a company (Antonides, Verhoef & van Aalst, 2002; Davis & Heineke 1998; Tom et al., 1997; Whiting & Donthu, 2006). Fewer have dealt with what occurs surrounding the emotion or mood aspect of affect when customers are kept waiting, which will be discussed later. Studies 2 and 3 of the current research are designed to examine reducing the negative mood/affect and dissatisfaction that arises due to waiting for telephone service. Several theories of how one may try and reduce these factors are put forth. Theory and findings related to the operative concepts of perceived wait time, perceived control, and mood regulation are discussed.

23 5.1. Reducing perceived wait time as a method of reducing negative affect/dissatisfaction . There are two ways that marketers have examined increasing customer satisfaction by reducing wait time during a period on-hold. These include operations management and perceptions management (Katz et al., 1991). Operations management looks to try and reduce actual wait times through modifying company operations – altering the number of staff serving customers or changing queuing methods. Perception management does not reduce actual waiting time, but aims to reduce the perceived time that one has been waiting (Whiting & Donthu, 2006). Perception management can be used in situations where a wait is unavoidable due to staffing shortages or other extenuating circumstances. In our fast paced world, where time waits for no one and no one seems to want to wait for any period of time, making a necessary wait or transaction seem shorter is sometimes the best that a company can do (Katz et al., 1991; Dube-Rioux, Schmitt & Leclerc, 1989; Tom et al., 1997). Maister (1985, p.115) proposes that “unoccupied time feels longer than occupied time”. He also states that this effect depends on what the time is filled with, which is often overlooked in the literature. He suggests that what occupies a person’s time should be informative about the organization a person is waiting for (but mentions this may not be the best idea in intimidating situations like a dentist’s rooms). This idea of “filled time” while on-hold is important because it may change a person’s perceived wait time (Whiting & Donthu, 2006). It has been suggested that this perceived reduction in wait time is due to cognitive restraints. For example, it has been proposed that when there is nothing else to think about apart from how long a call is taking, it seems long. However, when something else engages cognition also, there may be less cognitive power focussed on the wait, so it is perceived as being shorter (Cameron et al., 2003). Zakay and Block (1997) propose an attentional gate theory of cognition to explain how prospective time estimates occur. Some form of gating mechanism is opened when an external stimulus indicates the beginning of a period of time. This gate allows a rhythmic signal through to a cognitive counter. When external stimuli indicate that a time period has concluded, this count is stored in working memory. The gate explanation allows for attention to be paid to other tasks, but the more complex the other task, the less the gate is activated. This means that the count

24 is lower when a complex task is being attempted and therefore estimation of time lower. Zakay and Block (1997) advise that this theory is for prospective time estimates, when participants are aware that they will be estimating time at the end of an experiment. However, a similar resource allocation model was proposed by Zakay (1989, as cited in Cameron et al., 2003) as a general explanation for how people may estimate time. An opposing theory is that if the filler during a wait time is too complex, that perceived wait time will be longer (Antonides et al., 2002). An explanation for why complex tasks may lead to longer estimates of time is that we measure time by the number of thoughts we have during any period. Using this storage size model, if we have many thoughts due to a complex task or changing types of stimuli we may estimate a wait as longer (Ornstein, 1969). Zakay & Block (1997) have a similar theory called the contextual change model, which proposes that the more contextual changes in the environment we have to think back to, the longer our estimate of time will be for this period. This is their theory for how participants retrospectively estimate time. Examining the attentional gate theory, we can see that just filling the wait period with any sort of stimulus may not mean that the perceived wait time will be less. The wait period would need to be filled with some sort of distracting element that effectively draws focus away from the passing of time. When waiting for a service, a customer’s perceived wait time can differ markedly from their actual wait time (Davis & Heineke, 1998; Katz et al., 1991). However, there are mixed findings as to whether or not filling a wait will actually reduce a perceived wait. Some studies have shown trends toward perceived wait time being lowered by filling an on-hold wait with music (Tom et al., 1997; Whiting & Donthu, 2006). However, these findings were not particularly convincing, as each of the studies consisted of two experiments, of which only one in each case showed a relevant reduction in perceived wait time, presumably due to the fillers. The type of filler that is used to try and reduce perceived wait, and possibly affect satisfaction, may have an influence on the efficacy of any intervention. For example, there is some evidence for music decreasing the length of perceived wait (Tom et al., 1997; Whiting & Donthu, 2001). But when television is readily available to watch while waiting, this has been associated with an increase in perceived wait. Wait times were overestimated by those who were able to watch television while they

25 wait (Pruyn & Smidts, 1998). It has been hypothesised that the Pruyn and Smidts (1998) finding may have been due to the complexity of the stimulus that was encountered during the wait (Antonides et al., 2002). There is, however an alternative explanation for the finding. Television generally has markers of time in the form of advertisements and programmes starting and finishing. These could fit in with the contextual change model, as each advertisement, and programme changeover, is another external indicator of time passing. In a very recent study by Peevers, McInnes, Morton, Matthews and Jack (2009), lower perceived wait and higher satisfaction with a bank phone service were found when a standard apology message was interspersed by music (from a recent advertising campaign), updated wait time estimates, or both, compared to when interspersed with ringing. However, this study asked customers to adopt a specific persona/situation and rate their satisfaction with each of the on-hold conditions based on their persona/situation. I believe that demand characteristics possibly could have been present due to this procedure. Participants may have picked up on the fact that they should prefer the conditions with more interesting aural excerpts rather than actually doing so. An important aspect of the Peevers et al. (2009) study was the addition of time as a variable in the ANOVA. Overall, the trends discussed above were found; however, when time was included as a variable, a slightly different pattern of results appears. For a wait of one minute, what was heard on-hold did not affect perception of the wait. For a wait of five minutes, only music showed a significant difference (increase) in perceived wait using the repeated measures ANOVA. Participants were more satisfied with music or updates compared to ringing when a wait was five minutes long, but only updates when the wait was one minute. The authors believe that this may be due to the average acceptable wait time being listed as 1.5 minutes, and that any wait over this will result in larger differences due to different aural stimuli. More people hung up when they were instructed that a wait would be two to five minutes. Apologetic statements may be played to a customer to try and inform the customer or to apologise for a wait. But is this reducing the perceived wait or increasing it? It appears to do neither. Apologetic statements have been examined as a possible way of increasing satisfaction by waiting in two studies by Munichor and

26 Rafaeli (2007). In one of the studies they used realistic telephone on-hold scenarios with either music, music interwoven with apologies, or music interwoven with progress in the queue being played. No significant differences in perceived wait were found among the groups. Hui and Zhou (1996) carried out an experiment where participants sat at a computer under the guise of testing a new computerised registration system for university courses. This registration comprised a number of steps. In between the steps, experimenters could control wait length, and wait duration information. Wait lengths of 8, 12 and 16 minutes were compared with, and without, informing participants of the expected wait length. No differences in perceived wait were found between the participants who had wait duration information and those who did not. One of the Maister (1985) proposals has not been tested. The effect that playing advertorial information specific to a business has on perceived wait appears not to have been examined in any depth. Although reducing perceived wait may be harder than Maister (1985) suggests, some studies have reported an inverse relationship between perceived wait time and satisfaction. If it can be reduced, it may be useful for this reason. Katz et al. (1991) found that increases in perceived wait lead to lower self reported levels of satisfaction and a greater feeling of stress while waiting for service in line at a bank. Whiting and Donthu (2006) carried out two experiments to examine the same relationship. In the first, participants were simply asked to recall a recent voice to voice encounter they had, what sort of features were provided on-hold, and whether they found the length of wait to be acceptable. Responses showed that those who had music playing believed that their wait was more acceptable and shorter, whether the respondents enjoyed the music or not. In a second experiment, a private call centre co-operated by providing different groups with different soundscapes while they were on-hold. Later that day experimenters called them and asked for information regarding call length, whether music had played, whether they enjoyed the music, and their satisfaction with the wait. In this experiment position in queue, estimated time to wait, and music in general had no significant effect on lowering perceived wait time. Only when music was enjoyed by the respondents was there a significant reduction in perceived waiting time. Importantly a correlation was seen in both studies that showed higher perceived waiting time was significantly related to decreasing satisfaction with the wait.

27 All but one of the principles set forth by Maister (1985) regarding the underlying psychology of a wait involve possible ways of reducing perceived wait. But, does reducing the perception of a wait really make the wait any better for the customer? Although it may be true that a shorter perceived wait does significantly influence the satisfaction of the customer in some cases, this relationship has not always been found. A number of studies have altered perceived waits, without seeing the relationship seen by Whiting and Donthu (2006). Hui, Dube and Chebat (1997) found that playing participants positively valenced (liked) music significantly increased the length of perceived wait that participants experienced, but that this perceived wait did not negatively influence approach behaviours toward an organisation. This study is explained in further depth on page 33. Attempting to reduce perceived wait time has not always been shown to lead to an increase in satisfaction, or a more enjoyable wait. In some studies a reduction in perceived wait has seen no improvement in customer satisfaction (e.g. Hui & Tse, 1996). In a review of 18 waiting studies from 1984 -1997, Durrande – Moreau (1999) found that manipulating situational factors during the wait (a technique in 14 of the studies) was a disappointing method of impacting on the way clients experienced their wait. This technique has been used in the past to try and reduce perceived wait time in face to face waiting studies, like playing music or showing television in a waiting room (e.g. Pruyn & Smidts, 1998) or in line waiting at a bank (e.g. Taylor, 1994). Maister (1985) proposed that discussing matters topical to the business that one is waiting on may be the best way to shorten the perceived length of a wait. This suggests that on-hold messaging would be the best way to shorten the perceived length of time on-hold. In the current experiment a group is included with this type of manipulation to see whether it will do what is claimed by Maister, and assumed by many businesses, or whether it increases the perceived wait length. Many businesses use this strategy, and there are a number of companies that deal in producing this kind of on-hold messaging. Experiment 2 of the current work compared informative fillers to others to see whether they were more or less effective in decreasing perceived wait length. 5.2. Providing choice and increasing perceived control as a method for reducing negative affect and dissatisfaction

28 Perceived control is a term which does not seem to have a solid definition and the theoretical frameworks used to explain its effects are varied (see Jacelon, 2007, for a review). For the purposes of the current study we will borrow the definition of Collins, Luszcz, Lawson and Keeves (1997, p 295): “People’s perceptions of how much control they have over situations in their lives”. This implies that a simple intervention can be put in place, increasing the control a person has in any given situation. What does seem to be relatively clear about perceived control is that a lack of it may negatively impact a person’s affect and general wellbeing; a finding that has been replicated in a number of studies (Jacelon, 2007; Rothbaum, Weisz & Snyder, 1982; Wortman, 1975). Choice is a key consideration of the current study, as it is a possible way to reduce negative affect or dissatisfaction due to a feeling of increased control. In the past, increased satisfaction has been linked to the addition of choice into a service environment (Clemmer & Schneider, 1989). Providing choice has also seen increased performance and personal investment in classroom settings and workplace safety routines (Geller, 2001). In a study of face to face banking by Clemmer and Schneider (1989) it was proposed, based on cognitive dissonance theory, that if customers were told when a bank was at its busiest, this would, in essence, give the customer a choice between going when it was busy or not. This then meant that the customer felt more responsible for, and in control of, the conditions of their own wait. The authors reported a significant effect of greater satisfaction due to this intervention. Choice of waiting condition has also been examined in a previous study of voice to voice interaction by Tom et al. (1997). In this piece of research, the authors asked three groups of participants to call a phone line for a fictitious company where they were placed on-hold for three minutes. One of the groups had silence while they were waiting, one had music interlaced with informative statements about the company, and the final group had a choice between the music condition and the silent condition. The findings of this study showed that participants rated the company more favourably and enjoyed their wait more in the choice condition than in the silence or music conditions. Those in the music condition rated the company more favourably and enjoyed their wait significantly more than those in the silence condition. The silence condition had a significantly longer perceived wait than the other two conditions; 5.9 minutes, compared to music (3.65minutes) and choice (3.45minutes).

29 In a second study by Tom et al. (1997), four conditions were set up; silence, Classical music, Jazz music or a choice consisting of silence, Jazz, Classical or a ringing tone. This second study had some interesting results. The choice group reported enjoying their wait significantly more than the Classical and silence groups, but did not significantly differ from the Jazz music group. This raises the question “Is preference or choice more important in deciding wait enjoyment?” They also found that enjoyment was significantly less in the silence group, than even just the phone ringing. Interestingly, in this second study no difference in perceived wait time was detected across conditions. This may be because they let people ring in their own time and from cell phones, whereas in the first study there were controlled laboratory conditions. It could mean that, in the second study, participants were filling their time with other activities while waiting – a type of choice in itself, allowing greater personal control. Tom et al. (1997) also asked participants how long that they would usually stay on-hold before hanging up. They found that about 3-4 minutes was the length of time that participants in each condition estimated that they would usually hold on for before hanging up. Note that this is much longer than the 1.5 minute acceptable wait reported by Peevers et al. (2009). Because of the 12 years that has passed between the two studies it is possible that immediate service has become even more important in driving down acceptable waiting time. This is, however, purely conjecture. Tom et al. (1997) did not suggest theoretical reasons why choice may have lead to more favourable satisfaction ratings, other than to refer vaguely to one past finding from Hui and Bateson (1991) that found perceived choice increased customer satisfaction. Hui and Bateson, in actual fact, see perceived choice as just one part of perceived control, and that providing customers with a choice increases their perceived control of a situation. They studied participants’ responses to banking scenarios where a customer had a choice of when to deposit money. In the first scenario, the deposit is not urgently needed, another time will suffice, and a machine is working that can be used to deposit the money. In a second scenario the customer was restricted to only the time that they were currently in the bank e.g. the bank is about to close and money must be deposited to cover a cheque that has already been written. The machine that could be used to deposit the cash is out of order. The positive effect that perceived control (caused in part by choice) had on pleasure and

30 approach-avoidance behaviours was found to be significant in participants rating the scenarios. In the Hui and Zhou (1996) duration information computer study, an example of increased perceived control without explicit choice is shown. Service quality was judged more favourably in the duration of wait information group. It was suggested by the authors that this was due to the increased levels of perceived control that subjects felt when their wait was more predictable in length. It was unlikely that wait perception had altered the service quality judgements, as no significant difference in perceived wait was found. Donnerstein and Wilson (1976) conducted a study relevant to the current research, which examined the link between aggression and perceived control. In one of their experiments Donnerstein and Wilson (1976) exposed a group of participants to blasts of high intensity white noise while they were working on arithmetic problems. There were three conditions. In one condition the participants were given no option of control over this sound. The second group was given the option to stop the sound should they wish, but it would be appreciated if they did not. None in the choice group stopped the sound. The third group was a control group with no noise. Following seven minutes of arithmetic, participants were then either angered through electric shocks (supposedly as a form of feedback following writing an essay), or were not shocked. Questionnaires to measure various aspects of the experiment were then applied. Results revealed that the angered participants who had no perceived control over noise had higher ratings of aggression than those with the choice and the control group. The latter two groups did not differ significantly. Interestingly the same trend was not evidenced in the non-shocked (non-angered) participants. If one was calling through to a call centre, becoming angry and feeling as if they were not in control of the situation, it is plausible that they may be more likely to become aggressive. Likewise, it is possible that increasing a caller’s perceived control of a situation could see a reduction in anger and possibly verbal aggression.

5.3. Affect/mood regulation as a possible method to reduce customer negative affect and dissatisfaction.

31 Although customer satisfaction is an important factor in marketing, the current research also aims to look at negative affect as a consequence of waiting on-hold, rather than just dissatisfaction by itself. Mood is not only a dependent variable in the current research, but also a possible mechanism for change. Mood or affective regulation during a wait may reduce negative affect and also act on dissatisfaction. In the current work, emotion and mood are treated largely as interchangeable terms, as they both relate to general affect. In two studies carried out by Bougie et al. (2003) anger (a form of negative mood) and dissatisfaction were found to be two discrete factors that combined to influence customer behaviour. In their study it was proposed that negative behavioural responses in a consumption setting, previously attributed to dissatisfaction, were not caused by dissatisfaction alone, but were in fact mediated by anger. To ascertain whether the two factors were distinct emotions, the authors used retrospective sampling techniques to compare thoughts, feelings, actions, action tendencies and emotivational goals (emotional motives; goals that accompany discrete emotions). Participants were asked to recall either an instance when they were angry with a service interaction or an instance when they were dissatisfied with a service interaction. They were then asked to answer some questions regarding their experiences in terms of feelings, actions, action tendencies and emotivational goals. Bougie et al. (2003) formed their questions based on past literature that suggested which feelings, thoughts etc. were associated with anger or dissatisfaction, e.g. “During the event did you have a feeling like you’d explode?” (A feeling expected to be associated with anger), or “During the event did you have a feeling of unfulfillment?” (A feeling expected to be associated with dissatisfaction). Participants responded to these questions on a scale of 1 (not at all) to 9 (very much). Participants were also asked to recall their behaviour at the time that was due to the incident. Bougie et al. (2003) found that the participants who recalled a time that they were angry in a service encounter answered with high ratings on the questions directed at gauging anger. Results showed these angered participants were likely to have leanings towards acting aggressively, being violent and wanting to get back at a company. This type of thought process actually tended to result in complaints and nasty comments in the recalled instance, rather than actual violence.

32 A second study by the same authors showed that anger had a mediating response on negative word of mouth, complaint behaviours and switching service providers that may have been caused by dissatisfaction. Using multiple regression analysis, the authors showed that dissatisfaction by itself only predicted switching providers, but was not a significant predictor of the other outcomes following the service encounter. Anger was found to be a significant predictor for all of these outcomes. Therefore, although they may be separate and discrete factors, the two seem to have some sort of relationship. The authors propose that dissatisfaction is, in fact, an antecedent to anger. As anger predicted the most negative outcomes in their results, it seems that regulating this might be the best way of reducing negative affect and outcome. However, because the two may be separate and discrete they were measured separately in the laboratory experiment that was a part of the current research (Studies 2 and 3). Negative affect has been seen as a product of waiting for service; however, simply filling this wait may reduce these negative feelings. Taylor (1994) studied the effects that a pre-process delay had in creating negative affect in airline passengers. Delayed flight passengers were asked to fill out questionnaires while they waited just prior to boarding their aircraft, and again at the completion of their flight. The first questionnaire included four questions asking passengers to relate on a 7 point scale from “not at all” to “very” how angry (irritated, frustrated, annoyed and angry) they were and how uncertain (anxious, uncertain, uneasy, unsettled) they were. A significant relationship was found between delay and anger. The longer a delay was the angrier a person became. The same sort of relationship between uncertainty and delay was also found to be significant. The more uncertain a person was, the angrier they became. In the same study Taylor (1994) asked participants to what extent their time had been filled during the delay. This factor had a significant effect on the feelings of uncertainty and anger, with more time perceived as being filled during the wait leading to less anger and uncertainty in passengers. A further finding of Taylor (1994) was that overall service evaluations were negatively related to anger and uncertainty.

5.3.1. Music

33 Music is one filler that has been used in the past as a way to try and reduce arousal due to stress. A meta-analysis carried out by Pelletier (2004) examined the efficacy of music in reducing stress. The term stress in the analysis encompassed measures of anxiety, negative emotion and physiological arousal. Overall stress was reduced by the music in the 22 studies examined. Although passive listening to music was effective in reducing stress, the most effective method was to use music as a part of relaxation techniques. A study by Cameron et al. (2003) examined the effect that filling a wait with music has on mood. They played music to some participants waiting for an experimenter to return during an experiment. They then asked participants to rate the likeability of the music, their mood, their evaluation of the experience, and an evaluation of wait length. Mood was found to be related to likeability of the music. Mood was found to be the only significant predictor of evaluation of the entire experience. Hui et al. (1997) asked participants to watch a video of a bank waiting queue and imagine they were the next person in that line. They played either positively or negatively valenced music that had been rated as having low familiarity and high likeability during this wait. Participants were then asked to estimate their wait time, to emotionally evaluate their wait (rating whether it was stressful, tense, rushed) and to give an emotional response to their wait (rating whether they were irritated, frustrated or dissatisfied), and whether they would recommend this bank to others and continue to go there themselves. They found that both positively and negatively valenced music positively affected the emotional evaluation of the situation which, in turn, increased positive approach behaviours towards the organisation. This effect was stronger for the positively valenced music.

5.3.2. Humour and comedy The current research makes use of humour as a form of mood regulation to try and reduce the negativity of customers waiting for service. Humour has been used in the past to try and influence affect with some success; both creating positive emotion, and reducing negative mood (Martin, 2007). Thus it seems that comedy could be a mood regulator as well as being a filler that could reduce perceived wait time.

34 Danzer, Dale and Klions (1990) attempted to reduce induced depression in a group of female college students, using humour. Depressed mood was induced by showing 20 incrementally more depressing slides of the Velten (1968) mood statements. The Multiple Affect Adjective Check List (MAACL) was filled in by participants both pre- (baseline) and post-depression induction. Following the induction phase, participants either listened for 11.5 minutes to a humorous audio excerpt (a Bill Cosby and Robin Williams comedic discussion), a non humorous audio excerpt (an informational geology tape on formation of volcanoes), or to nothing at all. Following this treatment phase the MAACL was once again completed by participants. Only the humour group returned to the baseline levels of depression on the MAACL. The other groups were both significantly reduced, but not to the same extent. Humour did not significantly decrease hostility on the MAACL following the mood induction. Humour has received some support in its ability to reduce stress as well as manipulate emotion. Both physiological and emotional outcomes due to mild stressors have been positively affected by having comedy exposure during, before, or after a mildly stressful event in several experiments. These findings have not always been replicated though (Martin, 2007). Humour has also been used in psychoanalysis and psychotherapy, but with some contention (Mindess, 1996).

6. The current work . There are several theories as to what could contribute to decreasing dissatisfaction and negative affect or mood (i.e. anger, frustration) caused by waiting. Reduction in the perceived wait, increasing perceived control and regulating mood are all techniques that have been suggested. The current research first gauged whether call centre workers may support an on-hold intervention to try and reduce the negative outcomes discussed. It also attempted to test the efficacy of the several different techniques in ameliorating negative affect and dissatisfaction in customers waiting on-hold. The effects of different accents on customer experience was also tested, to see the possible prejudices that customers have towards someone in an outsourced call centre, with a accents differential from their own. The current research consisted of two laboratory studies that replicated a call centre experience. In addition, surveys were conducted of

35 some individual New Zealand customer service and call centre staff to explore the issues they have. Finally a music preference study was conducted in the laboratory to see if there were any discernable trends in undergraduate preference for certain genres while on-hold. The first study was a survey and several interviews with a number of call centre staff from two call centres. This was carried out to examine the attitudes of a sample of call centre workers on the problems they face at work, and views of those within the industry on some call centre practices, as well as whether their opinions suggested any support for an on-hold intervention to reduce negativity and increase satisfaction. The second experiment was a laboratory experiment similar to that of Tom et al. (1997). This aim of this experiment was to see which, of a number of different manipulations might lead to lower levels of negative mood (particularly anger) and higher levels of satisfaction. In total, eight groups were used for comparison. Measures of perceived wait, perceived control, acceptable wait and likeability were also recorded to examine theoretical possibilities underlying any successful manipulations. It was hypothesised, based on previous work (e.g. Hui & Bateson 1991; Hui & Zhou, 1996; Tom et al., 1997), that by increasing perceived control (in this case by providing choices) we might see a decrease in negativity. Two perceived control groups were tested. One was specifically given options to choose from and told that there was choice. It was not explicitly stated to the other group that there were choices, reading material was present and a certain amount of choice was implicit in the situation. They could choose to read the material, and choose which articles they read. The various methods of trying to increase positive mood and satisfaction were compared to examine which had the greatest satisfaction ratings and mood ratings. The relationship that perceived wait has with satisfaction and mood was also examined. In addition, perceived wait was compared across different on-hold situations in part to examine in further depth whether the propositions made by Maister (1985) hold true, e.g. whether information specific to the company would reduce perceived wait. If mood and satisfaction were not predicted either by reduced perceived wait time or increased perceived control (through making choices available), then it would

36 be possible mood regulation through some other form (e.g. valence/likeability) may have been acting on participants which was also measured in case this occurred. By providing a choice, we were also providing stimuli that may reduce perceived wait time. A number of alternatives have been set up that are not choices, but are “fillers” to try and decrease perceived wait time. These were compared to the control conditions and to the choice alternatives. It was of great interest to find the most effective technique to reduce negative mood and dissatisfaction; such a technique could then be trialled in a call centre with high turnover in an ABAB design in future studies. The third study was similar to the second experiment, but examined the interventions that showed the most promise in greater depth, and also examined the effect that different accents had on the same measured variables, and several others, relating to speaker characteristics. Finally, a music preference study was carried out in the laboratory to ascertain if there were any trends in university undergraduate preference for certain styles of on-hold music and whether this differed from styles reportedly preferred when listening in public areas.

37

38 Study 1: Working in the call centre: Issues from a Customer Service Representative point of view.

This study examined the opinions of call centre workers. Specifically, in this study we sought to examine whether the opinions of front line workers at two New Zealand call centres validated the principles of an on-hold intervention to reduce the number of angry callers that they have to deal with. There is a reasonable body of evidence that shows that working in a call centre can be stressful (Deery, Iverson & Walsh, 2002; Jackson, 2008; Sprigg, Smith & Jackson, 2003; Taylor & Bain, 1999) or lead to emotional exhaustion (Lewig & Dollard, 2003), and, anecdotally, sick leave and turnover are considered to be high within call centres (Lewig & Dollard, 2003). In addition to anecdotes, an Australian industry study of absenteeism found that call centre workers had a higher than average absenteeism rate with workers absent on average 9.2 days a year compared to 8.62 days for all workers (Direct Health Solutions, 2008). However, no mention was made as to whether this difference was statistically significant. Further, Jackson (2008), compared a large sample of call centre employees (N = 10,488) to non call centre organisation personnel (N = 276,408) in Canada. They found that employees in call centres were more likely to seek assistance through Employee Assistance Programs (EAPs), and were more likely to report stress. They estimate that in a 30 person call centre, the turnover in any year could cost that centre $400,000. They also reported that up to 10% of the entire call centre workforce may be absent on any given day. The two main reasons given for calling in sick included not feeling appreciated (37%) and not liking their job (19%). The New Zealand Contact Centre Industry Benchmarking Report (2007; as cited in Boyte, 2009) gives the turnover rate in New Zealand as 24%, and absenteeism rate at 7%, with an average of 8 days sick leave yearly by each worker. In New Zealand the overall average worker turnover rate, as of 2008, was 17.2% (Statistics New Zealand, 2008, p. 2), but no information regarding sick leave or absenteeism was available (Mike Moore, Respondent advocate, Statistics New Zealand, Personal Communication, Sep. 4, 2009). Unfortunately, seemingly little work has been done to accurately compare New Zealand call centres to nationwide cross-industry averages of turnover or sick

39 leave, but personal communication suggests call centres may have higher than average absenteeism and sick leave rates (Manager, Call Centre 2, Personal Communication, Jan. 13, 2010; Team Manager, Call Centre 1, Personal Communication, Jun. 12, 2009). Research conducted into the possible reasons for the absenteeism in some call centre staff of the Netherlands by Schalk and van Rijckevorsel (2007) showed that intentions to a leave an organisation, or self reported levels of absenteeism, had little to do with job characteristics, but rather contract issues and attitudes to the workplace. However, in a study carried out in the same country, Bakker, Demerouti and Schaufeli (2003) found that job demands and a lack of resources indirectly affected sick leave through an increased incidence of health problems. The job demands that they examined included work pressure, computer problems, emotional demands and changes in task. Job characteristics have also been linked with poor health or wellbeing outcomes by a number of other studies (e.g. Deery et al., 2002; Holman, 2003; Sprigg et al., 2003). Included in negative job characteristics with poor outcomes is customer interaction with hostile callers (Deery et al., 2002). Indeed, Taylor and Bain (1999, p.110) have proposed that “nuisance and abusive calls ... are a source of incalculable stress”. Call centre workers often have to engage in emotional regulation when dealing with customers (Holman et al., 2002; Totterdell & Holman, 2003; Zapf et al., 2003). They may have to appear happy and helpful, regardless of whether a caller is angry, whatever their own true emotions may be (Grandey, 2000). This emotional dissonance has been found to intensify the level of a workers emotional exhaustion (Lewig & Dollard, 2003). Emotional exhaustion brought about by customer interaction has in turn been linked with higher rates of absence (Deery et al., 2002). Angry callers may not be uncommon either. Jackson (2008) reported that over 40% of call centre workers in the United States deal with angry callers every day. Based on the possible prevalence of angry callers, and effects they may have on worker well being, we sought to examine which job characteristics were preferred and disliked by New Zealand call centre workers, and focussed on whether New Zealand call centre workers found angry callers to be problem.

40 To establish whether a sample of New Zealand call centre workers found angry callers to be a problem (and therefore a possible contributor to stress and sick leave), a survey of two call centres was conducted. If angry callers are a problem then reducing the number of these callers may increase job satisfaction and, based on the findings of Bakker et al. (2003; Deery et al., 2002), could seemingly lower rates of sick leave and turnover. At the very least job enjoyment should be positively altered. The survey also examined whether call centre workers feel callers are becoming angry while on-hold. If so, then the use of some form of intervention to prevent angry callers during the on-hold period may be warranted, for this reason as well as for the benefit of callers.

Why a survey? Rather than relying solely on statements from managers or individual personal communications from other call centre workers, a survey of a larger number of customer service representatives (CSRs) seemed more appropriate to obtain a picture of what the likes and dislikes of the workers may be. These CSRs are the ones “at the coal face” every day. They are the workers having to deal with the angry customers and the other stressors associated with the job. Further, they are also conversing with the customers for hours every day so they could have a greater insight into what the customers may want to change in a call centre interaction. Little academic research has been carried out into call centre work and the possible associated issues in New Zealand. This study was used to gain some personal insights, from people working within the industry, into what may be done to improve call centre interaction from the point of view of both customer and call handler. Accordingly, for this study a survey and interview process was undertaken in two inbound call centres. Using these techniques, it was hoped we could get a general picture of some of the issues that these call centre workers personally face in dealing with callers, and also, what they themselves found to be frustrating when they called through to call centres.

41 Methods

Participants Participants who completed questionnaires were CSRs from two New Zealand based call centres. Respondents from both of the participating organisations volunteered to fill in the surveys. Call Centre 1 : participants were 8 (of a possible 28) call centre employees at an inbound inquiry service centre for a government organisation taking calls from across New Zealand. Of the 8 respondents from the government organisation, 5 were female, and the ages ranged from 22-47 years of age. Mean age was 28.5 (median = 26.5), with a standard deviation of 8.01. Call Centre 2: participants were 20 (of a possible 44) employees at an inbound call centre for a privately owned manufacturing company. This call centre receives calls from both national and international callers. Of the respondents from the private sector company, 14 were female, and ages ranged from 19-60 years of age. Mean age was 38.05 (median = 37), with a standard deviation of 14.48. Aside from the questionnaires, 5 employees from the government run agency were also interviewed for approximately 20 minutes. Two of these interviews were carried out with two customer service representatives; the third was with one of the team managers of the national call centre. An interview was also carried out with the manager of the private sector call centre, but no CSRs from this organisation volunteered to do the same.

Apparatus Apparatus included a questionnaire about personal opinions around working in a call centre environment, and the issues facing call centre workers (see Appendix A). Questions asked included: • “What is your favourite part of the job as a CSR?” • “What is your least favourite part of the Job (Note: If it is not angry callers, please be honest and state what it really is)? ”1 • “How do you personally deal with angry callers? ” • “How do you personally deal with abusive callers?”

1 As the questionnaire was introduced substantively as being about angry and abusive callers, this note was added to try and reduce any demand characteristic the introduction may have had. 42 • “Have you ever had callers angry or abusive due to what they have listened to while waiting for service?” • “In your opinion do abusive/angry/aggressive callers lead to longer call times?” • “Do you have any suggestions to reduce the number of callers becoming angry and aggressive as they wait for assistance?” A space was also left to allow participants to add additional comments should they should wish to. For both call centres a sealed response box was included to collect questionnaires so responses would be anonymous. Also included in the apparatus was an A4 poster positioned above the questionnaire response box, notifying employees of its presence, and thanking them for responses (example in Appendix B).

Design The design of this study was a qualitative survey that was coded into different response groups for quantitative examination, and a semi-structured interview process to gain further detail and insight.

Procedure Before any surveys were handed out to respective call centres, informed consent was obtained from management, and any changes to procedure that the participating organisations required were made. Call Centre 1: For the government agency, the questionnaires were left in the tearoom that is used by staff every day for each break. Copies were placed on a table that features items of interest such as a radio, comical articles and, on occasion, book sales. An A4 hand written poster was included that read “Hi guys, I would appreciate everybody’s help by filling out a questionnaire. This is purely voluntary so only fill one in if you want to. Please let me know any suggestions you have, be honest, and do not give any information that could give your identity. Thank you for your help. Isaac Malpass.” This poster was torn upon removal, so is not a part of the appendices. A sealed box that had written on the top and sides: “Caller Questionnaire Return Box. Thank you” was placed under the sign and 30 questionnaires were placed next to the box. Two emails were sent around the office by management reminding

43 customer service representatives (CSRs) that the questionnaires were there and their help was appreciated in filling them out. The box remained at the office from 12/6/09 to 23/6/09. When it was removed the previous poster was replaced with a simple thank you note that read “Thank you to those who helped out by filling in questionnaires. It is really appreciated. Thanks, Isaac Malpass”. Additionally, a number of interviews were carried out that examined the opinions of some of the CSRs in more depth, and one of the team managers at the call centre. These were non-structured interviews designed to provoke thought and opinion surrounding call centre issues and possible solutions. Notes that were taken were read back to participants so they could be sure they agreed that what they were trying to convey was noted down. Call Centre 2: For the private sector call centre, a similar procedure was carried out, but questionnaires were instead placed on the desks of the CSRs. A response box was again placed in the tea room, with a black and white A4 poster advertising for volunteers to carry out interviews above it (see Appendix C). The response box was present for two weeks between 11/2/10 and 26/2/10. Prior to the surveys being handed out an interview was conducted with the manager of this call centre. No CSRs from this organisation volunteered for an interview, but 20 CSRs did fill out questionnaires.

Results

Data collection For the survey that was carried out, responses were examined for any trends that could constitute response categories. Once examined, responses to each question were sorted into these categories. Respondents were arbitrarily numbered 1-8 for Call Centre 1 and 1-20 for Call Centre 2 . In each category the arbitrary number of the individual respondent was noted in the appropriate column if their response fitted that category. Due to the qualitative open-ended nature of questioning in the survey, a response could fit multiple categories. By having the respondents’ numbers noted, item by item inter-observer reliability tests could be carried out. A copy of all responses including additional comments is included in Appendix D.

44 Interview responses were used as an additional source of information to further build on, and interpret, the survey responses in the results and discussion. As such they are not coded, but are discussed in relation to the survey responses. Details from these interviews are listed as personal communication.

Reliability To ensure that the categorisation of responses was not completely idiosyncratic, a consensus inter-observer reliability was calculated. To do this a secondary observer also categorised responses into the formulated categories for each question. For each response category the numbers of the respondents were compared, so that if the primary observer noted that number 20 responded “yes” to question 6, and so did the secondary observer, it was considered an agreement. However, if the primary observer had noted that number 20 responded “yes”, but the secondary observer had not, then a disagreement had occurred. This initially occurred for 27 of 216 categorisations (88% reliability). Discussion was then entered into until a consensus of 100% reliability was reached. An example of the sheet used for reliability checking is found in Appendix E.

For the data that is presented in this report:

Out of 216 categorisations, observers were initially in disagreement over 27.

Initial percentage reliability = # of agreements . x 100 # agreements + # disagreements

Initial percentage reliability = 189 . x 100 = 87.5% 189+27

Data analysis To present the data, overall percentages that included both call centres were formulated. Every response category was divided by 28 (the total number of respondents) and multiplied by 100 to give the percentage of total respondents that made a response within that category. Note that the percentages within a graph can

45 add to over 100% because responses can fall into more than one response category. For example, in response to the question “What is your least favourite part of the Job (Note: If it is not angry callers, please be honest and state what it really is)? ” the statement “Hours, pay, felt like you are just a number” could fall under both “roster/hours/pay” and the “dehumanised” response categories. Responses have been split to show what proportion of the percentage bar is from Call Centre 1 and Call Centre 2. The raw data that were used to calculate the percentages were all tested in SPSS 15 using a Chi-Square Goodness-of-Fit Test, to examine whether patterns in responding differed significantly from a model that assumed all responses were equally likely. The exact sig. p-value was used for those questions that had any expected frequencies less than five. This was calculated using non-parametric exact testing methods (SPSS 15.0.1, 2006).

Data presentation The first question (Question 1): “What is your favourite part of the job as a CSR?” lead to customer service oriented responses for the most part. Responses were significantly different from a model assuming that frequency of response would be equal among categories, X2 (5, N = 28) = 29.19, p <.001 . An overall majority and a majority CSRs from both Call Centre 1 and Call Centre 2 participants showed customer service oriented responses. Overall 57% of responses were categorised as regarding helping people, or satisfying customers (see Figure1). This was the only category that was greater than the expected values of an equal model.

46 "What is your favourite part of the job as CSR?"

60 50

40 Centre 2 30 Centre 1 20

10 0 Other people Workplace Helping camaraderie widerange of Percentage of total respondents Percentage total of Dealingwith a satisfaction Goinghome at theend theday of people/Customer Nofollow up work

Response categories

Figure 1: Categorical breakdown of respondents’ favourite part of being a CSR. Call Centre 1 is referred to as “Centre 1”, and Call Centre 2 as “Centre 2”.

Following helping people, dealing with a wide range of people had the next greatest rate of response (18%), followed by workplace camaraderie (14%), and going home at the end of the day (11%). The lowest categorisation of responses fell under “other” with 7% and having no follow up work to attend to (4%). For Question 2: “ What is your least favourite part of the Job (Note: If it is not angry callers, please be honest and state what it really is)? ” There was a great deal of variation (see Figure 2). Response categories did not differ significantly from a model assuming all equal rates of response X2 (6, N = 28) = 4.29, p = .67.

47 "What is your least favourite part of the Job"

30 25 Centre 2 20 Centre 1 15 10 5 0 Percentage of total respondents Other clients Dehumanised presure Roster/hours/pay Internal staff and staff Internal /Strictguidelines Unreasonable/angry equipment problems equipment Relentless calls/Time Relentless Restrictions/Limitations Response Categories

Figure 2: Categorical breakdown of respondents’ greatest dislikes of the CSR position. Call Centre 1 is referred to as “Centre 1”, and Call Centre 2 as “Centre 2”.

Although no significant differences were found amongst response categories, 29% of respondents gave the least favourite part of the job as unreasonable/angry clients. “Other” was the next group with 21%. Following these two responses Restrictions/limitations/strict guidelines and Roster/hours/pay were the next biggest dislikes (18%), followed by relentless calls/time pressure and internal staff and equipment problems (14%) and finally feeling dehumanised (7%). In response to Question 3: “ How do you personally deal with angry callers? ” most answers generally implied keeping calm and patient and trying to assist (see Figure 3). No significant difference from a model assuming equal rates of response was found X2 (5, N = 28) = 7.45, p = .11. This indicates that no response categories of dealing with angry callers were, statistically speaking, any more, or less, common than any of the other responses. This may, however, be due to the similarity of response categories.

48 "How do you personally deal with angry callers?"

45 40 35 30 25 Centre 2 20 Centre 1 15 10 respondents 5

Percentage of total total of Percentage 0 Say little/let Problem Be patient Calm and Other. customer solve. Fix the and take collected. rant/vent issue if customer Always try then try and fixable point of view and resolve assist the problem Response Categories

Figure 3: Categorical breakdown of how respondents deal with angry callers. Call Centre 1 is referred to as “Centre 1”, and Call Centre 2 as “Centre 2 ”.

As Figure 3 shows, there appears to be a non-significant trend showing that the majority of respondents advised that letting the customer vent their frustration before trying to assist was what they felt was the best way with dealing with angry customers. Overall 43% of responses were categorised as recommending this strategy of dealing with angry customers. The second highest category was to be “Calm and collected. Always try and resolve the problem” (29%). The next highest categories were to “Be patient and take the customers point of view” and “other” (both with 18%). Finally 11% of responses were categorised as “Problem solve. Fix the issue if fixable”. For Question 4: “How do you personally deal with abusive callers?” there was a significant different pattern of responses from a model assuming equal frequencies of response in each category X2 (4, N = 28) = 10.83, p <.05. A total of 43% of responses fell into a response category that indicated that the CSR would warn the customer, and (if they continued to be abusive) end the call (see Figure 4). An extra category was required for this question compared to question 3. This was the transfer/defer category. Transfer/defer indicates that the CSR would try and pass the

49 call on to someone else, rather than deal with the abusive person. This only had 7% of responses.

"How do you personally deal with abusive callers?"

45 40 35 30 25 Centre 2 20 Centre 1 15

respondents 10

Percentage total of 5 0 Be patient and Warn the Say little/Let Transfer/defer Other take the clients customer then customer perspective end the call rant/vent then try and assist Response categories

Figure 4: Categorical breakdown of how respondents deal with abusive callers. Call Centre 1 is referred to as “Centre 1”, and Call Centre 2 as “Centre 2”.

As Figure 4 shows, the “other” category is the second largest response category for this question (25%). Responses in this category included physical and emotional coping strategies not listed e.g. “Eat lots of candy ad [sic] clench fists” or “Take a deep breath and roll my eyes” or “Relax and smile”. These first two categories were greater than the expected values of a model assuming equal response frequencies. When compared to one another, there was no significant difference between the two major response categories X2 (1, N = 19) = 1.32, p = .08. The next largest category (18%) dealt with abusive callers in the same way that the majority of the CSRs deal with the angry callers, by leaving customers to rant/vent before attempting to assist. Following this category was to “Be patient and take the client’s perspective” with 11% (see Figure 4). When asked Question 5: “Have you ever had callers angry or abusive due to what they have listened to while waiting for service?” the pattern of responding significantly differed from a model assuming an equal rate of response in each category X2 (3, N = 28) = 18.59, p <.001. Over half (57%) of respondents answered in the affirmative (see Figure 5). However, both “No” and “Yes” responses were greater

50 than the expected values. When a further analysis of whether “Yes” and “No” differed was carried out in isolation, they were not found to be significantly different from equal, X2 (1, N = 27) = 3.00, p = .16.

“Have you ever had callers angry or abusive due to what they have listened to while waiting for service?”

60 50 40 Centre 2 30 Centre 1 20

respondents 10

Percentage of total 0 No Yes Yes due to wait Other time Response categories

Figure 5: Categorical breakdown of whether respondents had ever had callers say they were angry or abusive due to what they have listened to while waiting for service. Call Centre 1 is referred to as “Centre 1”, and Call Centre 2 as “Centre 2”.

As Figure 5 indicates, the next largest response was from respondents who had not ever had this experience (32%). Some of these respondents had caveats though e.g. “Agitated but reasonable good not angry [sic]”. The “other” and “Yes due to wait time” categories each had 7% of responses. “Yes due to wait time” was included as a separate category to “Yes” because this response did not really gel with the intention of the question. It was about what is listened to during, rather than just the length of, the wait. In response to Question 6: “In your opinion do abusive/angry/aggressive callers lead to longer call times?” 79% of respondents answered in the affirmative (see Figure 6).

51 “In your opinion do abusive/angry/aggressive callers lead to longer call times?”

90 80 70 60 50 Centre 2 40 Centre 1 30

respondents 20 10 Percentage of total 0 No Yes Angry callers Sometimes do, abusive callers don't Response categories

Figure 6: Categorical breakdown of whether respondents had ever had callers say they were angry or abusive due to what they have listened to while waiting for service. Call Centre 1 is referred to as “Centre 1”, and Call Centre 2 as “Centre 2”.

Participants indicated in their responses that these types of callers do not seem to listen, they wanted to vent first instead of dealing with an issue e.g. “Yes. They do not Listen [sic]!!!” and “Yes they do take longer as the customer can tend to ramble on longer than a customer who is not angry.” Several of respondents (11%) indicated that angry callers lead to longer calls, but abusive callers do not because either the abusive caller hangs up, or the CSR is forced to do so. All of these respondents were from Call Centre 1. Only 7% of participants responded in the negative, and 4% responded in the “sometimes” category. The frequency of responses among categories was statistically different from a model assuming equal frequencies of response X2 (3, N = 28) = 43.14, p <.001. “Yes” was greater than the expected equal frequencies, and the other response categories were lower. The final question (Question 7) asked “Do you have any suggestions to reduce the number of callers becoming angry and aggressive as they wait for assistance?” This question saw the most variation in response (see Figure 7). No significant

52 differences in the frequency of responses among categories were seen X2 (7, N = 28) = 11.58, p = .12.

“Do you have any suggestions to reduce the number of callers becoming angry and aggressive as they wait for assistance?”

35 30 25 20 15 Centre 2 10 Centre 1 5 0

c y er aff ait tt Percentage of total respondents total of Percentage st usi eall m r f w be Other e ot o s th er e/n m n eng chang l to ploy more / No m the cus s at sages Allow follow ntup time e es ie Tr times/e cl it m al wa on orm i Inf wer o izat L Informing of anygan nationwide problems or e v emo R

Response categories

Figure 7: Categorical breakdown of what suggestions respondents had to reduce the number of callers becoming angry and aggressive as they wait for assistance. Call Centre 1 is referred to as “Centre 1”, and Call Centre 2 as “Centre 2”.

As an idea of what some of the suggestions were, Figure 7 shows some 14% had no suggestions to reduce the number of angry callers. The largest response category (32%) concerned lowering wait times or employing more staff to try and fix the issue. The next largest response category, with 21% of CSRs making the suggestion, was to either remove organizational messages or to change the music played for customers while on-hold. The percentage of responses categorised as “Other” suggestions were on an even par with the “none/not really” group at 14%. Allowing follow up time, informing clients of the length of their wait and treating 53 customers better all received 7% of responses. Informing customers of any nationwide problems received 4% of responses (see Figure 7). However, none of the response categories had a significantly higher or lower frequency than any other for this question. An SPSS 15 crosstabs examination of responding was also carried out to examine differences in responding between the two call centres. The only significant difference between the centres that was seen was in the pattern of responses to question 6, X2 (3, N = 28) = 9.07, p <.05. Crosstab examinations were also carried out to examine whether certain responses on one question led to a higher likelihood in responding in a particular category on another. No relevant or significant results were found following Chi square exact tests.

Discussion

The results for Question 1 are somewhat unique, as most previous work has examined which factors cause absenteeism/stress/intent to leave, or which factors protect against these things occurring. What call centre workers enjoy about their jobs is often overlooked for a more negative and systematic attempt to gauge what stops them getting stressed. Results showed that the most responses for what CSRs most like about their job concerned helping people. Though no dislike response was significantly more likely than another, dislike categories included unreasonable and angry callers, and restriction and strict guidelines. These findings fall in line with emotional dissonance theories of possible causes of stress and emotional exhaustion (e.g. Holman et al., 2002; Totterdell & Holman, 2003; Zapf et al., 2003; Grebner, et al., 2003; Lewig & Dollard, 2003). Feelings of failure may also arise (Yagil, 2008). The CSRs want to help people, but when they are unable to, or have to deal with clients projecting an emotional response that conflicts with their own, attempts to regulate emotion to be friendly and helpful can lead to emotional dissonance or emotional exhaustion. This, in turn, may lead to psychosomatic complaints (Grebner, et al., 2003; Lewig & Dollard, 2003). Further, strict guidelines and restriction have also been linked with stress and lower ratings of job satisfaction or wellbeing (e.g. Holman, 2003; Sprigg et al., 2003), and the current findings lend further support to this earlier work in identifying it as a problem once more.

54 Implications for research Findings do seem to suggest that from a CSR point of view it is worthwhile attempting an applied intervention to try and reduce the number of callers with negative affect (particularly angry callers), through the use of an on-hold media intervention. Although not significantly more likely than other responses, statistically speaking, angry/abusive/aggressive callers were listed as one of the common dislikes that CSRs had in their job, and a large percentage have had callers who they felt were angry due to something they listened to during their wait, suggesting that the on-hold media may be contributing to the negative affect. Furthermore, though there were no suggestions that were significantly more likely to occur than any other, altering media during the on-hold period was the second most common CSR suggestion to reduce angry callers. This followed hiring more staff or reducing wait times. Hiring staff and reducing wait times may not be a pragmatic way to decrease the incidence of angry callers due to cyclical trends in call volume seeing only temporary periods where customers are placed on-hold for long periods, or the cost of training and employing more staff. Bearing this in mind the second most common suggestion may be a wise option to try and reduce a common CSR dislike. Possible on-hold interventions that interviewed CSRs suggested included using radio type broadcasts while on-hold, feedback about the wait or information about what the CSR can do for you. The Manager of Call Centre 2 suggested aiming to please the target market with any music played while on-hold, or letting the CSRs know how long people have been waiting so they can start a call with thanks for waiting (Manager, Call Centre 2, Personal Communication, Jan. 13, 2010). A strong theme that came across in the CSR interviews was that repetition while on-hold is annoying (Customer Service Representatives, Call Centre 1, Personal Communication, Jun. 12, 2009). This should probably be avoided in any on-hold interventions trying to reduce the incidence of angry callers. In addition to the fact that CSRs disliked angry callers, a significant majority of CSRs believed angry callers lead to longer calls. Management interviewed felt that listening and letting people rant then trying to help can be shorter in the long run than interrupting people and being combative (Manager, Call Centre 1, Personal Communication, Jun. 17, 2009). However, if there were fewer angry callers this time would not need to be used up. In addition to the survey results, some CSRs

55 interviewed felt that the angry callers did lead to longer wait times, and affected their call statistics. Therefore angry callers, even if not the main dislike of some CSRs, may have an indirect effect on job satisfaction also, through time and performance pressures. Longer calls can mean a) longer waits for other customers and b) mounting time pressure on the CSR, especially if short call handling time is a performance indicator for the company a CSR works for. Time pressure and performance monitoring related to call handling and wrap up time are both common occurrences in call centres (Taylor & Bain, 1999; Bain, Watson, Mulvey, Taylor & Gall, 2002). This is another possible cause of stress (Michie, 2002), that the intended intervention could affect. Not only is time pressure a stressor in itself, but the idea of getting through calls very quickly also conflicts with the goal of helping people, one of the common responses about why CSRs like the job. This conflict may, in turn, further add to emotional dissonance and exhaustion. In conclusion, it appears that the intended applied research examining whether changing on-hold stimuli affects the mood of callers and the sick leave/stress levels of said workers is supported by the CSRs opinion.

Limitations Not all of the staff at either call centre co-operated in the study, as filling out the questionnaire was voluntary. They may not have wanted to spend their breaks ruminating on their work to fill in surveys, or may just not have been interested in responding. This could have skewed the nature of responses. The sample size was also limited as a consequence; 28 as opposed to over 70 if all possible workers had responded from both of the call centres.

Reasons for the differences in responding between the call centres Statistical analyses yielded only one significant difference in responding between the two call centres (in the pattern of responding for question 6: “In your opinion do abusive/angry/aggressive callers lead to longer call times?” ), but an interesting trend that we saw in responses from the two different call centres related to the question regarding the dislikes that the CSRs had. A possible reason for the different responses to this question is the different performance indicators that the

56 organizations set for their CSRs. Call Centre 1 has strict limits regarding wrapping up any computer work required after the call (after call work or ACW) and call handling time (CHT). Call Centre 2 does not impose guidelines which are as strict for CHT (no limit) and ACW. Because the restrictions for Call Centre 1 are stricter than those for Call Centre 2, this could explain why more CSRs at Call Centre 1 saw this as their main occupational dislike. Likewise, Call Centre 2 works almost around the clock on different shifts for the global market, where Call Centre 1 operates closer to standard hours for New Zealand work places working hours (around 8 – 6) which may be why proportionately more CSRs at Call Centre 2 disliked the hours/pay/roster.

Summary In summary, the current study, though limited to a small sample size spread across two call centres that had different monitoring practices, gave an insight into what the front line call centre workers like and dislike dealing with on a day-to-day basis. It leant further support to the theories of emotional dissonance or emotional exhaustion in CSRs possibly caused by conflicting goals of helping the customer, but finding the customer unreasonable or angry when they call through. Also, there was significant indication that unreasonable and angry callers lead to longer call times – which can contribute to the stressor of time pressure and may further exacerbate emotional dissonance and exhaustion. One of the common suggestions on how to reduce angry callers was to change on-hold music or messaging. This lends valuable support to the idea of an on-hold intervention from the workers who have to deal with angry, unreasonable and abusive callers. The responses in this survey were not restricted to scoring on a scale or answering only but allowed a full range of responses which were then categorised. As such, we feel a full range of response has allowed a valuable insight into the call centre industry

57 58 Study 2: Reducing customer negativity and increasing customer satisfaction with a telephone service, through the use of an on-hold intervention.

This study was carried out to examine the effects that different on-hold listening conditions have on customer mood and satisfaction. The longer we wait, the more we may become dissatisfied with a service (Davis & Heineke, 1998; Davis & Vollman, 1990; Clemmer & Schneider, 1989; Tom et al., 1997). But, as mentioned in the introduction, attempts have been made to try and reduce this dissatisfaction with waiting (e.g. Davis & Heineke 1998; Tom et al., 1997; Whiting & Donthu, 2006). Also noted was the mixed success that different studies had on altering mood or satisfaction (see Intervention Techniques in the introduction). In this study we put some of the possible techniques of reducing anger or dissatisfaction to the test. Reducing perceived wait time is one of the methods proposed to try and make a wait better for customers. As such, we asked participants to estimate how long they were on-hold for in the current study. Complex aural stimuli or tasks should either reduce (Zakay & Block, 1997) or increase (Antonides et al., 2002) perceived wait, depending on the theory to which one subscribes. Maister (1985) proposed that presenting aural information about the company that is being called, during an on-hold period, may be a good way to engage a listener, and reduce the perceived duration of a call. This may in turn have positive effects on satisfaction and mood. On-hold messaging is used today for several reasons including entertainment, information and marketing (Business Voice, 2009). One of the alternatives tested in the current study used call related messages during an on-hold period to examine their effects. Another proposed method for creating positive mood and satisfaction is mood regulation. Listening conditions that people like may positively influence their mood (Cameron et al., 2003), which has been shown, in the past, to play a mediating role on satisfaction (Bougie et al., 2003). The current study asked participants how much they enjoyed what they listened to on-hold, as well as how satisfied they were. Mood measurements were also taken to examine if there was evidence of a difference in mood among listening conditions, and whether this had any effect on satisfaction. A third method proposed to possibly increase satisfaction and decrease negative mood is by increasing perceived control, through the introduction of choice

59 to a situation that would otherwise have none. Providing choice has seen some positive effects in face to face (Clemmer & Schneider, 1989), and telephone service environments (Tom et al., 1997). Increasing perceived control by offering some predictability of how long a wait will be has had positive effects on service quality ratings in a laboratory setting (Hui & Zhou, 1996). In the current study, two listening conditions were trialled to increase perceived control; a group with explicit choice, and one with an implicit choice. As a measure of control we asked participants to rate how much control they had over their telephone on-hold situation. Music was used in the current study as a stimulus to try and reduce negative mood and dissatisfaction. It is often played to customers (Tom et al., 1997), and past work has shown that music can be effective in combating stress (Pelletier, 2004), and dissatisfaction (Peevers et al., 2009), as well as positively affecting mood if the music is likeable (Cameron et al., 2003). Two music listening conditions were included in this study to try and reduce negative mood or increase satisfaction. We used current popular music in one condition. Pop is a genre that tests very highly as a preferred on- hold music type in an undergraduate Population (see Study 4). In addition, an elevator type music condition (which could be considered stereotypical hold music) was included. Encarta defines elevator music as “bland recorded music: bland instrumental background music played over loudspeakers in elevators, stores, and other public places” (“Elevator music”, n. d.). Another, rather novel, approach was taken to try and increase satisfaction and decrease negative mood; the use of humour. Humour has been used to reduce depression (Danzer et al., 1990), and stress (Martin, 2007), with some success. A comedy listening condition could seemingly therefore be used to try increase satisfaction or reduce negative affect, and was included in the current study. If successful, like music, this technique could also have either been working via decreasing perceived wait, or regulating mood. Two controls, a floor and ceiling, were included to provide reference levels against which to compare responses of the experimental groups. The floor control was a modified call waiting tone, and the ceiling control was a group which basically went straight through to service without being on-hold for more than 32 seconds from the moment the call first started (see Figure 8). Due to the number and variation of

60 listening conditions, alternative hypotheses were not included for which manipulation would be best or worst. Instead, null hypotheses were proposed:

Hypothesis 1: That there would be no difference in satisfaction among groups.

Hypothesis 2: That there would be no difference in mood among groups.

Hypothesis 3: That there would be no difference in hang-up rate among groups.

Measures of perceived wait, perceived control, acceptable wait and likeability were also recorded to examine theoretical possibilities underlying any successful manipulations. It was hypothesised, based on previous work (e.g. Hui & Bateson, 1991; Hui & Zhou, 1996; Tom et al., 1997), that, by increasing ‘perceived control’ (in this case by providing choices), we might see a decrease in negativity. Two ‘perceived control’ groups were tested. One was specifically given options to choose from and told that there was choice. It was not explicitly stated to the other group that there were choices, but a selection of reading materials was present and a certain amount of choice was implicit in the situation; the participants could choose whether or not to read, and which articles they would like to read.

Hypothesis 4: Participants in groups with increased perceived control would have a more positive mood than groups without.

Hypothesis 5 : Participants in groups with increased perceived control would have higher satisfaction ratings than groups without.

Because of the mixed findings regarding the influence that perceived wait has on affect and satisfaction, the following null hypotheses were proposed.

Hypothesis 6 : Reducing perceived wait would not increase positive mood.

Hypothesis 7 : Reducing perceived wait would not increase satisfaction.

61

The various methods of trying to increase positive mood and satisfaction (thereby reducing negative mood and dissatisfaction) were compared to examine which had the greatest satisfaction ratings and mood ratings. In addition, perceived wait was compared across different on-hold situations in part to examine in further depth whether the propositions made by Maister (1985) hold true e.g. whether information specific to the company would reduce perceived wait (when personal communication suggests it would not (e.g. Kelly Lindsay, Customer Service Representative, Personal Communication, Jun. 12, 2009; Callumn James, Customer Service Representative, Personal Communication, Jun. 12, 2009)). In this study a simulation of a real call centre was created to test these hypotheses. Participants were instructed to phone a call centre in order to get a code, under the guise that it would be used to assign them to a group for a mood assessment study. While waiting to receive this code, participants experienced different on-hold listening conditions. These included getting straight through, listening to a call waiting type of tone, a “hands-free” option where the waiting tone was followed by a loud blast to let participants know they were about to be connected; music, comedy, elevator type music, elevator type music interspersed with information, or a menu where there were a number of options to choose from. Measures of mood, satisfaction, perceived wait, perceived control and enjoyment were taken following the call.

Methods

Participants Participants were 226 Otago University undergraduates who could satisfy a small portion of course assessment by completing a worksheet based on the experiment. The initial sample consisted of participants aging in range from 17 years, to 50 years of age. The mean age was 19.82 (median = 19), with a standard deviation of 3.29. A total of 175 of the participants were female. Advertisements asked for participants not seeing a mental health practitioner for any reason.

62

Apparatus Equipment used included modified basic handset telephones, connected to Computers with a LabJack TM U12 I/O (Input/Output) box. These telephones were modified to allow signals to be sent to and from the telephone and computer. They did not have an active speaker phone function (see Appendix F for a photograph). A purpose written program called “Phone” was created that read the signals being received from pressing the telephone buttons, and in turn activated audio responses in the form of Waveform Audio File Format (.WAV) files. The Profile of Mood States (POMS) Standard Form Questionnaire (McNair, Lorr & Droppleman, 1992) (see Appendix G for a copy), was used to record the mood states of participants following their exposure to the aural excerpts played. This measure was chosen because it was proposed by its authors as a fast way of measuring the current mood states of participants (McNair, Lorr & Droppleman, 1992). It has also been examined by Norcross, Guadagnoli and Prochaska (1984), and was considered to have a high level of internal consistency, and quite stable factor structure. Moreover, Barker-Collo (2003) found that the POMS was more culturally valid in New Zealand than another test, the SCL-90-R, because results varied less from American undergraduate norms in a population of New Zealand undergraduates. A secondary questionnaire was used to measure their perception of, and satisfaction with, the phone service offered (see Appendix H). This questionnaire included questions regarding how satisfied participants were with the service and how much they enjoyed/disliked what they heard on the telephone; the level of control they felt they had over the interaction, and some questions pertaining to their cell phone use. An experimental log was kept to record to which condition participants had been exposed, the computer they had used, what their sorting code was, whether they had a hearing problem, and additional notes (see Appendix I). An attempt was also made to record the order in which a group of participants finished their call and obtained their sorting code on the same sheet, however, as most conditions ended at around the same time, the order was rather difficult to observe and most likely inaccurate. No reliability information was collected for this measure so it is not included in any analyses.

63 Music, comedy and narration in the form of Waveform Audio File Format (.WAV) files were used for the programmable audio responses (see Appendix J for a full list). In addition, telephone sounds, such as ringing and button tones were sourced from Stritof and Stritof (2006) in .WAV format. The computer program Audacity 1.2.4 (2009) was used to edit .WAV files so that they were the correct length and format for playback. All of the narrative sections were voiced by acquaintances of the researcher. An American female (who had done past voice work) voice was used for the automated on-hold messages, whereas a male American voice was used to simulate a customer service representative (CSR) at the end of the call. All files were transferred to computers and backed up on a USB stick. The comedy segments played were routines performed by the late American comic, Mitch Hedberg. These were performances with elements of stand up routines from the album “Mitch All Together” (Hedberg, 2003). However, these versions had no swearing. This comedy was chosen because each joke had a fast set up and punch line. Each musical excerpt consisted of one full song, and a secondary song, so each condition had a single contextual change (discriminative stimulus) of one song changeover. The music used came from several different genres and was chosen using the criteria that they were popular pieces within the genre or easily identifiable as a part of the genre (see Appendix J for details). Popularity alone was not used because genre crossover became a problem. For example, the top rated Country song in New Zealand was also highly rated on the contemporary charts, and may have been recognized as Pop rather than Country. Also, a secondary music preference study followed, which already had music arranged for it. So that the secondary music study was not affected by this study, some of the other top rated songs had to be replaced. A complete list of the songs, their sources, and how they were chosen are included in Appendix J. As this paper is academic research, and no direct commercial gain will be made from it, rights to play musical pieces were generally not sought. However, the partner of the late Mitch Hedberg was approached to request permission to use his comedy. Any on-hold interventions that may be trialled commercially as a result of this paper should pay royalties to the appropriate copyright holders through their collecting agency e.g. the Australasian Performing Right Association (APRA ) and/or the Recording Industry Association of New Zealand (RIANZ). Those participants who

64 had the opportunity to peruse a magazine while on the phone were all given the same University of Otago magazine; Issue 25, February 2010.

Design The design of this experiment was a between participants control group design. Participants were randomly assigned to one of eight possible groups. Participants in each group listened to a different aural alternative while waiting on- hold. Seven of the groups listened to whichever the aural alternative they were exposed to for a period of five minutes. One of the groups did not listen to any aural alternative but rather was connected 10 seconds after being exposed to an initial statement. This group acted as one of the control groups. Another group which acted as a secondary control group was a call waiting tone group. This group heard a quiet beeping tone for the whole five minutes. This was used as a control rather than having a group with no sound whatsoever, as this could have lead participants to believe that there was a line fault. Lowest levels of negative responding were expected for the group with no wait, whereas highest levels of negative responding were expected for the call waiting tone group.

Procedure Prior to participants arriving, the experimenter randomly assigned serial numbers to each. These serial numbers were then randomly assigned to an on-hold listening condition. Random assignment was achieved by printing off random sequences of integers from one to eight using a generator on Random.org (Haahr, 2010). Serial numbers were simply assigned to the next condition that came up on the random sequence. After all of the eight conditions had been filled, another random sequence was started. This method was devised as a way of trying to keep numbers close to equal in each condition. Following random allocation, the four computers with a computer-phone interface were readied by loading .WAV files to be played to each participant in the “Phone” program, so the computer would play through the telephone handset, the appropriate response for the condition to which each participant was assigned. The computer screen was then turned off so that participants did not realise that a program was running the telephone interaction.

65 When they arrived, participants were asked to take a seat while the experimenter ran through the details of the experiment with them, and gave them an information sheet to read over and a consent form to sign (see Appendix K). Participants were then asked to turn off their cell phones and either place them in their bag or give them to the experimenter to keep in a secure lockbox until the end of the experiment, along with their watch if they had one. Participants were informed that this was because they would be filling out a questionnaire regarding perceptions following the mood measure and we did not wish anything to influence their perceptions. This measure prevented the actual/objective time spent waiting affecting the participants’ perceived/subjective experience of time. Participants were advised that the experiment involved comparing moods of different groups. They were advised that they would have to call through to a call centre for a “sorting code” before filling out the measure of mood, to ascertain to which group they would be assigned. Participants were then asked to move to a booth and call through to the fictitious “Psychnet” organisation via telephone, and that this service was being employed to randomly assign participants to a condition. Participants had to wait on the line for their sorting code before they could continue with the rest of the experiment. They could hang up and end the experiment, but if they did they would not receive credit for participation. The sorting code, when collected, acted as a check that the correct condition was heard by each participant. Following instructions, participants were led through to the computers. Each computer was visually partitioned off. All participants were given the same number to phone using the telephone keypad. This activated the computer program and initiated the audio responses coming through the telephone handset. The first response was the same for all groups. It stated: “Thank you for calling Psychnet. Unfortunately our lines are currently busy. Please hold for assistance. We apologise for the wait and will try and connect you as soon as possible.” After this initial statement, the different aural alternatives began for the different groups. These alternatives included: Group 1: Elevator music: Elevator type music which consisted of midi generated versions of Für Elise (Bagatelle in A minor) composed by Ludwig van Beethoven and Greensleeves (anon).

66 Group 2: Elevator music plus information: The elevator type music above interspersed with informative statements about the company (see Appendix K for a list of statements). Group 3: Comedy: This condition consisted of stand-up comedy, performed by the late Mitch Hedberg, similar to that found on the album “Mitch All Together”, but without any swearing. Group 4: Pop: This condition included the songs “Black box” (Secon, Hector, Jeberg & Hansen, 2009), followed by “Replay” (City, Jones, Rotem & Derülo, 2009). Group 5: Straight-through: Following the initial statement the phone simply rang for 10 seconds and was then answered. Group 6: Quiet beeping: A quiet beeping tone (a modified call waiting signal) acted as the control group. This call waiting tone was sourced from Stritof and Stritof (2006). For all of these groups, following the initial statement, the aural output described began playing. There were, however, another two groups that require further explanation. These were the two groups with which we attempted to try and increase participant perceived control. Group 7: Quiet beeping plus alert: This was a “hands-free” group. Participants were advised: “A quiet beeping tone will sound to let you know that you are still on the line. When you hear this, please remove your handset from your ear as a very loud tone will sound to notify you when your call is about to be connected. Feel free to use this time as you like. Do not put the handset to your ear again until after you hear the loud tone finish. Thank you”. Participants in this group had a magazine at the desk when they arrived so they could choose to read it while waiting for the loud tone. The magazine provided them an implicit choice; to read the magazine or not, and to choose among the articles. Group 8: Choice: Following the initial statement, this group heard “We offer a selection of listening options to choose from while on-hold. Please press 1 for a choice of music, 2 for a comedy channel; 3 for some informative statements about the company or 4 if you would like to carry out other tasks while you wait. With this option, a quiet beeping tone will sound to let you know that you are still on the line. When you hear this, please remove your handset from your ear as a very loud tone will sound to notify you when your call is about to be connected. Feel free to use this

67 time as you like. Do not put the handset to your ear again until after you hear the loud tone finish. Press 5 to hear these options again”. This menu provided an explicit choice for the participants. Single magazines were at the desk of these participants when they arrived, in case they chose the hands-free option from the menu. For those who chose the music option, a further statement was issued: “To listen to country music, press 1; to listen to classical, press 2; to listen to jazz, press 3; or, to listen to pop, press 4. Press 5 to hear these options again” For those that chose the “hands-free” option in the choice menu, the same statement was given as for the group that was assigned this condition. All of the groups, except Group 5 which was answered 10 seconds after the initial statement, listened to their aural alternative for five minutes (300 seconds), before the final statement for their respective group started or until they hung up (see Figure 8). All of the statements and alternatives were matched to play at the same level of volume except for the loud tone at the end of the “hands-free” alternatives. This was done because variation in volume could possibly influence how long people were willing to wait on-hold, or alter their perceived wait time (see Garlin & Owen, 2006; Turley & Milliman, 2000 for reviews). Once they had obtained the sorting code, or had hung up, participants were then presented questionnaires to fill in, starting with the POMS state measure of mood. Following the POMS, participants were also asked to fill in a questionnaire that related to the perception of, and satisfaction with, their experience while waiting for their call to be placed. To try and reduce the number of people placing the perceptual anchor of five minutes exactly as their answer for estimated time waiting, participants were asked to estimate how long they waited for in minutes and seconds. Taylor (1994) had found that when they asked respondents to estimate the time they waited, using an open ended question, there were large spikes in the data at five and ten minutes. These were proposed as perceptual anchors. By asking for the time in minutes and seconds, we hoped to reduce this effect. Following the completion of questionnaires, participants were fully debriefed, given a debriefing sheet (see Appendix K), and an explanation of the true nature of the experiment was given

68 Pop, Elevator music, Elevator and Info, Quiet beep, comedy Listening condition Ringing 10 seconds (300seconds) Start Initial statement (12 seconds) Ringing and final message. 33 seconds.

For these groups the actual wait time (or wait until service) was 322seconds, and total wait time for entire call to finish was 355seconds.

Straight-through Ringing 10 seconds Ringing 10 seconds Start Initial statement (11 seconds) Ringing and final message. 32 seconds.

For this group the actual wait (or wait until service) was 31 seconds, and total wait time for entire call to finish was 63 seconds.

Listening condition Hands-free Ringing 10 seconds (300seconds)

Start Initial statement and Ringing and final instruction (35 seconds) message. 34 seconds.

For this group the actual wait (or wait until service) was 345 seconds, and total wait time for entire call to finish was 379 seconds. Time for choices and listening Choice Ringing 10 seconds (300seconds) Start Initial statement and Ringing and final instruction (58 seconds) message. 33 seconds.

For this group the actual wait (or wait until service) was 368 seconds, and total wait time for entire call to finish was 401 seconds. Discrepencies in the final times are due to slight variations in playback times following multiple file conversions. Originally all were recorded with a playback time of 33 seconds.

Figure 8: Flow chart of the time spent in different parts of a call for each condition

69 Results

Exclusion Criteria for group difference analyses In total, 29 participants had to be excluded from the final analyses. All participants aged 17 (five) were removed as our ethical approval did not include participation of those under 18. Furthermore, three participants who were supposed to have a magazine in front of them as an implicit choice option, but did not due to experimenter error, were removed from the analysis. Four who had experienced technical difficulties such as the computer updating during the experiment, or the phones disconnecting for some other reason (i.e. pulling too hard on the cords to move the phones) were also removed, as was anyone who took noticeably longer than seven minutes to get a sorting code, or had to be fetched by the experimenter. The eight participants who hung up and redialled a number of times were also removed from the analysis of differences among groups (they were examined in another analysis). Five participants had been diagnosed with hearing problems and were removed, as were two who had not filled in demographic information correctly. Two participants signed up twice for the experiment accidentally. The secondary data from these participants were removed from all analyses. For the first analyses, that did not include actual wait data, 197 participants, ranging in age from 18 to 47 were included. The mean age was 20.1 years (median = 19), with a standard deviation of 4 years. Of the 197, 155 were female.

Analyses A number of measured variables were collected to examine differences in response for the different listening alternatives. The SPSS 15 statistical package was used for all analyses, except where otherwise specified.

Differences in satisfaction amongst listening conditions To examine differences in self rated satisfaction, a Kruskal-Wallis one way non- parametric ANOVA was conducted in PASW Statistics (SPSS) 18, with on-hold listening group as the independent variable (IV), and a one to seven Likert type scale satisfaction rating as the dependent variable (DV). A highly significant difference in rankings among

70 the groups was found χ2 (7, N = 197) = 57.97, p < .001. Using the same software, non- parametric pairwise comparisons were carried out post-hoc to further examine the differences among the groups. All Kruskal Wallis post-hoc comparisons and actual p values can be found in Appendix L. These tests showed that those in the Straight-through group reported significantly greater satisfaction with service than those in the Beep alert (p < .001), Quiet beep ( p < .001), Elevator music ( p < .001), Elevator music with information ( p < .001), and Pop music groups ( p = .005). There were no significant differences in satisfaction among the Straight-through, Comedy and Choice groups. The Choice group showed significantly higher satisfaction ratings than the Quiet beep ( p < .02), and Beep alert groups ( p < .02). The Comedy condition also had significantly higher satisfaction ratings than both the Quiet beep ( p < .03), and Beep alert conditions ( p < .03). No other significant differences were found at the p < .05 significance level. Figure 9 shows the mean satisfaction ratings for the different on-hold listening groups.

7

6

5

4

3

2

Mean Satisfaction Rating Mean 1

0 Beep Quiet Elevator Elevator Pop Comedy Choice Straight alert beep info only through On-Hold listening condition

Figure 9: Mean satisfaction ratings (+SE) for the different on-hold listening conditions.

Differences in perceived control amongst listening conditions To examine differences in self rated perceived control, the same process was used as for the satisfaction ratings. A significant difference among the groups was found χ 2 (7, N = 197) =18.06, p < .02. Post hoc tests revealed that those in the Choice group had

71 significantly higher ratings of perceived control over the call than those in the Quiet beep (p < .005) and Pop music ( p < .03) listening conditions. No other significant differences were found. Figure 10 shows the mean perceived control ratings across the different on- hold listening conditions.

4.5

4

3.5

3

2.5

2

1.5

1

0.5

Mean rating control Mean of perceived 0 Quiet Pop Elevator Straight Comedy Beep Elevator Choice beep info through alert only On-Hold Listening conditions

Figure 10 : Mean ratings of perceived control (+SE) for the different on-hold listening conditions.

Group differences in Enjoyment/Dislike To examine differences in self rated listening enjoyment (reverse scored to give an indication of dislike), the same process was used as for the satisfaction ratings. A highly significant difference in dislike amongst groups was found χ 2 (7, N = 197) = 75.31, p < .001. Post hoc analyses showed that listening to the Quiet beep condition was rated higher (more strongly disliked), on average, than the Comedy ( p < .001), Elevator music (p < .001), Pop music ( p < .001), and Elevator music with information ( p = .001) conditions. Listening to the Beep alert condition was disliked more strongly than listening to the Comedy ( p < .001), Elevator music ( p < .005), Choice ( p < .001), and Pop music conditions ( p < .05). Listening to the Straight-through condition was more strongly disliked than listening to the Comedy condition ( p = .001). No further significant

72 relationships were found. See Figure 11 for a comparison of the mean ratings amongst groups.

7

6

5

4

3

2 Mean Dislike Rating MeanDislike 1

0 Comedy Elevator Choice Pop Elevator Straight Beep Quiet only info through alert beep On-Hold listening Condition

Figure 11 : Mean rating of Dislike (+SE) for the different on-hold listening conditions.

Group differences in mood This next set of analyses had a sample of 183 participants, 145 female. Ages ranged from 18 to 47. Mean age was 19.32 (median = 19), with a standard deviation of 3.70. Fourteen further exclusions were required for two reasons. One: participants failed to record perceived wait or acceptable wait time; Two: actual wait data (the actual time that participants waited until they got their sorting code) and the total time spent on the phone were not recorded for all participants due to technical difficulties or human error. As reported earlier, calls which included hanging up and redialling were excluded also, regardless of how long the total call took. These calls were analysed separately. The POMS gave a total mood disturbance score, along with scores on Tension- Anxiety, Depression-Dejection, Anger-Hostility, Vigor- Activity, Fatigue-Inertia and Confusion-Bewilderment scales. Prior to analyses of the POMS data, a transformation was carried out to reduce variance among groups. Fifty was added to every score to make them positive, and a log (base 10) transform was carried out.

73 To examine whether there were any differences in the POMS mood scores, among the participants in different on-hold listening groups, several analyses were conducted. The first was a MANCOVA with an independent variable (IV) of listening condition that examined differences in the dependent variables (DVs) of Total Mood Disturbance (TMD), Tension-Anxiety, Depression-Dejection, Anger-Hostility, Fatigue-Inertia and Confusion-Bewilderment scales. Covariates of age, acceptable wait time and actual wait time were included in the analysis also. The multivariate analysis was considered appropriate for these measures as they were significantly correlated. Gender was excluded from this analysis because of the unequal numbers of males and females. Small cell counts and inequality of variance were the result. Twenty-one cells had counts of 4. By removing the factor of gender, Levene's Test of Equality of Error Variances was non-significant for every scale except for Depression-Dejection (.033) and Anger-Hostility (.021). In addition, any findings would be more generalizable to practical application, if any relationships held across genders, assuming most call centres service people of both genders. Using Wilks’ Lamda for multivariate significance tests, no significant differences were found amongst listening conditions F (49, 847.18) =.35, across the POMS measures overall. A follow up MANOVA analysis with a between subjects factor of listening condition was carried out that echoed the non-significant results of the MANCOVA. Vigor- Activity was examined separately as it was not correlated significantly with the other POMS measures. A between subjects ANCOVA was carried out with listening condition as the IV and acceptable wait, actual wait and age as covariates. No significant differences were found for any of the IVs. When a follow up ANOVA was conducted with group as the IV, again, no significant differences were found among listening groups.

Group differences in perceived wait Perceived wait time frequencies were first examined to check whether the perceptual anchor of 5 minutes was selected with a much greater frequency than other times. The straight-through estimates were removed from this examination. The five minute mark did not have the greatest response frequency, despite the wait being near

74 five minutes for most participants. There did appear to be anchors generally at 3, 3.5, 4, 4.5, 5 and 5.5 minutes, but a major anchoring at five minutes skewing results did not appear present. To establish whether there was a significant difference in perceived wait amongst on-hold listening groups, a between subjects ANCOVA with perceived wait (in seconds) as a DV and listening condition and gender as IVs was carried out, with age, and actual wait time (in seconds) as covariates. No significant differences in perceived wait among groups, F (7,165) = 1.82, p = .09, or between genders, F (1,165) = .20, p = .65, were found when these covariates were included.

Correlations of measured variables A correlation matrix was produced to examine any possible relationships among measured variables. As some of the variables were ordinal in nature (Likert type scales), Spearman’s Rank Correlation Coefficient was used rather than Pearson’s Correlation Coefficient. The results of most of the measured variables are shown below in Table 1. For a matrix that includes subscales of the POMS measure see Appendix M. As Table 1 shows, Total Mood Disturbance is significantly positively correlated only with dislike of a listening condition r (181) = .17, p < .05. Strangely, higher perceived waits were associated with lower levels of dislike r (181) = -.17, p < .05. Higher perceived wait times were strongly associated with lower levels of satisfaction, r (181) = -.21, p < .01; higher acceptable wait times, r (181) = .24, p < .01 and (not surprisingly) higher actual wait times, r (181) = .29, p < .01. Higher satisfaction scores were strongly associated with higher levels of perceived control, r (181) = .23, p < .01; higher acceptable wait times, r (181) = .30, p < .01; and with lower levels of actual wait time, r (181) = -.24, p < .01, and dislike, r (181) = -.33, p < .01. Higher levels of perceived control were strongly associated with lower levels of dislike r (181) = -.24, p < .01. Acceptable wait was negatively correlated with dislike, r (181) = -.18, p < .01. Not shown in Table 1, but included in the POMS subscales appendix (Appendix M), is the significant negative correlation of the Anger subscale of the POMS with satisfaction, r (181) = -.15, p < .05

75 Table 1: Correlations of Measured variables, Age and Actual wait time.

PercCo Age TMD Percwt Satisfactn ntrol Acctblwt Dislike Actualwt rho Age CC 1.000 -.119 -.001 -.107 -.052 -.042 .012 -.098

Sig . .107 .994 .148 .488 .573 .872 .186

N 183 183 183 183 183 183 183 183

TMD CC -.119 1.000 .026 -.130 -.059 -.016 .170(*) .114

Sig. .107 . .724 .080 .428 .835 .022 .124

N 183 183 183 183 183 183 183 183

Percwt CC -.001 .026 1.000 -.209(**) -.076 .236(**) -.167(*) .290(**)

Sig. .994 .724 . .005 .309 .001 .024 .000

N 183 183 183 183 183 183 183 183

Satisfactn CC -.107 -.130 -.209(**) 1.000 .229(**) .299(**) -.330(**) -.242(**)

Sig. .148 .080 .005 . .002 .000 .000 .001

N 183 183 183 183 183 183 183 183

PercCont CC -.052 -.059 -.076 .229(**) 1.000 .066 -.238(**) .095 rol Sig. .488 .428 .309 .002 . .371 .001 .203

N 183 183 183 183 183 183 183 183

Acctblwt CC -.042 -.016 .236(**) .299(**) .066 1.000 -.180(*) .104

Sig. .573 .835 .001 .000 .371 . .015 .162

N 183 183 183 183 183 183 183 183

Dislike CC .012 .170(*) -.167(*) -.330(**) -.238(**) -.180(*) 1.000 .130

Sig. .872 .022 .024 .000 .001 .015 . .079

N 183 183 183 183 183 183 183 183

Actualwt CC -.098 .114 .290(**) -.242(**) .095 .104 .130 1.000

Sig .186 .124 .000 .001 .203 .162 .079 .

N 183 183 183 183 183 183 183 183

* Correlation is significant at the 0.05 level (2-tailed). ** Correlation is significant at the 0.01 level (2-tailed). Key : TMD=Total Mood Disturbance. Percwt=Perceived Wait. Satisfactn=Satisfaction. PercControl=Perceived Control. Acctblwt=Acceptable wait. Actualwt=Actual wait. Rho=Spearmans rho. CC=Correlation coefficient. Sig.=Significance level (2-tailed).

76 What may be the reasons that people become dissatisfied? In an attempt to answer the question “what may be the reasons that people are angry or dissatisfied?” an ordinal regression was carried out. Although there were no reliable mood differences seen among groups, differences in satisfaction for the different groups were highly significant. A regression analysis was therefore used to see how satisfaction was related to other measures, proposed in the literature as possible predictors (see Table 2).

Table 2: Parameter Estimates for Ordinal Regression of Satisfaction.

Std. 95% Confidence Estimate Error Wald df Sig. Interval Upper Lower Bound Bound Threshold [Satisfactn = 1.00] -15.068 9.664 2.431 1 .119 -34.008 3.872 [Satisfactn = 2.00] -13.248 9.645 1.887 1 .170 -32.152 5.656 [Satisfactn = 3.00] -11.169 9.637 1.343 1 .246 -30.058 7.720 [Satisfactn = 4.00] -9.101 9.626 .894 1 .344 -27.968 9.765 [Satisfactn = 5.00] -7.804 9.617 .658 1 .417 -26.652 11.045 [Satisfactn = 6.00] -6.401 9.615 .443 1 .506 -25.246 12.445 Location Percwt -.005 .002 7.963 1 .005** -.008 -.001 Acctblwt .004 .001 17.147 1 .000** .002 .006 Totalonphone -.181 .295 .376 1 .540 -.760 .398 Actualwt .173 .297 .338 1 .561 -.409 .755 [PercControl=1.00] -2.446 1.873 1.704 1 .192 -6.117 1.226 [PercControl=2.00] -2.174 1.882 1.334 1 .248 -5.862 1.515 [PercControl=3.00] -1.977 1.895 1.088 1 .297 -5.690 1.737 [PercControl=4.00] -2.106 1.896 1.234 1 .267 -5.822 1.610 [PercControl=5.00] -1.398 1.927 .526 1 .468 -5.173 2.378 [PercControl=6.00] -.567 1.917 .087 1 .768 -4.323 3.190 [PercControl=7.00] 0(a) . . 0 . . . [Dislike=1.00] 1.567 .638 6.036 1 .014* .317 2.817 [Dislike=2.00] 1.849 .601 9.465 1 .002** .671 3.028 [Dislike=3.00] 1.238 .598 4.284 1 .038* .066 2.410 [Dislike=4.00] .775 .572 1.840 1 .175 -.345 1.896 [Dislike=5.00] -.262 .621 .178 1 .673 -1.480 .956 [Dislike=6.00] .177 .558 .100 1 .751 -.917 1.271 [Dislike=7.00] 0(a) . . 0 . . . Link function: Logit. a This parameter is set to zero because it is redundant. * Predictor is significant at the 0.05 level. ** Predictor is significant at the 0.01 level.

Key : TMD=Total Mood Disturbance. Percwt=Perceived Wait. Satisfactn=Satisfaction. PercControl=Perceived Control. Acctblwt=Acceptable wait. Actualwt=Actual wait. Wald=The Wald statistic – this is used to test the significance of a predictor in the regression model. Sig.=Significance level.

77

Satisfaction was the dependent (predicted) variable. Perceived control and dislike were included as ordinal factors, and actual wait, perceived wait, acceptable wait and total time on the phone were included as covariates. The ordinal regression uses seven on the satisfaction Likert type scale as a reference category to compare how well the predictors (factors and covariates) contribute to scores being closer to or farther away from seven. The estimates column shows us the direction of this relationship. Negative estimates indicate that satisfaction ratings are moving away from seven, and positive estimates indicate that satisfaction ratings are moving towards seven. As can be seen from Table 2, results showed that perceived wait (Percwt; p < .01), and acceptable wait (Acctblwt; p < .001), were related to satisfaction in a highly significant manner. Analysis shows us that the higher the perceived wait, the lower satisfaction was. The negative estimate indicates that as perceived wait increases, the satisfaction scores decrease in comparison with the reference category (get further from seven). Whereas, the longer that people find acceptable to wait, the more satisfied they were with the call (the closer they get to the reference category of seven). Actual wait (Actualwt; p = .56) appears not to be as useful in predicting satisfaction as perceived wait time, with no significant predictive relationship seen, though examining correlations alone would suggest differently. It seems from these regression results that the relationship between actual wait and satisfaction may be mediated by perceived wait. Perceived control did not show any significance in predicting satisfaction, though the two were highly correlated. The lower categories of dislike were also significant predictors of satisfaction. These are more difficult to interpret. Now we have two reference categories to deal with. For dislike, as with satisfaction, seven on the Likert type scale is the reference category. We can examine how the increasing distance from this reference category of dislike predicts the distance from the reference category of satisfaction. The lowest three dislike categories significantly predicted differences in satisfaction ratings. From the estimates column, we can see that they are leading to an increase in satisfaction ratings. This indicates that the further from the dislike reference category (of seven) that these lower dislike ratings were, the closer to the satisfaction reference category the satisfaction ratings became i.e.within the lower 3 dislike ratings, the lower the rating becomes, the higher the satisfaction rating becomes. 78 Choice data An examination of the button press choices made by those participants in the choice condition was carried out, as a behavioural measure of what their preferences would be; given the options they were offered. Initial choices made and secondary choices for any that chose music were examined. Chi-square Goodness-of-Fit tests were used to determine whether any choice was significantly favoured. Choice data was combined with that collected from the next study described (Study 3: The effect of accent on appraisals of a call centre experience). Fifty-one participants in total were included as a part of the combined dataset. For any participant who had opted to hear the menu a second time, the initial choice was taken to be the first choice made after they had listened to the menu a second time. In the data that was examined consequently, only four responses were made. Participants either chose to listen to music, to comedy, to informative statements about the company (which had elevator type music interspersed), or did not make a choice (see Figure 12). If participants did not make a choice they essentially heard nothing until the five minute waiting period was over.

70 60 50 40 30 20

made 10 0 Music Comedy Information No response Percentageof total choices Choice category

Figure 12 : A percentage breakdown of the choices made by those allocated to the choice on-hold listening condition.

As can be seen from Figure 4, a greater percentage of people appeared to choose comedy (29%) and music (63%) compared to information (4%) or nothing (4%). To ascertain whether there was a significant difference to a theoretically equal distribution of responses, a Chi-square Goodness-of-Fit test was used. The pattern of 79 response significantly differed from a theoretical equal distribution, χ2 (3, N = 51) = 47.59, p < .001. A further Chi-square test then confirmed that music was chosen significantly more often than comedy χ2 (1, N = 47) = 6.149, p < .05. The next question to ask is “Of the given music genres available, which was chosen most often?” Results show that responses were spread across the four possible music choices (see Figure 13). An initial Chi-square Goodness-of-Fit test once again showed that the response frequency distribution significantly differed from a theoretical distribution of equal response frequencies for each choice, χ2 (4, N = 32) = 54.562, p < .001.

80 70 60 50 40 30 20 10

Percentageof total choices made 0 Country Classical Jazz Pop Nothing Secondary choice for those who chose music

Figure 13 : Percentage breakdown of the music which those who chose music listened.

Pop was the most frequent musical choice compared with all the rest e.g. a Chi-square test revealed that Pop, with 72% of choices, was chosen significantly more often than the next most frequent choice, Classical (12%), χ2 (1, N = 27) = 13.37, p < .001. There were no significant differences among the music choices when Pop was ignored, χ2 (3, N = 9) = 2.11, p = .55.

Hang-up data Hang up data was also combined to include the hang-up data recorded in Study 3. Because some participants who hung up appeared to be dialling again very

80 soon after dialling the first time, an operational definition for real hang-ups was devised.

Operational definition of a hang-up : any hang-up that occurred more than 10 seconds after the initial statement for that condition finished was considered an intentional hang up. Any people redialling before this were considered to be redialling because they thought the call had not been placed, because they thought they had dialled an incorrect number or did not understand instructions. Those who hung up after this time were considered to be tired of waiting and wanted to try again.

Analysis The number of premature hang-ups that occurred for each on-hold listening condition was recorded for comparison, as was the length of the time before the hang- up occurred. The number of participants hanging up in each condition was compared to a theoretical distribution of equal rates of hanging up for each group using Chi- square Goodness-of-Fit tests. The exact method (SPSS 15.0.1, 2006), of comparison in SPSS was used, as cell counts were less than five. Only four of the eight conditions saw a participant hang up within the terms of the operational definition. There were very low numbers of hang-ups (call terminations) in general. Three (of a possible 51) people in the choice group terminated the call, two (of a possible 26) in the “hands- free” condition did, two (of a possible 49) in the comedy, and one (of a possible 25) in the elevator music and information group did. The distribution of responses did not significantly differ from a theoretical distribution of equal responses in each group, χ2 (3, N = 8) = 1, p = .962. Differences in time to hang up for each condition were tested with two one way ANOVAs. The first used time recorded from the start of the call to termination as a dependent variable; the second used adjusted data for the time when the initial statement finished to termination. There was no significant difference among listening groups in time to hang up from the start of the call F (2, 5) = .32, p = .74, or the end of the initial statement, F (2, 5) = .02, p = .98.

81 Discussion

The main purpose of this study was to examine which on-hold listening conditions during a telephone service might reduce negative mood or increase customer satisfaction. We saw a significant difference in satisfaction ratings among groups, meaning that we would reject null hypothesis 1: That there would be no difference in satisfaction among groups. Participants in the straight-through, choice and comedy conditions were all significantly more satisfied than those in the floor control (the quiet beeping group). Most interesting were where there were no significant differences. There was no significant difference between the straight- through group and the choice group, or the comedy group. These listening conditions appear to be promising intervention material for application in a real world call centre setting – especially the choice group. The finding that introducing choice into the on- hold environment increases call satisfaction is in agreement with past findings of Clemmer and Schneider (1989; Tom et al.,1997), that introducing choice during a wait increases satisfaction. No significant differences in mood from the POMS were seen amongst groups, meaning that we failed to reject hypothesis 2, that there would be no difference in mood among groups. We failed to reject the third hypothesis, that there would be no difference in hang-up rate among groups. There were very few call terminations overall, and neither the number of call terminations, nor the length of time to terminate significantly differed among groups. Hypothesis 4, that participants in groups with increased perceived control would have a more positive mood than those without, was not supported, as there were no significant differences in mood amongst groups. There were also no significant correlations between perceived control and mood measures. Hypothesis 5, that participants in groups with increased perceived control would have higher satisfaction ratings than those without was partially supported. Correlational evidence suggests that there was generally a significant positive association between perceived control and satisfaction. However, of the groups that we created to increase perceived control, only the explicit choice group significantly differed from the floor control (quiet beeping) in satisfaction ratings, whereas the “hands-free” implicit choice/control group was no different from the floor control

82 group. This group did not have as high a level of perceived control as the choice group did. We failed to reject null hypothesis 6, that reducing perceived wait would not increase positive mood. There were no differences in mood or perceived wait among groups, or correlational relationships between mood and perceived wait. Null hypothesis 7, that reducing perceived wait would not increase satisfaction was partially rejected. Though there was no significant difference in perceived wait amongst groups, there was a significant difference in satisfaction, and satisfaction showed a significant negative correlation with perceived wait time. In fact, the ordinal regression adds to the evidence supporting perceived duration on-hold as a significant predictor of satisfaction. Along with perceived wait, what people thought was an acceptable time to wait was a significant predictor of whether they were satisfied or not. The higher a participant’s acceptable wait time was, the more satisfied they were with the call. It is of interest to note that perceived control had no predictive validity in the model tested, despite being highly correlated with satisfaction. Enjoyment (or dislike) was also related to satisfaction, in both correlational and regression results. Correlational and regression results of this study are both consistent with those of Tom et al. (1997; Whiting & Donthu, 2006) that there is some relationship between perceived duration and satisfaction. This study found no evidence of any on-hold listening conditions regulating mood directly using ANCOVA analyses. However, there was a positive correlation between dislike and Total Mood Disturbance suggesting that the more a condition was disliked, the higher mood disturbance was, echoing the findings of Cameron et al. (2003), that the more likeable the listening material was, the more positive a persons mood was. The results regarding the listening condition including informative statements about a company do not provide support for Maister’s (1985) assertions that they would reduce perceived wait time and increase satisfaction. The participants in the information group were not significantly more satisfied than the floor control group of quiet beep. Nor were there any significant differences in perceived wait time amongst any groups at all. The Pop music condition was not rated significantly higher in satisfaction than the floor control group. This is an interesting finding, because the choice data

83 indicates that Pop music was chosen by the majority of those in the choice group. They then listened to the same pieces of music as those in the Pop music group, waited a longer time in total, due to the extended menu, but were still more satisfied than the floor control, where those who had no choice about picking Pop music were not. The humour condition showed promise for on-hold listening material as it was highly enjoyed, and participants listening to comedy were no less satisfied than those in the straight-through group. Practically, however, the use of comedy for this purpose may be limited. Jokes need to be short, so punch lines are heard before an operator answers; they need to be devoid of swearing, and non-inflammatory. Because of these reasons, using comedy in a real world high-cost waiting situation may not be very practical, as sourcing appropriate humorous material may not always be possible. However, call centres or on-hold media suppliers could commission comics to create such material. Data concerning choices made suggested that music was most likely chosen to listen to on-hold. When music was chosen, Pop music was most likely the style to be picked by an undergraduate population. However, this group still scored significantly higher in satisfaction than the Pop music group. The mechanism behind the differing satisfaction levels among the different listening conditions is not totally clear, but results from the ordinal regression in this study suggest that changing the perceived duration on-hold had a very significant effect on satisfaction, along with (to a lesser extent) whether participants disliked, or enjoyed, what they were listening to. Correlations also suggested that the level of perceived control that participants experienced influenced their satisfaction, though the regression did not provide further support to bolster this theory. The anger subscale on the POMS was significantly negatively correlated with satisfaction. The less angry that participants were, the more satisfied they were. No causal inference can be made from this finding. No other POMS scores were related to satisfaction in correlation or regression, so the current work does not support the mood regulation theory. The evidence suggests that there is possibly some form of complex relationship among enjoyment/dislike, perceived wait time and possibly perceived control, leading to satisfaction with the service in this study.

84 An interesting peripheral finding, not mentioned in the results, is that the average acceptable wait time that participants reported was 3 minutes and 36 seconds (rounded to the nearest second). This fits in with the 3-4 minute wait participants felt was acceptable in the Tom et al. (1997) study, but is a lot longer than the Peevers et al. (2009) finding that 1.5 minutes was what participants felt was acceptable. We would have expected the acceptable wait to be closer to 1.5 minutes, as the Peevers et al. study is temporally much closer to our own. There are two possible explanations for the difference we see from the recent work of Peevers et al. The first is a cultural difference. New Zealand culture may be more relaxed about waiting for service compared to the culture in the United Kingdom where Peevers et al. carried out their research. The other possibility is a that a university experimental participant sample is possibly more relaxed about waiting for telephone service compared to actual bank customers that were the participants in Peevers et al.’s work. University students have a more flexible timetable than an eight to five job where calls to service providers may be squeezed into lunch or snack breaks at work. Another finding of interest is that those in the Straight-through group were very satisfied, but did not enjoy the condition to which they listened. This is probably due to the fact that the only audio they heard while waiting was ringing. However, the low level of enjoyment in listening to the ringing was not enough to offset the satisfaction of getting through to service quickly. The implications that this study has for customers are clear; comedy and choice should leave them more satisfied than other on-hold listening conditions like music by itself (even if it is Popular), information and music, call waiting tones and our attempt at a hands-free condition. Those in the comedy and choice listening conditions were not significantly less satisfied than those in the straight-through group, despite waiting much longer than those in the straight-through group. Comedy was the same as the straight-through condition 53 times out of every 100, whereas the choice group was no different to the straight-through 70 times out of every 100. This study found no differences in mood amongst the groups in different listening conditions; there was no reduction in the negative affect (aside from dissatisfaction) in callers for any one group more than any others. In terms of a reduction in angry callers, or callers with negative mood, the on-hold listening conditions trialled here seemed to have little effect. This could simply mean that

85 generally our samples were not angry enough for any differential effects in mood regulation to show up. It could also mean that a reduction in angry or abusive callers, for the purposes of improving CSR well being, may not be possible using the techniques we trialled. However, if customers are more satisfied, as in our results, then an on-hold intervention may be able to reduce some of the emotional dissonance that customer service representatives (CSRs) feel when they are trying to help a dissatisfied customer. Moreover, the anger subscale of the POMS was significantly negatively correlated with satisfaction. It is plausible to expect that great dissatisfaction could indeed lead to angry callers, and thus, reducing this satisfaction may go some way to improving customer mood. We believe that the effects that the choice and comedy on- hold listening conditions had on satisfaction warrant further examination in a real world setting for this reason. A major limitation of the current study was a methodological one. The mood measure did not take into account the mood of participants before the telephone interaction. By neglecting to take a pre-test measure we reduced the likelihood of statistical tests picking up slight differences in mood. Also, the measure used may not have been sensitive at picking up slight, but important, differences of mood that were occurring but did not reach significance e.g. Bond and Lader (1974). Another possible reason for no differences in mood is that the length of time that participants had to wait in the listening conditions was not long enough to induce a change in mood. Cameron et al. (2003) created differences in mood using different music during a wait of ten minutes. Also, mood measurement in Cameron et al.’s study was measured using three scales; pleased-annoyed, happy-unhappy and satisfied-dissatisfied. We attempted to examine mood in a more in-depth fashion using a dedicated mood measure, examining satisfaction separately. A secondary limitation with the current study is that the wait carried a very low perceived cost for participants, because they had already set aside the time to do the experiment. In a real life situation, one may call a company expecting immediate service before waiting on the telephone for minutes, and the call may be about something important e.g. a large insurance claim or a large amount of over taxation. The perceived cost of the wait in this study may have been low enough that no negative mood was induced, where, had the cost of the wait been higher, a negative

86 mood may have been induced and the effects of the different conditions observed. In conjunction with cost, benefits may also be lower than they could be in call centres.

87 88 Study 3: The effect of accent on appraisals of a call centre experience.

This study was carried out in conjunction with a third year psychology honours research project authored by Skye Hignett.

Study 3 was carried out for two reasons. The first was to further test the top listening conditions from Study 2, and the second was to examine the effect that customer service representative (CSR) accent had on the appraisal of the call centre experience. The Comedy, Choice, Pop and Straight-through conditions were tested on the same measures as for Study 2, but for this study, the POMS was used before and after the call to account for prior variations in mood of the individual participants taking part in the experiment. Additionally, a behavioural measure of satisfaction/frustration was trialled, using response force (key push) as an index of decisiveness in giving a positive, neutral or negative answer for a given question. Studies have shown that the more intense a stimulus is, the more forceful a response will be (Kunde, 2001). In our situation this stimulus is a feeling of satisfaction or frustration. The more intense this feeling is, the more forceful a positive response should be. How different accents affect the appraisal of a call centre experience is of importance due to the incidence of outsourcing/offshoring call centres to countries other than those that they are servicing. The accents of the CSRs in these countries can differ from those in the Country being serviced, and these accents alone may impact on customer satisfaction (Stringfellow, et al., 2008), affective response (Bresnahan et al., 2002), brand perception (Bennett & Loken, 2008), and purchasing intentions (DeShields & de los Santos, 2000). Recent research suggests that speakers with non-native accents are considered less credible than native speaking counterparts, which may have negative consequences for those who often speak in a language other than their own native tongue (Lev-Ari & Keysar, 2010). Lev-Ari and Keysar (2010) carried out two experiments where they measured a) the perceived truthfulness of a number of trivia statements and b) truthfulness and difficulty in understanding the trivia statements. The trivia statements were spoken by

89 either native American English speakers, mildly accented non-native speakers, or heavily accented non-native speakers. In the first experiment the authors found that significantly more of the statements were considered untrue for both groups of accented speakers compared to the native speakers. However, in the second experiment, where participants were also asked to rate how difficult the accents were to understand, there was no difference in perceived truthfulness between the mildly accented non-native and native speaker, but the heavily accented trivia statements were still regarded as less truthful. Difficulty in understanding was found to increase, the heavier the accent. The authors concluded that participants in the first experiment misattributed a decrease in processing fluency to a lack of speaker credibility, rather than a difficulty in understanding the accent. They proposed that in the second experiment, by making the difficulty in understanding more salient, the attribution of a lack of processing fluency was adjusted correctly for the mildly accented statements, but not for the heavily accented speakers. These recent findings are in line with previous research that has found non-native accented speakers are rated as less competent (Watanabe, 2008), and intelligent (Lindemann, 2003), than those native to speaking English. In contrast with the cognitive ‘ease of understanding/fluency of processing’ explanation of why foreign, non-native, English accents lead to negative ratings, are two more social explanations. The first is Tajfel and Turner’s (1979) Social Identity Theory (SIT). This theory posits that to maintain a positive self image, individuals categorise the speaker as a part of a social out-group based on their accent, then, to boost self image, denigrates the out-group compared to their own in-group. The theory has seen support from a piece of research by DeShields and de los Santos (2000). They examined purchasing intentions when sales people had North American-English accents and Mexican-English accents in North America and American-Spanish and Mexican-Spanish accents in Mexico. When the North American sample was tested, the North American-English accented sales people garnered significantly more positive purchase intentions than the Mexican-English accented sales people. The authors took this to be support for Tajfel and Turner’s SIT. However, in the Mexican sample, there was no difference in purchasing intentions between the two accent conditions, where the SIT would expect that Mexicans would prefer Spanish with a Mexican accent compared to American

90 accented Spanish. The differences seen could be due to lack of processing fluency being misattributed as a poor sales pitch, reducing the purchasing intentions for the Mexican-English accent sales people. In fact, DeShields and de los Santos (2000) analysed how easy the American sample reported being able to understand the North American-English and Mexican-English accents, and found that the Mexican-English accent was rated as harder to understand, and less pleasant listening. The second social explanation is that participants have stereotypes associated with accents, and a given stereotype is activated upon hearing a given accent. Wells (1982) proposes that accents are a significant element of various stereotypes. Evidence that supports this proposal can be seen in work by Ladegaard (1998), where students from Denmark rated Received Pronunciation (RP) English, also known as The Queen’s English, highly in scales relating to both status and competence, compared to American, Australian and Scottish English accents. These ratings were consistent with the affable and traditional stereotypes of English culture that students held. However, the stereotyping explanation for differential ratings of accent has not always been supported in the research. Lindemann (2003) found that native English speaking Americans’ ratings of Korean accents did not adhere to an associated stereotype of intelligence. Further, Gill (1994) found that while North American, British and Malaysian English accents affected North American perceptions of teachers, and comprehension of subject matter, the stereotypes that participants held relating to North Americans, Britons and Malaysians did not. In New Zealand there have been mixed findings with regards to which accents are aurally preferred, or have positive appraisals. The New Zealand accent has been rated both higher (Watanabe, 2008) and lower (Bayard, Weatherall, Gallois & Pittam, 2001) than a North American English accent on a number of traits relating to both status and competence. In the case of call centre outsourcing, there is another possible explanation that arises as to why appraisals of non-native accents can differ from native accents. There may be a negative reaction to the accent because of the perceived loss of potential jobs in the caller’s own Country (Cowie, 2007). This could be thought of as an example of realistic group conflict theory (RCT; Campbell, 1965). Conflict may be caused by moving jobs offshore that could seemingly, otherwise, be available to those

91 in the Country that is being serviced. The accented, non-native, speakers become a part of a competing out-group that could have hostile reactions directed toward it. Regardless of what the causal mechanism behind differing appraisals for various accents is, with the globalisation of the international market today, it is of interest to see whether major differences in appraisal occur in a telephone service situation. A 2009 survey of Chief Financial Officers (CFOs) from leading American technology businesses by American company BDO Seidman found that India was the most used location for outsourcing outside of the United States with a 50% market share. South East Asia, including the Philippines, was the next most commonly used outsourcing destination by the sample (31%; Pappas, 2009). Indian cities made up six of the top eight outsourcing destinations in a 2008 global report of outsourcing destinations, with a city each from the Philippines and Ireland rounding off the eight. In the same report, the top emerging global outsourcing city was also found in the Philippines (Vashistha & Khan, 2008). In New Zealand, there have been a number of call centre positions move off shore recently to outsourced call centres. Telecom (Campbell, 2009), Yellow (NZPA, 2009), Vodafone (Drinnan, 2009), and TelstraClear (Speedy, 2009), have all shifted some of their customer service operations to The Philippines recently. Due to the recent moves of New Zealand call centre jobs to the Philippines, and India being considered the top outsourcing destination globally, these accents were considered important to trial in a call centre experience. Two accents which have been suggested in the past as more favoured by New Zealanders were used as bases for comparison; North American (Bayard et al., 2001) and New Zealand (Watanabe, 2008) accents.

Methods

Participants Participants were 103 Otago University undergraduates who could satisfy a small portion of course assessment by completing a worksheet based on the experiment. Advertisements asked for participants that were not seeing a mental health practitioner for any reason.

92 The initial sample consisted of participants ranging in age approximately from 18 years to 46 years of age (age information was missing for three of these). The mean age was 19.93 (median = 19), with a standard deviation of 3.75. Of these initial participants, 79 were female.

Apparatus The apparatus used in the current study included the same modified telephone handsets used in Study 3 (see Appendix F). There were also several of the same files Waveform Audio File Format (.WAV) used; the same comedic excerpts performed by the late Mitch Hedberg, the same Pop music, and the same pieces of music as in the choice condition of the previous experiment (see Appendix J for full details). A number of other .WAV files to simulate the call centre experience were recorded in different accents. These were the same as the statements spoken in Study 2 for Choice, Non-choice Comedy, Non-choice Pop and Straight-through conditions (see Appendix J). All of the accented statements were spoken by adult females. Females were chosen as past research has shown that they make up the majority of call centre staff (e.g. Taylor & Bain, 1999; Holman et al., 2007). The speakers were from North America (Approximately 50 years of age, from the Mid West); The Philippines (21 years, born in and raised in Manila, English speaker since Primary school); India (27 years, born in Northern India) and New Zealand (25, Born and raised). All vocal actors had at least a University undergraduate qualification. Attempts were made to match the age of the speakers as closely as possible, but limited interest/numbers of speakers with the desired accents meant that we had to make concessions. The computer program Audacity 1.3 beta (Audacity, 2010) was used to edit .WAV files so that they were the correct length and format for playback. All of the accented files were matched in length and in general sound profile. The waveforms of each vocal track were examined visually, then manipulated so that they all matched approximately, with each major peak and trough lined up as closely as possible, without making the vocal tracks sound unnatural. A computer version of the Profile of Mood States (POMS) mood measure (McNair, Lorr & Droppleman, 1992; see Appendix G), was used as a state measure of mood and an additional questionnaire was used to rate the service of the call centre and the accents on a number of attributes (see Appendix N).

93 A purpose built apparatus was constructed as a behavioural measure of satisfaction and frustration (see Appendix O for photograph). Three buttons were mounted over top of Tekscan FlexiForce ® A201 transducers that communicated with a computer via a Phidget 1120 - FlexiForce Adapter and the PhidgetInterfaceKit 8/8/8, using a USB connection. The buttons were labelled to correspond with a response to a given question; yes, no or maybe. Lights were wired into the circuit so that when a button was pressed by a participant, a light would appear above that button until an experimenter reset the apparatus. The output that the device relayed was ratiometric 2, so provided a measure of difference between responses, but not an actual measure of force in Newtons. The apparatus was mounted on the side of a desk so that the heel of the palm could be used to press the buttons. In addition, an experimental log was included for the experimenter to note down the participants’ responses and ratiometric scores from the behavioural apparatus (see Appendix P).

Design The experiment was primarily a mixed design with between subjects’ factors of listening condition, and accent condition. The listening condition consisted of four levels: Pop, Comedy, Choice and Straight Through. The Accent Condition consisted of four levels also: North American, Filipino, Indian and New Zealand. Within subjects factors were pre vs. post measurements of mood using the POMS.

Procedure Prior to participants arriving, the experimenter arbitrarily assigned serial numbers to each, based on the order that they signed up to the experiment. These were used to link all data collected for each participant, while preserving anonymity. Participants were then randomly assigned to an on-hold listening condition, and an accent condition. Random allocation to conditions was achieved by assigning two numbers (from one to eight) to each accent condition, and each listening condition. The Random.org integer generator (Haahr, 2010), was then used to create two random numbers for each participant. The first random number was used to allocate accent. Numbers ending in either numeral associated with an accent condition were assigned

2 “Ratiometric - Describing any system in which an output is directly proportional to an input” (“Ratiometric”, 2010). 94 to that condition. Numbers produced ending in 1 or 5 = American; 2 or 6 = Filipino; 4, or 8 = Indian; 3 or 7 = NZ. If a number 9 or 0 was produced, this was skipped and the next number was used. The second number produced by the random integer generator was used in the same way to allocate listening conditions. Following random assignment, the computer/phone setup was then readied so the computer would play back the appropriate response for the assigned conditions through the telephone handset to each participant. This was done by activating the purpose written program called “Phone” used in Study 2, and inputting the appropriately accented narration and listening condition .WAV files ready for playback. The computer screen was then turned off so that participants would not realise that a program was running the telephone interaction. When they arrived, participants were asked to take a seat while the experimenter ran through the details of the experiment with them, and gave them an information sheet to read (see Appendix Q). Participants were then asked to turn off their cell phones and either place them in their bag or give them to the experimenter to keep in a secure lockbox until the end of the experiment, along with their watch, if they had one. Participants were informed that this was because they would be filling out a questionnaire regarding perceptions following the mood measure and we did not wish anything to influence these. This measure prevented the actual/objective time spent waiting affecting the participants’ perceived/subjective experience of time. Once consent was obtained (see Appendix Q for consent form), the next step involved the participants taking an initial test of mood using a computerized version of the POMS (paper copies of the forms had been purchased for this purpose, but for practical reasons a computerised version was used to collect responses). The first page of this computerised POMS asked for the participants Client I.D.; their age, and their gender. Participants were given their serial number to input as their Client I.D., and were asked to fill out the remaining demographic questions, before continuing to the next two pages to fill in the POMS. Following this step, participants were then asked to call through to Psychnet; a fictional call centre. They were told the Psychology department had organised Psychnet to take calls and give out sorting codes required for the next part of the experiment. The phone call started with an initial statement that was the same for each group. It stated: “Thank you for calling Psychnet. Unfortunately our lines are

95 currently busy. Please hold for assistance. We apologise for the wait and will try and connect you as soon as possible”. Following this statement, the listening condition that they had been assigned to was played. This could either be: Group 1: Choice Group 2: Comedy Group 3: Pop Group 4: Straight-through Refer back to Study 2 methods for details of each group. All of the conditions aside from the Straight-through played for 5 minutes following the initial statement. The initial statement did not vary in length for the choice group, but they had an additional menu added before the required 5 minute period during which they could listen to the choice(s) they made (see Figure 14 for a flow chart of call progress).

96 Pop and Comedy Listening condition Ringing 10 seconds (300seconds)

Start Initial statement (12 seconds) Ringing and final message. 38 seconds.

Total time: 360 Actual wait: 322 Straight through Ringing 10 seconds Ringing 10 seconds Start Initial statement (12 seconds) Ringing and final message. 38 seconds.

Total time: 70 Actual wait: 32

Choice Time for choices and listening (300seconds) Ringing 10 seconds Start Initial statement and instruction Ringing and final (58 seconds) message. 38 seconds.

Total time: 406 Actual wait: 368

Figure 14 : Flow chart of calls in different listening conditions. This includes time to answer/connection to CSR (actual wait), and the total time it took for the call to complete, provided there were no hang ups (Total time). The accents were matched exactly for the time each statement took.

97

Following the listening condition that played, a final statement was heard by all participants; “Welcome to Psychnet. Thank you for waiting. Ok, I’ll just get your sorting code….. Your sorting code is [code here]. Thank you. Goodbye”. Codes varied depending on the accent and listening condition to which the participant had been listening. This way they could then be used as a double check that the correct condition had been played to that participant. All of the dialogue that occurred during the entire call was heard spoken in the accent of the condition to which a participant had been assigned. After the call, participants were once again asked to take a seat at one of the computers and complete the POMS. For this secondary completion of the POMS, participants were asked to add B to the end of their serial number for the purposes of identifying these responses as being on the second trial. After this secondary POMS questionnaire, participants were asked to fill out an additional questionnaire regarding their perceptions of the call and the accent they heard. The final phase of the experiment was an attempt to attain a behavioural measure of the frustration or satisfaction that a participant may have experienced during their call. Participants were asked to take a seat at the measurement apparatus. To ensure an equal distance from the apparatus for all participants they were asked to make sure the front legs of the chair were equal to a line on the ground made by the experimenter (see Appendix O). Participants were then instructed that they would be read a few questions and indicate their answer by pressing one of the buttons in front of them labelled ‘Yes’, ‘Maybe’ and ‘No’. They were instructed to press the button that indicated their choice with the heel of the palm of their dominant hand, such that the fingers were pointing downward when the choice was made. The labels were counterbalanced so that ‘yes’ and ‘no’ responses were both situated for half of the experiment on the right, and half of the experiment on the left, to control for preferences of responding to the right or left. Question 1 was a control: “Do you prefer 2B pencils over HB pencils?” If participants hesitated in answering at all, they were prompted by the experimenter that they could respond ‘maybe’. The next two questions were the experimental questions. Question 2 was “Were you frustrated by your phone call to Psychnet?”, and question 3

98 was “Were you satisfied with your phone call to Psychnet?” Participants received no prompts during their responses to these questions. The amount of pressure registered on the ratiometric scale was then read off a computer connected to the device by an experimenter. On the last 30 trials item by item reliability was gathered, with two experimenters individually noting every ratiometric score from the computer for comparison. Once participants completed the three questions, and responses were noted, they were debriefed fully as to the nature of the experiment and thanked for their participation (see Appendix Q for a copy of the debrief sheet).

Results

Self report Accent Likert type scales To examine differences among conditions for the self report measures that were taken, a number of different tests were used. To analyse main effects of the accent on a number of Likert type scales, several Kruskal-Wallis non-parametric one way ANOVAs were carried out in SPSS 18. For scales with significant results in these tests, Non- parametric pair-wise comparisons were carried out post-hoc to examine where the differences lay. Three participants were excluded from the analyses as they had not been given the questionnaire containing the scales due to experimenter error, or had not responded to some of the questions. A total of 100 participants’ data were used; 76 of whom were female; 24 male. Ages ranged from 18 to 46, with a mean age of 19.93 (median = 19), and a standard deviation of 3.75. Analyses revealed that there were no significant differences among the different accents on Likert type scale measures of satisfaction, χ 2 (3, N = 100) = 4.58, p = .21; perceived control χ 2 (3, N = 100) = 1.53, p = .67; and dislike (of listening condition), χ 2 (3, N = 100) = 5.83, p = .12. There was a significant difference among accents on a Likert type scale measurement of speaker competence χ 2 (3, N = 100) = 8.10, p < .05. Post hoc tests revealed that the American accent was rated as significantly more competent than the Filipino accent, p<.05; with the New Zealand and Indian accents not rated as

99 significantly more or less competent than any other (see Figure 15 for a comparison of means).

7 6 5 4 3 2 1 Mean competence Meanrating competence 0 American Indian New Zealand Filipino Operator accent

Figure 15 : Mean Likert type ratings (+SE) of speaker competence across accent conditions .

There was also a significant difference in the Likert type scale ratings of how much participants disliked each accent, χ2 (3, N = 100) = 13.24, p < .01. Post hoc analyses showed that the New Zealand accent was liked significantly more than the Filipino accent. The American and Indian accents were not liked significantly more or less than any other accent (see Figure 16 for comparison of means).

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Mean dislike rating of operator 0 American Indian New Zealand Filipino Operator accent

Figure 16 : Mean Likert type ratings (+SE) of dislike across accent conditions. 100

Finally, a significant main effect of accent on a Likert type scale measurement of speaker likeability was discovered, χ2 (3, N = 100) = 8.12, p < .05. Oddly, post hoc analyses did not reveal any differences amongst groups. However, of the mean ranks that the Kruskal-Wallis test generated, the greatest difference lay between the New Zealand speaker (rated most likeable), and the Filipino speaker (rated least likeable). We have to assume that this where the significant difference, seen in the initial analyses, lay (see Figure 17 for a comparison of means).

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0 American Indian New Zealand Filipino Operator accent

Figure 17 : Mean Likert type rating (+SE) of speaker likeability across accent conditions.

Profile of Mood States for accent For this analysis a further 6 participants were excluded, as they had incorrectly filled out the POMS computer form making us unable to distinguish which was before, and which after, the Psychnet call. To examine the effects of accent on the self reported mood of participants a repeated measures ANCOVA analysis was carried out. There were 71 females and 23 males included in the analysis; and ages ranged from 18 to 46. The mean age was 19.98 (median = 19), with a standard deviation of 3.85. Within subjects factors included whether the POMS test had been filled out before (pre) or after (post) the call to Psychnet, and the different POMS scales. Between subject factors included

101 operator accent and gender, and age was included as a covariate. To increase homogeneity of variance, a log transformation was carried out, following increasing all scores by 50 so no negative scores were present. Mauchly’s test for sphericity was significant so Greenhouse-Geisser statistics were used for significance tests. Not surprisingly, there were differences in the scores for the different POMS scales. However, there was no pre-post x accent interaction, F (3, 85) = .91, p = .44; or pre-post x POMS scale x accent interaction, F (6.01, 170.39) = 1.092, p = .37, indicating that operator accent during the call had no effect on overall participant mood. The same analysis was carried out for each subscale of the POMS, with no significant findings for any of the subscales. Full analyses can be found in Appendix R.

Listening condition Likert type scales The main effects of listening condition on the same set of scales as those analysed for accent were examined. As could be reasonably expected, listening condition (Comedy, Pop, Straight-through and Choice) had no main effect on ratings of accent dislike, χ 2 (3, N = 100) = 2.38, p = .50; competence of the speaker, χ 2 (3, N = 100) = 6.25, p = .10; or likeability of the speaker, χ 2 (3, N = 100) = .308, p = .96. Also non significant was the effect of listening condition on levels of perceived control, χ 2 (3, N = 100) = 6.12, p = .11 Listening condition did have a significant main effect on satisfaction ratings, χ 2 (3, N = 100) = 27.90, p < .05. Post hoc analyses revealed that those in the straight through listening condition were significantly more satisfied than Comedy, Pop or Choice. There were no significant differences among the three less satisfactory groups. See Figure 18 for a comparison of means.

102 7

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0 comedy pop straight through choice Listening condition

Figure 18 : Mean Likert type ratings (+SE) of satisfaction with the Psychnet telephone service.

Listening condition also had a significant main effect on the dislike ratings of what was listened to during the call, χ 2 (3, N = 100) = 27.904, p < .05. Post hoc analyses revealed that participants listening to the straight through condition disliked what they heard significantly more than those in the Choice and Pop conditions, p < .05 (see Figure 19 for means). No other significant differences were found amongst groups.

103 7

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Mean dislike for what listened was rating 1 comedy pop straight through choice Listening condition

Figure 19 : Mean Likert type rating (+SE) of dislike of what was listened to on-hold, across listening conditions.

Profile of Mood States for listening condition To examine the effects of listening condition on the self reported mood of participants, a repeated measures ANCOVA analysis was carried out. There were 71 females and 23 males used in this analysis. Ages ranged from 18 to 46. The mean age was 19.98 (median = 19), with a standard deviation of 3.85. Within subjects factors included whether the POMS test had been filled out before (pre), or after (post), the call to Psychnet, and the different POMS scales. Between subject factors included listening condition and gender, and age was included as a covariate. Mauchly’s test for spheiricity was significant so Greenhouse-Geisser statistics were used for significance tests. Not surprisingly, there were differences in the scores for the different POMS scales. However, as with accent, there was no pre-post x listening condition interaction, F (3, 89) = .84, p = .47 or pre-post x POMS scale x listening condition interaction, F (6.25, 177.01) = .87, p = .52. There was no evidence to suggest a significant effect of listening condition on participant mood. The same analysis was carried out for each subscale of the POMS, with only one significant finding. There was a gender by pre/post interaction on the Fatigue-Inertia scale, F (1, 85) = 5.18, p < .05. Males reduced in fatigue to a greater extent than females

104 from the pre to the post test. There were no significant effects of listening condition whatsoever on any other POMS subscale. Full analyses are included in Appendix S.

Perceived wait (for accent and listening condition) To analyse the effect of perceived wait time, both accent and listening condition were included in the same analysis. This analysis included 61 females and 18 males. The 15 further exclusions were due to 13 participants missing responses on the questionnaire and 2 participants actual wait times not being registered. Differences in perceived wait were examined using an ANCOVA analysis with the dependent variable of perceived wait time (in seconds), independent variables of accent, listening condition and gender; and covariates of age, actual wait time and acceptable wait time. No significant differences in perceived wait time were found among the accent, F (3, 46) = .06, p = .98 or listening, F (3, 46) = 1.79, p = .16 conditions, when the covariates above were included in the analysis. There was no significant effect of an accent by listening condition interaction on perceived wait time either, F (9, 46) = .84, p = .58

Correlations Finally, as for Study 2, a correlation matrix was constructed to analyse the relationship among the various measured variables in this study. For a complete matrix which includes all of the subscales of the POMS see Appendix T. An abridged version can be seen in Table 3. Satisfaction ratings were highly correlated with levels of perceived control, r (92) = .32, p < .01, and ratings of speaker competence, r (92) = .35, p < .01. Satisfaction ratings were also correlated with speaker likeability, r (92) = .26, p < .05, and acceptable wait time, r (90) = .24, p < .05. Satisfaction showed a strong negative correlation with actual wait time, r (87) = -.42, p < .01; and perceived wait time, r (83) = -.49, p < .01, and was also negatively correlated with initial Total Mood Disturbance (TMD), r (92) = -.21, p < .05, and post test TMD, r (92) = -.24, p < .05.

105 Table 3: Abridged correlation matrix for self report measures and actual wait in Study 3. Continued on next page, with key.

satisfacti compete dislikea Acctblw PreTM Post on control dislike nce ccent likeability Actualwt Percwt t Age D TMD Spearm satisfactio Correlation - an's rho n Coefficient 1.000 .316(**) -.097 .355(**) -.077 .257(*) -.423(**) -.493(**) .244(*) .141 -.212(*) .242( *) Sig. (2-tailed) . .002 .354 .000 .463 .012 .000 .000 .019 .176 .041 .019 N 94 94 94 94 94 94 89 85 92 94 94 94 control Correlation .316(**) 1.000 -.027 .241(*) -.122 .094 -.102 -.114 .129 -.030 -.090 -.138 Coefficient Sig. (2-tailed) .002 . .796 .019 .240 .368 .343 .301 .222 .772 .390 .185 N 94 94 94 94 94 94 89 85 92 94 94 94 dislike Correlation -.097 -.027 1.000 -.064 -.181 -.183 -.355(**) -.338(**) -.116 -.074 .046 .083 Coefficient Sig. (2-tailed) .354 .796 . .540 .080 .078 .001 .002 .272 .480 .658 .426 N 94 94 94 94 94 94 89 85 92 94 94 94 competen Correlation .355(**) .241(*) -.064 1.000 -.237(*) .303(**) -.166 -.125 .252(*) .091 -.050 -.035 ce Coefficient Sig. (2-tailed) .000 .019 .540 . .021 .003 .120 .254 .015 .385 .635 .737 N 94 94 94 94 94 94 89 85 92 94 94 94 dislikeacc Correlation -.077 -.122 -.181 -.237(*) 1.000 -.008 .055 -.014 -.148 -.058 .124 .103 ent Coefficient Sig. (2-tailed) .463 .240 .080 .021 . .938 .606 .900 .160 .578 .233 .322 N 94 94 94 94 94 94 89 85 92 94 94 94 likeability Correlation .257(*) .094 -.183 .303(**) -.008 1.000 -.016 .053 .267(*) .079 -.094 -.085 Coefficient Sig. (2-tailed) .012 .368 .078 .003 .938 . .880 .632 .010 .450 .368 .415 N 94 94 94 94 94 94 89 85 92 94 94 94 Actualwt Correlation -.423(**) -.102 -.355(**) -.166 .055 -.016 1.000 .647(**) -.003 .055 .121 .105 Coefficient Sig. (2-tailed) .000 .343 .001 .120 .606 .880 . .000 .976 .609 .260 .326 N 89 89 89 89 89 89 89 80 87 89 89 89

106

satisfacti compete dislikea Acctblw PreTM control dislike likeability Actualwt Percwt Age PostTMD on nce ccent t D Correlation -.493(**) -.114 -.338(**) -.125 -.014 .053 .647(**) 1.000 .177 .135 .227(*) .167 Percwt Coefficient Sig. (2-tailed) .000 .301 .002 .254 .900 .632 .000 . .106 .219 .037 .128 N 85 85 85 85 85 85 80 85 84 85 85 85 Acctblwt Correlation .244(*) .129 -.116 .252(*) -.148 .267(*) -.003 .177 1.000 -.018 -.142 -.100 Coefficient Sig. (2-tailed) .019 .222 .272 .015 .160 .010 .976 .106 . .867 .177 .344 N 92 92 92 92 92 92 87 84 92 92 92 92 Age Correlation .141 -.030 -.074 .091 -.058 .079 .055 .135 -.018 1.000 -.130 -.181 Coefficient Sig. (2-tailed) .176 .772 .480 .385 .578 .450 .609 .219 .867 . .212 .081 N 94 94 94 94 94 94 89 85 92 94 94 94 PreTMD Correlation -.212(*) -.090 .046 -.050 .124 -.094 .121 .227(*) -.142 -.130 1.000 .933(**) Coefficient Sig. (2-tailed) .041 .390 .658 .635 .233 .368 .260 .037 .177 .212 . .000 N 94 94 94 94 94 94 89 85 92 94 94 94 PosTMD Correlation -.242(*) -.138 .083 -.035 .103 -.085 .105 .167 -.100 -.181 .933(**) 1.000 Coefficient Sig. (2-tailed) .019 .185 .426 .737 .322 .415 .326 .128 .344 .081 .000 . N 94 94 94 94 94 94 89 85 92 94 94 94 * Correlation is significant at the 0.05 level (2-tailed). ** Correlation is significant at the 0.01 level (2-tailed). Key : Acctblwt = Acceptable wait time. Percwt = Perceived wait time. Actualwt = Actual wait time. Dislike = dislike of what was listened to on-hold. Disikeaccent = how much the speakers accent was disliked. Pre = prior on-hold treatment, Post = following on-hold treatment. TMD = Total Mood Disturbance. Sig. = Significance level (2-tailed).

107

Other than satisfaction, control was the only measure significantly correlated with speaker competence, r (92) = .24, p < .05. Dislike showed a strong negative correlation with actual wait time, r (87) = -.35, p < .01, and perceived wait time, r (83) = -.34, p < .01. The strange negative correlation of dislike and perceived wait, r (83) = -.34, p < .01, can be explained by the level of dislike for the straight-through condition. Though there were no statistically significant differences in perceived wait among groups, there was a trend toward the straight-through condition having a lower perceived wait. Callers only had ringing to listen to during their wait in this condition so dislike what they heard aurally. The same type of negative correlation between actual wait and dislike can be explained in the same way. Apart from the relationships already discussed pertaining to speaker competence, competence was significantly negatively correlated with accent dislike, r (92) = -.24, p < .05; showed a strong positive correlation with speaker likeability, r (92) = .30, p < .01; and was positively correlated acceptable wait time, r (90) = .25, p < .05. Speaker likeability was correlated positively with acceptable wait, r (90) = .27, p = .01. Actual wait was, not surprisingly, positively correlated with perceived wait in a highly significant manner, r (78) = .65, p < .01. Perceived wait was positively correlated with initial Total Mood Disturbance (TMD; before treatment), r (83) = .23, p < .05, but not with post test TMD. Pre and post TMD were highly correlated, r (92) = .93, p < .01.

Behavioural measure (button press) For the analysis of behavioural data, 98 participants were included. The behavioural apparatus was not operational for the first two participants, and three were excluded because they had not followed the same procedure as other participants (they had not filled in the additional questionnaire before moving on to the behavioural measure). There were 74 female and 24 male participants. Ages of participants ranged from 18-46. The mean age of participants was 19.94 (median = 19), with a standard deviation of 3.78. Each participant was asked three questions. Question 1 was: “Do you prefer 2B pencils over HB pencils?” Question 2 was: “Were you frustrated by your phone call to Psychnet?” Question 3 was “Were you satisfied with your phone call to

108 Psychnet?” Unfortunately the first 30 participants were not asked question 3, as this question was not included until part way through running participants.

Apparatus test-retest reliability Unfortunately the apparatus used, though a promising idea, needs refinement as it was not a reliable measure. Post testing, a 1.5329 kg weight was measured 100 times on each button to see if the device was reliably giving the same result on the ratiometric scale. The results of that test are included in Appendix U. There was a large amount of variability in the repeated tests, indicating a lack of test-retest reliability in the measured ratiometric output relating to the force of response.

Force analyses for behavioural apparatus Prior to analysing the button press data, a log (base 10) transformation of the ratiometric scale was carried out to reduce heterogeneity of variance. To examine whether there were any general differences in the force among responses that participants made for each different question, ANOVA analyses for each question were carried out separately. Each ANOVA had the dependent variable of the log transformed pressure for that question. Independent variables included were response choice, accent condition and listening condition. These analyses are included in Appendix U also, but not reported due to the lack of test-retest reliability.

Choice analyses for behavioural apparatus A multinomial regression analysis was conducted for each question that was measured using the behavioural apparatus. This was done to see which factors aided in predicting whether a ‘yes’, ‘no’ or ‘maybe’ response was more likely. Predictor variables included listening condition, accent condition, and the age and gender of respondent. Separate regression analyses were undertaken for each question (see Appendix U). For Question 1: “Do you prefer 2B pencils over HB pencils?” listening condition, accent, and age showed no significant power in predicting participants’ response of ‘no’ or ‘maybe’, compared to the reference category of ‘yes’. For Question 2: “Were you frustrated by your phone call to Psychnet?” the listening condition that participants were in had significant predictive power in which response

109 they chose. Compared to the reference category of ‘yes’, those who listened to the straight through group were significantly more likely to respond ‘maybe’ to the question than the choice group. Question 3: “Were you satisfied with your phone call to Psychnet?” did not have a large sample size, and many empty cells, negating the efficacy of carrying out a multinomial regression successfully for the question. None of the predictors were found to significantly predict participant response for this question (see Appendix U for full analyses). To examine whether an answer on one of the behaviourally measured questions influenced answers on the other, crosstab examinations were carried out using SPSS 15. The Crosstab examination of Questions 2 (“Were you frustrated by your phone call to Psychnet?”) and 3 (“Were you satisfied with your phone call to Psychnet?”) generally followed a pattern that could be reasonably expected (see Table 4). The largest response combination was “no” to Question 2, and “yes” to Question 3 (55.7%). Those who were not frustrated most often indicated that they were satisfied. Strangely, some who indicated that they were frustrated also indicated that they were satisfied (10%). Some indicated “no” to both questions (2.9%). This suggests that some participants may view frustration and satisfaction as less interdependent than others; the levels of frustration or satisfaction induced in these participants may not have been high enough to cause a complete change in the other. Conversely these participants may view the two feelings as mutually exclusive.

Table 4: Crosstab examination of Questions 2 and 3. Question 2 maybe no yes Total Question maybe Count 0 6 1 7 3 % of Total .0% 8.6% 1.4% 10.0% no Count 2 2 10 14 % of Total 2.9% 2.9% 14.3% 20.0% yes Count 3 39 7 49 % of Total 4.3% 55.7% 10.0% 70.0% Total Count 5 47 18 70 % of Total 7.1% 67.1% 25.7% 100.0%

110 Discussion

Accent The current finding that New Zealanders score their own accent highly on likeability ratings provides support for past work by Watanabe (2008) that found a New Zealand English accent was rated highly compared to others, on similar traits of friendliness and sense of humour. The findings of Watanabe were also supported in that a) New Zealand and American English accents did not significantly differ from one another in pleasantness (what we measured as like/dislike to listen to), and, b) that a New Zealand English accent was rated significantly higher in pleasantness than lowly rated accents. In our case this accent was Filipino, in Watanabe’s it was Japanese. The findings also support research by Bayard et al. (2001), regarding the higher competence ratings of an American accent compared to some other accents by a New Zealand accented sample of participants. However, our findings did not support their previous finding that American accents were seen as more competent than a New Zealand accented speaker. We found no significant differences between the two. However, the results suggest a non-significant trend in the same direction. Our findings also do not lend support to previous assertions by (Stringfellow et al., 2008), that accent alone could affect levels of customer satisfaction. No significant differences were found in satisfaction due to the accent that participants heard. Nor do our findings support previous research by Bresnahan et al. (2002) showing that accent can change affective response. In our case the affective response was monitored by way of mood measurement, and no significant changes in the mood due to the accents that the participants heard were evidenced in the POMS testing that participants undertook. In summary, the accent of a CSR, though having no direct effect on satisfaction with service during an on-hold period, may affect the perception that a customer has of the competence of that CSR. This has implications for the use of offshore call centres. Our results suggest that this is especially the case for call centres in the Philippines. Though not rated significantly less competent than a New Zealand accent, participants in this group considered the speaker significantly less competent than did those who heard the American accent. From the means (see Figure 15), we

111 can hypothesize that given a larger sample size and the same trend, the New Zealand accent may also have been rated as more competent. We cannot completely explain the cause of this disparity in competences through our results, but we can formulate several hypotheses based on past research. These hypotheses include: a) that participants have a stereotype, activated by hearing the accent, that Filipino CSRs will be less competent, regardless of their true ability to carry out their job professionally and competently, b) there is a negative backlash due to hearing a CSR with an accent from a Country that is being used for jobs that could be in New Zealand, c) that the Filipino accent was less intelligible and a lack of processing fluency was misattributed to the (in)competence of the speaker, and d) that to solidify their own social identity, participants denigrated the out-group. We CAN discount the last hypothesis as an explanation, as we should have seen the Indian and American accents also denigrated if this were the case. There is, however, a fifth possibility; that the age of speaker was a causal factor here too. The Filipino accented speaker was the youngest, at 21 years of age, and the American accented speaker was over 40. Borrowing from Wells (1982) and Ladegaard (1998), the age could be affecting results by way of stereotype. The recognition that the American speaker is older may activate a stereotype that they were also more competent. If age difference is not causing the low competence ratings for the Filipino accent group, these findings have implications on intentions to offshore. It seems clients in the next generation of consumer, and current users of telecommunication technology, rate the Filipino accent unfavourably regarding competence. If a client has a pre-conceived idea of competence (or lack thereof) simply due to the accent of a speaker, this may see those in offshore Philippines call centres being taken less seriously or treated like incompetents. This is hardly ideal for a company wishing to be taken seriously and competing for a share of the market, and possibly harmful to the psychological well being of Filipino CSRs – unless they can disguise their accents. In India, it is common practice to teach customer service representatives to adopt a neutral accent, or an accent fitting that of the Country they are servicing (Cowlie, 2007). Filipino call centres may do well to train their CSRs to adopt Standard American or New Zealand accents. They may also do well to familiarise their staff to the accents and general speech with those whom they will be dealing

112 with for another reason - to avoid comprehension problems like those that the Yellow staff had when first dealing with New Zealanders (see NZPA, 2009). Unfortunately, being rated as lower in competence than the American accent was not the only trait that the Filipino accent scored poorly on. It was also significantly higher than the New Zealand accent on the dislike measure, and significantly lower on the likeability than the New Zealand accent. These could be explained due to backlash against the business practice of outsourcing taking jobs offshore, negative stereotypes of Filipino people as unfriendly and annoying to listen to, or misattribution of a lack of processing fluency. Although social identity theory (SIT; Tajfel & Turner, 1979) falls short of explaining why only the Filipino accent was denigrated, there is another social psychology theory that could be used to explain the backlash particularly against the Filipino accent, due to competition in the job market. This is realistic group conflict theory (RCT), a term coined by Campbell (1965). This theory maintains that if there is a conflict of interests between two groups, then a group will begin to feel threatened, and hostility can arise towards the threatening out-group. Filipino call centre employment may be seen to have a negatively interdependent relationship with New Zealand employment opportunity. If someone in the Philippines is working in a position answering calls from New Zealand, this may be seen as displacing the same position in New Zealand. This competition (a potentially threatening conflict of interest) could cause negative ratings of the Filipino accent due to the hostility that the intergroup competition causes.

Listening condition The findings relating to listening condition are interesting in regard to Study 2. When focussing on the four highest ranked conditions from Study 2 the straight- through condition, although disliked from a listening point of view, made the customers most satisfied. This again provides further support for previous findings (Davis & Heineke, 1998; Davis & Vollman, 1990; Clemmer & Schneider, 1989; Tom et al., 1997), that the longer an actual wait is, the less satisfied a customer becomes. This was even though participants enjoyed listening to the Pop and Choice listening conditions significantly more than the straight-through condition.

113 Interestingly, the similarities in satisfaction rating that were seen in the previous study were not seen in this study, and the Comedy condition was not the most liked condition, though it did not significantly differ from Pop and Choice (see Study 2 methods for details of the choices) . Further, the lack of significant difference in perceived control is interesting also. This may be due to the smaller sample size used in the current study compared to Study 2, or because the longer time it took participants to fill in the second computerised version of the POMS may have seen a reduction in the strength of their feelings about the level of the control they had as a part of their telephone interaction. Although no differences in mood were again seen in the current experiment, and the findings regarding satisfaction were not replicated, we feel that the Choice listening condition as an on-hold intervention for customer well being and satisfaction and worker well being continues to hold promise. Participants in this condition enjoyed what they listened to more so than those in the straight through condition.

Limitations Although a more stringent, within participants, test of change in mood was carried out for this study than for Study 2, two of the same limitations apply. The POMS measure of mood, as an analogue scale, may not pick small, but important, differences in mood (e.g. Bond & Lader, 1974). In addition, the wait may not have been long enough, or have a high enough psychological cost, for what was being played on-hold to affect the mood of the participants in the study. As discussed, the behavioural measure was not reliable enough to draw any conclusions, but developing an accurately calibrated version of the device, or a similar apparatus to test strength of conviction, is definitely a direction for future research. More control questions should be used in further development of such an apparatus to ensure that any novelty effects have worn off, and participants are accustomed to using the device, before the time experimental questions are asked. Another limitation already mentioned is the age differences in the vocal actors. This could possibly have resulted in some of the difference seen in competence ratings. Due to the non- parametric nature of the testing on the Likert type scales, we were unable to include the age of the speakers as a covariate in the analyses.

114 115 Study 3a: Accent Appraisal

As discussed, in Study 3 there were a number of possible confounds that could have affected the validity of the results relating to differences in the ratings of the different accents. For example, the age of the different speakers may have been related to the competence ratings, as could the level of intelligibility. Questions relating to speaker age, intelligibility and Country of origin were originally included in the questionnaire that participants were to fill out in Study 3. However, due to experimenter error, a past draft of the intended questionnaire was used throughout the experiment. In an attempt to find out if the proposed confounds were present, a study was carried out that included those questions originally intended in Study 3.

Methods

Participants Participants were 18 University of Otago and Otago Polytechnic students; 12 male and 6 female. Participants were volunteers recruited through word of mouth. Ages ranged from 19 to 27. Mean age was 22.28 (median = 22.5), with a standard deviation of 2.54 years. Lollipops were offered as a form of thanks following participation.

Apparatus Apparatus included a portable compact disc (CD) player, and a CD containing four vocal tracks. Each track consisted of the initial and final statement that every participant in Study 3 heard; spoken in Filipino (track 1), Indian (track 2), New Zealand (track 3) and American (track 4) accents. The speakers and the statements were the same as those used in Study 3. Each track had the same sorting code spoken as a part of the final statement. The apparatus also included a questionnaire that asked the same questions relating to accent as Study 3, regarding speaker competence, likeability, and how well participants enjoyed their accent. In addition, this questionnaire also had questions pertaining to speaker age, intelligibility (understanding), and Country of origin (see Appendix V).

116

Procedure Participants were asked to be seated near the CD player, and were given an information sheet to read through. If they consented to taking part in the study, they were then given a consent form to sign, before starting the experiment (see Appendix W for information sheet and consent form). If they consented, participants were then asked to listen to four vocal tracks, one after the other, and answer questions relating to the speaker for each track. They were instructed that they could begin filling in the questionnaire while the excerpt was playing, or fill it in during a 30 second break between the tracks. In reality, the experimenter did not start another track before the responses for a previous track were complete. Participant numbers were arbitrarily assigned using the order in which participants took part in the study. To ensure there would be no order effects, the order that the tracks were played to participants was randomised by following random sequences generated using the Random.org sequence generator (Haahr, 2010). The order in which the tracks were played to each participant was noted on their questionnaire. After they had heard and rated each track, participants were then verbally debriefed, and the previous study explained. Each participant was thanked for their time, and a gift in the form of a lollipop was offered to each.

Results

The results for all except the question regarding speaker Country of origin were analysed in SPSS 15 using Friedman’s non-parametric repeated measures ANOVA by ranks. Non-parametric post hoc analyses were carried out using a purpose written program based on Tukey tests for Friedmans as described in Zar (1984). The within participants variable was speaker accent. No exclusions were required. For the Likert type scale question: “Please indicate how competent you think the announcer is.” there was a significant difference among accents χ 2 (3, N = 18) = 12.50, p < .01 (see Figure 20 for comparison of means).

117 7

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Figure 20 : Mean competence ratings (+SE) for speakers with different accents.

Post hoc analyses indicated that this difference was between the Indian and American accents, with no other significant differences found. The Indian accented speaker was rated as significantly less competent that the American speaker. For the question “Did you like the announcer’s accent?” there was a significant difference among speakers, χ2 (3, N = 18) = 7.97, p < .05 (see Figure 21 for comparison of means).

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Figure 21 : Mean accent dislike ratings (+SE) for speakers with different accents.

118 Conservative post-hoc analyses showed no significant differences, so we have to assume that the initial finding relates at least to the two accents rated furthest apart, with the New Zealand accent liked significantly more than the Filipino accent. For the question “How easy was it to understand the speaker?” there was a significant difference among the speakers, χ2 (3, N = 18) = 39.48, p < .001 (see Figure 22 for a comparison of means).

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Figure 22 : Mean intelligibility ratings (+SE) for speakers with different accents.

Post hoc tests revealed that the Indian accent was rated significantly less intelligible than New Zealand, American and Filipino accents. For the question “How old do you estimate each speaker was?” there was a significant difference among the age estimates for the different speakers χ 2 (3, N = 18) = 35.33, p < .001 (see Figure 23 for a comparison of means).

119 60

Mean 50 Actual age

40

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Figure 23 : Mean age estimates (+SE) for the different speakers. Actual ages are included for comparison.

Post-hoc analyses revealed that the age estimation of the American speaker was significantly older than the Filipino and New Zealand speakers, but not the Indian speaker. No other significant differences in age estimation were found. There were no significant differences in intelligibility among the three latter accents. For the question “Do you think the announcer is a likeable person?” there was no difference in the Likert type scale rating for any of the accents χ 2 (3, N = 18) = 1.85, p = .60. A correlation matrix was created to examine the relationships among the various measures (see Table 5).

120 Table 5: Correlations of the dependent measures in Study 3a.

compete Actspkr intelligibil nce Dislike likeability ageest age ity Spearman's competence Correlation 1.000 -.542(**) .433(**) .053 .150 .526(**) rho Coefficient Sig. (2-tailed) . .000 .000 .659 .210 .000 N 72 72 72 72 72 72 Dislike Correlation -.542(**) 1.000 -.337(**) -.003 -.169 -.392(**) Coefficient Sig. (2-tailed) .000 . .004 .981 .155 .001 N 72 72 72 72 72 72 likeability Correlation .433(**) -.337(**) 1.000 -.251(*) -.038 .072 Coefficient Sig. (2-tailed) .000 .004 . .033 .750 .549 N 72 72 72 72 72 72 ageest Correlation .053 -.003 -.251(*) 1.000 .600(**) .029 Coefficient Sig. (2-tailed) .659 .981 .033 . .000 .810 N 72 72 72 72 72 72 Actspkrage Correlation .150 -.169 -.038 .600(**) 1.000 .114 Coefficient Sig. (2-tailed) .210 .155 .750 .000 . .341 N 72 72 72 72 72 72 intelligibility Correlation .526(**) -.392(**) .072 .029 .114 1.000 Coefficient Sig. (2-tailed) .000 .001 .549 .810 .341 . N 72 72 72 72 72 72 ** Correlation is significant at the 0.01 level (2-tailed). * Correlation is significant at the 0.05 level (2-tailed). Key: ageest= Age estimeate for speaker. Actspkrage=Actual speaker age.

As seen in Table 5, competence was highly correlated with likeability, r (70) = .43, p < .01, and intelligibility, r (70) = .53, p < .01, and highly negatively correlated with accent dislike, r (70) = -.54, p < .01. In addition to the relationship with competence, dislike was highly negatively correlated with likeability r (70) = -.34, p < .01 and intelligibility r (70) = -.39, p < .01. Likeability was negatively correlated with the estimated age of the speaker r (70) = -.25, p < .01. Age estimates of the speakers were, in addition, highly correlated with the actual age of the speakers. An analysis was carried out to examine whether a lack of ability to identify an accent correctly might play a role in the rating of the speaker on the Likert type scale variables. Responses to the final question relating to speaker Country of origin were coded as either correctly identifying a speaker’s Country of origin, or incorrectly

121 identifying their Country of origin. Incorrectly identifying a Country of origin included answers of “unsure”. Though the data were not strictly from independent samples, Mann-Whitney-U tests were carried out to provide a rough indication of whether an inability to correctly identify an accent had an effect on the Likert type scale measures mentioned above. The results provide an indication that likeability, U (70) = 326, Z = -2.674, p < .01, and intelligibility, U (70) = 367.5, Z = -2.20, p < .05, ratings were lower for those voices that were not correctly identified. There were no significant differences in dislike for an accent or competence of the speaker between the correctly and incorrectly identified countries of origin. However, the data shows a general trend towards less favourable ratings for those speakers whose country of origin was not correctly identified (see Table 6).

Table 6: Means for correct vs. incorrect identifications of Country of origin on measures of speaker competence, likeability, intelligibility and accent dislike.

Country ID Competence dislike likeability intelligibility Correct Mean 5.4314 3.7647 4.7843 5.9020 N 51 51 51 51 Std. Deviation 1.20424 1.42251 1.20522 1.52650

Incorrect Mean 4.8571 4.3333 3.9524 5.1429 N 21 21 21 21 Std. Deviation 1.31475 1.39044 1.11697 1.59015

Total Mean 5.2639 3.9306 4.5417 5.6806 N 72 72 72 72 Std. Deviation 1.25589 1.42736 1.23263 1.57289

As seen in Table 6, the means show a trend toward disliking incorrectly identified accents more than correctly identified accents, and a trend towards rating correctly identified accents as more competent. Out of the 21 incorrect identifications, 13 were incorrect identifications of the Filipino speaker’s Country of origin, 3 were incorrect identifications of the Indian speaker’s Country of origin, and 5 were incorrect identifications of the New Zealand speaker’s Country of origin! In regards to the Filipino accent, this means that 13 of

122 the 18 participants in the study (72%) were unable to correctly identify the speaker’s Country of origin.

Discussion

The results of this study were slightly puzzling. Why was the Indian accent rated less favourably in competence compared to the American by the sample in the current study (3a) population, whereas the Filipino accent was less favoured compared to the American in Study 3? The answer may be the basis for comparison that is provided in the current study. Though the order in which the tracks were played was randomised to try and reduce order effects, whatever order they were heard in, the Filipino and Indian pieces were still both being heard by the same participant. This means they could effectively compare the accents to one another, rather than giving an overall first impression, not influenced by hearing another accent at all, as in Study 3. In addition, the participants in this study listened to excerpts through a CD player, and did not receive the fully simulated call centre experience. Had they been listening through the phone and experiencing the full simulation for each accent, it may have changed their responses. For example, participants may be more familiar with the Indian accented speaker in the role of a call centre worker, thus rating them more favourably than the Filipino accent, given a call centre simulation. The direction of the results pertaining to accent dislike was consistent with those of Study 3. The accent of the Filipino speaker was disliked compared to that of the New Zealand speaker. However, in contrast to Study 3, there was no significant difference in likeability among the different accented speakers. Using the results from this study we can tentatively discount one possible confound of Study 3. Looking at the correlation table in the results of this study we can see that neither age estimate, nor actual age of the speakers was found to be associated with competence rating at all, so the possible confound of the age difference among speakers affecting competence is not likely to have been a confound in Study 3 after all. Taken in combination with the results from Study 3 the results of this study confirm that, although overall mood or satisfaction with a call may not be influenced

123 by the accent of an operator, the accent can influence how participants perceive the operator. In support of previous work by Watanabe (2008; Lev-Ari & Keysar, 2010), we found that the non-native English speakers were rated as less competent than the American. In Study 3 it was a Filipino speaker, and in Study 3a it was an Indian speaker who was rated less competent. The correlational results of this study suggest that age was not a contributing factor in this competence rating. In addition, the Indian operator who was rated significantly less competent than the American was not rated significantly different in age. Intelligibility could well play a role in the competence rating of the different speakers. The correlational results of the current work certainly suggest that there is a strong relationship between the two measures. This evidence supports the Lev-Ari and Keysar (2010) theory that a listener’s lack of fluency in cognitive processing of speech may be misattributed as a lack of speaker competence. How much a listener enjoys the accent of the person they are listening to, and how likeable they feel the speaker is, were also correlated with competence ratings. There does also appear to be a trend towards denigrating the accent which is unknown or hard to identify – but this is only statistically significant for the extent to which the accent is enjoyed and the likeability of the speaker, not the competence of the speaker. The evidence in the current study argues against an explanation of stereotyping leading to the denigration of the Filipino accented speaker in Study 3, or a backlash against the outsourcing of call centres to The Philippines. Of the 18 participants who were included, 13 (72%) could not correctly identify that the speaker was from the Philippines. If the sample in Study 3 were similar in their ability to identify the Country of origin, the Filipino stereotype would not have been activated for many, and the participants would not have known that the speaker was from a group that may be in competition for possible New Zealand based jobs. Although we found no difference in mood or satisfaction caused by the accents in Study 3, non-native English speakers in Study 3 (Filipino) and Study 3a (Indian and Filipino) were reported as less competent compared to an American accent. Results from Study 3a suggest this may be due in part to a lack of processing fluency due to speech that is harder to understand being misattributed to the speaker’s competence, in addition to the enjoyment of an accent, and the likeability of the speaker.

124 A non-native English speaker from The Philippines was rated as less likeable compared to a New Zealand speaker in Study 3, and the Filipino accent was disliked significantly compared to the New Zealand accent in both Study 3 and Study 3a, though participants in Study 3a struggled to identify the accent as Filipino. With the findings of Study 3 and Study 3a in mind, it is not suggested that companies halt outsourcing their calls based solely on the possibility that their operators or CSRs will be considered less competent. However, the accent training practice that some Indian call centres take part in may be put to use in a more widespread fashion in other outsourcing destinations. The workers could be trained to adopt the accent of the Country which they service. This could plausibly see less complaints, call backs and denigration of workers, if it increases the level of competence and likeability that callers attribute to the worker. Based on the results of Study 3 and Study 3a, the reasons for the lower ratings of the non-native English speakers does not seem to be explained by social identity theory (SIT) (Tajfel & Turner, 1979), realistic group conflict theory (RCT) (Campbell, 1965), or stereotyping (Wells, 1982), in our undergraduate samples. I propose that it was more a combination of a lack of processing fluency, and simply aural dislike (possibly due to unfamiliarity) causing the denigration of these speakers. This lends support to Lev-Ari and Keysar’s (2010) finding that a lack of processing fluency caused non-native English speakers to be rated less credible. The Filipino accent, which participants struggled to identify the most, was the most disliked accent. Though the Indian and American accents may not be familiar in day to day interactions, they may be recognisable due to being more common in popular culture.

125

126 Study 4: Undergraduates self reported music preferences in an on-hold situation vs. a public social situation

Technological limitations may mean that some call centres will not be able to use the “choice” on-hold intervention proposed in the Study 2 and 3. However, if a call centre is only able to play music, it is important to find out whether it matters which music they play. Do individuals prefer certain types of music on-hold, and do these preferences differ from their musical preferences in other situations? This study attempted to answer these questions. Music use has received attention in the Marketing and Service fields for its behavioural change applications with on-site customers in shopping scenarios. It has been used to try and increase purchasing behaviours, and has been used to increase the expense of the products that customers buy with some success (see Garlin & Owen, 2006; Turley & Milliman, 2000 for reviews). It has also been used to improve emotional evaluations and service appraisals in an experimental setting (Hui et al., 1997). The success that music has in changing behaviours, and attitudes towards a service provider, seems to be dependent on a number of factors including tempo, complexity, pleasurability, familiarity and volume (see Garlin & Owen, 2006; Turley & Milliman, 2000 for reviews). But, one factor does not appear to have been examined in any depth within the marketing literature - situation. Depending on the situation in which music is played, efficacy in keeping customers shopping for longer, or satisfied, may vary. Music may not always be a positive addition to the servicescape; depending on the situation, music has also been described as a stressor by customers (Aylott & Mitchell, 1998)! Why the conflicting findings? How can music be an effective marketing tool, but also be a stressor? It is possible that the style, volume, tempo etc. of music played is inappropriate for the demographic audience, or, even if it is preferred by the target demographic in general , that the music may not be suitable for the situation . The ‘music in marketing’ literature has mostly been based on-site in face-to- face service environments, and has largely ignored the effects that music may have in voice-to-voice telephone on-hold periods – a completely different situation. In the telephone service industry there are very few senses to appeal to when marketing a product or creating a pleasant queuing experience. Colours, senses, and structural

127 design concepts can not be used to influence the customer. A company can only use aural stimuli to try and woo customers. As such, when placed on-hold, customers are often played music. There is evidence that suggests playing this music during the on-hold period is better than playing nothing for customer satisfaction (Whiting & Donthu, 2006), service appraisal, positive perceptions and enjoyment (Tom et al., 1997). Previous work also provides some evidence that likeable music (as rated by participants) played in a low cost on-hold situation can positively affect mood, wait length appraisal, and overall appraisal of that situation (Cameron et al., 2003). However, on-hold music also appears anecdotally in the discussion of why people dislike being placed on-hold e.g. “Oh! yeah!! there is nothing more annoying than when your calling your phone supplier, or your tv, satilite supplier, they put you on-hold, and play rubbish music in your ear,. i hate it! [sic]” or “…And sometimes the music they play while you are waiting is quite horrible…” (Experience Project, Inc., 2010). These comments are from a forum topic set up for the sole reason to remonstrate about why people hate being on-hold. In fact, in 2001, a survey was even conducted to find the most annoying music played on-hold. What was the consensus? The anonymously penned folk tune “Greensleeves” was the tune that Britons surveyed found most annoying while waiting on-hold (Freedman, 2001). It appears from the evidence, that music is better than nothing for customers placed on-hold – but perhaps not all music is created equal. There is little evidence to suggest which music style may be better to use on-hold for a given demographic. Tom et al. (1997) has examined whether Jazz or Classical music was better to use for on-hold programming. Their study examined how four groups varied in rating their experiences. These groups listened to either: Silence, Jazz music, Classical music or a choice of the above, with the addition of a telephone ringing option. Jazz music was rated as equally enjoyable as having a listening choice, but Classical was not. This study only examined two musical styles which were considered stereotypical on-hold music at the time. As discussed, music choice may be an important consideration of any on-hold programming that a company may put in place. But what to play?! Music preference is seemingly very subjective, with some very different findings arising in the literature

128 regarding music appraisal (see Brattico & Jacobsen, 2009 for a review). However, there may be preferential trends within age/peer groups or, in view of marketing applications, certain target demographics. For example, Hui (2009) found that Macau high school students had clear preferences for popular local music (Cantopop), with significant age differences in ratings for all styles. Very little research has examined how music preference differs across situations. A couple of key questions therefore arise. For a specific Population does on-hold music preference differ from the music that participants prefer in a more casual setting or situation? And which styles are more suited for the on-hold environment? The current work expands on previous work Mitchell and Leland (2008) carried out with Otago high school Populations. Using the same musical excerpts they used, we set out to test how long university undergraduates were willing to listen to different music styles in two different situations; a public area, and on-hold. Four main hypotheses were formulated:

That:

1) Participants would report that they are willing to listen to Pop music for longer than other music styles. 2) Participants would differ in how long they are willing to listen to different music styles for telephone on-hold listening versus outdoor public area listening situations. 3) Males and females report that they would prefer to listen to different types for differential lengths of time. 4) There would be differences in willingness to listen to the different styles for different age groups.

Methods

Participants Participants were 155 Otago University undergraduates who could satisfy a small portion of course assessment by completing a worksheet based on the

129 experiment. They were participating following completion of another short experimental task (Study 2). The initial sample consisted of participants ranging in age from 17 to 50 years of age; 117 of the participants were female. The mean age was 19.82 (median = 19), with a standard deviation of 3.29.

Apparatus The apparatus included a portable CD player and four 20 track CDs which each had all of the 30 second music excerpts to be played in differing orders. The excerpts used were the same used by Mitchell and Leland (2008) (see Appendix X). Nine musical styles made up the 20 excerpts; Asian Karaoke (performed by Wing), Opera, Classical (orchestral), Jazz, Country, Pop, Piano, Sixties and Manilow. There were four Pop excerpts, and two each of the other styles. A music rating sheet was also included, that had rating scales for both on-hold and public area listening situations for every musical excerpt (see Appendix Y). The order of the situation to be scored first for each style was randomised (by tossing a coin), as was whether the scoring was forward or backwards. For each piece of music the scale allowed participants to rate, from zero to fifteen or more minutes, how long they would be prepared to listen to that style in both situations. The ratings sheet is a modified version of that used by Mitchell and Leland (2008). A Digitech QM 1588 sound level meter was used to ensure that the excerpts played did not exceed 80 decibels (dB) or drop below 55dB.

Setting The setting of the experiment was a partitioned off area in a psychology department computer laboratory. Participants were seated separately along the same row of desks. The closest participant was around two metres from the CD player, and the furthest around four metres from the CD player. Up to four participants were tested in session, depending on the number that attended the previous experiment (Study 2).

Design This study had a control group mixed design. Music style and situation were both within subjects factors; each participant rated all of the music styles for both

130 situations. Controls were the Asian karaoke style (expected to produce floor effects, and contemporary/Pop music style (expected to produce ceiling effects). These assumptions were made on the basis of findings of Mitchell and Leland (2008). The between subjects factors were age and gender.

Procedure Following a previous experiment (Study 2), the participants were presented with the information sheet for the experiment followed by a music ratings sheet. No consent forms were required because by filling in the music ratings sheet participants gave their implied consent. Serial numbers allocated to each participant for the previous experiment were noted on their ratings sheet, so that the data from each participant could be linked to that of the previous experiment. Prior to starting the experiment for each group, the experimenter randomly selected one of four CDs, each with a different order of music styles, to get the playing order for that group (see Appendix Z for list of the order of songs on each CD). Random assignment was achieved by generating a list of 300 integers from one to four using a random integer generator on Random.org. As the number of participants neared 120, the numbers of those who had listened to each CD were noted, and attempts were made to even out the numbers of participants who listened to each CD. The CD number (1-4) that was played to them was noted down on their ratings sheets. The number of participants in each experimental session varied from one to four, depending on how many participants showed up for the four available positions in the previous experiment. All participants were seated separately, facing in the same direction to try and negate social influence on ratings. Once the participants had read through the information sheet (see Appendix AA) and the rating sheet, the experimenter then made sure that the participants understood fully what they were being asked to do before starting the portable CD player. For each piece of music, participants were advised to rate how long they would be prepared to listen to that style in two different situations; “public area” and “on-hold”. For the “Public area” situation, participants were asked to imagine that they were in a local, public, outdoor area, with friends present; and they had to rate how long they would stay in the area, given the style of music they were listening to was playing. In the “On-hold” situation, participants were asked to imagine that they

131 had been overcharged $10 by a bank, telephone company or power company, and they were trying to recover their money. They were then asked to rate how long they would stay on the line, given the style of music that was playing. Each track lasted 30 seconds, with 10 seconds given between, to allow participants to finish noting down responses. A Digitech QM 1588 sound level meter was placed next to the participant closest to the CD player and monitored throughout the experiment to ensure that the sound level did not go above 80dB or below 55dB. Volume was adjusted track to track to make sure that the sound stayed within these parameters. The sound level meter was set at A, Lo, Fast weighting. Following the ratings, participants were asked to fill out some final questions at the back of the rating sheet (see Appendix Y), and were advised that these questions did not relate specifically to the styles/genres that they had just heard, but could include any. After all of the tracks were played and rated, and the qualitative questions at the end had been answered, participants were thanked for their participation, given a debriefing sheet regarding the experiments in which they had taken part, and their attendance registered.

Results

Exclusions The initial sample of 155 was reduced to 135 for final analysis. Participants who reported having had a hearing problem, or who left out any demographic/rating information, were excluded. Any participants under 18 or over 22 years of age were also removed from analysis. This was done to give a more accurate representation of the trends in music preference of an undergraduate population. The majority (80.37 %) of undergraduates at the University of Otago are aged between 18 and 22 (Andrea Howard, Senior Analyst (Planning and Funding), University of Otago, Personal Communication. May 19, 2010). The mean age of the sample was 19.36 (median = 19), with a standard deviation of 1.00.

132 Data Analysis and Presentation Cronbach’s alpha reliability tests were carried out to check the construct validity of the music styles. All styles showed acceptable internal consistency (Pop 0.92, Piano 0.84, Classical 0.85, Opera 0.87, Sixties 0.76, Manilow 0.78, Jazz 0.80, Asian karaoke 0.83 and Country 0.84). All intra class correlation coefficients were highly significant (p< 0.001). See Appendix AB for details. Raw data collected were the times that participants reported they would be willing to listen to the style of music played for each individual musical excerpt. Participants’ ratings of excerpts were then averaged across the style to find a mean time that each participant was willing to listen to each style. This average was the measured variable included in analyses. Prior to final analysis, a mixed design ANOVA with within subject factors of music and situation, and a between subjects factor of group (from the previous experiment) was carried out to see if the conditions that participants had been assigned to for the previous experiment affected responses in this study. Results showed no significant main effect of group, F (7, 127) = 1.02, p = .420, or interaction with music ( F (38.72, 702.56) = 1.062, p = .37) or situation ( F (7, 127) = .54, p = .80) caused by the group allocation of Study 2, so this was dropped as a factor in the final analysis (analysis in Appendix AC). For final analysis of the data, a mixed design ANOVA was used, with two within subjects’, and two between subjects’ variables (see Appendix AD for full analysis). The first within subjects’ variable was situation which had two levels; public area vs. on-hold. The second was music style, with nine levels; Asian Karaoke (Wing), Opera, Classical, Jazz, Country, Pop, Piano, Sixties and Manilow. Between subjects variables included gender (male vs. female) and age (18, 19, 20, 21 and 22). Data for 21 and 22 year olds were combined to increase cell numbers for analysis as there were very few 22 year olds. To adjust for inequality of variance, a square root transformation was carried out on the data. As Mauchly’s Test of Sphericity was violated Greenhouse-Geisser test statistics were used for interpretation. All statistics reported in figures are before transformation. As expected, our results showed that the average time participants reported that they were willing to listen to music varied significantly depending on the style

133 played, F (5.55, 699.34) = 49.65, p < .001. See Figure 24 for a pictorial representation of the differences.

10 9 8 7 6 5 4 3 2

prepared to listen (in minutes). (in listen to prepared 1

Mean time participants report they are they report participants time Mean 0 Wing Opera Classical Jazz Piano Manilow Country Sixties Pop Music Style

Figure 24 : Mean durations that undergraduates reported they would be willing to listen to different musical styles (+SE).

Post hoc pairwise tests with Bonferroni adjustments were carried in out SPSS 15 to further examine this main effect. Results showed that, on average across situations, the undergraduate population had a statistically significant preference for intent to listen to Pop music for a longer period than all other styles ( M = 8.57, SE = .47). Sixties music was preferred significantly less than Pop (M = 6.92, SE = .39), but was rated significantly higher than Wing, Opera and Jazz but was not preferred significantly more than Country, Manilow or Piano. Country (M = 5.90, SE = .41), Manilow (M = 5.85, SE = .39) and Piano (M = 5.79, SE = .40) were all very similarly rated and moderately tolerated. They were rated significantly better than Wing and Opera, and significantly worse than Pop. Jazz was seen in a slightly less favourable light (M = 5.29, SE = .37), and was rated significantly lower than Sixties and Pop, but higher than Wing and Opera. Classical (M = 4.82, SE = .37) was also rated significantly lower than Sixties and Pop, but higher than Wing and Opera. Opera was the second least preferred musical style with participants only willing to listen to it an average of 3.87 minutes (SE = .36) before removing themselves from the situation. It was rated significantly lower than all other styles except for Wing’s Karaoke style singing. As expected,

134 Wing’s music showed a floor effect, and acted as the ‘floor’ control, with participants only willing to put up with her music for an average of 1.95 minutes ( SE = .22). This was significantly lower than all other music styles, including Opera. Sorry Wing; University undergraduates may not be your market! Note: Means and Std Errors reported are non-transformed. For complete post hoc analysis of this main effect with transformed figures see Appendix AD. Also as predicted, we found a significant situation by music interaction F (6.38, 810.50) = 14.796, p < .001. Figure 25 shows how preparedness to listen to music alters as a function of listening situation.

12

On-hold 10 Public area

8

6

4

2 prepared to (inlisten minutes). prepared

Average time participants report they are are timereport they participants Average 0

z z try ing low ano n Pop W pera Ja i u O ani P o Sixties assical C Cl M Music style

Figure 25 : Mean durations that participants report they would be prepared to listen to various musical styles when on-hold compared to in a public area (+SE).

To examine the interaction further, individual ANOVAs were carried out for each situation followed by Post Hoc pairwise tests with Bonferroni adjustments to see where significant differences in response patterns lay (see Appendix AE complete results including post hoc analyses). The ANOVA for both On-hold (F (5.59, 1016) = 37.31, p < .001.) and Public area ( F (5.68, 1016) = 47.91, p < .001.) situations showed highly significant differences between music ratings. Post hoc analyses showed that there were several areas where there were differences in the pattern of response for on-hold vs. public area listening situation. Wing, again, provided a floor for both conditions; but in the on-hold condition, 135 differences between Opera, Classical and Jazz were not present, whereas in a public listening situation, Classical and Jazz were rated significantly higher than Opera. Manilow was rated the same in both conditions; significantly higher than Wing and Opera and significantly lower than Pop. Then, a very interesting difference between the two situations; given an on-hold situation, the Piano music style was not significantly different in rating to Country, Sixties and Pop. Given a listening situation in a public area, both Sixties and Pop are rated significantly higher than Piano music. Given a public situation, participants rate Pop significantly higher than both Country and Sixties also. No other significant main effects or interactions were found. No significant relationship was seen between age group and music F (16.52, 1016) = 1.03, p = .42. However, a trend toward males reporting they would be prepared to listen to music, for longer than females, in an on-hold situation compared to in a public area F (1, 127) = 3.78, p = .054 was seen. This trend is shown in Figure 26.

7 On-hold 6 Public

5

4

3

2

prepared to listen (in minutes). (in listen to prepared 1

Average time participants report they are report they participants Average time 0 Female Male Gender

Figure 26 : Mean durations participants of each gender reported that they were willing to listen to music in an on-hold situation compared to in a public area (+SE).

136 In addition to rating how long they would be prepared to listen to the styles of music played, participants were also asked a number of general questions. These questions consisted of: Question 1: “What genre/style of music would you like to listen to while placed on- hold?” Question 2: “What is a genre/style of music that would encourage you to hang up while placed on-hold?” Question 3: “What is your favourite genre/style of music to listen to around friends?” Question 4: “What is a genre/style of music that you would never be seen listening to around friends?”

Fourteen common music categories were chosen to sort the responses into. Two experimenters categorised primary responses that participants made. Participants often specified more than one style in their response to a question, but the initial response was taken as a general measure of their strongest feeling. Any disparities in response categorisation were discussed until the differences in opinion were resolved, and consensus reliability for frequency of over 90% was reached.

Percentage reliability = # of agreements . x 100 # agreements + # disagreements

Percentage reliability = 262 . x 100 = 93.57% 262 + 18

An “other” category was included because some answers were not common musical styles. These answers were usually in reference to rhythmic elements or timbre of a musical piece e.g. “Anything too fast/loud”. Also, as many participants reported answers of “Pop/Rock” and this could not be teased out into either Pop or Rock genres, it was given a separate category. Percentages of the total number of responses are reported. Chi square goodness of fit tests, testing a model that assumed equal frequency of response for

137 each category, were carried out using the SPSS statistical package to see which genres/styles were more or less frequent than expected if all response frequencies were equal. Note: This test does not indicate which response categories are significantly greater than another, only that significant differences exist from the equal frequencies model. Actual frequencies and Chi Square tests including residuals are included in Appendix AF. In the graphs that are presented, the expected value for a model with no differences in response frequency (as a percentage) is presented as a line, whereas the bars show what percentage of the observed responses fell into each category. In response to Question 1: “What genre/style of music would you like to listen to while placed on-hold?” participants showed that they would prefer to listen to Pop (22%), on-hold, along with “Other” (21%). See Figure 27 for a comparison.

What genre/style of music would you like to listen to while placed on hold?

25 Observed Percent 20 Expected Percentage

15

10

5

0 Percentage of total primary Percentage responses l ry s ic p a al n ae es jazz ue Po u ock bl sic o Rock R ount s tr & Rap Bl Other c la p Regg & c Pop/ Elec ho & a Heavy Met ip ythm easy listening H Sk h R Musical style

Figure 27 : Genres/styles that participants say they would like to listen to while placed on-hold. The “Expected Percentage” line shows what percentage a theoretical distribution of equal responding across categories would give.

138 The response pattern for Question 1 differed significantly from a model that had equal response frequencies in each category, X2 (13, N = 135) = 125.19, p < .001. Further Chi square tests were carried out to test which of these categories were significantly greater than the expected equal frequency. Bonferroni corrections were carried out for these three tests to change the significance threshold (.05/3 = .017). Pop had a significantly greater response frequency than an equal response model, X2 (1, N = 40) = 10, p < .017. The “other” category also had significantly more responses than a model assuming equal response rates, X2 (1, N = 39) = 10, p < .017. Classical style, however, did not differ significantly from an equal rate of response, X2 (1, N = 27) = 1.81, p = .18. The responses to Question 2: “What is a genre/style of music that would encourage you to hang up while placed on-hold?” again showed a trend towards the “Other” category (36%) – either the genres listed did not match our fourteen general genres/styles, or the participants referred to the composition of pieces rather than a general style/genre. There were, however, some general genres that appear to be listed as a dislike more often than expected (see Figure 28).

139 What is a genre/style of music that would encourage you to hang up while placed on hold?

40 35 Observed Percent 30 Expected Percentage 25 20 15 10 5

Percentage of Percentage total responses 0

z g p k try ical in a c n az ues pera n Pop o u J Bl e R R Other O & Co ist p Class o h hm & Blues ip t Heavy Metal Easy l H Rhy Musical style

Figure 28 : Styles/Genre that participants reported would encourage them to hang up when placed on-hold. The “Expected Percentage” line shows what percentage a theoretical distribution of equal responding across categories would give.

The styles apart from “other” that appear most likely to encourage participants to hang up were Classical (13%) and Opera (17%). Again, the general response pattern did not differ significantly from a model with equal expected frequencies for each category, X2 (11, N = 135) = 174.24, p < .001. Further Chi Square tests with Bonferroni correction for two tests (.05/2 = p = .025) revealed that only the “other” category was significantly more likely to be reported than if the distribution of responses was even, X2 (1, N = 59) = 23.20, p <.001. The next highest musical style was Classical, but, following a conservative correction (with a significance level of below .025) this did not differ significantly from our theoretical distribution of even responses, X2 (1, N = 34) = 23.20, p = .04. For Question 3: “What is your favourite genre/style of music to listen to around friends?” there was again a significantly different pattern of responses from a model expecting equal frequencies of response, X2 (12, N = 135) = 202.90, p < .001. For this question, answers fell into general genres/style response categories more so

140 than those questions relating to on-hold music. Those styles that were significantly above the expected frequency, or more favoured, were Pop (33%), X2 (1, N = 54) = 21.41, p < .001; and Rock (22%), X2 (1, N = 40) = 10, p < .017. The next highest response categories (other or Pop/rock) did not significantly differ from our theoretical distribution of equal responses across categories, X2 (1, N = 24) = .67, p = .41 (see Figure 29).

What is your favourite genre/style of music to listen to around friends?

35 30

25 Observed Percent 20 Expected Percentage 15 responses 10 5 Percentage of Percentage total primary 0

l g p p e k try n a o a c tal i nic P g e her Jazz n o R g Ro ctr & e Ot Coun iste e Classica l l E hop Pop/Rock ip ythm & Blues Heavy M Easy H Ska & R h R Musical styles

Figure 29 : Participants’ favourite genres/styles of music to listen to around friends. The “Expected Percentage” line shows what percentage a theoretical distribution of equal responding across categories would give.

Finally, answers to Question 4: “What is a genre/style of music that you would never be seen listening to around friends?” had a significantly different pattern of response from a model assuming equal levels of responding for each category X2 (11, N = 135) = 104.73, p < .001. Results are graphed in Figure 30.

141 What is a genre/style of music that you would never be seen listening to around friends? 25 Observed Percent 20 Expected Percentage 15

10 responses 5

0 Percentage of Percentage total primary

l s l r ry a ra ck ta e t c e e Jazz Rap Pop Op tening & Blue Rock Oth Coun s & Classi vy M m Pop/Ro a sy Li yth He Ea Hip hop h R Musical style

Figure 30 : Genres/styles of music that participants report they would never be seen listening to around friends. The “Expected Percentage” line shows what percentage a theoretical distribution of equal responding across categories would give.

As Figure 30 shows; Country (10%), Classical (21%), Opera (19%), Heavy Metal (13%), and “Other” (17%) generally appear to be the styles more likely to be shunned by the undergraduate participants when around friends. However, further Chi Square goodness of fit tests were carried out for Classical, opera, and “other” styles. Each was compared in isolation against the expected equal rate of response given the number of respondents. Classical; X2 (1, N = 40) = 8.10, p < .017, and Opera; X2 (1, N = 37) = 6.08, p < .017, were both significantly more likely to be shunned than our theoretical distribution following Bonferroni corrections (.05/3 = p = .017). The “other” group was not significantly more likely to be shunned than a theoretical distribution assuming equal responses, X2 (1, N = 34) = 4.23, p = .04. Appendix AG contains all of the post-hoc Chi Square tests in full.

142 Discussion

Length of time participants were prepared to listen Using the period of time participants were willing to listen to music as an index of preference, the finding that our sample population listens longer to some styles of music than others is consistent with the findings of Hui (2009) in Macau high school students; and Mitchell and Leland (2008) in New Zealand high school students. The finding was that some musical styles were more preferred to others, and in addition, types of Pop music were rated among the most preferred in each study. Although Pop music was the most preferred style to listen to around friends by New Zealand high school students (Mitchell & Leland, 2008), and the undergraduate students in the current study, patterns in preference differed for the other styles. For example, Asian Karaoke (Wing) was preferred least in the current study, but Opera, Jazz, Classical and Piano did not differ significantly from Wing in the Mitchell and Leland (2008) study. It is suggested that a statistical comparison of the responses patterns from the two samples be undertaken in the future. Hypothesis 1, that participants would report that they are willing to listen to Pop music for longer than other music styles , was supported on average across situations. However, when the on-hold situation was examined in isolation, this relationship with the other styles was altered considerably. This is discussed further in relation to Hypothesis 2. Hypothesis 2, that participants would differ in how long they are willing to listen to different music styles for telephone on-hold listening versus outdoor public area listening situations, was also supported. Depending on the situation, participants changed their pattern of response, although not drastically. When examined individually, the statistically significant overall preference for Pop music was only present in the public area situation. Country, Piano and Sixties styles were not rated significantly different from Pop in the on-hold situation. In addition, in the on-hold situation, piano music was no longer less favoured than Pop, Country and Sixties music. This does imply a willingness to listen to other music styles than are otherwise generally favoured in a more casual public outdoor setting, when given motivation to stay online in an on-hold situation. This could either be due to the increased motivation to stay on-hold for monetary reward, or because the music

143 styles other than Pop could be more familiar, and therefore more accepted, in an on- hold setting. The latter theory concurs with past research by Ali and Peynircioglu (2009) that familiar music is preferred to unfamiliar music. In contrast to previous findings by Tom et al. (1997), there was no difference in listening preference between Jazz and Classical music in an on-hold situation, as indexed by the time participants were prepared to listen to each. Hypothesis 3, that males would prefer to listen to different types of music for longer than females was not supported for either situation or on average across situations. Hypothesis 4, that there would be differences in willingness to listen to the different styles for different age groups was not supported whatsoever either. This could well be due to the very limited age range that the experiment utilised. It is suggested that future research compares the ratings of high school students and older adults to the undergraduate sample in this study in further depth using statistical analyses. The findings concerning the average time that participants were prepared to listen to the different musical styles have implications for the choice of both music on-hold and in public areas. Though there was less range in the variability of ratings for the on-hold situation than the public area, there were clearly different patterns of preference for both.

Responses to questions Some interesting findings arise from the answers to the questions that were asked after participants had finished rating the musical styles on the time scale. We can reasonably assume that Pop music would be preferred following the time scale data; however from the qualitative responses, a non-significant trend shows Classical music is almost as preferred to Pop to listen to while on-hold. Not only this, but there is a trend that this may also be a music that would encourage participants’ to hang up! Why might this be? I propose that a wide scope of genre is a reason for this. Several sub-genres are included in the psychological classification of “Classical” music. Music from the Romantic period, Baroque period and modern day music written in a certain style, as well as music from the Classical period may all fall under the broad “Classical” umbrella. Indeed, what was classified as “piano” in the time scale data

144 may psychologically be considered “Classical” when defining the style of the piece of the music. Within these styles there can be much variation also. For example, Nikolai Rimsky-Korsakov’s phrenetic “Flight of the Bumblebee” can hardly be considered very similar to the slow, sad melodies of Frédéric Chopin’s “Prelude Op. 28, No. 4 in E minor” known as “Suffocation”, yet both composers and pieces hail from the Romantic period. The conflicting responses may be reflecting this difference in musical elements throughout Classical music. The differing preferences for certain musical elements may also account for the high frequency of responses falling into the “Other” category for Question 1 and 2, regarding the on-hold music. Many of the responses in this category related to elements of speed, rhythm, vocals or just a general feeling of the music. The large percentage of responses that fell in this category lend credence to the idea that particular elements of the music may play an important role in whether people will stay in a service area (or in this case on the telephone line) supporting a review of previous findings by Garlin and Owen (2006), revealing that slower, quieter, and more familiar music would keep participants in an area slightly longer than louder, faster or unfamiliar music. Pop music may be represented strongly in answers to the questions 1 and 3, because Pop (popular) music is likely to be familiar to participants. The music by situation interaction in the time scale findings may be partially explained by this theory also. Those pieces of music in the piano genre, for instance, may have met more of the elemental criteria for what participants liked to listen to on- hold, without the influence of peers or a social situation, but not the criteria of what they would prefer to listen to when actively trying to enjoy themselves in a social situation. It also suggests that the specific elements of the music may be more important in the choice of music that is played on-hold, rather than the style itself, as it may increase or decrease the likelihood of a customer hanging up, the closest equivalent to leaving a service area.

Summary To answer the questions posed in the introduction to this study… 1) for a specific Population does on-hold music preference differ from the music that participants prefer in a more casual setting or situation? The short answer is yes.

145 However, the way in which the preference differs is not straight forward. For instance, in our university undergraduate sample, Pop was preferred in both situations, but the extent to which it was preferred over other musical styles changed. 2) Which styles are more suited for the on-hold environment? For the university undergraduate demographic; Pop music, along with Country, Piano, and Sixties style music were most suited to the on-hold period according to the time participants were prepared to listen to them in that situation. According to qualitative questioning, most support was given to Pop music as a style of music that would encourage participants to stay on-hold. However, many participants indicated that certain musical elements were important. The findings suggest that companies may want to think twice about picking music that that they use in storefront applications for on-hold music or vice-versa. The time scale findings, in conjunction with the qualitative responses, lend further credence to the argument of Garlin and Owen (2006), that, due to such a wide range of variability within a genre, and difficulty in defining musical pieces to a genre objectively, research into changes of preference or behaviour due to style/genre may be somewhat futile. In the qualitative questions participant responses often had to be categorised as “other” because they were describing certain elements of a musical piece, rather than a specific style. Indeed, in agreement with Garlin and Owen (2006), I propose that future work involving music should focus on these elements, rather than genre. Specifically, in lieu of our findings that piano music is comparatively more preferred on-hold than in a public area, examination into how the instrument used to play a piece of music influences behaviour/preferences is proposed. The same pieces of music could be played by different instruments/ensembles and the differential effects examined in on-hold and face-to-face service environments. I suggest future research is conducted in this area to examine the behavioural effects that different musical elements have on customers placed on-hold; and whether those elements utilised to increase spending and satisfaction with wait, in physical face-to-face service environments, will also increase purchasing behaviours or customer retention for telephone service providers (i.e. slow, quiet, familiar music). In addition I feel there is a research possibility looking at the effects that the sound of different instruments has on behaviour or preference. Future research could examine

146 the differential preferences, or success in changing participants’ behaviour, of the same pieces of music played by different instruments. Further research into age group differences is also required. Older adults, the elderly and New Zealand may have different musical preferences to a university and high school students. There age range within the sample in the current study was indeed, very small, and the preferences found in this study may not be generalizable to all age groups.

147 148 Overall Discussion

Summary of research The four studies described in this thesis each provide unique findings to add to existing research regarding call centres. Study 1 provided an opportunity for customer service representatives (CSRs) to voice their opinions on what they disliked and liked about their job; whether they found angry or abusive callers a problem, and whether they felt that calls took longer because of this type of caller. In agreement with previous research by Deery et al. (2002), that negative job characteristics with poor outcomes included customer interaction with hostile callers, and Taylor and Bain’s (1999, p.110) proposition that “nuisance and abusive calls ... are a source of incalculable stress”, we found that angry or abusive callers were one of the commonly listed dislikes of the CSRs surveyed. We also found CSRs believe that angry callers lead to longer call times. This may add to stress due to time pressure, and performance monitoring related to call times, that are a common occurrence in call centres (Taylor & Bain 1999; Bain et al., 2002).The results of Study 1 also showed that, for most CSRs, the favourite part of the position was the opportunity to help people. In Study 2 we sought to examine the promise of a number of on-hold listening conditions in reducing negative affect (indexed by mood) and dissatisfaction. There were significant differences among groups in satisfaction ratings. The most promising listening conditions were a Comedy condition, and a Choice condition. These conditions did not significantly differ in satisfaction ratings from being put straight through to service. In contrast with previous findings of Cameron et al. (2003), that playing different aural alternatives (musical pieces) during a wait altered mood, there were no significant differences in mood found among the listening conditions. In Study 3 we examined the most promising on-hold listening conditions in further depth. Again, no notable differences in mood were seen among the listening conditions tested. In this study, there was a significant difference in satisfaction between the Straight-through condition and both the Comedy, and Choice, groups, but no difference between the Comedy and Choice groups. The results of Study 3 indicated that the only further differentiation within the top listening conditions was that the Straight-through condition led to greater levels of satisfaction than Comedy, Choice or Pop music conditions.

149 In addition, during Study 3, we manipulated the accent of the CSR speaker to see if this affected caller satisfaction, as suggested by Stringfellow et al. (2008), and the appraisal of the operator. Past research has suggested that accented non-native English speakers are rated lower in credibility (Lev-Ari & Keysar, 2010), competence (Watanabe, 2008), and intelligence (Lindemann, 2003). Our results showed altering speaker accent had no effect of on satisfaction, but a Filipino accented speaker was significantly less favoured than an American on the scale of competence, and a New Zealand speaker on the scale of likeability that we measured. They also disliked listening to the Filipino accent more than the New Zealand accent. A further study (3a) was carried out to examine whether age or intelligibility of the speaker was causing these negative appraisals of the Filipino accent. The results from Study 3a showed a significantly more negative appraisal of the competence of an Indian accented speaker than a Filipino speaker. Age of speaker was not found to play a role in the competence rating, but intelligibility was positively correlated, and aural dislike of accent was negatively correlated, with competence. The positive correlation of intelligibility and competence ratings provides some support for research findings of Lev-Ari and Keysar (2010), that credibility of a foreign accented speaker was positively related to speaker intelligibility. Music has been used extensively to try and influence customer behaviours in face to face shopping scenarios (see Garlin & Owen, 2006; Turley & Milliman, 2000 for reviews). Study 4 was carried out to examine the preferred on-hold music in an undergraduate population, and whether this preferred music differed from the types of music preferred in a more social setting. In addition, participants were asked qualitatively, which music was more likely to make them hang up, or stay on-hold. In agreement with previous research by Hui (2009), in Macau, and Mitchell and Leland (2008), in New Zealand, there were overall differences in preferred music styles within the sample population. Music preferences did differ slightly across situations, though Pop music was generally the most favoured. The results relating to qualitative questioning in this study suggest that more attention should be paid to elements of music (such as tempo, timbre, rhythm or volume), and their effects on on-hold customer behaviour, rather than the genre. This provides support of assertions made by Garlin and Owen (2006) that elements of music, rather than genres, should be examined in future research.

150

Turnover and Absenteeism Turnover and absenteeism are estimated to cost a great deal to the New Zealand call centre industry every year. A recent estimate placed the total cost of turnover and absenteeism in New Zealand call centres at approximately $124 million per annum (Boyte, 2009). Emotional exhaustion (Lewig & Dollard, 2003) and emotional dissonance (Grebner et al., 2003) have been posited to play a role in absenteeism and intentions to leave a job. We set out to carry out an applied study in a real-world call centre in an attempt to try and reduce CSR absenteeism and turnover. We aimed to examine whether using a form of perceptions management utilising the on-hold period would reduce the number of negative customers CSRs had to deal with. This should reduce emotional dissonance, and in turn could lower rates of turnover and absenteeism. However, due to a lack of ongoing co-operation from call centres we were unable to test the effects that an on-hold intervention had on the turnover and absenteeism of a call centre. We approached a number of call centres and invited them to take part in the research. Several were interested in the research, but were more interested in keeping their operation running smoothly, so did not wish to take part in an applied study. We were taken up on our offer twice, only for one centre to withdraw their consent due to a lack of confidence in their Information Technology administrators following an upgrade taking much longer than expected (after a great deal of baseline data had been collected and converted). The other centre that had agreed to take part inexplicably stopped replying to our correspondence when data collection was supposed to start. We really feel that an applied study is the only way to effectively test whether an on- hold intervention is decreasing turnover and absenteeism in a call centre, and reducing the rate of call attrition (people hanging up). We strongly recommend this form of applied study as a direction for future research.

Emotional Dissonance The survey in Study 1 had results that fit the theory of emotional dissonance being caused due to the paradoxical nature of emotional expression in call centres. A number of respondents indicated that the best parts of the job as a CSR was helping

151 people, whereas one of the worst parts of the job was dealing with angry callers. This is somewhat consistent with what we would expect to lead to emotional exhaustion through emotional dissonance from past theory (e.g. Holman et al., 2002; Totterdell & Holman, 2003; Zapf et al., 2003; Grebner, et al., 2003; Lewig & Dollard, 2003). When CSRs try to help someone, but are unable to do so due to the caller being unreasonable and angry, they themselves may become upset or angry, but must continue to project a helpful persona. This applies not only to the initial angry or unreasonable client, but for the subsequent calls that they have to take following such a call.

Angry Callers Study 1 provided us with some evidence to suggest that a) angry callers can lead to longer calls, and b) that if angry callers become abusive they may well find their call terminated by call centre staff. These findings are consistent with personal communication (Matthew Lee, Customer Service Representative, Personal Communication, Jun. 12). In addition, one of the recommendations that call centre staff put forth, to help reduce the incidence of this type of caller, was manipulating what occurred during the on-hold period. Based on this first study, it does appear that trying to influence customer mood and satisfaction using the on-hold period while customers are waiting (an on-hold intervention), has some support from the staff who could be affected by such a manipulation. However, though manipulating the on-hold period to reduce negative affect/mood is a promising idea with support from some workers in the call centre industry, in our laboratory studies we were unable to elicit any statistically significant difference in anger (or any other mood) on the POMS measure. We have to conclude that none of the on-hold listening conditions that we trialled in Study 2 or Study 3 differentially influenced the mood/affect of the participants in a simulated call centre on-hold experience. This was inconsistent with findings from Cameron et al. (2003), that playing music that was enjoyed during a ten minute wait had significant positive effects on mood. However, the measurement of mood they used was not as stringent as our own, nor did they examine subsets of mood, as well as overall mood disturbance.

152 The lack of difference we saw could be due to a number of methodological limitations. It could be due to the analogue mood measurement scale of the POMS being insensitive to small but important changes in mood, a lack of perceived cost associated with the wait in the laboratory, or too short a wait time to influence mood. The Cameron et al. study had participants wait for ten minutes, twice that of our own. The wait time in our own research was found to be greater than the average stated acceptable wait time of the participants in the study, but time had already been set aside to take part in an experiment by these participants, so this generally acceptable wait time may not have applied for our studies.

On-hold Messaging In Study 2 we trialled the use of on-hold messaging, in combination with music, to try and reduce negative affect and increase satisfaction. Using statements about the company (on-hold messaging) had been proposed by Maister (1985) as an effective way to fill the on-hold period. It was proposed to reduce perceived wait and increase customer satisfaction. In contrast with the effects that Maister had proposed, there were no differences in perceived wait found amongst any of the groups whatsoever in Study 2 and Study 3. Thus, the on-hold messages had no significant effect on perceived wait, compard to any other condition. Additionally, the satisfaction ratings for the Elevator music plus Information condition in Study 2 did not differ significantly from those of the Elevator music condition, which played the same music without messages interspersed. This provides evidence that the addition of the on-hold messages did not add to the level of satisfaction that the music alone created. Likewise, benefits advertised by Evolved Sound (n. d), that clients listening to on-hold messages would stay on the line for a longer period had no support from the results of Studies 2 or 3. There were no positive effects on call attrition. There were no significant differences in rates of hanging up among any groups. However, call attrition was very low in general so we can not read much into this finding. Our findings do not provide support for assertions of the efficacy of on-hold messaging compared to other on-hold conditions. However, the participants in our study were ringing through for a purpose not driven by self motivation to call through. Generally, someone may call through to a business only because they are interested in

153 their product or service initially, and would therefore be interested in hearing information from the business (Suzie Jones, Managing Director, On-hold Marketing Limited, Personal Communication, Nov. 4, 2009). I believe that it is short-sighted to assume that all customers will be calling through for these reasons though. If a disgruntled customer calls a complaints line, they could be angered further to hear about the great service a company provides, or their interesting new products. On-hold messaging should definitely not be used in all lines of a large organisation in the same way. Our results show, at least, that when a customer is potentially uninterested in on-hold messages played amongst music, they are no more satisfying than the music alone. There is a slight trend towards lower satisfaction for on-hold messages. If a customer was disgruntled to begin with, rather than just uninterested, the messaging could cause further problems.

Outsourcing/Offshoring Study 3 and Study 3a examined the effects that accents from common outsourcing destinations have on customer mood and satisfaction with service. The accents that we examined included: Indian, Filipino, American and New Zealand. We found no differences among accents on a measure of satisfaction, in contrast with previous assertions by Stringfellow et al. (2008). We also found no effect on affective response (mood), in contrast with previous of Bresnahan et al. (2002), in an American sample. We did see differences among accents with regards to speaker competence, likeability, and the enjoyment of their accents. In Study 3, the Filipino accented speaker was rated less competent than the American, and less likeable/less pleasant to listen to, than the New Zealand speaker. In Study 3a the Indian accented speaker was rated less competent than the American accented speaker. There were no differences in likeability found among the accents in this follow up study, but the Filipino voice was still enjoyed significantly less than the New Zealand voice. Results indicated that dislike of accent, the likeability of a speaker and the intelligibility of the speaker were all correlated with the competence of the speaker. We recommend that outsourcing businesses consider training their staff in the accents of their callers, or neutralizing to a more American accent, so that the operators may be thought of as more competent.

154

Customer satisfaction Studies 2 and 3 addressed the issue of how customer satisfaction may be affected by what is listened to during a pre-process on-hold wait period in a telephone interaction. Both studies provided further correlational support that increases in actual wait time were related with lower satisfaction (Clemmer & Schneider, 1989; Davis & Heineke, 1998; Davis & Vollman, 1990; Tom et al., 1997). However, the proposal of Maister, that satisfaction during a wait does not just depend on the actual amount of time that is waited, was supported, as the Choice and Comedy conditions in Study 2 did not differ significantly in satisfaction from the Straight-through group, despite participants in these conditions having to wait for a much greater period before being connected. Study 2 also provided support for past research (e.g. Davis & Heineke, 1998; Pruyn & Smidts, 1998; Tom et al., 1997), that satisfaction could be influenced by what occurs during a wait, with some on-hold listening conditions leaving participants more satisfied than others. Specifically, apart from the Straight-through group, the Choice and Comedy groups were favoured by participants. In addition, in partial support for the assertion of Bougie et al. (2003) that dissatisfaction is an antecedent to anger, in Study 2 a negative correlation between satisfaction and anger was found. Given more waiting time and a higher cost wait, according to the theory proposed by Bougie et al., dissatisfaction could lead to anger.

Intervention Techniques

Reducing perceived wait time as a method of reducing negative affect/dissatisfaction In none of our studies did we see perceived wait significantly differ among on- hold listening conditions, when actual wait was included in analyses as a covariate. This is in contrast with findings of Tom et al. (1997; Whiting & Donthu, 2006) that perceived wait duration was influenced by what was played on-hold. Our results are similar to those of Munichor and Rafaeli (2007), who also saw no difference in perceived wait duration, among differing on-hold listening conditions. There was no evidence that changes in perceived wait were related to a change in overall mood/affect in either Study 2 or Study 3.

155 However, though there were no significant differences among listening conditions, correlational and regression evidence from Study 2 indicated that perceived wait was related in some way to caller satisfaction. Regression results indicated that as perceived wait became higher, satisfaction decreased. These findings lend support to the research of Whiting and Donthu (2006) and Peevers et al. (2009), who found that lower perceived wait duration was associated with higher levels of satisfaction.

Providing choice and increasing perceived control as a method for reducing negative affect and dissatisfaction Increasing perceived control was attempted using two techniques in Study 2. An explicit choice was provided through the use of a push button menu for one group, and an implicit choice was provided by allowing participants to flip through a magazine with a Hands-free option in another. Self rated perceived control was significantly higher for the explicit choice (Choice) group than Pop music and the Quiet beeping tone, but perceived control ratings were not significantly different from these low rated groups for the Hands-free group. The Hands-free group was rated significantly lower on satisfaction, but no different in perceived control, than the Choice group. Correlational results suggest that the level of perceived control does appear to play some role in the level of satisfaction that participants experienced, although ordinal regression did not provide further support, suggesting that this relationship may be mediated by another factor. Our results lend partial support to the findings of Clemmer and Schneider (1989), and Tom et al. (1997), that introducing choice during a wait can increase satisfaction, as the Choice group in Study 2 was rated highly in satisfaction. However, the evidence is unclear whether this is due, in part, to greater perceived control than other groups, rather than greater enjoyment or lower perceived wait duration. Thus, we cannot confidently support the finding of Hui and Bateson (1991), that increasing perceived control has a positive effect on customer appraisal.

156 Affect/mood regulation as a possible method to reduce customer negative affect and dissatisfaction We saw very little evidence of mood regulation occurring in Study 2 or Study 3. Though participants enjoyed listening to some conditions more than others, we saw no significant differences in mood among conditions. However, there was some support of Cameron et al.’s (2003) finding that music rated as likeable positively affected mood during a wait. Our results in Study 2 showed a positive correlation of dislike of listening condition (basically the inverse of enjoyment) and total mood disturbance. The differences in satisfaction with the call among listening conditions, in Studies 2 and 3 do not appear to be influenced greatly by mood regulation, as mood was not regulated in any substantive manner among the groups. However, the negative correlation of anger and satisfaction in Study 2 provides some evidence for mood regulation affecting satisfaction. Past research by Taylor (1994) indicated that filling a wait time caused by delay reduced anger and uncertainty compared to when this time was not filled. In our studies we did not have a condition with no filler whatsoever; however, we found no noticeable differential effects of the different listening conditions on affect/mood. We also did not see a relationship between actual time on-hold and anger, which does not provide support for the finding of Taylor (1994), in American airports, that the longer a delay was, the more angry people were.

Music In contrast to the findings of Hui et al. (1997) with a Canadian undergraduate sample, and the Cameron et al. (2003) findings with an American undergraduate sample, that music during a wait could affect participant mood, there was no effect of music on mood or perceived wait in Studies 2 or 3. Music (both Pop and Elevator type) was not as effective in satisfying participants as Choice and Comedy were in Study 2. In Study 3, participants were just as satisfied with Pop music as with the Comedy and Choice conditions, and participants in this sample indicated that they enjoyed Pop music. Though music was not the most promising on-hold listening alternative we found, for those call centres that may not be able to put in place the suggested intervention of a choice of options and can only offer a single stream of music, we suggest Pop for university age clients. Undergraduate participants in Study 4 indicated that they were prepared to listen for a longer period to Pop, Sixties,

157 Country or Piano music compared to other styles. Qualitative questioning provided more support for Pop as a music style that the sample would like to listen to on-hold.

Humour and comedy There was no evidence from Study 2 or Study 3 to suggest that the effect of comedy was working through a mood regulating mechanism in any way. There was no difference in mood found for comedy compared to the other groups in either study, in contrast to the finding of Danzer et al. (1990), that the use of humour could reduce feelings of depression. It may be that if a change in mood was induced by the telephone interaction that we could see humour work in a mood regulating fashion similar to that described by Danzer et al., but we were unable to see any difference in mood among any of the groups. However, in Study 2 the satisfaction of the participants in the Comedy listening condition was on par with the Choice listening condition and Straight- through condition. Satisfaction was not significantly different for the three. Comedy was also considered the most enjoyable condition to listen to in Study 2. In Study 3 this enjoyment was not replicated, and Comedy was found to be less satisfying than being connected straight away. It is a promising intervention strategy judging by our Study 2 results, but may be a somewhat impractical technique to employ in a real world setting. However, there is the possibility, that to source comedy appropriate for on-hold use, companies creating on-hold media (or call centres themselves) could commission local comics to write and perform material to be included in their on-hold programming.

Limitations

Major methodological limitations There are a few major limitations of the studies reported in this thesis. The first is that the POMS may not have been sensitive enough to small changes or differences in mood in Study 2 and 3. Large changes in mood may not have been caused due to the low cost wait, but significant small differences in mood may have been occurring. The low cost of the wait studies 2 and 3 may not have been enough to induce any real changes in mood. Participants had already set aside time to do the

158 experiment, so waiting for five minutes during this period may not have presented them with a mood changing wait, where in a real world situation it may have. The music preference study was done with a small age group, and can not be generalised to music preference of other age groups. It does indicate that examining the music preferences for a given target audience could be useful. Further research is required in the comparison across ages. This study was relying on estimates of how long people were willing to listen to a piece of music in two situations, rather than placing participants in those situations and examining their behaviour. The latter would be a more effective way of gauging the actual preference, rather than reported preference. Finally, the concept of a genre or style is a very subjective label, and there is a lot of variation within a genre/style. Particular elements of music should be examined rather than just styles.

Research limitations In general, there was a lack of interest and co-operation from the industry when any technical changes were mentioned. It seemed that managers did not wish to risk upsetting the status quo, and trial a new system, even though it could be beneficial to their business. Then again, they may just be uninterested in taking part in research. An example of the lack of interest in participating in the research is a study that was attempted by the author of this thesis. The study was to a) try and get an idea of what the average sick leave and turnover rates for New Zealand call centres was and b) to compare turnover rates to national averages that Statistics New Zealand collect. The survey was a mail-out with postage paid return envelopes and instructions on how to fill out data sheets that were included. Over 70 surveys were mailed out to call centres throughout New Zealand. Five were returned. Only one data sheet had been filled in correctly. Previous findings by Boyte (2009) revealed that managers in several New Zealand call centres did not know about academic research pertaining to the management of call centres. It may be that they have no interest in the research field, and prefer to deal with operations management more than perception management to try and increase customer satisfaction and worker wellbeing.

Conclusions/Implications

159

Best on-hold alternatives we examined. In Study 2 we found that the best alternatives to going straight through to service were Choice and Comedy. Though there were no differences in mood found across the groups, these listening conditions provided the best aural alternatives to the straight through condition on the measure of satisfaction. We believe that the choice option is the most practical and promising. If there is a choice, customers can then choose to listen to comedy if they wish. In addition, even if companies insist on having on-hold messaging, customers can then choose whether to listen to it or not.

Mechanisms by which the listening conditions affect satisfaction It appears that there may be a complex interplay of enjoyment, perceived control, perceived wait and actual wait at work in determining whether a listening condition alters a person’s level of satisfaction. All were correlated with satisfaction, though perceived wait showed most predictive power in the ordinal regression of Study 2. This area deserves more research attention, to further tease out the relationship between these different factors in more specified research.

Implications of the findings A general picture can be painted of the direction that our findings have for the call centre industry. Call centre workers have a number of dislikes in their occupation, one of which is dealing with angry or aggressive callers. A common suggestion from the workers to reduce the incidence of these callers is to manipulate what they are listening to on-hold. Though our research did not find any differences in mood for different listening conditions, we did find some differences among groups in satisfaction. However, the perceived low cost wait that participants experienced may not have been enough to significantly produce a sufficiently negative mood in the ‘floor’ control to compare against. Past research by Bougie et al. (2003) has found that satisfaction in a waiting scenario is mediated by anger, so dissatisfaction may in fact be a sign of anger that our tests were not sensitive enough to pick up, or an antecedent to anger. Regardless, call centre staff are likely to find a satisfied customer easier to deal with than a less satisfied customer. We therefore suggest the use of a choice of aural alternatives to

160 listen to while on-hold in call centres to try and increase customer satisfaction, and possibly help worker well-being also. Comedy also tested well as on-hold filler, but we believe that, without choice, this would be less useful generally across industries. Jokes need to be short so punch lines are not cut off, swearing must be omitted, and the content must not act to infuriate callers more if it is a sensitive matter they are calling about. For example, imagine a customer calling about their home that has just been destroyed beyond repair by the recent Christchurch earthquake hearing inane jokes…they may not feel it is a joking matter, or be in the mood for laughter. The choice option would allow an insurance company to provide a choice of different music, comedy and even answers to frequently asked questions while a customer waits. This places control in the hands of the caller. As dissatisfied customers are reputed to switch service providers more readily than satisfied customers (Anton, 2000; Davis & Heineke, 1998; Tom et al., 1997), the use of a choice of aural alternatives to increase satisfaction during the waiting period could help in retaining clientele for companies who have busy call centres. Outsourced call centres in the Philippines and India should be used with the understanding that, aside from problems they have had with training, and understanding (NZPA, 2009), there may also be a negative reaction towards the workers merely because they have a non-native English accent. For example, the Filipino accent was considered less competent than an American accent, less likable than a New Zealand accent, and disliked more than the New Zealand accent in Study 3. This is without any form of true interaction with the operator. In Study 3a, Indian and Filipino accents were both rated poorly. What is clear is that, without even taking training into account, CSRs with Filipino or Indian accents may be taken less seriously than American accents and the Filipino accented worker may be treated more unfairly, due to their low likeability, than their New Zealand counterparts. The music that is played on-hold appears to matter also. Not all music is appreciated on-hold, and some make callers more likely to hang up than others. In our undergraduate sample, participants reported that Pop, Country and Piano styles were all more likely to keep them listening for longer, whereas Asian Karaoke (Wing) and opera styles would see them hang up faster. Call centres that do not have the technological capability to provide a choice should examine what their general

161 customer base is and survey what sort of music they enjoy. They should do this separately for in-store and on-hold music, as response patterns vary depending on the situation in which people find themselves. The call centre could then use this music while on-hold rather than something that may make their customers hang up faster, possibly costing them business, or affecting their performance goals.

Future research We propose that an applied study be undertaken in a call centre to trial the use of the promising on-hold alternatives of choice and comedy, and examine the effects they have on both call centre worker wellbeing, attendance, and retention and customer satisfaction and mood. This could prove financially advantageous for call centres to trial, if only they were co-operative enough to do so. If our Choice intervention was found to decrease worker turnover and absenteeism, this could save call centres a great deal in recruiting and training costs, as well as a lack of productivity. For example, Holman et al. (2007) advises that replacing a call centre worker costs, on average, 16% of a full time CSRs yearly salary. Using Visual Analogue Mood Scales (VAMS; e.g. Stern, Arruda, Hooper, Wolfner & Morey, 1997) may more advantageous in picking up small differences in mood if measured from a neutral to a happy or sad face. We also believe that the behavioural apparatus used in Study 3, if calibrated accurately and refined, may be a useful tool in measuring the strength of conviction one has with answering to a given question. In addition, further music studies examining how the different elements or instruments that are used for on-hold and in-store applications affect customer behaviour is suggested.

Conclusion The current research examined found some of the problems faced by call centres and how on-hold alternatives may be able to play a role in reducing some of these problems. We discussed which on-hold alternatives appear the most promising for this role, and why companies should examine the different accents and different music styles they use in call centres, as they may affect the perceptions and behaviours of a customer placed on-hold.

162 We did not successfully induce measurable mood changes in any of our studies, but saw promise in a Choice on-hold intervention that saw levels of satisfaction, which, in one of our studies, did not differ significantly from being connected straight through to service. This condition would allow flexibility for companies to offer listening choices including music, humourous or informative aural alternatives – and could increase their customer satisfaction. We feel that further research is required in this area, specifically the trial of a choice on-hold intervention in a call centre with long on-hold periods, high turnover and absenteeism.

163 164 References Affect. (2007). In VandenBos, G. R. (Ed). APA Dictionary of Psychology , p. 26. Washington: American Psychological Association. Ali, S. O. & Peynircioglu, Z. F. (2009). Intensity of emotions conveyed and elicited by familiar and unfamiliar music. Music Perception, 27 , 177–182. Anton, J. (2000). The past, present and future of customer access centers . International Journal of Service Industry Management , 11 , 120-130. Antonides, G., Verhoef, P. C. & van Aalst, M. (2002). Consumer Perception and Evaluation of Waiting Time: A Field Experiment. JOURNAL OF CONSUMER PSYCHOLOGY , 12 , 193–202 Audacity 1.2.4. (2009). Audacity: The Free, Cross-Platform Sound Editor [Computer software] . Retrieved, June 10, 2009. http://audacity.sourceforge.net/ . Audacity 1.3. (2010). Audacity: The Free, Cross-Platform Sound Editor [Computer software]. Retrieved May 11, 2010, from http://audacity.sourceforge.net/ . Audio Advantage. (2008). On-hold Statistics. In Say it on-hold . Retrieved 12th August, 2009, from http://www.sayitonhold.com/pages/statistics.html. Aylott, R. & Mitchell, V. W. (1998). An exploratory study of grocery shopping stressors. International Journal of Retail & Distribution Management , 26 , 362–373. Bain, P., Watson, A., Mulvey, G., Taylor, P. & Gall, G. (2002). Taylorism, targets and the pursuit of quantity and quality by call centre management . New Technology, Work and Employment, 17, 170-185. Bakker, A. B., Demerouti, E. & Schaufeli, W. B. (2003). Dual processes at work in a call centre: An application of the job demands – resources model. European Journal of Work and Organizational Psychology, 12, 393-417. Barker-Collo, S. L. (2003) Culture and validity of the Symptom Checklist–90– Revised and Profile of Mood States in a New Zealand student sample. Cultural Diversity and Ethnic Minority Psychology, 9 , 185-196. Bayard, D., Weatherall, A., Gallois, C. & Pittam, J. (2001). Pax Americana? Accent attitudinal evaluations in New Zealand, Australia and America. Journal of Sociolinguistics, 5, 22-49.

165 Bennett, C. & Loken, B. (2008). The brand has two faces: Examining the impact of an accented spokesperson on brand perceptions [Extended Abstract]. European Advances in Consumer Research, 8, 120-121. Berkowitz, L. (1989) Frustration –Aggression hypothesis: Examination and reformulation. Psychological Bulletin, 106 , 59-73. Berkowitz, L. (1999) Anger. In Dalgleish, T. & Power, M.J. (Eds.) Handbook of cognition and emotion (pp. 411-428). Chichester: John Wiley & Sons. Bond, A. & Lader, M. (1974). The use of analogue scales in rating subjective feelings. British Journal of Medical Psychology , 47, 211-218. Bougie, R., Pieters, R. & Zeelenberg, M. (2003). Angry customers don't come back, they get back: The experience and behavioural implications of anger and dissatisfaction in services. Academy of Marketing Science . 31, 377-393. Boyte, K. (2009). Job design and wellness in New Zealand contact centres: A paradigm shift or same old management (Unpublished master’s thesis). Massey University, Palmerston North, New Zealand. Brattico, E. & Jacobsen, T. (2009). Subjective appraisal of music: Neuroimaging evidence. The Neurosciences and Music III: Disorders and Plasticity: Annals of the New York Academy of Sciences, 1169: 308–317. Bresnahan, M. J., Ohashi, R., Nebashi, R., Liu, W. Y. & Shearman, S. M. (2002). Attitudinal and affective response toward accented English. Language & Communication, 22, 171–185. Buerki, S. & Adler, R.H. (2005) Negative affect states and cardiovascular disorders: a review and the proposal of a unifying biopsychosocial concept. General Hospital Psychiatry, 27 , 180-188. Burgers, A., de Ruyter, K., Keen, C. & Streukens, S. (2000). Customer expectation dimensions of voice-to-voice service encounters: a scale-development study. International Journal of Service Industry Management . 11, 142. BusinessVoice. (2009). On-hold Messaging: Benefits of Messages and Music by BusinessVoice. In BusinessVoice . Retrieved 2nd August, 2009, from http://www.businessvoice.com/on_hold_messaging_benefits.html. Cameron, M. A., Baker, J., Peterson, M. & Braunsberger, K. (2003). The effects of music, wait-length evaluation, and mood on a low-cost wait experience . Journal of Business Research, 56 , 421– 430

166 Campbell, D. T. (1965). Ethnocentric and other altruistic motives. In D. Levine (Ed.), Nebraska symposium on motivation , (pp. 283-311), Nebraska: University of Nebraska Press. Campbell, S. J. (February 4, 2009). Telecom Moving Call Center Jobs; Philippines to Benefit. In TMCnet.com . Retrieved September 26, 2010, from http://outbound-call-center.tmcnet.com/topics/hosted-call- center/articles/50008-telecom-moving-call-center-jobs-philippines- benefit.htm . City, R., Jones, K., Rotem, J. & Derülo, J. (2009). Replay [Recorded by K. Jones]. On Replay [mp3]. United States of America: Reprise Records. Clemmer, E.C. & Schneider, B. (1989) Toward Understanding and Controlling Customer Dissatisfaction with Waiting, Working Paper , 89-115 , Marketing Science Institute, Cambridge, MA Collins, K., Luszcz, M., Lawson, M. & Keeves, J. (1997). Everyday problem solving in elderly women: Contributions of residence, perceived control and age, The Gerontologist, 33, 293 -302 . Cowie, C. (2007). The accents of outsourcing: the meanings of “neutral” in the Indian call centre industry. World Englishes, 26 , 316–330. Danzer, A. J., Dale, A. & Klions, H.L. (1990). Effect of exposure to humorous stimuli on induced depression. Psychological reports, 66 , 1027-1036. Davis, M. & Heineke, J. (1998). How disconfirmation, perception and actual waiting times impact customer satisfaction. International Journal of Service Industry Management. 9, 64-73. Davis, M. M. & Vollman, T. E. (1990). A framework for relating waiting time and customer satisfaction in a service operation. The Journal of Services Marketing, 4, 61-69. Deery, S. J., Iverson, R.D. & Walsh, J.T. (2002). Work relationships in telephone call centres: Understanding emotional exhaustion and employee withdrawal.

Journal of Management Studies, 39, 471-496. DeShields, O. W. & de los Santos, G. (2000). Salesperson’s accent as a globalization issue. Thunderbird International Business Review, 42, 29–46.

167 Direct Health Solutions (2008). 2008 Absence Management Survey Release .[Press Release]. Retrieved August 4 th , 2010 from http://www.dhs.net.au/NewsDetail.aspx?pid=121 . Dollard, J., Miller, N. E., Doob, L. W., Mowrer, O. H. & Sears, R. R (1939). Frustration and aggression. London: Kegan Paul, Trench, Trubner & Co., Ltd. Donnerstein, E. & Wilson, D. W. (1976) Effects of noise and perceived control on ongoing and subsequent aggressive behavior. Journal of Personality and Social Psychology, 34 , 774-781. Drinnan, J. (November 17, 2009). Vodafone service off to Manila. In nzherald.co.nz . Retrieved September 26, 2010, from http://www.nzherald.co.nz/philippines/news/article.cfm?l_id=82&objectid =10609693 Dube, L., Schmitt, B.H. & Leclerc, F. (1991). Consumers’ affective response to delays at different phases of a service delivery. Journal of Applied Social Psychology, 21 , 810-820. Dube-Rioux, L., Schmitt, B.H. & Leclerc, F. (1989) Consumers' reactions to waiting: When delays affect the perception of service quality. Advances in Consumer Research, 16 , 59-63. Durrande-Moreau, A. (1999). Waiting for service: Ten years of empirical research. International Journal of Service Industry Management, 10, 171-194. Elevator music. (n. d.). In MSN Encarta Dictionary. Retrieved October 19, 2010, from http://encarta.msn.com/encnet/features/dictionary/DictionaryResults.aspx? refid=1861689734 . Elmuti, D. & Kathawala, Y. (2000). The effects of global outsourcing strategies on participants' attitudes and organizational effectiveness. International Journal of Manpower . 21, 112-128 . Evolved Sound. (n. d.). Facts. In Evolved Sound . Retrieved 15th July, 2009, from http://www.evolvedsound.com.au/on_hold_facts.php. Experience Project. (2010). I hate being put on-hold - Stories of experiences. In Experience Project. Retrieved January 8, 2010, from http://www.experienceproject.com/group_stories.php?g=29721

168 Feinberg, R. A., Kim, I.S., Hokama, L., de Ruyter, K. & Keen, C (2000). Operational determinants of caller satisfaction in the call center. International Journal of Service Industry Management , 11 ,131-141. Ferguson, E., McCracken, D. D., Kussmaul, C. & Robbert, M. A. (2004). Offshore Outsourcing: Current Conditions & Diagnosis. ACM SIGCSE Bulletin. 36 , 330-331. Filek, J (2006) Ways to put out the fire – six steps to managing angry callers. Phone Communications Skills Newsletter. Retrieved 14 th March 2009 from http://impactcommunicationsinc.com/newsletters.html. Folkes, V.L., Koletsky, S. & Graham, J.L (1987) A field study of causal inferences and consumer reaction: The view from the airport. Journal of Consumer Research, 13, 534-539. Freedman, P. (2001). Simply the worst: The most infuriating telephone hold music. In Stressbusting.co.uk. Retrieved 24, August 2009 from http://www.stressbusting.co.uk/articles/infuriating-hold-music/ Gans, N, Koole, G. & Mandelbaum, A. (2003). Telephone call centers: Tutorial, review, and research prospects. Manufacturing and Service Operations Management, 5, 79-141. Garlin, F. V. & Owen, K. (2006). Setting the tone with the tune: A meta-analytic review of the effects of background music in retail settings. Journal of Business Research, 59 , 755–764. Geller, E.S. (2001). The psychology of safety handbook . Boca Raton, Florida: CRC Press LLC. Gill, M. M. (1994). Accent and stereotypes: Their effect on perceptions of teachers and lecture comprehension. Journal of Applied Communication Research , 22 , 348-361. Grandey, A.A. (2000). Emotion regulation in the workplace: A new way to conceptualize emotional labor. Journal of Occupational Health Psychology, 5 , 95-110. Grandey, A.A., Dickter, D.N. & Sin, H.P. (2004). The customer is not always right: Customer aggression and emotion regulation of service employees, Journal of Organizational Behavior 25, 397–418.

169 Grebner, S., Semmer, N.K., Lo Faso, L., Gut, S., Kalin, W., & Elfering A. (2003). Working conditions, well-being, and job-related attitudes among call centre agents . European Journal of Work and Organizational Psychology, 12, 341–365 Gunelius, S. (n.d.). Tips for handling angry callers as a virtual call center agent. Retreived August 19, 2009, from http://hubpages.com/hub/Work_from_Home_Call_Center_Tips_for_Handl ing_Angry_Callers . Haahr, M. (2010). Random integer generator. In Random.org . Retrieved October 14, 2010, from http://www.random.org/integers/. Haahr, M.. (2010). Random Sequence Generator. In RANDOM.ORG . Retrieved September 7, 2010, from http://www.random.org/sequences/. Harris, L.C., & Reynolds, K.L. (2003). The consequences of dysfunctional customer behavior. Journal of Service Research, 6, 144-161 Hedberg, M. (2003). Mitch all together [CD]. Minneapolis: Comedy Central. Herring, H.B. (2006, Jun. 11 ). Hold On, Please. Do Not Turn the Page. Just Be Patient. The New York Times, p. 32. Holman, D. (2003). Phoning in sick? An overview of employee stress in call centres. Leadership & Organization Development Journal, 24, 123-130. Holman, D., Batt, R. & Holtgrewe, U. (2007). The global call centre report: International perspectives on management and employment. Report of the Global Call Centre Network. Ithaca, NY: Authors. Holman, D., Chissick, C. & Totterdell, P. (2002). The effects of performance monitoring on emotional labor and well-being in call centers. Motivation and Emotion . 26 , 57-80. Hui, M.K & Tse, D.K (1996). What to tell consumers in waits of different lengths: An integrative model of service evaluation. Journal of Marketing , 60 , 81-90 Hui, M.K. & Bateson, J. E. G (1991) Perceived control and the effects of crowding and consumer choice on the service experience. Journal of Consumer Research. 18, 174–184. Hui, M.K. & Zhou, L. (1996) How does waiting duration information influence customers’ reactions to waiting for services? Journal of Applied Social Psychology, 26, 1702-1717.

170 Hui, M.K., Dube, L. & Chebat, J.C. (1997). The impact of music on consumers’ reactions to waiting for services. Journal of Retailing, 73 , 87-104. Hui, W.V. (2009). Music listening preferences of Macau students. Music Education Research, 11 , 485–500. Jacelon, C.S. (2007). Theoretical perspectives of perceived control in older adults: a selective review of the literature. Journal of Advanced Nursing, 59 , 1–10. Jackson, M. (2008) Employee health and well being: Trends in the call centre sector. Insights from the Shepell·fgi research group , 2, 1-10. Katz, K.L, Larson, B.M & Larson R.C. (1991). Prescription for the waiting line blues: Entertain, enlighten and engage. Sloan Management Review , 32 , 44 -53. Keall, C. (July 13, 2009). Telstra pulls out of Philippines; Telecom stays in. In The national business review . Retrieved September 26, 2010, from http://www.nbr.co.nz/article/telstra-pulls-out-philippines-telecom-stays- 105379 . Khan N, Currie L., Weerakkody V. & Desai B.,(2003) “Evaluating Offshore IT Outsourcing in India: Supplier and Customer Scenarios.” Proceeding of the 36th Hawaii International conference on systems Science (HICSS) on the mini-track of Information Systems in Global Business. Kubzansky, L.D. & Kawachi, I. (2000) Going to the heart of the matter: do negative emotions cause heart disease? Journal of Psychosomatic Research, 48, 323-337. Kunde, W. (2001). Response-Effect compatibility in manual choice reaction tasks. Journal of Experimental Psychology: Human Perception and Performance, 27 , 387-394. Ladegaard, H. J. (1998). National stereotypes and language attitudes: the perception of British, American and Australian language and culture in Denmark. Language and Communication, 18, 251-274. Lev-Ari, S. & Keysar, B. (2010). Why don't we believe non-native speakers? The influence of accent on credibility. Journal of Experimental Social Psychology , 46 , 1093–1096.

171 Lewig, K.A. & Dollard, M.F. (2003) Emotional dissonance, emotional exhaustion and job satisfaction in call centre workers. European Journal of Work and Organizational Psychology. 12 , 366-392. Lindemann, S. (2003). Koreans, Chinese or Indians? Attitudes and ideologies about non native English speakers in the United States. Journal of Sociolinguistics, 7, 348-364. Maister, D. H. (1985).The psychology of waiting lines. In J.Czepiel, M. Solomon, and C. Surprenant, (Eds.), The Service Encounter (pp 113-123). Massachusetts: Lexington Books, D.C. Heath and Co. Martin, R.A. (2007). The psychology of humour: An integrative approach . San Diego: Elsevier Inc. McNair, D. M., Lorr, M. & Droppleman, L. F. (1992). EdITS Manual for the Profile of Mood States. San Diego, California: EdITS/Educational and Industrial Testing Service. Messages on-hold. (2005). Messages On-hold : : Statistics. In Messages on-hold . Retrieved July 19, 2009, from http://www.messagesonhold.co.nz/statistics.htm . Michie, S. (2002) Causes and management of stress at work. Occupational and Environmental Medicine, 59 , 67-72. Milgram, S. (1974). Obedience to authority: An experimental view . New York: Harper & Row. Mindess, H. (1996). The use and abuse of humour in psychotherapy. In A.J. Chapman and H.C. Foot (Eds.). Humour and laughter: Theory, research and applications (pp. 331-341). New Brunswick: Transaction Publishers. Mitchell, A. & Leland, L. S. (2008). Musical magic: Digging deeper into the Mozart Method. Unpublished manuscript. Mood. (2007). In VandenBos, G. R. (Ed). APA Dictionary of Psychology , p. 26. Washington: American Psychological Association. Munichor, N. & Rafaeli, A. (2007). Numbers or Apologies? Customer reactions to telephone waiting time fillers. Journal of Applied Psychology, 92 , 511–518 New Zealand Press Association (NZPA). (May 15, 2009). Job cuts across NZ - Yellow Pages jobs head to Manila. In nzherald.co.nz . Retrieved September 26, 2010, from

172 http://www.nzherald.co.nz/business/news/article.cfm?c_id=3&objectid=10 572463 . Norcross, J. C., Guadagnoli, E. & Prochaska, J. O. (1984). Factor structure of the Profile of Mood States (POMS): Two partial replications. Journal of Clinical Psychology, 40, 1270-1277 . Nyer, P.U. (2000). An investigation into whether complaining can cause increased consumer satisfaction. Journal of Consumer Marketing, 17 , 9-19. Offshoring. (2010). In Cambridge Advanced Learner’s Dictionary. Retrieved October 29, 2010, from http://dictionary.cambridge.org/dictionary/british/offshoring . On-hold Messaging Association. (2009). On-hold Messaging Association. In On-hold Messaging Association . Retrieved July 15, 2009, from http://www.ohma.org/home.php . On-Hold Marketing Ltd. (2004). Statistics at On-Hold Marketing Ltd. In On-Hold Marketing . Retrieved July 21, 2009, from http://www.onhold.co.nz/statistics.htm . On-hold Marketing Services Inc. (2010). Just the facts. In On-hold Company . Retrieved November 26, 2010, from http://www.onholdcompany.com/facts.html. Ornstein, R.E. (1969). On the experience of time . Harmondsworth: Penguin Outsource. (2010). In Merriam-Webster’s Online Dictionary. Retrieved October 29, 2010, from http://www.merriam-webster.com/dictionary/outsourcing . Pappas, J. (2009, March 3). Technology CFOs Choose U.S. as future outsourcing location over China and India, according to BDO Seidman survey [Press Release]. Retrieved October 8, 2010, from http://www.bdo.com/news/pr/1016 . Peevers, G., McInnes, F., Morton, H., Matthews, A. & Jack, M.A. (2009) The mediating effects of brand music and waiting time updates on customers’ satisfaction with a telephone service when put on-hold. International Journal of Bank Marketing, 27, 202-217. Pelletier, C.L. (2004). The effect of music on decreasing arousal due to stress: A meta-analysis. Journal of Music Therapy, 41, 192-214.

173 Program Software. (Voice call central; 2008). Hold on-hold: Save your time when you are on-hold. In Hold on-hold . Retrieved July 20, 2009, from http://www.holdonhold.com/. Pruyn A. & Smidts, A. (1998). Effects of waiting on the satisfaction with the service: Beyond objective time measures. International Journal of Research in Marketing,15 , 321–334. Ratiometric. (2010). In Wiktionary: A wiki-based open content dictionary. Retreived November 10, 2010, from http://en.wiktionary.org/wiki/ratiometric . Rothbaum, F., Weisz, J.R. & Snyder, S.S. (1982). Changing the world and changing the self: A two-process model of perceived control. Journal of Personality and Social Psychology,42, 5-37. Russell, B. (2008). Call centres: A decade of research. International Journal of Management Reviews, 10, 195-219. Schalk, R. & van Rijckevorsel, A. (2007) Factors influencing absenteeism and intention to leave in a call centre. New Technology, Work and Employment 22, 260-274. Secon, L., Hector, W., Jeberg, J. & Hansen, M.H. (2009). Black box. [Recorded by S. Walker]. On Introducing... Stan Walker [mp3]. Australia: Sony. Speedy, J. (May 18, 2010). Telstra Clear outsourcing could be 'brand suicide'. In 3news.co.nz . Retrieved September 26, 2010, from http://www.3news.co.nz/Telstra-Clear-outsourcing-could-be-brand- suicide/tabid/421/articleID/156390/Default.aspx. Sprigg, C.A., Smith, P.R. & Jackson, P.R. (2003) Psychosocial risk factors in call centres: An evaluation of work design and well-being. Research paper prepared by the University of Sheffield, Health and Safety Laboratory and UMIST for the Health and Safety Executive . Retrieved August 4 th , 2010 from http://www.hse.gov.uk/research/rrpdf/rr169.pdf . SPSS 15.0.1. (2006). Exact tests . In . SPSS help topics: SPSS for Windows Release15.0.1. Statistics New Zealand (2008). Linked employer-employee data: March 2008 Quarter. Retrieved July 18 th , 2009 from http://www.stats.govt.nz/~/media/Statistics/Browse%20for%20stats/Linke dEmployer-

174 EmployeeData/HOTPMar08qtr/linkedemployeremployeedatamar08qtrhot p.ashx Stern, R. A., Arruda, J. E., Hooper, C. R., Wolfner, G. D. & Morey, C. E. (1997). Visual analogue mood scales to measure internal mood state in neurologically impaired patients: Description and initial validity evidence', Aphasiology , 11 , 59-71. Stockford, P. (2006). Customer care perception vs. reality. Call Center Magazine, 19, 22-24. Stringfellow, A., Teagarden, M. B., & Winter, N. (2008). Invisible costs in offshoring services work. Journal of Operations Management, 26, 164–179. Stritof, B. & Stritof, S. (2006). Signals and Circuit Conditions. In Telephone Tribute . Retrieved July 7, 2009, from http://www.telephonetribute.com/signal_and_circuit_conditions.htm. Tajfel, H. & Turner, J. (1979). An integrated theory of intergroup conflict. In W. G. Austin & S. Worchel (Eds.), The social psychology of intergroup relations (pp.33-48). Monterey, California: Brooks/Cole Publishing company. Taylor, P. & Bain, P (1999). ‘An assembly line in the head’: Work and employee relations in the call centre. Industrial Relations Journal, 30, 101-117. Taylor, S. (1994). Waiting for service: The relationship between delays and evaluations of service. Journal of Marketing. 58, 56-69. Thompson, B. (n.d.). Inbound telemarketing strategies in dealing with irate callers. Retrieved August 19, 2009, from http://www.isnare.com/?aid=192668&ca=Business+Management . Tom, G., Burns, M. & Zeng, Y. (1997). Your life on-hold: The effect of telephone waiting time on customer perception. Journal of Direct Marketing, 11, 25- 31. Totterdell, P. & Holman, D. (2003). Emotion regulation in customer service roles: Testing a model of emotional labor. Journal of Occupational Health Psychology, 8, 55-73. Turley, L. W. & Milliman, R. E. (2000). Atmospheric effects on shopping behavior: A review of the experimental evidence. Journal of Business Research, 49, 193–211.

175 Unzicker, D. (1999) The psychology of being put on-hold: An exploratory study of service quality. Psychology & Marketing, 16 , 327-350. USA Today. (1999). "Please Hold" Makes callers fume . USA Today, 127 , p. 6. Vashistha, A. & Khan, I. (2008). Top 50 emerging global outsourcing cities [White Paper]. Retrieved October 8, 2010, from http://www.tholons.com/pdf/top50_eoc_report2.pdf . Velten, E. (1968). A laboratory task for induction of mood states. Behaviour Research and Therapy, 6, 473-482. Watanabe, Y. (2008). New Zealand attitudes towards foreign-accented English. Te Reo, 51, 99-127. Wells, J. C. (1982). Accents of English I: An Introduction. Cambridge, Great Britain: Cambridge University Press. Whiting, A. & Donthu, N. (2006). Managing voice-to-voice encounters: Reducing the agony of being put on-hold. Journal of Service Research , 8, 234-244. Will, J. (n.d.) The psychology of telephone ‘On-hold’ programming. Retrieved July, 29, 2009, from http://www.acat1.com/about.htm Wortman, C.B. (1975). Some determinants of perceived control. Journal of Personality and Social Psychology, 31, 282-294. Yagil, D. (2008). When the customer is wrong: A review of research on aggression and sexual harassment in service encounters. Aggression and Violent Behavior 13 , 141–152 Zakay, D. & Block, R. A. (1997). Temporal Cognition. Current Directions in Psychological Science, 6, 12-16. Zapf, D., Isic, A., Bechtoldt, M. & Blau, P. (2003).What is typical for call centre jobs? Job characteristics, and service interactions in different call centres. European Journal of Work and Organizational Psychology, 12, 311-340. Zar, J.H. (1984) Biostatistical Analysis . Englewood Cliffs, New Jersey, United States of America: Prentice Hall, Inc.

176

Appendices

177 Table of Appendices:

Appendix A : Example of questionnaire given to CSRs in Study 1. Note: organisation names are removed for anonymity...... 180 Appendix B: Example of return box poster for Study 1 (was A4)...... 182 Appendix C : Example of interview advertisement for Study 1(with names removed) ...... 183 Appendix D : All responses made by all respondents, including additional comments for the Study 1 survey ...... 184 Appendix E : Table used for reliability checking for Study 1 response categorization ...... 192 Appendix F : Telephone apparatus used in Studies 2 and 3 ...... 197 Appendix G : Profile of Mood States (POMS) Standard Form (removed for copyright reasons) ...... 198 Appendix H : Secondary questionnaire given to participants in “Study 2: Reducing customer negativity and increasing customer satisfaction with a telephone service, through the use of an on-hold intervention” ...... 200 Appendix I : Experimental log for Study 2 ...... 202 Appendix J : .WAV sound files and narration, other than general telephone sounds, used in playback during the Studies 2 and 3 ...... 203 Appendix K : Information sheet, consent form and debriefing for Study 2 ...... 208 Appendix L : Kruskal-Wallis Tests and Post-hoc pairwise comparisons for Study 2 ...... 213 Appendix M : Full Non-Parametric correlation table for Study 2 ...... 219 Appendix N : Additional questionnaire for “Study 3: The effect of accent on appraisals of a call centre experience.” ...... 222 Appendix O: Behavioural apparatus used for Study 3...... 224 Appendix P : Experimental Log Sheet for Study 3 ...... 226 Appendix Q: Information sheet, Consent form and Debriefing sheet for Study 3 ...227 Appendix R: Analysis of the effect of accent on the POMS subscales for Study 3. 230 Appendix S: Analysis of the effect of listening condition on the POMS subscales for Study 3 ...... 252 Appendix T: Non-parametric correlation matrix of self report results for Study 3 including POMS subscales ...... 282 Appendix U: ANOVA and Multinomial regression analysis for behavioural measure for Study 3 followed by variability information for button press apparatus ...... 287 Appendix V: Accent appraisal questionnaire for “Study 3a: Accent Appraisal” .....315 Appendix W: Information sheet and consent form for Study 3a ...... 317 Appendix X : Songs, artists, and albums of music played to University students in “Study 4: Undergraduates self reported music preferences in an on-hold situation vs. a public social situation” ...... 319 Appendix Y: Music rating sheet for the university undergraduates to fill in Study 4 ...... 320 Appendix Z : Order of the tracks from Appendix X played on each CD for Study 4 ...... 327 Appendix AA : Information sheet for participants in the music preference study (Study 4) ...... 331 Appendix AB: Cronbach’s alpha reliability tests to check for internal consistency of musical styles for Study 4 ...... 333

178 Appendix AC : Check of the effect of group allocation from Study 2 on music ratings for Study 4 ...... 342 Appendix AD : Analysis of music preference in Study 4. Situation and music style by gender (gender has been labelled sex) and age. Bonferroni post hoc analyses for main effect of music are under the title “Music Overall” ...... 347 Appendix AE : Separate ANOVAs for On-hold and Public area situations, with associated Post-hoc tests ...... 363 Appendix AF: Frequencies and Initial Chi Square tests for music preference qualitative questions in Study 4 ...... 380 Appendix AG: Post-hoc Chi Square tests for Study 4 ...... 385

179 Appendix A: Example of questionnaire given to CSRs in Study 1. Note: organisation names are removed for anonymity

Greetings …………..staff!

My name is Isaac Malpass, and I am a student at the University of Otago, completing a Masters degree in psychology. I am currently in the midst of research aimed at trying to reduce feelings of ill will caused, or added to, by what a customer listens to while on-hold. My main research project involves trialling different styles of on-hold media to try and reduce negativity, and increase satisfaction while waiting for service. In addition to this laboratory based study, we are also examining the issues facing New Zealand call centres in general. As a part of this, I am investigating employee perspectives on a number of questions surrounding angry and abusive callers, and the job in general. We are also gauging what those within the industry feel might be useful to calm callers down or decrease hostility. I have kindly been granted the opportunity to approach the ……………… call centre staff to ask questions relating to these matters. I would really appreciate your help by filling in this quick questionnaire. Participation is purely voluntary and results will be completely confidential. Please fill in both sides completely. DO NOT WRITE YOUR NAME ON THIS FORM.

Gender: M / F Age:

What is your favourite part of the job as a CSR?

What is your least favourite part of the Job (Note: If it is not angry callers, please be honest and state what it really is)?

How do you personally deal with angry callers?

How do you personally deal with abusive callers? PTO

180

Have you ever had callers angry or abusive due to what they have listened to while waiting for service?

In your opinion do abusive/angry/aggressive callers lead to longer call times?

Do you have any suggestions to reduce the number of callers becoming angry and aggressive as they wait for assistance?

If you run out of room or have any other comments please write below. Thank you for your time! ☺

181 Appendix B : Example of return box poster for Study 1.

182 Appendix C : Example of interview advertisement for Study 1(with names removed).

183

Appendix D : All responses made by all respondents, including additional comments for the Study 1 survey.

Questions:

Greetings …………..staff!

My name is Isaac Malpass, and I am a student at the University of Otago, completing a Masters degree in psychology. I am currently in the midst of research aimed at trying to reduce feelings of ill will caused, or added to, by what a customer listens to while on-hold. My main research project involves trialling different styles of on-hold media to try and reduce negativity, and increase satisfaction while waiting for service. In addition to this laboratory based study, we are also examining the issues facing New Zealand call centres in general. As a part of this, I am investigating employee perspectives on a number of questions surrounding angry and abusive callers, and the job in general. We are also gauging what those within the industry feel might be useful to calm callers down or decrease hostility. I have kindly been granted the opportunity to approach the …………….call centre staff to ask questions relating to these matters. I would really appreciate your help by filling in this quick questionnaire. Participation is purely voluntary and results will be completely confidential. Please fill in both sides completely. DO NOT WRITE YOUR NAME ON THIS FORM .

Call centre1: M: 3 / F: 5 Age: 22 - 47

Call Centre 2: M: 6 / F: 14 Age: 19 - 60

What is your favourite part of the job as a CSR? 1. When you help out a friendly cust. 2. Taking orders, talking to other staff members all over the globe 3. My coworkers 4. Just started 2 1/2 weeks ago, so not sure at this stage. 5. Getting to go home 6. Making the customer happy, turning a bad situation into a good one. 7. GETTING A GOOD RESOLUTION FOR CUSTOMER AND COMPANY. 8. Helping people, coaching staff, sharing my knowledge. 9. Help customers with problems helping resolve problems before a tech is called out. 10. Solving cust problems. 11. HELP SOLVING CUSTOMER’S ENQUIRIES 12. helping the customer. 13. Helping customers & following to the end. 14. The satisfaction of being able to help someone. 184 15. There is no favourite part. Oh no pushing log out at the end of the day. 16. Customer thanks after helping them. 17. Customer interaction 18. BEING ABLE TO COMPLETE CUSTOMER ENQUIRIES AS OPPOSED TO ONLY PARTIALLY COMPLETING IT. 19. I do enjoy trying help people when they are normal and reasonable. 20. TALKING TO DIFFERENT CULTURES HAS BEEN SOMEWHAT INTERESTING & ENJOYABLE

1. Not having to follow anything up the next day. 2. People I work with 3. Those times where you are actually able to give a great customer service experience, for example cheering someone up or correcting an error in their information. 4. going home at the end of the day. 5. Getting to deal with a wide range of people. 6. Finishing a call on a positive note. i.e. customer happy. 7. talking to people from all walks of life. 8. helping people

What is your least favourite part of the Job (Note: If it is not angry callers, please be honest and state what it really is)?

1. The angry and abusive cust’s 2. Exceptions to the rules 3. The way the company make the staff feel. Just a number, of no value and uncared for no flexibility, bullish 4. Again with the above, I haven’t found anything specific 5. Arriving to work knowing the customers will be rude & also the fact that they always say “I love your accent”….Also I don’t like how the customers feel they have to give me their life story or the life story of the product. 6. No breaks between calls at times. 7. ROTATIONAL HOURS FOLLOWED BY ABUSIVE CUSTOMERS 8. My roster @ the moment. 9. When a customer is agro and you cannot help them or help them fast enough. 10. Hours, pay, felt like you are just a number. 11. THE COMPUTER SYSTEM, LOG ON, SPEED OF SERVER ETC. 12. in house negativeaty 13. Saying no we can’t help. 14. Having no time to follow up on issues. If we don’t follow up customers will call back even more irate. 15. People always complaining. The lack of acknowledgement we get from management. Not enough helpers. Hours suck! 185 16. The constant clock-watching re lunch & tea breaks 17. Huge wait times 18. TIME PRESSURE NOT ALLOWING THE JOB TO BE DONE PROPERLY. APOLOGISING FOR OTHER PEOPLES MISTAKES 19. It is angry callers, people blaming you for their appliances breaking down. 20. DIFFERENT ATTITUDES, PEOPLE THAT THINK

1. Dealing with angry callers (also the repetitive nature & limitations of the role 2. Repition, Workload, lack of freedom. Strict guidelines 3. The crappy unreliable equipment – headsets, phones, slow/malfunctioning software! Also to the same or greater degree, other staff being unavailable, rude, or unprofessional. VERY BLOODY FRUSTRATING 4. relentless calls, having to convince people of stuff 5. Being tied to the phone all day. Having to say when you need to go to the toilet etc. 6. The frustration of case managers/co-ord. not doing there job properly and having to explain to clients that payment isn’t going through. i.e. med cert received but not loaded. 7. dealing with people incapable of being reasonable. 8. sitting at a desk inside all day & not really having face to face meetings or moving around and having physical variety in the job.

How do you personally deal with angry callers?

1. Eat lots of candy ad clench fists 2. Firstly, listen to the situation, let C vent. Once they have vented – tend to calm down. Take as much details as possible – go from there 3. Try hard to listen to & formulate ideas of how I can answer each area. Look for a solution that makes them feel cared for. 4. Have had a minimal number of angry callers, but let them vent and spoke with colleague on how 5. Deal with the facts/issues of call by becoming non-emotive & giving the customer a clear course of action. 6. I listen and empathize with the customer, let them express their view and get it off their chest. 7. LISTEN TO THEM UNTIL THEY HAVE FINISHED THEN TRY TO HELP THEM. WE CANT ALWAYS HELP 8. I let the customer talk, I don’t interrupt, I listen to their side of the story. I investigate the situation & come up with a fair and reasonable answer. Both parties need to be happy! 9. Keep cool. Listen to what they are saying 10. I don’t mind angry cust, I let them have the say and then try and help them.

186 11. KEEP CALM, LISTEN, MAKE A PLAN OF ACTION TO SORT OUT THE SITUATION. 12. Always calm, clear and understanding. 13. Just let the customer get everything out as this helps. 14. stay calm and explain things clearly to the customer. 15. Stay calm and let the them rant for as long as they want. 16. Relax & smile 17. Keep the emotion out & stay calm. Let caller vent 18. LET THEM GET IT OFF THEIR CHEST AND LISTEN 19. Take a deep breath and roll my eyes. 20. Smile and talk as pleasantly as I can, it either snaps them out of it or agitates them even more.

1. Let them vent then try & help/explain things to them. 2. Calm and collected. Always try and resolve the problem not increase anger. 3. LISTEN to their concerns and address them if I can; VALIDATE feelings where possible and attempt to assist. 4. breath slow, with a flatish tone. 5. say very little until they have got it out of their system, talk very slow and quietly. 6. Try to be patient, and see from there point of view. 7. I try to problem solve and fix the issue if its fixable. 8. I just let them rant then tell them in 1) a firm voice why what has happened has happened (if they are angry by nature and not for a good reason) or 2) sympathise with them & sort out their problem.

How do you personally deal with abusive callers? PTO 1. Rationally and calmly explain that we don’t take that kind of behaviour and give 3 warnings B4 hanging up. 2. They don’t bother me. Tend to wait for them to finish yelling (and swearing sometimes). Once quite get facts. 3. Listen & ignore initially – if unable to, the warn them, if no luck terminate the call. 4. Haven’t had any abusive callers….yet ☺ 5. Advise them that if they continue to use this language that I will terminate the conversation. 6. I listen, type and empathize with the customer. 7. AGAIN LISTEN TO THEM BUT INFORM THEM THAT IF I AM TRYING TO HELP AND IF REALLY ABUSIVE WILL INFORM THEM THAT IF THEY CONTINUE TO SPEAK TO ME THAT WAY I WILL TERMINATE THE CALL. 8. Let them talk, scream, yell. Don’t interrupt. Never raise my voice! I take down notes as they talk so I don’t have to get them to repeat things.

187 9. Carmly and put them through to Customer Relations or quickly Deal with a booking 10. I Haven’t had any that have abused me in nearly 18 months @ the job. 11. EXPLAIN THEY NEED TO CALM DOWN SO I CAN TRY TO HELP. 12. never take it personally, will listen and do the best to offer a solution. 13. pass them to Team Leader or Manager 14. take their anger as an opportunity 15. hang up. 16. Relax, smile and have a giggle. 17. Don’t take it personally & get a little bit stern & ask the caller to stop swearing before I can help. 18. IF THEY GET PERSONAL THREATEN TO HANG UP 19. The same as angry callers. 20. I have only had 1 in which I mentioned politely if they continued to yell I would disconnect the call. Customer became very apologetic.

1. Hang up on them if they’re abusing me (after a warning). 2. Warn and dismiss if Abuse continues 3. If a call starts with, or escalates to, abuse, I warn, and if the abuse continues I terminate. No exceptions. As a new CSR I found this difficult but not any more. 4. Same as above 5. say very little until they have got it out of their system, talk very slow and quietly. 6. Patience, try to diffuse the situation. Or fix the problem or at least get them through to speak with someon. 7. Warn them to stop being abusive, then end the call if they continue. 8. Grit my teeth or laugh & then terminate the call, suppressing smart comments.

Have you ever had callers angry or abusive due to what they have listened to while waiting for service?

1. Nope 2. DEFINITELY YES. Tend to use that call waiting to advise us that F&P has integrity and we stand behind our product. 3. Yes 4. No 5. Only when they have been misled into believing that they are entitled to things that they are not 6. Yes. The hold music and the voice can escalate the issue. 7. HAVE BEEN TOLD OUR MUSIC DRIVES THEM INSANE WHILE ON- HOLD. 8. Yes. Our hold music is not the best. 9. Yes. 10. Angry yes as they get sick of the hold music 11. YES

188 12. have had comments on how bad the wait music is but not angry or abusive because of it. 13. Yes. As it goes over & over again the same thing. 14. no 15. our hold music is crap but no. 16. Yes 17. Long wait times tend to wind customers up. 18. ONLY THE HOLD MUSIC 19. No 20. Agitated but reasonable good not angry.

1. Yes they sometimes say they hate listening to the man who tells them “what ACC can help with” 2. Yes due to wait time mostly 3. Yes. Some have found “informative” announcements extremely irritating (while others express appreciation). 4. no 5. Not that I can remember, however some have made comments. 6. Not really angry. Frustrated and they like to tell you how long they waited. I just apologise 7. I’ve had callers who have gone from what would have been angry to being abusive due to listening to the acc propaganda 8. No, but some comment that they dislike it.

In your opinion do abusive/angry/aggressive callers lead to longer call times?

1. Yes 2. NO. Passive Customers can take just as long. 3. Yes 4. Yes 5. Yes. They do not Listen!!! 6. Yes they do take longer as the customer can tend to ramble on longer than a customer who is not angry. 7. YES ALWAYS. A HAPPY CUSTOMER DOES NOT NEED TO VENT THEIR ISSUES. 8. Yes! 9. Yes the rant and Rave over and over. 10. of course. 11. YES 12. No 13. Sometimes 14. Yes 15. yes! They like to go on an on and repeat themselves over and over again. 16. Yes 17. yes because if they don’t get issue resolved they keep having to phone back 18. YES 189 19. Yes 20. Yes very long.

1. Abusive callers don’t, angry ones do. 2. Yes if they don’t end the call themselves 3. Sometimes angry ones do, but abusive or aggressive ones get warned and then terminated so that is shorter…when I was a new CSR it made for longer calls due to my “newbie skills” 4. yes, they take longer to understand what you’re saying. 5. yes – they don’t seem to listen. 6. Yes they do, and they usually like to vent first instead of dealing with the issue. 7. angry – yes abusive - no 8. Yes

Do you have any suggestions to reduce the number of callers becoming angry and aggressive as they wait for assistance?

1. Do not play trashy or elevator type music. 2. Change hold music. It’s crap. 3. More staff to reduce the wait times. 4. Perhaps some soothing music. And songs that aren’t cut off by a message which starts a new song. 5. If they played chill out music like Norah Jones (as she puts everyone to sleep) Or PINK FLOYD Because they ROCK 6. More time to follow up on files which will create less calls into the call centre. 7. MAYBE LET THEM KNOW HOW LONG THEY HAVE TO WAIT SO THEY CAN CHOOSE TO STAY ON-HOLD OR COME BACK LATER. 8. More people on the phones. Staff need to have time to follow up on work! 9. Shorter wait time. 10. tell them how long they may have to wait for. 11. SORT OUT BETTER TRAINING FOR OUR TECHS, TOO MANY VISITS TO FIX MACHINE PROPERLY. 12. no but would love to know what would help this. 13. Move staff to answer calls so no waiting too long. 14. get more staff and deal with 1 issue at a time. 15. make products better. 16. More call centre staff to shorten waiting time. 17. Better customer service & don’t treat customers like they are the enemy 18. GET MORE STAFF 19. Just to let them have their rant and still remain calm in your voice when trying to help them.

190 20. If you greet them with there name if they say it and a big hello they generally respond well, even if they ring irate.

1. Not really. 2. – 3. Play NZ music only, no “informative” announcements about the organisation as a whole, and consider adding ONE announcement about 30 secs or less into their call, advising of approx. queue no., approx wait times, and of some low traffic times to call. 4. – 5. Less wait times. 6. If wait time is because of a nationwide problem, then if this can be advised while waiting may reduce wait time, but prepare them for the reasons why. 7. Remove propaganda advise them when they are being forwarded from another line 8. 1. CMs should call back even if they have no news just to keep them informed. Minimal wait time.

If you run out of room or have any other comments please write below. Thank you for your time! ☺

2. Your Welcome. Hope you get an A from my awesome piece of info. 16. The computer systems we use are slow, clumsy, lack flexibility and are generally very frustrating. F & Ps holiday, days off and time management needs to be more accommodating of staff needs. 17. We are making reasonable customers angry as they are having to wait so long & even when they do get thru we can’t do any follow up.

191

Appendix E : Table used for reliability checking for Study 1 response categorization.

Directions for completing for reliability purposes.

Examine the F & P and ACC answers and categorise each response made. If the response covers two categories, place it in both. To get item by item reliability, please write the number of the response in each case e.g. if number 18 from F & P answered “I don’t like the hours, or angry callers” to question 2 then in both F & P boxes for this question the number 18 should be written. Once all of the responses have been recorded, then totals can be created for each category. Give the total for each cell in the bottom right corner. Reliability will be checked using percentage reliability following the secondary observers coding. Graphs of percentages will then be created….both totals and within each organisation.

192

“What is your favourite part of the job as a CSR?” Helping people/Customer Dealing with Going home at the end No follow up work Workplace Othe Satisfaction a wide range of the day Camaraderie r

CC 1

CC 2

“What is your least favourite part of the Job (Note: If it is not angry callers, please be honest and state what it really is)?”

Restrictions/Limit Internal Staff and Relentless Unreasonable/ Roster/Hours/ Dehumanised Other ations/Strict Equipment Calls/Time angry Clients Pay Guidelines Problems pressure CC 1

CC 2

193

“How do you personally deal with angry callers? ”

Say Little/ Let customer Rant/ Problem solve. Fix the Be patient and take Calm and collected. Other. Vent then try and assist issue if fixable customer point of Always try and view resolve the problem CC 1

CC 2

“How do you personally deal with abusive callers?”

Be patient and take the Warn the customer then Say Little/ Let customer Rant/ Transfer/Defer Other clients perspective end the call Vent then try and assist CC 1

CC 2

194 “Have you ever had callers angry or abusive due to what they have listened to while waiting for service?”

No Yes

Not angry, but frustrated Not, but some have No Due to wait time made comments

CC 1

CC 2

No Yes Other CC 1

CC 2

195

“In your opinion do abusive/angry/aggressive callers lead to longer call times?” Yes Angry callers Sometimes No Other do, abusive callers don't CC 1

CC 2

“Do you have any suggestions to reduce the number of callers becoming angry and aggressive as they wait for assistance?”

Lower wait Informing of any Remove None/not Allow Inform Treat Other times/employ nationwide organizational really follow up clients the customers more staff problems messages/change time length of better music wait CC 1

CC 2

196

Appendix F : Telephone apparatus used in Studies 2 and 3.

Telephone apparatus.

197 Appendix G : Profile of Mood States (POMS) Standard Form.

Removed due to copyright

198

Removed due to copyright

199 Appendix H : Secondary questionnaire given to participants in “Study 2: Reducing customer negativity and increasing customer satisfaction with a telephone service, through the use of an on-hold intervention.”

200

201 Appendix I : Experimental log for Study 2.

Experimental Log

Instructions: After each group that participates draw a line. If four participants are in a trial include the four serial numbers and associated details and then draw a line. If three, include three then draw a line. If one, include one and draw the line. The order that they gained the code is referring to the order within this group.

Serial number Condition Computer Lab Hearing Year Order Sorting Code Comments Allocation number Problem of gained Y/N study code

202 Appendix J : .WAV sound files and narration, other than general telephone sounds, used in playback during the Studies 2 and 3

.WAV files played for each listening condition. No Choice Comedy : Stand up comedy of Mitch Hedberg. Similar to routines found on the album “Mitch All Together”, but with swearing omitted. This was chosen because it was clean humour with fast set-ups and punch lines. Pop condition : “Black Box” Performed by Stan Walker, written by Lucas Secon, Wayne Hector, Jonas Jeberg, Mich Hedin Hansen. “Replay” Performed by Iyaz (Keidran Jones), written by Rock City, Keidran Jones, Jonathan Rotem, Jason Derülo. These pieces were chosen because they were the top two pop songs in the country respectively both on the iTunes store, and the official New Zealand music charts on the 12/01/10. “Black Box” played entirely through, and “Replay” rounded up the five minutes. Elevator music : midi generated versions of Für Elise ( Bagatelle in A minor ) composed by Ludwig van Beethoven and Greensleeves (anon). This music was chosen as it has been recognised by Britons as some of the most annoying on-hold music that is played (Freedman, 2001). Greensleeves was, in fact, voted the most annoying. Greensleeves was sourced from (Heart and Soul Music, 2004) Für Elise was sourced from (forelise.com, 2010). Information & Elevator music : Same as for elevator music, but interspersed with statements about the fictional company. Quiet beeping tone : This condition was a beeping tone that was similar to an old fashioned call wait tone. The file was created using Audacity 1.2.4 (2009) to modify a tone sourced from Stritof & Stritof (2006). Straight-through : Participants heard no .Wav files apart from the initial and final statement and general telephone sounds (i.e. button presses, ringing).

203 Beeping and alert tone (hands-free): This condition was a beeping tone that was similar to an old fashioned call wait tone, followed by a loud tone. The file was created using audacity to modify a tone sourced from Stritof & Stritof (2006).

Choice : Choice Menu Comedy : Same as above. Information : Same as for “Information and Elevator music” above. Music : Pop : same as above

Classical: “Sarabande from Keyboard Suite No. 11 in D Minor (Opening)” - Andrei Gavrilov. This was followed by “Concerto Grosso, Op. 6, No. 12 in B Minor: III. Larghetto e Piano” - Bath Festival Orchestra & Yehudi Menuhin, to round off the five minutes. Both pieces were from the top classical album on iTunes 10/1/10; 100 Best Relaxing Classics.

Jazz : “Move” written by Densil Best, followed by “Jeru” written by Gerry Mulligan. Both performed by Miles Davis. These instrumental jazz pieces were from the top rated jazz album on iTunes on the 12/1/10; “Birth of the cool” (1957). Los Angeles: Capitol Records.

Country : Rather than taking the top country song or album, two pieces from the top album under an iTunes search of “Classic country” was used. The top country album and singles at the time were also charting very highly in pop/contemporary charts. Something more stereotypically “country” was thought more appropriate given the choice. The album that was used was “The universal masters collection: Classic Johnny Cash” (2003). New York: Universal International Music B.V. The track “I walk the line”, written and performed by Johnny Cash, was used, followed by “Folsom prison blues”, written by Johnny Cash and Gordon Jenkins, performed by Johnny Cash.

204

Beeping and alert tone (hands-free): Same as above.

Statements Throughout course of experiment in each condition

Initial statement heard by all participants : “Thank you for calling psychnet. Unfortunately our lines are currently busy. Please hold for assistance. We apologise for the wait and will try and connect you as soon as possible.”

Further information following initial statement : Choice: “We offer a selection of listening options to choose from while on-hold. Please press 1 for a choice of music, 2 for a comedy channel, 3 for some informative statements about the company, 4 if you would like to carry out other tasks while you wait. With this option, a quiet beeping tone will sound to let you know that you are still on the line. When you hear this, please remove your handset from your ear, as a very loud tone will sound to notify you when the call is about to be connected. Feel free to use this time as you like. Do not put the handset to your ear again until after you hear the loud tone finish. Press 5 to hear these options again” Following the first choice:

- Music : “To listen to country music, press 1; to listen to classical, press 2; to listen to jazz, press 3; or, to listen to pop, press 4. Press 5 to hear these options again”.

- Comedy : N/A (begins playing).

- Information and elevator music : Begins playing. See “Non-choice information interspersed with elevator type music” below for the informative statements given.

205 - Alert Tone (hands-free): “A quiet beeping tone will sound to let you know that you are still on the line. When you hear this, please remove your handset from your ear as a very loud tone will sound to notify you when the call is about to be connected. Feel free to use this time as you like. Do not put the handset to your ear again until after you hear the loud tone finish. Thank you”

Non Choice Beeping tone : “A quiet beeping tone will sound to let you know that you are still on the line until your call is connected. Thank you.” Non Choice Beeping tone plus loud blast (hands-free): “A quiet beeping tone will sound to let you know that you are still on the line. When you hear this, please remove your handset from your ear as a very loud tone will sound to notify you when the call is about to be connected. Feel free to use this time as you like. Do not put the handset to your ear again until after you hear the loud tone finish. Thank you” Non-choice Elevator Music : Only initial statement. Non-choice comedy : Only initial statement. Non-choice pop : Only initial statement. Straight-through : Only initial statement Non-choice information interspersed with elevator type music : Initial statement and then every 30 seconds one of three advertorial information statements is made on repeat.

-Statement 1: Psychnet is a company that works with academic institutions and businesses alike to complement their work and development. We offer a range of psychology based services that can aid these groups in their research, personnel development and counselling support. As a multifaceted company we deal in a wide range of services and welcome inquiry.

-Statement 2: Last year Psychnet helped a number of companies with staff recruitment and performance reviews. Services we offer to businesses include psychometric testing, measurement of personality traits, and counselling services for company personnel. We

206 are happy to hear from anyone wanting to know more about our business support services.

-Statement3: Every year Psychnet co-operates with academic institutions, the world over to aid in various scientific studies. Prime generation, recording techniques and statistical analysis for large subject numbers are just some of the services we provide. If you are planning research, next time why not contact psychnet and see if we can help you too.

Final heard by all participants . Ringing tones followed by a connection and a voice saying “Welcome to Psychnet. Thank you for waiting. Ok, I’ll just get your sorting code….. Your sorting code is [insert code here]. Your sorting code is [insert code here]. Goodbye.” Each condition ended with a different code so that we knew which decision they made on the choice conditions.

207 Appendix K : Information sheet, consent form and debriefing for Study 2.

Mood Assessment

INFORMATION SHEET

Thank you for showing an interest in this project. Please take some time to read this information sheet before deciding whether or not to participate. If you decide to participate, thank you. If not, then there will be no disadvantage to you, apart from having to carry out a different experiment for credit, and we thank you for considering our request.

What is the Aim of the Project?

The major aim of the project is to examine differences in mood across different groups of people

What Type of Participants are Being Sought?

Otago University undergraduate students not currently seeing a mental health practitioner for any reason.

What will Participants be Asked to Do?

Should you agree to participate, you will be asked to call through to a call centre for a sorting code to decide to which group you are being assigned, and then you will be asked to fill in a Profile of Mood States (POMS) questionnaire and a perception questionnaire. Because perception ratings are involved in the study, we ask you to please hand over your watch and cell phone (switched off), so that these will not influence your subjective perceptual experiences. These will be handed back upon completion of the experiment. If you believe that the call is taking too long you may hang up, but you will not gain credit/payment if you did not receive the sorting code and complete the questionnaires.

Can Participants Change their Mind and Withdraw from the Project?

You may withdraw at any time and without any disadvantage to yourself, other than having to complete another experiment or the library exercise to gain credit.

What Data of Information will be Collected and What Use will be Made of it?

In this project we are collecting data regarding your mood and perception for comparison to other groups. Some demographic information will also be recorded (age and gender), but no names.

Results of this project may be published, but any data included will be in no way linked to any specific participant. The data are collected and stored without identifying information. You are welcome to request a copy of the results, should you wish to.

What if Participants have any Questions?

Any questions about the project, now or in the future, please feel free to contact either:

Isaac Malpass Dr Louis Leland Jr [email protected] [email protected] Ph: (03) 479 5779 (university office) Ph: (03) 479 7638 (university office)

This project has been reviewed and approved by the Ethics Committee of the University of Otago.

208 Mood Assessment

Consent Form

I have read the Information Sheet concerning this project and understand what the experiment entails. All of my questions have been answered to my satisfaction. I understand that I am free to request further information at any stage.

I know that:

1. My participation in the project is entirely voluntary.

2. I am free to withdraw from the project at any time without any disadvantage apart from not receiving credit/payment for the experiment.

3. The data are collected and stored without any identifying information

I agree to take part in this project.

………………………………………… ……………………………….. (Participant signature) (Date)

This project has been reviewed and approved by the Ethics Committee of the University of Otago

Any questions about project, now or in the future, please feel free to contact:

Isaac Malpass Dr Louis Leland Jr Malis188@Student .otago.ac.nz [email protected] Ph: 03 479 5779 (university office) Ph: (03) 479 7638 (university office)

209 Debriefing for participants who also took part in Study 4

Experiment 1: Hold the phone! Attempts to reduce negative mood, while waiting for phone service.

Isaac Malpass

Dealing with call centres is a rather commonplace occurrence in our day to day lives, and as a consequence, so is the chance of being placed on-hold while operators are busy. Due to society being used to fast, almost immediate satisfaction of goals, this wait can seem like an eternity, and people can become quite angered or hostile while waiting for service. This can further hinder the service encounter for both parties. A question of interest therefore is how can we reduce negative emotion (particularly anger) while waiting for a telephone operator to be available, and what mechanisms may underlie this change?

In an attempt to answer this question, I asked participants to call a fictitious call centre “Psychnet” which kept them on-hold waiting for a sorting code. Participants were randomly assigned to one of several different groups. There was one independent variable. This was aural alternative. It was a between subjects variable with eight levels . Participants either had one of several non-choice conditions where they had to listen to a pre determined soundtrack while they waited, or they were allowed to listen to their choice of hold media. The non choice groups included a standard “elevator music”; this type of music interspersed with information about the company, a comedy channel, pop music, a quiet beeping tone (a modified call waiting tone), or the quiet beeping tone followed by a loud blast of “pick up tone” to notify participants when they were about to be connected. Also a condition where there was no wait before service was included. In the choice condition, a choice of different music (Western, Classical, Jazz, or Pop) was available. Comedy, information and the “pick up tone” tone condition were also included as choices. Following their wait, participants were asked to fill out a Profile of Mood States (POMS) state measure of mood and a further questionnaire asking them about other aspects of the wait.

The five main dependent variables included score on the mood state questionnaire , ratings of enjoyment, satisfaction and contro l, and perceived wait time . Information was also collected regarding if, and when, participants hung up or pressed buttons. Gender and age of participants was also recorded. This study is predominantly in the field of applied psychology and has a control group between subjects design.

If the basis for reducing negative emotion while waiting is by increasing perceived control through choice, then the choice group should see lower negative mood ratings than the other groups. However, if lowering perceived wait time is the key to reducing negative mood, one of the other conditions may be better. This is because it is theorized that both too much and too little cognitive involvement may increase perceived wait time. If true, then one of the other conditions that require less cognitive effort may reduce the negative mood to a greater extent.

NOTE: Use only this debriefing form to fill out your experimental participation form.

210

Experiment 2: University student music popularity ratings

This experiment follows on from past work done by members of the community behaviour change laboratory. Previously, a similar rating experiment was carried out with high school students to examine their musical preferences. Music preference is of interest to us for several reasons, as music can be used to try and increase satisfaction with a service encounter, or conversely, to try and deter loiterers/improve their behaviour (by playing non-preferred music). By collecting information about University aged participants, we are asking the question “What musical styles do university students prefer in a public area situation and a telephone service situation?” However, we can also examine whether trends found with high school students in a public area situation-hold for this new population, or if preferences are different for this older population.

In an attempt to establish whether university students showed any clear preferences in musical style, I asked participants to listen to a number of different musical excerpts and indicate how long they would be prepared to listen to each. Participants made their indications on a 16 point scale ranging from zero minutes, to fifteen minutes and over, in two different situations; a public area or on-hold. The main independent variable was musical style. This was a within subjects variable, as all participants listened to all of the nine musical styles in one of four randomly selected orders. A second within subjects’ factor was listening situation. This had two levels – “in a public area”, and “on-hold”. The main dependent variable was mean number of minutes participants were prepared to listen to each style . Gender, age and years of study were also recorded, along with the answers to several additional questions for further analysis.

If there are clear musical preferences found, these may form the bases of further work to see how the certain styles of music can be put to use in the fields of community behaviour change and consumer psychology. If the ratings differ between the two listening situations, this is of interest also, as it means that choosing appropriate on-hold music may be more complicated than just picking what is popular at the time, or what is generally rated highly in a public listening context.

NOTE: Use the debriefing of EXPERIMENT 1 only when filling out your experimental participation form. Do not use this (Experiment 2) debriefing form.

PLEASE DO NOT DISCUSS THESE STUDIES WITH ANYONE WHO MAY POTENTIALLY TAKE PART.

Thank you for your help.

Any Questions? Please feel free to contact :

Isaac Malpass Dr Louis Leland Jr [email protected] [email protected] Ph: (03) 479 5779 (university office) Ph: (03) 479 7638 (university office)

211 Debriefing for those who did not take part in Study 4

Hold the phone! Attempts to reduce negative mood, while waiting for phone service.

Isaac Malpass

Dealing with call centres is a rather commonplace occurrence in our day to day lives, and as a consequence, so is the chance of being placed on-hold while operators are busy. Due to society being used to fast, almost immediate satisfaction of goals, this wait can seem like an eternity, and people can become quite angered or hostile while waiting for service. This can further hinder the service encounter for both parties. A question of interest therefore is how can we reduce negative emotion (particularly anger) while waiting for a telephone operator to be available, and what mechanisms may underlie this change?

In an attempt to answer this question, I asked participants to call a fictitious call centre “Psychnet” which kept them on-hold waiting for a sorting code. Participants were randomly assigned to one of several different groups. There was one independent variable. This was aural alternative. It was a between subjects variable with eight levels . Participants either had one of several non-choice conditions where they had to listen to a pre determined soundtrack while they waited, or they were allowed to listen to their choice of hold media. The non choice groups included a standard “elevator music”; this type of music interspersed with information about the company, a comedy channel, pop music, a quiet beeping tone (a modified call waiting tone), or the quiet beeping tone followed by a loud blast of “pick up tone” to notify participants when they were about to be connected. Also a condition where there was no wait before service was included. In the choice condition, a choice of different music (Western, Classical, Jazz, or Pop) was available. Comedy, information and the “pick up tone” tone condition were also included as choices. Following their wait, participants were asked to fill out a Profile of Mood States (POMS) state measure of mood and a further questionnaire asking them about other aspects of the wait.

The five main dependent variables included score on the mood state questionnaire , ratings of enjoyment, satisfaction and contro l, and perceived wait time . Information was also collected regarding if, and when, participants hung up or pressed buttons. Gender and age of participants was also recorded. This study is predominantly in the field of applied psychology and has a control group between subjects design.

If the basis for reducing negative emotion while waiting is by increasing perceived control through choice, then the choice group should see lower negative mood ratings than the other groups. However, if lowering perceived wait time is the key to reducing negative mood, one of the other conditions may be better. This is because it is theorized that both too much and too little cognitive involvement may increase perceived wait time. If true, then one of the other conditions that require less cognitive effort may reduce the negative mood to a greater extent.

PLEASE DO NOT DISCUSS THESE STUDIES WITH ANYONE WHO MAY POTENTIALLY TAKE PART. Thank you for your help.

Any Questions? Please feel free to contact:

Isaac Malpass Dr Louis Leland Jr [email protected] [email protected] Ph: (03) 479 5779 (university office) Ph: (03) 479 7638 (university office) 212 Appendix L: Kruskal-Wallis Tests and Post-hoc pairwise comparisons for Study 2

213

214 215

216 217

218 Appendix M : Full Non-Parametric correlation table for Study 2.

Percw Satisf PercCo Acctbl Dislik Stayo Actual Totalonp Age TMD t actn ntrol wt e n wt hone T D A V F C Spearma Age Correlation - n's rho Coefficient 1.000 -.119 -.001 -.107 -.052 -.042 .012 -.026 -.098 -.120 -.101 -.105 .018 .015 .171(* -.105 ) Sig. (2-tailed) . .107 .994 .148 .488 .573 .872 .728 .186 .107 .173 .158 .807 .839 .021 .158 N 183 183 183 183 183 183 183 183 183 183 183 183 183 183 183 183 TMD Correlation - .170(* .706(* .746(* .606(* .765(* .753(* Coefficient -.119 1.000 .026 -.130 -.059 -.016 -.108 .114 .133 .553(* ) *) *) *) *) *) *) Sig. (2-tailed) .107 . .724 .080 .428 .835 .022 .145 .124 .072 .000 .000 .000 .000 .000 .000 N 183 183 183 183 183 183 183 183 183 183 183 183 183 183 183 183 Percwt Correlation - - .236(* .290(* Coefficient -.001 .026 1.000 .209(* -.076 .167(* -.113 .245(**) .007 .027 .041 -.026 .073 -.010 *) *) *) ) Sig. (2-tailed) .994 .724 . .005 .309 .001 .024 .127 .000 .001 .921 .720 .580 .724 .325 .896 N 183 183 183 183 183 183 183 183 183 183 183 183 183 183 183 183 Satisfactn Correlation - - - - .299(* Coefficient -.107 -.130 .209(* 1.000 .229(**) .330(* .123 .242(* -.205(**) -.039 -.065 .146(* .115 -.106 -.103 *) *) *) *) ) Sig. (2-tailed) .148 .080 .005 . .002 .000 .000 .097 .001 .005 .598 .384 .048 .121 .153 .164 N 183 183 183 183 183 183 183 183 183 183 183 183 183 183 183 183 PercContr Correlation - .229(* ol Coefficient -.052 -.059 -.076 1.000 .066 .238(* .074 .095 .113 -.042 .050 .035 .081 -.096 -.022 *) *) Sig. (2-tailed) .488 .428 .309 .002 . .371 .001 .318 .203 .126 .572 .501 .634 .278 .198 .767 N 183 183 183 183 183 183 183 183 183 183 183 183 183 183 183 183 Acctblwt Correlation - .236(* .299(* Coefficient -.042 -.016 .066 1.000 .180(* .073 .104 .096 -.025 -.112 -.011 -.001 .050 -.048 *) *) ) Sig. (2-tailed) .573 .835 .001 .000 .371 . .015 .327 .162 .198 .734 .133 .879 .993 .501 .518 N 183 183 183 183 183 183 183 183 183 183 183 183 183 183 183 183 Dislike Correlation - - - - .170(* - .158(* Coefficient .012 .167(* .330(* .180(* 1.000 -.039 .130 .113 .116 .075 .074 .163(* .024 ) .238(**) ) ) *) ) ) Sig. (2-tailed) .872 .022 .024 .000 .001 .015 . .600 .079 .129 .118 .313 .323 .027 .033 .745 N 183 183 183 183 183 183 183 183 183 183 183 183 183 183 183 183 Stayon Correlation -.026 -.108 -.113 .123 .074 .073 -.039 1.000 -.128 -.127 -.096 -.100 -.098 .026 -.103 -.114

219 Coefficient Sig. (2-tailed) .728 .145 .127 .097 .318 .327 .600 . .085 .086 .197 .176 .186 .727 .164 .124 N 183 183 183 183 183 183 183 183 183 183 183 183 183 183 183 183 Actualwt Correlation - - .290(* Coefficient -.098 .114 .242(* .095 .104 .130 -.128 1.000 .924(**) .036 .045 .118 .170(* .019 -.010 *) *) ) Sig. (2-tailed) .186 .124 .000 .001 .203 .162 .079 .085 . .000 .633 .549 .112 .022 .798 .897 N 183 183 183 183 183 183 183 183 183 183 183 183 183 183 183 183 Totalonph Correlation - - .245(* .924(* one Coefficient -.120 .133 .205(* .113 .096 .113 -.127 1.000 .046 .087 .129 .157(* .025 .023 *) *) *) ) Sig. (2-tailed) .107 .072 .001 .005 .126 .198 .129 .086 .000 . .533 .244 .082 .033 .739 .754 N 183 183 183 183 183 183 183 183 183 183 183 183 183 183 183 183 T Correlation .706(* .558(* .468(* .432(* .547(* -.101 .007 -.039 -.042 -.025 .116 -.096 .036 .046 1.000 -.127 Coefficient *) *) *) *) *) Sig. (2-tailed) .173 .000 .921 .598 .572 .734 .118 .197 .633 .533 . .000 .000 .088 .000 .000 N 183 183 183 183 183 183 183 183 183 183 183 183 183 183 183 183 D Correlation - .746(* .558(* .552(* .504(* .529(* Coefficient -.105 .027 -.065 .050 -.112 .075 -.100 .045 .087 1.000 .163(* *) *) *) *) *) ) Sig. (2-tailed) .158 .000 .720 .384 .501 .133 .313 .176 .549 .244 .000 . .000 .028 .000 .000 N 183 183 183 183 183 183 183 183 183 183 183 183 183 183 183 183 A Correlation - .606(* .468(* .552(* .403(* .513(* Coefficient .018 .041 .146(* .035 -.011 .074 -.098 .118 .129 1.000 -.026 *) *) *) *) *) ) Sig. (2-tailed) .807 .000 .580 .048 .634 .879 .323 .186 .112 .082 .000 .000 . .723 .000 .000 N 183 183 183 183 183 183 183 183 183 183 183 183 183 183 183 183 V Correlation ------Coefficient .015 .553(* -.026 .115 .081 -.001 .163(* .026 .170(* -.157(*) -.127 .163(* -.026 1.000 .348(* .281(* *) ) ) ) *) *) Sig. (2-tailed) .839 .000 .724 .121 .278 .993 .027 .727 .022 .033 .088 .028 .723 . .000 .000 N 183 183 183 183 183 183 183 183 183 183 183 183 183 183 183 183 F Correlation - - .765(* .158(* .432(* .504(* .403(* .494(* Coefficient .171(* .073 -.106 -.096 .050 -.103 .019 .025 .348(* 1.000 *) ) *) *) *) *) ) *) Sig. (2-tailed) .021 .000 .325 .153 .198 .501 .033 .164 .798 .739 .000 .000 .000 .000 . .000 N 183 183 183 183 183 183 183 183 183 183 183 183 183 183 183 183

220 C Correlation - .753(* .547(* .529(* .513(* .494(* Coefficient -.105 -.010 -.103 -.022 -.048 .024 -.114 -.010 .023 .281(* 1.000 *) *) *) *) *) *) Sig. (2-tailed) .158 .000 .896 .164 .767 .518 .745 .124 .897 .754 .000 .000 .000 .000 .000 . N 183 183 183 183 183 183 183 183 183 183 183 183 183 183 183 183

Key: TMD=Total Mood Disturbance, T=Tension-Anxiety, D=Depression-Dejection, A=Anger-Hostility, F=Fatigue-Inertia and C=Confusion- Bewilderment scales. Percwt=Perceived Wait. Satisfactn=Satisfaction. PercControl=Perceived control. Acctblwt=Acceptable wait. Stayon=Staying on-hold for a code. Actualwt=Actual wait for service. Totalonphone=Total time spent on the telephone.

221 Appendix N: Additional questionnaire for “Study 3: The effect of accent on appraisals of a call centre experience.”

222 223 Appendix O: Behavioural apparatus used for Study 3.

Behavioural measurement apparatus

Behavioural measurement apparatus (view from above). Labels read “Yes”, “Maybe” and “No”.

224 Position of chair when participant is seated. Computer to the left is where readings were taken

Telephone apparatus.

225 Appendix P : Experimental Log Sheet for Study 3. Subject Random Condition Comp Hearing Sorting Answer to Force reading Answer to Force Comments Numbers # Probs? Code control Q for control Q exp Q reading for exp Q

1 54,16 2 4,98 3 7,99 4 22,61 5 92,25 6 ... 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23

226 Appendix Q: Information sheet, Consent form and Debriefing sheet for Study 3.

Behavioural and Mood Assessment

INFORMATION SHEET

Thank you for showing an interest in this project. Please take some time to read this information sheet before deciding whether or not you would like to participate. If you decide to participate, thank you. If you decide not to participate, there will be no disadvantage to you apart from having to carry out a different experiment for credit, and we thank you for considering our request.

What is the aim of the project?

The major aim of the project is to examine differences in self-report measures of mood and behavioural measures of mood across different groups of people.

What types of participants are being sought?

Otago University students who have attended a New Zealand high school, and are not currently seeing a mental health practitioner for any reason.

What will participants be asked to do?

Should you agree to participate, you will be asked to fill in a Profile of Mood States Questionnaire (POMS) and press three buttons. You will then be asked to call through to a call centre for a sorting code to assign you to a group for the second stage of the experiment. You will then complete a second POMS questionnaire, answer a question from the experimenter using the buttons, and complete a short questionnaire. If you believe the call is taking too long, you may hang up the phone, but you will not gain credit if you did not receive the sorting code and complete the questionnaires.

Can participants change their mind and withdraw from the project?

You may withdraw from the project at any time without any disadvantage to yourself, other than having to complete another experiment to gain credit.

What data and information will be collected and what use made of it?

In this project we are collected data regarding your mood for comparison to other groups. Some demographic data will also be recorded (eg: gender and ethnicity), but no names. Results of this data may be published, but any data included will be in no way linked to any specific participant. The data are collected and stored without identifying information. You are welcome to request a copy of the results, should you wish to.

What if participants have any questions?

Any questions about the project, now or in the future, can be directed to either:

Skye Hignett Isaac Malpass Dr. Louis Leland Jr. [email protected] [email protected] [email protected] Ph: (03) 479 5779 Ph: (03) 479 5779) Ph: (03) 479 7638 (university office) (university office) (university office)

227 Mood Assessment Consent Form

Consent Form

I have read the Information Sheet concerning this project and I understand what the experiment entails. All of my questions have been answered to my satisfaction. I understand that I am free to request further information at any stage.

I know that:

1. My participation in this project is entirely voluntary.

2. I am free to withdraw from the project at any time without any disadvantage apart from not receiving credit for the experiment.

3. The data are collected and stored without any identifying information.

I agree to take part in this project.

......

(Participant Signature) (Date)

This project has been reviewed and approved by the Ethics Committee of the University of Otago.

Please feel free to direct any questions about the project, now or in the future, to either:

Skye Hignett Isaac Malpass Dr. Louis Leland Jr. [email protected] [email protected] [email protected] Ph: (03) 479 5779 Ph: (03) 479 5779) Ph: (03) 479 7638 (university office) (university office) (university office)

228 Debrief Sheet

The Effect of Accent on Call Customer Satisfaction.

Skye Hignett and Isaac Malpass.

Dealing with call centres is a commonplace occurrence in our day to day lives, and can be a frustrating experience. Long wait times and misunderstandings in communication could increase feelings of frustration, particularly when a phone operator’s communication skills or accent hinders effective communication between the two parties. A question of interest is therefore: do different accents influence self-report and behavioural measures of satisfaction and mood in a call centre situation?

In an attempt to address this question, participants were asked to complete a mood states questionnaire, then call a fictitious call centre “Psychnet” which waiting on-hold for a sorting code. Participants were randomly assigned to one of several groups. In this experiment there were three independent (manipulated) variables: operator accent, aural alternative, and type of measure. Operator accent was a between- subjects variable with four levels- participants either heard an American, Filipino, New Zealand or Indian accent. Aural alternative was also a between subjects variable with four levels. Participants either had one of several non-choice conditions where they listened to a predetermined soundtrack of either: a comedy channel or pop music. There was also a no-wait condition where participants were connected immediately. In the choice condition a choice of different music (western, classical, jazz, or pop) was available. Comedy, information and a beeping tone, followed by an alert to notify participants when they were about to be connected, were also included as choices. The measures of mood were behavioural and self-report. This was a within-subjects variable, making the experiment a mixed subjects design . After participants completed the phone call they were asked to fill out a Profile of Mood States (POMS) questionnaire, answer several questions using one of the three buttons, and complete a further questionnaire asking about other aspects of the wait.

The dependent (measured) variables were: score on the mood state questionnaire , force of button press (as a measure of frustration), ratings of enjoyment, satisfaction and control , and perceived wait time . Information was also collected on whether subjects hung up the phone, or pressed buttons. Information was also collected on gender and age. This study is predominantly in the field of behavioural/ operant psychology.

PLEASE DO NOT DISCUSS THESE STUDIES WITH ANYONE WHO MAY POTENTIALLY TAKE PART IN THE FUTURE. Thank you for your help.

Questions?

Please feel free to direct any queries about the project, now or in the future, to either:

Skye Hignett Isaac Malpass Dr. Louis Leland Jr. [email protected] [email protected] [email protected] Ph: (03) 479 5779 Ph: (03) 479 5779) Ph: (03) 479 7638 (university office) (university office) (university office)

229 Appendix R: Analysis of the effect of accent on the POMS subscales for Study 3.

General Linear Model

Within-Subjects Factors

Measure: MEASURE_1 Dependent Prepostanger Variable 1 PreAnger 2 PostAnger

Between-Subjects Factors Value Label N Accent 1.00 American 26 2.00 Indian 22 3.00 NZ 22 4.00 Filipino 24 Gender .00 Female 71 1.00 Male 23

Descriptive Statistics Accent Gender Mean Std. Deviation N PreAnger American Female 1.7508 .03550 21 Male 1.7735 .07909 5 Total 1.7552 .04574 26 Indian Female 1.7597 .04483 15 Male 1.7619 .06045 7 Total 1.7604 .04884 22 NZ Female 1.7515 .03378 17 Male 1.7335 .02381 5 Total 1.7474 .03220 22 Filipino Female 1.7521 .03848 18 Male 1.8019 .06610 6 Total 1.7645 .05031 24 Total Female 1.7532 .03734 71 Male 1.7687 .06191 23 Total 1.7570 .04474 94 PostAnger American Female 1.7374 .03568 21 Male 1.7600 .08139 5 Total 1.7418 .04648 26 Indian Female 1.7501 .03261 15 Male 1.7523 .05016 7 Total 1.7508 .03780 22 NZ Female 1.7494 .03051 17 Male 1.7268 .02729 5 Total 1.7443 .03074 22 Filipino Female 1.7446 .04453 18 230 Male 1.7837 .08027 6 Total 1.7544 .05627 24 Total Female 1.7448 .03603 71 Male 1.7566 .06246 23 Total 1.7477 .04389 94

Box's Test of Equality of Covariance Matrices(a) Box's M 46.275 F 1.940 df1 21 df2 3312.346 Sig. .006 Tests the null hypothesis that the observed covariance matrices of the dependent variables are equal across groups. a Design: Intercept+Age+Accent+Gender+Accent * Gender Within Subjects Design: Prepostanger

Multivariate Tests(b) Effect Value F Hypothesis df Error df Sig. Prepostanger Pillai's Trace .027 2.368(a) 1.000 85.000 .128 Wilks' Lambda .973 2.368(a) 1.000 85.000 .128 Hotelling's .028 2.368(a) 1.000 85.000 .128 Trace Roy's Largest .028 2.368(a) 1.000 85.000 .128 Root Prepostanger Pillai's Trace .002 .211(a) 1.000 85.000 .647 * Age Wilks' Lambda .998 .211(a) 1.000 85.000 .647 Hotelling's .002 .211(a) 1.000 85.000 .647 Trace Roy's Largest .002 .211(a) 1.000 85.000 .647 Root Prepostanger Pillai's Trace .034 1.005(a) 3.000 85.000 .395 * Accent Wilks' Lambda .966 1.005(a) 3.000 85.000 .395 Hotelling's .035 1.005(a) 3.000 85.000 .395 Trace Roy's Largest .035 1.005(a) 3.000 85.000 .395 Root Prepostanger Pillai's Trace .012 1.023(a) 1.000 85.000 .315 * Gender Wilks' Lambda .988 1.023(a) 1.000 85.000 .315 Hotelling's .012 1.023(a) 1.000 85.000 .315 Trace Roy's Largest .012 1.023(a) 1.000 85.000 .315 Root Prepostanger Pillai's Trace * Accent * .015 .419(a) 3.000 85.000 .740 Gender Wilks' Lambda .985 .419(a) 3.000 85.000 .740 Hotelling's .015 .419(a) 3.000 85.000 .740 Trace Roy's Largest .015 .419(a) 3.000 85.000 .740 Root a Exact statistic b Design: Intercept+Age+Accent+Gender+Accent * Gender Within Subjects Design: Prepostanger

231

Mauchly's Test of Sphericity(b)

Approx. Within Subjects Mauchly's Chi- Effect W Square df Sig. Epsilon(a) Greenhou Greenh Greenhou se- Huynh- Lower- ouse- Huynh- Lower- se- Geisser Feldt bound Geisser Feldt bound Geisser Prepostanger 1.000 .000 0 . 1.000 1.000 1.000 Measure: MEASURE_1 Tests the null hypothesis that the error covariance matrix of the orthonormalized transformed dependent variables is proportional to an identity matrix. a May be used to adjust the degrees of freedom for the averaged tests of significance. Corrected tests are displayed in the Tests of Within-Subjects Effects table. b Design: Intercept+Age+Accent+Gender+Accent * Gender Within Subjects Design: Prepostanger

Tests of Within-Subjects Effects Measure: MEASURE_1 Type III Sum Mean Source of Squares df Square F Sig. Prepostanger Sphericity .000 1 .000 2.368 .128 Assumed Greenhouse- .000 1.000 .000 2.368 .128 Geisser Huynh-Feldt .000 1.000 .000 2.368 .128 Lower-bound .000 1.000 .000 2.368 .128 Prepostanger * Age Sphericity 2.79E- 2.79E-005 1 .211 .647 Assumed 005 Greenhouse- 2.79E- 2.79E-005 1.000 .211 .647 Geisser 005 Huynh-Feldt 2.79E- 2.79E-005 1.000 .211 .647 005 Lower-bound 2.79E- 2.79E-005 1.000 .211 .647 005 Prepostanger * Sphericity .000 3 .000 1.005 .395 Accent Assumed Greenhouse- .000 3.000 .000 1.005 .395 Geisser Huynh-Feldt .000 3.000 .000 1.005 .395 Lower-bound .000 3.000 .000 1.005 .395 Prepostanger * Sphericity .000 1 .000 1.023 .315 Gender Assumed Greenhouse- .000 1.000 .000 1.023 .315 Geisser Huynh-Feldt .000 1.000 .000 1.023 .315 Lower-bound .000 1.000 .000 1.023 .315 Prepostanger * Sphericity 5.53E- .000 3 .419 .740 Accent * Gender Assumed 005 Greenhouse- 5.53E- .000 3.000 .419 .740 Geisser 005 Huynh-Feldt 5.53E- .000 3.000 .419 .740 005 Lower-bound 5.53E- .000 3.000 .419 .740 005 Error(Prepostanger) Sphericity .011 85 .000 Assumed Greenhouse- .011 85.000 .000 Geisser Huynh-Feldt .011 85.000 .000 Lower-bound .011 85.000 .000 232

Tests of Within-Subjects Contrasts Measure: MEASURE_1 Type III Sum Mean Source Prepostanger of Squares df Square F Sig. Prepostanger Linear .000 1 .000 2.368 .128 Prepostanger * Age Linear 2.79E-005 1 2.79E-005 .211 .647 Prepostanger * Accent Linear .000 3 .000 1.005 .395 Prepostanger * Gender Linear .000 1 .000 1.023 .315 Prepostanger * Accent * Linear .000 3 5.53E-005 .419 .740 Gender Error(Prepostanger) Linear .011 85 .000

Levene's Test of Equality of Error Variances(a) F df1 df2 Sig. PreAnger 2.685 7 86 .015 PostAnger 2.621 7 86 .017 Tests the null hypothesis that the error variance of the dependent variable is equal across groups. a Design: Intercept+Age+Accent+Gender+Accent * Gender Within Subjects Design: Prepostanger

Tests of Between-Subjects Effects Measure: MEASURE_1 Transformed Variable: Average Type III Sum Source of Squares df Mean Square F Sig. Intercept 19.666 1 19.666 5265.698 .000 Age .005 1 .005 1.313 .255 Accent .015 3 .005 1.355 .262 Gender .006 1 .006 1.487 .226 Accent * Gender .017 3 .006 1.477 .226 Error .317 85 .004

General Linear Model

Within-Subjects Factors Measure: MEASURE_1 Dependent Preposttension Variable 1 PreTension 2 PostTension

Between-Subjects Factors Value Label N Accent 1.00 American 26 2.00 Indian 22 3.00 NZ 22 4.00 Filipino 24 Gender .00 Female 71

233 1.00 Male 23

Descriptive Statistics Accent Gender Mean Std. Deviation N PreTension American Female 1.7786 .02897 21 Male 1.7690 .05829 5 Total 1.7768 .03507 26 Indian Female 1.7842 .04821 15 Male 1.7560 .05445 7 Total 1.7752 .05076 22 NZ Female 1.7778 .02698 17 Male 1.7589 .05590 5 Total 1.7735 .03486 22 Filipino Female 1.7725 .05026 18 Male 1.8002 .03827 6 Total 1.7794 .04833 24 Total Female 1.7780 .03874 71 Male 1.7710 .05163 23 Total 1.7763 .04206 94 PostTension American Female 1.7721 .02862 21 Male 1.7570 .05964 5 Total 1.7692 .03551 26 Indian Female 1.7738 .04182 15 Male 1.7453 .05296 7 Total 1.7647 .04639 22 NZ Female 1.7691 .02801 17 Male 1.7597 .06241 5 Total 1.7670 .03683 22 Filipino Female 1.7585 .04979 18 Male 1.7835 .06643 6 Total 1.7648 .05398 24 Total Female 1.7683 .03742 71 Male 1.7609 .05783 23 Total 1.7665 .04307 94

Box's Test of Equality of Covariance Matrices(a) Box's M 44.214 F 1.854 df1 21 df2 3312.346 Sig. .010 Tests the null hypothesis that the observed covariance matrices of the dependent variables are equal across groups. a Design: Intercept+Age+Accent+Gender+Accent * Gender Within Subjects Design: Preposttension

Multivariate Tests(b) Hypothesis Effect Value F df Error df Sig. Preposttension Pillai's Trace .020 1.771(a) 1.000 85.000 .187

234 Wilks' Lambda .980 1.771(a) 1.000 85.000 .187 Hotelling's Trace .021 1.771(a) 1.000 85.000 .187 Roy's Largest .021 1.771(a) 1.000 85.000 .187 Root Preposttension * Age Pillai's Trace .003 .264(a) 1.000 85.000 .609 Wilks' Lambda .997 .264(a) 1.000 85.000 .609 Hotelling's Trace .003 .264(a) 1.000 85.000 .609 Roy's Largest .003 .264(a) 1.000 85.000 .609 Root Preposttension * Pillai's Trace .029 .835(a) 3.000 85.000 .478 Accent Wilks' Lambda .971 .835(a) 3.000 85.000 .478 Hotelling's Trace .029 .835(a) 3.000 85.000 .478 Roy's Largest .029 .835(a) 3.000 85.000 .478 Root Preposttension * Pillai's Trace .000 .000(a) 1.000 85.000 .987 Gender Wilks' Lambda 1.000 .000(a) 1.000 85.000 .987 Hotelling's Trace .000 .000(a) 1.000 85.000 .987 Roy's Largest .000 .000(a) 1.000 85.000 .987 Root Preposttension * Pillai's Trace .012 .334(a) 3.000 85.000 .801 Accent * Gender Wilks' Lambda .988 .334(a) 3.000 85.000 .801 Hotelling's Trace .012 .334(a) 3.000 85.000 .801 Roy's Largest .012 .334(a) 3.000 85.000 .801 Root a Exact statistic b Design: Intercept+Age+Accent+Gender+Accent * Gender Within Subjects Design: Preposttension

Mauchly's Test of Sphericity(b) Measure: MEASURE_1 Approx. Within Subjects Mauchly's Chi- Effect W Square df Sig. Epsilon(a) Greenhou Greenh Greenhou se- Huynh- Lower- ouse- Huynh- Lower- se- Geisser Feldt bound Geisser Feldt bound Geisser Preposttension 1.000 .000 0 . 1.000 1.000 1.000 Tests the null hypothesis that the error covariance matrix of the orthonormalized transformed dependent variables is proportional to an identity matrix. a May be used to adjust the degrees of freedom for the averaged tests of significance. Corrected tests are displayed in the Tests of Within-Subjects Effects table. b Design: Intercept+Age+Accent+Gender+Accent * Gender Within Subjects Design: Preposttension

Tests of Within-Subjects Effects Measure: MEASURE_1 Type III Sum of Mean Source Squares df Square F Sig. Preposttension Sphericity .000 1 .000 1.771 .187 Assumed Greenhouse- .000 1.000 .000 1.771 .187 Geisser Huynh-Feldt .000 1.000 .000 1.771 .187 Lower-bound .000 1.000 .000 1.771 .187 Preposttension * Age Sphericity 5.69E-005 1 5.69E-005 .264 .609 235 Assumed Greenhouse- 5.69E-005 1.000 5.69E-005 .264 .609 Geisser Huynh-Feldt 5.69E-005 1.000 5.69E-005 .264 .609 Lower-bound 5.69E-005 1.000 5.69E-005 .264 .609 Preposttension * Sphericity .001 3 .000 .835 .478 Accent Assumed Greenhouse- .001 3.000 .000 .835 .478 Geisser Huynh-Feldt .001 3.000 .000 .835 .478 Lower-bound .001 3.000 .000 .835 .478 Preposttension * Sphericity 5.47E-008 1 5.47E-008 .000 .987 Gender Assumed Greenhouse- 5.47E-008 1.000 5.47E-008 .000 .987 Geisser Huynh-Feldt 5.47E-008 1.000 5.47E-008 .000 .987 Lower-bound 5.47E-008 1.000 5.47E-008 .000 .987 Preposttension * Sphericity .000 3 7.19E-005 .334 .801 Accent * Gender Assumed Greenhouse- .000 3.000 7.19E-005 .334 .801 Geisser Huynh-Feldt .000 3.000 7.19E-005 .334 .801 Lower-bound .000 3.000 7.19E-005 .334 .801 Error(Preposttension) Sphericity .018 85 .000 Assumed Greenhouse- .018 85.000 .000 Geisser Huynh-Feldt .018 85.000 .000 Lower-bound .018 85.000 .000

Tests of Within-Subjects Contrasts Measure: MEASURE_1 Type III Sum of Mean Source Preposttension Squares df Square F Sig. Preposttension Linear .000 1 .000 1.771 .187 Preposttension * Linear 5.69E-005 1 5.69E-005 .264 .609 Age Preposttension * Linear .001 3 .000 .835 .478 Accent Preposttension * Linear 5.47E-008 1 5.47E-008 .000 .987 Gender Preposttension * Linear .000 3 7.19E-005 .334 .801 Accent * Gender Error(Preposttens Linear .018 85 .000 ion)

Levene's Test of Equality of Error Variances(a) F df1 df2 Sig. PreTension 1.365 7 86 .230 PostTension 2.132 7 86 .049 Tests the null hypothesis that the error variance of the dependent variable is equal across groups. a Design: Intercept+Age+Accent+Gender+Accent * Gender Within Subjects Design: Preposttension

236

Tests of Between-Subjects Effects Measure: MEASURE_1 Transformed Variable: Average Type III Sum Source of Squares df Mean Square F Sig. Intercept 20.384 1 20.384 6026.528 .000 Age .013 1 .013 3.985 .049 Accent .006 3 .002 .544 .654 Gender .001 1 .001 .393 .532 Accent * Gender .013 3 .004 1.290 .283 Error .288 85 .003

General Linear Model

Within-Subjects Factors Measure: MEASURE_1 Dependent Prepostdepression Variable 1 PreDepressio n 2 PostDepressi on

Between-Subjects Factors Value Label N Accent 1.00 American 26 2.00 Indian 22 3.00 NZ 22 4.00 Filipino 24 Gender .00 Female 71 1.00 Male 23

Descriptive Statistics Accent Gender Mean Std. Deviation N PreDepression American Female 1.7520 .03534 21 Male 1.7652 .11283 5 Total 1.7545 .05535 26 Indian Female 1.7881 .07460 15 Male 1.7462 .08263 7 Total 1.7748 .07785 22 NZ Female 1.7755 .05581 17 Male 1.7648 .09465 5 Total 1.7730 .06404 22 Filipino Female 1.7582 .05636 18 Male 1.8299 .09467 6 Total 1.7761 .07282 24 Total Female 1.7668 .05616 71 Male 1.7762 .09473 23 Total 1.7691 .06718 94 237 PostDepression American Female 1.7491 .03413 21 Male 1.7646 .12105 5 Total 1.7521 .05758 26 Indian Female 1.7712 .06413 15 Male 1.7408 .08202 7 Total 1.7615 .06981 22 NZ Female 1.7628 .05634 17 Male 1.7614 .10415 5 Total 1.7625 .06697 22 Filipino Female 1.7517 .05386 18 Male 1.8191 .12124 6 Total 1.7686 .07891 24 Total Female 1.7577 .05159 71 Male 1.7708 .10374 23 Total 1.7609 .06769 94

Box's Test of Equality of Covariance Matrices(a) Box's M 61.420 F 2.575 df1 21 df2 3312.346 Sig. .000 Tests the null hypothesis that the observed covariance matrices of the dependent variables are equal across groups. a Design: Intercept+Age+Accent+Gender+Accent * Gender Within Subjects Design: Prepostdepression

Multivariate Tests(b) Hypothesis Effect Value F df Error df Sig. Prepostdepression Pillai's Trace .008 .718(a) 1.000 85.000 .399 Wilks' Lambda .992 .718(a) 1.000 85.000 .399 Hotelling's Trace .008 .718(a) 1.000 85.000 .399 Roy's Largest .008 .718(a) 1.000 85.000 .399 Root Prepostdepression * Pillai's Trace .000 .008(a) 1.000 85.000 .928 Age Wilks' Lambda 1.000 .008(a) 1.000 85.000 .928 Hotelling's Trace .000 .008(a) 1.000 85.000 .928 Roy's Largest .000 .008(a) 1.000 85.000 .928 Root Prepostdepression * Pillai's Trace .032 .931(a) 3.000 85.000 .429 Accent Wilks' Lambda .968 .931(a) 3.000 85.000 .429 Hotelling's Trace .033 .931(a) 3.000 85.000 .429 Roy's Largest .033 .931(a) 3.000 85.000 .429 Root Prepostdepression * Pillai's Trace .014 1.219(a) 1.000 85.000 .273 Gender Wilks' Lambda .986 1.219(a) 1.000 85.000 .273 Hotelling's Trace .014 1.219(a) 1.000 85.000 .273 Roy's Largest Root .014 1.219(a) 1.000 85.000 .273 Prepostdepression * Pillai's Trace .026 .771(a) 3.000 85.000 .514 238 Accent * Gender Wilks' Lambda .974 .771(a) 3.000 85.000 .514 Hotelling's Trace .027 .771(a) 3.000 85.000 .514 Roy's Largest .027 .771(a) 3.000 85.000 .514 Root a Exact statistic b Design: Intercept+Age+Accent+Gender+Accent * Gender Within Subjects Design: Prepostdepression

Mauchly's Test of Sphericity(b) Measure: MEASURE_1 Approx. Within Subjects Mauchly' Chi- Effect s W Square df Sig. Epsilon(a) Green Greenho house- Greenho use- Huynh- Lower- Geisse Huynh- Lower- use- Geisser Feldt bound r Feldt bound Geisser Prepostdepress 1.000 .000 0 . 1.000 1.000 1.000 ion Tests the null hypothesis that the error covariance matrix of the orthonormalized transformed dependent variables is proportional to an identity matrix. a May be used to adjust the degrees of freedom for the averaged tests of significance. Corrected tests are displayed in the Tests of Within-Subjects Effects table. b Design: Intercept+Age+Accent+Gender+Accent * Gender Within Subjects Design: Prepostdepression

Tests of Within-Subjects Effects Measure: MEASURE_1 Type III Sum of Mean Source Squares df Square F Sig. Prepostdepression Sphericity .000 1 .000 .718 .399 Assumed Greenhouse- .000 1.000 .000 .718 .399 Geisser Huynh-Feldt .000 1.000 .000 .718 .399 Lower-bound .000 1.000 .000 .718 .399 Prepostdepression * Sphericity 1.23E-006 1 1.23E-006 .008 .928 Age Assumed Greenhouse- 1.23E-006 1.000 1.23E-006 .008 .928 Geisser Huynh-Feldt 1.23E-006 1.000 1.23E-006 .008 .928 Lower-bound 1.23E-006 1.000 1.23E-006 .008 .928 Prepostdepression * Sphericity .000 3 .000 .931 .429 Accent Assumed Greenhouse- .000 3.000 .000 .931 .429 Geisser Huynh-Feldt .000 3.000 .000 .931 .429 Lower-bound .000 3.000 .000 .931 .429 Prepostdepression * Sphericity .000 1 .000 1.219 .273 Gender Assumed Greenhouse- .000 1.000 .000 1.219 .273 Geisser Huynh-Feldt .000 1.000 .000 1.219 .273 Lower-bound .000 1.000 .000 1.219 .273 Prepostdepression * Sphericity .000 3 .000 .771 .514 Accent * Gender Assumed Greenhouse- .000 3.000 .000 .771 .514 239 Geisser Huynh-Feldt .000 3.000 .000 .771 .514 Lower-bound .000 3.000 .000 .771 .514 Error(Prepostdepres Sphericity .013 85 .000 sion) Assumed Greenhouse- .013 85.000 .000 Geisser Huynh-Feldt .013 85.000 .000 Lower-bound .013 85.000 .000

Tests of Within-Subjects Contrasts Measure: MEASURE_1 Type III Prepostdepre Sum of Mean Source ssion Squares df Square F Sig. Prepostdepression Linear .000 1 .000 .718 .399 Prepostdepression Linear 1.23E- 1.23E- 1 .008 .928 * Age 006 006 Prepostdepression Linear .000 3 .000 .931 .429 * Accent Prepostdepression Linear .000 1 .000 1.219 .273 * Gender Prepostdepression Linear .000 3 .000 .771 .514 * Accent * Gender Error(Prepostdepre Linear .013 85 .000 ssion)

Levene's Test of Equality of Error Variances(a) F df1 df2 Sig. PreDepression 2.220 7 86 .040 PostDepression 3.077 7 86 .006 Tests the null hypothesis that the error variance of the dependent variable is equal across groups. a Design: Intercept+Age+Accent+Gender+Accent * Gender Within Subjects Design: Prepostdepression

Tests of Between-Subjects Effects Measure: MEASURE_1 Transformed Variable: Average Type III Sum Source of Squares df Mean Square F Sig. Intercept 20.494 1 20.494 2333.897 .000 Age .017 1 .017 1.954 .166 Accent .026 3 .009 1.003 .396 Gender .004 1 .004 .503 .480 Accent * Gender .050 3 .017 1.889 .138 Error .746 85 .009

240

General Linear Model

Within-Subjects Factors Measure: MEASURE_1 Dependent prepostvigor Variable 1 PreVigor 2 PostVigor

Between-Subjects Factors Value Label N Accent 1.00 American 26 2.00 Indian 22 3.00 NZ 22 4.00 Filipino 24 Gender .00 Female 71 1.00 Male 23

Descriptive Statistics Accent Gender Mean Std. Deviation N PreVigor American Female 1.7962 .03275 21 Male 1.7915 .04987 5 Total 1.7953 .03549 26 Indian Female 1.7955 .03224 15 Male 1.8123 .02436 7 Total 1.8008 .03045 22 NZ Female 1.8045 .02889 17 Male 1.8056 .02613 5 Total 1.8047 .02768 22 Filipino Female 1.8001 .02741 18 Male 1.7893 .02828 6 Total 1.7974 .02742 24 Total Female 1.7990 .03000 71 Male 1.8003 .03198 23 Total 1.7993 .03033 94 PostVigor American Female 1.7948 .03418 21 Male 1.7860 .04916 5 Total 1.7931 .03652 26 Indian Female 1.7885 .03186 15 Male 1.8186 .03130 7 Total 1.7981 .03410 22 NZ Female 1.7987 .03170 17 Male 1.7994 .03832 5 Total 1.7988 .03234 22 Filipino Female 1.7918 .03003 18 Male 1.7950 .02952 6 Total 1.7926 .02929 24 Total Female 1.7936 .03160 71 Male 1.8012 .03650 23 241 Total 1.7955 .03282 94

Box's Test of Equality of Covariance Matrices(a) Box's M 11.997 F .503 df1 21 df2 3312.346 Sig. .970 Tests the null hypothesis that the observed covariance matrices of the dependent variables are equal across groups. a Design: Intercept+Age+Accent+Gender+Accent * Gender Within Subjects Design: prepostvigor

Multivariate Tests(b) Hypothesis Effect Value F df Error df Sig. prepostvigor Pillai's Trace .003 .233(a) 1.000 85.000 .631 Wilks' Lambda .997 .233(a) 1.000 85.000 .631 Hotelling's Trace .003 .233(a) 1.000 85.000 .631 Roy's Largest .003 .233(a) 1.000 85.000 .631 Root prepostvigor * Age Pillai's Trace .000 .034(a) 1.000 85.000 .853 Wilks' Lambda 1.000 .034(a) 1.000 85.000 .853 Hotelling's Trace .000 .034(a) 1.000 85.000 .853 Roy's Largest .000 .034(a) 1.000 85.000 .853 Root prepostvigor * Pillai's Trace .015 .417(a) 3.000 85.000 .741 Accent Wilks' Lambda .985 .417(a) 3.000 85.000 .741 Hotelling's Trace .015 .417(a) 3.000 85.000 .741 Roy's Largest .015 .417(a) 3.000 85.000 .741 Root prepostvigor * Pillai's Trace .024 2.074(a) 1.000 85.000 .153 Gender Wilks' Lambda .976 2.074(a) 1.000 85.000 .153 Hotelling's Trace .024 2.074(a) 1.000 85.000 .153 Roy's Largest .024 2.074(a) 1.000 85.000 .153 Root prepostvigor * Pillai's Trace .048 1.420(a) 3.000 85.000 .243 Accent * Gender Wilks' Lambda .952 1.420(a) 3.000 85.000 .243 Hotelling's Trace .050 1.420(a) 3.000 85.000 .243 Roy's Largest .050 1.420(a) 3.000 85.000 .243 Root a Exact statistic b Design: Intercept+Age+Accent+Gender+Accent * Gender Within Subjects Design: prepostvigor

Mauchly's Test of Sphericity(b) Measure: MEASURE_1 Approx. Within Subjects Mauchly's Chi- Effect W Square df Sig. Epsilon(a) Greenhou Greenh se- Huynh- Lower- ouse- Huynh- Lower- Greenhou Geisser Feldt bound Geisser Feldt bound se-Geisser prepostvigor 1.000 .000 0 . 1.000 1.000 1.000 Tests the null hypothesis that the error covariance matrix of the orthonormalized transformed dependent variables is proportional to an identity matrix. a May be used to adjust the degrees of freedom for the averaged tests of significance. Corrected tests 242 are displayed in the Tests of Within-Subjects Effects table. b Design: Intercept+Age+Accent+Gender+Accent * Gender Within Subjects Design: prepostvigor Tests of Within-Subjects Effects Measure: MEASURE_1 Type III Sum of Mean Source Squares df Square F Sig. prepostvigor Sphericity 3.05E-005 1 3.05E-005 .233 .631 Assumed Greenhouse- 3.05E-005 1.000 3.05E-005 .233 .631 Geisser Huynh-Feldt 3.05E-005 1.000 3.05E-005 .233 .631 Lower-bound 3.05E-005 1.000 3.05E-005 .233 .631 prepostvigor * Sphericity 4.50E-006 1 4.50E-006 .034 .853 Age Assumed Greenhouse- 4.50E-006 1.000 4.50E-006 .034 .853 Geisser Huynh-Feldt 4.50E-006 1.000 4.50E-006 .034 .853 Lower-bound 4.50E-006 1.000 4.50E-006 .034 .853 prepostvigor * Sphericity .000 3 5.47E-005 .417 .741 Accent Assumed Greenhouse- .000 3.000 5.47E-005 .417 .741 Geisser Huynh-Feldt .000 3.000 5.47E-005 .417 .741 Lower-bound .000 3.000 5.47E-005 .417 .741 prepostvigor * Sphericity .000 1 .000 2.074 .153 Gender Assumed Greenhouse- .000 1.000 .000 2.074 .153 Geisser Huynh-Feldt .000 1.000 .000 2.074 .153 Lower-bound .000 1.000 .000 2.074 .153 prepostvigor * Sphericity .001 3 .000 1.420 .243 Accent * Gender Assumed Greenhouse- .001 3.000 .000 1.420 .243 Geisser Huynh-Feldt .001 3.000 .000 1.420 .243 Lower-bound .001 3.000 .000 1.420 .243 Error(prepostvigor Sphericity .011 85 .000 ) Assumed Greenhouse- .011 85.000 .000 Geisser Huynh-Feldt .011 85.000 .000 Lower-bound .011 85.000 .000

Tests of Within-Subjects Contrasts Measure: MEASURE_1 Type III prepostvig Sum of Mean Source or Squares df Square F Sig. prepostvigor Linear 3.05E-005 1 3.05E-005 .233 .631 prepostvigor * Age Linear 4.50E-006 1 4.50E-006 .034 .853 prepostvigor * Linear .000 3 5.47E-005 .417 .741 Accent prepostvigor * Linear .000 1 .000 2.074 .153 Gender prepostvigor * Linear .001 3 .000 1.420 .243 Accent * Gender Error(prepostvigor) Linear .011 85 .000

243

Levene's Test of Equality of Error Variances(a) F df1 df2 Sig. PreVigor .463 7 86 .858 PostVigor .412 7 86 .893 Tests the null hypothesis that the error variance of the dependent variable is equal across groups. a Design: Intercept+Age+Accent+Gender+Accent * Gender Within Subjects Design: prepostvigor

Tests of Between-Subjects Effects Measure: MEASURE_1 Transformed Variable: Average Type III Sum Source of Squares df Mean Square F Sig. Intercept 19.847 1 19.847 10181.204 .000 Age .000 1 .000 .148 .702 Accent .003 3 .001 .581 .629 Gender .000 1 .000 .196 .659 Accent * Gender .005 3 .002 .881 .454 Error .166 85 .002

General Linear Model

Within-Subjects Factors Measure: MEASURE_1 Dependent Prepostconfusion Variable 1 PreConfusion

2 PostConfusio n

Between-Subjects Factors Value Label N Accent 1.00 American 26 2.00 Indian 22 3.00 NZ 22 4.00 Filipino 24 Gender .00 Female 71 1.00 Male 23

Descriptive Statistics Accent Gender Mean Std. Deviation N PreConfusion American Female 1.7723 .02351 21 Male 1.7649 .04057 5 Total 1.7709 .02673 26 Indian Female 1.7747 .04721 15 Male 1.7443 .02414 7 244 Total 1.7650 .04316 22 NZ Female 1.7651 .03021 17 Male 1.7586 .04445 5 Total 1.7636 .03285 22 Filipino Female 1.7684 .03319 18 Male 1.7869 .02907 6 Total 1.7730 .03263 24 Total Female 1.7701 .03306 71 Male 1.7630 .03580 23 Total 1.7684 .03369 94 PostConfusion American Female 1.7703 .02159 21 Male 1.7664 .05543 5 Total 1.7696 .02944 26 Indian Female 1.7642 .03927 15 Male 1.7486 .02654 7 Total 1.7593 .03585 22 NZ Female 1.7627 .02542 17 Male 1.7476 .04775 5 Total 1.7593 .03112 22 Filipino Female 1.7614 .03322 18 Male 1.7812 .05488 6 Total 1.7663 .03933 24 Total Female 1.7650 .02951 71 Male 1.7607 .04536 23 Total 1.7639 .03384 94

Box's Test of Equality of Covariance Matrices(a) Box's M 63.323 F 2.655 df1 21 df2 3312.346 Sig. .000 Tests the null hypothesis that the observed covariance matrices of the dependent variables are equal across groups. a Design: Intercept+Age+Accent+Gender+Accent * Gender Within Subjects Design: Prepostconfusion

Multivariate Tests(b) Hypothesis Effect Value F df Error df Sig. Prepostconfusion Pillai's Trace .008 .646(a) 1.000 85.000 .424 Wilks' Lambda .992 .646(a) 1.000 85.000 .424 Hotelling's Trace .008 .646(a) 1.000 85.000 .424 Roy's Largest .008 .646(a) 1.000 85.000 .424 Root Prepostconfusion * Pillai's Trace .002 .136(a) 1.000 85.000 .713 Age Wilks' Lambda .998 .136(a) 1.000 85.000 .713 Hotelling's Trace .002 .136(a) 1.000 85.000 .713 Roy's Largest .002 .136(a) 1.000 85.000 .713 Root Prepostconfusion * Pillai's Trace .021 .616(a) 3.000 85.000 .606 Accent Wilks' Lambda .979 .616(a) 3.000 85.000 .606 245 Hotelling's Trace .022 .616(a) 3.000 85.000 .606 Roy's Largest .022 .616(a) 3.000 85.000 .606 Root Prepostconfusion * Pillai's Trace .006 .472(a) 1.000 85.000 .494 Gender Wilks' Lambda .994 .472(a) 1.000 85.000 .494 Hotelling's Trace .006 .472(a) 1.000 85.000 .494 Roy's Largest Root .006 .472(a) 1.000 85.000 .494 Prepostconfusion * Pillai's Trace .052 1.558(a) 3.000 85.000 .205 Accent * Gender Wilks' Lambda .948 1.558(a) 3.000 85.000 .205 Hotelling's Trace .055 1.558(a) 3.000 85.000 .205 Roy's Largest .055 1.558(a) 3.000 85.000 .205 Root a Exact statistic b Design: Intercept+Age+Accent+Gender+Accent * Gender Within Subjects Design: Prepostconfusion

Mauchly's Test of Sphericity(b) Measure: MEASURE_1 Approx. Within Subjects Mauchly's Chi- Effect W Square df Sig. Epsilon(a) Greenho Greenh Greenho use- Huynh- Lower- ouse- Huynh- Lower- use- Geisser Feldt bound Geisser Feldt bound Geisser Prepostconfusio 1.000 .000 0 . 1.000 1.000 1.000 n Tests the null hypothesis that the error covariance matrix of the orthonormalized transformed dependent variables is proportional to an identity matrix. a May be used to adjust the degrees of freedom for the averaged tests of significance. Corrected tests are displayed in the Tests of Within-Subjects Effects table. b Design: Intercept+Age+Accent+Gender+Accent * Gender Within Subjects Design: Prepostconfusion

Tests of Within-Subjects Effects Measure: MEASURE_1 Type III Sum of Mean Source Squares df Square F Sig. Prepostconfusion Sphericity 8.49E-005 1 8.49E-005 .646 .424 Assumed Greenhouse- 8.49E-005 1.000 8.49E-005 .646 .424 Geisser Huynh-Feldt 8.49E-005 1.000 8.49E-005 .646 .424 Lower-bound 8.49E-005 1.000 8.49E-005 .646 .424 Prepostconfusion * Sphericity 1.78E-005 1 1.78E-005 .136 .713 Age Assumed Greenhouse- 1.78E-005 1.000 1.78E-005 .136 .713 Geisser Huynh-Feldt 1.78E-005 1.000 1.78E-005 .136 .713 Lower-bound 1.78E-005 1.000 1.78E-005 .136 .713 Prepostconfusion * Sphericity .000 3 8.10E-005 .616 .606 Accent Assumed Greenhouse- .000 3.000 8.10E-005 .616 .606 Geisser Huynh-Feldt .000 3.000 8.10E-005 .616 .606 Lower-bound .000 3.000 8.10E-005 .616 .606

246 Prepostconfusion * Sphericity 6.20E-005 1 6.20E-005 .472 .494 Gender Assumed Greenhouse- 6.20E-005 1.000 6.20E-005 .472 .494 Geisser Huynh-Feldt 6.20E-005 1.000 6.20E-005 .472 .494 Lower-bound 6.20E-005 1.000 6.20E-005 .472 .494

Prepostconfusion * Sphericity .001 3 .000 1.558 .205 Accent * Gender Assumed Greenhouse- .001 3.000 .000 1.558 .205 Geisser Huynh-Feldt .001 3.000 .000 1.558 .205 Lower-bound .001 3.000 .000 1.558 .205 Error(Prepostconfusi Sphericity .011 85 .000 on) Assumed Greenhouse- .011 85.000 .000 Geisser Huynh-Feldt .011 85.000 .000 Lower-bound .011 85.000 .000

Tests of Within-Subjects Contrasts Measure: MEASURE_1 Type III Prepostconfu Sum of Mean Source sion Squares df Square F Sig. Prepostconfusion Linear 8.49E- 1 8.49E-005 .646 .424 005 Prepostconfusion Linear 1.78E- 1 1.78E-005 .136 .713 * Age 005 Prepostconfusion Linear .000 3 8.10E-005 .616 .606 * Accent Prepostconfusion Linear 6.20E- 1 6.20E-005 .472 .494 * Gender 005 Prepostconfusion Linear * Accent * .001 3 .000 1.558 .205 Gender Error(Prepostconf Linear .011 85 .000 usion)

Levene's Test of Equality of Error Variances(a) F df1 df2 Sig. PreConfusion 1.844 7 86 .089 PostConfusion 2.122 7 86 .050 Tests the null hypothesis that the error variance of the dependent variable is equal across groups. a Design: Intercept+Age+Accent+Gender+Accent * Gender Within Subjects Design: Prepostconfusion

Tests of Between-Subjects Effects Measure: MEASURE_1 Transformed Variable: Average Type III Sum Source of Squares df Mean Square F Sig. Intercept 19.737 1 19.737 9101.967 .000 Age .003 1 .003 1.377 .244 Accent .007 3 .002 1.088 .359 Gender .001 1 .001 .348 .557 Accent * Gender .008 3 .003 1.212 .310 247 Error .184 85 .002

General Linear Model

Within-Subjects Factors Measure: MEASURE_1 Dependent Prepostfatigue Variable 1 PreFatigue 2 PostFatigue

Between-Subjects Factors Value Label N Accent 1.00 American 26 2.00 Indian 22 3.00 NZ 22 4.00 Filipino 24 Gender .00 Female 71 1.00 Male 23

Descriptive Statistics Accent Gender Mean Std. Deviation N PreFatigue American Female 1.7612 .02627 21 Male 1.7675 .04458 5 Total 1.7624 .02961 26 Indian Female 1.7611 .03516 15 Male 1.7512 .03719 7 Total 1.7579 .03524 22 NZ Female 1.7714 .03836 17 Male 1.7478 .01979 5 Total 1.7660 .03603 22 Filipino Female 1.7642 .03466 18 Male 1.8054 .04494 6 Total 1.7745 .04073 24 Total Female 1.7644 .03301 71 Male 1.7682 .04281 23 Total 1.7653 .03545 94 PostFatigue American Female 1.7607 .02282 21 Male 1.7647 .04326 5 Total 1.7615 .02680 26 Indian Female 1.7613 .03228 15 Male 1.7356 .03641 7 Total 1.7531 .03497 22 NZ Female 1.7622 .03872 17 Male 1.7379 .03025 5 Total 1.7567 .03775 22 Filipino Female 1.7592 .03472 18 Male 1.7849 .06509 6 Total 1.7656 .04406 24 Total Female 1.7608 .03152 71 Male 1.7553 .04776 23 248 Total 1.7595 .03596 94

Box's Test of Equality of Covariance Matrices(a) Box's M 28.284 F 1.186 df1 21 df2 3312.346 Sig. .252 Tests the null hypothesis that the observed covariance matrices of the dependent variables are equal across groups. a Design: Intercept+Age+Accent+Gender+Accent * Gender Within Subjects Design: Prepostfatigue

Multivariate Tests(b) Hypothesis Effect Value F df Error df Sig. Prepostfatigue Pillai's Trace .034 3.014(a) 1.000 85.000 .086 Wilks' Lambda .966 3.014(a) 1.000 85.000 .086 Hotelling's Trace .035 3.014(a) 1.000 85.000 .086 Roy's Largest .035 3.014(a) 1.000 85.000 .086 Root Prepostfatigue * Age Pillai's Trace .013 1.113(a) 1.000 85.000 .294 Wilks' Lambda .987 1.113(a) 1.000 85.000 .294 Hotelling's Trace .013 1.113(a) 1.000 85.000 .294 Roy's Largest .013 1.113(a) 1.000 85.000 .294 Root Prepostfatigue * Pillai's Trace .037 1.081(a) 3.000 85.000 .362 Accent Wilks' Lambda .963 1.081(a) 3.000 85.000 .362 Hotelling's Trace .038 1.081(a) 3.000 85.000 .362 Roy's Largest .038 1.081(a) 3.000 85.000 .362 Root Prepostfatigue * Pillai's Trace .038 3.377(a) 1.000 85.000 .070 Gender Wilks' Lambda .962 3.377(a) 1.000 85.000 .070 Hotelling's Trace .040 3.377(a) 1.000 85.000 .070 Roy's Largest .040 3.377(a) 1.000 85.000 .070 Root Prepostfatigue * Pillai's Trace .022 .627(a) 3.000 85.000 .600 Accent * Gender Wilks' Lambda .978 .627(a) 3.000 85.000 .600 Hotelling's Trace .022 .627(a) 3.000 85.000 .600 Roy's Largest .022 .627(a) 3.000 85.000 .600 Root a Exact statistic b Design: Intercept+Age+Accent+Gender+Accent * Gender Within Subjects Design: Prepostfatigue

Mauchly's Test of Sphericity(b) Measure: MEASURE_1 Approx. Within Subjects Mauchly' Chi- Effect s W Square df Sig. Epsilon(a) Greenho Greenh Greenho use- Huynh- Lower- ouse- Huynh- Lower- use- Geisser Feldt bound Geisser Feldt bound Geisser Prepostfatigue 1.000 .000 0 . 1.000 1.000 1.000 Tests the null hypothesis that the error covariance matrix of the orthonormalized transformed dependent 249 variables is proportional to an identity matrix. a May be used to adjust the degrees of freedom for the averaged tests of significance. Corrected tests are displayed in the Tests of Within-Subjects Effects table. b Design: Intercept+Age+Accent+Gender+Accent * Gender Within Subjects Design: Prepostfatigue

Tests of Within-Subjects Effects Measure: MEASURE_1 Type III Sum of Mean Source Squares df Square F Sig. Prepostfatigue Sphericity .001 1 .001 3.014 .086 Assumed Greenhouse- .001 1.000 .001 3.014 .086 Geisser Huynh-Feldt .001 1.000 .001 3.014 .086 Lower-bound .001 1.000 .001 3.014 .086 Prepostfatigue * Sphericity .000 1 .000 1.113 .294 Age Assumed Greenhouse- .000 1.000 .000 1.113 .294 Geisser Huynh-Feldt .000 1.000 .000 1.113 .294 Lower-bound .000 1.000 .000 1.113 .294 Prepostfatigue * Sphericity .001 3 .000 1.081 .362 Accent Assumed Greenhouse- .001 3.000 .000 1.081 .362 Geisser Huynh-Feldt .001 3.000 .000 1.081 .362 Lower-bound .001 3.000 .000 1.081 .362 Prepostfatigue * Sphericity .001 1 .001 3.377 .070 Gender Assumed Greenhouse- .001 1.000 .001 3.377 .070 Geisser Huynh-Feldt .001 1.000 .001 3.377 .070 Lower-bound .001 1.000 .001 3.377 .070 Prepostfatigue * Sphericity .000 3 .000 .627 .600 Accent * Gender Assumed Greenhouse- .000 3.000 .000 .627 .600 Geisser Huynh-Feldt .000 3.000 .000 .627 .600 Lower-bound .000 3.000 .000 .627 .600 Error(Prepostfatigue Sphericity .017 85 .000 ) Assumed Greenhouse- .017 85.000 .000 Geisser Huynh-Feldt .017 85.000 .000 Lower-bound .017 85.000 .000

Tests of Within-Subjects Contrasts Measure: MEASURE_1 Type III Prepostfatig Sum of Mean Source ue Squares df Square F Sig. Prepostfatigue Linear .001 1 .001 3.014 .086 Prepostfatigue * Age Linear .000 1 .000 1.113 .294 Prepostfatigue * Linear .001 3 .000 1.081 .362 Accent Prepostfatigue * Linear .001 1 .001 3.377 .070 250 Gender Prepostfatigue * Linear .000 3 .000 .627 .600 Accent * Gender Error(Prepostfatigue Linear .017 85 .000 )

Levene's Test of Equality of Error Variances(a) F df1 df2 Sig. PreFatigue .913 7 86 .500 PostFatigue 3.027 7 86 .007 Tests the null hypothesis that the error variance of the dependent variable is equal across groups. a Design: Intercept+Age+Accent+Gender+Accent * Gender Within Subjects Design: Prepostfatigue

Tests of Between-Subjects Effects Measure: MEASURE_1 Transformed Variable: Average Type III Sum Source of Squares df Mean Square F Sig. Intercept 19.183 1 19.183 8321.033 .000 Age 9.50E-006 1 9.50E-006 .004 .949 Accent .015 3 .005 2.147 .100 Gender 2.17E-005 1 2.17E-005 .009 .923 Accent * Gender .018 3 .006 2.541 .062 Error .196 85 .002

251

Appendix S: Analysis of the effect of listening condition on the POMS subscales for Study 3.

General Linear Model

Within-Subjects Factors

Measure: MEASURE_1 Dependent preposttension Variable 1 PreTension 2 PostTension

Between-Subjects Factors

Value Label N Gender .00 Female 71 1.00 Male 23 Listen 5.00 comedy 20 6.00 pop 26 7.00 straightthrou 27 gh 8.00 choice 21

Descriptive Statistics

Gender Listen Mean Std. Deviation N PreTension Female comedy 1.7884 .03977 16 pop 1.7823 .03846 21 straightthrough 1.7707 .03806 20 choice 1.7702 .03938 14 Total 1.7780 .03874 71 Male comedy 1.7683 .02814 4 pop 1.7990 .05736 5 straightthrough 1.7459 .04716 7 choice 1.7777 .05927 7 Total 1.7710 .05163 23 Total comedy 1.7844 .03797 20 pop 1.7855 .04189 26 straightthrough 1.7643 .04117 27 choice 1.7727 .04555 21 Total 1.7763 .04206 94 PostTension Female comedy 1.7799 .04112 16 pop 1.7726 .03562 21 straightthrough 1.7613 .04375 20 choice 1.7586 .02171 14 Total 1.7683 .03742 71 Male comedy 1.7394 .03277 4 pop 1.7908 .06940 5 252 straightthrough 1.7393 .05593 7 choice 1.7735 .06004 7 Total 1.7609 .05783 23 Total comedy 1.7718 .04219 20 pop 1.7761 .04288 26 straightthrough 1.7556 .04708 27 choice 1.7636 .03794 21 Total 1.7665 .04307 94

Box's Test of Equality of Covariance Matrices(a)

Box's M 36.762 F 1.528 df1 21 df2 2502.074 Sig. .059 Tests the null hypothesis that the observed covariance matrices of the dependent variables are equal across groups. a Design: Intercept+Age+Gender+Listen+Gender * Listen Within Subjects Design: preposttension

Multivariate Tests(b)

Hypothesis Effect Value F df Error df Sig. preposttension Pillai's Trace .023 1.968(a) 1.000 85.000 .164 Wilks' Lambda .977 1.968(a) 1.000 85.000 .164 Hotelling's Trace .023 1.968(a) 1.000 85.000 .164 Roy's Largest .023 1.968(a) 1.000 85.000 .164 Root preposttension * Age Pillai's Trace .003 .238(a) 1.000 85.000 .627 Wilks' Lambda .997 .238(a) 1.000 85.000 .627 Hotelling's Trace .003 .238(a) 1.000 85.000 .627 Roy's Largest .003 .238(a) 1.000 85.000 .627 Root preposttension * Pillai's Trace .002 .189(a) 1.000 85.000 .664 Gender Wilks' Lambda .998 .189(a) 1.000 85.000 .664 Hotelling's Trace .002 .189(a) 1.000 85.000 .664 Roy's Largest .002 .189(a) 1.000 85.000 .664 Root preposttension * Pillai's Trace .029 .852(a) 3.000 85.000 .469 Listen Wilks' Lambda .971 .852(a) 3.000 85.000 .469 Hotelling's Trace .030 .852(a) 3.000 85.000 .469 Roy's Largest .030 .852(a) 3.000 85.000 .469 Root preposttension * Pillai's Trace .044 1.289(a) 3.000 85.000 .284 Gender * Listen Wilks' Lambda .956 1.289(a) 3.000 85.000 .284 Hotelling's Trace .045 1.289(a) 3.000 85.000 .284 Roy's Largest .045 1.289(a) 3.000 85.000 .284 Root a Exact statistic b Design: Intercept+Age+Gender+Listen+Gender * Listen Within Subjects Design: preposttension

253

Mauchly's Test of Sphericity(b) Measure: MEASURE_1 Approx. Within Subjects Mauchly's Chi- Effect W Square df Sig. Epsilon(a) Greenhou Greenh Greenho se- Huynh- Lower- ouse- Huynh- Lower- use- Geisser Feldt bound Geisser Feldt bound Geisser preposttension 1.000 .000 0 . 1.000 1.000 1.000 Tests the null hypothesis that the error covariance matrix of the orthonormalized transformed dependent variables is proportional to an identity matrix. a May be used to adjust the degrees of freedom for the averaged tests of significance. Corrected tests are displayed in the Tests of Within-Subjects Effects table. b Design: Intercept+Age+Gender+Listen+Gender * Listen Within Subjects Design: preposttension

Tests of Within-Subjects Effects Measure: MEASURE_1 Type III Sum of Mean Source Squares df Square F Sig. preposttension Sphericity .000 1 .000 1.968 .164 Assumed Greenhouse- .000 1.000 .000 1.968 .164 Geisser Huynh-Feldt .000 1.000 .000 1.968 .164 Lower-bound .000 1.000 .000 1.968 .164 preposttension * Sphericity 5.07E-005 1 5.07E-005 .238 .627 Age Assumed Greenhouse- 5.07E-005 1.000 5.07E-005 .238 .627 Geisser Huynh-Feldt 5.07E-005 1.000 5.07E-005 .238 .627 Lower-bound 5.07E-005 1.000 5.07E-005 .238 .627 preposttension * Sphericity 4.04E-005 1 4.04E-005 .189 .664 Gender Assumed Greenhouse- 4.04E-005 1.000 4.04E-005 .189 .664 Geisser Huynh-Feldt 4.04E-005 1.000 4.04E-005 .189 .664 Lower-bound 4.04E-005 1.000 4.04E-005 .189 .664 preposttension * Sphericity .001 3 .000 .852 .469 Listen Assumed Greenhouse- .001 3.000 .000 .852 .469 Geisser Huynh-Feldt .001 3.000 .000 .852 .469 Lower-bound .001 3.000 .000 .852 .469 preposttension * Sphericity .001 3 .000 1.289 .284 Gender * Listen Assumed Greenhouse- .001 3.000 .000 1.289 .284 Geisser Huynh-Feldt .001 3.000 .000 1.289 .284 Lower-bound .001 3.000 .000 1.289 .284 Error(preposttensio Sphericity .018 85 .000 n) Assumed Greenhouse- .018 85.000 .000 Geisser Huynh-Feldt .018 85.000 .000

254 Lower-bound .018 85.000 .000

Tests of Within-Subjects Contrasts

Measure: MEASURE_1 Type III preposttensi Sum of Mean Source on Squares df Square F Sig. preposttension Linear .000 1 .000 1.968 .164 preposttension * Age Linear 5.07E-005 1 5.07E-005 .238 .627 preposttension * Linear 4.04E-005 1 4.04E-005 .189 .664 Gender preposttension * Linear .001 3 .000 .852 .469 Listen preposttension * Linear .001 3 .000 1.289 .284 Gender * Listen Error(preposttension) Linear .018 85 .000

Levene's Test of Equality of Error Variances(a)

F df1 df2 Sig. PreTension 1.180 7 86 .323 PostTension 2.754 7 86 .012 Tests the null hypothesis that the error variance of the dependent variable is equal across groups. a Design: Intercept+Age+Gender+Listen+Gender * Listen Within Subjects Design: preposttension

Tests of Between-Subjects Effects

Measure: MEASURE_1 Transformed Variable: Average Type III Sum Source of Squares df Mean Square F Sig. Intercept 20.322 1 20.322 6250.539 .000 Age .013 1 .013 3.850 .053 Gender .001 1 .001 .347 .557 Listen .019 3 .006 1.925 .132 Gender * Listen .010 3 .003 1.057 .372 Error .276 85 .003

GLM PreDepression PostDepression BY Gender Listen WITH Age /WSFACTOR = prepostdepression 2 Polynomial /METHOD = SSTYPE(3) /PRINT = DESCRIPTIVE HOMOGENEITY /CRITERIA = ALPHA(.05) /WSDESIGN = prepostdepression /DESIGN = Age Gender Listen Gender*Listen .

255

General Linear Model

Within-Subjects Factors

Measure: MEASURE_1 Dependent prepostdepression Variable 1 PreDepressio n 2 PostDepressi on

Between-Subjects Factors

Value Label N Gender .00 Female 71 1.00 Male 23 Listen 5.00 comedy 20 6.00 pop 26 7.00 straightthrou 27 gh 8.00 choice 21

Descriptive Statistics

Gender Listen Mean Std. Deviation N PreDepression Female comedy 1.7818 .05968 16 pop 1.7659 .05769 21 straightthrough 1.7605 .05948 20 choice 1.7600 .04682 14 Total 1.7668 .05616 71 Male comedy 1.7395 .05684 4 pop 1.8212 .11527 5 straightthrough 1.7408 .07616 7 choice 1.8005 .10902 7 Total 1.7762 .09473 23 Total comedy 1.7733 .06020 20 pop 1.7765 .07269 26 straightthrough 1.7554 .06326 27 choice 1.7735 .07331 21 Total 1.7691 .06718 94 PostDepression Female comedy 1.7744 .06372 16 pop 1.7590 .05412 21 straightthrough 1.7491 .04814 20 choice 1.7491 .03486 14 Total 1.7577 .05159 71 Male comedy 1.7279 .05233 4 256 pop 1.8106 .13819 5 straightthrough 1.7416 .08911 7 choice 1.7962 .11458 7 Total 1.7708 .10374 23 Total comedy 1.7651 .06326 20 pop 1.7689 .07635 26 straightthrough 1.7471 .05947 27 choice 1.7648 .07243 21 Total 1.7609 .06769 94

Box's Test of Equality of Covariance Matrices(a)

Box's M 55.096 F 2.290 df1 21 df2 2502.074 Sig. .001 Tests the null hypothesis that the observed covariance matrices of the dependent variables are equal across groups. a Design: Intercept+Age+Gender+Listen+Gender * Listen Within Subjects Design: prepostdepression

Multivariate Tests(b)

Hypothesis Effect Value F df Error df Sig. prepostdepression Pillai's Trace .007 .568(a) 1.000 85.000 .453 Wilks' Lambda .993 .568(a) 1.000 85.000 .453 Hotelling's .007 .568(a) 1.000 85.000 .453 Trace Roy's Largest .007 .568(a) 1.000 85.000 .453 Root prepostdepression * Pillai's Trace .000 .000(a) 1.000 85.000 .984 Age Wilks' Lambda 1.000 .000(a) 1.000 85.000 .984 Hotelling's .000 .000(a) 1.000 85.000 .984 Trace Roy's Largest .000 .000(a) 1.000 85.000 .984 Root prepostdepression * Pillai's Trace .005 .397(a) 1.000 85.000 .530 Gender Wilks' Lambda .995 .397(a) 1.000 85.000 .530 Hotelling's .005 .397(a) 1.000 85.000 .530 Trace Roy's Largest .005 .397(a) 1.000 85.000 .530 Root prepostdepression * Pillai's Trace .006 .185(a) 3.000 85.000 .906 Listen Wilks' Lambda .994 .185(a) 3.000 85.000 .906 Hotelling's .007 .185(a) 3.000 85.000 .906 Trace Roy's Largest Root .007 .185(a) 3.000 85.000 .906 prepostdepression * Pillai's Trace .030 .886(a) 3.000 85.000 .452 Gender * Listen Wilks' Lambda .970 .886(a) 3.000 85.000 .452 Hotelling's .031 .886(a) 3.000 85.000 .452 Trace 257 Roy's Largest .031 .886(a) 3.000 85.000 .452 Root a Exact statistic b Design: Intercept+Age+Gender+Listen+Gender * Listen Within Subjects Design: prepostdepression

Mauchly's Test of Sphericity(b)

Measure: MEASURE_1 Approx. Within Subjects Mauchly' Chi- Effect s W Square df Sig. Epsilon(a) Greenho Greenh Greenho use- Huynh- Lower- ouse- Huynh- Lower- use- Geisser Feldt bound Geisser Feldt bound Geisser prepostdepressi 1.000 .000 0 . 1.000 1.000 1.000 on Tests the null hypothesis that the error covariance matrix of the orthonormalized transformed dependent variables is proportional to an identity matrix. a May be used to adjust the degrees of freedom for the averaged tests of significance. Corrected tests are displayed in the Tests of Within-Subjects Effects table. b Design: Intercept+Age+Gender+Listen+Gender * Listen Within Subjects Design: prepostdepression

Tests of Within-Subjects Effects

Measure: MEASURE_1 Type III Sum of Mean Source Squares df Square F Sig. prepostdepression Sphericity 8.90E-005 1 8.90E-005 .568 .453 Assumed Greenhouse- 8.90E-005 1.000 8.90E-005 .568 .453 Geisser Huynh-Feldt 8.90E-005 1.000 8.90E-005 .568 .453 Lower-bound 8.90E-005 1.000 8.90E-005 .568 .453 prepostdepression * Sphericity 6.13E-008 1 6.13E-008 .000 .984 Age Assumed Greenhouse- 6.13E-008 1.000 6.13E-008 .000 .984 Geisser Huynh-Feldt 6.13E-008 1.000 6.13E-008 .000 .984 Lower-bound 6.13E-008 1.000 6.13E-008 .000 .984 prepostdepression * Sphericity 6.22E-005 1 6.22E-005 .397 .530 Gender Assumed Greenhouse- 6.22E-005 1.000 6.22E-005 .397 .530 Geisser Huynh-Feldt 6.22E-005 1.000 6.22E-005 .397 .530 Lower-bound 6.22E-005 1.000 6.22E-005 .397 .530 prepostdepression * Sphericity 8.70E-005 3 2.90E-005 .185 .906 Listen Assumed Greenhouse- 8.70E-005 3.000 2.90E-005 .185 .906 Geisser Huynh-Feldt 8.70E-005 3.000 2.90E-005 .185 .906 Lower-bound 8.70E-005 3.000 2.90E-005 .185 .906 prepostdepression * Sphericity .000 3 .000 .886 .452 Gender * Listen Assumed Greenhouse- .000 3.000 .000 .886 .452 Geisser Huynh-Feldt .000 3.000 .000 .886 .452 Lower-bound .000 3.000 .000 .886 .452 Error(prepostdepressi Sphericity .013 85 .000 on) Assumed 258 Greenhouse- .013 85.000 .000 Geisser Huynh-Feldt .013 85.000 .000 Lower-bound .013 85.000 .000

Tests of Within-Subjects Contrasts

Measure: MEASURE_1 Type III prepostdepressi Sum of Mean Source on Squares df Square F Sig. prepostdepression Linear 8.90E-005 1 8.90E-005 .568 .453 prepostdepression * Linear 6.13E-008 1 6.13E-008 .000 .984 Age prepostdepression * Linear 6.22E-005 1 6.22E-005 .397 .530 Gender prepostdepression * Linear 8.70E-005 3 2.90E-005 .185 .906 Listen prepostdepression * Linear .000 3 .000 .886 .452 Gender * Listen Error(prepostdepressi Linear .013 85 .000 on)

Levene's Test of Equality of Error Variances(a)

F df1 df2 Sig. PreDepression 2.613 7 86 .017 PostDepression 5.132 7 86 .000 Tests the null hypothesis that the error variance of the dependent variable is equal across groups. a Design: Intercept+Age+Gender+Listen+Gender * Listen Within Subjects Design: prepostdepression

Tests of Between-Subjects Effects

Measure: MEASURE_1 Transformed Variable: Average Type III Sum Source of Squares df Mean Square F Sig. Intercept 20.270 1 20.270 2300.328 .000 Age .013 1 .013 1.434 .235 Gender .003 1 .003 .393 .532 Listen .039 3 .013 1.470 .229 Gender * Listen .043 3 .014 1.643 .186 Error .749 85 .009

259

General Linear Model

Within-Subjects Factors Measure: MEASURE_1 Dependent prepostconfusion Variable 1 PreConfusion

2 PostConfusio n

Between-Subjects Factors

Value Label N Gender .00 Female 71 1.00 Male 23 Listen 5.00 comedy 20 6.00 pop 26 7.00 straightthrou 27 gh 8.00 choice 21

Descriptive Statistics Gender Listen Mean Std. Deviation N PreConfusion Female comedy 1.7765 .03309 16 pop 1.7732 .02631 21 straightthrough 1.7652 .03830 20 choice 1.7652 .03581 14 Total 1.7701 .03306 71 Male comedy 1.7690 .00705 4 pop 1.7899 .03424 5 straightthrough 1.7383 .02990 7 choice 1.7650 .04071 7 Total 1.7630 .03580 23 Total comedy 1.7750 .02969 20 pop 1.7764 .02804 26 straightthrough 1.7582 .03770 27 choice 1.7651 .03648 21 Total 1.7684 .03369 94 PostConfusion Female comedy 1.7701 .03296 16 pop 1.7687 .02685 21 straightthrough 1.7622 .03366 20 choice 1.7575 .02327 14 Total 1.7650 .02951 71 Male comedy 1.7537 .01589 4 pop 1.7851 .05773 5 straightthrough 1.7412 .03734 7 choice 1.7669 .05278 7 260 Total 1.7607 .04536 23 Total comedy 1.7669 .03070 20 pop 1.7718 .03396 26 straightthrough 1.7567 .03518 27 choice 1.7606 .03476 21 Total 1.7639 .03384 94

Box's Test of Equality of Covariance Matrices(a)

Box's M 42.331 F 1.759 df1 21 df2 2502.074 Sig. .018 Tests the null hypothesis that the observed covariance matrices of the dependent variables are equal across groups. a Design: Intercept+Age+Gender+Listen+Gender * Listen Within Subjects Design: prepostconfusion

Multivariate Tests(b)

Hypothesis Effect Value F df Error df Sig. prepostconfusion Pillai's Trace .003 .263(a) 1.000 85.000 .610 Wilks' Lambda .997 .263(a) 1.000 85.000 .610 Hotelling's .003 .263(a) 1.000 85.000 .610 Trace Roy's Largest .003 .263(a) 1.000 85.000 .610 Root prepostconfusion * Pillai's Trace .000 .000(a) 1.000 85.000 .986 Age Wilks' Lambda 1.000 .000(a) 1.000 85.000 .986 Hotelling's .000 .000(a) 1.000 85.000 .986 Trace Roy's Largest .000 .000(a) 1.000 85.000 .986 Root prepostconfusion * Pillai's Trace .002 .159(a) 1.000 85.000 .691 Gender Wilks' Lambda .998 .159(a) 1.000 85.000 .691 Hotelling's .002 .159(a) 1.000 85.000 .691 Trace Roy's Largest .002 .159(a) 1.000 85.000 .691 Root prepostconfusion * Pillai's Trace .040 1.166(a) 3.000 85.000 .328 Listen Wilks' Lambda .960 1.166(a) 3.000 85.000 .328 Hotelling's .041 1.166(a) 3.000 85.000 .328 Trace Roy's Largest .041 1.166(a) 3.000 85.000 .328 Root prepostconfusion * Pillai's Trace .031 .921(a) 3.000 85.000 .435 Gender * Listen Wilks' Lambda .969 .921(a) 3.000 85.000 .435 Hotelling's .032 .921(a) 3.000 85.000 .435 Trace Roy's Largest .032 .921(a) 3.000 85.000 .435 Root a Exact statistic b Design: Intercept+Age+Gender+Listen+Gender * Listen Within Subjects Design: prepostconfusion 261

Mauchly's Test of Sphericity(b) Measure: MEASURE_1 Approx. Within Subjects Mauchly's Chi- Effect W Square df Sig. Epsilon(a) Greenhou Greenh Greenho se- Huynh- Lower- ouse- Huynh- Lower- use- Geisser Feldt bound Geisser Feldt bound Geisser prepostconfusion 1.000 .000 0 . 1.000 1.000 1.000 Tests the null hypothesis that the error covariance matrix of the orthonormalized transformed dependent variables is proportional to an identity matrix. a May be used to adjust the degrees of freedom for the averaged tests of significance. Corrected tests are displayed in the Tests of Within-Subjects Effects table. b Design: Intercept+Age+Gender+Listen+Gender * Listen Within Subjects Design: prepostconfusion

Tests of Within-Subjects Effects Measure: MEASURE_1 Type III Sum of Mean Source Squares df Square F Sig. prepostconfusion Sphericity 3.52E-005 1 3.52E-005 .263 .610 Assumed Greenhouse- 3.52E-005 1.000 3.52E-005 .263 .610 Geisser Huynh-Feldt 3.52E-005 1.000 3.52E-005 .263 .610 Lower-bound 3.52E-005 1.000 3.52E-005 .263 .610 prepostconfusion * Sphericity 4.45E-008 1 4.45E-008 .000 .986 Age Assumed Greenhouse- 4.45E-008 1.000 4.45E-008 .000 .986 Geisser Huynh-Feldt 4.45E-008 1.000 4.45E-008 .000 .986 Lower-bound 4.45E-008 1.000 4.45E-008 .000 .986 prepostconfusion * Sphericity 2.14E-005 1 2.14E-005 .159 .691 Gender Assumed Greenhouse- 2.14E-005 1.000 2.14E-005 .159 .691 Geisser Huynh-Feldt 2.14E-005 1.000 2.14E-005 .159 .691 Lower-bound 2.14E-005 1.000 2.14E-005 .159 .691 prepostconfusion * Sphericity .000 3 .000 1.166 .328 Listen Assumed Greenhouse- .000 3.000 .000 1.166 .328 Geisser Huynh-Feldt .000 3.000 .000 1.166 .328 Lower-bound .000 3.000 .000 1.166 .328 prepostconfusion * Sphericity .000 3 .000 .921 .435 Gender * Listen Assumed Greenhouse- .000 3.000 .000 .921 .435 Geisser Huynh-Feldt .000 3.000 .000 .921 .435 Lower-bound .000 3.000 .000 .921 .435 Error(prepostconfusio Sphericity .011 85 .000 n) Assumed Greenhouse- .011 85.000 .000 Geisser Huynh-Feldt .011 85.000 .000 Lower-bound .011 85.000 .000

262

Tests of Within-Subjects Contrasts Measure: MEASURE_1 Type III prepostconfusi Sum of Mean Source on Squares df Square F Sig. prepostconfusion Linear 3.52E-005 1 3.52E-005 .263 .610 prepostconfusion * Linear 4.45E-008 1 4.45E-008 .000 .986 Age prepostconfusion * Linear 2.14E-005 1 2.14E-005 .159 .691 Gender prepostconfusion * Linear .000 3 .000 1.166 .328 Listen prepostconfusion * Linear .000 3 .000 .921 .435 Gender * Listen Error(prepostconfusio Linear .011 85 .000 n)

Levene's Test of Equality of Error Variances(a) F df1 df2 Sig. PreConfusion 1.105 7 86 .367 PostConfusion 2.371 7 86 .029 Tests the null hypothesis that the error variance of the dependent variable is equal across groups. a Design: Intercept+Age+Gender+Listen+Gender * Listen Within Subjects Design: prepostconfusion

Tests of Between-Subjects Effects Measure: MEASURE_1 Transformed Variable: Average Type III Sum Source of Squares df Mean Square F Sig. Intercept 19.674 1 19.674 9311.164 .000 Age .002 1 .002 1.133 .290 Gender .000 1 .000 .192 .663 Listen .014 3 .005 2.201 .094 Gender * Listen .007 3 .002 1.101 .353 Error .180 85 .002

263

General Linear Model

Within-Subjects Factors Measure: MEASURE_1 Dependent prepostvigor Variable 1 PreVigor 2 PostVigor

Between-Subjects Factors Value Label N Gender .00 Female 71 1.00 Male 23 Listen 5.00 comedy 20 6.00 pop 26 7.00 straightthrou 27 gh 8.00 choice 21

Descriptive Statistics Gender Listen Mean Std. Deviation N PreVigor Female comedy 1.7953 .03058 16 pop 1.7921 .03479 21 straightthrough 1.8033 .02714 20 choice 1.8074 .02509 14 Total 1.7990 .03000 71 Male comedy 1.7767 .04139 4 pop 1.8014 .02669 5 straightthrough 1.8257 .02028 7 choice 1.7877 .02643 7 Total 1.8003 .03198 23 Total comedy 1.7916 .03267 20 pop 1.7939 .03311 26 straightthrough 1.8091 .02706 27 choice 1.8009 .02664 21 Total 1.7993 .03033 94 PostVigor Female comedy 1.7871 .02883 16 pop 1.7910 .03926 21 straightthrough 1.7960 .02933 20 choice 1.8016 .02547 14 Total 1.7936 .03160 71 Male comedy 1.7852 .04813 4 pop 1.7960 .04625 5 straightthrough 1.8246 .02265 7 choice 1.7906 .02863 7

264 Total 1.8012 .03650 23 Total comedy 1.7867 .03198 20 pop 1.7920 .03974 26 straightthrough 1.8034 .03018 27 choice 1.7979 .02638 21 Total 1.7955 .03282 94

Box's Test of Equality of Covariance Matrices(a) Box's M 17.951 F .746 df1 21 df2 2502.074 Sig. .787 Tests the null hypothesis that the observed covariance matrices of the dependent variables are equal across groups. a Design: Intercept+Age+Gender+Listen+Gender * Listen Within Subjects Design: prepostvigor

Multivariate Tests(b) Hypothesis Effect Value F df Error df Sig. prepostvigor Pillai's Trace .000 .001(a) 1.000 85.000 .977 Wilks' Lambda 1.000 .001(a) 1.000 85.000 .977 Hotelling's Trace .000 .001(a) 1.000 85.000 .977 Roy's Largest .000 .001(a) 1.000 85.000 .977 Root prepostvigor * Age Pillai's Trace .001 .074(a) 1.000 85.000 .786 Wilks' Lambda .999 .074(a) 1.000 85.000 .786 Hotelling's Trace .001 .074(a) 1.000 85.000 .786 Roy's Largest .001 .074(a) 1.000 85.000 .786 Root prepostvigor * Pillai's Trace .033 2.931(a) 1.000 85.000 .091 Gender Wilks' Lambda .967 2.931(a) 1.000 85.000 .091 Hotelling's Trace .034 2.931(a) 1.000 85.000 .091 Roy's Largest .034 2.931(a) 1.000 85.000 .091 Root prepostvigor * Pillai's Trace .007 .211(a) 3.000 85.000 .889 Listen Wilks' Lambda .993 .211(a) 3.000 85.000 .889 Hotelling's Trace .007 .211(a) 3.000 85.000 .889 Roy's Largest .007 .211(a) 3.000 85.000 .889 Root prepostvigor * Pillai's Trace .036 1.061(a) 3.000 85.000 .370 Gender * Listen Wilks' Lambda .964 1.061(a) 3.000 85.000 .370 Hotelling's Trace .037 1.061(a) 3.000 85.000 .370 Roy's Largest .037 1.061(a) 3.000 85.000 .370 Root a Exact statistic b Design: Intercept+Age+Gender+Listen+Gender * Listen Within Subjects Design: prepostvigor

265

Mauchly's Test of Sphericity(b) Measure: MEASURE_1

Epsilon(a) Approx. Greenho Within Subjects Mauchly' Chi- Huynh- Lower- use- Effect s W Square df Sig. Feldt bound Geisser prepostvigor 1.000 .000 0 . 1.000 1.000 1.000 Tests the null hypothesis that the error covariance matrix of the orthonormalized transformed dependent variables is proportional to an identity matrix. a May be used to adjust the degrees of freedom for the averaged tests of significance. Corrected tests are displayed in the Tests of Within-Subjects Effects table. b Design: Intercept+Age+Gender+Listen+Gender * Listen Within Subjects Design: prepostvigor

Tests of Within-Subjects Effects Measure: MEASURE_1 Type III Sum of Mean Source Squares df Square F Sig. prepostvigor Sphericity 1.10E-007 1 1.10E-007 .001 .977 Assumed Greenhouse- 1.10E-007 1.000 1.10E-007 .001 .977 Geisser Huynh-Feldt 1.10E-007 1.000 1.10E-007 .001 .977 Lower-bound 1.10E-007 1.000 1.10E-007 .001 .977 prepostvigor * Age Sphericity 9.82E-006 1 9.82E-006 .074 .786 Assumed Greenhouse- 9.82E-006 1.000 9.82E-006 .074 .786 Geisser Huynh-Feldt 9.82E-006 1.000 9.82E-006 .074 .786 Lower-bound 9.82E-006 1.000 9.82E-006 .074 .786 prepostvigor * Sphericity .000 1 .000 2.931 .091 Gender Assumed Greenhouse- .000 1.000 .000 2.931 .091 Geisser Huynh-Feldt .000 1.000 .000 2.931 .091 Lower-bound .000 1.000 .000 2.931 .091 prepostvigor * Sphericity 8.36E-005 3 2.79E-005 .211 .889 Listen Assumed Greenhouse- 8.36E-005 3.000 2.79E-005 .211 .889 Geisser Huynh-Feldt 8.36E-005 3.000 2.79E-005 .211 .889 Lower-bound 8.36E-005 3.000 2.79E-005 .211 .889 prepostvigor * Sphericity .000 3 .000 1.061 .370 Gender * Listen Assumed Greenhouse- .000 3.000 .000 1.061 .370 Geisser Huynh-Feldt .000 3.000 .000 1.061 .370 Lower-bound .000 3.000 .000 1.061 .370 Error(prepostvigor) Sphericity .011 85 .000 Assumed Greenhouse- .011 85.000 .000 Geisser Huynh-Feldt .011 85.000 .000 266 Lower-bound .011 85.000 .000

Tests of Within-Subjects Contrasts Measure: MEASURE_1 Type III prepostvigo Sum of Mean Source r Squares df Square F Sig. prepostvigor Linear 1.10E-007 1 1.10E-007 .001 .977 prepostvigor * Age Linear 9.82E-006 1 9.82E-006 .074 .786 prepostvigor * Linear .000 1 .000 2.931 .091 Gender prepostvigor * Linear 8.36E-005 3 2.79E-005 .211 .889 Listen prepostvigor * Linear .000 3 .000 1.061 .370 Gender * Listen Error(prepostvigor) Linear .011 85 .000

Levene's Test of Equality of Error Variances(a) F df1 df2 Sig. PreVigor .546 7 86 .797 PostVigor 1.183 7 86 .321 Tests the null hypothesis that the error variance of the dependent variable is equal across groups. a Design: Intercept+Age+Gender+Listen+Gender * Listen Within Subjects Design: prepostvigor

Tests of Between-Subjects Effects Measure: MEASURE_1 Transformed Variable: Average Type III Sum Source of Squares df Mean Square F Sig. Intercept 19.945 1 19.945 10910.476 .000 Age 3.02E-006 1 3.02E-006 .002 .968 Gender .000 1 .000 .055 .815 Listen .012 3 .004 2.249 .088 Gender * Listen .009 3 .003 1.706 .172 Error .155 85 .002

General Linear Model

Within-Subjects Factors

Measure: MEASURE_1 Dependent prepostfatigue Variable 1 PreFatigue 2 PostFatigue

Between-Subjects Factors

Value Label N 267 Gender .00 Female 71 1.00 Male 23 Listen 5.00 comedy 20 6.00 pop 26 7.00 straightthrou 27 gh 8.00 choice 21

Descriptive Statistics

Gender Listen Mean Std. Deviation N PreFatigue Female comedy 1.7795 .04036 16 pop 1.7637 .03253 21 straightthrough 1.7541 .02434 20 choice 1.7628 .03233 14 Total 1.7644 .03301 71 Male comedy 1.7603 .03877 4 pop 1.7797 .03793 5 straightthrough 1.7536 .04703 7 choice 1.7790 .04754 7 Total 1.7682 .04281 23 Total comedy 1.7757 .03983 20 pop 1.7668 .03344 26 straightthrough 1.7540 .03072 27 choice 1.7682 .03766 21 Total 1.7653 .03545 94 PostFatigue Female comedy 1.7759 .04069 16 pop 1.7613 .03141 21 straightthrough 1.7525 .02560 20 choice 1.7548 .02320 14 Total 1.7608 .03152 71 Male comedy 1.7294 .03420 4 pop 1.7658 .04817 5 straightthrough 1.7491 .04865 7 choice 1.7688 .05539 7 Total 1.7553 .04776 23 Total comedy 1.7666 .04309 20 pop 1.7621 .03411 26 straightthrough 1.7516 .03205 27 choice 1.7594 .03628 21 Total 1.7595 .03596 94

Box's Test of Equality of Covariance Matrices(a)

Box's M 28.769 F 1.196 df1 21 df2 2502.074 Sig. .244 Tests the null hypothesis that the observed covariance matrices of the dependent variables are equal 268 across groups. a Design: Intercept+Age+Gender+Listen+Gender * Listen Within Subjects Design: prepostfatigue

Multivariate Tests(b) Hypothesis Effect Value F df Error df Sig. prepostfatigue Pillai's Trace .034 2.953(a) 1.000 85.000 .089 Wilks' Lambda .966 2.953(a) 1.000 85.000 .089 Hotelling's .035 2.953(a) 1.000 85.000 .089 Trace Roy's Largest .035 2.953(a) 1.000 85.000 .089 Root prepostfatigue * Age Pillai's Trace .009 .811(a) 1.000 85.000 .370 Wilks' Lambda .991 .811(a) 1.000 85.000 .370 Hotelling's .010 .811(a) 1.000 85.000 .370 Trace Roy's Largest .010 .811(a) 1.000 85.000 .370 Root prepostfatigue * Pillai's Trace .057 5.183(a) 1.000 85.000 .025 Gender Wilks' Lambda .943 5.183(a) 1.000 85.000 .025 Hotelling's .061 5.183(a) 1.000 85.000 .025 Trace Roy's Largest .061 5.183(a) 1.000 85.000 .025 Root prepostfatigue * Pillai's Trace .043 1.282(a) 3.000 85.000 .286 Listen Wilks' Lambda .957 1.282(a) 3.000 85.000 .286 Hotelling's .045 1.282(a) 3.000 85.000 .286 Trace Roy's Largest .045 1.282(a) 3.000 85.000 .286 Root prepostfatigue * Pillai's Trace .044 1.296(a) 3.000 85.000 .281 Gender * Listen Wilks' Lambda .956 1.296(a) 3.000 85.000 .281 Hotelling's .046 1.296(a) 3.000 85.000 .281 Trace Roy's Largest .046 1.296(a) 3.000 85.000 .281 Root a Exact statistic b Design: Intercept+Age+Gender+Listen+Gender * Listen Within Subjects Design: prepostfatigue

Mauchly's Test of Sphericity(b)

Measure: MEASURE_1 Approx. Within Subjects Mauchly' Chi- Effect s W Square df Sig. Epsilon(a) Greenho Greenh Greenhou use- Huynh- Lower- ouse- Huynh- Lower- se- Geisser Feldt bound Geisser Feldt bound Geisser prepostfatigue 1.000 .000 0 . 1.000 1.000 1.000 Tests the null hypothesis that the error covariance matrix of the orthonormalized transformed dependent variables is proportional to an identity matrix. a May be used to adjust the degrees of freedom for the averaged tests of significance. Corrected tests are displayed in the Tests of Within-Subjects Effects table. b Design: Intercept+Age+Gender+Listen+Gender * Listen Within Subjects Design: prepostfatigue 269

Tests of Within-Subjects Effects

Measure: MEASURE_1 Type III Sum of Mean Source Squares df Square F Sig. prepostfatigue Sphericity .001 1 .001 2.953 .089 Assumed Greenhouse- .001 1.000 .001 2.953 .089 Geisser Huynh-Feldt .001 1.000 .001 2.953 .089 Lower-bound .001 1.000 .001 2.953 .089 prepostfatigue * Age Sphericity .000 1 .000 .811 .370 Assumed Greenhouse- .000 1.000 .000 .811 .370 Geisser Huynh-Feldt .000 1.000 .000 .811 .370 Lower-bound .000 1.000 .000 .811 .370 prepostfatigue * Sphericity .001 1 .001 5.183 .025 Gender Assumed Greenhouse- .001 1.000 .001 5.183 .025 Geisser Huynh-Feldt .001 1.000 .001 5.183 .025 Lower-bound .001 1.000 .001 5.183 .025 prepostfatigue * Sphericity .001 3 .000 1.282 .286 Listen Assumed Greenhouse- .001 3.000 .000 1.282 .286 Geisser Huynh-Feldt .001 3.000 .000 1.282 .286 Lower-bound .001 3.000 .000 1.282 .286 prepostfatigue * Sphericity .001 3 .000 1.296 .281 Gender * Listen Assumed Greenhouse- .001 3.000 .000 1.296 .281 Geisser Huynh-Feldt .001 3.000 .000 1.296 .281 Lower-bound .001 3.000 .000 1.296 .281 Error(prepostfatigue Sphericity .016 85 .000 ) Assumed Greenhouse- .016 85.000 .000 Geisser Huynh-Feldt .016 85.000 .000 Lower-bound .016 85.000 .000

Tests of Within-Subjects Contrasts

Measure: MEASURE_1 Type III prepostfatigu Sum of Mean Source e Squares df Square F Sig. prepostfatigue Linear .001 1 .001 2.953 .089 prepostfatigue * Age Linear .000 1 .000 .811 .370 prepostfatigue * Linear .001 1 .001 5.183 .025 Gender prepostfatigue * Linear .001 3 .000 1.282 .286 270 Listen prepostfatigue * Linear .001 3 .000 1.296 .281 Gender * Listen Error(prepostfatigue) Linear .016 85 .000

Levene's Test of Equality of Error Variances(a)

F df1 df2 Sig. PreFatigue 1.605 7 86 .145 PostFatigue 3.030 7 86 .007 Tests the null hypothesis that the error variance of the dependent variable is equal across groups. a Design: Intercept+Age+Gender+Listen+Gender * Listen Within Subjects Design: prepostfatigue

Tests of Between-Subjects Effects

Measure: MEASURE_1 Transformed Variable: Average Type III Sum Source of Squares df Mean Square F Sig. Intercept 19.122 1 19.122 8115.447 .000 Age 3.81E-005 1 3.81E-005 .016 .899 Gender .000 1 .000 .080 .778 Listen .006 3 .002 .790 .503 Gender * Listen .010 3 .003 1.412 .245 Error .200 85 .002

General Linear Model

[DataSet1] S:\Louis Leland\Lab\Skye\Statistics\POMS50logdata.sav

Within-Subjects Factors

Measure: MEASURE_1 Dependent Fatigue Variable 1 PreFatigue 2 PostFatigue

Between-Subjects Factors

Value Label N Listen 5.00 comedy 20 6.00 pop 26 7.00 straightthrou 27 gh 8.00 choice 21 Gender .00 Female 71 1.00 Male 23

271

Multivariate Tests(b)

Hypothesis Effect Value F df Error df Sig. Fatigue Pillai's Trace .034 2.953(a) 1.000 85.000 .089 Wilks' Lambda .966 2.953(a) 1.000 85.000 .089 Hotelling's Trace .035 2.953(a) 1.000 85.000 .089 Roy's Largest .035 2.953(a) 1.000 85.000 .089 Root Fatigue * Age Pillai's Trace .009 .811(a) 1.000 85.000 .370 Wilks' Lambda .991 .811(a) 1.000 85.000 .370 Hotelling's Trace .010 .811(a) 1.000 85.000 .370 Roy's Largest .010 .811(a) 1.000 85.000 .370 Root Fatigue * Listen Pillai's Trace .043 1.282(a) 3.000 85.000 .286 Wilks' Lambda .957 1.282(a) 3.000 85.000 .286 Hotelling's Trace .045 1.282(a) 3.000 85.000 .286 Roy's Largest .045 1.282(a) 3.000 85.000 .286 Root Fatigue * Pillai's Trace .057 5.183(a) 1.000 85.000 .025 Gender Wilks' Lambda .943 5.183(a) 1.000 85.000 .025 Hotelling's Trace .061 5.183(a) 1.000 85.000 .025 Roy's Largest .061 5.183(a) 1.000 85.000 .025 Root Fatigue * Listen Pillai's Trace .044 1.296(a) 3.000 85.000 .281 * Gender Wilks' Lambda .956 1.296(a) 3.000 85.000 .281 Hotelling's Trace .046 1.296(a) 3.000 85.000 .281 Roy's Largest .046 1.296(a) 3.000 85.000 .281 Root a Exact statistic b Design: Intercept+Age+Listen+Gender+Listen * Gender Within Subjects Design: Fatigue

Mauchly's Test of Sphericity(b)

Measure: MEASURE_1 Approx. Within Subjects Mauchly' Chi- Effect s W Square df Sig. Epsilon(a) Greenh Greenho ouse- Greenho use- Huynh- Lower- Geisse Huynh- Lower- use- Geisser Feldt bound r Feldt bound Geisser Fatigue 1.000 .000 0 . 1.000 1.000 1.000 Tests the null hypothesis that the error covariance matrix of the orthonormalized transformed dependent variables is proportional to an identity matrix. a May be used to adjust the degrees of freedom for the averaged tests of significance. Corrected tests are displayed in the Tests of Within-Subjects Effects table. b Design: Intercept+Age+Listen+Gender+Listen * Gender Within Subjects Design: Fatigue

272

Tests of Within-Subjects Effects

Measure: MEASURE_1 Type III Sum of Mean Source Squares df Square F Sig. Fatigue Sphericity .001 1 .001 2.953 .089 Assumed Greenhouse- .001 1.000 .001 2.953 .089 Geisser Huynh-Feldt .001 1.000 .001 2.953 .089 Lower-bound .001 1.000 .001 2.953 .089 Fatigue * Age Sphericity .000 1 .000 .811 .370 Assumed Greenhouse- .000 1.000 .000 .811 .370 Geisser Huynh-Feldt .000 1.000 .000 .811 .370 Lower-bound .000 1.000 .000 .811 .370 Fatigue * Sphericity .001 3 .000 1.282 .286 Listen Assumed Greenhouse- .001 3.000 .000 1.282 .286 Geisser Huynh-Feldt .001 3.000 .000 1.282 .286 Lower-bound .001 3.000 .000 1.282 .286 Fatigue * Sphericity .001 1 .001 5.183 .025 Gender Assumed Greenhouse- .001 1.000 .001 5.183 .025 Geisser Huynh-Feldt .001 1.000 .001 5.183 .025 Lower-bound .001 1.000 .001 5.183 .025 Fatigue * Sphericity .001 3 .000 1.296 .281 Listen * Assumed Gender Greenhouse- .001 3.000 .000 1.296 .281 Geisser Huynh-Feldt .001 3.000 .000 1.296 .281 Lower-bound .001 3.000 .000 1.296 .281 Error(Fatigue) Sphericity .016 85 .000 Assumed Greenhouse- .016 85.000 .000 Geisser Huynh-Feldt .016 85.000 .000 Lower-bound .016 85.000 .000

Tests of Within-Subjects Contrasts Measure: MEASURE_1 Type III Sum Source Fatigue of Squares df Mean Square F Sig. Fatigue Linear .001 1 .001 2.953 .089 Fatigue * Age Linear .000 1 .000 .811 .370 Fatigue * Listen Linear .001 3 .000 1.282 .286 Fatigue * Gender Linear .001 1 .001 5.183 .025 273 Fatigue * Listen * Linear .001 3 .000 1.296 .281 Gender Error(Fatigue) Linear .016 85 .000

Tests of Between-Subjects Effects Measure: MEASURE_1 Transformed Variable: Average Type III Sum Source of Squares df Mean Square F Sig. Intercept 19.122 1 19.122 8115.447 .000 Age 3.81E-005 1 3.81E-005 .016 .899 Listen .006 3 .002 .790 .503 Gender .000 1 .000 .080 .778 Listen * Gender .010 3 .003 1.412 .245 Error .200 85 .002

General Linear Model Within-Subjects Factors

Measure: MEASURE_1 Dependent Anger Variable 1 PreAnger 2 PostAnger

Between-Subjects Factors Value Label N Listen 5.00 comedy 20 6.00 pop 26 7.00 straightthrou 27 gh 8.00 choice 21 Gender .00 Female 71 1.00 Male 23

Descriptive Statistics Listen Gender Mean Std. Deviation N PreAnger comedy Female 1.7666 .04216 16 Male 1.7394 .03343 4 Total 1.7611 .04128 20 pop Female 1.7518 .03680 21 Male 1.7856 .07510 5 Total 1.7583 .04659 26 straightthrough Female 1.7393 .03565 20 Male 1.7459 .04747 7 Total 1.7410 .03818 27 choice Female 1.7597 .03081 14 Male 1.7962 .07200 7 274 Total 1.7719 .04983 21 Total Female 1.7532 .03734 71 Male 1.7687 .06191 23 Total 1.7570 .04474 94 PostAnger comedy Female 1.7639 .04531 16 Male 1.7289 .04078 4 Total 1.7569 .04571 20 pop Female 1.7422 .03203 21 Male 1.7726 .07594 5 Total 1.7481 .04351 26 straightthrough Female 1.7301 .03096 20 Male 1.7343 .03905 7 Total 1.7312 .03249 27 choice Female 1.7478 .02898 14 Male 1.7834 .07722 7 Total 1.7597 .05127 21 Total Female 1.7448 .03603 71 Male 1.7566 .06246 23 Total 1.7477 .04389 94

Box's Test of Equality of Covariance Matrices(a) Box's M 35.416 F 1.472 df1 21 df2 2502.074 Sig. .076 Tests the null hypothesis that the observed covariance matrices of the dependent variables are equal across groups. a Design: Intercept+Age+Listen+Gender+Listen * Gender Within Subjects Design: Anger

Multivariate Tests(b) Hypothesis Effect Value F df Error df Sig. Anger Pillai's Trace .043 3.831(a) 1.000 85.000 .054 Wilks' Lambda .957 3.831(a) 1.000 85.000 .054 Hotelling's .045 3.831(a) 1.000 85.000 .054 Trace Roy's Largest .045 3.831(a) 1.000 85.000 .054 Root Anger * Age Pillai's Trace .009 .808(a) 1.000 85.000 .371 Wilks' Lambda .991 .808(a) 1.000 85.000 .371 Hotelling's .010 .808(a) 1.000 85.000 .371 Trace Roy's Largest .010 .808(a) 1.000 85.000 .371 Root Anger * Listen Pillai's Trace .013 .384(a) 3.000 85.000 .765 Wilks' Lambda .987 .384(a) 3.000 85.000 .765 Hotelling's .014 .384(a) 3.000 85.000 .765 Trace Roy's Largest .014 .384(a) 3.000 85.000 .765 Root Anger * Gender Pillai's Trace .010 .842(a) 1.000 85.000 .361 Wilks' Lambda .990 .842(a) 1.000 85.000 .361 275 Hotelling's .010 .842(a) 1.000 85.000 .361 Trace Roy's Largest .010 .842(a) 1.000 85.000 .361 Root Anger * Listen * Pillai's Trace .005 .139(a) 3.000 85.000 .936 Gender Wilks' Lambda .995 .139(a) 3.000 85.000 .936 Hotelling's .005 .139(a) 3.000 85.000 .936 Trace Roy's Largest .005 .139(a) 3.000 85.000 .936 Root a Exact statistic b Design: Intercept+Age+Listen+Gender+Listen * Gender Within Subjects Design: Anger

Mauchly's Test of Sphericity(b) Measure: MEASURE_1 Approx. Within Subjects Mauchly' Chi- Effect s W Square df Sig. Epsilon(a) Greenh Greenho ouse- Greenho use- Huynh- Lower- Geisse Huynh- Lower- use- Geisser Feldt bound r Feldt bound Geisser Anger 1.000 .000 0 . 1.000 1.000 1.000 Tests the null hypothesis that the error covariance matrix of the orthonormalized transformed dependent variables is proportional to an identity matrix. a May be used to adjust the degrees of freedom for the averaged tests of significance. Corrected tests are displayed in the Tests of Within-Subjects Effects table. b Design: Intercept+Age+Listen+Gender+Listen * Gender Within Subjects Design: Anger

Tests of Within-Subjects Effects Measure: MEASURE_1 Type III Sum of Mean Source Squares df Square F Sig. Anger Sphericity .001 1 .001 3.831 .054 Assumed Greenhouse- .001 1.000 .001 3.831 .054 Geisser Huynh-Feldt .001 1.000 .001 3.831 .054 Lower-bound .001 1.000 .001 3.831 .054 Anger * Age Sphericity .000 1 .000 .808 .371 Assumed Greenhouse- .000 1.000 .000 .808 .371 Geisser Huynh-Feldt .000 1.000 .000 .808 .371 Lower-bound .000 1.000 .000 .808 .371 Anger * Listen Sphericity .000 3 5.23E-005 .384 .765 Assumed Greenhouse- .000 3.000 5.23E-005 .384 .765 Geisser Huynh-Feldt .000 3.000 5.23E-005 .384 .765 Lower-bound .000 3.000 5.23E-005 .384 .765 Anger * Gender Sphericity .000 1 .000 .842 .361 Assumed Greenhouse- .000 1.000 .000 .842 .361 Geisser Huynh-Feldt .000 1.000 .000 .842 .361 Lower-bound .000 1.000 .000 .842 .361 Anger * Listen * Sphericity 5.69E-005 3 1.90E-005 .139 .936 276 Gender Assumed Greenhouse- 5.69E-005 3.000 1.90E-005 .139 .936 Geisser Huynh-Feldt 5.69E-005 3.000 1.90E-005 .139 .936 Lower-bound 5.69E-005 3.000 1.90E-005 .139 .936 Error(Anger) Sphericity .012 85 .000 Assumed Greenhouse- .012 85.000 .000 Geisser Huynh-Feldt .012 85.000 .000 Lower-bound .012 85.000 .000

Tests of Within-Subjects Contrasts Measure: MEASURE_1 Type III Sum of Mean Source Anger Squares df Square F Sig. Anger Linear .001 1 .001 3.831 .054 Anger * Age Linear .000 1 .000 .808 .371 Anger * Listen Linear .000 3 5.23E-005 .384 .765 Anger * Gender Linear .000 1 .000 .842 .361 Anger * Listen * Linear 5.69E-005 3 1.90E-005 .139 .936 Gender Error(Anger) Linear .012 85 .000

Levene's Test of Equality of Error Variances(a) F df1 df2 Sig. PreAnger 2.443 7 86 .025 PostAnger 2.644 7 86 .016 Tests the null hypothesis that the error variance of the dependent variable is equal across groups. a Design: Intercept+Age+Listen+Gender+Listen * Gender Within Subjects Design: Anger

Tests of Between-Subjects Effects Measure: MEASURE_1 Transformed Variable: Average Type III Sum Source of Squares df Mean Square F Sig. Intercept 19.679 1 19.679 5633.730 .000 Age .006 1 .006 1.606 .209 Listen .027 3 .009 2.541 .062 Gender .004 1 .004 1.120 .293 Listen * Gender .019 3 .006 1.782 .157 Error .297 85 .003

GLM PreFatigue PostFatigue BY Listen Gender WITH Age /WSFACTOR = Fatigue 2 Polynomial /METHOD = SSTYPE(3) /PLOT = PROFILE( Fatigue*Gender ) /PRINT = DESCRIPTIVE HOMOGENEITY /CRITERIA = ALPHA(.05) /WSDESIGN = Fatigue 277 /DESIGN = Age Listen Gender Listen*Gender .

General Linear Model

Within-Subjects Factors

Measure: MEASURE_1 Dependent Fatigue Variable 1 PreFatigue 2 PostFatigue

Between-Subjects Factors Value Label N Listen 5.00 comedy 20 6.00 pop 26 7.00 straightthrou 27 gh 8.00 choice 21 Gender .00 Female 71 1.00 Male 23

Descriptive Statistics Listen Gender Mean Std. Deviation N PreFatigue comedy Female 1.7795 .04036 16 Male 1.7603 .03877 4 Total 1.7757 .03983 20 pop Female 1.7637 .03253 21 Male 1.7797 .03793 5 Total 1.7668 .03344 26 straightthrough Female 1.7541 .02434 20 Male 1.7536 .04703 7 Total 1.7540 .03072 27 choice Female 1.7628 .03233 14 Male 1.7790 .04754 7 Total 1.7682 .03766 21 Total Female 1.7644 .03301 71 Male 1.7682 .04281 23 Total 1.7653 .03545 94 PostFatigue comedy Female 1.7759 .04069 16 Male 1.7294 .03420 4 Total 1.7666 .04309 20 pop Female 1.7613 .03141 21 Male 1.7658 .04817 5 Total 1.7621 .03411 26 straightthrough Female 1.7525 .02560 20 Male 1.7491 .04865 7 278 Total 1.7516 .03205 27 choice Female 1.7548 .02320 14 Male 1.7688 .05539 7 Total 1.7594 .03628 21 Total Female 1.7608 .03152 71 Male 1.7553 .04776 23 Total 1.7595 .03596 94

Box's Test of Equality of Covariance Matrices(a) Box's M 28.769 F 1.196 df1 21 df2 2502.074 Sig. .244 Tests the null hypothesis that the observed covariance matrices of the dependent variables are equal across groups. a Design: Intercept+Age+Listen+Gender+Listen * Gender Within Subjects Design: Fatigue

Multivariate Tests(b) Hypothesis Effect Value F df Error df Sig. Fatigue Pillai's Trace .034 2.953(a) 1.000 85.000 .089 Wilks' Lambda .966 2.953(a) 1.000 85.000 .089 Hotelling's Trace .035 2.953(a) 1.000 85.000 .089 Roy's Largest .035 2.953(a) 1.000 85.000 .089 Root Fatigue * Age Pillai's Trace .009 .811(a) 1.000 85.000 .370 Wilks' Lambda .991 .811(a) 1.000 85.000 .370 Hotelling's Trace .010 .811(a) 1.000 85.000 .370 Roy's Largest .010 .811(a) 1.000 85.000 .370 Root Fatigue * Listen Pillai's Trace .043 1.282(a) 3.000 85.000 .286 Wilks' Lambda .957 1.282(a) 3.000 85.000 .286 Hotelling's Trace .045 1.282(a) 3.000 85.000 .286 Roy's Largest .045 1.282(a) 3.000 85.000 .286 Root Fatigue * Pillai's Trace .057 5.183(a) 1.000 85.000 .025 Gender Wilks' Lambda .943 5.183(a) 1.000 85.000 .025 Hotelling's Trace .061 5.183(a) 1.000 85.000 .025 Roy's Largest .061 5.183(a) 1.000 85.000 .025 Root Fatigue * Listen Pillai's Trace .044 1.296(a) 3.000 85.000 .281 * Gender Wilks' Lambda .956 1.296(a) 3.000 85.000 .281 Hotelling's Trace .046 1.296(a) 3.000 85.000 .281 Roy's Largest .046 1.296(a) 3.000 85.000 .281 Root a Exact statistic b Design: Intercept+Age+Listen+Gender+Listen * Gender Within Subjects Design: Fatigue

Mauchly's Test of Sphericity(b) Measure: MEASURE_1 279 Approx. Within Subjects Mauchly' Chi- Effect s W Square df Sig. Epsilon(a) Greenh Greenho ouse- Greenhou use- Huynh- Lower- Geisse Huynh- Lower- se- Geisser Feldt bound r Feldt bound Geisser Fatigue 1.000 .000 0 . 1.000 1.000 1.000 Tests the null hypothesis that the error covariance matrix of the orthonormalized transformed dependent variables is proportional to an identity matrix. a May be used to adjust the degrees of freedom for the averaged tests of significance. Corrected tests are displayed in the Tests of Within-Subjects Effects table. b Design: Intercept+Age+Listen+Gender+Listen * Gender Within Subjects Design: Fatigue

Tests of Within-Subjects Effects Measure: MEASURE_1 Type III Sum of Mean Source Squares df Square F Sig. Fatigue Sphericity .001 1 .001 2.953 .089 Assumed Greenhouse- .001 1.000 .001 2.953 .089 Geisser Huynh-Feldt .001 1.000 .001 2.953 .089

Lower-bound .001 1.000 .001 2.953 .089 Fatigue * Age Sphericity .000 1 .000 .811 .370 Assumed Greenhouse- .000 1.000 .000 .811 .370 Geisser Huynh-Feldt .000 1.000 .000 .811 .370

Lower-bound .000 1.000 .000 .811 .370 Fatigue * Sphericity .001 3 .000 1.282 .286 Listen Assumed Greenhouse- .001 3.000 .000 1.282 .286 Geisser Huynh-Feldt .001 3.000 .000 1.282 .286

Lower-bound .001 3.000 .000 1.282 .286 Fatigue * Sphericity .001 1 .001 5.183 .025 Gender Assumed Greenhouse- .001 1.000 .001 5.183 .025 Geisser Huynh-Feldt .001 1.000 .001 5.183 .025

Lower-bound .001 1.000 .001 5.183 .025 Fatigue * Sphericity .001 3 .000 1.296 .281 Listen * Assumed Gender Greenhouse- .001 3.000 .000 1.296 .281 Geisser Huynh-Feldt .001 3.000 .000 1.296 .281

Lower-bound .001 3.000 .000 1.296 .281 Error(Fatigue) Sphericity .016 85 .000 Assumed Greenhouse- .016 85.000 .000 Geisser Huynh-Feldt .016 85.000 .000

280 Lower-bound .016 85.000 .000

Tests of Within-Subjects Contrasts Measure: MEASURE_1 Type III Sum Source Fatigue of Squares df Mean Square F Sig. Fatigue Linear .001 1 .001 2.953 .089 Fatigue * Age Linear .000 1 .000 .811 .370 Fatigue * Listen Linear .001 3 .000 1.282 .286 Fatigue * Gender Linear .001 1 .001 5.183 .025 Fatigue * Listen * Linear .001 3 .000 1.296 .281 Gender Error(Fatigue) Linear .016 85 .000

Levene's Test of Equality of Error Variances(a) F df1 df2 Sig. PreFatigue 1.605 7 86 .145 PostFatigue 3.030 7 86 .007 Tests the null hypothesis that the error variance of the dependent variable is equal across groups. a Design: Intercept+Age+Listen+Gender+Listen * Gender Within Subjects Design: Fatigue

Tests of Between-Subjects Effects Measure: MEASURE_1 Transformed Variable: Average Type III Sum Source of Squares df Mean Square F Sig. Intercept 19.122 1 19.122 8115.447 .000 Age 3.81E-005 1 3.81E-005 .016 .899 Listen .006 3 .002 .790 .503 Gender .000 1 .000 .080 .778 Listen * Gender .010 3 .003 1.412 .245 Error .200 85 .002

281 Appendix T: Non-parametric correlation matrix of self report results for Study 3 including POMS subscales.

Nonparametric Correlations

[DataSet1] S:\Louis Leland\Lab\Skye\Statistics\file for correlations.sav

satisf comp dislik actio contr dislik etenc eacc likea Actua Perc Acctb PreT PostT Post Pre Post Post Post Post Pre n ol e e ent bility lwt wt lwt Age MD MD PreT T D D PreA A PreV V PreF F C PostC Spea sati Corre ------rman' sfa lation .316( .355( .257( .244( - - - 1.000 -.097 -.077 .423( .493( .141 .212( .225( -.143 .251( .329( .110 .109 .264( .325( .21 -.192 s rho ctio Coeff **) **) *) *) .242(*) .153 .149 **) **) *) *) *) **) *) **) 4(*) n icient Sig. (2- .03 . .002 .354 .000 .463 .012 .000 .000 .019 .176 .041 .019 .140 .029 .152 .169 .015 .001 .292 .294 .010 .001 .064 tailed 9 ) N 94 94 94 94 94 94 89 85 92 94 94 94 94 94 94 94 94 94 94 94 94 94 94 94 co Corre ntr lation .316( .241( - - .00 1.000 -.027 -.122 .094 -.102 -.114 .129 -.030 -.090 -.138 -.102 -.065 -.083 -.116 .172 .180 .002 -.117 -.053 ol Coeff **) *) .086 .087 5 icient Sig. (2- .95 .002 . .796 .019 .240 .368 .343 .301 .222 .772 .390 .185 .410 .326 .402 .531 .428 .265 .098 .082 .986 .262 .614 tailed 9 ) N 94 94 94 94 94 94 89 85 92 94 94 94 94 94 94 94 94 94 94 94 94 94 94 94 disl Corre - - ike lation - .00 -.097 -.027 1.000 -.064 -.181 -.183 .355( .338( -.116 -.074 .046 .083 .057 .055 -.027 .018 .009 -.122 -.168 .018 .135 .024 Coeff .041 5 **) **) icient Sig. (2- .96 .354 .796 . .540 .080 .078 .001 .002 .272 .480 .658 .426 .584 .602 .696 .796 .863 .934 .240 .106 .861 .194 .821 tailed 1 ) N 94 94 94 94 94 94 89 85 92 94 94 94 94 94 94 94 94 94 94 94 94 94 94 94 co Corre mp lation - - .355( .241( .303( .252( - - ete Coeff -.064 1.000 .237( -.166 -.125 .091 -.050 -.035 .014 .016 -.094 -.115 .044 .043 -.082 -.062 .03 -.009 **) *) **) *) .026 .030 nc icient *) 3 e Sig. (2- .75 .000 .019 .540 . .021 .003 .120 .254 .015 .385 .635 .737 .800 .890 .774 .878 .368 .268 .672 .682 .432 .555 .930 tailed 1 ) N 94 94 94 94 94 94 89 85 92 94 94 94 94 94 94 94 94 94 94 94 94 94 94 94

282 disl Corre - ike lation .08 -.077 -.122 -.181 .237( 1.000 -.008 .055 -.014 -.148 -.058 .124 .103 .092 .057 .136 .113 .131 .142 -.042 -.025 .174 .148 .060 acc Coeff 1 *) ent icient Sig. (2- .43 .463 .240 .080 .021 . .938 .606 .900 .160 .578 .233 .322 .380 .585 .190 .279 .207 .172 .685 .813 .093 .155 .563 tailed 5 ) N 94 94 94 94 94 94 89 85 92 94 94 94 94 94 94 94 94 94 94 94 94 94 94 94 like Corre - abil lation .257( .303( .267( - .094 -.183 -.008 1.000 -.016 .053 .079 -.094 -.085 .046 .025 -.103 -.189 -.152 .073 .029 -.120 -.165 .02 -.053 ity Coeff *) **) *) .152 7 icient Sig. (2- .79 .012 .368 .078 .003 .938 . .880 .632 .010 .450 .368 .415 .658 .813 .144 .323 .068 .143 .487 .782 .251 .111 .614 tailed 8 ) N 94 94 94 94 94 94 89 85 92 94 94 94 94 94 94 94 94 94 94 94 94 94 94 94 Act Corre - - ual lation .647( .245( .247( .05 .423( -.102 .355(* -.166 .055 -.016 1.000 -.003 .055 .121 .105 .058 .110 .072 .109 -.007 .012 .113 .105 .005 wt Coeff **) *) *) 4 **) *) icient Sig. (2- .61 .000 .343 .001 .120 .606 .880 . .000 .976 .609 .260 .326 .592 .306 .505 .310 .021 .020 .945 .908 .290 .326 .960 tailed 8 ) N 89 89 89 89 89 89 89 80 87 89 89 89 89 89 89 89 89 89 89 89 89 89 89 89 Per Corre - - cwt lation .647( .227( .233( .17 .493( -.114 .338(* -.125 -.014 .053 1.000 .177 .135 .167 .194 .159 .140 .115 .197 -.180 -.156 .166 .115 .113 Coeff **) *) *) 2 **) *) icient Sig. (2- .11 .000 .301 .002 .254 .900 .632 .000 . .106 .219 .037 .128 .075 .145 .203 .293 .032 .070 .100 .154 .129 .295 .305 tailed 6 ) N 85 85 85 85 85 85 80 85 84 85 85 85 85 85 85 85 85 85 85 85 85 85 85 85 Ac Corre - - - ctbl lation .244( .252( .267( - - .129 -.116 -.148 -.003 .177 1.000 -.018 -.142 -.100 -.103 -.111 .208( -.176 -.066 -.087 .220( -.189 .11 -.065 wt Coeff *) *) *) .117 .148 *) *) 3 icient Sig. (2- .28 .019 .222 .272 .015 .160 .010 .976 .106 . .867 .177 .344 .267 .327 .160 .294 .047 .093 .535 .409 .035 .072 .540 tailed 3 ) N 92 92 92 92 92 92 87 84 92 92 92 92 92 92 92 92 92 92 92 92 92 92 92 92 Ag Corre - - - - e lation - .141 -.030 -.074 .091 -.058 .079 .055 .135 -.018 1.000 -.130 -.181 .206 .221( .210( -.039 -.079 .073 .134 -.011 -.103 .19 -.187 Coeff .130 (*) *) *) 9 icient Sig. .176 .772 .480 .385 .578 .450 .609 .219 .867 . .212 .081 .046 .032 .213 .043 .708 .451 .486 .197 .915 .323 .05 .072

283 (2- 5 tailed ) N 94 94 94 94 94 94 89 85 92 94 94 94 94 94 94 94 94 94 94 94 94 94 94 94 Pre Corre - - - .84 TM lation .227( .933(* .907 .821( .887 .876( .778( .723( .775( .698( .806(* .212( -.090 .046 -.050 .124 -.094 .121 -.142 -.130 1.000 .543( .563( 8(** D Coeff *) *) (**) **) (**) **) **) **) **) **) *) *) **) **) ) icient Sig. (2- .00 .041 .390 .658 .635 .233 .368 .260 .037 .177 .212 . .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 tailed 0 ) N 94 94 94 94 94 94 89 85 92 94 94 94 94 94 94 94 94 94 94 94 94 94 94 94 Po Corre - - - .77 stT lation .933( .859 .889( .826 .885( .719( .768( .736( .806( .844(* .242( -.138 .083 -.035 .103 -.085 .105 .167 -.100 -.181 1.000 .520( .615( 8(** MD Coeff **) (**) **) (**) **) **) **) **) **) *) *) **) **) ) icient Sig. (2- .00 .019 .185 .426 .737 .322 .415 .326 .128 .344 .081 .000 . .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 tailed 0 ) N 94 94 94 94 94 94 89 85 92 94 94 94 94 94 94 94 94 94 94 94 94 94 94 94 Pre Corre - - - .79 T lation .907( .859(* 1.00 .881( .778 .789( .633( .629( .654( .620( .771(* -.153 -.086 .057 -.026 .092 .046 .058 .194 -.117 .206( .443( .468( 4(** Coeff **) *) 0 **) (**) **) **) **) **) **) *) *) **) **) ) icient Sig. (2- .00 .140 .410 .584 .800 .380 .658 .592 .075 .267 .046 .000 .000 . .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 tailed 0 ) N 94 94 94 94 94 94 89 85 92 94 94 94 94 94 94 94 94 94 94 94 94 94 94 94 Po Corre - - - - .71 stT lation .821( .889(* .881 1.00 .685 .765( .634( .706( .604( .663( .775(* .225( -.102 .055 .014 .057 .025 .110 .159 -.103 .221( .432( .465( 4(** Coeff **) *) (**) 0 (**) **) **) **) **) **) *) *) *) **) **) ) icient Sig. (2- .00 .029 .326 .602 .890 .585 .813 .306 .145 .327 .032 .000 .000 .000 . .000 .000 .000 .000 .000 .000 .000 .000 .000 tailed 0 ) N 94 94 94 94 94 94 89 85 92 94 94 94 94 94 94 94 94 94 94 94 94 94 94 94 Pre Corre - - .72 D lation .887( .826(* .778 .685( 1.00 .945( .682( .626( .620( .579( .691(* -.149 -.087 -.041 -.030 .136 -.152 .072 .140 -.148 -.130 .394( .450( 6(** Coeff **) *) (**) **) 0 **) **) **) **) **) *) **) **) ) icient Sig. (2- .00 .152 .402 .696 .774 .190 .144 .505 .203 .160 .213 .000 .000 .000 .000 . .000 .000 .000 .000 .000 .000 .000 .000 tailed 0 ) N 94 94 94 94 94 94 89 85 92 94 94 94 94 94 94 94 94 94 94 94 94 94 94 94 Po Corre -.143 -.065 -.027 .016 .113 -.103 .109 .115 -.111 - .876( .885(* .789 .765( .945 1.00 .696( .680( - - .617( .635( .70 .725(*

284 stD lation .210( **) *) (**) **) (**) 0 **) **) .411( .475( **) **) 2(** *) Coeff *) **) **) ) icient Sig. (2- .00 .169 .531 .796 .878 .279 .323 .310 .293 .294 .043 .000 .000 .000 .000 .000 . .000 .000 .000 .000 .000 .000 .000 tailed 0 ) N 94 94 94 94 94 94 89 85 92 94 94 94 94 94 94 94 94 94 94 94 94 94 94 94 Pre Corre - - - - .54 A lation .245( .233( .778( .719(* .633 .634( .682 .696( 1.00 .872( .628( .531( .527(* .251( -.083 .018 -.094 .131 -.189 .208( -.039 .228( .239( 6(** Coeff *) *) **) *) (**) **) (**) **) 0 **) **) **) *) *) *) *) *) ) icient Sig. (2- .00 .015 .428 .863 .368 .207 .068 .021 .032 .047 .708 .000 .000 .000 .000 .000 .000 . .000 .027 .020 .000 .000 .000 tailed 0 ) N 94 94 94 94 94 94 89 85 92 94 94 94 94 94 94 94 94 94 94 94 94 94 94 94 Po Corre - - .51 stA lation .247( .723( .768(* .629 .706( .626 .680( .872( 1.00 .614( .626( .562(* .329( -.116 .009 -.115 .142 -.152 .197 -.176 -.079 -.177 .224( 4(** Coeff *) **) *) (**) **) (**) **) **) 0 **) **) *) **) *) ) icient Sig. (2- .00 .001 .265 .934 .268 .172 .143 .020 .070 .093 .451 .000 .000 .000 .000 .000 .000 .000 . .088 .030 .000 .000 .000 tailed 0 ) N 94 94 94 94 94 94 89 85 92 94 94 94 94 94 94 94 94 94 94 94 94 94 94 94 Pre Corre ------V lation 1.00 .845( .41 .110 .172 -.122 .044 -.042 .073 -.007 -.180 -.066 .073 .543( .520(* .443 .432( .394 .411( .228( -.177 .266( .313( .367(* Coeff 0 **) 0(** **) *) (**) **) (**) **) *) **) **) *) icient ) Sig. (2- .00 .292 .098 .240 .672 .685 .487 .945 .100 .535 .486 .000 .000 .000 .000 .000 .000 .027 .088 . .000 .009 .002 .000 tailed 0 ) N 94 94 94 94 94 94 89 85 92 94 94 94 94 94 94 94 94 94 94 94 94 94 94 94 Po Corre ------stV lation .845( 1.00 .45 .109 .180 -.168 .043 -.025 .029 .012 -.156 -.087 .134 .563( .615(* .468 .465( .450 .475( .239( .224( .338( .403( .426(* Coeff **) 0 2(** **) *) (**) **) (**) **) *) *) **) **) *) icient ) Sig. (2- .00 .294 .082 .106 .682 .813 .782 .908 .154 .409 .197 .000 .000 .000 .000 .000 .000 .020 .030 .000 . .001 .000 .000 tailed 0 ) N 94 94 94 94 94 94 89 85 92 94 94 94 94 94 94 94 94 94 94 94 94 94 94 94 Pre Corre - - - - .63 F lation .775( .736(* .654 .604( .620 .617( .628( .614( 1.00 .812( .643(* .264( .002 .018 -.082 .174 -.120 .113 .166 .220( -.011 .266( .338( 3(** Coeff **) *) (**) **) (**) **) **) **) 0 **) *) *) *) **) **) ) icient Sig. .00 .010 .986 .861 .432 .093 .251 .290 .129 .035 .915 .000 .000 .000 .000 .000 .000 .000 .000 .009 .001 . .000 .000 (2- 0

285 tailed ) N 94 94 94 94 94 94 89 85 92 94 94 94 94 94 94 94 94 94 94 94 94 94 94 94 Po Corre - - - .55 stF lation .698( .806(* .620 .663( .579 .635( .531( .626( .812( 1.00 .667(* .325( -.117 .135 -.062 .148 -.165 .105 .115 -.189 -.103 .313( .403( 8(** Coeff **) *) (**) **) (**) **) **) **) **) 0 *) **) **) **) ) icient Sig. (2- .00 .001 .262 .194 .555 .155 .111 .326 .295 .072 .323 .000 .000 .000 .000 .000 .000 .000 .000 .002 .000 .000 . .000 tailed 0 ) N 94 94 94 94 94 94 89 85 92 94 94 94 94 94 94 94 94 94 94 94 94 94 94 94 Pre Corre - - - C lation .848( .778(* .794 .714( .726 .702( .546( .514( .633( .558( 1.0 .877(* .214( .005 .005 -.033 .081 -.027 .054 .172 -.113 -.199 .410( .452( Coeff **) *) (**) **) (**) **) **) **) **) **) 00 *) *) **) **) icient Sig. (2- .039 .959 .961 .751 .435 .798 .618 .116 .283 .055 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 . .000 tailed ) N 94 94 94 94 94 94 89 85 92 94 94 94 94 94 94 94 94 94 94 94 94 94 94 94 Po Corre - - .87 stC lation .806( .844(* .771 .775( .691 .725( .527( .562( .643( .667( -.192 -.053 .024 -.009 .060 -.053 .005 .113 -.065 -.187 .367( .426( 7(** 1.000 Coeff **) *) (**) **) (**) **) **) **) **) **) **) **) ) icient Sig. (2- .00 .064 .614 .821 .930 .563 .614 .960 .305 .540 .072 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 . tailed 0 ) N 94 94 94 94 94 94 89 85 92 94 94 94 94 94 94 94 94 94 94 94 94 94 94 94 ** Correlation is significant at the 0.01 level (2-tailed). * Correlation is significant at the 0.05 level (2-tailed).

Key: Control=Perceived control. Dislike=dislike of listening condition. Dislikeaccent=dislike of the accent of the speaker. Actualwt=actual wait. Percwt=Perceived wait. Acctblwt=Acceptable wait. Pre=Prior to manipulation. Post=following manipulation. TMD=Total Mood Disturbance, T=Tension-Anxiety, D=Depression-Dejection, A=Anger-Hostility, F=Fatigue-Inertia and C=Confusion- Bewilderment scales

286 Appendix U: ANOVA and Multinomial regression analysis for behavioural measure for Study 3 followed by variability information for button press apparatus.

Univariate Analysis of Variance

Between-Subjects Factors

Value Label N Listening 5.00 22 6.00 26 7.00 27 8.00 23 Accent 1.00 26 2.00 23 3.00 24 4.00 25 Contolans 1 m 34 2 n 35 3 y 29

Descriptive Statistics

Dependent Variable: Controllog =log10 transformation of control question force responses Listening Accent Contolans Mean Std. Deviation N 5.00 1.00 m 2.5051 . 1 y 2.4535 .14878 2 Total 2.4707 .10935 3 2.00 m 2.2342 .19723 3 n 2.1004 . 1 Total 2.2007 .17439 4 3.00 m 2.3151 .52415 2 n 2.2876 .13357 2 y 2.1409 .17521 2 Total 2.2479 .26771 6 4.00 m 2.3024 .26180 4 n 2.1452 .11837 3 y 2.2632 .05354 2 Total 2.2413 .18712 9 Total m 2.3048 .26109 10 n 2.1852 .12555 6 y 2.2859 .17604 6 Total 2.2670 .20746 22 6.00 1.00 m 2.0446 .45265 3 n 2.2669 .23969 5 y 1.9912 . 1 Total 2.1621 .30925 9 2.00 m 2.1492 . 1 n 1.9823 . 1

287 y 2.1963 .23586 3 Total 2.1441 .19082 5 3.00 m 2.4188 .15748 2 n 2.0538 .04608 2 y 2.7316 . 1 Total 2.3354 .29851 5 4.00 m 2.1790 . 1 n 2.2953 .12515 2 y 2.3888 .08345 4 Total 2.3321 .11221 7 Total m 2.1857 .31739 7 n 2.2015 .20501 10 y 2.3185 .24354 9 Total 2.2377 .24884 26 7.00 1.00 m 2.4004 .07918 2 n 2.2688 .43812 2 y 2.2595 .27176 5 Total 2.2929 .25580 9 2.00 m 2.5119 . 1 n 2.3732 .15175 4 y 2.4065 . 1 Total 2.4019 .12999 6 3.00 m 2.5831 .18792 2 n 2.4782 .05510 3 y 2.4374 .58867 2 Total 2.4965 .26170 7 4.00 m 2.2630 .07190 2 n 2.2788 . 1 y 2.1759 .07356 2 Total 2.2313 .07244 5 Total m 2.4293 .16220 7 n 2.3744 .19174 10 y 2.2931 .28578 10 Total 2.3585 .22397 27 8.00 1.00 m 2.5642 .45470 4 n 2.2833 . 1 Total 2.5080 .41333 5 2.00 m 2.1592 .17127 3 n 2.3566 .47100 3 y 2.1039 .34017 2 Total 2.2194 .31896 8 3.00 m 2.2619 .05538 2 n 2.1984 .18099 4 Total 2.2196 .14609 6 4.00 m 2.6294 . 1 n 2.2380 . 1 y 2.3606 .28181 2 Total 2.3972 .23191 4 Total m 2.3887 .34210 10 n 2.2650 .27063 9 y 2.2322 .29500 4 288 Total 2.3131 .30185 23 Total 1.00 m 2.3696 .41163 10 n 2.2694 .24553 8 y 2.2744 .25719 8 Total 2.3095 .31425 26 2.00 m 2.2301 .18422 8 n 2.2939 .29247 9 y 2.2005 .24007 6 Total 2.2474 .23790 23 3.00 m 2.3947 .25580 8 n 2.2646 .19200 11 y 2.3777 .39425 5 Total 2.3316 .25878 24 4.00 m 2.3180 .21847 8 n 2.2204 .11224 7 y 2.3154 .14087 10 Total 2.2897 .16265 25 Total m 2.3306 .28523 34 n 2.2644 .21503 35 y 2.2911 .24248 29 Total 2.2953 .24828 98

Levene's Test of Equality of Error Variances(a)

Dependent Variable: Controllog =log10 transformation of control question force responses

F df1 df2 Sig. 2.556 43 54 .001 Tests the null hypothesis that the error variance of the dependent variable is equal across groups. a Design: Intercept+Listening+Accent+Contolans+Listening * Accent+Listening * Contolans+Accent * Contolans+Listening * Accent * Contolans

Tests of Between-Subjects Effects

Dependent Variable: Controllog =log10 transformation of control question force responses

Type III Sum Source of Squares df Mean Square F Sig. Corrected Model 2.125(a) 43 .049 .692 .893 Intercept 373.739 1 373.739 5236.145 .000 Listening .227 3 .076 1.062 .373 Accent .123 3 .041 .575 .634 Contolans .110 2 .055 .770 .468 Listening * Accent .680 9 .076 1.059 .408 Listening * Contolans .192 6 .032 .447 .844 Accent * Contolans .081 6 .013 .189 .979 Listening * Accent * Contolans .611 14 .044 .611 .844 Error 3.854 54 .071 Total 522.267 98 Corrected Total 5.979 97 a R Squared = .355 (Adjusted R Squared = -.158) 289

Univariate Analysis of Variance

Key: m = maybe, n = no, y = yes.

Between-Subjects Factors

Value Label N Listening 5.00 22 6.00 26 7.00 27 8.00 23 Accent 1.00 26 2.00 23 3.00 24 4.00 25 Frustans 1 m 7 2 n 73 3 y 18

Descriptive Statistics

Dependent Variable: Frustlog =log10 transformation of frustration question force responses

Listening Accent Frustans Mean Std. Deviation N 5.00 1.00 n 2.1139 . 1 y 2.2870 .03804 2 Total 2.2293 .10345 3 2.00 n 2.0537 .12550 4 Total 2.0537 .12550 4 3.00 n 2.0863 .16030 4 y 2.3884 .36492 2 Total 2.1870 .25766 6 4.00 m 1.9777 . 1 n 2.1158 .15021 6 y 2.3280 .14160 2 Total 2.1476 .17062 9 Total m 1.9777 . 1 n 2.0912 .13283 15 y 2.3344 .18170 6 Total 2.1524 .18226 22 6.00 1.00 m 1.8451 . 1 n 2.0339 .36808 5 y 2.3065 .20059 3 Total 2.1038 .32346 9 2.00 m 1.8976 . 1 n 2.0534 .20781 3 y 1.9494 . 1 Total 2.0015 .16429 5 3.00 m 2.1790 .25310 2 n 2.2401 .25569 3 290 Total 2.2157 .22321 5 4.00 n 2.1494 .18428 6 y 2.0864 . 1 Total 2.1404 .16990 7 Total m 2.0252 .23098 4 n 2.1145 .25337 17 y 2.1910 .21784 5 Total 2.1155 .23985 26 7.00 1.00 n 2.0891 .29794 9 Total 2.0891 .29794 9 2.00 n 2.0721 .15627 6 Total 2.0721 .15627 6 3.00 n 2.2316 .32580 7 Total 2.2316 .32580 7 4.00 m 1.7404 . 1 n 2.0854 .06555 4 Total 2.0164 .16440 5 Total m 1.7404 . 1 n 2.1230 .25268 26 Total 2.1088 .25849 27 8.00 1.00 n 2.2398 .35056 5 Total 2.2398 .35056 5 2.00 n 2.0529 .31467 6 y 2.0153 .20657 2 Total 2.0435 .27772 8 3.00 n 2.1503 .23129 2 y 2.1071 .22860 4 Total 2.1215 .20628 6 4.00 m 2.0128 . 1 n 2.0344 .33246 2 y 1.9243 . 1 Total 2.0015 .19899 4 Total m 2.0128 . 1 n 2.1257 .30066 15 y 2.0547 .19602 7 Total 2.0992 .26354 23 Total 1.00 m 1.8451 . 1 n 2.1142 .31292 20 y 2.2987 .14351 5 Total 2.1393 .29455 26 2.00 m 1.8976 . 1 n 2.0592 .20443 19 y 1.9933 .15094 3 Total 2.0436 .19439 23 3.00 m 2.1790 .25310 2 n 2.1867 .25349 16 y 2.2008 .28122 6 Total 2.1896 .24887 24 4.00 m 1.9103 .14822 3 n 2.1112 .15893 18 y 2.1667 .21394 4 Total 2.0960 .17538 25 Total m 1.9759 .19434 7 n 2.1150 .24002 73 291 y 2.1858 .22207 18 Total 2.1181 .23676 98

Levene's Test of Equality of Error Variances(a)

Dependent Variable: Frustlog = log10 transformation of frustration question force responses

F df1 df2 Sig. 1.335 30 67 .164 Tests the null hypothesis that the error variance of the dependent variable is equal across groups. a Design: Intercept+Listening+Accent+Frustans+Listening * Accent+Listening * Frustans+Accent * Frustans+Listening * Accent * Frustans

Tests of Between-Subjects Effects

Dependent Variable: Frustlog = log10 transformation of frustration question force responses

Type III Sum Source of Squares df Mean Square F Sig. Corrected Model 1.140(a) 30 .038 .593 .942 Intercept 184.254 1 184.254 2872.686 .000 Listening .037 3 .012 .191 .902 Accent .221 3 .074 1.147 .337 Frustans .154 2 .077 1.203 .307 Listening * Accent .170 9 .019 .294 .974 Listening * Frustans .132 4 .033 .516 .724 Accent * Frustans .075 5 .015 .234 .946 Listening * Accent * Frustans .059 3 .020 .307 .820 Error 4.297 67 .064 Total 445.105 98 Corrected Total 5.438 97 a R Squared = .210 (Adjusted R Squared = -.144)

Univariate Analysis of Variance

Between-Subjects Factors

Value Label N Listening 5.00 15 6.00 20 7.00 18 8.00 17 Accent 1.00 18 2.00 16 3.00 17 4.00 19 satans 1.00 m 7 2.00 n 14 3.00 y 49

292

Descriptive Statistics

Dependent Variable: Satlog = log10 transformation of satisfaction question force responses

Listening Accent satans Mean Std. Deviation N 5.00 1.00 n 2.2201 . 1 y 2.2833 . 1 Total 2.2517 .04468 2 2.00 y 1.6532 . 1 Total 1.6532 . 1 3.00 m 2.1644 . 1 n 1.9905 .24171 2 y 1.4314 . 1 Total 1.8942 .34840 4 4.00 n 2.0129 .14763 2 y 1.9209 .24562 6 Total 1.9439 .21912 8 Total m 2.1644 . 1 n 2.0454 .17240 5 y 1.8771 .30145 9 Total 1.9523 .26526 15 6.00 1.00 m 2.2504 . 1 n 1.8976 .16892 2 y 2.0515 .32405 5 Total 2.0379 .27621 8 2.00 n 2.0170 . 1 y 1.9479 .14676 3 Total 1.9652 .12471 4 3.00 m 2.1732 . 1 n 2.1239 . 1 y 2.1832 .01410 2 Total 2.1658 .02953 4 4.00 m 2.3634 .21004 2 n 2.1430 . 1 y 1.8129 . 1 Total 2.1707 .28701 4 Total m 2.2876 .15283 4 n 2.0158 .14521 5 y 2.0255 .24076 11 Total 2.0755 .22470 20 7.00 1.00 m 2.3784 . 1 y 2.0965 .49745 5 Total 2.1435 .45957 6 2.00 y 2.0157 .20057 5 Total 2.0157 .20057 5 3.00 y 2.3511 .18125 3 Total 2.3511 .18125 3 4.00 y 1.8695 .17314 4 Total 1.8695 .17314 4 Total m 2.3784 . 1 293 y 2.0643 .32805 17 Total 2.0817 .32676 18 8.00 1.00 y 2.3461 .76350 2 Total 2.3461 .76350 2 2.00 m 1.9590 . 1 n 2.6149 . 1 y 1.9216 .15025 4 Total 2.0434 .30359 6 3.00 n 2.1236 .05768 2 y 1.8855 .41573 4 Total 1.9649 .34565 6 4.00 n 1.8808 . 1 y 2.0089 .05709 2 Total 1.9662 .08423 3 Total m 1.9590 . 1 n 2.1857 .30995 4 y 1.9948 .36789 12 Total 2.0376 .34395 17 Total 1.00 m 2.3144 .09049 2 n 2.0051 .22122 3 y 2.1320 .42289 13 Total 2.1311 .37314 18 2.00 m 1.9590 . 1 n 2.3160 .42275 2 y 1.9432 .17890 13 Total 1.9908 .23161 16 3.00 m 2.1688 .00625 2 n 2.0704 .14408 5 y 2.0393 .39180 10 Total 2.0637 .30546 17 4.00 m 2.3634 .21004 2 n 2.0124 .13684 4 y 1.9103 .18953 13 Total 1.9795 .22269 19 Total m 2.2360 .17456 7 n 2.0749 .20799 14 y 2.0042 .31392 49 Total 2.0415 .29053 70

Levene's Test of Equality of Error Variances(a)

Dependent Variable: Satlog = log10 transformation of satisfaction question force responses

F df1 df2 Sig. 2.027 31 38 .020 Tests the null hypothesis that the error variance of the dependent variable is equal across groups. a Design: Intercept+Listening+Accent+satans+Listening * Accent+Listening * satans+Accent * satans+Listening * Accent * satans

Tests of Between-Subjects Effects

Dependent Variable: Satlog = log10 transformation of satisfaction question force responses

294 Type III Sum Source of Squares df Mean Square F Sig. Corrected Model 2.424(a) 31 .078 .874 .647 Intercept 134.665 1 134.665 1505.115 .000 Listening .134 3 .045 .501 .684 Accent .153 3 .051 .570 .638 satans .387 2 .193 2.163 .129 Listening * Accent .964 9 .107 1.197 .325 Listening * satans .141 4 .035 .393 .812 Accent * satans .274 5 .055 .613 .690 Listening * Accent * satans .260 4 .065 .727 .579 Error 3.400 38 .089 Total 297.567 70 Corrected Total 5.824 69 a R Squared = .416 (Adjusted R Squared = -.060)

295 Multinomial regressions for answers on the behavioural apparatus

Nominal Regression for Question 1: “Do you prefer 2B pencils over HB pencils?”

Case Processing Summary

Marginal N Percentage Control maybe 34 34.7% Question no 35 35.7% Answer yes 29 29.6% Listening comedy 22 22.4% pop 26 26.5% straight through 27 27.6% choice 23 23.5% Accent American 26 26.5% Indian 23 23.5% New Zealand 24 24.5% Filipino 25 25.5% Gender female 75 76.5% male 23 23.5% Valid 98 100.0% Missing 30 Total 128 Subpopulation 66(a) a The dependent variable has only one value observed in 51 (77.3%) subpopulations.

296

Model Fitting Information

Model Fitting Criteria Likelihood Ratio Tests

-2 Log Model Likelihood Chi-Square df Sig. Intercept Only 177.251 Final 168.302 8.950 16 .915

Pseudo R-Square Cox and Snell .087 Nagelkerke .098 McFadden .042

Likelihood Ratio Tests

Model Fitting Criteria Likelihood Ratio Tests -2 Log Likelihood of Reduced Effect Model Chi-Square df Sig. Intercept 168.302(a) .000 0 . Age 168.816 .515 2 .773 Listening 173.762 5.460 6 .486 Accent 171.653 3.351 6 .764 Gender 168.341 .039 2 .980 The chi-square statistic is the difference in -2 log-likelihoods between the final model and a reduced model. The reduced model is formed by omitting an effect from the final model. The null hypothesis is that all parameters of that effect are 0. a This reduced model is equivalent to the final model because omitting the effect does not increase the degrees of freedom.

297

Parameter Estimates 95% Confidence Interval for Control Exp(B) Question Answer(a) B Std. Error Wald df Sig. Exp(B) Lower Bound Upper Bound maybe Intercept .718 1.729 .173 1 .678 Age -.015 .067 .049 1 .825 .985 .865 1.123 [comedy ] -.319 .826 .149 1 .700 .727 .144 3.674 [pop] -1.154 .797 2.097 1 .148 .315 .066 1.504 [Straight-through] -1.335 .787 2.878 1 .090 .263 .056 1.230 [Choice] 0(b) . . 0 . . . . [American] .651 .728 .800 1 .371 1.917 .460 7.979 [Indian] .481 .764 .397 1 .529 1.618 .362 7.225 [New Zealand] .732 .768 .909 1 .340 2.080 .462 9.373 [Filipino] 0(b) . . 0 . . . . [Female] .057 .617 .009 1 .926 1.059 .316 3.547 [male] 0(b) . . 0 . . . . no Intercept 1.245 1.879 .439 1 .507 Age -.052 .077 .448 1 .503 .950 .817 1.105 [comedy ] -.796 .875 .827 1 .363 .451 .081 2.509 [pop] -.603 .783 .592 1 .442 .547 .118 2.540 [Straight-through] -.834 .771 1.170 1 .279 .435 .096 1.967 [Choice] 0(b) . . 0 . . . . [American] .267 .734 .132 1 .716 1.306 .310 5.502 [Indian] .605 .748 .655 1 .418 1.832 .423 7.930 [New Zealand] 1.103 .744 2.197 1 .138 3.013 .701 12.956 [Filipino] 0(b) . . 0 . . . . [Female] .121 .614 .039 1 .843 1.129 .339 3.762 [male] 0(b) . . 0 . . . . a The reference category is: yes. b This parameter is set to zero because it is redundant.

298

Nominal Regression for Question 2: “Were you frustrated by your phone call to Psychnet?”

Warnings There is possibly a quasi-complete separation in the data. Either the maximum likelihood estimates do not exist or some parameter estimates are infinite. The NOMREG procedure continues despite the above warning(s). Subsequent results shown are based on the last iteration. Validity of the model fit is uncertain.

Case Processing Summary Marginal N Percentage Frustration maybe 7 7.1% Question no 73 74.5% Answer yes 18 18.4% Listening comedy 22 22.4% pop 26 26.5% straight through 27 27.6% choice 23 23.5% Accent American 26 26.5% Indian 23 23.5% New Zealand 24 24.5% Filipino 25 25.5% Gender female 75 76.5% male 23 23.5% Valid 98 100.0% Missing 30 Total 128 Subpopulation 66(a)

299 a The dependent variable has only one value observed in 56 (84.8%) subpopulations.

Model Fitting Information Model Fitting Criteria Likelihood Ratio Tests

-2 Log Model Likelihood Chi-Square df Sig. Intercept Only 118.428 Final 91.935 26.493 16 .047

Pseudo R-Square Cox and Snell .237 Nagelkerke .311 McFadden .188

Likelihood Ratio Tests

Model Fitting Criteria Likelihood Ratio Tests -2 Log Likelihood of Reduced Effect Model Chi-Square df Sig. Intercept 91.935(a) .000 0 . Age 96.880 4.945 2 .084 Listening 111.535 19.601 6 .003 Accent 96.513 4.578 6 .599 Gender 92.376 .442 2 .802 The chi-square statistic is the difference in -2 log-likelihoods between the final model and a reduced model. The reduced model is formed by omitting an effect from the final model. The null hypothesis is that all parameters of that effect are 0.

300 a This reduced model is equivalent to the final model because omitting the effect does not increase the degrees of freedom.

Parameter Estimates Frustration Question Answer(a) B Std. Error Wald df Sig. Exp(B) 95% Confidence Interval for Exp(B)

Lower Bound Upper Bound Lower Bound Upper Bound Lower Bound Upper Bound Lower Bound Upper Bound maybe Intercept 1.958 8.789 .050 1 .824 Age -.148 .462 .102 1 .749 .863 .349 2.135 [comedy ] -.024 1.595 .000 1 .988 .976 .043 22.238 [pop] 1.914 1.332 2.065 1 .151 6.778 .498 92.178 [Straight-through] 945285091.2 18585119670.94 20.667 1.520 184.934 1 .000 48079534.574 69 2 [Choice] 0(b) . . 0 . . . . American -1.820 1.413 1.660 1 .198 .162 .010 2.583 Indian -.828 1.457 .323 1 .570 .437 .025 7.588 New Zealand -.771 1.230 .393 1 .531 .463 .042 5.150 Filipino 0(b) . . 0 . . . . [Female] -.534 1.093 .239 1 .625 .586 .069 4.991 [Male] 0(b) . . 0 . . . . no Intercept -5.948 5.234 1.291 1 .256 Age .347 .271 1.636 1 .201 1.414 .831 2.406 [comedy ] .373 .725 .265 1 .607 1.452 .351 6.005 [pop] .513 .730 .494 1 .482 1.670 .399 6.982 [Straight-through] 1705186209. 21.257 .000 . 1 . 1705186209.046 1705186209.046 046 [Choice] 0(b) . . 0 . . . . American -.418 .849 .243 1 .622 .658 .125 3.475 Indian .407 .908 .201 1 .654 1.502 .253 8.903 New Zealand -.802 .836 .921 1 .337 .448 .087 2.308 Filipino 0(b) . . 0 . . . . [Female] .133 .711 .035 1 .852 1.142 .283 4.606 [Male] 0(b) . . 0 . . . . a The reference category is: yes.

301 b This parameter is set to zero because it is redundant. Nominal Regression for Question 3: “Were you satisfied with your phone call to Psychnet?”

Warnings There is possibly a quasi-complete separation in the data. Either the maximum likelihood estimates do not exist or some parameter estimates are infinite. The NOMREG procedure continues despite the above warning(s). Subsequent results shown are based on the last iteration. Validity of the model fit is uncertain.

Case Processing Summary Marginal N Percentage Satisfaction maybe 7 10.0% Question no 14 20.0% answer yes 49 70.0% Listening comedy 15 21.4% pop 20 28.6% straight through 18 25.7% choice 17 24.3% Accent American 18 25.7% Indian 16 22.9% New Zealand 17 24.3% Filipino 19 27.1% Gender female 50 71.4% male 20 28.6% Valid 70 100.0% Missing 58 Total 128 Subpopulation 53(a) a The dependent variable has only one value observed in 49 (92.5%) subpopulations.

302

Model Fitting Information Model Fitting Criteria Likelihood Ratio Tests

-2 Log Model Likelihood Chi-Square df Sig. Intercept Only 102.890 Final 79.273 23.618 16 .098

Pseudo R-Square Cox and Snell .286 Nagelkerke .358 McFadden .210

Likelihood Ratio Tests

Model Fitting Criteria Likelihood Ratio Tests -2 Log Likelihood of Reduced Effect Model Chi-Square df Sig. Intercept 79.273(a) .000 0 . Age 86.889 7.617 2 .022 Listening 89.435 10.162 6 .118 Accent 81.069 1.796 6 .937 Gender 80.381 1.109 2 .574 The chi-square statistic is the difference in -2 log-likelihoods between the final model and a reduced model. The reduced model is formed by omitting an effect from the final model. The null hypothesis is that all parameters of that effect are 0. a This reduced model is equivalent to the final model because omitting the effect does not increase the degrees of freedom.

303

Parameter Estimates Satisfacti 95% Confidence Interval for on Exp(B) question answer(a ) B Std. Error Wald df Sig. Exp(B) Lower Bound Upper Bound maybe Intercept -15.026 7.808 3.704 1 .054 Age .615 .361 2.906 1 .088 1.850 .912 3.754 [comedy ] 1.121 1.709 .430 1 .512 3.068 .108 87.435 [pop] 1.362 1.340 1.033 1 .310 3.904 .282 54.004 [Straight-through] -.088 1.558 .003 1 .955 .916 .043 19.413 [Choice] 0(b) . . 0 . . . . American 1.152 1.605 .515 1 .473 3.164 .136 73.521 Indian .425 1.716 .061 1 .805 1.529 .053 44.180 New Zealand 1.209 1.521 .631 1 .427 3.350 .170 66.097 Filipino 0(b) . . 0 . . . . [Female] -.969 1.061 .834 1 .361 .379 .047 3.034 [Male] 0(b) . . 0 . . . . no Intercept -7.428 5.656 1.725 1 .189 Age .318 .284 1.255 1 .263 1.375 .788 2.400 [comedy ] .474 .911 .271 1 .603 1.606 .270 9.570 [pop] .349 .864 .163 1 .686 1.418 .261 7.708 [Straight-through] -20.421 .000 . 1 . 1.35E-009 1.35E-009 1.35E-009 [Choice] 0(b) . . 0 . . . . American -.187 1.024 .033 1 .855 .829 .111 6.171 Indian -.574 1.083 .281 1 .596 .563 .067 4.703 New Zealand .353 .913 .150 1 .699 1.424 .238 8.524 Filipino 0(b) . . 0 . . . . [Female] .276 .843 .107 1 .743 1.318 .253 6.877 [Male] 0(b) . . 0 . . . . a The reference category is: yes.

304 b This parameter is set to zero because it is redundant

305

Variability information for button press apparatus.

Key:

Right, left and middle indicate which button was being tested.

Rightlog, Leftlog, and middlelog are log (base 10) transformations of the output from each button.

Explore Case Processing Summary Cases Valid Missing Total N Percent N Percent N Percent Leftbutton 100 100.0% 0 .0% 100 100.0% Middlebutton 100 100.0% 0 .0% 100 100.0% Rightbutton 100 100.0% 0 .0% 100 100.0%

Descriptives Statistic Std. Error Leftbutton Mean 22.3300 .57893 95% Confidence Lower Bound 21.1813 Interval for Mean Upper Bound 23.4787

5% Trimmed Mean 21.7889 Median 21.0000 Variance 33.516 Std. Deviation 5.78932 Minimum 13.00 Maximum 58.00 Range 45.00 Interquartile Range 5.75 Skewness 2.894 .241 Kurtosis 14.497 .478 Middlebutton Mean 20.8300 .43902 95% Confidence Lower Bound 19.9589 Interval for Mean Upper Bound 21.7011

5% Trimmed Mean 20.9333 Median 21.0000 Variance 19.274 Std. Deviation 4.39020 Minimum 2.00

306 Maximum 32.00 Range 30.00 Interquartile Range 4.00 Skewness -.655 .241 Kurtosis 2.570 .478 Rightbutton Mean 24.6300 1.05444 95% Confidence Lower Bound 22.5378 Interval for Mean Upper Bound 26.7222

5% Trimmed Mean 23.8333 Median 21.0000 Variance 111.185 Std. Deviation 10.54443 Minimum 7.00 Maximum 68.00 Range 61.00 Interquartile Range 14.75 Skewness 1.358 .241 Kurtosis 2.661 .478

M-Estimators Huber's M- Tukey's Hampel's M- Andrews' Estimator(a) Biweight(b) Estimator(c) Wave(d) Leftbutton 21.3747 21.2154 21.4463 21.2121 Middlebutton 20.9415 20.9984 20.9913 21.0000 Rightbutton 22.6796 22.1511 22.9117 22.1362 a The weighting constant is 1.339. b The weighting constant is 4.685. c The weighting constants are 1.700, 3.400, and 8.500 d The weighting constant is 1.340*pi.

Extreme Values Case Number Value Leftbutton Highest 1 1 58.00 2 13 42.00 3 36 34.00 4 2 31.00 5 3 31.00 Lowest 1 68 13.00 2 49 15.00 3 94 16.00 4 87 16.00 5 76 16.00 Middlebutton Highest 1 2 32.00 2 1 29.00 3 6 29.00 4 5 28.00 5 4 27.00(a) Lowest 1 23 2.00 2 98 12.00 3 89 13.00 307 4 82 13.00 5 50 14.00(b) Rightbutton Highest 1 1 68.00 2 72 60.00 3 77 48.00 4 3 47.00 5 8 41.00(c) Lowest 1 75 7.00 2 100 11.00 3 65 12.00 4 64 12.00 5 55 12.00 a Only a partial list of cases with the value 27.00 are shown in the table of upper extremes. b Only a partial list of cases with the value 14.00 are shown in the table of lower extremes. c Only a partial list of cases with the value 41.00 are shown in the table of upper extremes.

Leftbutton

Leftbutton Stem-and-Leaf Plot

Frequency Stem & Leaf

1.00 13 . 0 .00 14 . 1.00 15 . 0 3.00 16 . 000 5.00 17 . 00000 13.00 18 . 0000000000000 12.00 19 . 000000000000 5.00 20 . 00000 18.00 21 . 000000000000000000 4.00 22 . 0000 8.00 23 . 00000000 5.00 24 . 00000 4.00 25 . 0000 2.00 26 . 00 4.00 27 . 0000 7.00 28 . 0000000 2.00 29 . 00 1.00 30 . 0 2.00 31 . 00 3.00 Extremes (>=34.0)

Stem width: 1.00 Each leaf: 1 case(s)

Middlebutton

Middlebutton Stem-and-Leaf Plot

Frequency Stem & Leaf

308 2.00 Extremes (=<12.0) 2.00 13 . 00 3.00 14 . 000 5.00 15 . 00000 3.00 16 . 000 3.00 17 . 000 4.00 18 . 0000 11.00 19 . 00000000000 12.00 20 . 000000000000 16.00 21 . 0000000000000000 6.00 22 . 000000 9.00 23 . 000000000 5.00 24 . 00000 3.00 25 . 000 6.00 26 . 000000 6.00 27 . 000000 1.00 28 . 0 2.00 29 . 00 1.00 Extremes (>=32.0)

Stem width: 1.00 Each leaf: 1 case(s)

Rightbutton

Rightbutton Stem-and-Leaf Plot

Frequency Stem & Leaf

1.00 0 . 7 12.00 1 . 122233344444 21.00 1 . 555556667777788889999 28.00 2 . 0000000011111111112222333344 9.00 2 . 555667779 9.00 3 . 001122223 12.00 3 . 555666788889 4.00 4 . 0011 2.00 4 . 78 2.00 Extremes (>=60)

Stem width: 10.00 Each leaf: 1 case(s)

EXAMINE VARIABLES=leftlog middlelog rightlog /PLOT BOXPLOT STEMLEAF NPPLOT /COMPARE GROUP /MESTIMATORS HUBER(1.339) ANDREW(1.34) HAMPEL(1.7,3.4,8.5) TUKEY(4.685) /STATISTICS DESCRIPTIVES EXTREME /CINTERVAL 95 309 /MISSING LISTWISE /NOTOTAL.

Explore

Notes Output Created 16-SEP-2010 17:02:15 Comments Input Active Dataset DataSet0 Filter Weight Split File N of Rows in Working Data File 100 Missing Value Definition of Missing User-defined missing values for Handling dependent variables are treated as missing. Cases Used Statistics are based on cases with no missing values for any dependent variable or factor used. Syntax EXAMINE VARIABLES=leftlog middlelog rightlog /PLOT BOXPLOT STEMLEAF NPPLOT /COMPARE GROUP /MESTIMATORS HUBER(1.339) ANDREW(1.34) HAMPEL(1.7,3.4,8.5) TUKEY(4.685) /STATISTICS DESCRIPTIVES EXTREME /CINTERVAL 95 /MISSING LISTWISE /NOTOTAL.

Resources Processor Time

0:00:01.78

Elapsed Time 0:00:02.09

Case Processing Summary Cases Valid Missing Total N Percent N Percent N Percent leftlog 100 100.0% 0 .0% 100 100.0% middlelog 100 100.0% 0 .0% 100 100.0% 310 rightlog 100 100.0% 0 .0% 100 100.0%

Descriptives Statistic Std. Error leftlog Mean 1.3377 .00946 95% Confidence Lower Bound 1.3189 Interval for Mean Upper Bound 1.3564

5% Trimmed Mean 1.3329 Median 1.3222 Variance .009 Std. Deviation .09458 Minimum 1.11 Maximum 1.76 Range .65 Interquartile Range .11 Skewness 1.189 .241 Kurtosis 3.601 .478 middlelog Mean 1.3045 .01322 95% Confidence Lower Bound 1.2782 Interval for Mean Upper Bound 1.3307

5% Trimmed Mean 1.3154 Median 1.3222 Variance .017 Std. Deviation .13220 Minimum .30 Maximum 1.51 Range 1.20 Interquartile Range .08 Skewness -4.538 .241 Kurtosis 33.064 .478 rightlog Mean 1.3561 .01744 95% Confidence Lower Bound 1.3215 Interval for Mean Upper Bound 1.3907

5% Trimmed Mean 1.3546 Median 1.3222 Variance .030 Std. Deviation .17439 Minimum .85 Maximum 1.83 Range .99 Interquartile Range .27 Skewness .152 .241 Kurtosis .068 .478

311

M-Estimators Huber's M- Tukey's Hampel's M- Andrews' Estimator(a) Biweight(b) Estimator(c) Wave(d) leftlog 1.3261 1.3248 1.3274 1.3248 middlelog 1.3205 1.3255 1.3223 1.3256 rightlog 1.3465 1.3452 1.3508 1.3452 a The weighting constant is 1.339. b The weighting constant is 4.685. c The weighting constants are 1.700, 3.400, and 8.500 d The weighting constant is 1.340*pi.

Extreme Values

Case Number Value leftlog Highest 1 1 1.76 2 13 1.62 3 36 1.53 4 2 1.49 5 3 1.49 Lowest 1 68 1.11 2 49 1.18 3 94 1.20 4 87 1.20 5 76 1.20 middlelog Highest 1 2 1.51 2 1 1.46 3 6 1.46 4 5 1.45 5 4 1.43(a) Lowest 1 23 .30 2 98 1.08 3 89 1.11 4 82 1.11 5 50 1.15(b) rightlog Highest 1 1 1.83 2 72 1.78 3 77 1.68 4 3 1.67 5 8 1.61(c) Lowest 1 75 .85 2 100 1.04 3 65 1.08 4 64 1.08 5 55 1.08 a Only a partial list of cases with the value 1.43 are shown in the table of upper extremes. b Only a partial list of cases with the value 1.15 are shown in the table of lower extremes. c Only a partial list of cases with the value 1.61 are shown in the table of upper extremes.

312

Tests of Normality Kolmogorov-Smirnov(a) Shapiro-Wilk Statistic df Sig. Statistic df Sig. leftlog .145 100 .000 .927 100 .000 middlelog .203 100 .000 .665 100 .000 rightlog .097 100 .021 .985 100 .293 a Lilliefors Significance Correction

leftlog leftlog Stem-and-Leaf Plot

Frequency Stem & Leaf

1.00 11 . 1 1.00 11 . 7 8.00 12 . 00033333 25.00 12 . 5555555555555777777777777 27.00 13 . 000002222222222222222224444 17.00 13 . 66666666888889999 13.00 14 . 1133334444444 5.00 14 . 66799 1.00 15 . 3 2.00 Extremes (>=1.62)

Stem width: .10 Each leaf: 1 case(s)

Middlelog

middlelog Stem-and-Leaf Plot

Frequency Stem & Leaf

7.00 Extremes (=<1.15) 5.00 11 . 77777 .00 11 . 3.00 12 . 000 3.00 12 . 333 4.00 12 . 5555 11.00 12 . 77777777777 .00 12 . 12.00 13 . 000000000000 16.00 13 . 2222222222222222 6.00 13 . 444444 9.00 13 . 666666666 8.00 13 . 88888999 6.00 14 . 111111 6.00 14 . 333333 1.00 14 . 4 2.00 14 . 66 1.00 Extremes (>=1.51)

313 Stem width: .10 Each leaf: 1 case(s)

rightlog rightlog Stem-and-Leaf Plot

Frequency Stem & Leaf

1.00 8 . 4 .00 9 . 4.00 10 . 4777 13.00 11 . 1114444477777 16.00 12 . 0003333355557777 31.00 13 . 0000000022222222224444666688999 10.00 14 . 1133367799 17.00 15 . 00001444555677779 6.00 16 . 001178 1.00 17 . 7 1.00 18 . 3

Stem width: .10 Each leaf: 1 case(s)

314 Appendix V: Accent appraisal questionnaire for “Study 3a: Accent Appraisal”.

315

316 Appendix W: Information sheet and consent form for Study 3a.

Accent Appraisal

INFORMATION SHEET

Thank you for showing an interest in this project. Please take some time to read this information sheet before deciding whether or not to participate. If you decide to participate, thank you. If not, we thank you for considering our request.

What is the Aim of the Project?

The major aim of the project is to examine how appraisals of several speakers differ according to their accents.

What Type of Participants are Being Sought?

Otago University and Polytechnic students who attended a New Zealand high school.

What will Participants be Asked to Do?

Should you agree to participate, you will be asked to listen to vocal excerpts from four speakers and fill out a questionnaire regarding the attributes of each speaker.

Can Participants Change their Mind and Withdraw from the Project?

You may withdraw at any time and without any disadvantage to yourself.

What Data or Information will be Collected and What Use will be Made of it?

In this project we are collecting data regarding your appraisals of four speakers with different accents. Questions relate to the competence, intelligibility, likeability, age, country of origin and aural pleasantness of each speaker. All data is kept completely anonymous.

Results of this project may be published, but any data included will be in no way linked to any specific participant. The data are collected and stored without identifying information. You are welcome to request a copy of the results, should you wish to.

What if Participants have any Questions?

Any questions about the project, now or in the future, please feel free to contact either:

Isaac Malpass Dr Louis Leland Jr [email protected] [email protected] Ph: (03) 479 5779 (university office) Ph: (03) 479 7638 (university office)

This project has been reviewed and approved by the Psychology Department on behalf of the Ethics Committee of the University of Otago.

317

Accent Appraisal

Consent Form

I have read the Information Sheet concerning this project and understand what the experiment entails. All of my questions have been answered to my satisfaction. I understand that I am free to request further information at any stage.

I know that:

1. My participation in the project is entirely voluntary.

2. I am free to withdraw from the project at any time without any disadvantage.

3. The data are collected and stored without any identifying information

I agree to take part in this project.

………………………………………… ……………………………….. (Participant signature) (Date)

This project has been reviewed and approved by the Psychology Dept. on behalf of the Ethics Committee of the University of Otago

Any questions about project, now or in the future, please feel free to contact:

Isaac Malpass Dr Louis Leland Jr Malis188@Student .otago.ac.nz [email protected] Ph: 03 479 5779 (university office) Ph: (03) 479 7638 (university office)

318 Appendix X : Songs, artists, and albums of music played to University students in “Study 4: Undergraduates self reported music preferences in an on-hold situation vs. a public social situation”.

319 Appendix Y: Music rating sheet for the university undergraduates to fill in Study 4.

Music Rating Sheet (University)

Thank you for participating in this research project. Filling in this rating sheet implies that you are allowing us to use the data collected in published research. Please be aware that the results of this questionnaire will remain completely anonymous. The only reason for the number on the top right of this form is to match the data with that of the experiment “Hold the phone! Attempts to reduce negative mood, while waiting for phone service ”. You may withdraw from the project at any time. If you have any questions please feel free to ask, or direct correspondence to : Isaac Malpass C/O Dr Louis Leland Jr Department of Psychology, University of Otago, P.O. Box 56, Dunedin 9054

Please fill in the information below:

Age: Year of University Study: Gender: Male / Female Have you ever suffered from or been diagnosed with a hearing problem? Yes / No Instructions: A short clip of music will be played, then you will be asked to make a rating on the corresponding scale by ticking one of the boxes which corresponds the number of minutes you would listen to it, for two different scenarios. One is listening to the music on-hold while attempting to recoup an overcharged payment of $10 via telephone. The other is listening to the music in a public area with friends present, where you are free to come and go as you choose. Please note : the scenarios and numbers of minutes are not always presented in the same order. Please pay attention to which scenario you are being asked to respond, and the order in which the number of minutes are presented on each question.

Music Style 1 Please tick the box that would indicate the number of minutes you would be likely spend listening to this style of music if it were playing:

On-hold: □ □ □ □ □ □ □ □ □ □ □ □ □ □ □ □ 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15+ minutes

In a public area: □ □ □ □ □ □ □ □ □ □ □ □ □ □ □ □ 15+ 14 13 12 11 10 9 8 7 6 5 4 3 2 1 0 minutes

320

Music Style 2 Please tick the box that would indicate the number of minutes you would be likely spend listening to this style of music if it were playing:

On-hold: □ □ □ □ □ □ □ □ □ □ □ □ □ □ □ □ 15+ 14 13 12 11 10 9 8 7 6 5 4 3 2 1 0 minutes

In a public area: □ □ □ □ □ □ □ □ □ □ □ □ □ □ □ □ 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15+ minutes

Music Style 3 Please tick the box that would indicate the number of minutes you would be likely spend listening to this style of music if it were playing:

In a public area: □ □ □ □ □ □ □ □ □ □ □ □ □ □ □ □ 15+ 14 13 12 11 10 9 8 7 6 5 4 3 2 1 0 minutes

On-hold: □ □ □ □ □ □ □ □ □ □ □ □ □ □ □ □ 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15+ minutes

Music Style 4 Please tick the box that would indicate the number of minutes you would be likely spend listening to this style of music if it were playing:

On-hold: □ □ □ □ □ □ □ □ □ □ □ □ □ □ □ □ 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15+ minutes

In a public area: □ □ □ □ □ □ □ □ □ □ □ □ □ □ □ □ 15+ 14 13 12 11 10 9 8 7 6 5 4 3 2 1 0 minutes

Music Style 5 Please tick the box that would indicate the number of minutes you would be likely spend listening to this style of music if it were playing:

In a public area: □ □ □ □ □ □ □ □ □ □ □ □ □ □ □ □ 15+ 14 13 12 11 10 9 8 7 6 5 4 3 2 1 0 minutes

On-hold: □ □ □ □ □ □ □ □ □ □ □ □ □ □ □ □ 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15+ minutes

321

Music Style 6 Please tick the box that would indicate the number of minutes you would be likely spend listening to this style of music if it were playing:

On-hold: □ □ □ □ □ □ □ □ □ □ □ □ □ □ □ □ 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15+ minutes

In a public area: □ □ □ □ □ □ □ □ □ □ □ □ □ □ □ □ 15+ 14 13 12 11 10 9 8 7 6 5 4 3 2 1 0 minutes

Music Style 7 Please tick the box that would indicate the number of minutes you would be likely spend listening to this style of music if it were playing:

On-hold: □ □ □ □ □ □ □ □ □ □ □ □ □ □ □ □ 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15+ minutes

In a public area: □ □ □ □ □ □ □ □ □ □ □ □ □ □ □ □ 15+ 14 13 12 11 10 9 8 7 6 5 4 3 2 1 0 minutes

Music Style 8 Please tick the box that would indicate the number of minutes you would be likely spend listening to this style of music if it were playing:

On-hold: □ □ □ □ □ □ □ □ □ □ □ □ □ □ □ □ 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15+ minutes

In a public area: □ □ □ □ □ □ □ □ □ □ □ □ □ □ □ □ 15+ 14 13 12 11 10 9 8 7 6 5 4 3 2 1 0 minutes

Music Style 9 Please tick the box that would indicate the number of minutes you would be likely spend listening to this style of music if it were playing:

On-hold: □ □ □ □ □ □ □ □ □ □ □ □ □ □ □ □ 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15+ minutes

In a public area: □ □ □ □ □ □ □ □ □ □ □ □ □ □ □ □ 15+ 14 13 12 11 10 9 8 7 6 5 4 3 2 1 0 minutes

322

Music Style 10 Please tick the box that would indicate the number of minutes you would be likely spend listening to this style of music if it were playing:

On-hold: □ □ □ □ □ □ □ □ □ □ □ □ □ □ □ □ 15+ 14 13 12 11 10 9 8 7 6 5 4 3 2 1 0 minutes

In a public area: □ □ □ □ □ □ □ □ □ □ □ □ □ □ □ □ 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15+ minutes

Music Style 11 Please tick the box that would indicate the number of minutes you would be likely spend listening to this style of music if it were playing:

In a public area: □ □ □ □ □ □ □ □ □ □ □ □ □ □ □ □ 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15+ minutes

On-hold: □ □ □ □ □ □ □ □ □ □ □ □ □ □ □ □ 15+ 14 13 12 11 10 9 8 7 6 5 4 3 2 1 0 minutes

Music Style 12 Please tick the box that would indicate the number of minutes you would be likely spend listening to this style of music if it were playing:

On-hold: □ □ □ □ □ □ □ □ □ □ □ □ □ □ □ □ 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15+ minutes

In a public area: □ □ □ □ □ □ □ □ □ □ □ □ □ □ □ □ 15+ 14 13 12 11 10 9 8 7 6 5 4 3 2 1 0 minutes

Music Style 13 Please tick the box that would indicate the number of minutes you would be likely spend listening to this style of music if it were playing:

On-hold: □ □ □ □ □ □ □ □ □ □ □ □ □ □ □ □ 15+ 14 13 12 11 10 9 8 7 6 5 4 3 2 1 0 minutes

In a public area: □ □ □ □ □ □ □ □ □ □ □ □ □ □ □ □ 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15+ minutes

323

Music Style 14 Please tick the box that would indicate the number of minutes you would be likely spend listening to this style of music if it were playing:

On-hold: □ □ □ □ □ □ □ □ □ □ □ □ □ □ □ □ 15+ 14 13 12 11 10 9 8 7 6 5 4 3 2 1 0 minutes

In a public area: □ □ □ □ □ □ □ □ □ □ □ □ □ □ □ □ 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15+ minutes

Music Style 15 Please tick the box that would indicate the number of minutes you would be likely spend listening to this style of music if it were playing:

On-hold: □ □ □ □ □ □ □ □ □ □ □ □ □ □ □ □ 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15+ minutes

In a public area: □ □ □ □ □ □ □ □ □ □ □ □ □ □ □ □ 15+ 14 13 12 11 10 9 8 7 6 5 4 3 2 1 0 minutes

Music Style 16 Please tick the box that would indicate the number of minutes you would be likely spend listening to this style of music if it were playing:

In a public area: □ □ □ □ □ □ □ □ □ □ □ □ □ □ □ □ 15+ 14 13 12 11 10 9 8 7 6 5 4 3 2 1 0 minutes

On-hold: □ □ □ □ □ □ □ □ □ □ □ □ □ □ □ □ 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15+ minutes

Music Style 17 Please tick the box that would indicate the number of minutes you would be likely spend listening to this style of music if it were playing:

On-hold: □ □ □ □ □ □ □ □ □ □ □ □ □ □ □ □ 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15+ minutes

In a public area: □ □ □ □ □ □ □ □ □ □ □ □ □ □ □ □ 15+ 14 13 12 11 10 9 8 7 6 5 4 3 2 1 0 minutes

324

Music Style 18 Please tick the box that would indicate the number of minutes you would be likely spend listening to this style of music if it were playing:

On-hold: □ □ □ □ □ □ □ □ □ □ □ □ □ □ □ □ 15+ 14 13 12 11 10 9 8 7 6 5 4 3 2 1 0 minutes

In a public area: □ □ □ □ □ □ □ □ □ □ □ □ □ □ □ □ 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15+ minutes

Music Style 19 Please tick the box that would indicate the number of minutes you would be likely spend listening to this style of music if it were playing:

On-hold: □ □ □ □ □ □ □ □ □ □ □ □ □ □ □ □ 15+ 14 13 12 11 10 9 8 7 6 5 4 3 2 1 0 minutes

In a public area: □ □ □ □ □ □ □ □ □ □ □ □ □ □ □ □ 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15+ minutes

Music Style 20 Please tick the box that would indicate the number of minutes you would be likely spend listening to this style of music if it were playing:

On-hold: □ □ □ □ □ □ □ □ □ □ □ □ □ □ □ □ 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15+ minutes

In a public area: □ □ □ □ □ □ □ □ □ □ □ □ □ □ □ □ 15+ 14 13 12 11 10 9 8 7 6 5 4 3 2 1 0 minutes

325

What genre/style of music would you like to listen to while placed on-hold ?

What is a genre/style of music that would encourage you to hang up while placed on-hold ?

What is your favourite genre/style of music to listen to around friends ?

What is a genre/style of music that you would never be seen listening to around friends ?

Thank you for your participation

326 Appendix Z : Order of the tracks from Appendix X played on each CD for Study 4.

327

328

329

330 Appendix AA : Information sheet for participants in the music preference study (Study 4).

MUSIC RATING INFORMATION SHEET FOR PARTICIPANTS

Thank you for showing an interest in this music rating project. Please read this information sheet carefully before deciding whether or not to take part and help rate music.

What is the Aim of the Project?

Because music popularity is always changing we are interested in finding out which kinds of telephone on-hold music, and music played in public places are liked and disliked by Otago University Students. We hope to use this information to implement strategies to help alleviate negative affect that may arise while waiting on-hold and loitering behaviour that occurs in public places. This project is being undertaken as part of the requirements to fulfil a Masters (Science) qualification in psychology, and we would appreciate the help you can give us. What Type of Participants are being sought?

The participants in the study will be Otago University students.

What will Participants be Asked to Do?

Should you agree to take part in this project, you will be asked to rate a series of short clips of music as to how long you would be willing to listen to them while on-hold waiting for telephone service, and how long you would listen if you were in a public area with a group of friends. It is expected that the task will take no longer than 30 minutes and participants should not find the task difficult or discomforting in anyway.

Can Participants Change their Mind and Withdraw from the Project?

If you decide to help us we thank you. If you decide not to then there will be no disadvantage to you apart from not receiving credit/payment for the experiment. Either way, we thank you for taking the time to think about what we are asking you to do.

What Data or Information will be Collected and What Use will be Made of it? We will ask you to record your gender, age, year of study and whether you have had hearing difficulties before, but no information that will make you personally identifiable. Please do not write your name on the questionnaire! We will also record the ratings which you make after listening to the music to determine its popularity and the types of music you like and do not like in different listening situations.

The results of the project may be published and may be available in the University of Otago Library (Dunedin, New Zealand) but all data will be anonymous.

331 You are most welcome to request a copy of the results of the project should you wish.

Any hard copies of (anonymous) questionnaires will be held in the psychology department. Electronic data will be stored on password protected computers. None of this information will be identifiable as coming from any individual. There is no personal identifying information being collected. Any (anonymous) information will be archived for at least five years and may be used in future research. Serial numbers on questionnaires will be used only for the purposes of matching the data in this experiment, to that of the experiment “Hold the phone! Attempts to reduce negative mood while waiting for phone service”, and will not be associated with any names.

What if Participants have any Questions? If you have any questions about our project, either now or in the future, please feel free to contact either:-

Isaac Malpass Dr Louis Leland Jr. [email protected] [email protected] Ph: (03) 479 5779 (university office) Ph: (03) 479 7638 (university office)

332 Appendix AB: Cronbach’s alpha reliability tests to check for internal consistency of musical styles for Study 4.

Reliability Scale: Pop

Case Processing Summary

N % Cases Valid 135 100.0 Excluded( 0 .0 a) Total 135 100.0 a Listwise deletion based on all variables in the procedure.

Reliability Statistics

Cronbach's Alpha Based on Cronbach's Standardized Alpha Items N of Items .922 .923 8

Inter-Item Correlation Matrix

Pop1pu Pop2pu Pop3pu Pop4pu Pop1 Pop2 Pop3 Pop4 b b b b Pop1 1.000 .823 .617 .745 .682 .635 .382 .527

Pop2 .823 1.000 .642 .773 .597 .718 .430 .602

Pop3 .617 .642 1.000 .713 .345 .441 .682 .484

Pop4 .745 .773 .713 1.000 .496 .563 .443 .713 Pop1p .682 .597 .345 .496 1.000 .779 .415 .629 ub Pop2p .635 .718 .441 .563 .779 1.000 .558 .729 ub Pop3p .382 .430 .682 .443 .415 .558 1.000 .619 ub Pop4p .527 .602 .484 .713 .629 .729 .619 1.000 ub

Intraclass Correlation Coefficient

Intraclass Correlatio 95% Confidence n(a) Interval F Test with True Value 0 Lower Upper Lower Bound Bound Value df1 df2 Sig Bound Single .597(b) .531 .664 12.850 134 938 .000 Measures Average .922 .901 .941 12.850 134 938 .000 Measures Two-way random effects model where both people effects and measures effects are random. 333 a Type C intraclass correlation coefficients using a consistency definition-the between-measure variance is excluded from the denominator variance. b The estimator is the same, whether the interaction effect is present or not.

RELIABILITY /VARIABLES=Piano1 Piano2 Piano1pub Pianol2pub /SCALE('Pop') ALL/MODEL=ALPHA /STATISTICS=CORR /ICC=MODEL(RANDOM) TYPE(CONSISTENCY) CIN=95 TESTVAL=0 .

Reliability

[DataSet1] S:\Louis Leland\Lab\Isaac\Music Pref\matmus1.sav

Scale: Piano

Case Processing Summary N % Cases Valid 135 100.0 Excluded( 0 .0 a) Total 135 100.0 a Listwise deletion based on all variables in the procedure.

Reliability Statistics

Cronbach's Alpha Based on Cronbach's Standardized Alpha Items N of Items .841 .843 4

Inter-Item Correlation Matrix Piano1 Piano2 Piano1pub Pianol2pub Piano1 1.000 .759 .537 .409 Piano2 .759 1.000 .490 .507 Piano1pub .537 .490 1.000 .741 Pianol2pub .409 .507 .741 1.000

Intraclass Correlation Coefficient Intraclas s Correlati 95% Confidence on(a) Interval F Test with True Value 0 Lower Upper Lower Bound Bound Value df1 df2 Sig Bound Single .569(b) .488 .648 6.287 134 402 .000 Measures Average .841 .792 .881 6.287 134 402 .000 Measures Two-way random effects model where both people effects and measures effects are random. a Type C intraclass correlation coefficients using a consistency definition-the between-measure variance is excluded from the denominator variance. b The estimator is the same, whether the interaction effect is present or not. 334

RELIABILITY /VARIABLES=Classical1 Classical2 Classical1pub Classical2pub /SCALE('Classical') ALL/MODEL=ALPHA /STATISTICS=CORR /ICC=MODEL(RANDOM) TYPE(CONSISTENCY) CIN=95 TESTVAL=0 .

Reliability

Scale: Classical

Case Processing Summary N % Cases Valid 135 100.0 Excluded( 0 .0 a) Total 135 100.0 a Listwise deletion based on all variables in the procedure.

Reliability Statistics

Cronbach's Alpha Based on Cronbach's Standardized Alpha Items N of Items .848 .858 4

Inter-Item Correlation Matrix Classical1 Classical2 Classical1 Classical2 pub pub Classical1 1.000 .763 .570 .519 Classical2 .763 1.000 .426 .616 Classical1pub .570 .426 1.000 .714 Classical2pub .519 .616 .714 1.000

Intraclass Correlation Coefficient Intraclass Correlatio 95% Confidence n(a) Interval F Test with True Value 0 Lower Upper Lower Bound Bound Value df1 df2 Sig Bound Single .583(b) .502 .660 6.587 134 402 .000 Measures Average .848 .802 .886 6.587 134 402 .000 Measures Two-way random effects model where both people effects and measures effects are random. a Type C intraclass correlation coefficients using a consistency definition-the between-measure variance is excluded from the denominator variance. b The estimator is the same, whether the interaction effect is present or not.

RELIABILITY /VARIABLES=Opera1 Opera2 Opera1pub Opera2pub /SCALE('Opera') ALL/MODEL=ALPHA /STATISTICS=CORR /ICC=MODEL(RANDOM) TYPE(CONSISTENCY) CIN=95 TESTVAL=0 . 335

Reliability

Scale: Opera

Case Processing Summary N % Cases Valid 134 99.3 Excluded( 1 .7 a) Total 135 100.0 a Listwise deletion based on all variables in the procedure.

Reliability Statistics

Cronbach's Alpha Based on Cronbach's Standardized Alpha Items N of Items .872 .874 4

Inter-Item Correlation Matrix Opera1 Opera2 Opera1pub Opera2pub Opera1 1.000 .828 .606 .455 Opera2 .828 1.000 .521 .561 Opera1pub .606 .521 1.000 .837 Opera2pub .455 .561 .837 1.000

Intraclass Correlation Coefficient Intraclass Correlatio 95% Confidence n(a) Interval F Test with True Value 0 Lower Upper Lower Bound Bound Value df1 df2 Sig Bound Single .630(b) .554 .702 7.808 133 399 .000 Measures Average .872 .832 .904 7.808 133 399 .000 Measures Two-way random effects model where both people effects and measures effects are random. a Type C intraclass correlation coefficients using a consistency definition-the between-measure variance is excluded from the denominator variance. b The estimator is the same, whether the interaction effect is present or not.

RELIABILITY /VARIABLES=Sixties1 Sixties2 Sixties1pub Sixties2pub /SCALE('Sixties') ALL/MODEL=ALPHA /STATISTICS=CORR /ICC=MODEL(RANDOM) TYPE(CONSISTENCY) CIN=95 TESTVAL=0 .

336

Reliability

Scale: Sixties

Case Processing Summary N % Cases Valid 135 100.0 Excluded( 0 .0 a) Total 135 100.0 a Listwise deletion based on all variables in the procedure.

Reliability Statistics

Cronbach's Alpha Based on Cronbach's Standardized Alpha Items N of Items .764 .769 4

Inter-Item Correlation Matrix Sixties1 Sixties2 Sixties1pub Sixties2pub Sixties1 1.000 .514 .571 .302 Sixties2 .514 1.000 .232 .555 Sixties1pub .571 .232 1.000 .552 Sixties2pub .302 .555 .552 1.000

Intraclass Correlation Coefficient Intraclass Correlatio n(a) 95% Confidence Interval F Test with True Value 0 Lower Upper Lower Bound Bound Value df1 df2 Sig Bound Single .448(b) .360 .538 4.246 134 402 .000 Measures Average .764 .692 .823 4.246 134 402 .000 Measures Two-way random effects model where both people effects and measures effects are random. a Type C intraclass correlation coefficients using a consistency definition-the between-measure variance is excluded from the denominator variance. b The estimator is the same, whether the interaction effect is present or not.

RELIABILITY /VARIABLES=Manilow1 Manilow2 Manilow1pub Manilow2pub /SCALE('Manilow') ALL/MODEL=ALPHA /STATISTICS=CORR /ICC=MODEL(RANDOM) TYPE(CONSISTENCY) CIN=95 TESTVAL=0 .

337

Reliability Scale: Manilow

Case Processing Summary N % Cases Valid 135 100.0 Excluded( 0 .0 a) Total 135 100.0 a Listwise deletion based on all variables in the procedure.

Reliability Statistics

Cronbach's Alpha Based on Cronbach's Standardized Alpha Items N of Items .777 .783 4

Inter-Item Correlation Matrix Manilow1 Manilow2 Manilow1pub Manilow2pub Manilow1 1.000 .633 .548 .301 Manilow2 .633 1.000 .326 .566 Manilow1pub .548 .326 1.000 .468 Manilow2pub .301 .566 .468 1.000

Intraclass Correlation Coefficient Intraclass Correlatio 95% Confidence n(a) Interval F Test with True Value 0 Lower Upper Lower Bound Bound Value df1 df2 Sig Bound Single .465(b) .377 .554 4.475 134 402 .000 Measures Average .777 .708 .832 4.475 134 402 .000 Measures Two-way random effects model where both people effects and measures effects are random. a Type C intraclass correlation coefficients using a consistency definition-the between-measure variance is excluded from the denominator variance. b The estimator is the same, whether the interaction effect is present or not.

RELIABILITY /VARIABLES=Jazz1 Jazz2 Jazz1pub Jazz2pub /SCALE('Jazz') ALL/MODEL=ALPHA /STATISTICS=CORR /ICC=MODEL(RANDOM) TYPE(CONSISTENCY) CIN=95 TESTVAL=0 .

338

Reliability

Scale: Jazz

Case Processing Summary N % Cases Valid 135 100.0 Excluded( 0 .0 a) Total 135 100.0 a Listwise deletion based on all variables in the procedure.

Reliability Statistics

Cronbach's Alpha Based on Cronbach's Standardized Alpha Items N of Items .803 .806 4

Inter-Item Correlation Matrix Jazz1 Jazz2 Jazz1pub Jazz2pub Jazz1 1.000 .757 .539 .353 Jazz2 .757 1.000 .336 .399 Jazz1pub .539 .336 1.000 .671 Jazz2pub .353 .399 .671 1.000

Intraclass Correlation Coefficient Intraclass Correlatio 95% Confidence n(a) Interval F Test with True Value 0 Lower Upper Lower Bound Bound Value df1 df2 Sig Bound Single .504(b) .418 .590 5.068 134 402 .000 Measures Average .803 .742 .852 5.068 134 402 .000 Measures Two-way random effects model where both people effects and measures effects are random. a Type C intraclass correlation coefficients using a consistency definition-the between-measure variance is excluded from the denominator variance. b The estimator is the same, whether the interaction effect is present or not.

RELIABILITY /VARIABLES=Wing1 Wing2 Wing1pub Wing2pub /SCALE('Asian Karaoke') ALL/MODEL=ALPHA /STATISTICS=CORR /ICC=MODEL(RANDOM) TYPE(CONSISTENCY) CIN=95 TESTVAL=0 . 339

Reliability

Scale: Asian Karaoke (Wing).

Case Processing Summary N % Cases Valid 135 100.0 Excluded( 0 .0 a) Total 135 100.0 a Listwise deletion based on all variables in the procedure.

Reliability Statistics

Cronbach's Alpha Based on Cronbach's Standardized Alpha Items N of Items .827 .827 4

Inter-Item Correlation Matrix Wing1 Wing2 Wing1pub Wing2pub Wing1 1.000 .677 .648 .435 Wing2 .677 1.000 .401 .363 Wing1pub .648 .401 1.000 .741 Wing2pub .435 .363 .741 1.000

Intraclass Correlation Coefficient Intraclass Correlatio 95% Confidence n(a) Interval F Test with True Value 0 Lower Upper Lower Bound Bound Value df1 df2 Sig Bound Single .544(b) .461 .626 5.779 134 402 .000 Measures Average .827 .774 .870 5.779 134 402 .000 Measures Two-way random effects model where both people effects and measures effects are random. a Type C intraclass correlation coefficients using a consistency definition-the between-measure variance is excluded from the denominator variance. b The estimator is the same, whether the interaction effect is present or not.

340

Reliability

Scale: Country

Case Processing Summary N % Cases Valid 135 100.0 Excluded( 0 .0 a) Total 135 100.0 a Listwise deletion based on all variables in the procedure.

Reliability Statistics

Cronbach's Alpha Based on Cronbach's Standardized Alpha Items N of Items .840 .843 4

Inter-Item Correlation Matrix Country1 Country2 Country1pub Country2pub Country1 1.000 .747 .599 .332 Country2 .747 1.000 .504 .552 Country1pub .599 .504 1.000 .705 Country2pub .332 .552 .705 1.000

Intraclass Correlation Coefficient Intraclass Correlatio n(a) 95% Confidence Interval F Test with True Value 0 Lower Upper Lower Bound Bound Value df1 df2 Sig Bound Single .567(b) .485 .646 6.239 134 402 .000 Measures Average .840 .791 .880 6.239 134 402 .000 Measures Two-way random effects model where both people effects and measures effects are random. a Type C intraclass correlation coefficients using a consistency definition-the between-measure variance is excluded from the denominator variance. b The estimator is the same, whether the interaction effect is present or not.

341 Appendix AC : Check of the effect of group allocation from Study 2 on music ratings for Study 4.

General Linear Model

Within-Subjects Factors

Measure: MEASURE_1 Dependent Situation Music Variable 1 1 Wingsqrt 2 Operasqrt 3 Classicalsqrt

4 Jazzsqrt 5 Manilowsqrt 6 Pianosqrt 7 Countrysqrt 8 Sixtiessqrt 9 Popsqrt 2 1 Wingpubsqrt 2 Operapubsqrt

3 Classicalpubs qrt 4 Jazzsqrtpubs qrt 5 Manilowpubsq rt 6 Pianopubsqrt

7 Countrypubsq rt 8 Sixtiespubsqrt

9 Poppubsqrt

Between-Subjects Factors

N Group 1.00 17 2.00 17 3.00 17 4.00 17 5.00 17 6.00 17 7.00 15 8.00 18

342

Multivariate Tests(c) Hypothesis Effect Value F df Error df Sig. Situation Pillai's Trace .001 .071(a) 1.000 127.000 .790 Wilks' Lambda .999 .071(a) 1.000 127.000 .790 Hotelling's .001 .071(a) 1.000 127.000 .790 Trace Roy's Largest .001 .071(a) 1.000 127.000 .790 Root Situation * Group Pillai's Trace .029 .545(a) 7.000 127.000 .799 Wilks' Lambda .971 .545(a) 7.000 127.000 .799 Hotelling's .030 .545(a) 7.000 127.000 .799 Trace Roy's Largest .030 .545(a) 7.000 127.000 .799 Root Music Pillai's Trace 76.503( .836 8.000 120.000 .000 a) Wilks' Lambda 76.503( .164 8.000 120.000 .000 a) Hotelling's 76.503( 5.100 8.000 120.000 .000 Trace a) Roy's Largest 76.503( 5.100 8.000 120.000 .000 Root a) Music * Group Pillai's Trace .402 .959 56.000 882.000 .561 Wilks' Lambda .654 .955 56.000 651.531 .569 Hotelling's .450 .951 56.000 828.000 .578 Trace Roy's Largest .187 2.938(b) 8.000 126.000 .005 Root Situation * Music Pillai's Trace 21.846( .593 8.000 120.000 .000 a) Wilks' Lambda 21.846( .407 8.000 120.000 .000 a) Hotelling's 21.846( 1.456 8.000 120.000 .000 Trace a) Roy's Largest 21.846( 1.456 8.000 120.000 .000 Root a) Situation * Music * Pillai's Trace .474 1.144 56.000 882.000 .223 Group Wilks' Lambda .599 1.160 56.000 651.531 .205 Hotelling's .555 1.172 56.000 828.000 .187 Trace Roy's Largest .275 4.324(b) 8.000 126.000 .000 Root a Exact statistic b The statistic is an upper bound on F that yields a lower bound on the significance level. c Design: Intercept+Group Within Subjects Design: Situation+Music+Situation*Music

Mauchly's Test of Sphericity(b) Measure: MEASURE_1 Approx. Within Subjects Mauchly's Chi- Effect W Square df Sig. Epsilon(a) Greenhou Greenh Greenh se- Huynh- Lower- ouse- Huynh- Lower- ouse- Geisser Feldt bound Geisser Feldt bound Geisser Situation 1.000 .000 0 . 1.000 1.000 1.000 Music .202 198.639 35 .000 .691 .766 .125

343 Situation * Music .380 120.167 35 .000 .784 .875 .125 Tests the null hypothesis that the error covariance matrix of the orthonormalized transformed dependent variables is proportional to an identity matrix. a May be used to adjust the degrees of freedom for the averaged tests of significance. Corrected tests are displayed in the Tests of Within-Subjects Effects table. b Design: Intercept+Group Within Subjects Design: Situation+Music+Situation*Music

Tests of Within-Subjects Effects Measure: MEASURE_1 Type III Sum of Mean Source Squares df Square F Sig. Situation Sphericity .133 1 .133 .071 .790 Assumed Greenhouse- .133 1.000 .133 .071 .790 Geisser Huynh-Feldt .133 1.000 .133 .071 .790 Lower-bound .133 1.000 .133 .071 .790 Situation * Group Sphericity 7.131 7 1.019 .545 .799 Assumed Greenhouse- 7.131 7.000 1.019 .545 .799 Geisser Huynh-Feldt 7.131 7.000 1.019 .545 .799 Lower-bound 7.131 7.000 1.019 .545 .799 Error(Situation) Sphericity 237.282 127 1.868 Assumed Greenhouse- 237.282 127.000 1.868 Geisser Huynh-Feldt 237.282 127.000 1.868 Lower-bound 237.282 127.000 1.868 Music Sphericity 579.107 8 72.388 101.582 .000 Assumed Greenhouse- 579.107 5.532 104.683 101.582 .000 Geisser Huynh-Feldt 579.107 6.132 94.443 101.582 .000 Lower-bound 579.107 1.000 579.107 101.582 .000 Music * Group Sphericity 42.395 56 .757 1.062 .355 Assumed Greenhouse- 42.395 38.724 1.095 1.062 .370 Geisser Huynh-Feldt 42.395 42.923 .988 1.062 .366 Lower-bound 42.395 7.000 6.056 1.062 .392 Error(Music) Sphericity 724.011 1016 .713 Assumed Greenhouse- 724.011 702.563 1.031 Geisser Huynh-Feldt 724.011 778.740 .930 Lower-bound 724.011 127.000 5.701 Situation * Music Sphericity 30.800 8 3.850 27.491 .000 Assumed Greenhouse- 30.800 6.275 4.908 27.491 .000 Geisser Huynh-Feldt 30.800 7.001 4.399 27.491 .000 Lower-bound 30.800 1.000 30.800 27.491 .000 Situation * Music * Sphericity 7.886 56 .141 1.006 .465 Group Assumed Greenhouse- 7.886 43.928 .180 1.006 .464 Geisser Huynh-Feldt 7.886 49.006 .161 1.006 .465 344 Lower-bound 7.886 7.000 1.127 1.006 .431 Error(Situation*Music Sphericity 142.288 1016 .140 ) Assumed Greenhouse- 142.288 796.975 .179 Geisser Huynh-Feldt 142.288 889.111 .160 Lower-bound 142.288 127.000 1.120

Tests of Within-Subjects Contrasts Measure: MEASURE_1 Type III Situatio Sum of Mean Source n Music Squares df Square F Sig. Situation Linear .133 1 .133 .071 .790 Situation * Group Linear 7.131 7 1.019 .545 .799 Error(Situation) Linear 237.282 127 1.868 Music Linear 490.401 1 490.401 456.455 .000 Quadrat 17.261 1 17.261 20.581 .000 ic Cubic 61.301 1 61.301 95.620 .000 Order 4 .067 1 .067 .097 .757 Order 5 .332 1 .332 .670 .415 Order 6 2.462 1 2.462 5.274 .023 Order 7 1.823 1 1.823 2.940 .089 Order 8 5.460 1 5.460 6.296 .013 Music * Group Linear 8.769 7 1.253 1.166 .327 Quadrat 1.773 7 .253 .302 .952 ic Cubic 4.212 7 .602 .938 .479 Order 4 4.729 7 .676 .969 .457 Order 5 5.608 7 .801 1.617 .136 Order 6 1.361 7 .194 .417 .891 Order 7 3.813 7 .545 .879 .526 Order 8 12.129 7 1.733 1.998 .060 Error(Music) Linear 136.445 127 1.074 Quadrat 106.513 127 .839 ic Cubic 81.418 127 .641 Order 4 88.539 127 .697 Order 5 62.919 127 .495 Order 6 59.283 127 .467 Order 7 78.746 127 .620 Order 8 110.149 127 .867 Situation * Music Linear Linear 17.934 1 17.934 109.724 .000 Quadrat 3.096 1 3.096 19.016 .000 ic Cubic 3.676 1 3.676 24.820 .000 Order 4 1.686 1 1.686 14.245 .000 Order 5 1.806 1 1.806 11.811 .001 Order 6 .042 1 .042 .427 .515 Order 7 1.469 1 1.469 11.869 .001 Order 8 1.092 1 1.092 7.129 .009 Situation * Music * Linear Linear .835 7 .119 .730 .647 Group Quadrat .684 7 .098 .600 .755 ic Cubic .721 7 .103 .695 .676 Order 4 1.048 7 .150 1.266 .273 Order 5 2.119 7 .303 1.980 .063 Order 6 1.021 7 .146 1.490 .176 Order 7 .920 7 .131 1.062 .392 345 Order 8 .538 7 .077 .502 .831 Error(Situation*Musi Linear Linear 20.758 127 .163 c) Quadrat 20.674 127 .163 ic Cubic 18.811 127 .148 Order 4 15.030 127 .118 Order 5 19.419 127 .153 Order 6 12.427 127 .098 Order 7 15.723 127 .124 Order 8 19.446 127 .153

Tests of Between-Subjects Effects Measure: MEASURE_1 Transformed Variable: Average Type III Sum Source of Squares df Mean Square F Sig. Intercept 10065.233 1 10065.233 1812.777 .000 Group 39.645 7 5.664 1.020 .420 Error 705.153 127 5.552

346 Appendix AD : Analysis of music preference in Study 4. Situation and music style by gender (gender has been labelled sex) and age. Bonferroni post hoc analyses for main effect of music are under the title “Music Overall”.

Suffix key: Sqrt = square root transformation. Pubsqrt = public area, square root transformation

General Linear Model

Within-Subjects Factors

Measure: MEASURE_1 Dependent Situation Music Variable 1 Wing Wingsqrt Opera Operasqrt Classi cal Classicalsqrt Jazz Jazzsqrt Manilo w Manilowsqrt Piano Pianosqrt Countr y Countrysqrt Sixties Sixtiessqrt

Pop Popsqrt 2 Wing Wingpubsqrt Opera Operapubsqrt

Classi Classicalpubs cal qrt Jazz Jazzsqrtpubs qrt Manilo Manilowpubsq w rt Piano Pianopubsqrt

Countr Countrypubsq y rt Sixties Sixtiespubsqrt

Pop Poppubsqrt

Between-Subjects Factors Value Label N Sex 1.00 102 2.00 33 Age 18.00 18 24 19.00 19 60 20.00 20 34 2122.00 17 347

Descriptive Statistics

Sex Age Mean Std. Deviation N Wingsqrt 1.00 18 .9649 .89303 21 19 .8409 .64116 50 20 1.0526 .81338 21 2122.00 1.6376 .95343 10 Total .9881 .78920 102 2.00 18 1.0787 1.12011 3 19 1.3304 .96036 10 20 1.2969 .98467 13 2122.00 1.5987 .65956 7 Total 1.3512 .89703 33 Total 18 .9791 .89670 24 19 .9225 .71829 60 20 1.1460 .87639 34 2122.00 1.6216 .82149 17 Total 1.0769 .82834 135 Operasqrt 1.00 18 1.3166 .77769 21 19 1.5784 .81975 50 20 1.9478 .87540 21 2122.00 1.6698 .75418 10 Total 1.6095 .83107 102 2.00 18 1.6221 .23104 3 19 2.3231 .78415 10 20 2.1380 .84534 13 2122.00 2.4235 .75136 7 Total 2.2077 .77367 33 Total 18 1.3548 .73566 24 19 1.7025 .85452 60 20 2.0205 .85621 34 2122.00 1.9801 .82331 17 Total 1.7558 .85447 135 Classicalsqrt 1.00 18 1.7389 .61233 21 19 1.9150 .74417 50 20 2.0854 .78024 21 2122.00 2.0342 .82451 10 Total 1.9255 .73336 102 2.00 18 1.6560 .62153 3 19 2.3600 .80312 10 20 2.3967 .88168 13 2122.00 2.4294 .66914 7 Total 2.3252 .79235 33 Total 18 1.7286 .60035 24 19 1.9892 .76569 60 20 2.2044 .82171 34 2122.00 2.1970 .76843 17 Total 2.0232 .76486 135 Jazzsqrt 1.00 18 1.5185 .76077 21 348 19 1.7581 .86252 50 20 2.0363 .78818 21 2122.00 2.2459 .97512 10 Total 1.8139 .85540 102 2.00 18 2.0356 .18521 3 19 2.8632 .94407 10 20 2.1583 .76068 13 2122.00 2.0721 .99531 7 Total 2.3425 .88254 33 Total 18 1.5832 .73265 24 19 1.9423 .96244 60 20 2.0829 .76847 34 2122.00 2.1744 .95610 17 Total 1.9431 .88854 135 Manilowsqrt 1.00 18 1.8476 .93045 21 19 2.1984 .74703 50 20 2.3096 .82096 21 2122.00 2.4581 .67319 10 Total 2.1745 .80640 102 2.00 18 2.2331 .51961 3 19 2.6832 .70730 10 20 2.2818 .78057 13 2122.00 2.3511 .84730 7 Total 2.4137 .74455 33 Total 18 1.8958 .89065 24 19 2.2792 .75696 60 20 2.2990 .79386 34 2122.00 2.4140 .72600 17 Total 2.2330 .79572 135 Pianosqrt 1.00 18 1.9971 .88720 21 19 2.1086 .64197 50 20 2.3135 1.03920 21 2122.00 2.3956 .97823 10 Total 2.1560 .81984 102 2.00 18 1.9649 .84185 3 19 2.9270 .65197 10 20 2.5930 .82109 13 2122.00 2.4232 .75254 7 Total 2.6011 .77520 33 Total 18 1.9931 .86383 24 19 2.2450 .70832 60 20 2.4204 .95848 34 2122.00 2.4069 .86651 17 Total 2.2648 .82885 135 Countrysqrt 1.00 18 1.9556 .85701 21 19 2.1557 .85891 50 20 2.1114 .81811 21 2122.00 2.2382 .73805 10 Total 2.1135 .83188 102 2.00 18 2.2758 .48099 3 19 2.8877 .71580 10 349 20 1.9858 .98888 13 2122.00 2.5274 .89323 7 Total 2.4004 .90768 33 Total 18 1.9956 .81883 24 19 2.2777 .87551 60 20 2.0634 .87468 34 2122.00 2.3573 .79190 17 Total 2.1836 .85654 135 Sixtiessqrt 1.00 18 2.0659 .64693 21 19 2.3070 .81312 50 20 2.4731 .69120 21 2122.00 2.7475 .66792 10 Total 2.3347 .75779 102 2.00 18 2.2982 .27775 3 19 2.7485 .57305 10 20 2.3913 .68697 13 2122.00 2.7019 .69505 7 Total 2.5570 .63105 33 Total 18 2.0950 .61384 24 19 2.3806 .79166 60 20 2.4418 .68029 34 2122.00 2.7287 .65775 17 Total 2.3891 .73288 135 Popsqrt 1.00 18 2.3942 .99066 21 19 2.5780 .75841 50 20 2.4675 .85545 21 2122.00 2.7513 .90073 10 Total 2.5344 .83739 102 2.00 18 2.3742 .21082 3 19 2.8001 .71470 10 20 2.5797 .78995 13 2122.00 2.6773 .96389 7 Total 2.6485 .75563 33 Total 18 2.3917 .92591 24 19 2.6150 .75005 60 20 2.5104 .82066 34 2122.00 2.7208 .89788 17 Total 2.5623 .81689 135 Wingpubsqrt 1.00 18 .9393 .92125 21 19 .7114 .73120 50 20 .9340 .73071 21 2122.00 1.4683 1.22207 10 Total .8783 .84613 102 2.00 18 .8047 .72705 3 19 .7865 1.04427 10 20 .8197 .66240 13 2122.00 1.0744 .85819 7 Total .8623 .80936 33 Total 18 .9224 .88658 24 19 .7239 .78178 60 20 .8903 .69738 34 350 2122.00 1.3061 1.07526 17 Total .8744 .83433 135 Operapubsqrt 1.00 18 1.1584 1.12517 21 19 1.2997 1.04039 50 20 1.7601 .97327 21 2122.00 1.7863 1.22884 10 Total 1.4131 1.07547 102 2.00 18 1.5255 .09637 3 19 1.2463 1.10387 10 20 1.9877 1.12322 13 2122.00 2.0031 .80763 7 Total 1.7243 1.02907 33 Total 18 1.2043 1.05691 24 19 1.2908 1.04175 60 20 1.8471 1.02248 34 2122.00 1.8756 1.05171 17 Total 1.4892 1.06897 135 Classicalpubsqrt 1.00 18 1.4969 1.17144 21 19 1.7618 1.04788 50 20 1.9833 .91913 21 2122.00 2.4253 1.08922 10 Total 1.8179 1.06896 102 2.00 18 1.5260 .71210 3 19 1.6882 .57745 10 20 2.2839 .98694 13 2122.00 2.4896 .72038 7 Total 2.0781 .84758 33 Total 18 1.5005 1.11242 24 19 1.7495 .98161 60 20 2.0982 .94244 34 2122.00 2.4518 .92899 17 Total 1.8815 1.02246 135 Jazzsqrtpubsqrt 1.00 18 1.6728 .99334 21 19 1.8302 1.00017 50 20 2.0407 1.24595 21 2122.00 2.7213 1.33104 10 Total 1.9285 1.10869 102 2.00 18 2.5223 .45459 3 19 1.9860 .67158 10 20 1.9900 1.02134 13 2122.00 2.1588 .90169 7 Total 2.0730 .84207 33 Total 18 1.7790 .97896 24 19 1.8562 .95027 60 20 2.0213 1.14925 34 2122.00 2.4897 1.17597 17 Total 1.9638 1.04867 135 Manilowpubsqrt 1.00 18 1.9584 1.09445 21 19 2.3507 .80452 50 20 2.3504 1.03661 21 2122.00 2.7754 .74326 10 351 Total 2.3115 .92818 102 2.00 18 1.9413 .31187 3 19 2.4169 .91806 10 20 2.1466 .79577 13 2122.00 2.1876 .76385 7 Total 2.2185 .78219 33 Total 18 1.9563 1.02473 24 19 2.3617 .81654 60 20 2.2724 .94426 34 2122.00 2.5334 .78643 17 Total 2.2888 .89278 135 Pianopubsqrt 1.00 18 1.6827 1.26563 21 19 1.8732 1.00063 50 20 2.0756 .82258 21 2122.00 2.3229 1.18036 10 Total 1.9197 1.04642 102 2.00 18 2.1966 .87328 3 19 2.1524 .86108 10 20 2.4840 .87970 13 2122.00 2.0981 .88373 7 Total 2.2755 .85012 33 Total 18 1.7470 1.22038 24 19 1.9197 .97758 60 20 2.2317 .85562 34 2122.00 2.2304 1.04383 17 Total 2.0067 1.01068 135 Countrypubsqrt 1.00 18 2.0509 .89912 21 19 2.1549 .98260 50 20 2.1658 1.07237 21 2122.00 2.7672 .90836 10 Total 2.1958 .98359 102 2.00 18 2.2341 .71632 3 19 2.1624 1.18973 10 20 2.0559 .98109 13 2122.00 2.3904 1.08018 7 Total 2.1754 1.01301 33 Total 18 2.0738 .86685 24 19 2.1562 1.00885 60 20 2.1238 1.02465 34 2122.00 2.6121 .96861 17 Total 2.1908 .98708 135 Sixtiespubsqrt 1.00 18 2.5772 .85213 21 19 2.3814 .86250 50 20 2.6942 .95042 21 2122.00 3.2493 .65980 10 Total 2.5712 .88819 102 2.00 18 2.2709 .87455 3 19 2.1464 .99609 10 20 2.4415 .84211 13 2122.00 2.7227 .77533 7 Total 2.3962 .86639 33 352 Total 18 2.5389 .84180 24 19 2.3422 .88146 60 20 2.5976 .90602 34 2122.00 3.0325 .73598 17 Total 2.5284 .88292 135 Poppubsqrt 1.00 18 2.9899 .83724 21 19 3.0901 .76016 50 20 2.8877 .95867 21 2122.00 3.4654 .39610 10 Total 3.0646 .79938 102 2.00 18 2.8391 .39897 3 19 3.0477 .63379 10 20 3.0085 .79511 13 2122.00 3.0955 1.03482 7 Total 3.0234 .75192 33 Total 18 2.9711 .79119 24 19 3.0830 .73582 60 20 2.9339 .88907 34 2122.00 3.3131 .72459 17 Total 3.0545 .78548 135

Multivariate Tests(c) Hypothesis Effect Value F df Error df Sig. Situation Pillai's Trace .002 .310(a) 1.000 127.000 .579 Wilks' Lambda .998 .310(a) 1.000 127.000 .579 Hotelling's .002 .310(a) 1.000 127.000 .579 Trace Roy's Largest .002 .310(a) 1.000 127.000 .579 Root Situation * Sex Pillai's Trace 3.780(a .029 1.000 127.000 .054 ) Wilks' Lambda 3.780(a .971 1.000 127.000 .054 ) Hotelling's 3.780(a .030 1.000 127.000 .054 Trace ) Roy's Largest 3.780(a .030 1.000 127.000 .054 Root ) Situation * Age Pillai's Trace 1.987(a .045 3.000 127.000 .119 ) Wilks' Lambda 1.987(a .955 3.000 127.000 .119 ) Hotelling's 1.987(a .047 3.000 127.000 .119 Trace ) Roy's Largest 1.987(a .047 3.000 127.000 .119 Root ) Situation * Sex * Pillai's Trace 1.123(a .026 3.000 127.000 .343 Age ) Wilks' Lambda 1.123(a .974 3.000 127.000 .343 ) Hotelling's 1.123(a .027 3.000 127.000 .343 Trace ) Roy's Largest 1.123(a .027 3.000 127.000 .343 Root ) Music Pillai's Trace 38.065( .717 8.000 120.000 .000 a) Wilks' Lambda 38.065( .283 8.000 120.000 .000 a) 353 Hotelling's 38.065( 2.538 8.000 120.000 .000 Trace a) Roy's Largest 38.065( 2.538 8.000 120.000 .000 Root a) Music * Sex Pillai's Trace .048 .761(a) 8.000 120.000 .637 Wilks' Lambda .952 .761(a) 8.000 120.000 .637 Hotelling's .051 .761(a) 8.000 120.000 .637 Trace Roy's Largest .051 .761(a) 8.000 120.000 .637 Root Music * Age Pillai's Trace .203 1.105 24.000 366.000 .335 Wilks' Lambda .808 1.110 24.000 348.638 .329 Hotelling's .226 1.115 24.000 356.000 .324 Trace Roy's Largest 2.198(b .144 8.000 122.000 .032 Root ) Music * Sex * Pillai's Trace .127 .672 24.000 366.000 .878 Age Wilks' Lambda .877 .674 24.000 348.638 .877 Hotelling's .137 .675 24.000 356.000 .876 Trace Roy's Largest 1.474(b .097 8.000 122.000 .173 Root ) Situation * Music Pillai's Trace 13.024( .465 8.000 120.000 .000 a) Wilks' Lambda 13.024( .535 8.000 120.000 .000 a) Hotelling's 13.024( .868 8.000 120.000 .000 Trace a) Roy's Largest 13.024( .868 8.000 120.000 .000 Root a) Situation * Music * Pillai's Trace 1.424(a .087 8.000 120.000 .193 Sex ) Wilks' Lambda 1.424(a .913 8.000 120.000 .193 ) Hotelling's 1.424(a .095 8.000 120.000 .193 Trace ) Roy's Largest 1.424(a .095 8.000 120.000 .193 Root ) Situation * Music * Pillai's Trace .265 1.476 24.000 366.000 .071 Age Wilks' Lambda .753 1.490 24.000 348.638 .067 Hotelling's .304 1.502 24.000 356.000 .063 Trace Roy's Largest 3.057(b .200 8.000 122.000 .004 Root ) Situation * Music * Pillai's Trace .120 .637 24.000 366.000 .908 Sex * Age Wilks' Lambda .883 .634 24.000 348.638 .910 Hotelling's .128 .631 24.000 356.000 .912 Trace Roy's Largest 1.210(b .079 8.000 122.000 .299 Root ) a Exact statistic b The statistic is an upper bound on F that yields a lower bound on the significance level. c Design: Intercept+Sex+Age+Sex * Age Within Subjects Design: Situation+Music+Situation*Music

354

Mauchly's Test of Sphericity(b) Measure: MEASURE_1 Within Subjects Mauchly's Approx. Effect W Chi-Square df Sig. Epsilon(a) Greenh ouse- Greenhous Huynh- Lower- Greenhous Huynh- Lower- Geisse e-Geisser Feldt bound e-Geisser Feldt bound r Situation 1.000 .000 0 . 1.000 1.000 1.000 Music .194 203.686 35 .000 .688 .763 .125 Situation * .397 114.733 35 .000 .798 .891 .125 Music Tests the null hypothesis that the error covariance matrix of the orthonormalized transformed dependent variables is proportional to an identity matrix. a May be used to adjust the degrees of freedom for the averaged tests of significance. Corrected tests are displayed in the Tests of Within-Subjects Effects table. b Design: Intercept+Sex+Age+Sex * Age Within Subjects Design: Situation+Music+Situation*Music

Tests of Within-Subjects Effects Measure: MEASURE_1 Type III Sum of Source Squares df Mean Square F Sig. Situation Sphericity .545 1 .545 .310 .579 Assumed Greenhouse- .545 1.000 .545 .310 .579 Geisser Huynh-Feldt .545 1.000 .545 .310 .579 Lower-bound .545 1.000 .545 .310 .579 Situation * Sex Sphericity 6.649 1 6.649 3.780 .054 Assumed Greenhouse- 6.649 1.000 6.649 3.780 .054 Geisser Huynh-Feldt 6.649 1.000 6.649 3.780 .054 Lower-bound 6.649 1.000 6.649 3.780 .054 Situation * Age Sphericity 10.487 3 3.496 1.987 .119 Assumed Greenhouse- 10.487 3.000 3.496 1.987 .119 Geisser Huynh-Feldt 10.487 3.000 3.496 1.987 .119 Lower-bound 10.487 3.000 3.496 1.987 .119 Situation * Sex * Sphericity 5.924 3 1.975 1.123 .343 Age Assumed Greenhouse- 5.924 3.000 1.975 1.123 .343 Geisser Huynh-Feldt 5.924 3.000 1.975 1.123 .343 Lower-bound 5.924 3.000 1.975 1.123 .343 Error(Situation) Sphericity 223.390 127 1.759 Assumed Greenhouse- 127.0 223.390 1.759 Geisser 00 Huynh-Feldt 127.0 223.390 1.759 00 Lower-bound 127.0 223.390 1.759 00 Music Sphericity 283.893 8 35.487 49.646 .000 Assumed Greenhouse- 283.893 5.507 51.555 49.646 .000 Geisser Huynh-Feldt 283.893 6.102 46.522 49.646 .000 Lower-bound 283.893 1.000 283.893 49.646 .000 Music * Sex Sphericity 6.584 8 .823 1.151 .326 Assumed Greenhouse- 6.584 5.507 1.196 1.151 .331 Geisser Huynh-Feldt 6.584 6.102 1.079 1.151 .330 Lower-bound 6.584 1.000 6.584 1.151 .285 Music * Age Sphericity 17.688 24 .737 1.031 .421 Assumed Greenhouse- 16.52 17.688 1.071 1.031 .421 Geisser 0 Huynh-Feldt 18.30 17.688 .966 1.031 .421 7 Lower-bound 17.688 3.000 5.896 1.031 .381 Music * Sex * Age Sphericity 9.318 24 .388 .543 .965 Assumed Greenhouse- 16.52 9.318 .564 .543 .928 Geisser 0 Huynh-Feldt 18.30 9.318 .509 .543 .940 7 Lower-bound 9.318 3.000 3.106 .543 .654 Error(Music) Sphericity 726.232 1016 .715 Assumed Greenhouse- 699.3 726.232 1.038 Geisser 40 Huynh-Feldt 774.9 726.232 .937 96 Lower-bound 127.0 726.232 5.718 00 Situation * Music Sphericity 16.521 8 2.065 14.796 .000 Assumed Greenhouse- 16.521 6.382 2.589 14.796 .000 Geisser Huynh-Feldt 16.521 7.126 2.318 14.796 .000 Lower-bound 16.521 1.000 16.521 14.796 .000 Situation * Music * Sphericity 1.300 8 .162 1.164 .318 Sex Assumed Greenhouse- 1.300 6.382 .204 1.164 .323 Geisser Huynh-Feldt 1.300 7.126 .182 1.164 .321 Lower-bound 1.300 1.000 1.300 1.164 .283 Situation * Music * Sphericity 4.933 24 .206 1.473 .067 Age Assumed Greenhouse- 19.14 4.933 .258 1.473 .087 Geisser 6 Huynh-Feldt 21.37 4.933 .231 1.473 .077 9 Lower-bound 4.933 3.000 1.644 1.473 .225 Situation * Music * Sphericity 2.588 24 .108 .772 .774 Sex * Age Assumed Greenhouse- 19.14 2.588 .135 .772 .743 Geisser 6 Huynh-Feldt 21.37 2.588 .121 .772 .758 9 Lower-bound 2.588 3.000 .863 .772 .511 Error(Situation*Musi Sphericity 141.811 1016 .140 c) Assumed Greenhouse- 810.5 141.811 .175 Geisser 02 Huynh-Feldt 905.0 141.811 .157 37 Lower-bound 127.0 141.811 1.117 00

356

Tests of Within-Subjects Contrasts Measure: MEASURE_1 Situatio Type III Sum Mean Source n Music of Squares df Square F Sig. Situation Linear .545 1 .545 .310 .579 Situation * Sex Linear 6.649 1 6.649 3.780 .054 Situation * Age Linear 10.487 3 3.496 1.987 .119 Situation * Sex * Linear 5.924 3 1.975 1.123 .343 Age Error(Situation) Linear 223.390 127 1.759 Music Linear 222.66 239.017 1 239.017 .000 9 Quadr 11.068 1 11.068 13.823 .000 atic Cubic 31.541 1 31.541 48.658 .000 Order .212 1 .212 .306 .581 4 Order .069 1 .069 .138 .711 5 Order 1.137 1 1.137 2.442 .121 6 Order .295 1 .295 .479 .490 7 Order .555 1 .555 .600 .440 8 Music * Sex Linear 1.667 1 1.667 1.553 .215 Quadr 1.075 1 1.075 1.343 .249 atic Cubic .324 1 .324 .500 .481 Order .263 1 .263 .379 .539 4 Order 1.604 1 1.604 3.225 .075 5 Order .134 1 .134 .289 .592 6 Order .107 1 .107 .174 .677 7 Order 1.409 1 1.409 1.525 .219 8 Music * Age Linear 4.178 3 1.393 1.298 .278 Quadr 2.221 3 .740 .925 .431 atic Cubic 1.323 3 .441 .680 .566 Order 2.973 3 .991 1.428 .238 4 Order 2.071 3 .690 1.388 .250 5 Order .582 3 .194 .417 .741 6 Order 3.468 3 1.156 1.878 .137 7 Order .872 3 .291 .315 .815 8 Music * Sex * Age Linear .195 3 .065 .060 .980 Quadr 1.462 3 .487 .609 .611 atic Cubic .788 3 .263 .405 .749 Order 2.319 3 .773 1.114 .346 4 Order .905 3 .302 .607 .612 5 Order .255 3 .085 .182 .908 6 Order 2.666 3 .889 1.444 .233 7

357 Order .728 3 .243 .263 .852 8 Error(Music) Linear 136.324 127 1.073 Quadr 101.685 127 .801 atic Cubic 82.322 127 .648 Order 88.130 127 .694 4 Order 63.170 127 .497 5 Order 59.121 127 .466 6 Order 78.167 127 .615 7 Order 117.312 127 .924 8 Situation * Music Linear Linear 10.325 1 10.325 61.617 .000 Quadr 1.122 1 1.122 7.050 .009 atic Cubic 3.020 1 3.020 20.327 .000 Order .627 1 .627 5.806 .017 4 Order .720 1 .720 4.473 .036 5 Order .030 1 .030 .304 .582 6 Order .538 1 .538 4.311 .040 7 Order .138 1 .138 .939 .334 8 Situation * Music * Linear Linear .002 1 .002 .010 .921 Sex Quadr .020 1 .020 .123 .726 atic Cubic .122 1 .122 .823 .366 Order .151 1 .151 1.401 .239 4 Order .324 1 .324 2.010 .159 5 Order .155 1 .155 1.544 .216 6 Order .298 1 .298 2.387 .125 7 Order .228 1 .228 1.551 .215 8 Situation * Music * Linear Linear .013 3 .004 .026 .994 Age Quadr .591 3 .197 1.238 .299 atic Cubic .393 3 .131 .881 .453 Order 2.076 3 .692 6.404 .000 4 Order .282 3 .094 .583 .627 5 Order .186 3 .062 .620 .603 6 Order .235 3 .078 .627 .599 7 Order 1.158 3 .386 2.621 .054 8 Situation * Music * Linear Linear .158 3 .053 .315 .814 Sex * Age Quadr .572 3 .191 1.199 .313 atic Cubic .056 3 .019 .126 .944 Order .421 3 .140 1.299 .278 4

358 Order .221 3 .074 .459 .712 5 Order .095 3 .032 .315 .815 6 Order .132 3 .044 .353 .787 7 Order .931 3 .310 2.108 .102 8 Error(Situation*Mus Linear Linear 21.281 127 .168 ic) Quadr 20.208 127 .159 atic Cubic 18.869 127 .149 Order 13.719 127 .108 4 Order 20.446 127 .161 5 Order 12.728 127 .100 6 Order 15.855 127 .125 7 Order 18.704 127 .147 8

Tests of Between-Subjects Effects Measure: MEASURE_1 Transformed Variable: Average Type III Sum Source of Squares df Mean Square F Sig. Intercept 5931.572 1 5931.572 1127.857 .000 Sex 4.462 1 4.462 .848 .359 Age 26.688 3 8.896 1.692 .172 Sex * Age 9.367 3 3.122 .594 .620 Error 667.913 127 5.259

Estimated Marginal Means

1. Music Overall

Estimates

Measure: MEASURE_1 95% Confidence Interval Music Mean Std. Error Lower Bound Upper Bound Wing 1.084 .086 .914 1.253 2 1.737 .097 1.545 1.928 3 2.017 .092 1.835 2.199 4 2.101 .096 1.911 2.290 5 2.281 .087 2.108 2.453 6 2.226 .094 2.040 2.411 7 2.257 .097 2.065 2.450 8 2.514 .081 2.353 2.674 9 2.815 .088 2.642 2.989

359

Pairwise Comparisons

Measure: MEASURE_1 95% Confidence Interval for Difference(a) Mean Difference (I) Music (J) Music (I-J) Std. Error Sig.(a) Upper Bound Lower Bound Wing Opera -.653(*) .099 .000 -.978 -.328 Classical -.933(*) .103 .000 -1.271 -.596 Jazz -1.017(*) .107 .000 -1.366 -.668

Manilow -1.197(*) .102 .000 -1.529 -.865 Piano -1.142(*) .104 .000 -1.481 -.803 Country -1.174(*) .107 .000 -1.525 -.823 Sixties -1.430(*) .100 .000 -1.757 -1.103 Pop -1.732(*) .110 .000 -2.090 -1.373 Opera Wing .653(*) .099 .000 .328 .978 Classical -.280(*) .076 .012 -.528 -.032 Jazz -.364(*) .079 .000 -.624 -.104 Manilow -.544(*) .115 .000 -.920 -.168

Piano -.489(*) .078 .000 -.745 -.233 Country -.521(*) .111 .000 -.883 -.159 Sixties -.777(*) .099 .000 -1.102 -.452 Pop -1.079(*) .114 .000 -1.451 -.706 Classical Wing .933(*) .103 .000 .596 1.271 Opera .280(*) .076 .012 .032 .528 Jazz -.084 .090 1.000 -.377 .210 Manilow -.264 .100 .349 -.592 .065 Piano -.209 .073 .185 -.448 .031 Country -.241 .094 .434 -.549 .068 Sixties -.497(*) .081 .000 -.763 -.231 Pop -.798(*) .109 .000 -1.154 -.443 Jazz Wing 1.017(*) .107 .000 .668 1.366 Opera .364(*) .079 .000 .104 .624 Classical .084 .090 1.000 -.210 .377 Manilow -.180 .106 1.000 -.526 .166 Piano -.125 .088 1.000 -.411 .161 Country -.157 .104 1.000 -.496 .182 Sixties -.413(*) .093 .001 -.718 -.108 Pop -.715(*) .118 .000 -1.100 -.329 Manilow Wing 1.197(*) .102 .000 .865 1.529 Opera .544(*) .115 .000 .168 .920 Classical .264 .100 .349 -.065 .592 Jazz .180 .106 1.000 -.166 .526 Piano .055 .109 1.000 -.300 .410 Country .023 .097 1.000 -.293 .339 Sixties -.233 .075 .081 -.477 .011

Pop -.535(*) .077 .000 -.788 -.282

360 Piano Wing 1.142(*) .104 .000 .803 1.481 Opera .489(*) .078 .000 .233 .745 Classical .209 .073 .185 -.031 .448 Jazz .125 .088 1.000 -.161 .411 Manilow -.055 .109 1.000 -.410 .300 Country -.032 .108 1.000 -.386 .322 Sixties -.288 .101 .184 -.618 .042 Pop -.590(*) .116 .000 -.968 -.212

Country Wing 1.174(*) .107 .000 .823 1.525 Opera .521(*) .111 .000 .159 .883 Classical .241 .094 .434 -.068 .549 Jazz .157 .104 1.000 -.182 .496 Manilow -.023 .097 1.000 -.339 .293 Piano .032 .108 1.000 -.322 .386 Sixties -.256 .080 .059 -.516 .004 Pop -.558(*) .112 .000 -.923 -.193 Sixties Wing 1.430(*) .100 .000 1.103 1.757

Opera .777(*) .099 .000 .452 1.102 Classical .497(*) .081 .000 .231 .763 Jazz .413(*) .093 .001 .108 .718 Manilow .233 .075 .081 -.011 .477 Piano .288 .101 .184 -.042 .618 Country .256 .080 .059 -.004 .516 Pop -.302(*) .078 .007 -.558 -.045 Pop Wing 1.732(*) .110 .000 1.373 2.090 Opera 1.079(*) .114 .000 .706 1.451

Classical .798(*) .109 .000 .443 1.154 Jazz .715(*) .118 .000 .329 1.100 Manilow .535(*) .077 .000 .282 .788 Piano .590(*) .116 .000 .212 .968 Country .558(*) .112 .000 .193 .923 Sixties .302(*) .078 .007 .045 .558 Based on estimated marginal means * The mean difference is significant at the .05 level. a Adjustment for multiple comparisons: Bonferroni.

Multivariate Tests Value F Hypothesis df Error df Sig. Pillai's trace .717 38.065(a) 8.000 120.000 .000 Wilks' lambda .283 38.065(a) 8.000 120.000 .000 Hotelling's trace 2.538 38.065(a) 8.000 120.000 .000 Roy's largest root 2.538 38.065(a) 8.000 120.000 .000 Each F tests the multivariate effect of Music. These tests are based on the linearly independent pairwise comparisons among the estimated marginal means. a Exact statistic

2. Situation * Music Measure: MEASURE_1

361 95% Confidence Interval Situation Music Mean Std. Error Lower Bound Upper Bound 1 1 1.225 .094 1.039 1.411 2 1.877 .094 1.691 2.064 3 2.077 .087 1.904 2.250 4 2.086 .098 1.892 2.280 5 2.295 .092 2.114 2.477 6 2.340 .094 2.154 2.527 7 2.267 .098 2.072 2.462 8 2.467 .084 2.301 2.633 9 2.578 .097 2.386 2.769 2 1 .942 .097 .751 1.134 2 1.596 .123 1.353 1.839 3 1.957 .117 1.726 2.188 4 2.115 .121 1.875 2.355 5 2.266 .104 2.060 2.471 6 2.111 .118 1.878 2.343 7 2.248 .116 2.018 2.478 8 2.560 .101 2.361 2.760 9 3.053 .092 2.870 3.236

362 Appendix AE : Separate ANOVAs for On-hold and Public area situations, with associated Post-hoc tests.

Suffix key: Sqrt = square root transformation. Pubsqrt = public area, square root transformation

General Linear Model

Within-Subjects Factors Measure: MEASURE_1 Dependent Music Variable 1 Wingsqrt 2 Operasqrt 3 Classicalsqrt

4 Jazzsqrt 5 Manilowsqrt 6 Pianosqrt 7 Countrysqrt 8 Sixtiessqrt 9 Popsqrt

Between-Subjects Factors Value Label N Sex 1.00 102 2.00 33 Age 18.00 18 24 19.00 19 60 20.00 20 34 2122.00 17

Descriptive Statistics Sex Age Mean Std. Deviation N Wingsqrt 1.00 18 .9649 .89303 21 19 .8409 .64116 50 20 1.0526 .81338 21 2122.00 1.6376 .95343 10 Total .9881 .78920 102 2.00 18 1.0787 1.12011 3 19 1.3304 .96036 10 20 1.2969 .98467 13 2122.00 1.5987 .65956 7 Total 1.3512 .89703 33 Total 18 .9791 .89670 24 19 .9225 .71829 60 20 1.1460 .87639 34 2122.00 1.6216 .82149 17 Total 1.0769 .82834 135 Operasqrt 1.00 18 1.3166 .77769 21

363 19 1.5784 .81975 50 20 1.9478 .87540 21 2122.00 1.6698 .75418 10 Total 1.6095 .83107 102 2.00 18 1.6221 .23104 3 19 2.3231 .78415 10 20 2.1380 .84534 13 2122.00 2.4235 .75136 7 Total 2.2077 .77367 33 Total 18 1.3548 .73566 24 19 1.7025 .85452 60 20 2.0205 .85621 34 2122.00 1.9801 .82331 17 Total 1.7558 .85447 135 Classicalsqrt 1.00 18 1.7389 .61233 21 19 1.9150 .74417 50 20 2.0854 .78024 21 2122.00 2.0342 .82451 10 Total 1.9255 .73336 102 2.00 18 1.6560 .62153 3 19 2.3600 .80312 10 20 2.3967 .88168 13 2122.00 2.4294 .66914 7 Total 2.3252 .79235 33 Total 18 1.7286 .60035 24 19 1.9892 .76569 60 20 2.2044 .82171 34 2122.00 2.1970 .76843 17 Total 2.0232 .76486 135 Jazzsqrt 1.00 18 1.5185 .76077 21 19 1.7581 .86252 50 20 2.0363 .78818 21 2122.00 2.2459 .97512 10 Total 1.8139 .85540 102 2.00 18 2.0356 .18521 3 19 2.8632 .94407 10 20 2.1583 .76068 13 2122.00 2.0721 .99531 7 Total 2.3425 .88254 33 Total 18 1.5832 .73265 24 19 1.9423 .96244 60 20 2.0829 .76847 34 2122.00 2.1744 .95610 17 Total 1.9431 .88854 135 Manilowsqrt 1.00 18 1.8476 .93045 21 19 2.1984 .74703 50 20 2.3096 .82096 21 2122.00 2.4581 .67319 10 Total 2.1745 .80640 102 2.00 18 2.2331 .51961 3 19 2.6832 .70730 10

364 20 2.2818 .78057 13 2122.00 2.3511 .84730 7 Total 2.4137 .74455 33 Total 18 1.8958 .89065 24 19 2.2792 .75696 60 20 2.2990 .79386 34 2122.00 2.4140 .72600 17 Total 2.2330 .79572 135 Pianosqrt 1.00 18 1.9971 .88720 21 19 2.1086 .64197 50 20 2.3135 1.03920 21 2122.00 2.3956 .97823 10 Total 2.1560 .81984 102 2.00 18 1.9649 .84185 3 19 2.9270 .65197 10 20 2.5930 .82109 13 2122.00 2.4232 .75254 7 Total 2.6011 .77520 33 Total 18 1.9931 .86383 24 19 2.2450 .70832 60 20 2.4204 .95848 34 2122.00 2.4069 .86651 17 Total 2.2648 .82885 135 Countrysqrt 1.00 18 1.9556 .85701 21 19 2.1557 .85891 50 20 2.1114 .81811 21 2122.00 2.2382 .73805 10 Total 2.1135 .83188 102 2.00 18 2.2758 .48099 3 19 2.8877 .71580 10 20 1.9858 .98888 13 2122.00 2.5274 .89323 7 Total 2.4004 .90768 33 Total 18 1.9956 .81883 24 19 2.2777 .87551 60 20 2.0634 .87468 34 2122.00 2.3573 .79190 17 Total 2.1836 .85654 135 Sixtiessqrt 1.00 18 2.0659 .64693 21 19 2.3070 .81312 50 20 2.4731 .69120 21 2122.00 2.7475 .66792 10 Total 2.3347 .75779 102 2.00 18 2.2982 .27775 3 19 2.7485 .57305 10 20 2.3913 .68697 13 2122.00 2.7019 .69505 7 Total 2.5570 .63105 33 Total 18 2.0950 .61384 24 19 2.3806 .79166 60 20 2.4418 .68029 34

365 2122.00 2.7287 .65775 17 Total 2.3891 .73288 135 Popsqrt 1.00 18 2.3942 .99066 21 19 2.5780 .75841 50 20 2.4675 .85545 21 2122.00 2.7513 .90073 10 Total 2.5344 .83739 102 2.00 18 2.3742 .21082 3 19 2.8001 .71470 10 20 2.5797 .78995 13 2122.00 2.6773 .96389 7 Total 2.6485 .75563 33 Total 18 2.3917 .92591 24 19 2.6150 .75005 60 20 2.5104 .82066 34 2122.00 2.7208 .89788 17 Total 2.5623 .81689 135

Multivariate Tests(c)

Hypothesis Effect Value F df Error df Sig. Music Pillai's Trace 27.902(a .650 8.000 120.000 .000 ) Wilks' Lambda 27.902(a .350 8.000 120.000 .000 ) Hotelling's Trace 27.902(a 1.860 8.000 120.000 .000 ) Roy's Largest 27.902(a 1.860 8.000 120.000 .000 Root ) Music * Sex Pillai's Trace .048 .753(a) 8.000 120.000 .645 Wilks' Lambda .952 .753(a) 8.000 120.000 .645 Hotelling's Trace .050 .753(a) 8.000 120.000 .645 Roy's Largest .050 .753(a) 8.000 120.000 .645 Root Music * Age Pillai's Trace .205 1.118 24.000 366.000 .321 Wilks' Lambda .805 1.129 24.000 348.638 .309 Hotelling's Trace .230 1.139 24.000 356.000 .298 Roy's Largest .154 2.345(b) 8.000 122.000 .022 Root Music * Sex * Pillai's Trace .179 .965 24.000 366.000 .512 Age Wilks' Lambda .830 .965 24.000 348.638 .512 Hotelling's Trace .195 .965 24.000 356.000 .512 Roy's Largest .120 1.834(b) 8.000 122.000 .077 Root a Exact statistic b The statistic is an upper bound on F that yields a lower bound on the significance level. c Design: Intercept+Sex+Age+Sex * Age Within Subjects Design: Music

Mauchly's Test of Sphericity(b)

Measure: MEASURE_1

366 Approx. Within Subjects Mauchly's Chi- Effect W Square df Sig. Epsilon(a) Greenhou Greenh se- Huynh- Lower- ouse- Huynh- Lower- Greenhous Geisser Feldt bound Geisser Feldt bound e-Geisser Music .207 195.223 35 .000 .699 .775 .125 Tests the null hypothesis that the error covariance matrix of the orthonormalized transformed dependent variables is proportional to an identity matrix. a May be used to adjust the degrees of freedom for the averaged tests of significance. Corrected tests are displayed in the Tests of Within-Subjects Effects table. b Design: Intercept+Sex+Age+Sex * Age Within Subjects Design: Music

Tests of Within-Subjects Effects Measure: MEASURE_1 Type III Sum of Mean Source Squares df Square F Sig. Music Sphericity 95.188 8 11.899 37.308 .000 Assumed Greenhouse- 95.188 5.592 17.023 37.308 .000 Geisser Huynh-Feldt 95.188 6.201 15.350 37.308 .000 Lower-bound 95.188 1.000 95.188 37.308 .000 Music * Sex Sphericity 2.596 8 .325 1.018 .421 Assumed Greenhouse- 2.596 5.592 .464 1.018 .410 Geisser Huynh-Feldt 2.596 6.201 .419 1.018 .413 Lower-bound 2.596 1.000 2.596 1.018 .315 Music * Age Sphericity 8.834 24 .368 1.154 .276 Assumed Greenhouse- 8.834 16.775 .527 1.154 .298 Geisser Huynh-Feldt 8.834 18.604 .475 1.154 .292 Lower-bound 8.834 3.000 2.945 1.154 .330 Music * Sex * Sphericity 6.711 24 .280 .877 .636 Age Assumed Greenhouse- 6.711 16.775 .400 .877 .601 Geisser Huynh-Feldt 6.711 18.604 .361 .877 .611 Lower-bound 6.711 3.000 2.237 .877 .455 Error(Music) Sphericity 324.030 1016 .319 Assumed Greenhouse- 324.030 710.152 .456 Geisser Huynh-Feldt 324.030 787.563 .411 Lower-bound 324.030 127.000 2.551

Tests of Within-Subjects Contrasts Measure: MEASURE_1 Type III Sum Source Music of Squares df Mean Square F Sig. Music Linear 74.993 1 74.993 155.551 .000 Quadratic 9.618 1 9.618 25.277 .000 Cubic 7.521 1 7.521 24.002 .000

367 Order 4 .785 1 .785 2.831 .095 Order 5 .617 1 .617 3.105 .080 Order 6 .770 1 .770 3.900 .050 Order 7 .815 1 .815 3.006 .085 Order 8 .070 1 .070 .161 .689 Music * Sex Linear .887 1 .887 1.840 .177 Quadratic .402 1 .402 1.057 .306 Cubic .024 1 .024 .077 .782 Order 4 .406 1 .406 1.466 .228 Order 5 .243 1 .243 1.225 .270 Order 6 .000 1 .000 .002 .966 Order 7 .381 1 .381 1.407 .238 Order 8 .251 1 .251 .583 .447 Music * Age Linear 2.134 3 .711 1.475 .224 Quadratic 2.405 3 .802 2.107 .103 Cubic 1.176 3 .392 1.251 .294 Order 4 .182 3 .061 .219 .883 Order 5 .444 3 .148 .744 .528 Order 6 .344 3 .115 .580 .629 Order 7 1.829 3 .610 2.249 .086 Order 8 .320 3 .107 .247 .863 Music * Sex * Linear .123 3 .041 .085 .968 Age Quadratic 1.207 3 .402 1.057 .370 Cubic .283 3 .094 .301 .824 Order 4 1.380 3 .460 1.659 .179 Order 5 .726 3 .242 1.217 .306 Order 6 .261 3 .087 .440 .725 Order 7 1.698 3 .566 2.088 .105 Order 8 1.033 3 .344 .799 .497 Error(Music) Linear 61.229 127 .482 Quadratic 48.326 127 .381 Cubic 39.793 127 .313 Order 4 35.206 127 .277 Order 5 25.231 127 .199 Order 6 25.072 127 .197 Order 7 34.437 127 .271 Order 8 54.737 127 .431

Tests of Between-Subjects Effects Measure: MEASURE_1 Transformed Variable: Average Type III Sum Source of Squares df Mean Square F Sig. Intercept 3022.912 1 3022.912 938.425 .000 Sex 11.002 1 11.002 3.415 .067 Age 13.748 3 4.583 1.423 .239 Sex * Age 11.227 3 3.742 1.162 .327 Error 409.100 127 3.221

368 Estimated Marginal Means

Music On-hold

Estimates Measure: MEASURE_1 95% Confidence Interval Music Mean Std. Error Lower Bound Upper Bound Wing 1.225 .094 1.039 1.411 Opera 1.877 .094 1.691 2.064 Classi cal 2.077 .087 1.904 2.250 Jazz 2.086 .098 1.892 2.280 Manilo w 2.295 .092 2.114 2.477 Piano 2.340 .094 2.154 2.527 Countr y 2.267 .098 2.072 2.462 Sixties 2.467 .084 2.301 2.633

Pop 2.578 .097 2.386 2.769

Pairwise Comparisons Measure: MEASURE_1 95% Confidence Interval for Difference(a) Mean Difference (I) Music (J) Music (I-J) Std. Error Sig.(a) Upper Bound Lower Bound Wing Opera -.652(*) .098 .000 -.973 -.331 Classical -.852(*) .099 .000 -1.175 -.529 Jazz -.861(*) .104 .000 -1.202 -.520

Manilow -1.070(*) .098 .000 -1.391 -.750 Piano -1.115(*) .103 .000 -1.452 -.779 Country -1.042(*) .104 .000 -1.384 -.701 Sixties -1.242(*) .096 .000 -1.555 -.928 Pop -1.353(*) .110 .000 -1.711 -.994 Opera Wing .652(*) .098 .000 .331 .973 Classical -.200 .072 .240 -.436 .037 Jazz -.209 .075 .235 -.455 .038 Manilow -.418(*) .100 .002 -.743 -.093

Piano -.463(*) .080 .000 -.726 -.200 Country -.390(*) .099 .005 -.714 -.066 Sixties -.589(*) .088 .000 -.877 -.302 Pop -.700(*) .106 .000 -1.048 -.353 Classical Wing .852(*) .099 .000 .529 1.175 Opera .200 .072 .240 -.037 .436 Jazz -.009 .086 1.000 -.289 .271 Manilow -.218 .092 .666 -.518 .081 Piano -.263(*) .070 .008 -.491 -.036

369 Country -.190 .087 1.000 -.473 .093 Sixties -.390(*) .073 .000 -.628 -.152 Pop -.501(*) .099 .000 -.825 -.177 Jazz Wing .861(*) .104 .000 .520 1.202 Opera .209 .075 .235 -.038 .455 Classical .009 .086 1.000 -.271 .289 Manilow -.209 .099 1.000 -.534 .115 Piano -.254 .084 .109 -.529 .021 Country -.181 .104 1.000 -.520 .158 Sixties -.381(*) .091 .002 -.679 -.083 Pop -.492(*) .115 .001 -.868 -.116 Manilow Wing 1.070(*) .098 .000 .750 1.391 Opera .418(*) .100 .002 .093 .743 Classical .218 .092 .666 -.081 .518 Jazz .209 .099 1.000 -.115 .534 Piano -.045 .103 1.000 -.380 .290 Country .028 .081 1.000 -.238 .294 Sixties -.171 .065 .333 -.383 .041

Pop -.282(*) .075 .009 -.528 -.037 Piano Wing 1.115(*) .103 .000 .779 1.452 Opera .463(*) .080 .000 .200 .726 Classical .263(*) .070 .008 .036 .491 Jazz .254 .084 .109 -.021 .529 Manilow .045 .103 1.000 -.290 .380 Country .073 .101 1.000 -.258 .404 Sixties -.126 .093 1.000 -.431 .179 Pop -.237 .115 1.000 -.613 .138

Country Wing 1.042(*) .104 .000 .701 1.384 Opera .390(*) .099 .005 .066 .714 Classical .190 .087 1.000 -.093 .473 Jazz .181 .104 1.000 -.158 .520 Manilow -.028 .081 1.000 -.294 .238 Piano -.073 .101 1.000 -.404 .258 Sixties -.199 .072 .242 -.436 .037 Pop -.311 .103 .112 -.648 .026 Sixties Wing 1.242(*) .096 .000 .928 1.555

Opera .589(*) .088 .000 .302 .877 Classical .390(*) .073 .000 .152 .628 Jazz .381(*) .091 .002 .083 .679 Manilow .171 .065 .333 -.041 .383 Piano .126 .093 1.000 -.179 .431 Country .199 .072 .242 -.037 .436 Pop -.111 .073 1.000 -.350 .128 Pop Wing 1.353(*) .110 .000 .994 1.711 Opera .700(*) .106 .000 .353 1.048

Classical .501(*) .099 .000 .177 .825 Jazz .492(*) .115 .001 .116 .868

370 Manilow .282(*) .075 .009 .037 .528 Piano .237 .115 1.000 -.138 .613 Country .311 .103 .112 -.026 .648 Sixties .111 .073 1.000 -.128 .350 Based on estimated marginal means * The mean difference is significant at the .05 level. a Adjustment for multiple comparisons: Bonferroni.

Multivariate Tests Value F Hypothesis df Error df Sig. Pillai's trace .650 27.902(a) 8.000 120.000 .000 Wilks' lambda .350 27.902(a) 8.000 120.000 .000 Hotelling's trace 1.860 27.902(a) 8.000 120.000 .000 Roy's largest root 1.860 27.902(a) 8.000 120.000 .000 Each F tests the multivariate effect of Music. These tests are based on the linearly independent pairwise comparisons among the estimated marginal means. a Exact statistic

General Linear Model

Within-Subjects Factors

Measure: MEASURE_1 Dependent Music Variable 1 Wingpubsqrt 2 Operapubsqrt

3 Classicalpubs qrt 4 Jazzsqrtpubs qrt 5 Manilowpubsq rt 6 Pianopubsqrt

7 Countrypubsq rt 8 Sixtiespubsqrt

9 Poppubsqrt

Between-Subjects Factors

Value Label N Sex 1.00 102 2.00 33 Age 18.00 18 24 19.00 19 60 20.00 20 34 2122.00 17

371 Descriptive Statistics

Sex Age Mean Std. Deviation N Wingpubsqrt 1.00 18 .9393 .92125 21 19 .7114 .73120 50 20 .9340 .73071 21 2122.00 1.4683 1.22207 10 Total .8783 .84613 102 2.00 18 .8047 .72705 3 19 .7865 1.04427 10 20 .8197 .66240 13 2122.00 1.0744 .85819 7 Total .8623 .80936 33 Total 18 .9224 .88658 24 19 .7239 .78178 60 20 .8903 .69738 34 2122.00 1.3061 1.07526 17 Total .8744 .83433 135 Operapubsqrt 1.00 18 1.1584 1.12517 21 19 1.2997 1.04039 50 20 1.7601 .97327 21 2122.00 1.7863 1.22884 10 Total 1.4131 1.07547 102 2.00 18 1.5255 .09637 3 19 1.2463 1.10387 10 20 1.9877 1.12322 13 2122.00 2.0031 .80763 7 Total 1.7243 1.02907 33 Total 18 1.2043 1.05691 24 19 1.2908 1.04175 60 20 1.8471 1.02248 34 2122.00 1.8756 1.05171 17 Total 1.4892 1.06897 135 Classicalpubsqrt 1.00 18 1.4969 1.17144 21 19 1.7618 1.04788 50 20 1.9833 .91913 21 2122.00 2.4253 1.08922 10 Total 1.8179 1.06896 102 2.00 18 1.5260 .71210 3 19 1.6882 .57745 10 20 2.2839 .98694 13 2122.00 2.4896 .72038 7 Total 2.0781 .84758 33 Total 18 1.5005 1.11242 24 19 1.7495 .98161 60 20 2.0982 .94244 34 2122.00 2.4518 .92899 17 Total 1.8815 1.02246 135 Jazzsqrtpubsqrt 1.00 18 1.6728 .99334 21 19 1.8302 1.00017 50 20 2.0407 1.24595 21

372 2122.00 2.7213 1.33104 10 Total 1.9285 1.10869 102 2.00 18 2.5223 .45459 3 19 1.9860 .67158 10 20 1.9900 1.02134 13 2122.00 2.1588 .90169 7 Total 2.0730 .84207 33 Total 18 1.7790 .97896 24 19 1.8562 .95027 60 20 2.0213 1.14925 34 2122.00 2.4897 1.17597 17 Total 1.9638 1.04867 135 Manilowpubsqrt 1.00 18 1.9584 1.09445 21 19 2.3507 .80452 50 20 2.3504 1.03661 21 2122.00 2.7754 .74326 10 Total 2.3115 .92818 102 2.00 18 1.9413 .31187 3 19 2.4169 .91806 10 20 2.1466 .79577 13 2122.00 2.1876 .76385 7 Total 2.2185 .78219 33 Total 18 1.9563 1.02473 24 19 2.3617 .81654 60 20 2.2724 .94426 34 2122.00 2.5334 .78643 17 Total 2.2888 .89278 135 Pianopubsqrt 1.00 18 1.6827 1.26563 21 19 1.8732 1.00063 50 20 2.0756 .82258 21 2122.00 2.3229 1.18036 10 Total 1.9197 1.04642 102 2.00 18 2.1966 .87328 3 19 2.1524 .86108 10 20 2.4840 .87970 13 2122.00 2.0981 .88373 7 Total 2.2755 .85012 33 Total 18 1.7470 1.22038 24 19 1.9197 .97758 60 20 2.2317 .85562 34 2122.00 2.2304 1.04383 17 Total 2.0067 1.01068 135 Countrypubsqrt 1.00 18 2.0509 .89912 21 19 2.1549 .98260 50 20 2.1658 1.07237 21 2122.00 2.7672 .90836 10 Total 2.1958 .98359 102 2.00 18 2.2341 .71632 3 19 2.1624 1.18973 10 20 2.0559 .98109 13 2122.00 2.3904 1.08018 7

373 Total 2.1754 1.01301 33 Total 18 2.0738 .86685 24 19 2.1562 1.00885 60 20 2.1238 1.02465 34 2122.00 2.6121 .96861 17 Total 2.1908 .98708 135 Sixtiespubsqrt 1.00 18 2.5772 .85213 21 19 2.3814 .86250 50 20 2.6942 .95042 21 2122.00 3.2493 .65980 10 Total 2.5712 .88819 102 2.00 18 2.2709 .87455 3 19 2.1464 .99609 10 20 2.4415 .84211 13 2122.00 2.7227 .77533 7 Total 2.3962 .86639 33 Total 18 2.5389 .84180 24 19 2.3422 .88146 60 20 2.5976 .90602 34 2122.00 3.0325 .73598 17 Total 2.5284 .88292 135 Poppubsqrt 1.00 18 2.9899 .83724 21 19 3.0901 .76016 50 20 2.8877 .95867 21 2122.00 3.4654 .39610 10 Total 3.0646 .79938 102 2.00 18 2.8391 .39897 3 19 3.0477 .63379 10 20 3.0085 .79511 13 2122.00 3.0955 1.03482 7 Total 3.0234 .75192 33 Total 18 2.9711 .79119 24 19 3.0830 .73582 60 20 2.9339 .88907 34 2122.00 3.3131 .72459 17 Total 3.0545 .78548 135

Multivariate Tests(c) Hypothesis Effect Value F df Error df Sig. Music Pillai's Trace 38.996(a .722 8.000 120.000 .000 ) Wilks' Lambda 38.996(a .278 8.000 120.000 .000 ) Hotelling's Trace 38.996(a 2.600 8.000 120.000 .000 ) Roy's Largest 38.996(a 2.600 8.000 120.000 .000 Root ) Music * Sex Pillai's Trace .052 .820(a) 8.000 120.000 .586 Wilks' Lambda .948 .820(a) 8.000 120.000 .586 Hotelling's Trace .055 .820(a) 8.000 120.000 .586 Roy's Largest .055 .820(a) 8.000 120.000 .586 Root

374 Music * Age Pillai's Trace .198 1.076 24.000 366.000 .369 Wilks' Lambda .814 1.071 24.000 348.638 .375 Hotelling's Trace .215 1.065 24.000 356.000 .382 Roy's Largest .123 1.875(b) 8.000 122.000 .070 Root Music * Sex * Pillai's Trace .085 .447 24.000 366.000 .990 Age Wilks' Lambda .916 .445 24.000 348.638 .990 Hotelling's Trace .090 .443 24.000 356.000 .990 Roy's Largest .064 .970(b) 8.000 122.000 .463 Root a Exact statistic b The statistic is an upper bound on F that yields a lower bound on the significance level. c Design: Intercept+Sex+Age+Sex * Age Within Subjects Design: Music

Mauchly's Test of Sphericity(b) Measure: MEASURE_1 Approx. Within Subjects Mauchly's Chi- Effect W Square df Sig. Epsilon(a) Greenhou Greenh se- Huynh- Lower- ouse- Huynh- Lower- Greenhous Geisser Feldt bound Geisser Feldt bound e-Geisser Music .230 182.463 35 .000 .710 .788 .125 Tests the null hypothesis that the error covariance matrix of the orthonormalized transformed dependent variables is proportional to an identity matrix. a May be used to adjust the degrees of freedom for the averaged tests of significance. Corrected tests are displayed in the Tests of Within-Subjects Effects table. b Design: Intercept+Sex+Age+Sex * Age Within Subjects Design: Music

Tests of Within-Subjects Effects Measure: MEASURE_1 Type III Sum of Mean Source Squares df Square F Sig. Music Sphericity 205.226 8 25.653 47.910 .000 Assumed Greenhouse- 205.226 5.677 36.152 47.910 .000 Geisser Huynh-Feldt 205.226 6.300 32.574 47.910 .000 Lower-bound 205.226 1.000 205.226 47.910 .000 Music * Sex Sphericity 5.287 8 .661 1.234 .275 Assumed Greenhouse- 5.287 5.677 .931 1.234 .288 Geisser Huynh-Feldt 5.287 6.300 .839 1.234 .285 Lower-bound 5.287 1.000 5.287 1.234 .269 Music * Age Sphericity 13.788 24 .574 1.073 .368 Assumed Greenhouse- 13.788 17.030 .810 1.073 .376 Geisser Huynh-Feldt 13.788 18.901 .729 1.073 .374 Lower-bound 13.788 3.000 4.596 1.073 .363 Music * Sex * Sphericity 5.195 24 .216 .404 .995 Age Assumed Greenhouse- 5.195 17.030 .305 .404 .985 Geisser Huynh-Feldt 5.195 18.901 .275 .404 .989

375 Lower-bound 5.195 3.000 1.732 .404 .750 Error(Music) Sphericity 544.013 1016 .535 Assumed Greenhouse- 544.013 720.956 .755 Geisser Huynh-Feldt 544.013 800.136 .680 Lower-bound 544.013 127.000 4.284

Tests of Within-Subjects Contrasts Measure: MEASURE_1 Type III Sum Mean Source Music of Squares df Square F Sig. Music Linear 174.349 1 174.349 229.748 .000 Quadratic 2.571 1 2.571 4.439 .037 Cubic 27.040 1 27.040 55.932 .000 Order 4 .055 1 .055 .104 .747 Order 5 .172 1 .172 .374 .542 Order 6 .397 1 .397 1.079 .301 Order 7 .018 1 .018 .039 .844 Order 8 .623 1 .623 .974 .326 Music * Sex Linear .782 1 .782 1.030 .312 Quadratic .693 1 .693 1.195 .276 Cubic .422 1 .422 .874 .352 Order 4 .008 1 .008 .015 .904 Order 5 1.684 1 1.684 3.664 .058 Order 6 .289 1 .289 .784 .378 Order 7 .024 1 .024 .051 .822 Order 8 1.386 1 1.386 2.165 .144 Music * Age Linear 2.058 3 .686 .904 .441 Quadratic .408 3 .136 .235 .872 Cubic .539 3 .180 .372 .773 Order 4 4.866 3 1.622 3.091 .029 Order 5 1.909 3 .636 1.384 .251 Order 6 .425 3 .142 .384 .764 Order 7 1.873 3 .624 1.331 .267 Order 8 1.710 3 .570 .891 .448 Music * Sex * Linear .230 3 .077 .101 .959 Age Quadratic .828 3 .276 .476 .699 Cubic .561 3 .187 .387 .763 Order 4 1.360 3 .453 .864 .462 Order 5 .401 3 .134 .291 .832 Order 6 .088 3 .029 .080 .971 Order 7 1.100 3 .367 .781 .507 Order 8 .627 3 .209 .326 .806 Error(Music) Linear 96.376 127 .759 Quadratic 73.568 127 .579 Cubic 61.398 127 .483 Order 4 66.643 127 .525 Order 5 58.385 127 .460

376 Order 6 46.777 127 .368 Order 7 59.585 127 .469 Order 8 81.279 127 .640

Tests of Between-Subjects Effects Measure: MEASURE_1 Transformed Variable: Average Type III Sum Source of Squares df Mean Square F Sig. Intercept 2909.205 1 2909.205 766.211 .000 Sex .109 1 .109 .029 .866 Age 23.427 3 7.809 2.057 .109 Sex * Age 4.064 3 1.355 .357 .784 Error 482.203 127 3.797

Estimated Marginal Means

Music in Public

Estimates

Measure: MEASURE_1 95% Confidence Interval Music Mean Std. Error Lower Bound Upper Bound Wing .942 .097 .751 1.134 Opera 1.596 .123 1.353 1.839 Classi cal 1.957 .117 1.726 2.188 Jazz 2.115 .121 1.875 2.355 Manilo w 2.266 .104 2.060 2.471 Piano 2.111 .118 1.878 2.343 Countr y 2.248 .116 2.018 2.478 Sixties 2.560 .101 2.361 2.760

Pop 3.053 .092 2.870 3.236

Pairwise Comparisons Measure: MEASURE_1 95% Confidence Interval for Difference(a) Mean Difference (I) Music (J) Music (I-J) Std. Error Sig.(a) Upper Bound Lower Bound Wing Opera -.654(*) .121 .000 -1.050 -.257 Classical -1.015(*) .128 .000 -1.432 -.597 Jazz -1.173(*) .130 .000 -1.599 -.747

Manilow -1.324(*) .123 .000 -1.726 -.921 Piano -1.168(*) .126 .000 -1.581 -.755

377 Country -1.305(*) .125 .000 -1.713 -.898 Sixties -1.618(*) .120 .000 -2.011 -1.225 Pop -2.111(*) .127 .000 -2.525 -1.697 Opera Wing .654(*) .121 .000 .257 1.050 Classical -.361(*) .096 .010 -.676 -.046 Jazz -.519(*) .103 .000 -.857 -.181 Manilow -.670(*) .143 .000 -1.138 -.202

Piano -.515(*) .090 .000 -.811 -.219 Country -.652(*) .139 .000 -1.107 -.197 Sixties -.965(*) .128 .000 -1.382 -.547 Pop -1.457(*) .139 .000 -1.913 -1.001 Classical Wing 1.015(*) .128 .000 .597 1.432 Opera .361(*) .096 .010 .046 .676 Jazz -.158 .111 1.000 -.522 .205 Manilow -.309 .123 .489 -.713 .095 Piano -.154 .094 1.000 -.462 .155 Country -.291 .118 .545 -.677 .095 Sixties -.604(*) .104 .000 -.942 -.265 Pop -1.096(*) .132 .000 -1.528 -.664 Jazz Wing 1.173(*) .130 .000 .747 1.599 Opera .519(*) .103 .000 .181 .857 Classical .158 .111 1.000 -.205 .522 Manilow -.151 .130 1.000 -.576 .275 Piano .005 .112 1.000 -.362 .371 Country -.132 .122 1.000 -.531 .266 Sixties -.445(*) .114 .006 -.819 -.071 Pop -.938(*) .140 .000 -1.395 -.481 Manilow Wing 1.324(*) .123 .000 .921 1.726 Opera .670(*) .143 .000 .202 1.138 Classical .309 .123 .489 -.095 .713 Jazz .151 .130 1.000 -.275 .576 Piano .155 .129 1.000 -.268 .578 Country .018 .123 1.000 -.383 .419 Sixties -.295 .098 .113 -.614 .025

Pop -.787(*) .093 .000 -1.091 -.483 Piano Wing 1.168(*) .126 .000 .755 1.581 Opera .515(*) .090 .000 .219 .811 Classical .154 .094 1.000 -.155 .462 Jazz -.005 .112 1.000 -.371 .362 Manilow -.155 .129 1.000 -.578 .268 Country -.137 .130 1.000 -.563 .289 Sixties -.450(*) .127 .019 -.863 -.036 Pop -.942(*) .135 .000 -1.383 -.501

Country Wing 1.305(*) .125 .000 .898 1.713 Opera .652(*) .139 .000 .197 1.107 Classical .291 .118 .545 -.095 .677 Jazz .132 .122 1.000 -.266 .531 Manilow -.018 .123 1.000 -.419 .383

378 Piano .137 .130 1.000 -.289 .563 Sixties -.313 .100 .081 -.641 .015 Pop -.805(*) .132 .000 -1.238 -.373 Sixties Wing 1.618(*) .120 .000 1.225 2.011

Opera .965(*) .128 .000 .547 1.382 Classical .604(*) .104 .000 .265 .942 Jazz .445(*) .114 .006 .071 .819 Manilow .295 .098 .113 -.025 .614 Piano .450(*) .127 .019 .036 .863 Country .313 .100 .081 -.015 .641 Pop -.493(*) .098 .000 -.814 -.171 Pop Wing 2.111(*) .127 .000 1.697 2.525 Opera 1.457(*) .139 .000 1.001 1.913

Classical 1.096(*) .132 .000 .664 1.528 Jazz .938(*) .140 .000 .481 1.395 Manilow .787(*) .093 .000 .483 1.091 Piano .942(*) .135 .000 .501 1.383 Country .805(*) .132 .000 .373 1.238 Sixties .493(*) .098 .000 .171 .814 Based on estimated marginal means * The mean difference is significant at the .05 level. a Adjustment for multiple comparisons: Bonferroni.

Multivariate Tests Value F Hypothesis df Error df Sig. Pillai's trace .722 38.996(a) 8.000 120.000 .000 Wilks' lambda .278 38.996(a) 8.000 120.000 .000 Hotelling's trace 2.600 38.996(a) 8.000 120.000 .000 Roy's largest root 2.600 38.996(a) 8.000 120.000 .000 Each F tests the multivariate effect of Music. These tests are based on the linearly independent pairwise comparisons among the estimated marginal means. a Exact statistic

379 Appendix AF: Frequencies and Initial Chi Square tests for music preference qualitative questions in Study 4.

Frequencies

Frequency Table

Like to listen to on-hold Cumulative Frequency Percent Valid Percent Percent Valid 1.00 4 2.3 3.0 3.0 2.00 8 4.6 5.9 8.9 3.00 1 .6 .7 9.6 4.00 17 9.7 12.6 22.2 6.00 11 6.3 8.1 30.4 7.00 3 1.7 2.2 32.6 8.00 5 2.9 3.7 36.3 9.00 30 17.1 22.2 58.5 10.00 1 .6 .7 59.3 11.00 4 2.3 3.0 62.2 12.00 9 5.1 6.7 68.9 13.00 12 6.9 8.9 77.8 14.00 1 .6 .7 78.5 15.00 29 16.6 21.5 100.0 Total 135 77.1 100.0 Missing System 40 22.9 Total 175 100.0

Encourage to hang up on-hold Cumulative Frequency Percent Valid Percent Percent Valid 1.00 10 5.7 7.4 7.4 2.00 3 1.7 2.2 9.6 3.00 1 .6 .7 10.4 4.00 18 10.3 13.3 23.7 5.00 23 13.1 17.0 40.7 6.00 3 1.7 2.2 43.0 8.00 5 2.9 3.7 46.7 9.00 8 4.6 5.9 52.6 11.00 1 .6 .7 53.3 12.00 8 4.6 5.9 59.3 14.00 7 4.0 5.2 64.4 15.00 48 27.4 35.6 100.0 Total 135 77.1 100.0 Missing System 40 22.9 Total 175 100.0

Like to listen to around friends

380 Cumulative Frequency Percent Valid Percent Percent Valid 1.00 1 .6 .7 .7 2.00 1 .6 .7 1.5 4.00 2 1.1 1.5 3.0 6.00 1 .6 .7 3.7 7.00 12 6.9 8.9 12.6 8.00 11 6.3 8.1 20.7 9.00 44 25.1 32.6 53.3 10.00 2 1.1 1.5 54.8 11.00 2 1.1 1.5 56.3 12.00 30 17.1 22.2 78.5 13.00 14 8.0 10.4 88.9 14.00 1 .6 .7 89.6 15.00 14 8.0 10.4 100.0 Total 135 77.1 100.0 Missing System 40 22.9 Total 175 100.0

Never listen to around friends Cumulative Frequency Percent Valid Percent Percent Valid 1.00 14 8.0 10.4 10.4 2.00 3 1.7 2.2 12.6 4.00 29 16.6 21.5 34.1 5.00 26 14.9 19.3 53.3 6.00 2 1.1 1.5 54.8 8.00 6 3.4 4.4 59.3 9.00 8 4.6 5.9 65.2 11.00 1 .6 .7 65.9 12.00 4 2.3 3.0 68.9 13.00 1 .6 .7 69.6 14.00 18 10.3 13.3 83.0 15.00 23 13.1 17.0 100.0 Total 135 77.1 100.0 Missing System 40 22.9 Total 175 100.0

NPar Tests

381

Chi-Square Test

Frequencies

Like to listen to on-hold Observed N Expected N Residual country 4 9.6 -5.6 jazz 8 9.6 -1.6 blues 1 9.6 -8.6 classical 17 9.6 7.4 easy listening 11 9.6 1.4 Electronic 3 9.6 -6.6 Hip hop & Rap 5 9.6 -4.6 Pop 30 9.6 20.4 Ska & Reggae 1 9.6 -8.6 Rhythm & Blues 4 9.6 -5.6 Rock 9 9.6 -.6 Pop/Rock 12 9.6 2.4 Heavy Metal 1 9.6 -8.6 Other 29 9.6 19.4 Total 135

Test Statistics listeningcat Chi- 125.193 Square(a) df 13 Asymp. Sig. .000 a 0 cells (.0%) have expected frequencies less than 5. The minimum expected cell frequency is 9.6.

NPar Tests

Chi-Square Test

Frequencies

Encourage to hang up on-hold

Observed N Expected N Residual Country 10 11.3 -1.3 Jazz 3 11.3 -8.3 Blues 1 11.3 -10.3 Classical 18 11.3 6.8 Opera 23 11.3 11.8 Easy listening 3 11.3 -8.3 Hip hop & Rap 5 11.3 -6.3 Pop 8 11.3 -3.3 Rhythm & Blues 1 11.3 -10.3

382 Rock 8 11.3 -3.3 Heavy Metal 7 11.3 -4.3 Other 48 11.3 36.8 Total 135

Test Statistics

Hangupcat Chi- 174.244 Square(a) df 11 Asymp. Sig. .000 a 0 cells (.0%) have expected frequencies less than 5. The minimum expected cell frequency is 11.3.

NPar Tests

Chi-Square Test

Frequencies

Like to listen to around friends Observed N Expected N Residual Country 1 10.4 -9.4 Jazz 1 10.4 -9.4 Classical 2 10.4 -8.4 Easy listening 1 10.4 -9.4 Electronic 12 10.4 1.6 Hip hop & Rap 11 10.4 .6 Pop 44 10.4 33.6 Ska & Reggae 2 10.4 -8.4 Rhythm & Blues 2 10.4 -8.4 Rock 30 10.4 19.6 Pop/Rock 14 10.4 3.6 Heavy Metal 1 10.4 -9.4 Other 14 10.4 3.6 Total 135

Test Statistics arndfrnscat Chi- 202.904 Square(a) df 12 Asymp. Sig. .000 a 0 cells (.0%) have expected frequencies less than 5. The minimum expected cell frequency is 10.4.

383 NPar Tests

Chi-Square Test

Frequencies

Never listen to around friends Observed N Expected N Residual Country 14 11.3 2.8 Jazz 3 11.3 -8.3 Classical 29 11.3 17.8 Opera 26 11.3 14.8 Easy Listening 2 11.3 -9.3 Hip hop & Rap 6 11.3 -5.3 Pop 8 11.3 -3.3 Rhythm & Blues 1 11.3 -10.3 Rock 4 11.3 -7.3 Pop/Rock 1 11.3 -10.3 Heavy Metal 18 11.3 6.8 Other 23 11.3 11.8 Total 135

Test Statistics nvrarnfrndscat Chi- 104.733 Square(a) df 11 Asymp. Sig. .000 a 0 cells (.0%) have expected frequencies less than 5. The minimum expected cell frequency is 11.

384 Appendix AG: Post-hoc Chi Square tests for Study 4.

NPar Tests

Follow up Chi-Square Tests for Question 1: “What genre/style of music would you like to listen to while placed on-hold?”

Chi-Square Test

Frequencies

Pop vs. Equal Observed N Expected N Residual pop 30 20.0 10.0 Equal 10 20.0 -10.0

Total 40

Test Statistics Pop vs. equal Chi- 10.000 Square(a) df 1 Asymp. Sig. .002 a 0 cells (.0%) have expected frequencies less than 5. The minimum expected cell frequency is 20.0.

NPar Tests

Chi-Square Test Frequencies

Other vs. equal Observed N Expected N Residual other 29 19.5 9.5 Equal 10 19.5 -9.5

Total 39

Test Statistics VAR00014 Chi- 9.256 Square(a) df 1 Asymp. Sig. .002 a 0 cells (.0%) have expected frequencies less than 5. The minimum expected cell frequency is 19.5.

385

Frequencies

Classical vs Equal Observed N Expected N Residual classical 17 13.5 3.5 Equal 10 13.5 -3.5 Total 27

Test Statistics Classical vs. equal Chi- 1.815 Square(a) df 1 Asymp. Sig. .178 a 0 cells (.0%) have expected frequencies less than 5. The minimum expected cell frequency is 13.5.

Follow Up Chi Square Tests for Question 2: “What is a genre/style of music that would encourage you to hang up while placed on-hold?”

NPar Tests

Chi-Square Test

Frequencies

Other vs. Equal

Observed N Expected N Residual Other 48 29.5 18.5 Equal 11 29.5 -18.5

Total 59

Test Statistics

Other vs. Equal Chi- 23.203 Square(a) df 1 Asymp. Sig. .000 a 0 cells (.0%) have expected frequencies less than 5. The minimum expected cell frequency is 29.5.

386 NPar Tests

Chi-Square Test

Frequencies

Opera vs. Equal

Observed N Expected N Residual Opera 23 17.0 6.0 Equal 11 17.0 -6.0 Total 34

Test Statistics Opera vs. Equal Chi- 4.235 Square(a) df 1 Asymp. Sig. .040 a 0 cells (.0%) have expected frequencies less than 5. The minimum expected cell frequency is 17.0.

Chi-Square Test

[DataSet3]

Frequencies

Classical vs. Equal Observed N Expected N Residual Classical 18 14.5 3.5 Equal 11 14.5 -3.5 Total 29

Test Statistics Classical vs. Opera Chi- 1.690 Square(a) df 1 Asymp. Sig. .194 a 0 cells (.0%) have expected frequencies less than 5. The minimum expected cell frequency is 14.5.

387 NPar Follow Up Chi Square Tests For Question 3: “What is your favourite genre/style of music to listen to around friends?”

Chi-Square Test

Frequencies

Pop vs. Equal Observed N Expected N Residual Pop 44 27.0 17.0 Equal 10 27.0 -17.0

Total 54

Test Statistics arndfrnscat Chi- 21.407 Square(a) df 1 Asymp. Sig. .000 a 0 cells (.0%) have expected frequencies less than 5. The minimum expected cell frequency is 27.0.

NPar Tests

Chi-Square Test

Frequencies

Rock vs. Equal Observed N Expected N Residual rock 30 20.0 10.0 Equal 10 20.0 -10.0

Total 40

Test Statistics VAR00006 Chi- 10.000 Square(a) df 1 Asymp. Sig. .002 a 0 cells (.0%) have expected frequencies less than 5. The minimum expected cell frequency is 20.0.

NPar Tests

Chi-Square Test

388

Frequencies

Pop/Rock vs. Equal Observed N Expected N Residual Pop/rock 14 12.0 2.0 Equal 10 12.0 -2.0 Total 24

Test Statistics VAR00009 Chi- .667 Square(a) df 1 Asymp. Sig. .414 a 0 cells (.0%) have expected frequencies less than 5. The minimum expected cell frequency is 12.0.

Follow Up Chi Square Tests For Question 4: “What is a genre/style of music that you would never be seen listening to around friends?”

Chi-Square Test

Frequencies

Classical vs. Equal Observed N Expected N Residual Classical 29 20.0 9.0 Equal 11 20.0 -9.0 Total 40

Test Statistics VAR00005 Chi- 8.100 Square(a) df 1 Asymp. Sig. .004 a 0 cells (.0%) have expected frequencies less than 5. The minimum expected cell frequency is 20.0.

389 NPar Tests

Chi-Square Test

Frequencies

Opera vs. Equal Observed N Expected N Residual Opera 26 18.5 7.5 Equal 11 18.5 -7.5 Total 37

Test Statistics VAR00008 Chi- 6.081 Square(a) df 1 Asymp. Sig. .014 a 0 cells (.0%) have expected frequencies less than 5. The minimum expected cell frequency is 18.5.

NPar Tests

Chi-Square Test

Frequencies

Other vs. Equal Observed N Expected N Residual other 23 17.0 6.0 Equal 11 17.0 -6.0

Total 34

Test Statistics

VAR00011 Chi- 4.235 Square(a) df 1 Asymp. Sig. .040 a 0 cells (.0%) have expected frequencies less than 5. The minimum expected cell frequency is 17.0.

390