VARIATION IN JOB PERFORMANCE AMONG TELECOMMUTERS
A thesis submitted to the faculty of AS San Francisco State University 3G In partial fulfillment of the requirements for the Degree
Master of Science
In
Industrial/Organizational Psychology
by
Zachary Glenn DeRossette
San Francisco, California
May 2016 Copyright by Zachary Glenn DeRossette 2016 CERTIFICATION OF APPROVAL
I certify that I have read Variation in Job Performance Among Telecommuters by
Zachary Glenn DeRossette, and that in my opinion this work meets the criteria for
approving a thesis submitted in partial fulfillment of the requirement for the degree
Masters of Science: Industrial/Organizational Psychology at San Francisco State
University.
-fir Chirk/ttfLf- Chris Wright, Ph.D. Associate Professor of Psychology
Kevin Eschleman, Ph.D. Assistant Professor of Psychology VARIATION IN JOB PERFORMANCE AMONG TELECOMMUTERS
Zachary Glenn DeRossette San Francisco, Ca 2016
This study examined the variation in job performance of employees in regards to the amount of time they spend telecommuting (i.e. telecommuting intensity). The majority of the previous research only compared telecommuting groups to non-telecommuting groups, and did not take into consideration the amount of time an employee spends away from the office. It was hypothesized that the more time an employee spent away from the office the lower their job performance would be. This hypothesis was supported. It was also hypothesized that the more time an employee spent telecommuting the more time they would spend using the internet for non-work purposes (i.e cyberloafing). This hypothesis was also supported. Personality characteristics were also examined, specifically conscientiousness. It was hypothesized that conscientiousness would play a moderating role between telecommuting intensity and both job performance and cyberloafing. These hypotheses were not supported. Limitations of the study and suggestions for future research are discussed.
I certify that the Abstract is a correct representation of the content of this Thesis
Chair, Thesis Committee Date ACKNOWLEDGEMENTS
I would like to acknowledge and thank my advisor Chris Wright, Ph.D. for his support through this process and throughout my two years in the program at SFSU. I would also like to thank Kevin Eschleman, Ph.D. for his insight to improve this study.
v TABLE OF CONTENTS
List of Tables...... vii
List of Appendices...... viii
Introduction...... 1
Significance of Study...... 1
Cyberloafing...... 10
Personality...... 11
Method...... 13
Participants...... 13
Measures...... 13
Procedures...... 15
Results...... 16
Discussion...... 17
Strengths and Limitations...... 19
Practical Implications and Future Directions...... 21
References...... 24
Tables...... 30
Appendices...... 34 LIST OF TABLES
Table Page
1. Means, Standard Deviations, and Correlation Matrix...... 30 2. Means, Standard Deviations, and Correlation Matrix...... 31 3. Moderation Table...... 32 4. Moderation Table...... 33
vii LIST OF APPENDICES
Appendix Page
1. Demographic Items...... 34 2. Questions About Time Spent Telecommuting...... 35 3. Personality Characteristics...... 36 4. Questions About Job Performance...... 37 5. Questions About Cyberloafmg...... 38 6. Additional Questions About Cyberloafmg...... 39 1
The amount of employees telecommuting is constantly growing (from 18.7% in
2004 to 23.3% in 2014 according to the Bureau of Labor Statistics), yet there is still much to be researched on the effects it has on an employee’s job performance. For the purpose
of this study, telecommuting is defined as, “A form of work organization in which the
work is partially or completely done outside the conventional company workplace with
the aid of information and telecommunication services” (Konradt, Schmook, & Malecke,
2000). Data will be collected to determine how the amount of time an employee spends
telecommuting, also referred to as telecommuting intensity, is related to both their job
performance and the frequency of engagement in cyberloafing. With so many employees
doing work at home, 23% of Americans in 2014 (Bureau of Labor Statistics, 2015), it is
important to understand what type of impact this has on their job performance.
Significance of the study
Advances in technology have made telecommuting an affordable and realistic
possibility for many companies and employees. More and more work can be done away
from the office. This trend is unlikely to stop or slow down, so it is important to examine
how this type of working arrangement can affect the job performance of an employee.
Early Research. Earlier research on telecommuting generally compared a
telecommuting group to a non-telecommuting group (Allen, Golden, & Shockley, 2015).
This does not take into consideration that many people today spend time both
telecommuting and working in the office. Some people telecommute exclusively, but 2
more frequently people have a wide range of time spent telecommuting. Someone who is telecommuting a few hours per week, compared to someone who spends almost their entire work week telecommuting, is likely to have a very different experience. It was only recently that telecommuting studies started taking telecommuting intensity into consideration, (Allen et al., 2015) which is why this study is needed.
Research suggesting positive business outcomes. The existing research on telecommuting has mixed results. In general, the research on telecommuting is mostly positive. For example, a widely cited meta-analysis by Gajendran & Harrison (2007) suggests there are a number of benefits to telecommuting. The authors examined 46 studies in natural settings involving 12,833 employees assessing how telecommuting affects many factors, including employees’ job performance. The results were divided into self-rated employee performance, supervisor ratings, or objective measures. The collective results showed that telecommuting had no impact on self-rated employee performance, but there was an improvement on the supervisor rating and objective measures. This may seem like very promising news for telecommuting. However, it is important to take into consideration that only four of the studies in this meta-analysis used supervisor ratings as criteria. The authors also mentioned that telecommuting intensity was not taken into consideration when looking at job performance.
Another study that suggests telecommuting can lead to improved job performance was conducted at a Chinese travel agency (Bloom, Liang, Roberts, & Ying, 2015). The results showed that telecommuting led to a 13% increase in job performance and also a 3
4% increase in productivity. Workers at the company volunteered for a telecommuting experiment and were randomly assigned to work either from home or the office. This experiment took place over a nine-month period, so the increase is not attributed to a temporary increase in performance due to the fact the workers knew they were being monitored. This is a very compelling study considering it was a true experiment and job performance was measured using objective data such as phone calls answered and orders placed.
Yet another study revealing promising results about telecommuting and employee job performance involves a quasi-experiment done at IBM. They compared a group of telecommuting employees to traditional employees and the results revealed that employees felt more productive at home and had fewer distractions (Hill, Miller, Weiner,
& Colihan, 1998). This survey was given to 249 service and marketing employees at
IBM. While the results of the study were positive, another study was done at the same company with slightly different results. IBM collected data from 25,822 employees throughout the world. Working from home compared to a traditional office was not found to produce a significant improvement in job performance (Hill, Ferris, & Martinson,
2003). It is worth noting, however, that telecommuting did not decrease job performance.
The differences in these results could be due to the different ways job performance was measured. The first study used self-reported job performance, and the second study used self-reported performance appraisals, which could be a reason for the conflicting results. 4
Controversy over telecommuting. Due to telecommuting being a work practice that is rather new, it is no surprise that people do not always agree about the value it provides. Despite the positive effects on business outcomes suggested by telecommuting research, sometimes telecommuting is met with resistance from managers or businesses.
In 2013, a very controversial decision was made by Yahoo CEO, Marissa Mayer, to ban telecommuting for all employees. She was widely criticized in the media for being unfair to her employees and for discontinuing a program is good for business. The New York
Times published an article criticizing Yahoo saying “Managers are tempted to use “face time” in the office as the de facto measurement of commitment and productivity. They are often suspicious about employees who work out of sight, believing they will shirk or drift if not under constant supervision” (Glass, 2013). Even Richard Branson criticized
Yahoo when he responded to Mayer’s decision by saying, “This seems a backwards step in an age when remote working is easier and more effective than ever” (Branson, 2013).
While Branson does believe telecommuting to be beneficial, he says that there needs to be a balance of remote and office working. Subsequently, researchers have started to consider the impact of telecommuting intensity.
Telecommuting intensity. More recent research has started to take into account telecommuting intensity and has shown it to be a moderating variable on many outcomes.
For example, telecommuting intensity has been found to moderate the relationship between professional isolation and job performance (Golden, Veiga, & Dino, 2008).
Researchers looked at the consequences of professional isolation as a result from 5
telecommuting. Mid-level managers were asked to rate the job performance of their direct reports who spent time telecommuting. Employees that telecommuted extensively exhibited the lowest job performance due to professional isolation. For the employees that spent limited time telecommuting, the impact on performance was negligible. This reinforces the point that not all telecommuting is equal. It is important to distinguish between telecommuters who work exclusively from home and telecommuters who also spend time in the office, as this has an impact on individual outcomes.
One study with a within-subjects design compared the performance of employees when working from home or at the office in a large government agency (Vega, Anderson
& Kaplan, 2014). They found that telecommuting significantly predicted job performance, such that individuals rated their own job performance higher when they were working from home. These findings align with the results of the study that Hill et. al
(1998) conducted at IBM. Vega et. al (2014) also investigated the impact of telecommuting on creative tasks. Participants were asked to perform a creative task while working from home. There was no significant difference in their self-rated scores of creativity, but individuals scored significantly higher on the task by trained raters.
However, this study only took place over one week, and the average time spent telecommuting was only 2 days (M= 2.13 days, SD = 1.58) out of the week. This means they spent more time in the office than at home, allowing them to still have frequent interactions with their co-workers. 6
Group Cohesion. A study done at a Bank of America call center reveals the importance of working in the same location as your co-workers (Waber, Olguin, Kim, &
Pentland, 2010). Workers were given Sociometric badges which record information such as with whom the employee talked to and for how long. The researchers were also given access to employees’ emails and their individual performance records. To measure productivity, they used average handle time, which is how many seconds the employee took to solve a customer’s problem. Calls were monitored to ensure that employees were not hanging up on customers prematurely in order to receive a higher performance rating.
Each employee’s network cohesion was measured using a kith index, which is essentially a measurement of how frequently an employee’s contacts are also connected to each other. They found that network cohesion was the single greatest predictor of productivity
(r = -.61,/? < .001, Mkuh= 1.07, SD = .41, MAHT= 263 seconds, SD = 38). In addition, the relationship between productivity and tenure was positive but not significant (r = .11, p >
.05, MTenure = 832 days, SD = 331). After the authors found these results, they decided they would try to increase the employee’s network cohesion to see if productivity would improve.
The existing break structure at the banks was designed so that no more than one employee per team would be on break at the same time. This was done so that the team would only be missing one person at a time, so there would always be someone available to answer the phone. While a strategy like this might be necessary at a very small company, call centers at a major banking company are large enough where teams do not 7
have one specialty, and the call load can easily be shifted to another team. Waber et al.
(2010) found that letting the call center workers take breaks together increased their group cohesion. It is important to note that these increases in group cohesion were not just attributed to face-to-face interactions, which were found not to be significantly predictive in this study. Neither scheduled meetings, nor people chatting at their desks significantly increased cohesion, but rather informal interactions away from the desks.
This type of serendipitous interaction is much less likely to occur when someone is not in the same physical location as their co-workers. While it cannot be guaranteed that employees will form these types of relationships if they work in an office, it is much more likely than if the employee spends all or most of their time telecommuting. They found that by scheduling workers breaks at the same time, the kith index significantly changed across the different waves of the study (p < .05) and the mean difference between the two waves was .19. At the time the study was published the performance data had not been released. However, Waber (2013) later reported, “The performance
increases associated with this intervention would conservatively yield $ 15 million in yearly savings on call center costs across BoA.” Even though precise numbers and
correlations are not available, it is clear that changing the break structure so that the
employees could have more informal interactions is positively related to productivity.
This demonstrates how co-worker relationships can contribute to job performance.
Co-worker relationships. It has been found that telecommuting intensity and co worker relationships have a negative relationship (Gajendran and Harrison, 2007). This is 8
another reason why telecommuting too much could actually hinder performance. Low
intensity telecommuting was unrelated to coworker relationship, but had a negative effect
for high intensity telecommuting. The weaker the ties with your co-workers, the less
information will be shared. A strong relationship with your coworkers is important because with more frequent communication, employees are less inhibited from seeking
information and asking for clarification in a cohesive network. Therefore, they are more
likely to understand how to correctly use the information sooner. Face-to-face
communication offers the maximal knowledge transfer in each information exchange due to it using communication channels with the richest social cues, which improves
absorptive capacity in a short amount of time (Wu, Waber, Aral, Brynjolfsson, &
Pentland, 2008).
Knowledge Sharing. Another aspect of telecommuting where the literature is
lacking is in regards to knowledge sharing. Knowledge sharing refers to the process by
which knowledge diffuses from one individual to others within the organization (Taskin
& Bridoux, 2010). Very little research has addressed the impact of telecommuting on
knowledge sharing (Allen et al., 2015). An exception to this is one study that has
examined telecommuters and knowledge sharing (Golden & Raghuram, 2010).
Telecommuters were examined over a 6-month period. Telecommuters reported on
relationship qualities (i.e. trust, interpersonal bond, and organizational commitment),
knowledge sharing, technology support, face-to-face interactions, and use of electronic
tools once at the beginning of the study, and then again 6 months later. Telecommuters 9
who reported higher trust, interpersonal bonds, and organizational commitment also reported higher knowledge sharing after 6 months. They also found that technological support and frequency of face-to-face interactions moderated the relationship between trust and knowledge sharing. The relationship was stronger with higher amounts of technological support and more frequent face-to-face interactions. This supports the idea that it is important for telecommuters to also spend time in the office, and that extensively telecommuting could lead to diminished knowledge sharing.
Reduced Distractions. While conversations with coworkers can be very useful, there is a limit to productivity gains when it comes to conversations. It has been found that disruptions during task execution can be especially distracting, as the cognitive cost of switching tasks can impede the rate of task completion (Aral, Brynjolfsson & Van
Alstyne, 2006). One of the reasons people say telecommuting increases productivity is that working from home results in fewer distractions especially when employees generally feel the office is an unpleasant or distracting environment (Chiaburu and
Harrison, 2008). While it is true distractions can get in the way of completing work tasks, there is also evidence that informal conversations with your co-workers can greatly improve your productivity (Waber, 2013).
Due to the inconsistent findings in research, it is necessary to examine the relationship between telecommuting intensity and job performance. While some studies have suggested there is a positive relationship between telecommuting and job 10
performance, it has also been shown that extensive telecommuting can lead to many negative results. Therefore, the following hypothesis is proposed:
Hypothesis 1: The relationship between the telecommuting intensity and job performance is negative and significant.
Cyberloafing
One of the most common criticisms and fears of telecommuting is that employees are not being supervised as closely, and therefore spend less time working. Cyberloafing has been defined as “any voluntary act of employees using their companies’ Internet access during office hours to surf nonwork-related Web sites for nonwork purposes, and access
(including receiving and sending) nonwork-related email” (Lim, Teo, & Loo, 2002, p.
67). Measuring how frequently an employee engages in cyberloafing while working from home when compared to the traditional office is a way to measure how valid this concern is.
Measuring cyberloafing is also another way to get an idea of an employee’s performance since cyberloafing has been associated with decreased employee performance (O’Neill, Hambley, Bercovich, 2014). While it can be argued taking small breaks and checking non-work related things during work is not harmful, and could even be considered beneficial (Lim & Chen, 2012), more severe cyberloafing would mean the
employee is not performing as well as they could. 11
Hypothesis 2: Telecommuting intensity and cyberloafing will have a significant positive relationship.
Personality
It is reasonable to assume that telecommuting will not have the same impact on every employee. Personality could play a moderating role between telecommuting intensity and both job performance and cyberloafing. It needs to be taken into consideration when looking at telecommuters. Many would agree that the Five-Factor
Model (FFM) is the best representation of personality traits, which consists of five dimensions - conscientiousness, extraversion, agreeableness, openness to experience, and neuroticism (Digman, 1990; McCrae & Costa, 1987). A summarized description of those five traits is as follows (Jia, Jia, Karau, 2013). Conscientiousness describes an individual’s impulse control that facilitates goal and task oriented behavior.
Conscientious individuals follow norms and rules, are able to delay gratification, and excel in organizing and planning. Individuals high in extraversion have an energetic approach to the social and material world. They are socially assertive, and enthusiastic.
Agreeable individuals are prosocial and have a desire to fit in with others. They are courteous, generous, warm, trusting, good-natured, and flexible. Individuals who score high on openness to experience are open-minded, original, and have a complex mental and experiential life. They are accepting of new ideas and experiences. Individuals who score high on the neuroticism scale are erratic, impulsive, and depressive. Neuroticism is opposite of emotional stability which embodies even-temperedness in contrast with 12
negative emotionality that includes feelings such as anxiousness, insecurity, and nervousness.
Conscientiousness. Of the five factors, conscientiousness has been found to be the most predictive trait in relation to job performance (Schmidt & Hunter, 2004). This study looked at all five of the characteristics and after controlling for general mental ability, found that conscientiousness was the only factor that contributed to job success.
Building on this idea, a more recent study found that conscientiousness has a positive relationship with job performance of jobs that require “independence in completing one’s work” (Judge & Zapata, 2015). This means that individuals that score high on conscientiousness will excel in jobs that require guiding oneself with little or no supervision. It would follow that these individuals would not take advantage of working out of sight of their supervisor and still maintain a high job performance regardless of the extent of their telecommuting.
Hypothesis 3: The negative relationship between telecommuting intensity and job performance is moderated by conscientiousness. Individuals who score higher in conscientiousness will score high on job performance regardless of extent of telecommuting.
Big Five Personality Traits and Cyberloafing. Multiple studies have found that conscientiousness is negatively related to cyberloafing (Jia, Jia, Karau, 2013; O’Neill,
Hambley, Bercovich, 2014). Extraversion has been found to be positively related to 13
cyberloafmg, where conscientiousness, agreeableness, emotional stability, and openness to experience were all negatively related (Jia, Jia, Karau, 2013). One of the aspects of conscientious individuals is that they tend to follow rules, so it would be expected that they would spend less time engaging in non-work activities on company time regardless of where they are working. Therefore, the following hypothesis is proposed:
Hypothesis 4: The positive relationship between telecommuting intensity and cyberloafmg will be moderated by conscientiousness. Individuals who score high on the conscientious scale will score low on the cyberloafmg scale regardless o f the amount o f time the spend telecommuting.
Method
Participants
Participants were recruited through Amazon’s M-Turk. They were age 18 or older and employed. They were provided with information about the study and the researcher’s contact information.
Measures
Demographics. Participants provided demographic information, which included age, gender, and race/ethnicity (See Appendix A). They were also asked to provide the number of years they have been working in their current job, number of hours worked per week, number of hours telecommuted per week (See Appendix B.l). The final sample 14
consisted of 196 participants, 127 were male, 68 were female, and 1 respondent did not report their gender. (Mage = 34 years, S D = 10.7, age range = 20-64 years).
Time Spent Telecommuting. Time spent telecommuting was measured using self-reported data. Participants were asked how many hours they spent working in a typical workweek, followed by asking them how many hours per week they spent telecommuting. All participants were employed. (M hoursworked= 39.5, SD = 11.6, M hours telecommuted = 8.9, SD = 12.2 )
Job Performance. Job Performance was measured using a 21-item scale developed by Williams and Anderson (1991). Participants used a 5-point scale to specify the frequency from 1 (Never) to 5 (Very Often) they engaged in certain behaviors (e.g., I meet the formal performance requirements of my job)(See Appendix B.2). Job performance was measured by averaging all of the items. (MJobperformance = 3.65, S D = .57, a = .88).
Big Five Personality. Personality was measured using the Big-Five Personality domains. In order to keep the survey brief, a modified version was used (See Appendix
B.3). Gosling, Rentfrow, and Swann Jr. (2003) developed a 10-item Big-Five inventory.
Generally, the Big-Five Inventory (John & Srivastava, 1999) is used to asses the personality domains, but in order to keep the survey brief the 10-item version was used.
The convergent correlations (mean r = .77) between the shortened scale used in this 15
study and the Big-Five Inventory are acceptable to get a general idea of a person’s personality.
Participants used a 7-point scale to indicate how strongly they agreed or disagreed
1 {Disagree strongly) to 7 (Agree strongly) about how a pair of personality traits applied to them (e.g., I see myself as dependable, self-disciplined). Personality factors were measured by averaging all of the items (a = .88).
Cyberloafing. This variable was measured using a 22-item scale developed by
Blanchard and Henle (2008). Participants used a 5-point scale to specify the frequency from 1 (Never) to 5 (Very Often) over the past month that they engaged in cyberloafing behavior (e.g., Checked non-work related email)(See Appendix B.4). Cyberloafing was measured by averaging all of the items. (MCyberloafing - 2.12, SD = 0.89).
An additional two questions were added to the scale that asked participants about the frequency that they visited social media sites and online dating sites (See Appenix
B.5). Due to the rise in the popularity of these two behaviors, they were added to the scale (original a = .97 and a = .97 if social media was removed and a = .97 if online dating was removed).
Procedures
Participants were instructed to complete an initial online survey via Qualtrics, which included items about demographic information, telecommuting frequency, personality traits, job performance, and cyberloafing frequencies. 16
There were two attention checks in the survey that instructed the participant to select a specific answer in order to determine if they were reading the questions (i.e.
“Select ‘Fairly Often’ for this question”). 22 participants who failed to answer the selected answer were removed from the data set. It is assumed these participants were not actually paying attention to the questions and answering truthfully.
Results
Pearson correlation was used to examine the relationship between telecommuting intensity and both job performance and cyberloafing. As predicted, there was a significant negative relationship between the telecommuting intensity and job performance (r = -.\S ,p < .05), and a significant positive relationship between telecommuting intensity and cyberloafing (r = .20, p < .01). Hypotheses 1 and 2 were supported (See Table 1).
Conscientiousness was not found to be a significant moderator (p = .055) between telecommuting intensity and job performance by conventional standards, but did increase the explained variance (from R2 = .28 to R2 = .30). Hypothesis three was not supported
(See Table 2).
Conscientiousness was not found to be a significant moderator (p = .64) between telecommuting intensity and cyberloafing. The interaction term did not increase the explained variance (from R2 = .09 to R2 = .09). Hypothesis four was not supported (See
Table 3). 17
Exploratory Analyses
Two additional items were added to the cyberloafing scale. “Visited social media sites” and “Visited online dating sites” (See Appendix B.5). One criticism of the
Blanchard and Henle (2008) scale is that it does not take social media into consideration
(Kim & Byrne, 2011). This is a common problem when measuring cyberloafing because technology is always changing, and the type of sites people visit is not stagnant. That is why this study also measured time spent on online dating sites, due to the recent rise in popularity. The addition of these items had a negligible impact on the scale’s reliability
(original a = .97 and a = .97 if social media was removed and a = .97 if online dating was removed), and were not included in the analysis.
Previous research has found extraversion is positively related to cyberloafing, where conscientiousness, agreeableness, emotional stability, and openness were all negatively related (Jia, Jia, Karau, 2013). The results of this study supported that finding
(See Table 2). In addition to conscientiousness, openness to experience (r = -A5,p < .01) and agreeableness (r = -.21 ,P< .01) were found to be significant.
Discussion
The purpose of this study was to examine the job performance of individuals who telecommute and assess if there was a relationship between the amount of time they spent working away from the office and their job performance. Contrary to the most recent meta-analysis, telecommuting intensity was negatively related to job performance. This 18
study also examined the relationship between telecommuting intensity and cyberloafing to see if employees were more likely to spend time on non-work related activities while away from the office. It was found that the more time an employee spent away from the office working, the more likely they were to cyberloaf.
An interesting observation about the cyberloafing data is how prevalent cyberloafing is among workers. Blanchard and Henle (2008), reported high frequencies for various cyberloafing activities: checked non-work email (90%), visiting news sites
(90%), shopping online (70%), visiting sports sites (50%), booking vacations (50%), and job hunting (40%). The participants in this study reported slightly lower frequencies: checked non-work email (81.5%), visiting news sites (79.7%), shopping online (65.6%), visiting sports sites (57.9%), booking vacations (46.2%), and job hunting (31.6%).
Blanchard and Henle (2008) reported low frequencies for more major forms of cyberloafing: visiting adult sites (5%) and visiting gambling sites (5%). However, the participants in this study reported much higher frequencies of these activities: visiting adult sites (31.6%) and visiting gambling sites (30.1%). These results indicate that cyberloafing may be prevalent and should be considered in future telecommuting research.
Finally, this study also aimed to show that individual differences in employees may moderate those relationships. No moderating effect was found, but conscientiousness was negatively associated with cyberloafing and positively related to job performance. 19
Strengths and Limitations
This study contributes to research on telecommuting by taking into consideration the intensity of the telecommuting arrangement. It also looks at how individual differences between employees could play a role in performance when working away from the office. It specifically examines how individuals who score high on conscientiousness might perform regardless of where they work.
This study uses data from all over the world, and is not limited to just companies in the United States. Many studies look at only one company at a time and compare a telecommuting group to a non-telecommuting group (Allen et al., 2015). That methodology is problematic because there could be other factors that have to do with that company’s culture that are influencing the results. For example, it could be that high performers are allowed to telecommute, therefore skewing the data. This study gets around that by interviewing people all over the world, and that work at different companies to get a less limited perspective.
There are several limitations to this study. The first is that it relies on self-reported data, including job performance. There is research that suggests the correlations between self-rated performance and supervisor-related performance is low (r = .35) with self- reports generally inflated (Harris & Schaubroeck, 1988). In an effort to decrease this artificial inflation of performance ratings, this study kept respondents’ identities anonymous to increase their trust and chances of being honest (Podsakoff, MachKenzie, 20
Lee, & Podsakoff, 2003). Also, the participants had nothing to gain from rating their performance higher than reality in this study, where in a traditional work setting they would have an incentive to rate their performance higher than it actually is.
Another limitation is that the way job performance was measured could lead to lower “performance” due to the employee not being at the office. The instrument used to measure performance was not specifically designed for telecommuting employees and contains items which may have resulted in artificially lowered performance score for telecommuters. Items such as “I assist my supervisor with his/her work (when not asked)” and “I take the time to listen to my coworkers’ problems and worries” could potentially have low scores due to a lack of awareness of the remote employee. It is easier to know your supervisor needs help with their work and your coworkers have a problem when you are working in the same location as them. However, this could also just be considered another negative outcome of telecommuting that is actually hindering one’s performance.
Another limitation is that the population was limited to Amazon’s Mechanical
Turk user base. While attention check questions were used to ensure the user was paying attention to the surveys, there is still the problem that it might not be a representative sample of the general working population. 21
Practical Implications and Future Directions
This study makes a contribution to the literature because it takes telecommuting intensity into consideration. It has been said that more research needs to examine this variable when researching telecommuting (Allen et al. 2015). The main finding of this study, that telecommuting intensity is negatively related with job performance, adds to the mixed findings in the literature. This shows that there are other variables that need to be taken into consideration. There could potentially be an amount of time that telecommuting is most beneficial, but after that threshold is exceeded the benefits begin to diminish. This could be one explanation for the mixed results the research has about telecommuting. Not all telecommuting is created equal, and telecommuting intensity needs to be included in future studies.
This study highlights the idea that telecommuting is a complex activity with many different variables impacting individual and company outcomes. Not all telecommuting is created equally, and businesses need to understand that just because one company had success with telecommuting, does not mean the results are generalizable to every company. They also need to look at the details of the telecommuting arrangements and know about the potential benefits and risks when employees telecommute. Too often the results are generalized, and this study shows that not all telecommuting results in higher job performance. There is no doubt that an employee can benefit from limiting distractions at the workplace, however they need to know that limiting these distractions 22
comes at a cost, and the negative consequences can start to outweigh the benefits, especially at extensive amounts.
One major variable that was not taken into consideration in this study is the type of job one is performing. Some jobs are more suited for telecommuting than others. It is possible that some jobs can be done more effectively remotely rather than in person and visa versa. Unfortunately, research is lacking about what job characteristics are most suited for telecommuting. Currently, telecommuting results are often generalized and presented as an average dollar figure a company could expect to see their bottom line increase. For example, GlobalWorkplaceAnalytics.com suggests that if companies in the
United States let employees with “suitable jobs” work from home half the time they would save an average of $11,000.00 per person per year (Global Workplace Analytics
(2016)). More research needs to be done to determine what qualifies as a “suitable job” before this advice should be taken by a company without any experience with telecommuting.
Future research should study individual differences in regards to telecommuting.
Just as telecommuting results are too often assumed to be generalizable to every company, they are also incorrectly assumed to apply to every employee the same. This is clearly not the case. While this study did not find that conscientiousness moderated the relationship between telecommuting intensity and job performance, the results showed a trend in that direction, suggesting the idea should be explored more in depth and with sufficient statistical power. Conscientiousness was very strongly related to job 23
performance and negatively related to cyberloafing. Cyberloafing was also negatively related to job performance. Future studies should investigate what type of personality characteristics contribute to an employee performing better or worse while working away from the office.
More studies should also be done using objective performance data.
Unfortunately, there are many limitations when using self-reported, or manager related performance data. This is generally the most convenient way to collect data, but it is very easy for the bias of the rater to get in the way of accurate data.
It is possible that a balance of working from the office and telecommuting could lead to the best performance. An employee who can take advantage of both strong relationships with their co-workers, but also have a quiet environment where they can work uninterrupted could reap the benefits from telecommuting without experiencing the negative effects. One study, however, suggested that the relationship between telecommuting intensity and job satisfaction is curvilinear in the shape of an upside down
U (Golden & Viega, 2005). Job satisfaction starts to increase as employees spend time telecommuting, but reaches a point where the benefits peak and then start to decline. It is possible this could be true for the relationship between telecommuting and job performance. It is important to remember that while it may be true that telecommuting helps limit distractions, coworkers are not only disruptions, but also valuable sources of knowledge. Future research should be conducted to clarify these relationships and see how telecommuting intensity impacts them. 24
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Table 1
Means, standard deviations and correlations Variable Mean SD 1 2 3 1. Telecommuting Intensity (Hours) 8.94 12.2
2. Conscientiousness 5.44 1.27 -.024
3. Cyberloafing 2.12 .89 .201** -.211**
4. Job Performance 3.88 .57 -.153* .508** -.454**
*p<.05. **p<.01 31
Table 2
Means, standard deviations and correlations Variable Mean SD \______2 3 4 5 1. Cyberloafing 2.12 .89
2. Conscientiousness 5.44 1.27 -.211**
3. Open To Experience 5.09 1.27 -.154* .483**
4. Agreeableness 5.13 1.29 -.210** .548** .451**
5. Emotional Stability 5.04 1.39 -.082 .540** .275** .436**
6. Extraversion 4.30 1.50 .068 .267** .466** .194** .337**
*p<.05. **p<.01 32
Table 3
Effect o f Telecommuting Intensity of Job Performance moderated by Conscientiousness Variable BSEB P
Stepl
TI -.007 .003 -.156*
Step2
TI -.006 .003 -1.24
C .226 .028 .509**
Step 3
TI x C .004 .002 .123
Note:ir , R2 =_ .024 fore . .. Step _ 1;1 . r.2R2 =_ .281 ->o 1 forr . . . Step 2;o . R2 r .2 = _ .295o n e fore_— Step 'j 3. TI = Telecommuting Intensity C = Conscientiousness Score *p < .05 **p < .01 33
Table 4
Effect o f Telecommuting Intensity o f Cyberloafing moderated^ by Conscientiousness Variable B SE B P Stepl
TI .015 .005 .205**
Step2
TI -.014 .005 .193**
C -.145 .049 -.207**
Step 3
TI x C -.002 .004 -.034
Note: R2t.2 = _ .024 for Step 1;1 . rR2>2 =_ .281 001 rfor_ Step 2;o. R2 r>2 =_ .295one ^for Step 3.'i TI = Telecommuting Intensity C = Conscientiousness Score *p < .05 **p < .01 34
Appendix A: Demographic items
1. A ge___ 2. Gender a. Male b. Female c. Other 3. Race/Ethnicity a. African American/Black b. Asian c. Caucasian d. Native American e. Hispanic/Latino f. Multi-Ethinc g. Other: Please specify__ 35
Appendix B.l Questions about Time Spent Telecommuting
For this study telecommuting is defined as:
A form o f work organization in which the work is partially or completely done outside the conventional company workplace with the aid of information and telecommunication services.
1. How long have you been in your current jo b ___years____ months 2. In a typical workweek I work______hours 3. In a typical workweek I telecommute (work remotely)_____ hours 4. At my workplace telecommuting is a. Mandatory b. Optional c. Does Not Exist 36
Appendix B.2: Questions about Job Performance
1 2 3 4 5
Never Infrequently Sometimes Fairly Often Very Often
1. I adequately complete my assigned duties. 1 2 3 4 5 2. I fulfill the responsibilities specified in my job description. 1 2 3 4 5 3. I perform the tasks that are expected of me. 1 2 3 4 5 4. I meet the formal performance requirements of my job. 1 2 3 4 5 5. I engage in the activities that will directly affect my performance evaluation. 1 2 3 45 6. I neglect aspects of my job that I am obligated to perform. 1 2 3 4 5 7. I fail to perform essential duties of my job. 1 2 3 4 5 8. I help others who have been absent. 1 2 3 4 5 9. I help others who have heavy work loads. 1 2 3 4 5 10.1 assist my supervisor with his/her work (when not asked). 1 2 3 4 5 11.1 take time to listen to my coworkers’ problems and worries. 1 2 3 4 5 12.1 go out of my way to help new employees. 1 2 3 4 5 13.1 take a personal interest in other employees. 1 2 3 4 5 14.1 pass along information to my coworkers. 1 2 3 4 5 15. My attendance at work is above the norm. 1 2 3 4 5 16.1 give advance notice when I am unable to come to work. 1 2 3 4 5 17.1 take undeserved work breaks. 1 2 3 4 5 18.1 spend a great deal of time on personal phone conversations. 1 2 3 4 5 19.1 complain about insignificant things at work. 1 2 3 4 5 20.1 conserve and protect organizational property. 1 2 3 4 5 21.1 adhere to informal rules devised to maintain order at work. 1 2 3 4 5 37
Appendix B.3 Survey Items about personality characteristics
Disagree Disagree Disagree Neither Agree a Agree Agree strongly moderately a little agree nor little moderately strongly disagree 1______2______3______4______5______6______7 I see myself as:
1. Extraverted, enthusiastic.
2. Critical, quarrelsome.
3. Dependable, self-disciplined.
4. Anxious, easily upset.
5. Open to new experiences, complex.
6. Reserved, quiet.
7. Sympathetic, warm.
8. Disorganized, careless.
9. Calm, emotionally stable.
10 ._____Conventional, uncreative. 38
Appendix B.4 Questions about Cyberloafing
1 2 3 4 5
Never Infrequently Sometimes Fairly Often Often
1. Checked non-work related email. 1 2 3 4 5 2. Sent non-work related email. 1 2 3 4 5 3. Visited general news sites. 1 2 3 4 5 4. Visited stock or investment related web sites. 1 2 3 4 5 5. Checked online personals. 1 2 3 4 5 6. Viewed sports related web sites. 1 2 3 4 5 7. Received non-work related email. 1 2 3 4 5 8. Visited banking or financial related web sites. 1 2 3 4 5 9. Shopped online for personal goods. 1 2 3 4 5 10. Visited online auctions sites (e.g., Ebay). 1 2 3 4 5 11. Sent/received instant messaging .1 2 3 4 5 12. Participated in online games. 1 2 3 4 5 13. Participated in chat rooms. 1 2 3 4 5 14. Visited newsgroups or bulletin boards. 1 2 3 4 5 15. Booked vacations/travel. 1 2 3 4 5 16. Visited virtual communities. 1 2 3 4 5 17. Maintained a personal web page. 1 2 3 4 5 18. Downloaded music. 1 2 3 4 5 19. Visited job hunting or employment related sites. 1 2 3 4 5 20. Visited gambling web sites. 1 2 3 4 5 21. Read blogs. 1 2 3 4 5 22. Viewed adult oriented (sexually explicit) web sites. 1 2 3 4 5 39
Appendix B.5 Additional Questions about Cvberloafing
1 2 3 4 5
Never Infrequently Sometimes Fairly Often Often
1. Visited social media sites. 1 2 3 4 5 2. Visited online dating sites. 1 2 3 4 5