An Examination of Customer Advocacy in a Services Context

A Thesis Submitted in Fulfilment of the Requirement for the Degree of Doctor of Philosophy

Dan Liu

School of Marketing Australian School of Business University of New South Wales August, 2012

Abstract

Customer advocacy involves strong and passionate recommendations being made by customers. However, there is little published research indicating how and why customers act as advocates for service providers for whom they have strong feelings. The primary aim of this research is to investigate the characteristics of and the contributors to customer advocacy.

The research project consists of three studies. Study 1 conceptualizes and develops a scale of customer advocacy based on both qualitative and quantitative research. Results suggest that customer advocacy is a second-order construct characterized by two dimensions - spontaneous endorsement and proactive promotion. The scale exhibits solid reliability, predictive validity and discriminant validity from the theoretically- related measure of general positive WOM.

Study 2 draws on self-determination theory to examine the determinants and contingent conditions of customer advocacy. Structural equation modelling analysis indicates that advocacy is largely driven by evaluative and motivational antecedents, while the effect of these antecedents on advocacy is moderated by situational and relational factors. In addition, the determinants of customer advocacy differ in their impact on general positive WOM. For example, the relative importance of the motivational dimensions is stronger in advocacy than in general positive WOM.

Study 3 profiles customers with varying degrees of strength of recommendation across a range of demographic, consumer-related and psychographic factors. It is found that customers who are female, heavy users and referred by other customers are more likely to produce the strongest level of recommendation. Advocates and general positive WOM communicators tend to have a greater level of agreeableness and risk-aversion. Further, latent class analysis identifies four segments of customers. These customer segments display varying degrees of recommendation strength and other attitudinal/behavioural loyalty outcomes with unique demographic characteristics.

This research appears to make the first attempt to conceptually delineate customer advocacy and to reveal the contributing influences behind the higher levels of recommendation strength. It should assist service managers in prioritizing their targeting of customers and their service improvement efforts in exploiting the power of customer recommendation as an important component of customer strategy.

To

My Dearest Late Father

Acknowledgements

First and foremost, to my husband and my family, for their utmost support throughout the PhD process. Eddie, by encouraging your wife to continuously pursue her dreams, you have shown the greatest love from a man and a husband. To my mother and my sister, you are two extraordinary women who have always given me strength in facing every challenge that comes along. Without the three of you (㘱ཤˈྸྸˈကက ), I would not be myself.

I cannot fully express my gratitude to my two supervisors. My thanks to Professor Adrian Payne, who led me to this important and interesting topic of customer advocacy. Thank you especially to Dr. Jennifer Harris, who has been so generous with her time and attention. This PhD journey could have been much lonelier and darker without your constant support. You are such a beautiful soul and you are my mentor and role model.

My sincere appreciation to colleagues in the School of Marketing who have provided invaluable advice and inspiration throughout. Special thanks to Professor Paul Patterson, Professor Mark Uncles, Associate Professor Jack Cadeaux, Professor Ashish Sinha, Dr. Julien Cayla and Dr. Christine Mathies who have offered precious suggestions during the stage of proposal development. The Australian School of Business and SYLFF have provided financial support to my research, without which the completion of this thesis would have been impossible.

And finally, I am grateful to my fellow PhD students and friends for being right by my side during moments of joy, stress and frustration. A special thank you to Heather. The decision to move in and become your flatmate has turned into such an important, enjoyable and memorable chapter of my Australian experiences. Not every Chinese student is as lucky as I am - being able to find a sister like you in a western culture. I have been also very fortunate to get to know you all - MJ, Hazel, Zhirong, Euphie and Jimmy Liao, who have become, and will remain, my lifetime friends.

Table of Contents

Chapter 1: Introduction...... 1

1.1 Overview ...... 1

1.2 Research on Customer Recommendation ...... 3

1.3 Research Objectives ...... 5

1.4 Research Context ...... 6

1.5 Research Approach...... 8

1.6 Structure of the Thesis ...... 9

1.7 Contributions ...... 10

Chapter 2: Customer Advocacy: Conceptualization and Scale Development ...... 21

2.1 Introduction ...... 22

2.2 Conceptualization of Customer Advocacy ...... 24

2.2.1 Advocacy in Marketing ...... 25

2.2.2 Philosophical and Social Foundations of Advocacy ...... 26

2.2.3 Conceptual Dimensions of Customer Advocacy ...... 27

2.2.4 Study 1: Exploratory Qualitative Study ...... 29

2.3 Development of a Customer Advocacy Scale ...... 34

2.3.1 Item Generation ...... 34

2.3.2 Study 2: Pre-test - Item Purification...... 35

2.3.3 Study 3: Item Purification ...... 36

2.3.4 Study 4: Reassessment of the Reliability and Validity of Customer Advocacy Scale ...... 43

2.4 Discussion ...... 44

2.4.1 Theoretical Contribution ...... 45

2.4.2 Managerial Implications...... 46

2.5 Further Research and Limitations ...... 47

Chapter 3: Determinants of Customer Advocacy and their Differential Impact on General Positive Word-of-Mouth ...... 61

3.1 Introduction ...... 62

3.2 Literature Review ...... 67

3.3 Hypotheses ...... 74

3.3.1 Service Quality and Customer Expertise as Antecedents of Confidence ...... 74

3.3.2 Confidence as a Mediator of the Effects of Service Quality and Customer Expertise on Customer Advocacy ...... 77

3.3.3 Altruism towards Service Providers and Opinion Leadership as Antecedents of Customer Advocacy ...... 80

3.3.4 Moderating Effects of Service Types and Relationship Quality ...... 83

3.4 Method ...... 88

3.4.1 Study 1 ...... 88

3.4.2 Study 2 - Extended study ...... 104

3.5 Discussion ...... 109

3.6 Theoretical and Managerial Implications ...... 114

3.7 Limitations and Future Research ...... 119

Chapter 4: Profiling Customers with Varying Degrees of Recommendation Strength ...... 148

4.1 Introduction ...... 149

4.2 Stage 1 Study - Profiling Customer Referrers ...... 153

4.2.1 Demographic Characteristics ...... 153

4.2.2 Consumer-related Characteristics ...... 158

4.2.3 Psychographic Characteristics ...... 162

4.3 Method ...... 166

4.3.1 Sample ...... 166

4.3.2 Measures ...... 168

4.3.3 Data Analysis and Results ...... 169

4.4 Stage 2 Study - Profiling Customer Loyalty Segments ...... 174

4.4.1Literature Review ...... 175

4.4.2 Sample and Measures ...... 176

4.4.3 Data Analysis ...... 177

4.4.4 Results ...... 178

4.5 Discussion ...... 181

4.6 Managerial Implications...... 186

4.7 Limitations and Future Research ...... 188

Chapter 5: Conclusions ...... 212

5.1 Synopsis ...... 212

5.2 Managerial Implications...... 216

5.3 Limitations and Future Research ...... 218

5.3.1 Empirical Extensions ...... 219

5.3.2 Conceptual Extensions ...... 220

5.3.3 Alternative methodologies ...... 223

Chapter 1: Introduction

1.1 Overview

Customer recommendation by word-of-mouth (hereafter ‘WOM’) has attracted increasing attention from practitioners and academics in the past decade as a result of its unambiguous power and benefits. It is a key force that influences how people think and what people buy (Day, 1971; Leskovec, Adamic and Huberman, 2006; Liu, 2006). For customers, recommendations from a non-commercial source provide a credible information channel (Mangold, Miller and Brockway, 1999; Murray, 1991). Recommendation by WOM is also convenient for customers in saving search time and effort (East, Vanhuele and Wright, 2008). For firms, customer recommendations increase new customer acquisition and sales revenues efficiently (e.g., Godes and Mayzlin, 2009; Schmitt, Skiera and Bulte, 2011).

While recommendation refers to the conative advice in favour of a focal object (product/firm/brand) (Mazzarol, Sweeney and Soutar, 2007; Swan and Oliver, 1989), WOM has a slightly broader meaning that also includes mere discussions or exchange of any information about a product or service offerings of a firm among individuals (Arndt, 1967; Brown et al., 2005; Dichter, 1966). Unsurprisingly, the impact of both customer recommendation and WOM has been observed in a wide range of product and services sectors such as entertainment (e.g., Chevalier and Mayzlin, 2006; Zhu and Zhang, 2010), hospitality (Hartline and Jones, 1996), automobiles (Brown et al., 2005) or the healthcare industry (Iyengar, Bulte and Valente, 2010).

Research has made substantial progress in understanding the consequences (e.g., Trusov, Bucklin and Pauwels, 2009; Wangenheim and Bayon, 2004) or contributors (e.g., Anderson, 1998; Zeithaml, Berry and Parasuraman, 1996) of customer recommendation and WOM. Nonetheless, they are probably the world's most effective, yet least understood marketing forces (Misner, 1999). For example, there are different forms of

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customer recommendation and WOM, which can be negatively or positively, implicitly or explicitly, weakly or strongly expressed. As a form of passionately and strongly expressed recommendation, customer advocacy has been proposed as the ultimate test of customers’ relationship with an organization (Bendapudi and Berry, 1997; Christopher, Payne and Ballantyne, 1991). It provides strategic value for firms seeking to maintain their superiority in the competitive environment (Lawer and Knox, 2006; Urban, 2004). However, despite the rich and growing body of literature on customer recommendation or WOM, research to date mostly focuses on the more general form of positive WOM that does not emphasize the strength of recommendation. Little is understood about what characterizes customer advocacy and what leads to passionate and strong levels of customer recommendations. Hence, the focus of the thesis is customer advocacy that features passionate and strong recommendations in the context of customer-customer communication. This is in contrast to previous organizational perspective that tends to view customer advocacy as a strategic customer-oriented behaviour (Lawer and Knox, 2006; Urban, 2004).

Today's information-abundant and competitive business environment (Shardanand and Maes, 1995) has resulted in a marketing reality where the strength of recommendation matters. Interactions among customers are frequent (Berger and Schwartz, 2011), while the sheer volume of WOM information has increased explosively along with the widespread use of the Internet and the emergence of user-generated WOM websites (Godes and Mayzlin, 2004). In the midst of a voluminous amount of WOM information, strong recommendations from trustworthy customers are likely to assist firms in standing out from the crowd, given that the strength of WOM expression has a significant influence on its effectiveness (East, Hammond and Lomax, 2008). Healthcare practitioners have long recognized the impact of strong recommendations (Ferguson, Strauss and Toy, 1994) and the merits of an explicit grading system of recommendations publicized to patients (Guyatt et al., 2008; Schünemann et al., 2006). Practitioners' proactively initiated and strongly-expressed recommendations substantially influence patients' follow-up behaviours (Ferguson et al., 1994). By contrast, moderate recommendations featured by reactive responses and less assertive

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comments such as 'mentioned it, but said it was up to me' are much less successful in driving patients' desirable follow-up actions. Therefore, the study of customer advocacy that focuses on the strength of recommendation is strategically important for businesses.

In the following sections, major research streams in customer recommendation are reviewed. This is followed by an outline of the research context, research approach and the structure of the thesis. Lastly, the overall contributions of the research are discussed.

1.2 Research on Customer Recommendation

Customer recommendation has been viewed from different perspectives in the literature. It has been studied as an input, when it is seen as the information that customers receive from others. The effect of recommendations on the audience is the focus of this research stream. Customer recommendation has been found to reduce perceived risks (Roselius, 1971) and to impact product evaluation and judgement (e.g. Bone, 1995; Herr, Kardes and Kim, 1991). On an aggregate level, customer referrals positively influence new product adoption, product sales and customer lifetime value (e.g., Chevalier and Mayzlin, 2006; Villanueva, Yoo and Hanssens, 2008). Researchers have also examined the conditions under which referrals are likely to be more effective. For example, the source credibility, the closeness of the relationship or the similarity between the communicator and the audience (e.g., Bansal and Voyer, 2000; Gilly et al., 1998), as well as the product characteristics (Zhu and Zhang, 2010) are all relevant to the effectiveness of customer recommendation.

Another perspective adopts a process approach, typically exploring individuals' network configuration and information structure in the process of spreading information. This stream is related to the impact of social contagion on product diffusion (Christakis and Fowler, 2011), the referral flows in the social environment (e.g., Brown and Reingen, 1987; Reingen and Kernan, 1986), or the underlying process of WOM (Goldenberg, Libai and Muller, 2001). The work of this stream is in line with the increasing 3 | Page

importance placed on the role of customer communities, networks and groups (Cova and Cova, 2002; Muniz Jr and O'Guinn, 2001).

The third approach, which is adopted in this research, is the outcome perspective. This research stream regards customer recommendation as a consequence of certain stimuli. The perspective focuses on the production of customer recommendation and its contributing factors. Findings of this stream closely reflect the characteristics of WOM communication. For example, although the majority of WOM conversations are produced spontaneously (Carl, 2006), behind the spontaneity customers tend to have cognitive evaluations in the first place. East et al. (2008a) maintain that a major determining factor of the production of WOM is customers' evaluation of the product or the service. Hence, typically embedded in the antecedents of customer recommendation or WOM is the evaluative dimension, such as customer satisfaction (e.g., Anderson, 1998; Swan and Oliver, 1989), service quality (e.g., Zeithaml et al., 1996) and service recovery efforts (e.g., Bowman and Narayandas, 2001; Maxham, 2001; Swan and Oliver, 1989). While these antecedents concern the evaluation of the product/service performance, some other determinants of recommendation or WOM are related to the evaluation of the benefits received versus the sacrifice paid, such as the perceived value (e.g., Hartline and Jones, 1996).

Additionally, WOM communication is inherently a social phenomenon (Brown and Reingen, 1987). Work on social capital (Burt, 1992) and social learning (Ellison and Fudenberg, 1995) argues that individuals produce or transmit information for social purposes. For example, the engagement in WOM or customer recommendation is motivated by the desire to help friends or relatives make a better purchase decision (Hennig-Thurau et al., 2004; Sundaram, Mitra and Webster, 1998), to enhance one's self-image (Wojnicki, 2006), or to gain attention in a social environment such as the need to be seen as being unique (Cheema and Kaikati, 2010) or being smart in shopping (Wangenheim, 2005). Hence, the motivational dimension represents another major line of contributors to the generation of customer recommendation.

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On the other hand, WOM is a relational outcome. Based on social exchange theory (Blau, 1964), commitment (Bettencourt, 1997; Gruen, 1995) and trust (Garbarino and Johnson, 1999; Gremler, Gwinner and Brown, 2001; Ranaweera and Prabhu, 2003) have been confirmed to directly contribute to the tendency of WOM production, while these constructs are typical manifestations of relationship development (Moorman, Deshpande and Zaltman, 1993; Morgan and Hunt, 1994).

Therefore, the generation of customer recommendation or WOM involves the evaluation of the product or the service. Customers are motivated to spread information based on social purposes. Additionally, customer recommendation is the consequence of relational bonds. Thus, the evaluative, motivational and relational dimensions are important aspects when considering the generation of customer recommendations.

1.3 Research Objectives

Despite the variety of research perspectives on customer recommendation or WOM, customer recommendation has been commonly treated as a uniform and undifferentiated concept with respect to its manner or strength. The majority of existing measurement instruments focus on the occurrence, the valence (the positive or negative nature) or the frequency of WOM and customer recommendation activities (e.g., File, Cermak and Prince, 1994; Swan and Oliver, 1989; Zeithaml et al., 1996). The details of message content and delivery style have been largely ignored. Thus, marketing scholarship fails to capture certain extreme forms of recommendation such as customer advocacy. More importantly, managers are uncertain as to what makes customers go the extra mile to act as a strong and passionate promoter for a firm. Strong recommendations are much more desirable for firms, compared to moderate recommendation strength that typifies positive comments with less passion, conviction and faith in the firm. Therefore, the present research aims to investigate what contributes to customer advocacy.

To address this focal research question, customer advocacy needs to be clearly defined and measured. A further step involves the identification of the determinants of customer 5 | Page

advocacy, as well as the contingent factors that influence the relationship between these determinants and customer advocacy. Moreover, it will be useful to identify the unique profiling characteristics of advocates in comparison to customers with a weaker recommendation strength, which will enhance the understanding of the contributors to customer advocacy. Hence, this research has three specific objectives:

1. To conceptualize and develop a scale of customer advocacy that captures strongly expressed recommendations.

2. To identify and empirically examine the antecedents and moderators of customer advocacy.

3. To profile customers with varying degree of recommendation strength on a comprehensive range of customer characteristics and loyalty outcomes.

1.4 Research Context

This research is conducted in the services context. Customer recommendation is highly desirable in services sectors. In contrast to goods, intangible elements usually dominate the value creation in service deliveries. This makes it harder for customers to evaluate the service and distinguish the performance of a provider from its competitors (Lovelock, Patterson and Walker, 2007). As services are produced and consumed at the same time, the pre-purchase trial of services is impossible (Zeithaml, Parasuraman and Berry, 1985). Thus, the perceived risk is amplified in the context of services due to its unique characteristics (Murray and Schlacter, 1990).

Customer recommendation serves to minimize the problems of low comparability and few search qualities associated with services (Bristor, 1990; File, Judd and Prince, 1992). With no vested interest in selling the service, the source of recommendation is seen as independent from the service provider (Murray, 1991; Silverman, 2001). Hence,

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customer recommendation facilitates problem solving and enhances risk reduction (Lampert and Rosenberg, 1975).

Customer recommendation has been shown to be influential in almost all types of services. Examples are seen from complex credence services (Duhan et al., 1997; East et al., 2005) and professional services, such as medical services and legal services (File et al., 1992), to experience services that are easier to evaluate such as hairdressing (Maxham, 2001), or to more standardized services with less human contact such as retailing, dry cleaning and banking services (East et al., 2005; Gilly et al., 1998; Higie, Price and Feick, 1987). The present study aims to capture the diverse nature of services by examining customer advocacy in four service categories based on two important service dimensions - the degree of customer-employee contact and the credence/experience nature of services (Bowen, 1990; Lovelock, 1983). These four categories are experience and high contact services; experience and low/medium contact services; credence and high contact services; and credence and low/medium contact services. Figure 1 illustrates these four categories and services involved in each service category as research settings in the study.

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FIGURE 1 Research Service Settings

HiDegree of Customer-Employee Contact Low

•Hairdressing •Basic banking services •Personal trainers (excluding investment) •Education •Telecommunications Experience •Beauty salons •Dry cleaning Services •Massage services •Airlines •Childcare

•Car servicing •Medical services (e.g., •Financial advisors GPs, Dentists, •Computer repairs Credence Physiotherapists) Services •Accountants •Legal services •White goods repairs (e.g., fridge, TV)

1.5 Research Approach

This research adopts a mixed-method approach based on a combination of both qualitative and quantitative studies. The focus of the research - customer advocacy - is an under-explored concept in the extant literature, thus, qualitative research is appropriate in providing insightful meaning for the exploration of this central phenomenon (Creswell, 1994; 2002). There are a number of forms of qualitative research such as semi-structured interviews, narrative analysis, focus groups and participant observations. The current research employs in-depth interviews, as they provide more opportunities for the individuals to discuss their own experiences in depth and at length (Bogdan and Biklin, 1982).

The qualitative research sheds light on the relationship among variables found in the literature, whereas the quantitative research aims for the prediction and the explanation of the relationship (Creswell, 2002; Salomon, 1991). It is therefore important to use the

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quantitative method in validating the proposed conceptual model of customer advocacy. Surveys, the specific technique employed in the present study, have been widely used in the services context in prior WOM research (e.g., Bowman and Narayandas, 2001; Maxham, 2001; Swanson and Kelley, 2001). They are appropriate here as they can be readily adapted to obtain personal and social facts, perceptions and attitudes (Kerlinger, 1973) as the primary objective of the present study is to understand customer advocacy as an individual and social phenomenon.

1.6 Structure of the Thesis

In exploring the focal question of what contributes to customer advocacy, this thesis is structured around a series of independent yet closely related research papers. The first study (Chapter 2) addresses the first research objective by providing the conceptualization and measurement of a customer advocacy scale. It examines the characteristics of customer advocacy based on a synthesized literature review from the work in the marketing, philosophy, sociology and communications domains, as well as a number of interviews with customer advocates. The subsequent multi-staged scale development process involves the test of the scale's reliability, convergent validity and discriminant validity from conceptually related measurements. Results indicate that customer advocacy is characterized by two distinctive dimensions - spontaneous endorsement and proactive promotion. The predictive validity of customer advocacy on behavioural loyalty is examined and established.

Using the scale developed in Study 1, Study 2 (Chapter 3) addresses the second research objective and investigates the antecedents and moderators of customer advocacy. The study proposes a conceptual model of customer advocacy building on self- determination theory (SDT). The model identifies the determining factors of customer advocacy from the evaluative and motivational perspectives which are analysed with structural equation modelling. The study further indicates that the extent to which the predictive power of these determinants of customer advocacy is moderated by relational

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and situational factors. Additionally, the study explores and reveals that these determinants have a differential impact on general positive WOM.

To address the third research objective, Study 3 (Chapter 4) discriminates between advocates, general positive WOM communicators and non positive WOM communicators on a comprehensive set of customer characteristics. Demographic, consumer-related and psychographic variables are examined and are found to be effective in profiling customers of varying degrees of recommendation strength. Moreover, as customer recommendation is commonly considered a loyalty outcome, these customers are also profiled along a broader range of loyalty dimensions. Latent class analysis uncovers four customer segments that exhibit customer loyalty heterogeneity with respect to the recommendation strength and other dimensions of attitudinal and behavioural loyalty.

Finally, Chapter 5 discusses the theoretical and managerial implications of the research. Limitations and directions for future research are also presented.

1.7 Contributions

The current research contributes to the customer recommendation and WOM literature in several important ways.

First, the study identifies and conceptualizes a unique and extreme form of customer recommendation. It reveals the directness, explicitness and proactive-ness of customer advocacy when customers actively promote a service provider. It distinguishes customer advocacy from general positive WOM that mainly characterizes positive comments and an exchange of product and brand-related marketing messages and meanings (Arndt, 1967; Brown et al., 2005). Relative to general positive WOM, customer advocacy has different manifestations in customer-customer communication, different influencing forces and even different loyalty outcomes. These differences should have specific implications for marketers who wish to leverage customer communicative power both 10 | Page

online and offline, such as building customer confidence to encourage advocacy behaviour.

Second, this research is among the first to examine factors leading to strong recommendations using a structured approach. Determinants of strong recommendations have been previously assumed to be similar to those of general positive WOM, such as service quality (Zeithaml et al., 1996), satisfaction (Anderson, 1998), altruism towards other customers (Sundaram et al., 1998) or involvement (Richins and Root-Shaffer, 1988). However, this investigation identifies different and specific antecedents of upper level of recommendations (i.e., customer advocacy). It shows that the forces contributing to customer advocacy are complex, charged with customer confidence and shaped by the desire to help a service provider and to influence fellow customers. Additionally, the study reveals the contingency conditions under which advocacy behaviour is more or less likely to occur. For service managers, this enriches the understanding of the possibilities, and limits, of recommendation strength improvement strategies.

Third, customer advocacy signals an advanced level of loyalty behaviour. The newly developed measurement of customer advocacy has sound psychometric properties and lays a practical groundwork for further examination of this highly desirable customer outcome. Understanding this customer outcome should provide firms with opportunities to create more meaningful service experiences. Moreover, the new scale presents managers with a diagnostic tool for service performance assessment and tracking purposes. Managers may now establish baseline levels of advocacy within a service firm and then target areas for improvements.

Fourth, the present study appears to be the first in profiling customers with varying degrees of recommendation strength on a broad range of demographic, consumer- related and psychographic characteristics. It sheds light on consumer segments that are more or less predisposed to making recommendations and especially strong recommendations. These findings inform service firms of reliable and useful customer

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characteristics based on which effective segmentation and targeting strategies to achieve customer advocacy can be formulated.

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Chapter 2: Customer Advocacy: Conceptualization and Scale Development

Abstract

This chapter conceptualizes and develops a scale of Customer Advocacy - the strongly expressed recommendations as an ultimate test of customer-firm relationship. The research involves both qualitative and quantitative phases comprising four studies across four major service categories. Item purification and assessment processes generated a parsimonious and psychometrically sound scale of customer advocacy. Results indicate that customer advocacy is two-dimensional and is characterized by spontaneous endorsement and proactive promotion. Advocates tend to be both explicit and passionate in urging people to patronize a service provider. They proactively promote a service provider and they defend the provider in the face of criticisms. This is in contrast with general positive WOM which is often passively generated by enquiries and may be much less explicit. The research advances the understanding of WOM phenomena by revealing the content and style characteristics of strong recommendations. This study represents the initial attempt to specifically investigate the dimensions of strength of recommendation from the communicators' perspective.

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2.1 Introduction

Customer advocacy has recently developed into one of the most exciting areas in marketing. Companies in a wide range of industry sectors have newly established Customer Advocacy Units or Departments. These sectors include: financial services (e.g., Citibank, Bank of America, ANZ Bank); telecommunication services (e.g., Foxtel) and IT services (e.g., Cisco). Both practitioners and academics acknowledge that customer advocacy is a strategic imperative. Hitachi Consulting Corporation believes that “customer advocacy will become the single most important strategic initiative that cutting-edge, forward-thinking, innovative companies will adopt in the very near future” (Hitachi, 2006, p.3). In the marketing literature, advocacy has been recognized as the ultimate test of customers’ relationship with an organization (Bendapudi and Berry, 1997; Christopher, Payne and Ballantyne, 1991) and a key goal for companies seeking to achieve a sustainable competitive position (Urban, 2004).

Despite its importance, customer advocacy is a comparatively new concept in the marketing literature and its conceptualization remains unclear. Advocacy has been used interchangeably with positive word-of-mouth (hereafter ‘WOM’) (e.g., Chung and Darke, 2006; White and Schneider, 2000) or recommendations (e.g., Peck et al., 1999). Both WOM and recommendations are broad terms (Swan and Oliver, 1989) including weak or strong, implicit or explicit, and plain or passionate communications. Advocacy, however, is strongly expressed recommendations (Wilson, 1994) associated with a somewhat forceful manner (Krapfel, 1985). Hence, advocacy is a specific form of communication (O'Keefe, 1998) or an extreme form of positive WOM communication. In contrast, most positive WOM research concerns the more general form of positive WOM, which features informal conversations between non-commercial communicators (Harrison-Walker, 2001). Therefore, the emphasis is more on information exchange than the strength of recommendations.

The specifics of strong expressions in advocacy have rarely been explored with respect to the dimensions of content, language or manner of expression, all of which are 22 | Page

important characteristics underlying WOM messages (Sweeney, Soutar and Mazzarol, 2012). This gap in the extant research reflects the notion that many conceptualizations of WOM are still simplistic (Mazzarol, Sweeney and Soutar, 2007). To date, WOM literature has been primarily concerned with the valence and the frequency of WOM and recommendation activities (e.g., Anderson, 1998; Harrison-Walker, 2001; Zeithaml, Berry and Parasuraman, 1996), with much less attention paid to the message itself or the manner by which the WOM message is delivered. For a few exceptions, see studies by Mazzarol et al. (2007), Schellekens, Verlegh and Smidts (2010) and Sweeney et al. (2012).

In this article, the focus is the strength of recommendations with the development of both a conceptual analysis and a customer advocacy scale. With consumers' growing distrust of marketing claims (Urban, 2004) and the rising levels of consumer sophistication (Macdonald and Uncles, 2007), there has been an increasing level of recommendation seeking and provision activities by customers (Bernoff and Li, 2008) in both online and offline environments. For a firm to stand out from its competitors, an effective and fast means to achieve distinction is through advocacy, based on a substantial number of strong recommendations. Social psychologists and communication scholars have confirmed the effectiveness in attitude change of explicit arguments such as advocacy (Cialdini, 1971; O'Keefe, 1997). Similarly, recent studies in marketing observe that the strength of WOM expression has a significant influence on its effectiveness, for example, the change of a listener's perception of a service organization (East, Hammond and Lomax, 2008; Sweeney et al., 2012). A strong recommendation about a product or service experience provided by advocates, along with detailed and passionate explanation of why it is recommended, is much more likely to attract attention and action from an audience than mild WOM recommendations. Hence, a fuller understanding of the strength and nature of advocacy is required if firms are to improve their organizational competitiveness.

By assessing the strength of recommendation in customer advocacy, this research contributes to WOM literature in three important ways: (1) this multi-phased and multi-

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context study is the first attempt to provide a conceptualization and a psychometrically sound measure of customer advocacy; (2) the research offers insights into the extent to which customers passionately support a firm with intensified effort, which signals an advanced level of customer loyalty worthy of further examination and, (3) it identifies a unique form of customer-customer communication which is related to but distinct from general positive WOM, providing a new context for research in the WOM area.

This article is organized as follows. First, the concept of customer advocacy is explored. Various perspectives of advocacy are synthesized based on the literature in the marketing, philosophy, sociology and communications domains. Second, the development of a preliminary scale of customer advocacy is outlined and the assessment of the scale's psychometric properties is presented. This is followed by the discussion of the results of an additional study undertaken to further assess the reliability and validity of the scale. Last, the chapter concludes with the theoretical and managerial implications of the work and directions for future research.

2.2 Conceptualization of Customer Advocacy

Advocacy has been recognized since ancient Greek and Roman times (Hanrahan, 2003). The act of advocating is defined as “to plead in favour of” (Merriam-Webster Online Dictionary, 2011). As such, advocacy is positive communication (O'Keefe, 1998) with intensified explicitness and strength of support.

To further conceptualize customer advocacy, this section starts with a review of customer advocacy in the extant marketing literature and then considers its philosophical and social perspectives. The communication and language style literature is reviewed next in order to better understand key dimensions of customer advocacy as a specific form of customer-customer communication.

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2.2.1 Advocacy in Marketing

Customer advocacy is a relatively new concept in marketing and work in this area has generally followed one of two streams. One school of research takes a simplified view, by using advocacy interchangeably with positive WOM or recommendations (e.g., White and Schneider, 2000). WOM is an aggregate term, suggesting any person-to- person communication about the target object at any point in time (Rosen, 2000). There are various forms of WOM including simple information exchange (discussion), further favourable advice given (moderate recommendation), or strong recommendation (advocacy). For example, Swan and Oliver (1989) define discussions as the evaluative aspect, while recommendation is an additional dimension characterized by the conative advice made in favour of the firm. In marked contrast, by giving ‘public support’, advocacy tends to suggest an explicit and strong statement with a somewhat forceful manner (Krapfel, 1985). Hence, advocacy is always strong, passionate and explicit in favour of the focal object (e.g., a product or service). Thus, advocacy could be considered as an extreme form of positive WOM recommendation.

A second stream of customer advocacy research in marketing adopts a managerial perspective in which customer advocacy is considered as an advanced form of customer orientation through developing mutual transparency, dialogue and partnership with customers (Lawer and Knox, 2006; Urban, 2004). In return, customers are likely to reciprocate through their advocacy behaviour. However, the manifestations and processes of this reciprocal behaviour have not yet been explored.

Therefore, extant marketing literature falls short of developing a clear and appropriate definition of customer advocacy with its unique characteristics, although it does shed light on its prerequisite (organizational effort) as well as some of its characteristics (WOM and recommendation). In order to fully explore the nature of customer advocacy, it is pertinent to review relevant work in philosophy, sociology, and other disciplines in which advocacy has been discussed and practised.

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2.2.2 Philosophical and Social Foundations of Advocacy

Roman philosophers believed that advocates represented a high order of justice for civic improvement and made persuasive public speeches (Aristotle, 1960). Cicero pointed out that key dimensions of advocacy were the authenticity of the speaker and the appeal of emotion and logic to the audience (the skills of ethos, pathos, logos) (Hanrahan, 2003). Hence, advocates stand up for what they believe and deliver in an overt and straightforward manner (Greenwald and Albert, 1968). They do so proactively, as the disparity between ‘what is’ and ‘what ought to be’ propels them to take an active public stand (Nelson, 1966).

Advocacy has been practised in a wide variety of social functions in modern society including politics, public health, legal affairs and political economy. Advocates either passionately favour a standpoint or move the audience’s attitude away from an original standpoint, in an effort to effect a change or improvement of existing conditions (Mallik, 1997). Advocates spontaneously stand up and publicly support a cause or a party whenever it is possible, with defending a cause frequently seen in political and legal advocacy (Dewatripont and Tirole, 1999; McAdam, 1988). Fundamentally, the advocated position is considered to be in the best interests of the audience and/or certain groups in a society (e.g., patients) (Kohnke, 1982).

When customers act as advocates, advocacy can be considered a customer extra-role discretionary behaviour, which is not required and formally rewarded by a firm (Groth, 2005). Customers’ extra-role behaviour is grounded in social exchange theory (Blau, 1964). When customers receive perceived benefits from an organization, they may engage in voluntary behaviour that is beneficial to the organization (Bettencourt, 1997). In addition, as advocates communicate what they perceive as being in the best interests of their audience, this satisfies individuals' interpersonal goals in communication to maintain or enhance the relationship with others (Higgins et al., 1981; Rubin, Perse and Barbato, 1988). 

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The philosophical origin, existing practices and social grounding of advocacy provide clarification of some fundamental characteristics of customer advocacy. It is the communication of a strong belief and support of a subject that is considered as benefiting the audience. Additionally, advocacy is delivered publicly, voluntarily and proactively, involving passion, persuasion and even defence of the advocate’s point of view.

Drawing on these perspectives, this research adopts the following definition of customer advocacy:

Customer advocacy is the strong, passionate and explicit recommendation from customers, who voluntarily promote the focal object (products or services) against the market alternatives.

2.2.3 Conceptual Dimensions of Customer Advocacy

The construct of customer advocacy is now further explicated by reviewing the communication behaviour and ‘language style’ literature. At its core, advocacy is communication (O'Keefe, 1998; Thackeray and Hunter, 2010). Every communication has two inextricable components – a message content and a style (Watzlawick, Beavin and Jackson, 1967). The content suggests the literal meaning and the depth of information involved in the message, while the style refers to the manner that the message is delivered (Sweeney et al. 2012) which results in how the literal meaning “should be taken, interpreted, filtered or understood” (Norton, 1983, p.19). These two aspects have been considered fundamental to WOM communication (Gremler, 1994; Mazzarol et al., 2007).

In the context of customer advocacy, the content component will focus on communication that highly praises the service provider. The style component, however, can be more complex. There have been various perspectives on the dimensions of communication styles explored by scholars (e.g., Burgoon and Hale, 1987; Norton,

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1978). The five dimensions identified by Wish, D'Andrade and Goodnow (1980) are adopted in this research because their work was based on real interpersonal conversations in contrast to experimental or survey settings. Therefore these dimensions are able to capture the actual dynamics of social interactions typified by advocacy as a communication behaviour. These dimensions are: asking versus informing, initiatory versus reactive, dissension versus approval, forceful versus forceless, and judgmental versus non-judgmental.

The first dimension focuses on the nature of information exchange based on requesting information, as opposed to giving information spontaneously. The second dimension concerns the initiation of communication, measuring the degree to which an individual initiates or leads the conversation. The third dimension is dissension versus approval. It refers to the likelihood of agreement or disagreement between the communicator and the listener. The fourth and fifth dimensions are forceful versus forceless and judgmental versus non-judgmental. These last two dimensions involve the degree to which the communicator’s opinion is strongly expressed as forceful and judgmental, and are closely related to two important aspects in communications - language intensity and language abstraction.

Language intensity, such as forcefulness, refers to the degree and direction of distance from neutrality (Burgoon, Jones and Stewart, 1975). It is associated with passion surrounding an issue or an opinion, which can increase the effectiveness of persuasion (Huffaker, 2010; Ng and Bradac, 1993). Language abstraction refers to the degree of abstraction of verbs and adjectives that individuals use in their everyday descriptive language (Semin and Fiedler, 1988). According to the linguistic category model (LCM) of Semin and Fiedler (1988), more abstract language such as judgmental adjectives (e.g., “Bruce is aggressive”), as opposed to the concrete level of descriptive verbs (e.g., “Bruce hits him”), displays specific characteristics of the subject as well as the ‘bias’ of the communicator (Douglas and Sutton, 2003). As explained by McCroskey and Wheeless (1976), our beliefs, whether true or not, are fundamental to the communicative strategies we adopt. Often, despite the conscious intention to

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communicate objectively and truthfully, individuals produce utterances that are flavoured by their own opinions and beliefs (e.g., Maass et al., 1989). Recent WOM research has confirmed that the use of more abstract language in positive WOM increases the persuasiveness of WOM and the listeners’ buying intentions (Schellekens et al., 2010).

Taken together, these theories on communication content and style have specific implications for the dimensions of customer advocacy behaviour. As advocates are fuelled with passion or even 'bias' towards a subject, the dimensions of their communication behaviours regarding, for example, a service provider are likely to be associated with these characteristics: (1) Content: pertaining to highly positive comments such as details of great experiences as well as a strong recommendation and, (2) Style: it is anticipated that the nature of the communication style is informing and initiatory in customer advocacy. Alternatively stated, advocates are likely to initiate the conversation and pass the information to the audience. Furthermore, given the strong belief in and the passionate support of the service provider, they are not likely to be convinced by criticisms from listeners. Instead, they tend to use intensified and abstract language with highly subjective opinions about the provider to persuade the audience. As such, the communication style of customer advocacy may also involve dissension (rather than approval in the face of criticisms) and the inclination of being forceful and judgmental in the conversation.

The degree to which these predictions align with actual advocacy behaviours needs to be assessed. This assessment was performed through a qualitative study (Study 1) as discussed next.

2.2.4 Study 1: Exploratory Qualitative Study

To further explore the concept of customer advocacy, fourteen exhaustive interviews were conducted. The context of services was considered appropriate for advocacy research, as recommendations and WOM are suggested as being more desirable in 29 | Page

services (e.g., Brown et al., 2005; File, Judd and Prince, 1992), attributable to the low comparability, few search qualities and high risk associated with services (Murray and Schlacter, 1990).

Interviewees were recruited through convenience sampling. They were aged from 22 to 64 years with a broad balance between the genders (57% male; 43% female), covering a wide spread of occupations (e.g., dental technician, IT director and defence force officer), education levels and nationalities (see Appendix 1 for profiles of interviewees). All had made a very strong recommendation (score 6 or 7 out of 7 on a 7 point scale, 1= very weak; 7 = very strong) about a service provider within the past six months. They had also made positive comments about other service providers in various WOM communications over the same period, but with lower recommendation strength. Each interview lasted between 60-90 minutes, allowing individuals to discuss their own experiences in depth.

All interviews were semi-structured. Discussions focused on areas of: (1) situational context (e.g., the type of the service provider involved, the length of relationship with the service provider); (2) the interviewee's thoughts and feelings about the service provider at the point of advocacy; (3) any encounters or experiences that might contribute to the interviewees' strong recommendations about the service provider; (4) the specific circumstances surrounding the advocacy incident (e.g., who were there, who initiated the communication and what triggered the topic); (5) how the interviewees strongly recommended the service provider (e.g., the tone, the adjectives, verbs and other words used, manner or any follow-ups after the conversation); (6) the reactions of the listener(s) and how the interviewees responded and; (7) how the interviewees gave positive comments about other service providers, but not with the same level of recommendation strength. This latter topic enabled a comparison between customer advocacy and more general positive WOM in the analysis. Following the process outlined by Miles and Huberman (1994), verbatim transcripts of the interviews and field notes were coded. Concept frequencies were calculated and recurring themes within the data were identified to represent relevant facets of advocacy behaviour.

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Moreover, the themes were grouped by their conceptual consistency to establish characteristics of customer advocacy. These themes were confirmed as relevant in subsequent verification with two interviewees.

A distinctive content and communication style of advocacy was revealed in this qualitative phase of the research. It was in accordance with the anticipated conceptual dimensions of customer advocacy derived from the literature review.

First, customer advocacy is distinguished by 'proactive-ness' in the initiation of the topic about a service provider, which reflects the dimensions of informing and being initiatory in a communication style. Advocates tended not to wait until the audience asked for advice. Instead, they proactively mentioned a service provider whenever there was a relevant conversation, without being asked: “So once I heard someone was talking about this general topic and the need to find a dermatologist then I am proactive. I don’t wait for them to ask me” (Interviewee 10); "Any time the topic came up you would need to say it. You would be compelled in some way to add, this was really good, and not be able to keep quiet about it” (Interviewee 8). Thus, advocates appeared to have the urge to talk about the service provider. Conversely, WOM is less likely to be proactive as WOM is largely generated by listeners’ direct inquiries (Mangold, Miller and Brockway, 1999).

Second, advocates tended to defend the focal service provider against criticisms and downplay the competitors in the marketplace in the face of disagreement. This indicates that the dimension of dissension instead of approval is involved in response to the audience's challenges. For example, an American interviewee (Interviewee 9) dismissed the perceived super value of Wal-Mart firmly - “Like I would say to people, ‘oh God, don’t go to Wal-Mart. Go to Target. Well, you thought Wal-Mart was cheaper? No, I think that’s a total lie. I think that’s advertising”. A British interviewee (Interviewee 11) behaved similarly by saying: “Don’t ever go for BT. It doesn’t make any sense - I think that this [the smaller company as the subject of advocacy] is the best provider and they are much cheaper and they can answer your calls at 11:30 at night if you need to.”

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Third, advocates exhibited a judgmental and forceful communication style. Advocates were explicit and enthusiastic in the conversation, such as “This guy is absolutely brilliant. I couldn’t recommend him more highly. Here is his email address why don’t you get in touch with him?” (Interviewee1). By contrast, the delivery in general positive WOM communications can be much more subdued with reduced degrees of positivity and enthusiasm: “There is a different excitement and energy about it [advocacy] as opposed to when I used to recommend other companies in the past. It was - yeah they're good, they did the job. Whereas for this one it's like - it is superb. It is simply different” (Interviewee 5).

Moreover, advocates believed they acted as a promoter or a salesperson for the service provider. They urged listeners to actually use the provider’s service. Additionally, they followed up people’s interests and actively prepared information (e.g., company materials and website links) to help others access the service provider immediately after the conversation. They even contacted the service provider on behalf of their friends or relatives, trying to encourage a deal: “So I rang up [the company] and said this is what my friend has been offered, what can you do?” (Interviewee 6); “The next day I would send them an email saying ‘remember how you said you would be potentially interested, if you ever follow up on it give this guy a call’. So I’d almost be like a salesperson for him (laughs)” (Interviewee 2); “I recommend it so strongly I should get some money out of it” (Interviewee 14).

Obviously, these advocates are passionate and subjective. The subjectivity and certainty portrayed in advocacy was based on the interviewee’s own experience with the service provider, which had been transformed into a belief and conviction: “Features of the strong recommendation? Conviction, yes” (Interviewee 11); “For someone to strongly recommend, they need to believe in it. They need to have experienced it. I’ve tried it and it’s taken a while for me to get to the point … That’s how strong I feel about it” (Interviewee 12). Customers were willing to nurture providers that offer service excellence through advocacy; otherwise they even feared that these providers might not be sustainable in the future: “I’m just trying to be an advocate for a really good thing.

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How could you not be in this day and age? There’s some cynical part of you that thinks it might disappear, it might not last if we don’t nurture it. It’s almost fear” (Interviewee 9).

Notably, all interviewees revealed that they had engaged in numerous positive WOM interactions with others, however, they had only acted as advocates for a very limited number of service providers. Collectively, the findings of the exploratory study revealed that customer advocacy is a unique form of WOM communication with distinctive content and style characteristics. These aspects have not been captured by existing WOM measurement scales.

Early WOM scales adopted single indicators that focused mainly on the occurrence of WOM (e.g., File, Cermak and Prince, 1994; Singh, 1990; Swan and Oliver, 1989). One single-item scale that measures recommendation likelihood - Net Promoter Score (Reichheld, 2003) has received criticisms regarding its predictive validity (e.g., Keiningham et al., 2007). A commonly used WOM scale was developed by Zeithaml and her colleagues (1996) that examined the tendency to generate positive WOM and recommendations. Harrison-Walker (2001) introduced a more comprehensive measure which includes WOM activity and WOM praise, however, the details of message content and delivery style were not explored. A more recent WOM scale developed by Sweeney, Soutar and Mazzarol (2012) offers significant advantages over previous scales, capturing both WOM content and delivery dimensions with a wide applicability in positive and negative WOM, as well as situations of WOM production and reception. However, with limited words such as 'strong' and 'powerful' to describe the strength of message delivery, the scale was not designed to investigate an extreme form of WOM production. As a result, the unique content and style of customer advocacy necessitate the development of a distinctive measurement scale, which will be outlined in the following section.

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2.3 Development of a Customer Advocacy Scale

This section details the development, refinement and validation of an advocacy scale consisting of three quantitative studies.

2.3.1 Item Generation

Existing theoretical work has identified a set of conceptual dimensions that may underpin customer advocacy. These dimensions were confirmed in the findings of Study 1. Hence, the combination of literature review and results of the qualitative study formed the basis for the initial item development of a customer advocacy scale.

For example, to reflect the content of customer advocacy, items such as ‘I always give this service provider a strong recommendation (score 6 or 7 out of 7)’ and ‘When talking about this service provider, I usually compare it to its competitors, explaining why competitors are not as good’ were included. With regards to the style component, ‘Whenever there is a conversation about the service category, I always strongly recommend this service provider without being asked’ was used to capture the proactive-ness in initiating the conversation that characterizes an informing and initiatory style. Items such as ‘I defend this service provider if people raise negative comments about it directly with me’ were included to reflect the dissension in the communication. Items such as 'When discussing this service provider, I urge people to consider using it' and ‘I described this service provider as the best of its kind’ were employed to capture the forceful and judgmental dimensions in an advocate’s conversation.

An initial pool of 15 items that focused on the behavioural (not intention) side of advocacy communication was developed. The items were subsequently evaluated by four academic experts chosen for their experiences and expertise in the services area or scale development. To ensure that these items were representative of the conceptual

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domain of customer advocacy, each judge was provided the definition of advocacy from the Oxford Dictionary and the definition of customer advocacy that is adopted in this research. Judges agreed that these items represented the domain of interest with the suggestion of an additional item that emphasized the provision of feedback to service providers. In total 16 items (see Appendix 2) were carried forward into the next stage of the study.

2.3.2 Study 2: Pre-test - Item Purification

The pre-test of the scale involved a collection of two waves of survey data. The first wave was collected from 133 second year university students in a large Australian university. Students were from non-marketing backgrounds with 51.2% female and 48.8% male. Participants were asked to recall the service provider about whom they had given the strongest recommendation in the previous six months. They were then asked to evaluate this service provider and to what extent the 16 advocacy items described their actual recommendation behaviours (from 1 ‘Does not describe me at all’ to 7 ‘Describes me very well’). A second wave of sampling was conducted among a non-student population. A small incentive (e.g., a chocolate bar) was used to recruit 104 respondents in a large shopping mall in a major Australian city. These customers aged from 20 to above 70 with 64% female. In both studies, the variety of recommended service providers was broad, ranging from banking, telecommunications to car repair services.

Exploratory factor analysis (EFA) using Principal Component Analysis (Oblimin rotation) was employed to obtain an initial understanding of scale dimensionality. Items with either low factor loadings (lower than .6), high cross-loading on two or more factors, or sharp drops in item-to-total correlations were examined individually (Churchill, 1979). Both samples revealed a three-factor solution with eigenvalues greater than 1 (68% variance explained in the student sample and 74% explained in the non-student sample). Factor 1 focused primarily on the endorsement for a service provider, such as being enthusiastic in recommendations or urging people to try the provider. Factor 2 was concerned with criticism reflection, such as ignoring criticism or 35 | Page

defending the service provider. Factor 3 focused on the dimension of proactive-ness, such as promoting the service provider even without being asked in the conversation. Three items that were common as candidates for removal in both samples were deleted from the item pool. This left a refined pool of 13 items after the first stage of scale purification.

2.3.3 Study 3: Item Purification

The second phase of item purification involved a large scale survey administered to members of an online panel selected in proportion to the state populations of Australia. Respondents were recruited based on two screening criteria: (1) the respondent had recommended a service provider in the previous six months and, (2) they had made a strong recommendation about the service provider. This was to ensure that there was a sufficient number of responses on the upper end of the advocacy scale and consequently an adequate level of variance of scale items for analysis. Therefore, a 7-point screening question ‘If you have recommended this service provider to others, how strongly was the recommendation expressed?’ was asked. Participants selecting above 4 in this question were qualified to continue the survey. Respondents were then asked to evaluate the extent to which the advocacy items described their past recommendation behaviours about the service provider on a 7-point Likert scale (1 = does not describe me at all; 7 = describes me very well) ( = 5.01; SD = 1.08). In return, respondents received a small cash credit (equivalent to $2 or less) offered via the standard operation of the online panel.

Multiple service categories were specified as the research contexts. This aimed to capture the diverse natures of services so as to maximize the generalizability of the developed scale. Two important dimensions were selected in classifying service industries - the extent of customer-employee contact as well as the experience versus credence properties (Bowen, 1990; Darby and Karni, 1973; Lovelock, 1983). The extent of customer-employee contact is widely accepted as a key dimension of service categories in service typology studies (e.g., Bowen, 1990; Lovelock, 1983). On the 36 | Page

other hand, the experience versus credence attribute is important to services. Customers perceive a lack of confidence and increased risk in the evaluation of quality of credence- based services (e.g., psychotherapy or legal services) (Darby and Karni, 1973; Murray and Schlacter, 1990). This results in customers of credence services placing greater reliance on personal information sources such as WOM (Mitra, Reiss and Capella, 1999).

Therefore, the research setting based on these two dimensions (extent of customer- employee contact and experience/credence properties), allowed us to examine meaningful differences across service categories in studying customer advocacy. Specifically, four service categories were studied: ‘experience and high contact services’ (e.g., hairdressing or beauty salon); ‘experience and low/medium contact services’ (e.g., telecommunication services or basic banking services); ‘credence and high contact services’ (e.g., medical services); and ‘credence and low/medium contact services’ (e.g., financial advisors or car servicing).

In total 1,045 respondents completed the survey. After data screening for dubious response patterns, missing values and outliers, a total sample of 975 respondents was retained. The sample was representative of metropolitan Australia (see Appendix 3 for detailed demographic distribution of the sample).

An initial CFA model, estimated with maximum likelihood method using AMOS 18.0, did not yield satisfactory fit indices. Subsequent scale purification eliminated items one by one based on criteria specified by Netemeyer, Sharma and Bearden (2003). In addition to the three factor structure revealed in the pre-test, two- and one-factor solutions were also considered. The comparison of CFA results among the competing models indicated that the two-factor solution was most appropriate. As shown in Table 1, the two-factor model demonstrated a significant improvement over the other two models in terms of chi-square statistic (p < .01). Other fit indices also outperformed its competing models: GFI = .975, CFI = .976, TLI = .961, RMSEA = .075, SRMR = .028.

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Although overall chi-square was significant (X2 (13) = 84.69, p < .01), this was expected in the presence of a large sample size (Bollen, 1989).

TABLE 1 Study 3: Confirmatory Factor Analysis Model Fit Comparisons

Model Chi-Square d.f. Chi-Square Difference X2/d.f. GFI CFI TLI RMSEA SRMR

One factor 225.27 14 16.089 0.936 0.930 0.894 0.124 0.050

Three factor 135.59 18 89.68, p < .001 7.532 0.965 0.963 0.942 0.082 0.035

Two factor 84.69 13 50.90, p < .001 6.514 0.975 0.976 0.961 0.075 0.028 Note: Chi-square differences represent comparisons of subsequent models (e.g., one-factor versus three-factor model, three-factor model versus two-factor model).

Hence, advocacy is best conceptualized as having two dimensions (shown in Figure 1). Spontaneous endorsement (SE) denotes the passion in the conversation (e.g., praising a provider as the best of its kind, defending the service provider and telling more people about the provider). Proactive promotion (PP) entails the proactive-ness in initiating the topic about a service provider and the proactive-ness in promoting the provider (e.g., helping others access the provider, or contact the provider on behalf of others if needed) (see Appendix 2 for a complete list of final items).

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FIGURE 1 CFA of Two-factor Measurement Model

.60 e1 Enthusiasm .78

.62 e2 Urge consideration .78 .52 e3 .72 Best of kind Spontaneous .70 Endorsement .49 e4 Defend .73 .54 e5 Told more .77

.55 e6 Proactive in Non- .74 conversation Proactive .82 Promotion .67 e7 Promoter

Cronbach’s alpha (α) and composite reliability (CR) exceeded the threshold of .7 for each factor (Hair et al., 2006; Nunnally and Bernstein, 1994): SE factor - α= .856, CR = .860; PP factor - correlation between two items = .61, CR = .76 (Table 2). Thus, reliability of the scale was established. Evidence of convergent validity was demonstrated by high and unambiguous item loadings on their own factor (.70 or above) (Figure 1). All t values were significant (p < .01). Average variance extracted (AVE) for SE and PP was .55 and .61 respectively (Table 2). An AVE value of .5 or above suggests that variance explained by the construct is greater than the measurement error (Fornell and Larcker, 1981). This evidence, along with the high reliability of dimensions, supported the adequate convergent validity of the advocacy scale.

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TABLE 2 Measurement, Reliability and Convergent Validity of the Advocacy Scale EFA Loadings CFA Loadings Study 3 Study 3 Study 3 Study 3 Study 3 Study 3 Study 4 a (Entire (Service (Service (Service (Service Study 4 a sample) category 1) category 2) category 3) category 4) n = 975 n = 265 n = 975 n = 232 n = 247 n = 247 n = 249 n = 265 Spontaneous Endorsement Enthusiasm 0.80 0.69 0.78 0.86 0.78 0.80 0.79 0.91 Urge consideration 0.81 0.68 0.78 0.86 0.84 0.83 0.79 0.88 Best of kind 0.76 0.84 0.72 0.80 0.75 0.71 0.80 0.83 Defend 0.73 0.97 0.70 0.81 0.68 0.69 0.71 0.72 Told more 0.78 0.77 0.73 0.71 0.83 0.79 0.68 0.81 Proactive Promotion Proactive non conversation 0.69 0.92 0.74 0.75 0.75 0.76 0.82 0.87 Promoter 0.74 0.87 0.82 0.83 0.88 0.77 0.86 0.89

Cronbach's α Composite Reliability Average Variance Extracted Study 3 Study 4 Study 3 Study 4 Study 3 Study 4 Spontaneous Endorsement 0.86 0.92 0.86 0.92 0.55 0.69 Proactive Promotion N/A b 0.76 0.87 0.61 0.78

CFA Goodness-of-fit indices X2 df GFI CFI TLI NFI RMSEA SRMR Study 3 84.688 13 0.975 0.976 0.961 0.972 0.075 0.028 Study 4 55.013 13 0.943 0.970 0.952 0.962 0.111 0.026 Notes: a Study 4 reassesses the reliability and validiy of customer advocacy scale and will be discussed later in Section 2.3.4 b Correlations of the two items of dimension 'Proactive promotion' are .61 (Study 3) and .77 (Study 4).

To investigate discriminant validity, all items were paired for the calculation of squared correlations and AVE. As Table 3 indicates, AVE of item pairs exceeded the corresponding squared correlation of the pairs, thus demonstrating discriminant validity (Fornell and Larcker, 1981). At the dimension level, a chi-square test was applied (Anderson and Gerbing, 1988). The correlation between two factors of ‘spontaneous endorsement’ and ‘proactive promotion’ was constrained to unity in the measurement model and the CFA process was repeated. The constrained CFA reported a significant 2 increase in the chi-square statistics (X diff (1) = 13.92, p < .01), providing additional evidence of the distinctiveness of the scale’s dimensions (Anderson and Gerbing, 1988; Bagozzi and Phillips, 1982).

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TABLE 3 Study 3: Squared Correlation, Correlation and Average Variance Extracted (AVE) between Measurement Items 1234567

1. Enthusiasm 0.66** 0.54** 0.56** 0.53** 0.44** 0.46** 2. Urge consideration 0.43 0.53** 0.51** 0.55** 0.48** 0.53** 3. Best of kind 0.29 0.28 0.54** 0.58** 0.40** 0.44** 4. Defend 0.31 0.26 0.30 0.53** 0.35** 0.40** 5. Told more 0.28 0.31 0.33 0.29 0.41** 0.49** 6. Proactive Non conversation 0.20 0.23 0.16 0.12 0.17 0.61** 7. Promoter 0.21 0.28 0.19 0.16 0.24 0.37

1. Enthusiasm 2. Urge consideration 0.61 3. Best of kind 0.56 0.57 4. Defend 0.54 0.55 0.50 5. Told more 0.57 0.58 0.53 0.51 6. Proactive Non conversation 0.58 0.58 0.53 0.52 0.54 7. Promoter 0.64 0.64 0.59 0.58 0.6 0.61 Notes: N = 975. The upper section is correlations, with squared correlations between measurement items below the diagonal and zero-order correlations above the diagonal; the lower section is average variance extracted (AVE) between items. **p < .01

Discriminant validity was further examined between advocacy and a closely related external scale - general positive WOM. The WOM scale developed by Zeithaml et al. (1996) consists of items such as: ‘Say positive things about this service provider to others’ and ‘Recommend this service provider to someone who seeks my advice’. The scale reflects general positive comments about a service provider without any specific reference to the extreme strength and passion in the recommendation. Hence, this is a scale of the general form of positive WOM with moderate recommendation strength. In the subsequent EFA analysis, a distinct pattern emerged - the items of the two advocacy dimensions and WOM loaded on three different factors (Table 4). This provided preliminary evidence that customer advocacy and general positive WOM are conceptually different.

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TABLE 4 Study 3: Exploratory Factor Analysis - Advocacy and General Positive Word-of-Mouth (PWOM) Scale

Factor 123 Advocacy_Enthusiasm .493 .270 .260 Advocacy_Urge consideration .470 .115 .393 Advocacy_Defend .816 .107 -.092 Advocacy_Told more .862 -.131 .044 Advocacy_Best of kind .815 .011 -.001 Avocacy_Proactive in Nonconversation -.096 .022 .946 Advocacy_Promoter .123 -.064 .805 General PWOM _ Say positive things -.017 .960 .001 General PWOM _ Recommend .013 .954 -.030

Notes: Standardized coefficients reported. Bold values indicate the factor on which each item predominantly loads.

A subsequent confirmatory factor analysis (CFA) using chi-square difference test 2 revealed a significant increase of chi-square statistics (X diff (3) = 251.96, p < .01) when the two components of customer advocacy and general positive WOM scale were constrained to unity. This provided additional evidence that customer advocacy is distinct from general positive WOM.

Predictive validity of the advocacy scale was ascertained by estimating a structural equation model incorporating two related constructs – satisfaction and behavioural loyalty. As Figure 2 shows, satisfaction was expected to have a direct positive impact on advocacy and behavioural loyalty, while advocacy was expected to positively predict behavioural loyalty (Anderson, 1998; Zeithaml et al., 1996). Satisfaction was measured using three 7-point scale items adopted from Oliver (1997). Behavioural loyalty was measured by customer exclusive patronage and the willingness to spend more on the service provider adopted from Oliver (1997), Zeithaml et al. (1996) and De Wulf, Odekerken-Schroder and Iacobucci's (2001) studies (see Appendix 2 for measurement item details).

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FIGURE 2 Study 3: Predictive Validity of the Customer Advocacy Scale

Spontaneous Proactive Endorsement Promotion

Customer Advocacy .18** .71**

.45** Behavioural Satisfaction Loyalty

**p < .01 Note: All coefficient values are standardized and appear near the associated path.

The estimated model was satisfactory: X2 (40) = 169.13, X2/df = 4.23, GFI = .92, AGFI = .88, CFI = .84, and RMSEA = .06, SRMR = .07. All path coefficients in the model were significant at .01 level. As predicted, satisfaction has a direct impact on customer advocacy and behavioural loyalty (β = .71 and .45 respectively, p < .01), while customer advocacy directly predicts behavioural loyalty (β = .18, p < .01). In addition, satisfaction has an indirect effect of .13 (β = .71 x .18, p < .01) on behavioural loyalty through advocacy. Consequently, the predictive validity of the customer advocacy scale was established.

2.3.4 Study 4: Reassessment of the Reliability and Validity of Customer Advocacy Scale

Study 4 aimed at reassessing the scale through the introduction of a broader sample within a single ‘service category’. This was to provide further validation of the scale developed in previous studies. The restrictions on recommendation strength imposed in

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Study 3 were removed; hence the new sample consisted of a full spectrum of recommendation strength from ‘not strong at all’ to ‘very strong’. The service category of high contact and experience service (e.g., hairdressing, beauty salon, education or massage) was selected as it is a category with which many people have had experience.

Respondents were recruited from the same online panel used in Study 3, without any screening on the strength of recommendation. However, screening was administered to ensure that members who participated in Study 3 were not involved in this new survey. Respondents who had experienced the specified service industries in the previous six months were asked to recall a service provider whose performance they were willing to evaluate, as well as their advocacy behaviours about this provider. This process yielded 271 participants with 265 retained for data analysis after screening for dubious response patterns and outliers (demographic details provided in Appendix 3).

The resulting factor structure, reliability and convergent validity confirmed previous results, providing additional validation of the advocacy scale. EFA revealed a clean two-factor structure explaining 75% of the variance. Next, CFA yielded a two-factor measurement model of good fit: X2 (13) = 55.01, p < .01; GFI = .943; CFI = .970; TLI = .952; RMSEA = .111; SRMR = .026. As shown in Table 2, each item loaded highly on their corresponding factor, with standardized coefficients ranging from .72 to .91. CR were .92 and .87 and AVE were .69 and .78 for the dimensions of spontaneous endorsement and proactive promotion respectively.

2.4 Discussion

The current research represents what appears to be the first attempt to systematically and empirically explore customer advocacy. This multi-context, multi-phased study provides rich insights into the specifics of customers' strength of recommendations during informal conversations with fellow customers. It reveals two conceptual dimensions of customer advocacy - spontaneous endorsement and proactive promotion. It develops and validates a scale that measures actual advocacy behaviours. Based on 44 | Page

findings from a series of reliability and validity tests, the scale is parsimonious and practical consisting of seven items that display sound psychometric properties. Notably, it displays discriminant validity from the theoretically related measure of general positive WOM. Additionally, the customer advocacy scale has predictive validity. It indicates positive impacts of advocacy on customers’ exclusive patronage and their willingness to spend more on a service provider.

2.4.1 Theoretical Contribution

This study makes an important contribution to marketing theory. It shows that advocates' behavioural manifestations have two dimensions: spontaneous endorsement and proactive promotion. In the first dimension, customer advocacy exhibits more than just highly positive content (e.g., the praise of ‘best of kind’) and passionate manner (e.g., acting enthusiastically and urging listeners’ consideration of the service provider), it also presents the quantity (e.g., tell more people about the focal provider than about other providers) and reflection of criticism (e.g., defend the provider in face of negative comments). The second dimension adds that the delivery mode of advocacy is highly proactive, in other words, advocates are proactive in bringing up the topic and proactive in acting as the promoter of the service provider.

Conceptually, the study identifies a unique form of customer-customer communication which is distinct from general positive WOM. While general positive WOM involves sharing of pleasant, vivid or novel experiences and recommendations to others (Anderson, 1998), it appears that customer advocacy is more forceful and persuasion- oriented. This is interesting as there are always perceived risks in being subjective and forceful in general WOM communications (Mazzarol et al., 2007). However, advocates appear to ignore these risks. In addition, general WOM is less likely to be proactive as WOM is frequently generated by listeners’ direct inquiries (Mangold et al., 1999), while advocacy is characterized by proactive promotion of a service provider with best effort. Overall, the present study suggests that customer advocacy represents more than a subset of general positive WOM which primarily focuses on information exchange. 45 | Page

Instead, it highlights the strong support towards a service provider from advocates, who volunteer to highly recommend a service provider with no reservation and attempt to persuade the audience to patronize the provider.

As a result, customer advocacy emerges from the research as an advanced level of loyalty behaviour, based on the recognition of positive WOM as a loyalty dimension (e.g., Gremler and Brown, 1999; Palmatier et al., 2006; Zeithaml et al., 1996) characterized by a weaker level of promotion effort relative to that of advocacy. Hence, the measure of advocacy provides building blocks for further empirical research into an advanced level of customer loyalty.

The current research may also have implications for relationship marketing theories. Much WOM theory has been studied through the lens of relationship marketing (e.g., Brown et al., 2005; Gremler and Brown, 1999), without clarifying the association between different types of positive WOM and their relationship emphasis. This research serves as a foundation for further exploration of forms of WOM in relation to varying levels of relationship bonding.

2.4.2 Managerial Implications

A number of managerial implications result from our research. First, customer advocacy provides managers with an understanding of the extent to which customers can become passionate promoters of a company. As voluntary ‘marketers’ of a firm, advocates are likely to reach new customers and retain existing customers more effectively, which makes them a valuable asset to a firm (Schmitt, Skiera and Bulte, 2011). The past focus on customers' transactional value (e.g., customers' own retention or cross-buying) has led to an increasing level of competition among rival loyalty programs, however, the level of efficiency among these programs has been decreasing (Ferguson and Hlavinka, 2007; Liu and Yang, 2009). This research emphasizes the need to develop initiatives to exploit customers' non-transactional value such as advocacy and has implications for the allocation of corporate resources. 46 | Page

Second, the distinction between advocacy and general positive WOM revealed in this study suggests that firms should not stop at the success of gaining positive comments from customers. A substantial number of WOM conversations are related to a neutral attitude (neither satisfied nor dissatisfied) towards a product or service (Anderson, 1998; East, Vanhuele and Wright, 2008). By contrast, the unrewarded passion and strong promotion by advocates are more likely to be a true reflection of customers' support of a firm and should be an ultimate goal pursued by organizations.

Third, the advocacy scale can be used as a diagnostic tool for identifying excellent service performance or improving less satisfactory service functions. Managers may establish baseline levels of advocacy within a service firm and then target areas for improvements. The advocacy scale can be used to gather benchmark data across multiple locations or multiple functions for assessment, planning and tracking purposes. Further, the diagnostic value can be enhanced by comparing advocacy scores over time, across an organisation’s divisions or between industry competitors.

Fourth, the scale can be applied to identify segments of advocates in the customer base that differentiate themselves in terms of demographic, purchase and lifestyle characteristics. As a result, existing advocates will be more valued while potential advocates will be approached earlier to maximize the economics of customer advocacy.

2.5 Further Research and Limitations

Certain limitations have marked the study and suggested potential directions for additional research. First, the research focuses only on the positive side of strong recommendations. Customers may engage in strongly expressed negative WOM about a service provider. They are extreme "detractors" of a company who post a threat to a company's reputation and market competitiveness (Reichheld, 2003). Hence, future research could extend into the opposite of customer advocacy while the measurement scale developed in the current research may provide a useful starting point.

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Second, this study does not examine the antecedents and consequences of customer advocacy, which would be a fruitful area for future research. For example, what aspects of relationship building or brand experiences will result in customer advocacy? Which one has the stronger predictive power of customer advocacy – the relative attitude derived from superior performance among competitors, or the long-term consistency in excellent service quality? Will successful service recovery ever be able to generate customer advocacy, or will it only lead to general positive WOM? Likewise, it would be important to investigate more specific consequences associated with customer advocacy, such as repurchase intentions or customer lifetime value.

Third, this study investigates customer advocacy behaviours regarding a service provider. Future research may explore if and to what extent customers advocate a service employee and the entire service firm differently. Personal loyalty to the sales person has been shown to positively influence customer loyalty to the business (Bove and Johnson, 2006; Reynolds and Beatty, 1999), but research has also shown that the relationship between the customer and sales person may not necessarily extend to the service business (Chow and Holden, 1997). Hence, future research is needed to clarify customer advocacy of a service employee relative to that of a service business. Additionally, future research could examine advocacy in the product context or a business-to-business context, given that the mechanism and influence of informal communication (e.g., WOM or advocacy) in an industrial service setting remains under- researched.

Fourth, the study explores advocacy in the context of direct customer interactions, which accounts for the majority of incidences of customer informal conversations (Keller and Berry, 2006). However, there has been a remarkable increase of online consumer ratings and forums (Bernoff and Li, 2008). The exploration of online advocacy will add insights to a growing body of research devoted to the dynamics and effectiveness of online WOM and recommendations (e.g., Duan, Gu and Whinston, 2008; Liu, 2006).

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Fifth, echoing practices of prior WOM scale development, this study focuses on the non-physical aspects of communication behaviours. It may be useful to assess the physical dimensions of advocacy including body language, facial expression or voice, which may vary considerably with gender, personality or even cultures. This leads to another important future investigation direction - customer advocacy in a cross-cultural context. While culture has been shown to impact customers’ seeking of WOM (Money, Gilly and Graham, 1998; Mooradian and Swan, 2006) and effectiveness of WOM (e.g., Takada and Jain, 1991), research determining how the strength of recommendation may vary across different cultures (e.g., individualist versus collectivist) may represent a fruitful new line of inquiry.

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Appendix 1 Interviewee Profiles Interviewee Service provider advocated Age Gender Occupation Education Ethnic background

1 Mobile network provider 35-39 Female OHS Advisor Undergraduate Arabian 2 Personal trainer 30-35 Female Academic Postgraduate Austrian 3 Credict card company 26-29 Male Student Postgraduate German 4 Removalist 26-29 Male Defense force Year 10 Italian 5 Internet provider 26-29 Female University administration Postgraduate Arabian 6 Music store 30-35 Male Administration TAFE Diploma New Zealand 7 Car mechanic 20-25 Male Software engineer Undergraduate Indonesian/Chinese 8 Hairdresser 20-25 Male Dental Technician TAFE Diploma Vietnamese 9 Retail store 45-49 Female Home service TAFE Diploma American 10 Medical service 60-69 Male Academic Postgraduate Australian 11 Cleaning service 35-39 Male IT director Postgraduate British 12 Woodwork tool shop 70-75 Male Retiree Undergraduate Australian 13 Home improvement 40-45 Female Housewife Postgraduate British 14 Beauty salon 20-25 Female Student Undergraduate Singaporean/Chinese

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APPENDIX 2 Measures of Study Constructs

Advocacy (Developed in this study) 1 = does not describe me at all, 7 = describe me very well Original item pool contains 16 items. The first 6 items are final items of advocacy scale as the result of EFA and CFA analyses. 1 (Enthusiasm) I am enthusiastic in my recommendations of this service provider. a 2 (Urge consideration) When discussing this service provider, I urge people to consider using it. a 3. (Best of kind) I describe this service provider as the best of its kind. a 4. (Defend) I defend this service provider if people raise negative comments about it directly with me. a 5. (Told more) I have told more people about my positive experience with this service provider than I have with most other service providers, regardless of the service category . a 6. (Proactive in non-conversation) Even when there is no conversation, but if I think there are people who have an interest in the service category (e.g., dentist, car cleaning, hairdressing or banking), I strongly recommend this service provider, without being asked. a 7. (Promoter) I take the initiative to act as a 'promoter' of this service provider (e.g., help others have access to this service provider, contact the service provider on behalf of others if needed) . a 8. I always give this service provider a strong recommendation (score 5 or 6 out of 6). b 9. Whenever there is a conversation about the service category (e.g., dentist, car cleaning, hairdressing or banking), I seldom miss an opportunity to tell others about this service provider. b 10. Whenever there is a conversation about the service category (e.g., dentist, car cleaning, hairdressing or banking), I always strongly recommend this service provider, without being asked. b 11. I directly encourage people to actually use this service provider. b 12. I have only good things to say about this service provider. b 13. When practical, I provide written feedback on this service provider (e.g., recommendation blogs, ratings and comments on review websites, or letters/emails back to the service provider). b 14. Provided it is accessible to people, I ONLY recommend this service provider in the service category (e.g., dentist, car cleaning, hairdressing or banking). b 15. When talking about this service provider, I usually compare it to its competitors, explaining why competitors are not as good. b 16. If I hear criticisms about this service provider, I tend to ignore them. b

Word-of-Mouth (WOM) (Zeithaml et al. 1996) 1 = not at all likely, 7 = extremely likely 1. Say positive things about this service provider to other people. 2. Recommend this service provider to someone who seeks your advice.

Satisfaction (Oliver 1997; Hennig-Thurau,Gwinner, and Gremler 2002) 1 = strongly disagree, 7 = strongly agree 1. I am always delighted with this service provider’s service. 2. Overall I am satisfied with this service provider. 3. I feel good about using this service provider.

Behavioural Loyalty (De Wulf et al. 2001; Oliver 1997; Zeithaml et al. 1996) 1 = strongly disagree, 7 = strongly agree 1. Compared with other service providers, I have spent more money on this provider. 2. When I have a need for this type of service, I buy only from this service provider. NOTE: a. Final retained advocacy items. b. Rejected advocacy items during the scale purification process.

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APPENDIX 3 Sample Demographic Profiles Study 3 Study 4 Service Category 1 Service Category 2 Service Category 3 Service Category 4 Entire Sample (Experience/High (Experience/Low- (Credence/High (Credence/Low-Med Entire Sample contact Services) Med contact Services) contact Services) contact Services) n = 975 n = 232 n = 247 n = 247 n = 249 n = 265 Gender Male 47.8 28.9 47.8 50.4 62.8 41.1 Female 52.2 71.1 52.2 49.6 37.2 58.9 Total 100% 100% 100% 100% 100% 100% Age 18-29 yrs 22.9 34.9 31.2 11.3 15.6 26.0 30-39 yrs 21.9 28.0 26.3 15.7 18.0 24.2 40-49 yrs 24.0 20.3 20.2 29.8 25.2 20.0 Above 50 yrs 31.2 16.8 22.3 43.1 41.2 29.8 Total 100% 100% 100% 100% 100% 100% Income less than $10,000 11.5 13.0 13.8 9.3 10.0 10.6 $10,000-$30,000 22.9 22.6 24.4 25.1 19.7 25.7 $30,000-$80,000 47.8 49.1 46.7 45.3 50.2 41.1 over $80,000 17.7 15.2 15.0 20.2 20.1 22.6 Total 100% 100% 100% 100% 100% 100% Education Under Yr 11 15.5 14.2 11.3 15.3 20.8 13.6 HSC or VCE 15.1 13.4 17.4 16.9 12.4 13.6 TAFE/Trade qualification 27.7 24.1 28.7 26.6 31.2 30.2

Undergraduate or above 41.7 48.3 42.5 41.1 35.6 42.6 Total 100% 100% 100% 100% 100% 100% Note: Service Category 1 (Experience/High contact Services): Hairdressing, beauty salon, personal trainer, education, massage and childcare provider services. Service Category 2 (Experience/Low-Med contact Services): Telecommunication, basic banking, dry cleaning and airline services. Service Category 3 (Credence/High contact Services): Medical services such as GP, dentist or physiotherapist. Service Category 4 (Credence/Low-Med contact Services): Financial advisor, car servicing, computer repair, accountants, legal services and white goods repair services.

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Chapter 3: Determinants of Customer Advocacy and their Differential Impact on General Positive Word-of-Mouth

Abstract

This chapter aims to examine the determinants and the contingent conditions of customer advocacy. A conceptual model of customer advocacy is developed drawing on self-determination theory. It is proposed that advocacy is driven by evaluative and motivational antecedents, while the effect of these antecedents on advocacy is moderated by situational and relational factors. This is confirmed by the results of structural equation modelling and moderated regression analysis. Specifically, the evaluative factor of confidence, along with motivational factors of opinion leadership and altruism towards a service provider are the direct predictors of customer advocacy. The evaluative factors of service quality and customer expertise indirectly influence customer advocacy through confidence. Service type and relationship quality moderate the impact of these determinants on customer advocacy. Additionally, the determinants of customer advocacy differ in their impact on general positive word-of-mouth. For example, the relative importance of the motivational dimensions (altruism towards a service provider and opinion leadership) is stronger in advocacy than in general positive word-of-mouth. The implications of these findings, as well as the limitations and future research opportunities are discussed fully in the chapter.

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3.1 Introduction

Customer recommendation is a significant marketing force. It is important in brand switching decisions and it is more important than marketing communications in influencing product adoption (Katz and Lazarsfeld, 1955; Rogers, 1962). In the quest for deep and meaningful customer loyalty behaviour in an era of growing consumer power (Holt, 2002; Klein, Smith and John, 2004) and consumer sophistication (Macdonald and Uncles, 2007), companies have realized that a most attractive but difficult outcome is to turn customers into advocates (Bhattacharya and Sen, 2003). Advocates are passionate and active promoters, who strongly recommend a company to other customers with the willingness to publicly stand by or even defend the focal company in their communications (Liu, Harris and Payne, 2011). Perceived as the ultimate test of customers’ relationship with an organization (Bendapudi and Berry, 1997; Christopher, Payne and Ballantyne, 1991), customer advocacy is highly desirable for businesses. Such advocacy occurs when customers volunteer to be champions for a company without expectation of rewards.

However, research that attempts to delineate customer advocacy conceptually and empirically is scarce. Customer advocacy is a form of positive word-of-mouth (hereafter ‘WOM’), which refers to informal communications between customers and includes discussions or recommendations regarding brands, products or services (Anderson, 1998; Arndt, 1967). To date, most research has been concerned with the general form of positive WOM whereas advocacy represents an extreme form of positive WOM. General positive WOM is friendly positive comments about a service or product firm. It is not always firm and explicit, as a consequence of the risks perceived by the WOM communicator (Mazzarol, Sweeney and Soutar, 2007). Further, it is less likely to be proactive, as more than half of WOM incidences are reactive responses to others’ inquiries or interest (Mangold, Miller and Brockway, 1999). Thus, general positive WOM most likely involves reactive and moderate strength of recommendations.

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By contrast, the term ‘advocacy’ refers specifically to strongly expressed recommendations (Wilson, 1994) and is associated with a somewhat forceful manner (Krapfel, 1985). It is defined as the strong, passionate and explicit recommendation in favour of a service provider against the market alternatives in this research. The passion, conviction and recommendation strength in customer advocacy suggest that it is conceptually and psychometrically different from general positive WOM (Liu et al., 2011). Advocates tend to engage in higher levels of recommendation strength than general positive WOM communicators.

The lack of specific research on customer advocacy limits our understandings of why some customers are willing to recommend a company passionately and proactively, while many others tend to restrain their recommendation strength by stopping at general positive comments. As a result, the power of customer recommendation as "a dominant force in the marketplace" (Mangold et al., 1999, p.73) remains under-exploited. Also, managers lack guidance in maximizing the highly desirable business outcomes that may result from customer advocacy.

Thus, this research focuses on what contributes to the upper level of recommendation strength. More specifically, it investigates three research questions:

1. What are the determinants of customer advocacy?

2. Under what contingent conditions do these determinants vary in their impact on customer advocacy?

3. Do the determinants of customer advocacy differ in their impact on general positive WOM?

These research questions are important for several reasons. Firstly, the variation of recommendation strength can make an important difference in communication and business outcomes. Research suggests that the strength of recommendation enhances the effectiveness of communications (East, Hammond and Lomax, 2008). A study of 2,000 63 | Page

extremely dedicated customers in China, Europe and Japan reveals that these customers possess exclusive loyalty towards the brand/company (Eisingerich et al., 2010). More importantly, the passion of these customers is contagious. They promote the company via multi-channels, speak up for the brand/company against potential attacks and regularly disparage competing brands. In a recent study, Moe and Trusov (2011) point out that the more highly rated products may encourage greater customer discussion and consequently more product sales. Therefore, it is of particular interest and relevance to understand what may cause customers' recommendation strength to increase to the level of advocacy.

Secondly, despite the realization of the positive influence of customer referrals, companies typically fall short in capitalizing on the attractive economic impact of customer advocacy (Hamilton, Symonds and Hogbin, 2011). There are some companies who are exceptions to this including Harley-Davidson, The Body Shop, Patagonia and Southwest Airlines (Bhattacharya and Sen, 2003). Clearly, companies and their managers are in need of deeper knowledge regarding how to unleash the power of customer recommendations.

Thirdly, general positive WOM may be insufficient in securing a competitive advantage for companies. General positive WOM is customers' natural behavioural response towards a satisfied and pleasurable product or service experience (Oliver, 1997; White and Schneider, 2000; Zeithaml, Berry and Parasuraman, 1996). However, even when customers have a neutral attitude towards a product or service (neither satisfied nor dissatisfied), they still produce a substantial amount of positive comments (Anderson, 1998; East, Vanhuele and Wright, 2008). This is further complicated by customers' tendency to make positive comments rather than negative ones (East, Hammond and Wright, 2007). These findings imply that general positive WOM is not always a true reflection of customers’ strong support. What is more certain is that WOM is associated with the social benefit of sharing information and gaining social capital (Brown and Reingen, 1987; Coleman, Katz and Menzel, 1957). By contrast, advocates promote an organization with intensified positivity, strength and passion. This extra effort that

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customers undertake in advocacy behaviour receives neither a reward from the organization nor extra social benefit from their social network. Therefore, customer advocacy is more likely to be a genuine reflection of customers' support towards the firm. Research on factors that explain the difference between moderate recommendations (i.e., general positive WOM) and strong recommendations (i.e., customer advocacy) will assist in the understanding of how to strengthen the competitive advantage of a firm.

In addressing the research questions, the present study focuses on the services sectors. Customers have a greater demand for other people’s opinions regarding services than products (Murray, 1991). This is the result of a greater level of risk associated with service characteristics such as intangibility or heterogeneity in service provision (Guseman, 1981; Mitchell and Boustani, 1993; Murray, 1991). Thus, strong and confident recommendations are highly sought-after in services sectors as they are more likely to reduce consumer perceived risks. On the other hand, however, customers are less likely to be certain in giving strong recommendations about services. This is largely due to the perceived double-layered risk associated with services as well as the risk in making recommendations (Mazzarol et al., 2007). Hence, research pertinent to the strength of recommendations is specifically meaningful to services industries.

Overall, this research contributes to extant WOM and customer-customer communication literature from three perspectives. First, it is among the first attempts to explore the determinants of strong recommendations within the context of four distinctive service types. Recent research has advanced the understanding of the complexity of customer-customer communication in various respects, such as the effect of WOM communication versus other consumer learning channels (Chen, Wang and Xie, 2011; Trusov, Bucklin and Pauwels, 2009), psychological and social influences on WOM production (Cheema and Kaikati, 2010; Moe and Trusov, 2011), language style in WOM communications (Schellekens, Verlegh and Smidts, 2010) or WOM over different time horizons (Berger and Schwartz, 2011). However, the strength of recommendation remains under-explored. The current research specifically addresses

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this topic and adds knowledge to customers’ strong responses to firms’ effort, following prior research on customers' strength of satisfaction (Chandrashekaran et al., 2007) and strong brand attachment (Thomson, 2006).

Second, the research examines both direct and indirect contributors to customer advocacy and highlights the need to take a contingent approach towards advocacy. The research identifies two moderating factors (i.e., service types and relationship quality) of customer advocacy, in contrast to previous WOM research considering them as a research context or an antecedent. This study enriches the understanding of the role of these situational and relational dimensions in the context of the upper level of recommendation strength.

Third, this research is of particular importance and relevance to service marketers. A large body of research has tried to understand how to turn customers into strong and committed supporters of firms from different perspectives, including customer satisfaction (e.g., Fournier and Mick, 1999), customer orientation (e.g., Brady and Cronin, 2001), relationship marketing (e.g., De Wulf, Odekerken-Schroder and Iacobucci, 2001), customer-company identification (e.g., Bhattacharya and Sen, 2003), and recently, customer engagement (e.g., van Doorn et al., 2010). The current research offers additional insights in that it explicates what contributes to strong, explicit and enthusiastic recommendations about a service provider. The results should provide guidelines for managers challenged with exploiting the economics of customer recommendations in service businesses.

The remainder of this article is organized as follows. First, since customer advocacy is a type of strong extra-role behaviour that is not required as a formal responsibility of a customer, major perspectives of extra-role behaviours are reviewed drawing on work from organizational management, sociology and social psychology. A special focus is placed on self-determination theory (SDT) that provides insights into the strength and magnitude of extra-role behaviours. Second, a conceptual model of customer advocacy is proposed and the rationale for a number of hypotheses is offered. Third, the method

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and the results of a two-stage empirical study are discussed. The last section presents the implications of findings for researchers and managers, limitations of the research and opportunities for further research.

3.2 Literature Review

Advocacy is predominantly voluntary in nature (Mallik, 1997), thus, it is a typical customer ‘extra-role’ behaviour or customer citizenship behaviour (Bettencourt, 1997; Groth, 2005; Morhart, Herzog and Tomczak, 2009). These extra-role behaviours are “not required for the successful production and/or delivery of the service but that, in the aggregate, help the service organization overall” (Groth, 2005, p.11). They are dispensed at the free will of the subject and are not generally rewarded by an organization (Organ, 1988). Additionally, they are opposite to customer ‘in-role’ behaviours that facilitate the service delivery, such as the description of symptoms to a doctor (Bowen and Waldman, 1999; Rodie and Kleine, 2000).

Social exchange theory has been commonly used to explain extra-role or citizenship behaviours (Konovsky and Pugh, 1994; Podsakoff et al., 2000; Yi and Gong, 2008) as a form of reciprocation following the receipt of perceived benefit (Blau, 1964). The act of reciprocation is a psychological pressure as well as a pleasure (Becker, 1986; Buck, 2004; Cialdini, 2001). In other words, it is an innate desire of individuals to repay favours received, even in cases where favours are unwanted (Regan, 1971). For customers, service delivery is a form of social exchange, in which participants normally seek to maximize the rewards and minimize the costs of the transaction (Lutz and Kakkar, 1976; Solomon et al., 1985). When customers perceive benefits during service deliveries, they are willing to engage in reciprocal behaviour with or on behalf of the service provider. This may involve discretionary behaviour with customers acting as a promoter or an 'employee' for the firm (Bettencourt, 1997). These perceived benefits include fairness of interpersonal treatment, providing reliable service as promised, spontaneous employee behaviours and responses to special requests (Bitner, Booms and Tetreault, 1990; Parasuraman, Zeithaml and Berry, 1985; 1988). Even in situations 67 | Page

where there are less apparent benefits, such as firms exerting extra effort in making or displaying their products - without changing the quality of the products - customers may reward them with increased willingness to pay higher prices, increased store patronage and the provision of favourable service evaluations (Morales, 2005).

Positive WOM, as an extra-role behaviour beneficial to a company, has also been typically explained by social exchange theory (Groth, 2005; Harrison-Walker, 2001). However, there is an extended emphasis of extra-role behaviour in customer advocacy than in general positive WOM. In circumstances of general positive WOM, customers provide advice mostly upon request, with the joy being the sharing of personal experience and gaining of social benefit (Coleman et al., 1957; Mangold et al., 1999; Richins and Root-Shaffer, 1988). In contrast, advocates proactively express their strong opinions by persuading the audience or even defending the service provider in the face of criticisms. Hence, the benefit of sharing information and gaining social benefit tends to be less pronounced in customer advocacy. Instead, what is more evident is the conviction and passion exhibited by an advocate towards or on behalf of the service provider. This heightened strength and magnitude of extra-role behaviour does not appear to be sufficiently explained by social exchange theory, because positive comments with moderate recommendation strength (i.e., general positive WOM) already serves the purpose of reciprocation. Hence, it is believed that there exists additional theoretical perspectives that underlie the enhanced magnitude of extra-role behaviour exhibited in customer advocacy. A promising one is self-determination theory (SDT) (Ryan and Deci, 2000a) that highlights the role of intrinsic motivation in the strength and magnitude of extra-role behaviours.

Motivation is effective in explaining extra-role behaviour as it refers to “an inner desire to make an effort” (Dowling and Sayles, 1978, p. 16). It concerns the direction and intensity of effort (Sage, 1977) as well as the underlying process that directs, energizes and sustains activation and intention (Ryan and Deci, 2000b). Klehe and Anderson (2007) note that motivation is a main reason in explaining the difference between typical and maximum performance.

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Motivation derives from both external and internal sources. While extrinsic motivation is driven by outcomes external to the work itself such as external rewards or recognition, intrinsic orientation is based on the pleasure and enjoyment of engaging in an activity purely for its own sake (Amabile, 1993; Deci, 1975). As intrinsic motivation is authentic and typically self-determined (Ryan and Deci, 2000b), it forms the basis of self-determination theory (Ryan and Deci, 2000a). The theory focuses on the degree to which an individual’s behaviour is self-motivated without external influence. SDT suggests that intrinsically motivated individuals feel naturally drawn to, or pulled to, expend effort in completing a task or work (Grant, 2008; Ryan and Deci, 2000a). This might be explained by the stimulating sensations such as fun and excitement derived from one’s engagement in the process. Unsurprisingly, self-determination of intrinsic motivation positively influences service workers’ adaptive behaviour (Gwinner et al., 2005; Roman and Iacobucci, 2010) and their willingness to report service complaints (Luria, Gal and Yagil, 2009). More importantly, SDT contends that intrinsic motivation is associated with enhanced creativity (Deci and Ryan, 1991; Sheldon et al., 1997) and heightened vitality (Nix et al., 1999). Furthermore, actions of intrinsic orientation are more powerful and are likely to result in highest levels of effort, quality and persistence in work (Deci and Ryan, 1985; Grant, 2008; Meyer, Becker and Vandenberghe, 2004).

Recent marketing and management literature has build on SDT in studying a variety of strong responses and behaviours from customers and employees. For example, Thomson (2006) draws on SDT and confirms that if a brand satisfies the three underlying psychological needs of SDT (competence, relatedness and autonomy), a person is likely to develop strong attachment to the brand. McGinnis, Gentry and Tao (2008) compare the impact of psychological and social antecedents of customers’ enduring involvement in an extended service encounter. They discover that the psychological effect of ‘flow’ has a stronger influence. In line with SDT, flow reflects the role of intrinsic motivation and features the process whereby a person is intensively involved and addictive to an activity without the self-consciousness of time (Csikszentmihalyi, 1996). Similarly, Grant (2008) established that under the impact of

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high intrinsic motivation, employees go beyond the call of duty to persist in performing tough work.

To summarize, SDT provides insights into maximum effort as well as intensified passion and vitality in an extra-role behaviour. This type of behaviour resembles customer advocacy as an extreme form of positive WOM. Hence, SDT establishes a theoretical underpinning for the analysis of customer advocacy, while social exchange theory offers a theoretical foundation for positive WOM communication in general.

Central to SDT are three psychological needs - competence, autonomy and relatedness (Deci and Ryan, 1985; Ryan and Deci, 2000a). The fulfilment of these three factors explains the variability in intrinsic motivation (Sheldon et al., 2003). In other words, a high level of intrinsic motivation will only occur when the psychological needs of competence, autonomy and relatedness are satisfied. For example, customers’ strong attachment to a brand is likely to be developed when the brand enhances a person's feelings of autonomy and relatedness and does not suppress feelings of competence (Thomson, 2006). Competence refers to the belief of possession of skills or qualities for performing a particular task satisfactorily (Brashear et al., 2003; Ganesan, 1994; Newell and Swan, 2000). The sense of autonomy reflects one’s own values or interests in which one feels like an originator, rather than being influenced by external sources of value or pressure (deCharms, 1968). The dimension of relatedness indicates the sense of closeness or attachment with others (Ryan and Deci, 2000a). This social factor highlights social environments that facilitate intrinsic motivation by supporting people’s innate needs to experience "connected with and cared for another" (La Guardia et al., 2000, p. 368). Customers' connection with other customers or even the service provider may be a manifestation of this dimension.

Given the fundamental role of competence, autonomy and relatedness to SDT, it is conceivable that these three core psychological needs are highly relevant to customer advocacy. However, this thesis argues that three specific factors (confidence, opinion

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leadership and altruism towards service providers) may provide clearer insights into the direct determinants of customer advocacy based on two important considerations.

First, competence, autonomy and relatedness are universal human needs (Ryan and Deci, 2000a), while the proposed determinants tend to be more specifically relevant to the situation of customer-customer communication. For example, competence is likely to be reflected in confidence in the strong recommendation of a service provider. Competence stems from successful experiences with an activity over time (Bandura, 1977; Deci and Ryan, 1985). Continued success gives rise to confidence that results in the belief in possession of a capability. In customer advocacy, the confidence concerns the capability in evaluating a service provider's service excellence based on direct experiences. Confidence is also related to the certainty in making the right recommendation to others in customer-customer communications.

With respect to the second psychological need, opinion leadership is considered to reflect autonomy in conversational situations. This is because that opinion leaders spread knowledge as an original influence on others (Richins and Root-Shaffer, 1988; Summers, 1970), which is in line with the originality in one’s own values or interests manifested in autonomous behaviours (deCharms, 1968). Opinion leaders are motivated to do so as a result of their interest and involvement in a product category. Thus, opinion leadership involves the desire to be seen as knowledgeable and to pass the information (Summers, 1970; Burt, 1982) and therefore it can be categorized as a motivating factor in the production of customer advocacy.

As the third essential psychological need, relatedness is believed to be displayed in altruism towards service providers in customer advocacy. Altruism refers to helping behaviours without any anticipation of compensations (Batson, 1987; Sundaram, Mitra and Webster, 1998). The willingness by customers to help a service provider is perceived as a result of a series of positive interactions with the service provider (Mazzarol et al., 2007). Thus, customers have become connected to the provider by caring for its well-being (Hennig-Thurau et al., 2004). They see the provider as more

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than an unrelated commercial merchant. Therefore, altruism towards service providers is believed to be associated with relatedness in the context of customers' strong recommendations.

Additionally, confidence is related to the evaluative dimension, whereas opinion leadership and altruism towards service providers characterize a motivational dimension of customer advocacy. Prior research indicates that the evaluative and motivational dimensions are essential to the production of customer recommendation (e.g., Anderson, 1998; Sundaram et al., 1998). In the case of customer advocacy, advocates are not normally rewarded by companies. On the contrary, strong statements are likely to cause embarrassment if the service provider fails to meet the audience's expectations. Therefore, advocates are expected to be highly confident with the service evaluation and are highly motivated to promote the service provider. This suggests that evaluative and motivational dimensions are critical to customer advocacy. As confidence, opinion leadership and altruism towards service providers closely reflect the evaluative and motivational dimensions that are central to the generation of strong recommendations, they are likely to provide insights into what contributes to customer advocacy.

The next section presents a further elaboration on the evaluative and motivational antecedents of customer advocacy. Situational and relational factors are also proposed. They are expected to moderate the relationship between the determinants and customer advocacy. Figure 1 summarizes these factors and illustrates the conceptual model of customer advocacy.

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FIGURE 1 Conceptual Model 1 (Study 1)

Situational Factor Relational Factor Relationship Service Type Quality

H6a,b H7a, b (-) Evaluative Factor H6c H7c Service Quality H1; H2 Customer Confidence Expertise H3a,b Customer H7d Advocacy

Motivational Factor Altruism towards Service Provider H4a; H5a Control Variables Opinion •Tie Streng th Leadership •Altruism towards Others •Involvement

As shown in Figure 1, the evaluative determinants include service quality and customer expertise that are mediated through confidence in a service provider and in making a strong recommendation to impact customer advocacy. The motivational factors include altruism towards the service provider and opinion leadership. Service type and relationship quality represent the situational and relational moderators respectively. The hypotheses specified in this conceptual model of customer advocacy will be tested in a large-scale survey study (Study 1).

In the next section, additional hypotheses are also proposed concerning the relative impact of these determinants on general positive WOM. Alternatively stated, it is expected that the proposed evaluative (e.g., service quality, confidence) and motivational determinants (e.g., altruism towards service providers) of customer advocacy may have a stronger or weaker impact on general positive WOM. This serves to enhance the understanding of the impact of the proposed determinants on customer advocacy revealed in Study 1. These additional hypotheses will be tested in a separate study (Study 2). 73 | Page

3.3 Hypotheses

3.3.1 Service Quality and Customer Expertise as Antecedents of Confidence

Two antecedents of confidence are proposed in this article - service quality and customer expertise. With a different focus, each of them is indispensable to the establishment of confidence in the service provider and subsequently in making a strong recommendation. While service quality concerns the evaluation of an individual service provider, customer expertise indicates the knowledge of a general level of performance in the service category. The interrelationship between these antecedents and confidence will be discussed in detail next together with a number of hypotheses derived from the discussion.

Service quality

Service quality has a fundamental impact on customers’ perceptions of value (Bolton and Drew, 1991; Sweeney, Soutar and Johnson, 1997), satisfaction (Cronin and Taylor, 1992; Oliver, 1993; Spreng and Mackoy, 1996), long term evaluation of trust (Chiou and Droge, 2006; Sharma and Patterson, 1999), commitment (Wetzels, Ruyter and Birgelen, 1998), and behavioural intentions of loyalty (Bloemer, Ruyter and Peeters, 1998; Boulding et al., 1993; Zeithaml et al., 1996). In a services context, customers place greater importance on the service quality they receive than on the cost they pay for the service (Cronin, Brady and Hult, 2000).

A high quality of service sustains customer confidence and competitive advantage (Berry, Parasuraman and Zeithaml, 1994). This is attributable to the link between various service quality dimensions and customers’ level of certainty towards the performance. Parasuraman et al. (1988) identify five dimensions of service quality, namely, reliability, responsiveness, assurance, empathy, and tangibles. All of these dimensions are important to customer confidence in a service provider. 74 | Page

For example, the assurance dimension of service quality refers to the knowledge and courtesy of employees and their ability to convey confidence in the service provider (Dabholkar et al., 1996; Parasuraman et al., 1988). The reliability dimension is naturally related to confidence, as being reliable is the ability to be “be relied upon, in which reliance or confidence may be put” (Merriam-Webster Online Dictionary, 2011). Reflecting this definition, reliability in service quality pertains to the fulfilment of promises and the consistency of service delivery that customers can rely upon (Parasuraman, Berry and Zeithaml, 1991). The other dimensions of service quality (i.e., empathy, responsiveness and tangibles) may not exhibit direct connections with confidence. For example, empathy suggests the tendency of having customers’ best interest at heart (Parasuraman et al., 1988). The responsiveness and tangibles dimensions of service quality embody, respectively, the promptness in responding to customer requests and visible cues of a service environment (Parasuraman et al., 1988). However, failure of delivery of these dimensions results in unsatisfactory service quality and ultimately affects confidence in a service provider.

Cognitive psychology indicates that the coherent representation and integration among elements play an important role in producing confidence (Myers et al., 1984). In addition, the stability of a belief and the ease of retrieving the belief are associated with certainty (Gill, Swann and Silvera, 1998; Pelham, 1991; Tormala, Petty and Briñol, 2002). Consequently, the integration among various service quality dimensions and the consistency of the integration lead to a positive and reliable representation of the service provider, which is likely to facilitate a positive belief about a service provider in customers’ mind. Therefore, it is hypothesized that:

H1: Service quality has a positive direct influence on confidence in a service provider. (Study 1)

Customer expertise

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Customer expertise represents a customer’s knowledge about the standard of performance by a typical provider of similar brands in a category (Cadotte, Woodruff and Jenkins, 1987). Therefore, customer expertise encompasses firm-specific as well as market-specific knowledge (Sharma and Patterson, 2000). The influence of customer expertise is powerful throughout a consumption experience. It shapes customer expectation (Cadotte et al., 1987; Zeithaml, Berry and Parasuraman, 1993). As a result, it affects the extent of post-purchase satisfaction and loyalty as a result of higher levels of exposure to competitive offerings and performances (Bell and Eisingerich, 2007; Mitchell and Dacin, 1996; Söderlund, 2002). This can be explained by the observation that as the exposure to positive experiences increases, an individual’s evaluation tends to polarize (Grush, 1976; Zajonc, 1968). Therefore, if the service experience is generally positive, highly experienced customers are more likely to engage in extreme positive activities (e.g., strong and passionate WOM such as customer advocacy) compared to low-familiarity customers. Underlying these differences is the more developed and complex cognitive structure or ‘schema’ of experienced customers with respect to the quantity, content and organization of knowledge about market offerings (Alba and Hutchinson, 1987; Rao and Monroe, 1988). Accumulated experience enables customers to use more attributes and more attribute levels to differentiate among offerings and performances (Mitchell and Dacin, 1996; Moorthy, Ratchford and Talukdar, 1997).

More importantly, expert customers process information in greater depth than novices (Chi, Feltovich and Glaser, 1981). For example, experienced customers base their evaluations on attributes that are most important (Butcher, 2005; Johnson and Russo, 1984). Consequently, novice customers rely more on perceptual and relational attributes. For someone to advocate a service provider, he/she should be confident about the most important attribute and this is more likely the case with experienced customers. In addition, customers with expertise are less affected by marketing communications (Alba and Hutchinson, 1987) and less reliant on social norm influences (East, 1992). Overall, they are more confident than the novices (Christensen-Szalanski and Bushyhead, 1981; Spence and Brucks, 1997). For example, research in the nursing arena has shown that

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nurses who were advocates were experienced and respected clinicians who had less perceived risk when acting on their convictions (Segesten, 1993). Therefore, the following is suggested:

H2: Customer expertise has a positive direct influence on confidence in a service provider. (Study 1)

3.3.2 Confidence as a Mediator of the Effects of Service Quality and Customer Expertise on Customer Advocacy

Confidence “reflects the degree of conviction or certainty with which a belief or attitude is held” (Krishnan and Smith, 1998, p.276). It is the degree to which individuals feel certain about their evaluative judgment or prediction about the firm they are dealing with (Howard, 1989; Morgan and Hunt, 1994). Confidence is highly predictive of the initiation and persistence of actions (Fazio and Zanna, 1978; Stajkovic, 2006). This relates to its role in determining and mediating the consistency between attitude and behaviour (Fazio and Zanna, 1978).

Confidence has been proven to be a critical determinant of advocacy in the areas of nursing (Chafey et al., 1998; Foley, Minick and Kee, 2002; Hanks, 2008), public health advocacy (Caira et al., 2003; Carver et al., 2003) and media advocacy (Holder and Treno, 1997). Advocacy builds on skills and knowledge, but more importantly, confidence to voice opinion to the public in an open and firm manner. In other words, advocates need to feel strongly enough about a situation or a sense of being ‘right’ about the issue prior to their advocacy communication (Chafey et al., 1998; McGrath, Holewa and McGrath, 2006). In fact, McGrath et al. (2006) regard confidence as the first necessary tool for advocacy.

Central to the importance of confidence is the perceived social risk involved in communications. This refers to the negative consequences arising from the individual’s behaviour in his/her social environment (Jacoby and Kaplan, 1972). Individuals are

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motivated to reduce risks in their social communication, according to uncertainty reduction theory in communication literature (Berger and Calabrese, 1975). Otherwise, undesired feelings such as ‘concern’ or ‘psychological discomfort’ (Zaltman and Wallendorf, 1983) will result. There is also a level of riskiness in offering WOM information, including social and psychological cost (Mazzarol et al., 2007; Moe and Trusov, 2011; Palmatier et al., 2006). This is complicated by the greater level of risks associated with services (when compared with products) as a consequence of their inherent properties such as intangibility or heterogeneity (Guseman, 1981; Mitchell and Boustani, 1993; Murray, 1991).

Risk perception is likely to be more evident in customer advocacy. Advocates do not receive rewards for recommending the subject service provider with extra strength and effort. If advocates are not fully confident that the service provider will provide the same experience to the audience, they are likely to experience substantial social risk without any potential benefits. It would be much safer to provide general positive WOM with reduced strength and passion. As a matter of fact, advocates assume a great responsibility and they do not wish to damage their self-image by a false statement (Nelson, 1966). Hence, confidence is a key determinant of customer advocacy. Here confidence refers to the certainty in the judgement of the service provider as well as the confidence in highly recommending the provider.

Given the fundamental position of confidence in advocacy, it is argued that despite the positive influence of service quality and customer expertise on WOM (e.g., Hartline and Jones, 1996; Zeithaml et al., 1996), it is the certainty of a service provider's excellence of performance that holds a more important position in the production of advocacy. Thus, the impact of service quality and customer expertise is mediated through confidence in a service provider in achieving customer advocacy. Expressed formally:

H3a: Confidence in a service provider mediates the relationship between service quality and customer advocacy. (Study 1)

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H3b: Confidence in a service provider mediates the relationship between customer experience and customer advocacy. (Study 1)

The preceding discussion highlights the direct impact of confidence in a service provider and the indirect impact of service quality and customer expertise on customer advocacy. This implies that confidence in a service provider is more crucial to customer advocacy than service quality and customer expertise, largely attributable to the central role of increased risks associated with firm and forceful statements in advocacy. Contrastingly, in the situation of general positive WOM, the strength of recommendation is milder as the message is delivered in a less passionate and reinforcing manner. Thus, the level of perceived risk is likely to be lower in general positive WOM. Therefore, it is expected that confidence is not as critical in leading to general positive WOM compared to its role in leading to customer advocacy.

While service quality is likely to be less important than confidence in the generation of customer advocacy as a result of heightened risk involved in strong statements, it is of substantial importance in the production of positive WOM (Hartline and Jones, 196; Zeithaml et al., 1996). Similarly, it is conceivable that the direct impact of customer expertise is stronger on general positive WOM than that on customer advocacy. Taken together, the following hypotheses are proposed:

H3c: Service quality has a stronger direct influence on general positive WOM than on customer advocacy. (Study 2)

H3d: Customer expertise has a stronger direct influence on general positive WOM than on customer advocacy. (Study 2)

H3e: Confidence in a service provider has a weaker direct influence on positive WOM than on customer advocacy. (Study 2)

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3.3.3 Altruism towards Service Providers and Opinion Leadership as Antecedents of Customer Advocacy

Altruism

Altruism is the act of doing something for others or helping others without any anticipation of personal gain (Batson, 1987; Sundaram et al., 1998, p.529). The tendency to be altruistic is determined by internalized values in terms of standards of behaviour (Piliavin and Charng, 1990; Schwartz and Howard, 1984), evoking feelings or obligations to do something for others. Although altruism can be affected by external forces such as social influence (Piliavin and Charng, 1990), diffusion of responsibility (Simmons, Klein and Simmons, 1977) or mood (Bower, 1981; Kelley and Douglas Hoffman, 1997), the willingness to be altruistic and to benefit others does exist as a part of human nature (Piliavin and Charng, 1990). Typically, people engaged in altruistic activities do not see helping situations as costly; rather, they see them as rewarding (Kerber, 1984). This is evidenced by benefits associated with altruism such as the feeling of ‘warm glow’ (Andreoni, 1990), the experience of moral satisfaction (Kahneman and Knetsch, 1992), and the enhancement of joy (Smith, Keating and Stotland, 1989) and self-esteem (Batson, 1987). Therefore, altruism is motivated by internal instead of external rewards in the form of hedonism and self-gratification (Baumann, Cialdini and Kendrick, 1981).

Empirically confirmed in recent studies (Hennig-Thurau et al., 2004; Smith et al., 2007), altruism is recognized as a fundamental motive in WOM communication behaviours (Dichter, 1966; Engel, Blackwell and Miniard, 1993; Sundaram et al., 1998). In most of these studies, altruism refers to altruism towards other customers, concern for other consumers (Engel et al., 1993) or ‘other-involvement’ (Dichter, 1966), as people desire to help other consumers with their buying decisions or save others from negative experiences.

Sundaram et al. (1998) introduce customers’ desire to help a company as a new dimension of altruistic motives in WOM. Impressive employee behaviours (such as 80 | Page

being helpful, responsive and friendly) may underline this motive, which contributed to nearly 18% of the initiation of positive WOM conversations based on their analysis of 363 critical incidents (Sundaram et al., 1998). Grounded in social exchange theory (Blau, 1964), this motive reflects individuals' tendency to return the favour when they receive exceptional service or a positive output/input ratio from an organization (Hennig-Thurau et al., 2004). Customers describe the reciprocation as “I want to see them (the company) survive” or “I want to see them do well” (Cheung, Anitsal and Anitsal, 2007) or simply a wish to help a service provider (Mazzarol et al., 2007). This manifests an internal joy and rewards when doing something without the expectation of financial gain. Notably, the impact of this motive is equivocal in WOM literature. While some studies revealed its positive relationship with WOM communication (Cheung et al., 2007; Sundaram et al., 1998), some failed to find a significant result in the context of virtual communities (Hennig-Thurau et al., 2004).

With respect to customer advocacy, the conventional altruism towards other customers still holds, as advocates help influence other customers make the right decision. On the other hand, the manifestation of internal joy towards helping the service provider is likely to be even stronger, because customers go the extra mile to convince others to experience the right service provider without additional reward. Thus, it is expected that altruism towards the service provider contributes to the extra effort from general positive WOM to customer advocacy. It is therefore hypothesized that:

H4a: Altruism towards service providers has a positive direct influence on customer advocacy. (Study 1)

H4b: Altruism towards service providers has a weaker direct influence on general positive WOM than on customer advocacy. (Study 2)

Opinion leadership

Diffusion and communication research has emphasized the important role of opinion leaders in the diffusion of innovation (Czepiel, 1974; Valente, 1996) and the brokerage 81 | Page

of information within and between social groups (Katz and Lazarsfeld, 1955). Opinion leaders do not simply relay messages from mass media to their personal networks, but exert direct influence on others by giving advice, usually regarding the purchase and the use of product categories (Flynn, Goldsmith and Eastman, 1996; Katz and Lazarsfeld, 1955; Rogers, 1962), including fashion products (Summers, 1970), energy consumption (Davis and Rubin, 1983) and even prescription drug choice (Nair, Manchanda and Bhatia, 2010). Their enduring and stable involvement with a particular product category (Richins and Root-Shaffer, 1988) results in the willingness to acquire and accumulate extensive knowledge over time (Bloch and Richins, 1983), which establishes them as an expert in a product category.

Opinion leaders are intrinsically motivated to share knowledge, largely attributable to the pleasure of talking about the product with which they are highly involved (Summers, 1970) and the social benefit of passing the needed information (Burt, 1982). Therefore, opinion leaders are distinct from advocates, who focus on the expression of support and conviction for a service provider. However, individuals high in opinion leadership possess certain attributes that make them more likely to produce strong and passionate recommendations instead of mere positive comments. For example, opinion leaders tend to have a greater level of assertiveness in their communication (Summers, 1970). They carefully evaluate key elements of consumption experiences such as the benefit/expenses ratio (Davis and Rubin, 1983). Drawing upon their own past experiences, opinion leaders develop a thorough understanding of industry performance and they are less likely to be influenced by consumption guidance (Childers, 1986; Hirschman, 1980). Consequently, their cognitive skills and expertise enhance their competence in judging good services and speaking out loudly for outstanding performers.

In addition, opinion leaders display high levels of participation in social and political activities (Weimann, 1994). They tend to be proactive and voluntary agents in promoting socially beneficial subjects and are ultimately more altruistic and active (Noelle-Neumann, 1999 ). Therefore, they are likely to be advocates if a passionate

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promotion of a service provider provides benefits for their social network. Programs that employed opinion leaders as effective advocates have been successful, typically in the public health sector in HIV/STD risk reduction (Kelly et al., 1991), promotion of mammography screening (Earp et al., 2002) and tobacco prevention in schools (Valente et al., 2003). Thus, although opinion leadership has been associated with WOM (Richins and Root-Shaffer, 1988), it is expected that it is more important in the production of customer advocacy. The following hypotheses are therefore proposed:

H5a: Opinion leadership has a positive direct influence on customer advocacy. (Study 1)

H5b: Opinion leadership has a weaker direct influence on general positive WOM than on customer advocacy. (Study 2)

3.3.4 Moderating Effects of Service Types and Relationship Quality

It is critical to identify the drivers behind customer advocacy, however, these drivers may not be equally important across different situations. Thus, another key issue this research considers involves understanding factors that leverage the impact of drivers on customer advocacy. Both situational and relational factors may have a leveraging effect, which is discussed next.

Service types

The nature of service types has been found to influence the perception of relative importance of service attributes, the evaluation of service experiences and the loyalty intent (e.g., Bloemer, de Ruyter and Wetzels, 1999; Ostrom and Iacobucci, 1995; Patterson and Smith, 2001). Hence, customer advocacy as a manifestation of loyalty is likely to be affected by the nature of service types.

Previous work postulates that the extent of customer-employee contact is important as employees’ attitude and behavioural responses affect customers’ evaluations of services

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(Bitner, 1990). Affected by service employees' role stress (Hartline and Ferrell, 1996; Schneider, 1980) or job dissatisfaction (Shamir, 1980), customer-employee contact can result in negative judgement regarding the quality of customer service. However, when employees exhibit authentic understandings in a good service delivery, extended customer-employee encounters can lead to highly positive customer responses (Price, Arnould and Tierney, 1995). Therefore, the emotional (Johnson and Zinkhan, 1991) and social dimension of service quality (Gwinner, Gremler and Bitner, 1998) are more apparent in customers' service evaluation for high contact services. Consequently, in high-contact and experience services, customers are likely to judge the service provider and develop the confidence of the judgment based on the service quality of a specific provider’s performance.

By contrast, low/medium contact services (e.g., retail banking) tend to have standardized service delivery processes with low levels of customization and employee discretion (Clemes, Mollenkopf and Burn, 2000). Hence, the quality control of low/medium contact services is comparatively more achievable than that of high contact services. However, compared to high contact services, customers tend to have fewer evaluation dimensions in low/medium contact and experience services. For example, the customer-firm affection is less likely to be established in transactional services typified by low/medium contact services (Yim, Tse and Chan, 2008). Therefore, to be able to reach a confident judgment of the service excellence of a service provider, customers are likely to not only rely on firm-specific performance, but also on market- specific knowledge of the standard of performance. In other words, customer expertise tends to be a stronger determinant on confidence in a service provider in low/medium contact services. As a result, it is hypothesized that:

H6a: Service quality has a stronger direct influence on confidence in a service provider in high contact and experience services than in low/medium contact and experience services. (Study 1)

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H6b: Customer experience has a weaker direct influence on confidence in a service provider in high contact and experience services than in low/medium contact and experience services. (Study 1)

Credence services such as medical or legal services require formal educational qualifications to make a fair assessment (Hill, 1988), hence perceived risk in purchasing credence services is the highest among service types (Mitra, Reiss and Capella, 1999). Common sources of this high level of risk perception are: minimal information about alternatives (Mitra et al., 1999), high information asymmetry due to the degree of service providers’ professionalism (Nayyar, 1993); higher degree of customization (Guiltinan, 1987; Zeithaml, 1981); the lack of ability to observe the service delivery process, or the lack of ability to judge if the actions of the provider are appropriate (Gallouj, 1996). Some credence services involve financial risks as the cost can not be determined until the end of the service delivery (e.g., legal services) (Nayyar, 1993). Additionally, psychological and social risks may be involved upon the selection of a poor attorney service in a divorce case (Mitra et al., 1999). Consequently, higher repeat purchases (Crocker, 1986) and the reluctance to switch (Sharma and Patterson, 2000) are associated with credence services. As many credence services are of critical importance to customers (Thakor and Kumar, 2000), given a greater level of perceived uncertainty in these services, it is likely that customers require a stronger sense of confidence before they advocate a service provider of credence services. Alternatively stated, confidence in a service provider plays a more important role in generating advocacy in credence services than services of a lower level of uncertainty such as experience services. Therefore it is expected that:

H6c: Confidence in a service provider has a stronger direct influence on customer advocacy in credence services than in experience services. (Study 1)

Relationship quality

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Relationship quality assesses overall relationship strength customers have with a firm (Crosby, Evans and Cowles, 1990; De Wulf et al., 2001). In the literature, there has been a considerable level of agreement that relationship quality is a higher-order multidimensional construct consisting of relationship satisfaction, trust and commitment (e.g., De Wulf et al., 2001; Dwyer, Schurr and Oh, 1987; Kumar, Scheer and Steenkamp, 1995). In other words, relationship quality is a global assessment of customers’ satisfaction with the relationship, their belief in the firm’s reliability and integrity (Morgan and Hunt, 1994) and their desire to maintain a valued relationship (Moorman, Zaltman and Deshpande, 1992). A number of studies have revealed that relationship quality is associated with positive customer-focused outcomes such as repeat purchase, cross-buying and extended relationship duration (Crosby et al., 1990; Doney and Cannon, 1997; Dwyer et al., 1987), as well as positive seller-focused outcomes such as cost reduction effects (Berry, 1995), enhanced sales or share of wallet (Doney and Cannon, 1997; Reynolds and Beatty, 1999; Siguaw, Simpson and Baker, 1998). In addition, relationship quality has its direct impact on WOM (Hennig-Thurau, Gwinner and Gremler, 2002; Verhoef, Franses and Hoekstra, 2002), greater than its impact on relationship continuity or behavioural loyalty (e.g., repeat purchase) (Palmatier et al., 2006).

Recently, the moderating effect of relationship quality on customer post-purchase responses has received attention. For example, in the context of customer desire for revenge and avoidance, compared with low relationship quality customers, the desire for revenge of high relationship quality customers decreases more slowly, while the desire for avoidance increases more rapidly (Gregoire, Tripp and Legoux, 2009).

In the context of customer advocacy, it is hypothesized that relationship quality leverages its impact on the positive association between service quality and confidence, as well as that between customer expertise and confidence. When there is high relationship quality, customers exhibit strong relationship satisfaction, trust and commitment. They believe they can depend on the company to have their best interests at heart and be capable of delivering services reliably (Doney and Cannon, 1997).

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Therefore, the presence of high relationship quality indicates customers’ confidence in the firm’s performance. The association between service quality and confidence is likely to be stronger at a high level of relationship quality. Naturally, high relationship quality is likely to enhance the impact of confidence on customer advocacy. On the other hand, customers of high relationship quality tend to hold favourite attitude towards a service provider's competence and benevolence (Morgan and Hunt, 1994). Thus, how well other firms perform in the market may become less critical in the development of their confidence in the service provider. This suggests that the impact of customer expertise that involves customer knowledge about other firms' performance may be weaker (stronger) at a high (low) level of relationship quality. Hence, the following hypotheses are proposed:

H7a: Service quality has a stronger direct influence on confidence in a service provider at a high level of relationship quality. (Study 1)

H7b: Customer experience has a stronger direct influence on confidence in a service provider at a low level of relationship quality. (Study 1)

H7c: Confidence in a service provider has a stronger direct influence on customer advocacy at a high level of relationship quality. (Study 1)

Further, altruism towards service providers denotes customers' desire to help a company and make it more successful (Hennig-Thurau et al., 2004; Sundaram et al., 1998). With the presence of a high level of relationship quality, customers value the relationship with the service provider and they are inclined to behave in a way that is beneficial to the company (Verhoef et al., 2002). It is therefore expected that when customers develop a strong relationship with a service provider, their desire to help and benefit the provider is stronger as well. Hence the following hypothesis is proposed:

H7d: Altruism towards service providers has a stronger direct influence on customer advocacy at a high level of relationship quality. (Study 1)

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3.4 Method

As noted previously, two studies were conducted to test the proposed hypotheses. Study 1 explored the determinants and moderating influences underlying customer advocacy. Structural equation modelling (SEM) and multiple regression were employed to analyse the correlation, mediation and moderation effects in this study.

A further study, Study 2, investigated if and how the determinants revealed in Study 1 differ in their impact on general positive WOM when compared with advocacy. In examining H3c, H3d, H3e, H4b and H5b, the study aimed to broaden the understanding of the impact of these determinants.

Next, the procedures and the results of these two studies will be presented in detail.

3.4.1 Study 1

Sample

Four major service types, including 17 service industries, were utilised in the research in order to maximize the generalizability and to test the moderating effect of service type. These four service types were: experience and high contact services (hairdressing, beauty salons, personal trainers, education, massage services and childcare); experience and low contact services (telecommunications, basic banking services, dry cleaning and airlines); credence and high contact services (medical services such as GPs, dentists or physiotherapists); credence and low contact services (financial advisors, car servicing, computer repairs, accountants, legal services and white goods repairs).

These service types were selected based on two reasons. First, these services represent the diverse nature of services as they capture two important dimensions in service 88 | Page

typologies - the extent of customer-employee contact as well as the experience versus credence properties (Bowen, 1990; Darby and Karni, 1973; Lovelock, 1983). The diversity also serves the purpose of testing the role of service types as a potential moderator of customer advocacy. Second, these services are highly subjective to the influence of customer recommendations. For example, customers tend to experience a lack of confidence in the evaluation of credence-based services (Darby and Karni, 1973; Murray and Schlacter, 1990) and they place greater reliance on WOM information. Hence, the selection of these services is appropriate in examining the influencing factors of the upper level of customer recommendation strength.

Respondents were recruited from members of an online panel proportionate to the state populations of Australia. A Web-based self-administered survey was employed to gather data. Upon completion of the survey, respondents received a small cash credit (equivalent to $2 or less) provided by the online panel.

To accomplish the research objective of exploring customer advocacy as the upper level of recommendation strength, two screening criteria were imposed when recruiting respondents during this phase of the study: (1) the respondent had recommended a service provider from the specified service industries within the previous six months, and (2) the recommendation made was above 4 out of 7 in terms of recommendation strength (1 = not strong at all, 7 = very strong). Respondents were then asked to separately evaluate to what degree the items of the customer advocacy scale described their recommendation behaviour. Therefore, all respondents had given a recommendation with the strength that was above the moderate level. This process ensured that Study 1 examined factors contributing to strong and passionate recommendations reflected in customer advocacy.

In total, 6,937 panel members were approached. 1,045 qualified respondents based on the screening criteria completed the survey. A total sample of 975 respondents was retained for further data analysis after excluding outliers or dubious response patterns.

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Demographic statistics suggest that the sample was representative of metropolitan Australia (Australian Bureau of Statistics, 2006), with the exception of generally higher levels of education and income. The demographic distribution of the sample is as follows: gender - 48% male and 52% female; age - 23% 18-29 years old, 22% 30-39 years old, 24% 40-49 years old, and 31% above 50 years old; education - 58% below undergraduate, 42% undergraduate or above; income - 12% less than $10,000 annual taxable income, 23% $10,000-$30,000, 48% $30,000-$80,000, and 18% over $80,000. Sample size was generally even across four service types (see Appendix 1 for details).

Measures

The measurement of customer advocacy adopted seven newly developed scale items that had demonstrated psychometrically sound properties (Liu et al., 2011). The Coefficient alpha for the measure was .87.

All other model constructs used multiple indicators adapted from existing scales. Service quality was measured on both the functional and technical dimensions, based on eight items adapted from Brady and Cronin (2001), Brady, Cronin and Brand (2002) and Hartline and Ferrell (1996). The Cronbach's alpha for the scale was .95. To measure customer experience, two items were used to assess customers’ self-evaluation of their level of experience and knowledge of the service type (Patterson, 2000), with the correlation between the two scale items being .80. The measurement of confidence was adapted from Laroche, Kim and Zhou (1996) anchored by statements: ‘How confident are you about your evaluations of this service provider’; ‘How confident are you that this service provider will provide the same experience to listeners of your recommendations’ and ‘How confident are you in giving strong positive evaluations about this service provider’, with a construct reliability estimate of .95. Measures of altruism towards service providers followed those used in Hennig-Thurau et al. (2004), while opinion leadership was measured according to studies by Flynn et al. (1996) and Kleijnen et al. (2009). Measures of relationship quality were adapted from De Wulf et

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al. (2001) and Sirdeshmukh, Singh and Sabol (2002). Items of these three scales proved to be reliable, with the Cronbach's alpha estimated at .82, .90 and .92 respectively.

To ensure that customer advocacy actually captures an extreme form of recommendation that is different from general positive WOM, the measurement of general positive WOM was also included for verification purposes. The WOM measurement proposed by Zeithaml et al. (1996) contains items such as - ‘Say positive things about this service provider to others’ and ‘Recommend this service provider to someone who seeks my advice’. Therefore, this WOM scale relates to general positive comments about a service provider without any specific reference to extreme strength and passion in the recommendation. Hence, this scale in essence captures the general form of positive WOM with moderate recommendation strength. The scale was therefore adopted and the correlation between the two scale items was .85. It was found that the sample mean of general positive WOM was much higher than that of customer advocacy ( = 6.49 vs. 5.01; SD = .67 vs. 1.08). This provided a preliminary indication that the measurement of customer advocacy effectively captured a construct distinct from general positive WOM and served the purpose of the study.

Appendix 2 provides a complete overview of the final measurement items, their standardized loadings and sources for each construct.

Control variables

To control for the possibility of other constructs that might have an impact on customer advocacy, measures of four control variables were included. Tie strength is the closeness of the relationship between the party who recommends the product/service and the party who receives the recommendation (Brown and Reingen 1987). It has been found to influence WOM and recommendation likelihood in consumer markets (e.g., Brown and Reingen 1987; Wirtz and Chew 2002), business markets (Money, Gilly and

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Graham, 1998) and online communities (Brown, Broderick and Lee, 2007). Tie strength was assessed using three items based on Bansal and Voyer (2000), with a construct reliability estimate of .84.

Another potential covariate was altruism towards other customers. The focus of the concern in this type of altruism is directed at fellow customers, based on the desire to assist others make a correct purchase decision. This is distinct from altruism towards a service provider in which the focus is on the service provider with the intention to help it succeed in the market place. Altruism towards other customers is a fundamental individual motive for WOM communication behaviour in early WOM literature (Dichter, 1966; Engel et al., 1993; Sundaram et al., 1998). Recently, the positive relationship between altruism towards others and WOM behaviour received quantitative empirical support (Hennig-Thurau et al., 2004; Smith et al., 2007). The measurement items of this variable followed those used in Hennig-Thurau et al. (2004). Cronbach's alpha for the scale was .86.

The third control variable was the level of involvement in the service category. Involvement results in exciting and pleasurable experiences with products or services, which contributes to customers' subsequent discussions and recommendations in order to repeat the joy of those experiences (Ditcher, 1966; Richins, 1988; Sundaram et. al., 1998). Therefore, the measure of involvement was included in the assessment of the conceptual model, with measurement items adapted from Zaichkowsky (1985). Cronbach's alpha was estimated at .81 for the scale.

In testing the moderating role of relationship quality on confidence and customer advocacy, another control variable was introduced - relationship length. This was to minimize the compounding effect between the relationship quality and relationship length. Relationship length was measured by the number of actual months since respondents first patronized the service provider.

Results

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Measurement model:

The psychometric properties of the constructs were evaluated through confirmatory factory analysis (CFA) using AMOS 18.0. Six latent variables and a moderator variable (relationship quality) with their observed indicators were included in the measurement model. The measurement model generated a significant chi-square (X2 = 1241.56, df = 328, p < .01). This was expected considering the sample size of the study (Marsh, Balla and McDonald, 1988), therefore other measures of fit should be considered as criteria for model acceptance or rejection (Hair et al., 2006). Other indices suggested that the measurement model fitted the data well (CFI = .958; TLI = .952; NFI = .944; RMSEA = .053; and SRMR = .087.) (Hu and Bentler, 1999). Items load unambiguously and highly (.70 or above) on their respective factor. All constructs were reliable with Composite Reliability (CR) ranging from .82 to .93, exhibiting internal reliability (Bagozzi and Yi, 1988). Average Variance Extracted (AVE) for each latent variable exceeded .50, indicating that the variance explained by the construct was greater than the measurement error (Fornell and Larcker, 1981). These findings provided support for convergent validity. Furthermore, as AVE of each latent construct was greater than its respective squared correlation between other constructs, evidence of discriminant validity was established (Fornell and Larcker, 1981). Complete results of AVE, CR, means, standard deviation, and correlations among the latent factors are provided in Table 1.

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TABLE 1 Means, Standard Deviations, Average Variance Extracted, and Composite Reliabilities (Study 1)

Constructs Mean SD AVE CR 1. Service Quality 6.09 0.79 0.69 0.93 2. Customer Experience 5.12 1.28 0.81 0.89 3. Confidence 7.87 1.11 0.87 0.95 4. Altruism towards Service Provider 4.17 0.68 0.69 0.82 5. Opinion Leaership 5.08 1.13 0.75 0.90 6. Customer Advocacy 5.01 1.08 0.78 0.87 7. Relationship Quality 6.06 0.80 0.69 0.90 Correlation Matrix Constructs 1 2 3 4 5 6 7 1. Service Quality 0.19** 0.75** 0.60** 0.21** 0.43** 0.82** 2. Customer Experience 0.04 0.32** 0.18** 0.29** 0.32** 0.26** 3. Confidence 0.56 0.10 0.56** 0.30** 0.49** 0.79** 4. Altruism towards Service Provider 0.36 0.03 0.31 0.39** 0.48** 0.62** 5. Opinion Leaership 0.05 0.08 0.09 0.15 0.53** 0.33** 6. Customer Advocacy 0.18 0.10 0.24 0.23 0.28 0.49** 7. Relationship Quality 0.68 0.07 0.63 0.39 0.11 0.24 **p < .01 NOTE: AVE = average variance extracted; CR = composite reliabilities; SD = standard deviation. Correlations are reported above the diagonal; squared correlations are reported below the diagonal.

Covariance structure model:

Following the confirmation of measurement quality of latent variables, the covariance structural model was evaluated using maximum likelihood estimation. Again the chi- square value was significant (X2 = 1539.64, df = 465, p < .01) given the large sample size (n = 975). However, the other fit measures indicated that the model was a plausible representation of the structure underlying the empirical data: CFI = .954; TLI = .947; NFI = .935; RMSEA = .049; and SRMR = .059 (Hu and Bentler, 1999). Moreover, all parameter estimates were significant in the expected direction (Table 2 reports the fully standardized solution), with none of the estimates of control variables being significant (β = -.02 to .08, ns). In total the proposed determinants explain 54% of the variance of customer advocacy.

In confirmation of H1 and H2, both service quality and customer experience were positively related to confidence (β = .76 and β = .20 respectively, p < .01). Altruism towards the service provider was positively related to customer advocacy (β = .22, p 94 | Page

< .01), supporting H4a. Opinion leadership had a significant and positive impact on customer advocacy (β = .34, p < .01), which supported H5a.

TABLE 2 Structural Model Results (Study 1)

Hypothesized Path Path Estimates a t-Value b R 2 Hypotheses Service Quality -> Confidence 0.76** 27.07 0.68 H1 (Supported) Customer Expertise -> Confidence 0.20** 8.66 H2 (Supported) Confidence -> Advocacy 0.35** 8.70 0.54 Altruism towards Service Provider -> Advocacy 0.22** 4.98 H4a (Supported) Opinion Leadership -> Advocacy 0.34** 9.24 H5a (Supported)

Tie Strength -> Advocacy (control) 0.02 Altruism towards Other Customers -> Advocacy (control) -0.02 Involvement -> Advocacy (control) 0.08

Goodness-of-fit indices X2 df GFI CFI TLI NFI RMSEA SRMR 1539.648 465 0.910 0.954 0.947 0.935 0.049 0.059 Note: GFI = Goodness-of-Fit Index; CFI = Comparative Fit Index; TLI = Tucker-Lewis Index; NFI = Normed Fit Index; RMSEA = Root Mean Square Error Approximation a. These are standardized loading estimates b. On the basis of one-tailed tests, t values greater than 1.65 were significant at p < .05; t values greater than 2.33 were significant at p < .01. **p < .01.

It should be noted that the model yielded a small, nonsignificant negative variance (0.09) associated with the second-order customer advocacy construct. Bentler and Chou (1987) and Dillon, Kumar and Mulani (1987) suggested a remedy by setting the estimate of the error variance to be a small, positive value (.01 in this study). The strategy is justifiable in light of the relatively large size of the sample (Dijkstra 1992). To ensure that the fixed value has a minimal impact on the interpretation of the results, five positive values starting from .01 to .05 at an increment of .01 were estimated and compared. All five models revealed similar results in terms of both fit and path estimates.

As all respondents had at least made a moderately strong recommendation as a result of screening on recommendation strength, their evaluation of service quality and confidence in the conceptual model tended to be highly skewed. In light of the potential issue of multivariate non-normality, the Bollen-Stine bootstrap procedure (Bollen and Stine 1992) were employed for correcting potential standard error and fit statistic biases 95 | Page

sourced from maximum likelihood-based estimation. Chi-square values remained significant given a large sample. For path parameters, the differences between the maximum likelihood-based estimation and the bootstrap-based estimation were very small, for example, the value of bias ranged from -.003 to .002 and the standard errors were similar. All hypothesized paths remained significant at .01 level in the bootstrap analysis. Therefore, the results based on maximum likelihood estimation can be trusted without the concern of biased estimation of parameters due to multivariate nonnormality.

Mediation Analysis

The procedure suggested by Baron and Kenny (1986) was followed to test the mediation relationship stated in H3a and H3b. Four conditions should be satisfied if confidence in a service provider mediated the relationship between service quality, customer expertise and customer advocacy: (a) service quality and customer expertise (the independent variables) must significantly affect confidence (the mediator), (b) confidence must significantly affect customer advocacy (the dependent variable), (c) service quality and customer expertise must significantly affect customer advocacy, when the impact of confidence is not present in the model and, (d) the direct relationship between service quality, customer expertise and customer advocacy becomes insignificant (full mediation), or remains significant but their impact becomes reduced (partial mediation) if the mediator is inserted back into the model.

As demonstrated in Table 2, the condition (a) and (b) have been satisfied. Both service quality and customer expertise were positively related to confidence in a service provider (β = .76 and β = .20 respectively, p < .01), while confidence significantly influenced customer advocacy (β = .35, p < .01). To test for condition (c), two new direct links were added from service quality to customer advocacy and from customer expertise to customer advocacy, while the effect of confidence on customer advocacy was constrained to zero. Both new paths were significant (β Service quality -> Advocacy = .29,

β Customer Expertise -> Advocacy = .17, p < .01), providing support for the third condition. Finally, a new model removing the constraint of the effect of confidence on customer

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advocacy was estimated to test for condition (d). This process caused the direct path from service quality to customer advocacy to be insignificant (β = .29 p < .01 to β =.09 ns). The direct association between customer expertise and customer advocacy remained significant. However, the direct impact of customer experience on customer advocacy decreased from β = .17 to .13 (p < .01). These results suggest that confidence in a service provider fully mediates the impact of service quality on customer advocacy, while it partially mediates the relationship between customer expertise and advocacy. Hence, H3a was fully supported while H3b received partial support.

Moderation Analyses

The moderation effects of service types were assessed using multigroup analysis in SEM. H6a predicted that service types moderate the effect of service quality on confidence in a service provider. To test this, the full sample was divided into four subsamples (groups) according to the corresponding service type. A baseline model was estimated in which an equality constraint was imposed on the service quality -> confidence path across all groups. Subsequently, a second model allowing the path to vary freely across groups was estimated. In both models, tie strength, altruism towards others and level of involvement remained as control variables. A Chi-square difference test was then conducted to verify the moderation effect.

The significant Chi-square difference result (∆X2 = 12.82, ∆df = 3, p < .01) provided support for H6a. As shown in Table 3, the effect of service quality on confidence in a service provider was highest in experience and high contact services (e.g., hairdressing) (path estimate = 1.12), followed by that in credence and low/medium contact services (e.g., car servicing) (path estimate = 1.08) and that in credence and high contact services (e.g., medical services) (path estimate = .96). The effect of service quality on confidence was lowest in experience and low/medium contact services (e.g., telecommunication) (path estimate = .78).

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Similar multigroup analysis was repeated in examining the moderation effect of service types stated in H6b and H6c. Moderation effect was evident for the relationship between customer expertise and confidence in a service provider (∆X2 = 7.31, ∆df = 3, p < .10). The impact of customer expertise on confidence was much stronger in experience and low/medium contact services than in three other service types. Specifically, the path estimate between customer expertise and confidence in experience and low/medium contact services was .27, contrasting the range of .13 - .16 in other service types (Table 3). Thus, H6b was supported. However, the moderation effect of service types on the Confidence -> Customer advocacy relationship was not significant (∆X2 = 3.54, ∆df = 3, ns), which failed to support H6c.

TABLE 3 Results of the Moderatated Regression Analysis (Service Type) Hypothesized Path Service Quality -> Confidence Customer Expertise -> Confidence Confidence -> Advocacy

Service Type 1 (Experience/High contact Services) 1.12 0.16 0.31 Service Type 2 (Experience/Low-Med contact Services) 0.78 0.27 0.11 Service Type 3 (Credence/High contact Services) 0.96 0.14 0.26 Service Type 4 (Credence/Low-Med contact Services) 1.08 0.13 0.20

∆df 333 ∆X2 12.819 7.314 3.539 (p = .01) (p = .10) (ns) Hypotheses H6a (Supported) H6b (Supported) H6c (Not supported) Note: Unstandardized path estimates are reported.

H7a to H7c predicted the moderation effects of relationship quality on the impact of various determinants of confidence in a service provider and customer advocacy. These effects were assessed using moderated regression analysis with two dependent variables involved – confidence and customer advocacy. Variable scores were obtained by averaging scale items and mean-centred all independent variables before the regression analysis was conducted (Aiken and West, 1991). All variance inflation factors were less than 4, suggesting that collinearity was not a problem.

Table 4 provides a summary of the moderated regression analysis. The relationship quality x service quality term in predicting confidence in a service provider was not significant, which did not support H7a. This result suggests that regardless of the magnitude of relationship quality, the impact of service quality on confidence remains 98 | Page

unchanged. In confirmation of H7b, the interaction term of relationship quality x customer expertise was significant and negative (β = -.07, p < .01). This indicates that with high levels of relationship quality, customer expertise exerts a lower impact on confidence in a service provider. The moderation effect can be seen in Figure 2A.

TABLE 4 Results of the Moderatated Regression Analysis Predictor DV = Confidence DV = Advocacy Hypotheses Model 1 Model 2 Model 3 Model 1 Model 2 Model 3 Constant 7.85** 4.74** 4.88** 5.00** 3.21** 2.73** (283.79) (17.69) (17.98) (160.90) (10.40) (8.74) Relationship Length (control) 0.00 0.00 0.00 0.00 0.00 0.00 (0.82) (0.01) (0.06) (0.17) (-0.27) (-0.42) Service Qualtiy 0.98** 0.56** 0.56** (32.67) (12.24) (11.96) Customer Expertise 0.17** 0.12** 0.12** (8.80) (6.76) (6.85) Relationship Quality 0.53** 0.51** (11.67) (11.16) Relationship Quality x Service Quality -0.01 H7a (Not supported) (-0.23) Relationship Quality x Customer Expertise -0.07** H7b (Supported) (-3.33) Tie Strength (control) 0.02 0.02 0.01 (0.78) (0.72) (0.54) Altuism towards Other Customers (control) -0.03 -0.01 -0.04 (-0.53) (-0.24) (-0.68) Involvement (control) 0.07 0.06 0.07* (1.82) (1.60) (1.83) Confidence 0.24** 0.12** 0.19** (7.62) (3.25) (4.87) Altruism towards Service Provider 0.23** 0.12* 0.09 (3.82) (1.93) (1.45) Opinion Leadership 0.44** 0.40** 0.39** (14.76) (13.61) (13.28) Relationship Quality 0.30** 0.37** (5.83) (7.03) Relationship Quality x Confidence 0.12** H7c (Supported) (4.01) Relationship Quality x Altruism towards Service Provider 0.02 H7d (Not supported) (0.23)

Adjusted R2 = 0.58 0.63 0.64 0.43 0.45 0.47 **p < .01. *p < .05 Notes: DV = Dependent variable. Unsatndardized coefficients are reported with t values in parentheses.

The hypothesized positive moderation effect of relationship quality on Confidence -> Customer advocacy relationship was confirmed with a significant interaction term of relationship quality x confidence (β = .12, p < .01), in support of H7c. This suggests that a higher degree of relationship quality results in greater confidence leading to customer advocacy. The moderation effect is shown in Figure 2B.

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Figure 2A and 2B depict the aforementioned significant interaction effects with the low/high level of relationship quality being set at one standard deviation below/above the mean. Figure 2A shows that customer expertise exerts a positive effect on confidence in a service provider at a low level of relationship quality. The effect becomes only marginally positive at a high level of relationship quality. This indicates that relationship quality affects the impact of customer expertise on confidence in a service provider negatively. Figure 2B shows the relationship between confidence and customer advocacy is positive at a low level of relationship quality. The relationship is even more positive at a high level of relationship quality, which illustrates the positive effect of relationship quality on the association between confidence in a service provider and customer advocacy.

FIGURE 2 Plots for Interaction Effects of Relationship Quality A. Moderating Effects of Relationship Quality on Customer Expertise -> Confidence Relationship

9

8.5

8

7.5

7 Confidence

6.5

6 Low Relationship Quality

High Relationship Quality 5.5 Low Customer Expertise High Customer Expertise

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B. Moderating Effects of Relationship Quality on Confidence -> Customer Advocacy Relationship

6.5

6

5.5

5

4.5 Customer Advocacy 4

3.5 Low Relationship Quality

High Relationship Quality 3 Low Confidence High Confidence

Note: Low/high level of relationship quality is set at one standard deviation below/above the mean.

The interaction between altruism towards service providers and relationship quality was not significant, which failed to support H7d. Thus, relationship quality does not moderate the impact of altruism towards service providers on customer advocacy. This was somewhat expected as the correlation between relationship quality and altruism towards service providers was relatively high at .62. It is possible that relationship quality gives rise to altruism towards a service provider. To confirm, several alternative models were tested. The first alternative model added relationship quality as the direct antecedent of altruism towards service providers (see Figure 3 Alternative 1). The fit indices of this new model were acceptable with a direct predictive influence of relationship quality on altruism towards service providers (β = .74, p < .01). However, the fit indices were inferior to those of the previously proposed conceptual model of customer advocacy (see Table 5, Alternative model 1 and Proposed model).

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A second competing model considered the possibility of relationship quality as a direct predictor in replacement of altruism towards service providers (see Figure 3, Alternative 2). The results revealed that the proposed theoretical model of customer advocacy still outperformed this competing model with regards to major model fit indices (see Table 5). This indicated that although relationship quality contributes to the development of altruism towards a service provider, the latter better explains customer advocacy.

FIGURE 3 Competing Models of Proposed Conceptual Model

Alternative 1: Service Quality

Customer Confidence Expertise Customer Relationship Altruism towards Advocacy Qua lity Service Provider

Opinion Leadership

Alternative 2: Service Quality

Customer Confidence Expertise Customer Relationship Advocacy Quality

Opinion Leadership

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TABLE 5 Model Fit Comparisons between Competing Models Competing Models X2 d.f. Chi-Square Difference GFI CFI TLI NFI RMSEA SRMR Alternative 1: RQ as antecedent of altruism twds service provider 2167.098 634 0.890 0.946 0.940 0.925 0.050 0.062 Alternative 2: RQ replace altruism twds service provider as antecedent 1886.266 564 280.832, p < .001 0.898 0.951 0.945 0.931 0.049 0.060 Proposed model: RQ as moderator with altruism twds service provider as antecedent 1539.648 465 346.618, p < .001 0.910 0.954 0.947 0.935 0.049 0.059 Note: RQ = Relationship quality; Chi-square differences represent comparisons of subsequent models (e.g., alternative 1 versus alternative 2 model, alternative 2 model versus proposed model).

As a moderator, relationship quality had a significant direct effect on confidence in a service provider (β = .53, p < .01) and on customer advocacy (β = .31, p < .01). Thus, relationship quality is a quasi-moderator according to Sharma, Durand and Gur-Arie (1981). Of the control variables, relationship length had no significant impact on either confidence or customer advocacy. Only involvement was significantly related to customer advocacy however its impact was minor (β = .07, p < .05).

To explore the possibility that the significant interaction terms were the result of potential curvilinear relationships, models that specified quadratic terms for relationship quality were examined. These quadratic terms were not significant in both models with either confidence or customer advocacy as the dependent variable. This confirms that when the level of relationship quality varies, the effect of independent variables on confidence or customer advocacy increases or decreases linearly.

Common method bias:

Both dependent and determinant variables in the study were perceptual measures derived from the same respondents at the same time in the study. Consequently, common method variance (CMV) may be a concern, as respondents may tend to provide consistent answers that are otherwise not (Podsakoff et al., 2003; Podsakoff and Organ, 1986). To assess the potential existence of CMV, the marker variable (MV) method was applied. The method selects a scale theoretically unrelated to at least one construct in the model as proxy for common method variance (Lindell and Whitney, 2001). A three-item construct ‘openness to experience’ was used as the MV marker. The scale measured openness to imaginative activities and novel solutions (Licata et al.,

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2003) (Cronbach’s alpha = 0.80). Its lowest positive correlation with other variables (r = .062) was used to adjust the construct correlations and statistical significance (Lindell and Whitney, 2001). As exhibited above the diagonal in Table 6, all originally significant correlations remained significant after adjustments. Therefore, common method bias was not likely to be a problem in the study.

TABLE 6 Correlations before and after Adjustment of Marker Variable (MV) Constructs 1 2 3 4 5 6 7 1. Service Quality 0.133** 0.732** 0.572** 0.163** 0.391** 0.821** 2. Customer Expertise .187** 0.276** 0.126** 0.242** 0.273** 0.213** 3. Confidence .749** .321** 0.532** 0.253** 0.459** 0.778** 4. Altruism towards Service Provider .599** .180** .561** 0.346** 0.446** 0.596** 5. Opinion Leadership .215** .289** .299** .387** 0.502** 0.285** 6. Customer Advocacy .429** .318** .493** .480** .533** 0.455** 7. Relationship Quality .832** .262** .792** .621** .329** .489** 8. Openness to Experience (MV marker) 0.062 .242** .154** .148** .355** .252** .130**

Reliability (Cronbach's alpha) 0.926 0.886 0.952 0.818 0.897 0.867 0.918 **p < .01. Notes: N = 975. Zero-order correlations are below the diagonal; adjusted correlations for potential common method variance (Lindell and Whitney, 2001) are above the diagonal.

3.4.2 Study 2 - Extended study

Study 1 revealed that evaluative and motivational factors, in line with SDT, contribute to the generation of customer advocacy. The findings prompted the need to further investigate if and how these factors may differ in their impact on general positive WOM and customer advocacy respectively. In examining H3c, H3d, H3e, H4b and H5b, Study 2 added general positive WOM as a second dependent variable in the original conceptual model and compared the impact of the same set of direct and indirect determinants (i.e., service quality, customer expertise, confidence in a service provider, opinion leadership and altruism towards service providers) on customer advocacy and general positive WOM respectively.

Sample

A new sample of respondents through the same online panel was collected. The questionnaire administered was identical to that of Study 1, however, the screening criterion on recommendation strength imposed in Study 1 was removed. Hence, the new 104 | Page

sample consisted of a full spectrum of recommendation strength from ‘not strong at all’ to ‘very strong’. Screening was administered to ensure that members who participated in Study 1 were not involved in this new study. Respondents were asked to evaluate and recall their WOM and advocacy behaviours regarding a service provider they have encountered in the previous six months.

To eliminate any potential compounding effect of service categories, one single service type was used as the research context. The service category of high contact and experience service was selected (e.g., hairdressing, beauty salons, childcare providers), as it is the service category with which many people have had experience.

This phase of study recruited 271 respondents. Six were excluded from the analysis due to extremely short survey completion time (less than five minutes). No outliers or missing values were identified. Both Study 1 and Study 2 adopted a uniform questionnaire using an identical measurement of general positive WOM and customer advocacy. In addition, they shared a same research context - high contact and experience services. Therefore, responses of this service type from both studies were combined. This enabled a new sample of 497 responses with reasonable sample size and an abundant level of variance in both customer advocacy and general positive WOM.

Measures

All constructs of Study 2 followed their measurements scales used in Study 1, including both customer advocacy and general positive WOM. The psychometric properties of these constructs were evaluated with CFA. The fit of the CFA was satisfactory, with X2 = 725.38, p < .01, CFI = .954; TLI = .945; NFI = .934; RMSEA = .066; and SRMR = .055 (Hu and Bentler, 1999). All factor loadings were significant (p < .01), in support of convergent validity. CR were above .70, demonstrating good reliability (Hair et al., 2006). Discriminant validity was established, due to AVE of each latent construct being greater than its respective squared correlation between other constructs (Fornell and

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Larcker, 1981). Complete results of AVE, CR, means, standard deviation, and correlations among the latent factors are listed in Table 7.

Table 7 Means, Standard Deviations, Average Variance Extracted, and Compsite Reliabilities (Study 2) Constructs Mean SD AVE CR 1. Service Quality 5.91 0.95 0.69 0.92 2. Customer Expertise 4.91 1.35 0.83 0.90 3. Confidence 7.57 1.42 0.79 0.92 4. Altruism towards Service Provider 4.24 0.62 0.63 0.77 5. Opinion Leaership 4.82 1.35 0.81 0.93 6. Customer Advocacy 4.68 1.34 0.83 0.91 7. General positive WOM 6.12 1.05 0.87 0.93

Correlation Matrix Constructs 1 2 3 4 5 6 7 1. Service Quality 0.29** 0.82** 0.66** 0.33** 0.57** 0.71** 2. Customer Expertise 0.09 0.36** 0.29** 0.35** 0.34** 0.31** 3. Confidence 0.67 0.13 0.63** 0.45** 0.63** 0.75** 4. Altruism towards Service Provider 0.44 0.08 0.40 0.42** 0.51** 0.54** 5. Opinion Leadership 0.11 0.12 0.20 0.18 0.69** 0.42** 6. Customer Advocacy 0.32 0.12 0.40 0.26 0.47 0.60** 7. General positive WOM 0.51 0.10 0.57 0.29 0.18 0.36 **p < .01 NOTE: AVE = average variance extracted; CR = composite reliabilities; SD = standard deviation. Correlations are reported above the diagonal; squared correlations are reported below the diagonal.

Results

Study 2 featured an extended structural model involving two dependent variables - general positive WOM and customer advocacy. The model was analysed with AMOS 18.0 using maximum likelihood estimation. To test the hypotheses, critical ratio differences were used to compare if path coefficients from the same determinants to customer advocacy and general positive WOM were significantly different (e.g., Confidence -> Customer advocacy vs. Confidence -> General positive WOM). For example, when a critical ratio difference exceeds 1.645, the difference between the pair of path coefficients is significant at .05 level.

As exhibited in Table 8, the impact of service quality on customer advocacy was not significant, while its impact on general positive WOM was direct and positive (β = .19, p < .05). Further, the impact of service quality on customer advocacy and WOM was significantly different (critical ratio difference = 1.73, p < .05). This result supported 106 | Page

H3c. Specifically, service quality has its influence on WOM directly, while it does not have a direct impact on customer advocacy. This result is in line with the confirmation of H3a in Study 1, that the impact of service quality on customer advocacy is not direct, instead, it is mediated through confidence in a service provider.

The critical ratio between the paths Customer expertise -> Customer advocacy and Customer expertise -> General positive WOM was not significant. Likewise, the critical ratio between the paths Confidence -> Customer advocacy and Confidence -> General positive WOM was not significant, therefore H3d and H3e were not supported. It suggests that the impact of customer expertise or confidence in a service provider on customer advocacy and general positive WOM is similar. Interestingly, little research to date has empirically recognized the importance of customer expertise and confidence in a service provider in eliciting positive WOM, not to mention their importance in driving advocacy.

H4b predicted that altruism towards service providers had a stronger impact on customer advocacy than on general positive WOM. This was supported given a significant critical ratio difference (-2.07, p < .01) between the impact of altruism towards service providers on customer advocacy (β = .20, p < .05) and its impact on positive WOM (β = .02, ns). While the willingness to help the service provider was crucial to customer advocacy, it was not significant in predicting general positive WOM.

Lastly, the influence of opinion leadership on general positive WOM was significant but rather minor (β = .10, p < .05), while its influence on customer advocacy was significantly stronger (β = .41, p < .01), as evidenced by a significant critical ratio difference of 6.20 (p < .01). Therefore, H5b was supported.

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TABLE 8 Structural Model Results (Study 2) DV Advocacy DV Positive WOM Critical Ratio Hypothesized Path Path Estimates a p Path Estimates a p Difference p Hypotheses Service Quality -> -0.04 ns 0.19 * 1.73 * H3c (Supported) Customer Expertise -> 0.06 * 0.00 ns -1.59 ns H3d (Not supported) Confidence -> 0.43 ** 0.62 ** 1.11 ns H3e (Not supported) Altruism towards Service Provider -> 0.20 * 0.02 ns -2.07 ** H4b (Supported) Opinion Leadership -> 0.41 ** 0.10 * -6.20 ** H5b (Supported)

Goodness-of-fit indices X2 df GFI CFI TLI NFI RMSEA SRMR 757.463 233 0.881 0.951 0.942 0.931 0.067 0.059 Note: GFI = Goodness-of-Fit Index; CFI = Comparative Fit Index; TLI = Tucker-Lewis Index; NFI = Normed Fit Index; RMSEA = Root Mean Square Error Approximation SRMR = Standardized Root Mean Square Residual a. These are standardized loading estimates **p < .01; * p < .05

Overall, the data support most of the hypothesized relationships between the independent variables and dependent variables, as well as the impact of moderator variables on some of these relationships in the conceptual model. Table 9 provides a summary of these hypotheses and the results. A discussion of these results and their implications are put forward in the following sections.

TABLE 9 Summary of Hypothesis Testing Hypothesized relationships Hypothesized directions Results H1: Service quality -> Confidence Positive Supported H2: Customer expertise -> Confidence Positive Supported H3a: Service quality -> Confidence -> Advocacy Mediation Supported H3b: Customer expertise -> Confidence -> Advocacy Mediation Partially supported H3c: Service quality -> Positive WOM vs. Service quality -> Advocacy Stronger effect Supported H3d: Customer expertise -> Positive WOM vs. Customer expertise -> Advocacy Stronger effect Rejected H3e: Confidence -> Positive WOM vs. Confidence -> Advocacy Weaker effect Rejected H4a: Altruism twds service providers -> Advocacy Positive Supported H4b: Altruism twds service providers -> Positive WOM vs. Weaker effect Supported Altruism twds service providers -> Advocacy H5a: Opinion leadership -> Advocacy Positive Supported H5b: Opinion leadership -> Positive WOM vs. Opinion leadership -> Advocacy Weaker effect Supported H6a: Service quality -> Confidence in high contact/experience services vs. Stronger effect Supported in low/medium contact/experience services H6b: Customer expertise -> Confidence in high contact/experience services vs. Weaker effect Supported in low/medium contact/experience services H6c: Confidence -> Advocacy in high contact/experience services vs. Stronger effect Rejected in low/medium contact/experience services H7a: Service quality x Relationship quality -> Confidence Positive Rejected H7b: Customer expertise x Relationship quality -> Confidence Negative Supported H7c: Confidence x Relationship quality -> Advocacy Positive Supported H7d: Altruism twds service providers * Relationship quality -> Advocacy Positive Rejected

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3.5 Discussion

A great deal of attention in the WOM literature has been devoted to the valence and the frequency of WOM and recommendation activities (e.g., Anderson, 1998; Harrison- Walker, 2001; Zeithaml et al., 1996), yet few studies examine the influences that give rise to the varying strength of recommendations. This study makes the first attempt towards narrowing this gap by developing and testing a range of determining and moderating influences that substantially explained the upper level of strong recommendations manifested in customer advocacy. Next, the findings will be discussed and aligned with three focal research questions of the study.

1. What are the determinants of customer advocacy?

The findings demonstrate that both evaluative and motivational dimensions play important roles in explaining advocacy. The evaluative factor of confidence in a service provider, along with the motivational factors of opinion leadership and altruism towards service providers are the direct predictors. Among these three determinants, confidence in a service provider has the greatest impact, closely followed by opinion leadership, while altruism towards service providers has a smaller yet significant impact. These results suggest that for customers to act as advocates, they must have substantial confidence in the service provider and consequently confidence in strongly recommending it to others. In addition, advocates possess characteristics of opinion leadership and altruism towards the service provider to help it to be more successful.

The role of confidence has rarely been considered, or has not been found to exert direct influence on WOM (Hennig-Thurau et al., 2002) in previous studies. The current research demonstrates that confidence in a service provider fully or partially mediates the influence of service quality and customer expertise on customer advocacy. A mediating relationship helps us understand how and why certain effects occur (Baron and Kenny, 1986). Hence, it is due to the presence of confidence that service quality and customer expertise contribute to customer advocacy. Confidence reflects the degree of 109 | Page

certainty with which an attitude is held (Berger and Mitchell, 1989), while previous studies suggest that relative attitude is related to the occurrence of post-purchase recommendation (East et al., 2005). Hence, this finding adds to the previous theory, that in the context of strong recommendations, the certainty of a favourable attitude is indispensable.

Opinion leadership is not an essential determinant in general WOM conversations, however, this study reveals that it is critical in the communication of strong recommendations. Mangold et al. (1999) identify that a significant number of WOM conversations are responses to individuals’ enquiries rather than proactive complements. In contrast to the production of general positive WOM, advocacy involves proactive communication of strong and explicit statements. Therefore, it is not surprising that opinion leadership, as characterized by the tendency to influence the attitude of other individuals in an intended direction (Reynolds and Wells, 1977), serves as one of the major determinants leading to a strong level of recommendations.

Equally noteworthy is the positive association between altruism towards service providers and customer advocacy. Some earlier empirical research did not find a significant impact of this factor in the generation of positive WOM (Hennig-Thurau et al., 2004). A potential explanation is that WOM itself may not always truly reflect customers’ commitment to a company, as individuals' engagement of positive WOM is largely motivated by self-related aspects such as the involvement of the product and the excitement of product experiences (Dichter, 1966; Richins and Root-Shaffer, 1988), the propensity of self-enhancement (Wojnicki and Godes, 2008), or the potential social benefit of passing on useful information to others (Coleman et al. 1957; Brown and Reingen 1987). By contrast, advocates are concerned about the success of the service provider and are willing to engage in strong voluntary promotions. This clear will to benefit the service provider lends support to the premise that customer advocacy is the ultimate relationship between the customer and the firm (Urban, 2004).

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Interestingly, some well-documented determinants of general positive WOM do not appear to directly predict customer advocacy, despite the close relationship between the two. For example, service quality has been typically recognized as a direct predictor of positive WOM (e.g., Hartline and Jones, 1996; Zeithaml et al., 1996). However, service quality only indirectly predicts customer advocacy through confidence in a service provider. This implies that service quality will not result in customer advocacy until customers' confidence towards the service provider is established in the production of a strong level of recommendations. This is in agreement with SDT that the engagement of extra-role behaviour (e.g., customer advocacy) requires a feeling of competence, which is difficult to achieve without the role of confidence.

Additionally, none of the control variables of tie strength, altruism towards others and involvement has a significant impact on customer advocacy. This suggests that while aspects such as the closeness between the communicator and the audience, the willingness to help fellow customers and the interest in the service category influence the production of general WOM (e.g., Bansal and Voyer, 2000; Richins and Root- Shaffer, 1988; Sundaram et al., 1998), they are inadequate in affecting the change to the upper level of recommendation strength.

It is also worth mentioning that customer expertise as another indirect determinant has been typically studied in the context of the effectiveness of WOM (e.g., Bansal and Voyer, 2000; Bone, 1995). This is from the perspective of the audience of the WOM communication. The present study shows that from the opposite perspective - that of the communicators, customer expertise is also relevant and is partially mediated through confidence in a service provider in its influence on customer advocacy.

2. Under what contingent conditions do these determinants vary in their impact on customer advocacy?

This study demonstrates that under situational (service type) and relational (relationship quality) conditions, the aforementioned determinants may vary in their impact on

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customer advocacy. First, the relative importance of the predictors of confidence in a service provider tends to vary with service types. Service quality has a stronger effect on confidence in high contact and experience services (e.g., hairdressing) than in low/medium contact and experience services (e.g., telecommunications), while the impact of customer expertise on confidence is stronger in low/medium contact and experience services. This corresponds with the heterogeneity of characteristics of service types. The consistency of service delivery is relatively easier to achieve in low/medium contact and experience services as a consequence of low levels of customization and employee discretion (Clemes et al., 2000). However, customers are less likely to embrace the social and emotional aspects of the service experience as a result of the lack of contact in these service encounters (Gwinner et al., 1998; Price et al., 1995). Accordingly, customer expertise, characterised by the experience with the same provider over time and the market experiences with other providers, is more important in encouraging confidence in a service provider in low/medium contact and experience services, in comparison to high contact and experience services.

Unexpectedly, the importance of confidence in customer advocacy is equal across all four major service types. Thus, despite the consensus that the degree of certainty in service evaluation is greater in experience services than in credence services (Darby and Karni, 1973; Mitra et al., 1999), the importance of confidence to the upper level of recommendations is similar in these services. This may be explained by the notion that advocates are concerned about their self-image in producing strong statements (Nelson, 1966), which underlines the criticality of confidence in the strength of recommendations.

Second, the impact of determinants is contingent on relationship quality. Relationship quality moderates the relationship between customer experience and confidence in a service provider negatively. Thus, when relationship quality is strong, the impact of customer experience on confidence decreases, while under the condition of weak relationship quality, the influence of customer experience on confidence is stronger. This result is appealing to marketers as it implies that strong relationship quality might mitigate the lack of customer expertise and encourage customer confidence in a service

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provider. In addition, relationship quality enhances the impact of confidence on customer advocacy. Therefore, when customers have confidence in the evaluation and recommendation of the service provider, the presence of relationship quality can further encourage their likelihood to go “the extra mile” to be advocates.

Intriguingly, however, relationship quality does not enhance or weaken the impact of service quality on confidence in a service provider. Stated differently, a relatively high level of relationship quality does not remedy a relatively lowly perceived service quality in strengthening customer confidence. This suggests that service quality and relationship quality act independently in encouraging strong levels of recommendations. A potential explanation is that relationship quality captures the closeness and strength of the customer-firm relationship (Crosby et al., 1990; De Wulf et al., 2001), which tends to be a personal and emotional perspective. By contrast, service quality pertains to the overall assessment of the service performance (Cronin and Taylor, 1992; Parasuraman et al., 1988), which is likely to be less emotional and more objective. Customer advocacy features strong statements involving the benefit of other customers, thus, a more objective evaluation (service quality) can't be replaced by the more subjective and emotional assessment of a relationship (relationship quality) when the well-being of other customers is taken into consideration.

3. Do the determinants of customer advocacy differ in their impact on general positive WOM?

The determinants of customer advocacy have different impacts on customer advocacy and general positive WOM. Service quality exerts a stronger and direct influence on general positive WOM, while it does not have direct influence on customer advocacy. Thus, service quality is fundamental to the production of general positive WOM, however, its impact is not as powerful in the generation of strong levels of customer recommendations. Conversely, opinion leadership has a significantly weaker effect on general positive WOM than on customer advocacy. Likewise, altruism towards service

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providers does not predict general positive WOM, however, it is important in bringing customers' recommendations to a strong and passionate level.

Remarkably, the importance of confidence is similar in the production of general positive WOM and customer advocacy. This is in agreement with the existence of perceived risk involved in post-purchase recommendations (Mazzarol et al., 2007). The direct effect of customer expertise on both general positive WOM and customer advocacy is relatively small. However, as Study 1 reveals, customer expertise impacts customer advocacy indirectly through its significant influence on confidence.

In summary, the contributing influences behind the upper level of recommendation strength (customer advocacy) are distinct from those behind the lower level of recommendation strength (general positive WOM). The relative importance of motivational factors (altruism towards service providers and opinion leadership) is stronger in strong recommendations. The relative importance of the evaluative factor of service quality is stronger in the lower level of recommendation such as general positive WOM, while the certainty of evaluation (confidence in a service provider) remains equally important in varying levels of recommendation strength.

3.6 Theoretical and Managerial Implications

Overall, this current research contributes to extant WOM research in several ways. First, it extends the study of WOM from its valence and frequency to its strength. The study is among the first in providing a structured approach to identify what leads to an upper level of recommendation strength (customer advocacy) in the services context. Second, the contingency approach in this study enriches the understanding of conditions that may enhance or weaken the effectiveness of determinants of strong recommendations. Third, the research empirically examines and distinguishes how these determinants vary their influence on general positive WOM and customer advocacy differently. Tested across four diverse service types in two studies, this study should advance existing

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knowledge of WOM and should have noteworthy implications for both academics and practitioners. This will be discussed in the next section.

The study indicates that the integration of evaluative and motivational aspects is important in understanding what leads to customer advocacy. The conceptual model based on the integration of evaluative and motivational aspects is derived from and consistent with self-determination theory (SDT) (Ryan and Deci, 2000a) that explains the strength and magnitude of extra-role behaviours. The direct determinants of customer advocacy - confidence, opinion leadership and altruism towards service providers represent the core psychological elements of competence, autonomy and relatedness respectively in SDT. These direct determinants generally have a stronger impact on customer advocacy than on general positive WOM. Therefore, SDT offers a rich source of theoretical underpinnings for strong levels of recommendations. While social exchange theory (Blau, 1964) has been prevalent in explaining positive WOM behaviour in general, the findings add new perspectives to the explanatory theories in the domain of WOM communication.

The identification of determinants of customer advocacy sheds light on the difference between the nature of general positive WOM and customer advocacy. Positive WOM is perceived as customers' behavioural responses towards a pleasurable level of consumption-related fulfilment (Oliver, 1997) or the disconfirmation of service-related expectations (White and Schneider, 2000; Zeithaml et al., 1996). These responses are attributable to the cognitive appraisal in combination with affective experience (Westbrook, 1987), which results in post-consumption positive psychological states. This, in turn, induces favourable WOM and recommendations (Swan and Oliver, 1989). Hence, positive WOM is a manifestation of favourable attitude towards a firm or a provider.

In contrast to general positive WOM, customer advocacy embodies customers' extra passion and conviction. This strong support is driven by customer confidence based on direct experiences and firm belief in the service provider's performance excellence. As

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confidence results from the amount of information and the repetition of the same information (Berger and Mitchell, 1989; Krishnan and Smith, 1998), advocacy is more likely to be internalized and long lasting. Customer advocacy is also propelled by customers' intention to help the service providers (altruism towards service providers). Taken together, the strong level of customer recommendation exhibits a more committed level of loyalty. This suggests the need for a more fine-grained approach in studying the association between loyalty and various levels of recommendation strength. Marketing research that neglects the strength of recommendation may fail to capture the full effects of customer communication on a company's competitiveness and longevity.

Additionally, this research provides substantive evidence for taking a contingent approach to recognising the factors that leads to customer advocacy. Service type has been commonly used as research settings in WOM studies. However, this research formally examines the role of service type as a moderating factor. It is revealed that the characteristics of service types influence the relative importance of service quality and customer expertise on confidence in a service provider, but have no influence on the role of confidence on customer advocacy. On the other hand, relationship quality has been positioned as one of the contributors leading to WOM (e.g., Gremler and Brown, 1999; Hennig-Thurau et al., 2002). This research indicates that relationship quality can be a moderating influence in strengthening the impact of confidence on customer advocacy. Moreover, the presence of relationship quality may mitigate the lack of customer expertise in the development of confidence. These findings advance the understanding of the influence of relationship quality in the promotion of a strong level of recommendations.

These theoretical implications have important contributions for the practice of marketing. To illustrate, although service heterogeneity results in different relative importance of the antecedents of confidence (i.e., service quality and customer expertise), it does not affect the criticality of confidence itself in promoting customer advocacy. Confidence pertains to the benefit of reduced anxiety and increased predictability in knowing what to expect in the service encounter (Gwinner et al., 1998;

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Hennig-Thurau et al., 2002). It has a positive relationship with the quantity of the information and more repetition of the same information or variations of information (Berger and Mitchell, 1989; Haugtvedt et al., 1994). Besides, customers need to perceive the superiority of a service provider compared to market competitors in gaining confidence (Howcroft, Hamilton and Hewer, 2007). Therefore, companies need to reduce uncertainty in service delivery through explicit pricing, return guarantees, telephone hotlines, e-mail support or error-free billing. Moreover, group social events, the creation of customer communities that present user feedback and ratings, cues of best-service awards and celebrity endorsements will be useful in providing varied and repeated positive information. Such effort should assist to increase customer confidence in a service provider in making the best choice. Additionally, customer education seminars, functional experts and direct-mail campaigns that attempt to instill confidence in existing customers are important. They are best to exploit the synergy between confidence and the likelihood to engaging in high levels of recommendation strength.

This research highlights the role of opinion leadership in effecting customer advocacy. As such, managers should facilitate opinion leadership by encouraging customers to voice their opinions and exert influence on fellow customers via events, social media sites, online and offline forums, and recommender systems. Meanwhile, the identification of opinion leaders followed by gradual enhancement of their expertise, confidence and the willingness to help the service provider can be a direct way of achieving customer advocacy based on the research findings. Opinion leaders can be identified through various techniques that have been shown to effectively promote behaviour changes among fellow customers, such as the recruitment of local or regional celebrities (Valente and Pumpuang, 2007) or the invitation of the nomination of peer opinion leaders in a community (Latkin, 1998). Opinion leaders can be targeted via strategic media planning, as leaders are not only more readily exposed to a variety of media source (Sheth, Mittal and Newman., 1999), but are also found to favour specific media vehicles within certain product/service categories (Vernette, 2004). If so, a broad range of opinion leader identification measures to encourage customer advocacy may be implemented.

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To increase the strength of recommendation, service firms should also seek to encourage customers' altruism towards service providers. This type of altruism relates to the willingness to reciprocate the benefits customers received from a service provider. It is driven by an imperative force of gratitude (Komter, 2004), which positively enhances customer commitment behaviours (e.g., share of wallet) (Palmatier et al., 2009). As a result, altruism and reciprocation towards the service provider can be realized by leveraging gratitude-enhancement investments, such as the implementation of customized offering that best suit the customer's needs (Surprenant and Solomon, 1987) or more discretion within reasonable financial boundaries to maximize satisfactory service experiences (Palmatier et al., 2009). Furthermore, central to customer advocacy is the development of mutual transparency and dialogue with customers, even with the honesty of sharing the offering of competitors (Lawer and Knox, 2006; Urban, 2004). This is likely to generate customers' appreciation. In the meantime, managers should include measures of altruism towards the service provider in existing customer satisfaction and loyalty surveys or sales audits, which may provide managers with a more complete picture of the quality of customer-firm relationships.

This study provides strategic implications for customer relationship management and service management as well. The magnitude of relationship quality may mitigate the lack of customer expertise in establishing customer confidence in a service provider. Therefore, relationship marketing effort can be targeted towards customers who are novices in the service area to strengthen their confidence, and subsequently their likelihood of engaging in advocacy. On the other hand, managers should maintain the investment in the improvement of service quality, as the role of service quality in the development of confidence may not be replaced by the degree of the closeness of the customer-firm relationship.

The contingency role of service types may assist service managers in tailoring advocacy-enhancement strategies to the specific industry characteristic. For example, service quality is particularly critical in eliciting customer confidence in a service provider and ultimately customer advocacy in high contact and experience services (e.g.,

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hairdressing, education). This reinforces the necessity of employee training programs that stress the need for maximizing service quality and customer experiences of every service encounter. By contrast, customer expertise is more important to facilitate confidence in low/medium contact and experience services. Thus, more efforts may be placed in the enhancement of customer education that spreads the knowledge of average market performance, for example, through the provision of customers' self-experiences with other service providers in the marketplace.

3.7 Limitations and Future Research

The results of the study provide a first look at the determining and moderating influences behind the strongest level of recommendation – customer advocacy. As the initial objective of this study was to examine the strength of recommendation, there are areas which may represent future research opportunities.

Although attempts such as multiple research contexts and multiple studies were employed to help maximize the reliability and validity of the results, the survey approach used in this study limits potential generalizations about the causal effects of the determinants. An improved experimental design or a longitudinal study will enable the investigation of causal linkages between the determinants and their effects on the variation of recommendation strength.

Future research may expand the current theoretical framework further by investigating the effects of current antecedents in greater depth or by considering other determinants. For example, only performance-based service quality was measured in this study. It would be interesting to examine if and how the determining and mediating mechanisms will be altered when service quality is measured on the comparison between customer expectation and service performance. Further, customer-firm identification can be a potential variable influencing customer advocacy, as it has indirect effect on WOM (Brown et al., 2005) and a direct impact on extra-role behaviours (Ahearne, Bhattacharya and Gruen, 2005). The present study did not include this variable in the 119 | Page

conceptual model. This was based on the consideration that customers tend to develop affiliations with a service firm when such affiliations are associated with social opportunities and prestige (Ashforth and Mael, 1989; Brown, 1969). These affiliations may only apply to a limited range of services. Nonetheless, the role of customer-firm identification in selected service contexts warrants future exploration.

This study investigates only a subset of contingent conditions. Therefore, additional research may explore a wider range of factors that could moderate the effect of evaluative and motivational drivers on strong recommendations. For example, customer characteristics of personality traits, consumption rates or the stage of relationship cycle may represent fruitful lines of inquiry. In addition, more service characteristics (e.g., service provider criticality and service brand equity) or market characteristics (e.g., degree of market competitiveness) may be considered. It is also unclear how the specific empirical results apply in other contexts including the physical product categories, the business environment and various cultural environments, though there is no strong reason to expect that the underlying theoretical rationale or the conceptual model would differ substantially.

Although customer advocacy was positioned as a manifestation of loyalty, the behavioural loyalty associated with advocates was not explicitly examined in the research. Thus, little is known about whether advocates are necessarily more loyal with respect to behaviours such as the resistance to switch or the increased share-of-wallet spending, compared to customers who only give positive comments about a service provider. It would be informative to determine if the endurance of varying levels of recommendation strength differ significantly. As the novelty of experience fades over time (Wu and Huberman, 2007), customers' interest of spreading WOM tends to decline depending on the product/service categories (East, Lomax and Narain, 2001). Hence, further research should examine if customer advocacy lasts longer than the lower levels of recommendation strength. Additionally, this research is concerned with the production of recommendations from the senders' perspective while the effectiveness of customer advocacy from the audience's point of view is relatively unknown. Although

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prior research indicates that the strength of WOM expression enhances its effectiveness (East et al., 2008), research is required to understand if customers are more receptive and responsive to an extreme form of positive WOM such as customer advocacy. These additional investigations will provide academics and practitioners with a more holistic view of customer advocacy and further confidence in reinforcing investment in this highly desirable customer outcome.

Despite the existence of limitations and the need for considerable future research, this research has a clear importance. It provides insights into determinants and contingent factors concerning the increase of customers' recommendation strength to customer advocacy. Customer advocacy possesses common attributes of WOM communications including perceived credibility (Gremler and Brown, 1999), vividness (Herr, Kardes and Kim, 1991) or diagnosticity (Herr et al., 1991; Mangold et al., 1999). Besides, it is strong, passionate, explicit and proactive. Thus, customer advocacy should be viewed by companies as a reward from customers. It also represents an excellent opportunity to promote the service provider in a trustworthy manner. The results of this study should provide initial guidance for managers who wish to expand their customer base of advocates and maximize the organizational competitive advantage in the marketplace.

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APPENDIX 1 Demographic Profiles of Respondents (Study 1) Service Type 1 Service Type 2 Service Type 3 Service Type 4 Entire Sample (Experience/High (Experience/Low- (Credence/High (Credence/Low-Med contact Services) Med contact Services) contact Services) contact Services) n = 975 n = 232 n = 247 n = 247 n = 249 Gender Male 47.8 28.9 47.8 50.4 62.8 Female 52.2 71.1 52.2 49.6 37.2 Total 100% 100% 100% 100% 100% Age 18-29 yrs 22.9 34.9 31.2 11.3 15.6 30-39 yrs 21.9 28.0 26.3 15.7 18.0 40-49 yrs 24.0 20.3 20.2 29.8 25.2 Above 50 yrs 31.2 16.8 22.3 43.1 41.2 Total 100% 100% 100% 100% 100% Income less than $10,000 11.5 13.0 13.8 9.3 10.0 $10,000-$30,000 22.9 22.6 24.4 25.1 19.7 $30,000-$80,000 47.8 49.1 46.7 45.3 50.2 over $80,000 17.7 15.2 15.0 20.2 20.1 Total 100% 100% 100% 100% 100% Education Under Yr 11 15.5 14.2 11.3 15.3 20.8 HSC or VCE 15.1 13.4 17.4 16.9 12.4 TAFE/Trade qualification 27.7 24.1 28.7 26.6 31.2 Undergraduate or above 41.7 48.3 42.5 41.1 35.6 Total 100% 100% 100% 100% 100% Note: Service Type 1 (Experience/High contact Services): Hairdressing, beauty salon, personal trainer, education, massage and childcare provider services. Service Type 2 (Experience/Low-Med contact Services): Telecommunication, basic banking, dry cleaning and airline services. Service Type 3 (Credence/High contact Services): Medical services such as GP, dentist or physiotherapist. Service Type 4 (Credence/Low-Med contact Services): Financial advisor, car servicing, computer repair, accountants, legal services and white goods repair services.

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APPENDIX 2 Questionnaire Items and Measurement Properties Constructs Loadings Items Scale .861 This service provider is courteous and friendly .789 This service provider has my best interests at heart Service quality (Brady .853 This service provider understands my specific needs and Cronin 2001; Brady .841 This service provider responds promptly to my requests 1 Strongly disagree; et al. 2002 and Hartline .876 This service provider delivers what I really want 7 Strongly agree & Ferrell 1996) .873 I am consistently pleased with the outcome I get from this service provider .881 I always have an excellent experience with this service provider .867 This service provider is competent (i.e. knowledgeable and skillful)

How would you characterize your level of experience with this type of service (e.g., dentist, car 1 Very Customer expertise 0.796* cleaning, hairdressing or banking)? inexperienced; (Patterson 2000) How knowledgeable are you of this type of service? 7 Very experienced

.950 How confident are you about your evaluations of this service provider? Confidence (Laroche et .967 How confident are you in giving strong positive evaluations about this service provider? 1 Not confident at al. 1996) How confident are you that this service provider will provide the same experience to listeners all; 9 Very confident .946 of your recommendations?

I am so satisfied with the service provider and its service that I want to help the service provider Altruism towards .880 to be successful 1 Strongly disagree; service provider (Hennig- .801 In my opinion, good service providers should be supported 5 Strongly agree Thurau et al. 2004) .882 Helping a good service provider is a pleasure

I enjoy being asked about this type of service (e.g., dentist, car cleaning, hairdressing or .758 banking) My friends think of me as a knowledgeable source of information about this type of service .861 (e.g., dentist, car cleaning, hairdressing or banking) Opinion leadership My friends ask for my opinion about this type of services (e.g., dentist, car cleaning, hairdressing .853 1 Strongly disagree; (Flynn et al. 1996; or banking) 7 Strongly agree Kleijnen et al. 2009) .849 People that I know select this type of service based on what I have told them. I often persuade other people to buy this type of service that I selected (e.g., dentist, car .861 cleaning, hairdressing or banking). I often influence people's opinions about this type of service (e.g., dentist, car cleaning, .876 hairdressing or banking).

.811 I am very committed to my relationship with this service provider .842 The relationship with this service provider is something I intend to maintain for a long time. .655 I put effort into maintaining this relationship for a long time. Relationship quality (De .857 I am satisfied with my relationship with this service provider 1 Strongly disagree; Wulf et al. 2001 and .841 My relationship with this service provider is quite good. 7 Strongly agree Sirdeshmukh et al. 2002) .850 I am happy with the effort this service provider is making towards consumers like me. .866 I felt that this service provider is very dependable .838 I felt that this service provider is of high integrity 1 Not close at all; .791 Relationship with the listener 7 Extremely close Tie strength (Bansal and .841 Likelihood of sharing personal views and thoughts with the listener Voyer 2000) 1 Very unlikely; .826 Likelihood of helping the listener with everyday matters 7 Very likely .849 Likelihood of spending free time together with the listener * Less than three items. Correlation was used instead.

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APPENDIX 2 (continued) Constructs Loadings Items Scale Altruism towards other .895 I want to save others from having the same negative experiences as me 1 Strongly disagree; customers (Hennig- .919 I want to help others with my own positive experiences 5 Strongly agree Thurau et al. 2004) .823 I want to give others the opportunity to have the right service

Involvement (Li et al. .931 This type of service (e.g., dentist, car cleaning, hairdressing or banking) is of concern to me 1 Strongly disagree; 2000 and Zaichkowsky .922 This type of service (e.g. dentist, car cleaning, hairdressing or banking) is important to me 7 Strongly agree 1985) .924 This type of service (e.g. dentist, car cleaning, hairdressing or banking) is relevant to me

General positive WOM Say positive things about this service provider to other 1 Very unlikely; 7 .846* (Zeithaml et al. 1996) Recommend this service provider to someone who seeks my advice Very likely .795 I am enthusiastic in my recommendations of this service provider .810 When discussing this service provider, I urge people to consider using it I have told more people about my positive experience with this service provider than I have .775 with most other service providers, regardless of the service category .761 I describe this service provider as the best of its kind 1 Does not describe Customer Advocacy .734 I defend this service provider if people raise negative comments about it directly with me me at all; 7 Describe Even when there is no conversation, but if I think there are people who have an interest in the me very well .686 service category (e.g., dentist, car cleaning, hairdressing or banking), I strongly recommend this service provider, without being asked I take the initiative to act as a 'promoter' of this service provider (e.g., help others have access to .735 this service provider, contact the service provider on behalf of others if needed) * Less than three items. Correlation was used instead.

124 | Page

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Chapter 4: Profiling Customers with Varying Degrees of Recommendation Strength

Abstract

The purpose of this chapter is to profile customers with varying degrees of recommendation strength across a range of customer characteristics and loyalty outcomes. Results indicate that customers who are female, heavy users and referred by other customers are more likely to produce the strongest level of recommendation. Advocates and general positive word-of-mouth (hereafter 'WOM') communicators tend to have a greater level of agreeableness and risk-aversion than customers who do not engage in positive WOM. Further, Latent Class Analysis identifies four segments of customers - the 'Hard-core Loyal', the 'Overall Loyal', the 'Lip-service Loyal' and the 'Attitudinally Loyal'. These customers display a combination of varying degrees of recommendation strength and other attitudinal/behavioural loyalty outcomes with unique demographic characteristics.

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4.1 Introduction

Firms can hardly underestimate the strategic importance of customer loyalty when small improvements in loyalty and retention yield large changes in profitability (Reichheld, 1993; Reichheld, Markey and Hopton, 2000). As an integral part of loyalty manifestations (e.g., Bloemer, de Ruyter and Wetzels, 1999; Palmatier et al., 2006; Sirdeshmukh, Singh and Sabol, 2002), customer recommendation has proven to be a prominent means to enhance customer lifetime value and customer acquisition with substantial efficiency and effectiveness (Trusov, Bucklin and Pauwels, 2009; Villanueva, Yoo and Hanssens, 2008).

Customer recommendation is vitally important for services (Murray, 1991). This is greatly attributable to risks associated with the low comparability and fewer extrinsic cues in service search and evaluation, as well as the intangibility and variability in service provision (Bristor, 1990; Ettenson and Turner, 1997; Murray and Schlacter, 1990). For service firms, stronger recommendations are likely to instil greater confidence and enhance risk-reduction. Thus, customer advocacy, which is defined as the strong, passionate and explicit recommendation in favour of a service provider against the market alternatives in this research, is of particular interest to service managers.

Considerable research in customer recommendation literature has been devoted to examining the determinants (e.g., Anderson, 1998; Wangenheim and Bayón, 2007; Zeithaml, Berry and Parasuraman, 1996) or the consequences of customer recommendations (e.g., Liu, 2006; Zhu and Zhang, 2010). What has not been systematically examined, however, is which consumer segments are more or less predisposed to making recommendations, especially making the strong and passionate recommendations that characterize ‘advocacy’. For example, are female consumers more likely to advocate a service provider than their male counterparts? Do factors such as usage rate, relationship length, or how the customer was acquired distinguish between customers with different levels of recommendation strength (i.e., advocates, 149 | Page

customers who produce very strong recommendations; WOMers, those who engage in general positive WOM with moderate recommendation strength; and non-WOMers, customers who do not generate positive WOM)? Are advocates or WOMers distinctive in their personalities, relative to non-WOMers?

Further, little research investigates the extent to which customers' communicative loyalty, as exhibited by recommendation strength, aligns with other dimensions of loyalty including attitudinal and behavioural loyalty. As a consequence, the connection between customer recommendation strength and other loyalty dimensions remains unclear. For example, do customers who highly recommend a service provider necessarily spend more on that service provider? Do advocates exclusively patronize the service provider they highly recommend? Are advocates and WOMers more willing to pay a price premium than the non-WOMers?

Answers to these questions will assist in profiling customers with varying levels of recommendation strength. This is important given the growing interest of managers wishing to harness the power of customer referrals (e.g., Chen, Wang and Xie, 2011; Garnefeld, Helm and Eggert, 2010). While customer referrals are considered a loyalty outcome (e.g., Palmatier et al., 2006; Sirdeshmukh et al., 2002), managers are concerned with declining levels of customer loyalty (Clancy, 2001) despite the continuous emphasis and investment in loyalty-building programs.

Linking customer characteristics to customer recommendation strength would be beneficial to enterprises by helping to identify and target those loyal customers who are more likely to produce recommendations with a high level of strength. Additionally, understanding how customers with varying degrees of recommendation strength exhibit other loyalty manifestations should assist in identifying segment(s) of customers on whom managers can focus their relationship building activities. Managers will gain improved insight into how to maximize the economic value of customer referrals and to more effectively manage and prioritize their loyalty enhancement efforts. Extant research indicates some potential connections between customer recommendation as a

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manifestation of loyalty and various customer characteristics. The determinants of customer recommendation, including service quality (Anderson, Pearo and Widener, 2008) and satisfaction ratings (Danaher, 1998; Johnson and Fornell, 1991; Ross et al., 1999) have been reported to be influenced by customer characteristics (e.g., education, income). Diverse demographic (e.g. age, sex, education) and consumer-related (e.g. usage rate) factors have also been associated with service loyalty outcomes such as: recommendations (Homburg and Giering, 2001); repurchase (Homburg and Giering, 2001; Mittal and Kamakura, 2001; Patterson, 2007); share of wallet (Cooil et al., 2007; Mägi, 2003); or, subsequent service consumption rate (Bolton and Lemon, 1999). Further, it is well documented in the communication literature that customer demographic and psychographic characteristics have an influence on individuals' communication style (e.g., Brody and Hall, 1993; Cuperman and Ickes, 2009; Mehl, Gosling and Pennebaker, 2006). Therefore, it seems reasonable to conjecture that customers who tend to give strong recommendations may possess certain characteristics that distinguish them from other customers. Alternatively stated, advocates and non- advocates may not only differ by degree of strength of recommendation, but also differ by some qualitative differences.

Additionally, previous research suggests that customers' magnitude of attitudinal and behavioural loyalty are not always in alignment. Customers may exhibit repatronage loyalty owing to situational reasons (e.g., location, stock shortage) and may lack the attitudinal commitment towards the brand or the service provider (Liljander and Strandvik, 1995; Storbacka, Strandvik and Groenroos, 1994). Likewise, customers may be willing to stay with the service provider attitudinally, however, they may not be willing to pay more if service fees increase (Zeithaml et al., 1996). Therefore, customers' loyalty levels may vary across different dimensions. This loyalty heterogeneity may be predetermined by customers’ characteristics to some extent, given their impact on consumer loyalty outcomes. This raises the possibility of segments of customers each displaying varying degrees of recommendation strength and other loyalty manifestations.

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This study, therefore, aims to investigate the extent to which selected customer characteristics are effective in discriminating advocates, general positive WOM communicators ('WOMers') and those who do not produce WOM ('non-WOMers'). These characteristics include demographic (gender, age, education and income), consumer-related (acquisition mode, relationship length, usage rate and number of people recommended) and psychographic factors (the ‘Big Five’ personality factors (McCrae and John, 1992) and risk aversion. Additionally, the current study seeks to develop profiles of customer segments which have different levels of recommendation strength as well as a broader range of loyalty outcomes.

This research contributes to the existing customer recommendation and loyalty literature in three important ways. First, the WOM research that has examined the association between individual differences and customer recommendation tends to mainly focus on demographic characteristics (Cheema and Kaikati, 2010; DeAnna and Kay, 2006; Garbarino and Strahilevitz 2004), while the current research involves a more comprehensive range of discriminating factors (demographic, consumer-related and psychographic variables).

Second, prior WOM studies are primarily concerned with the occurrence of WOM production (Singh, 1990; Swan and Oliver, 1989; Zeithaml et al., 1996). In other words, the understanding of the effect of individual differences on WOM is restricted to the quantity of recommendations. This research is among the early attempts to explore the impact of individual differences on a quality dimension of recommendation - as characterized by the strength of recommendation.

Third, most studies of customer loyalty heterogeneity consider traditional attitudinal and behavioural loyalty outcomes such as repeat purchase or price sensitivity (e.g., Krishnamurthi and Papatla, 2003; Lewis, 2004; Yim and Kannan, 1999), without taking into account the loyalty exhibited in customer-customer communications. This research makes an initial attempt to include advocacy and WOM as two additional and different

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loyalty dimensions. This helps enrich existing knowledge of the dynamics and heterogeneity of customer loyalty.

To achieve the objectives of the study, this research adopts a two-stage process. Stage 1 first explores demographic, consumer-related and psychographic characteristics that are likely to affect customer recommendation. These characteristics are selected following a review of literature on service quality, satisfaction, loyalty and communication. On the basis of this review, a series of hypotheses are developed concerning the differences between advocates and non-advocates. Non-advocates are then divided into WOMers and non-WOMers. This aims to investigate if the proposed customer characteristics can further distinguish three customer groups - advocates, WOMers and non-WOMers.

As an extension of the Stage 1 research, Stage 2 broadens the focus of loyalty manifestation from the recommendation strength to a wider range of loyalty dimensions. Latent class analysis (McCutcheon, 1987) is used to develop a loyalty segmentation schema based on customers' loyalty levels and demographic profiles. The last section concludes with a discussion of managerial implications, limitations and directions for future research.

4.2 Stage 1 Study - Profiling Customer Referrers

4.2.1 Demographic Characteristics

Gender

It has been generally acknowledged that men and women are different in cognitive processes and consumer behaviour (Fisher and Dubé, 2005; Homburg, Fürst and Koschate, 2010; McColl-Kennedy, Daus and Sparks, 2003; Meyers-Levy, 1988). Explanations for gender-based differences typically involve biological reasons including different hormones (Berenbaum, 1999) and brain structure (Geary, 1996), as well as

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social psychological aspects such as the social role (Powell and Ansic, 1997) or gender identity (Oakley, 2000; Spence, 1984). Maltz and Borker (1982) suggest that men and women communicate differently because they are socialized into separate positions in society. While men are concerned with independence or competition, women are socialized in ways that emphasize community and interdependence. These characteristics consequently manifest in their communication styles and language use. For example, males tend to be more dominant but can be more relaxed in conversations (Zimmerman, 1975). Females tend to be more friendly (Rosenfeld, 1966) and are more open in the display of feelings and emotions (Eakins and Eakins, 1978), using language with references to emotion (e.g., “I am happy”) more frequently than men (Palomares, 2004).

The effect of gender differences is also reflected in consumer involvement and evaluation. Men are more likely to be analytic and task or goal oriented while women are more subjective (Allinson and Hayes, 1996; Meyers-Levy, 1988). Women place more emphasis on customer service issues (McColl-Kennedy et al., 2003) and are more sensitive to the personal interactions (Zeithaml, 1985) or the relational aspects of services (Iacobucci and Ostrom, 1993). Additionally, women tend to have stronger connection and relationships with brands or service personnel when trust is high (Fournier, 1998; Ndubisi, 2006).

However, research to date presents a mixed picture of gender differences in communication or loyalty behaviours. While men are generally thought to be more assertive and instrumental (Meyers-Levy, 1989; Powell, Butterfield and Parent, 2002), some studies found women to be assertive and at times even more assertive than men (Canary, Cunningham and Cody, 1988; Papa and Natalie, 1989). Although women embrace stronger interpersonal and brand relationships than men (Fournier, Dobscha and Mick, 1998), men who are satisfied with a product are more likely to be behaviourally loyal, showing stronger repurchase likelihood than women (Homburg and Giering, 2001; Mittal and Kamakura, 2001).

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Despite the existing inconclusiveness of gender differences in consumer behaviour in general, women are expected to be more likely to act as advocates. First, in contrast to males, females prefer verbal and facial expression (Brody and Hall, 1993). This expression is fundamental to advocacy communications. Second, there is little dispute that women tend to be more comfortable with expressing emotions (Baird, 1976; Canary and Hause, 1993) in a public manner than men (Kelly and Hutson-Comeaux, 2000). Further, they tend to express positive and negative feelings more intensely (Brody, 1993). The expressiveness and intense positive feelings can be naturally translated into the enthusiasm and proactive promotion of advocacy.

Additionally, female consumers tend to develop and maintain loyalty to individual service providers (Melnyk, Osselaer and Bijmolt, 2009) with a greater level of intrinsic commitment and relationship strength and persistence (Bhagat and Williams, 2008). This is in line with self-construal theory which suggests that women view themselves as connected to other people and therefore they are psychologically oriented to engage in communal concerns characterized by attachment and affiliation with others (Cross and Madson, 1997). This communal nature of women is related to the ethic of caring (Gilligan, 1982). It is associated with women's greater altruistic desire to help other people (George et al., 1998) in charitable activities without the anticipation of rewards (Piliavin and Charng, 1990). Therefore, women are more likely to develop stronger attachment to a service provider and are more determined to help the provider compared to male customers. As altruism towards the service provider has been identified as a key determinant of customer advocacy in Chapter 3, it is expected that:

H1: Advocates are more likely to be female than non-advocates.

Age

There has been a lack of consensus regarding the impact of customer age on loyalty. The direction of the relationship appears to greatly depend on the product and the service category involved. In grocery shopping, age is negatively related to store loyalty and customer share-of-wallet, possibly due to the price sensitivity of older customers 155 | Page

(East et al., 2000; East et al., 1995). Some studies observe no differences in brand loyalty between younger and older consumers for packaged goods (Uncles and Ehrenberg, 1990). On the other hand, older customers display greater loyalty for high involvement goods and services. For example, older customers report less search effort and a greater level of satisfaction and repurchase for automotives (Furse, Punj and Stewart, 1984; Homburg and Giering, 2001; Lambert-Pandraud, Laurent and Lapersonne, 2005) and services such as dental, hairdressing, travel agencies and banking industries (Baumann, Burton and Elliott, 2005; Patterson, 2007).

This research draws on a range of theoretical perspectives to argue that older customers are more likely to become advocates. Cognitively, information processing theory (Moskovitch, 1982) contends that as a consequence of the decline in energy and the loss of brain tissue (Wood, 1982), there is a reduced level of information processing capacity and the ability to seek new information with greater age (Wells and Gubar, 1966). As a result, older consumers may rely on fewer evaluation criteria (Evanschitzky and Wunderlich, 2006) which may lead to a greater level of satisfaction towards a service provider. More importantly, the declining cognitive system contributes to the older adults' adoption of a more subjective mode in comparison to younger adults' engagement in a more objective and factual processing mode (Isaacowitz, Turk-Charles and Carstensen, 2000). This subjectivity, enhanced by the greater emphasis on emotion and feelings by older customers (Lambert-Pandraud et al., 2005), may influence them to be passionate and enthusiastic in their recommendations of a service provider.

Attitudinally, aging is associated with increased attitude persistence (Jennings and Niemi, 1981; Sears, 1983). The older adults are more likely to defend a service provider in light of challenges given their resistance to attitude change. Psychologically, the willingness to provide support for other people and the altruistic orientation increase with mature age (Filipp, 1996; Henry, 2000) and this may serve as a coping mechanism in enhancing the self-concept of older individuals (Midlarsky, 1991). Considering these multiple perspectives, older customers are more likely to engage in advocacy behaviour than younger customers. Therefore, it is hypothesized that:

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H2: Advocates are more likely to be older customers than non-advocates.

Education and Income

In marketing literature, income is highly correlated with the level of education and is even used as a proxy for education (Cooil et al., 2007; Farley, 1964). Thus, education and income will be discussed together.

Research is conflicting as to whether income and education positively influence loyalty behaviours (e.g., East et al., 1995; Farley, 1968; Keaveney and Parthasarathy, 2001). Some studies have determined a negative relationship between individuals' income and education level in relation to their loyalty tendency (Anderson et al., 2008; Crask and Reynolds, 1978; Korgaonkar, Lund and Price, 1985). As education involves the acquirement of knowledge and the development of reasoning and judgment (Hoch, 2002), the more developed cognitive capacity enables well-educated customers to be more comfortable with greater search effort and variety seeking behaviours (Claxton, Fry and Portis, 1974; Newman and Staelin, 1972). Therefore, the link between satisfaction and retention is weaker for customers with higher education attainments (Mittal and Kamakura, 2001). Likewise, wealthier customers will also engage in greater search effort prior to repurchase decisions (Claxton et al., 1974; Evanschitzky and Wunderlich, 2006).

Prior research, however, does not provide sufficient evidence of a relationship between education/income and the strength of recommendation. On the one hand, well-educated and wealthy customers tend to have greater exposure to more expensive and better services. This may lead to greater expectations of service quality from these customers. Customers may find fewer service providers who live up to their expectations. Scarcity theory suggests that consumers perceive a smaller availability set as being more valuable (Folkes, Martin and Gupta, 1993). Consequently, higher expectations may encourage well-educated and wealthy customers to strongly recommend the few 157 | Page

satisfactory service providers. On the other hand, higher-income/education customers may keep searching for better performance and may have limited faith in service providers. Additionally, education promotes greater sensitivity to alternative points of view and the philosophy that people hold a variety of values and opinion (Kohlberg, 1969). This may discourage well-educated and wealthy customers from engaging in strongly opinionated recommendations. Overall, there is a lack of solid theoretical evidence indicating the direction of the relationship between higher education/income level and the strength of recommendation. Therefore, it is hypothesized that:

H3a: Education does not differentiate between advocates and non-advocates.

H3b: Income does not differentiate between advocates and non-advocates.

4.2.2 Consumer-related Characteristics

Acquisition Mode

Wangenheim and Bayón (2004) report that 'referral customers' - customers who were acquired through positive WOM from other customers - exhibit a higher level of satisfaction and give more recommendations than non-referral customers. It is argued that the referral status of customers is also likely to make a difference in the strength of recommendation.

The Information Availability Explanation theory (Fern, Monroe and Avila, 1986; Tybout, Sternthal and Calder, 1983) suggests that both valence (i.e., favourability) and relative availability (i.e., retrievability) of previous memories have an impact on the subsequent attitudes and/or behaviours. WOM, as vividly presented information, is more accessible, retrievable and diagnostic than other channels of information (e.g., print advertisement) even when the content is similar (Herr, Kardes and Kim, 1991). Thus, prior favourable WOM is likely to influence subsequent attitude and recommendation behaviours including the strength of recommendation. On the other hand, as referrals are often responses to customers' enquiries about a product (Mangold,

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Miller and Brockway, 1999), customers recruited by referrals are likely to be more suited to the product than those recruited by marketing activities such as sales promotions. This may contribute to their willingness to make recommendations with greater strength.

More importantly, the perceived-risk of generating strong recommendations is likely to be lower for referral customers. The pre-consumption positive WOM they received indicates other customers' positive attitude towards the same service provider and serves as an effective means of risk-reduction (Arndt, 1967; Murray, 1991). Villanueva et al. (2008) note that customers acquired through WOM successfully convert more new customers by spreading WOM than those acquired through traditional marketing, adding two times the lifetime value of these customers. Hence, referral customers are likely to be more confident about a service provider's performance excellence than customers who have not been exposed to favourable comments. As confidence in a service provider is a key determinant of customer advocacy as revealed earlier in Chapter 3, it is therefore proposed that:

H4: Advocates are more likely to be referral customers than non-advocates.

Usage Rate

Another consumer-related characteristic that may distinguish advocates from non- advocates is customers' usage rate. Usage rate is related to customer satisfaction (Bolton and Lemon, 1999) and the evaluation of service quality (Danaher and Rust, 1996). High usage rate can also increase perceived switching costs (Bolton, Lemon and Verhoef, 2004; Sheth and Parvatlyar, 1995) and encourage cross-buying behaviour (Lemon and Wangenheim, 2009), thereby influencing the future level of consumption (Bolton and Lemon, 1999).

Although little research has examined the extent to which usage rate is related to WOM, Shih and Venkatesh (2004) confirm the positive association between the intensity of communication and a high variety of service use. Moreover, heavy users are more likely

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to appreciate the benefits of the product or service (Lim, Currim and Andrew, 2005). Their extensive experience and knowledge about the product/service category facilitates a better position from which to make confident judgments (Shim and Mahoney, 1992). Additionally, heavy users tend to be socially active and optimistic, for example, they are profiled as more enthusiastic about the product/service category and they are more likely to be opinion leaders than medium or light users (Goldsmith, Flynn and Bonn, 1994; Goldsmith and Litvin, 1999; Wansink and Park, 2000). Given the association of enthusiasm, confidence and opinion leadership with customer advocacy, it is expected that:

H5: Advocates have a higher level of usage rate than non-advocates.

Relationship Length

Various studies have observed the positive effect of relationship length on service evaluation, customer satisfaction and loyalty (e.g., Bolton, 1998; Dagger and Sweeney, 2007; Reinartz and Kumar, 2003) across a variety of service areas such as financial services, insurance services and mobile phone services (Bolton, 1998; Verhoef, 2003). Other benefits accrue from longer customer tenure include lower customer sensitivity to price increases (Dawes, 2009) and the sense of familiarity that breeds liking (Zajonc, 1984). More evidence of positive effect of relationship length can be seen in two streams of literature: 1) Social psychology suggests that people in a lengthy relationship are more motivated to integrate information into a coherent representation with greater confidence (Swann and Gill, 1997), and 2) Relationship marketing literature indicates that as a relationship increases in length, customers tend to develop increased trust and interpersonal bonds with, or dependence on, the service provider (Dawes, 2009; Ganesan, 1994; Gwinner, Gremler and Bitner, 1998) .

There are mixed findings on the effect of relationship length on customer recommendation. Franses and Hoekstra (2002) did not find any significant impact of relationship age on customer referrals. Ranaweera and Prabhu (2003) determined that long-term customers are less likely than new customers to engage in WOM. Likewise, 160 | Page

East, Lomax and Narain (2001) reported that the rate of recommendation declines with the duration of customer tenure, which can be attributable to the recency effect in which customers are more likely to talk about and share experiences that occurred lately. This is in line with research in psychology and marketing that found that the magnitude of customer responses tends to decrease over time (Fournier and Mick, 1999; Thompson and Spencer, 1966). Nonetheless, the level of satisfaction and confidence tends to increase with relationship length and these two factors are indispensable to WOM and advocacy (Anderson, 1998). Therefore, it is proposed that:

H6: Advocates tend to have a longer relationship length with the service provider than non-advocates.

Number of People Recommended

Number of people recommended concerns the volume of recommendation. Comparatively, little is known about what influences the volume of recommendations made by customers. Limited research indicates that product characteristics such as the novelty or the usefulness of the product (Berger and Schwartz, 2011; Moldovan, Goldenberg and Chattopadhyay, 2011), the accessibility of memory such as the recency of the product experience (Herr et al., 1991) or the network size of the WOM communicator (East, Vanhuele and Wright, 2008) may make a difference in the number of people with whom they communicate. Gremler and Brown (1999) discovered that loyal customers do make more recommendations than less loyal ones. Based on the preceding findings, advocacy is greatly driven by the desire to help a service provider and to be an opinion leader in the service category, therefore advocates are likely to be more loyal than non-advocates. As a result, it is expected that advocates generate a greater volume of recommendation about a service provider than non-advocates. A large body of research on WOM tends to capture the volume of recommendation by measuring the frequency occurrence of recommendation or the number of people told (e.g., Bowen and Narayandas, 2001; File, Judd and Prince, 1992; Westbrook, 1987). Thus, this research takes a similar approach and proposes the following hypothesis:

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H7: Advocates recommend a service provider to a greater number of customers than non-advocates.

4.2.3 Psychographic Characteristics

Personality

Personality psychologists have related a number of key personality traits to consumer behaviour (e.g., Bozionelos and Bennett, 1999; Bugental, Henker and Whalen, 1976). Personality refers to "the characteristics which determine general patterns of behaviour" (Engel, Kollat and Blackwell, 1969, p. 61) and it impacts individuals' perception of the service environment (Auh et al., 2011) or the advertising appeals (Mooradian, 1996). Personality also influences post-purchase procedures and responses in reaction to the marketing environment (Singh, 1990) including customer-customer communications. Indeed, communication theorists suggest that as people tend to use the style of communication that is most natural to them (de Vries et al., 2009), individuals' personal traits are expressed through their communication style to a considerable extent (McCroskey et al., 1998).

Although controversy remains regarding the classification of personality traits (Block, 1995; Eysenck, 1992), there is a widespread acceptance that the Big Five personality factors of Agreeableness, Extraversion/Introversion, Conscientiousness, Neuroticism (Emotional Instability), and Openness to Experience (McCrae and John, 1992) have proved a useful integrative structure of personality at a fairly high level of abstraction. These five personality traits have been found to manifest in daily activities (Mehl et al., 2006) via the frequency of contact and the quality of social relationships (Asendorpf and Wilpers, 1998) as well as individuals' communication styles (Bugental et al., 1976).

Among the five dimensions, Agreeableness, Extraversion/Introversion and Neuroticism are particularly relevant to this research as these three dimensions have positive links with people's social behaviour or affective experience (Goldberg et al., 1998; McCrae and Costa, 1989; Peabody and Goldberg, 1989). Agreeableness and 162 | Page

Extraversion/Introversion account for most of the significant personality effects contributing to the quality of personal interactions (Cuperman and Ickes, 2009).

The fourth dimension of Conscientiousness, defined as being careful, orderly, achievement oriented and deliberate (Barrick and Mount, 1991), is most associated with work behaviour (Barrick and Mount, 1991; Costa and McCrae, 1992). In relation to originality and open-mindedness, the fifth dimension - Openness to Experience - concerns people's intellectual life (Peabody and Goldberg, 1989). This dimension accounts for the least variance in personality ratings (John and Srivastava, 1999). Therefore, the dimensions of Conscientiousness and Openness to Experience are not explored due to their relatively low impact on customer-customer communication. The remaining three relevant dimensions are discussed now.

Agreeableness refers to the tendency to be kind, soft-hearted, trustworthy and warm (Barrick and Mount, 1991). It has been consistently associated with friendliness and sociability in personal interactions (Graziano and Eisenberg, 1997; John and Srivastava, 1999). As socially oriented personality traits or values are related to recommendation intentions (Ferguson, Paulin and Bergeron, 2010), it is likely that the level of agreeableness influences the strength of recommendation. Highly agreeable individuals tend to give high ratings (Bernardin, Cooke and Villanova, 2000). This tendency may encourage them to strongly praise a service provider, while individuals of relatively low levels of agreeableness may only produce milder strength of recommendations (e.g., positive WOM) in a similar situation. Additionally, the trait of agreeableness has been linked with positive affect in expression and communications (Cuperman and Ickes, 2009; Mehl et al., 2006). Based on these characteristics of highly agreeable individuals, it is predicted that:

H8: Advocates have a greater level of agreeableness than non-advocates.

Extraversion/Introversion is manifested in individuals’ daily communications, activities, mood, and language use (Costa and McCrae, 1992; Mehl et al., 2006). Defined as being outgoing, optimistic, gregarious, assertive and active (Barrick and Mount, 1991), 163 | Page

extraversion predicts optimistic feelings and more positive consumption-based emotions than introversion (e.g., Connor-Smith and Flachsbart, 2007). As a result, extraverted individuals are talkative and enthusiastic in communications (Costa and McCrae, 1992).

More importantly, the positive emotionality may contribute to the extroverts' greater confidence in their abilities to perform (Judge and Ilies, 2002). Hence, extraversion is not only associated with a greater amount of talking, but also with the greater propensity to take the lead in conversations with confidence and assertiveness (Cuperman and Ickes, 2009). Extroverts' characteristics of being enthusiastic, confident and assertive bear resemblance to those of advocates. While previous studies have found that WOM communicators are extraverted and gregarious (Mazzarol, Sweeney and Soutar, 2007), it is likely that advocates may possess a greater level of extraversion, or a lower level of introversion. Therefore, it is hypothesized that:

H9: Non-advocates have a greater level of introversion than advocates.

Contrary to the upbeat feeling and the confidence exhibited in extroversion, neuroticism has been associated with negative affect (Larsen and Ketelaar, 1991) and uncertainty in behaviours (Cuperman and Ickes, 2009). Neurotic individuals tend to be anxious, depressed, angry and unstable emotionally. Therefore, they are more likely to experience frustration or distress such as a lower level of job satisfaction (Furnham and Zacherl, 1986; Tokar and Subich, 1997). Moreover, neurotic individuals tend to be self- conscious, trying to follow others' lead to be 'in sync' with the environment (Cuperman and Ickes, 2009). The tendency to experience negative emotion and the lack of certainty in neuroticism does not encourage the production of strong recommendations. As a result, it is proposed that:

H10: Non-advocates have a greater level of neuroticism than advocates.

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Risk Aversion

It has been argued that a substantial part of consumer behaviour could best be understood as risk-taking behaviour (Bauer, 1967). Risk is considered as a subjectively- determined expectation of loss (Stone and Winter, 1987). This implies that the level of risk that a consumer will experience is a function of perceived negative consequences (Bauer, 1967), as well as the individual’s subjective certainty or importance of the negative consequences (Cox and Stuart, 1964; Dowling, 1986; Kogan and Wallach, 1964).

As a related concept, risk aversion is "the extent to which people feel threatened by ambiguous situations, and have created beliefs and institutions that try to avoid these” (Hofstede and Bond, 1984, p. 419). Highly risk-averse customers are inclined to feel threatened by risky or ambiguous situations as these customers' cognitive structures are characterized by increased availability of loss-related exemplars (Folkes, 1988).

Five dimensions of perceived risk are generally involved - financial risk, performance risk, physical risk, social risk and psychological risk (Jacoby and Kaplan, 1972). Among them, the psychological risk plays an important mediating function for the other types of risks (Stone and Grønhaug, 1993). Although prior WOM research has mainly focused on the impact of risk on the seeking or the effectiveness of WOM (Bansal and Voyer, 2000; Murray, 1991; Wangenheim, 2005), the production of WOM is related to the risk perceived by the WOM communicator (Mazzarol et al., 2007). In the context of strong recommendations, customers make explicit and strong statements about the subject firm. These statements are likely to raise the audience's expectations of the service quality and produce social risk for the WOM communicator in case the performance does not match expectations.

Social risk refers to the negative consequences arising from the individual’s behaviour in his/her social environment (Jacoby and Kaplan, 1972). This type of risk typically results in feelings such as ‘concern’ or ‘psychological (dis)comfort’ (Zaltman and

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Wallendorf, 1983). These feelings are likely to be stronger if the subject of the strong recommendation is a service provider instead of a product due to a greater level of perceived risk associated with services (Guseman, 1981; Mitchell and Boustani, 1993; Murray, 1991). When interpersonal communication involves transmitting information, as well as creating, sustaining and transforming relationships (Higgins, 1992), it is expected that individuals who are highly risk-averse are less likely to risk the social relationships to give strong recommendations relative to customers with low levels of risk-aversion. Hence, it is hypothesized that:

H11: Non-advocates have a greater level of risk aversion than advocates.

4.3 Method

4.3.1 Sample

A Web-based self-administered survey was used to gather data from members of an online panel in Australia. The research context was high contact and experience services (i.e., hairdressing, beauty salon, education, personal trainer, massage and childcare) with which many people have had experiences. High contact services provide customers opportunities to experience all dimensions of service performance, while customers find it easier to evaluate technical outcomes of experience services more confidently than credence services (Bowen, 1990; Zeithaml and Bitner, 1996). Therefore, the high contact and experience services provide an excellent context in collecting recommendations of varying levels of strength. The focus on a single service type was to minimize the potential compounding effect of multiple service types on WOM, as research has indicated that the impact of the determinants of WOM can be industry dependent (Harrison-Walker, 2001).

To ensure an ample level of variance in both customer advocacy and WOM scores, as well as a reasonably large sample size for sub-group analysis, the recruitment of

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respondents was completed in two phases. In phase 1, respondents were those who had made fairly strong recommendations (above 4 out of a possible 7 in terms of the recommendation strength) about a service provider in any of the specified services in the previous six months. In phase 2, respondents were those who had experienced a service provider of any of the specified services in the previous six months. They were asked to evaluate their actual WOM and advocacy behaviours concerning the provider without any restrictions on the recommendation strength. Hence, responses spanned the entire recommendation scale (from 1 to 7). In return, respondents received a small cash credit (equivalent to $2 or less) provided by the online panel.

The preceding process yielded a sample of 497 customers. No missing values were identified as respondents were not able to proceed without completing the previous questions. As certain analytical techniques used in the study were sensitive to outliers (e.g., MANOVA), multivariate outlier were assessed based on Mahalanobis D2 measure. 13 cases were excluded from data analysis when a conservative criterion of 0.001 was applied for the test of level of significance. As a result, 484 respondents were retained for further analysis (Refer to Table 1 for demographic profiles).

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Table 1: Demographic profile of sample Count Valid % Gender Male 168 34.7 Female 316 65.3 Total 484 100.0 Age 18-29 yrs 146 30.2 30-39 yrs 126 26.0 40-49 yrs 97 20.0 Above 50 yrs 115 23.8 Total 484 100.0 Income less than $10,000 57 11.6 $10,000-$30,000 118 24.5 $30,000-$80,000 217 45.0 over $80,000 92 18.9 Total 484 100.0 Education Under Yr 11 66 13.6 HSC or VCE 64 13.2 TAFE/Trade 134 27.7 Undergraduate or above 220 45.5 Total 484 100.0 Note: Service Type (Experience/High contact Services): Hairdressing, beauty salon, personal trainer, education, massage and childcare provider services.

4.3.2 Measures

Due to a lack of existing measures of customer advocacy, the study used a newly developed advocacy scale. It contains seven items that had demonstrated psychometrically sound properties (Liu, Harris and Payne, 2011). The WOM measurement proposed by Zeithaml et al. (1996) was adopted in the study to capture customers' production of general positive WOM. Demographic characteristics such as gender, age or education were collected from self-report items from respondents. Relationship length was defined as the number of actual months since respondents first patronized the service provider, and usage rate was assessed on a 7 point scale [1 = very light user, 7 = very heavy user] via the question ‘Compared to average users of the same service, how would you describe your usage rate?’ The referral status was measured by asking respondents to indicate how they got to know the particular service provider. The number of people recommended was based on respondents' recall of the actual number of people (e.g., 1, 2… 10) to whom they have recommended the service provider. 168 | Page

The measurement of agreeableness, introversion and neuroticism was adopted from the Big Five personality scale as operationalized by Licata et al. (2003). Each personality trait was captured by three or four items on a 9 point scale [1 = never; 9 = always] when respondents were probed ‘How often do you feel/act this way?’. Risk aversion was captured by three items based on Donthu and Gilliland (1996) (Refer to Appendix 1 for detailed measurement items).

4.3.3 Data Analysis and Results

To test the hypotheses, the sample was split into groups of advocates and non-advocates. Advocates were those with a high composite score on the advocacy scale (above 4 out of 7) while non-advocates were those who scored low on the scale (1-4 out of 7).

Chi-square tests were used to test H1 to H4. As per Table 2, gender was significantly associated with differences between advocates and non-advocates (X2 = .001, p < .01). There was a much higher proportion of females in advocates (72.4%) than in non- advocates (59.1%). In contrast, the percentage of males in advocates (27.6%) was lower than that in non-advocates (40.9%). Therefore, advocates are more likely to be female, providing support for H1. There was no significant association between age and the tendency to be advocates, hence H2 was not supported.

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TABLE 2: Results of Chi-square Test (Stage 1) Advocates vs. non-Advocates Advocates vs. WOMers vs. non-WOMers Advocates non-Advocates Chi-square a Advocates WOMers non-WOMers Chi-square Female 72.4% 59.1% 65.7% 66.7% 47.6% Gender 0.001** 0.097 b Male 27.6% 40.9% 34.3% 33.3% 52.4% Age 18-29 years 29.3% 30.9% 31.3% 28.8% 26.2% 30-39 years 25.3% 26.6% 26.9% 30.3% 23.8% 0.814 0.918 40-49 years 19.6% 20.5% 14.9% 16.7% 14.3% Above 50 years 25.8% 22.0% 26.9% 24.2% 35.7% Education Under Year 11 16.9% 10.8% 16.4% 13.6% 7.1% HSC or VCE 12.9% 13.5% 11.9% 10.6% 11.9% 0.099 0.658 TAFE/Trade qaulification 29.8% 25.9% 29.9% 33.3% 23.8% Undergraduate or above 40.4% 49.8% 41.8% 42.4% 57.1% Income Less than $10,000 10.3% 12.7% 9.1% 15.2% 11.9% $10,000 - $30,000 24.7% 24.3% 24.2% 28.8% 21.4% 0.208 0.458 $30,000 - $80,000 49.3% 41.3% 47.0% 30.3% 50.0% Over $80,000 15.7% 21.6% 19.7% 25.8% 16.7% Acquisition Referral 66.7% 52.5% 73.1% 54.5% 38.1% 0.001** 0.001** mode non-Referral 33.3% 47.5% 26.9% 45.5% 61.9% Cell size 225 259 67 66 42 Note: a. Chi-square refers to Pearson Chi-square. b. In the three-group comparison between advocates, WOMers and non-WOMers, p < 0.10 was accepted as a significant result due to a small sample size; in the two-group comparison between advocates and non-advocates, p < 0.10 was considered insignificant due to a much bigger sample size. ** p < 0.01

As shown in Table 2, the association between the level of education/income and the advocacy tendency was not significant, although there appeared to be some differences between advocates and non-advocates with respect to the level of the highest attained education qualification. For example, the percentage of customers with undergraduate level or above was higher among non-advocates (49.8%) than among advocates (40.4%), whereas the percentage of the education qualification of under year 11 was higher among advocates (16.9%) than among non-advocates (10.8%). As this trend was not significant, H3 was supported.

There was a significant association between the acquisition mode of referral status and the tendency to be advocates (X2 = .001, p < .01). As shown in Table 2, most advocates (66.7%) were referred to current service providers by other customers, while this percentage was significantly lower in non-advocates (52.5%). Therefore, H4 was supported.

Independent t-tests were employed to test H5 to H11. As detailed in Table 3, usage rate was significantly higher among advocates ( = 4.3) than among non-advocates ( = 3.4) (p < .01). Hence, advocates were more likely to be heavy users than non-advocates, 170 | Page

which supported H5. Relationship length was not found to be significantly different between the two groups, which failed to support H6. There was a significant difference (p < .01) between advocates and non-advocates with respect to the number of people recommended. On average advocates recommended a service provider to 3.9 people, while non-advocates only recommended 1.8 people. Hence advocates made more than twice the number of recommendations compared to non-advocates. Thus H7 was supported.

TABLE 3 : Results of T-test and ANOVA (Stage 1) Advocates vs. non-Advocates Advocates vs. WOMers vs. non-WOMers Advocates non-Advocates t value T-test sig. Advocates WOMers non-WOMers F value ANOVA sig. Usage rate 4.3 (1.4) 3.4 (1.3) 7.57 0.000** 4.3a (1.3) 3.4b (1.3) 2.7c (1.2) 23.20 0.000** Relationship length 40.6 (48.2) 35.1 (58.5) 1.13 0.257 36.1 (40.6) 35.3 (48.8) 24.9 (41.4) 0.94 0.393 Number of people recommended 3.9 (3.4) 1.8 (1.7) 8.71 0.000** 3.4a (2.3) 1.9b (1.7) 0.5c (0.8) 34.61 0.000** Cell size 225 259 67 66 42 Note: Standard deviations are shown in parentheses. ** p < 0.01 Means with different letters are significantly different from each other at p < 0.01.

As shown in Table 4, advocates were more agreeable than non-advocates ( = 7.2 vs. 6.8, p < .01), supporting H8. The two groups were not significantly different in introversion and neuroticism (p < .554 and p < .474 respectively). Therefore, H9 and H10 were not supported. These results indicate that while advocates tend to be more soft-hearted or kind than non-advocates, no difference is found between the two groups regarding the level of introversion and emotional stability.

Risk aversion was another personality trait that significantly differentiated advocates from non-advocates. However, contrary to H11, non-advocates were less risk averse than advocates ( = 3.8 vs. 4.0, with higher score suggesting a greater level of risk aversion) (p < .01). Hence, H11 was not supported.

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TABLE 4 : Results of Independent T-tests and MANOVA (Stage 1) Advocates vs. non-Advocates Advocates vs. WOMers vs. non-WOMers Advocates non-Advocates F value Sig. Advocates WOMers non-WOMers F value Sig. Agreeableness 7.2 (1.0) 6.8 (1.0) 21.05 0.000** 7.0a (1.0) 6.8a (0.9) 6.2b (1.1) 9.06 0.000** Neuroticism 4.6 (1.7) 4.5 (1.4) 0.51 0.474 4.5 (1.5) 4.8 (1.3) 4.4 (1.3) 1.59 0.206 Introversion 4.7 (1.9) 4.8 (1.5) 0.35 0.554 6.1 (1.7) 5.7 (1.5) 5.7 (1.2) 0.39 0.681 Risk Aversion 4.0 (0.6) 3.8 (0.6) 14.57 0.000** 4.1a (0.7) 3.9a (0.5) 3.6b (0.8) 7.20 0.001** Note: Standard deviations are shown in parentheses. ** p < 0.01 Means with different letters are significantly different from each other at p < 0.01.

The analysis so far has provided initial evidence that demographic, consumer-related and psychographic factors are associated with the differences between advocates and non-advocates. To obtain deeper insights into the extent to which these factors can effectively discriminate between customers with varying degrees of recommendation strength, non-advocates were further classified into two sub-groups – customers who produce general positive WOM about a service provider ("WOMers") and those who fail to do so ("non-WOMers").

WOMers are defined as customers who generate positive comments about a service provider with a weaker recommendation strength relative to that of advocates. Thus, WOMers scored high on the WOM scale (5-7 out of 7) but low on the advocacy scale (1-4 out of 7). By contrast, non-WOMers were those who were low on both the WOM (1-4 out of 7) and advocacy scale (1-4 out of 7). The sample yielded 42 non-WOMers. Given this relatively small sample, a small number of advocates (67) and WOMers (66) were randomly selected to balance the group sizes in the subsequent analysis.

Chi-square tests were employed to examine if there were differences with respect to demographic characteristics for the refined comparison (Advocates vs. WOMers vs. Non-WOMers). As shown in Table 2, Pearson Chi-square of gender association was significant (X2 = .097, p < .10). The percentage of females among non-WOMers was significantly lower (47.6%) than that of advocates (65.7%) and WOMers (66.7%). This suggests that WOMers are similar to advocates in being more likely to be female, while customers who fail to praise a service provider are equally likely to be males or females. This finding provides additional insights into the previous result of H1 regarding the gender difference between the advocates and non-advocates. In line with the H2 and H3,

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there was no significant difference with respect to age, education and income level between advocates, WOMers and non-WOMers.

Interestingly, although advocates and WOMers were similar with respect to gender, Chi-square tests reveal that they were significantly different (X2 = .001, p < .01) in how they were acquired by the service provider. As per Table 2, the percentage of referred customers was much higher among advocates (73.1%) than that among WOMers (54.5%) and non-WOMers (38.1%). This reinforces the result of H4 in that the more likely a customer is to be acquired through referrals, the stronger the recommendation that this customer is likely to make in the future.

Similarly, the results of one-way ANOVA in Table 3 show that usage rate and the number of people recommended not only differed between advocates and WOMers, but also differed between WOMers and non-WOMers. Advocates had a significantly higher level of usage rate ( = 4.3) than WOMers ( = 3.4) (p < .01), while WOMers had a significantly higher level of usage rate than non-WOMers ( = 2.7) (p < .01). Additionally, advocates recommended a service provider to more people than WOMers ( = 3.4 vs. 1.9, p < .01), whereas WOMers told more people than non-WOMers ( = 1.9 vs. 0.5, p < .01). Reinforcing the confirmation of H5, relationship age did not vary among the three customer groups.

Group differences regarding the personality traits were tested using MANOVA. The results in Table 4 indicate that the level of agreeableness significantly separated non- WOMers from advocates and WOMers (Bonferroni adjusted alpha level of .013, = 6.2 vs. 7.0 and 6.8, p < .01). However, advocates and WOMers did not differ. This is also the case with the level of risk aversion whereby non-WOMers were significantly different from the other two customer groups ( = 3.6 vs. 4.1 and 3.9, p < .01). No significant differences were found among the three groups with respect to neuroticism (p < .206) or introversion (p < .681).

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To summarize, non-WOMers are distinctly different from advocates and WOMers across demographic, consumer-related and psychographic variables. Comparatively, advocates and WOMers are similar in their demographic and psychographic attributes, hence advocates are not very different in kind by nature from WOMers. However, they are different in their consumption and recommendation behaviour, such as the acquisition mode, usage rate, quantity of recommendation, as well as the quality of recommendation (i.e., the strength).

4.4 Stage 2 Study - Profiling Customer Loyalty Segments

The Stage 1 study revealed that customers of different recommendation strength can be profiled on a number of demographic, consumer-related and psychographic characteristics. As recommendation is seen as a manifestation of customer loyalty (Anderson, 1998; Gremler and Brown, 1999), these findings prompted the need to further investigate the extent to which customers with varying degrees of recommendation strength can be profiled on other loyalty outcomes. Thus, Stage 2 extends the loyalty focus from customer recommendation to a broader range of loyalty dimensions, examining how customers of different recommendation strength exhibit loyalty on other attitudinal and behavioural dimensions.

Loyalty literature indicates that customers' loyalty levels do not always align with each other. For example, while the increased use of services and dollars spent may go hand in hand with the increased number of recommendations about a service provider (Gremler and Brown, 1999), the willingness to pay a price premium appears to be less pronounced than the willingness to stay with the provider (Zeithaml et al., 1996). Likewise, Wirtz (2007) found that the attractiveness of reward programs will have a positive impact on share of wallet even when the attitudinal loyalty is low.

As a result, the varied degrees of loyalty between attitudinal and behavioural dimensions suggests a possibility of segments of customers possessing a varying degree of recommendation and other loyalty manifestations. Because no previous loyalty 174 | Page

segmentation schemes have distinguished between different recommendation strength (i.e., general positive WOM and advocacy), no formal hypotheses were developed in the Stage 2 study. Instead, segmentation techniques are used to examine the existence of loyalty segments.

Prior to the examination of potential customer segments, the loyalty literature is reviewed and some important loyalty dimensions are identified for subsequent analysis.

4.4.1Literature Review

Much of the work on loyalty incorporates both attitudinal and behavioural components (e.g., Dick and Basu, 1994; Gremler and Brown, 1996; Jacoby and Chestnut, 1978). The behavioural dimension alone may not yield a comprehensive view of loyalty, as typical behavioural loyalty such as customers' repurchase behaviours (Day, 1970) may be attributable to convenience reasons or switching barriers rather than a preferential disposition (Liljander and Strandvik, 1995; Storbacka et al., 1994). Therefore, attitudinal loyalty is often considered. It reflects the extent to which customers hold favourable attitudes towards a service provider relative to the alternatives (Bloemer et al., 1999; Dick and Basu, 1994; Oliver, 1997).

Behavioural and attitudinal loyalty have various manifestations. Share of wallet (Keiningham, Perkins-Munn and Evans, 2003), patronage exclusivity (Gremler and Brown, 1999; Zeithaml et al., 1996) and increased service usage (Zeithaml et al., 1996) are common behavioural dimensions. Share of wallet reflects the proportion of expenditure devoted to a specific brand or a service provider (Jacoby and Chestnut, 1978; Jones and Sasser, 1995) and has attracted increasing interest from academics and practitioners (e.g., Cooil et al., 2007; Mägi, 2003; Uncles, Dowling and Hammond, 2003). Customers tend to diversify their spending instead of completely discontinuing patronage with a company (Perkins-Munn et al., 2005). Thus, studies find that share of wallet is a better measure of loyalty than repurchase or retention alone (Coyles and Gokey, 2002; Reichheld, 1996). Likewise, the exclusive selection of a service provider 175 | Page

within the service category and increased service usage from the same provider are less likely to be subject to situational restrictions (e.g., convenience reasons or the unavailability of a service) and are more likely to be true reflections of behavioural loyalty.

Attitudinal loyalty commonly involves the willingness to stay loyal and pay a premium to the company even when the prices increase, as a consequence of customers' psychological attachment to a service provider (Bloemer et al., 1999; Zeithaml et al., 1996). Customer recommendation or positive WOM represents another dimension of attitudinal loyalty. Although positive WOM has been positioned as a dimension of behavioural loyalty by some scholars (e.g., Zeithaml et al., 1996), recommendation is largely predicted by attitude (East et al., 2005). Attitude denotes the degree to which a consumer’s disposition towards a service is favourably inclined (Azjen and Fishbein, 1980), and this favourable inclination is typically reflected in the communication of general positive WOM and customer advocacy. Consequently, in line with a large body of research (Gremler and Brown, 1999; Homburg and Giering, 2001; Jain, Pinson and Malhotra, 1987; Selnes, 1993), general positive WOM and customer advocacy are categorized as manifestations of attitudinal loyalty in this study.

4.4.2 Sample and Measures

The Stage 2 study was based on the same sample that was previously collected in the Stage 1 study. To serve the research purpose of examining loyalty segments, Stage 2 involves additional loyalty measures. The behavioural loyalty dimension of share of wallet was measured by ‘Compared with other providers, I have spent more money on this service provider’ adopted from Oliver (1997), Zeithaml et al. (1996) and De Wulf Odekerken-Schroder and Iacobucci (2001). Patronage exclusivity was captured by ‘When I have a need for this type of service, I buy only from this service provider’ (Oliver, 1997). Increased service usage was adopted from Zeithaml et al. (1996) measured by statement ‘I have used more of the services offered by this service provider’. These measures were not loyalty intentions which may be an incomplete 176 | Page

proxy for what actually happened (Keaveney, 1995); rather, they focused on actual past behaviours.

In addition to WOM and customer advocacy, attitudinal loyalty was measured by the willingness to stay loyal and pay a price premium - ‘I continue to do business with this service provider even if its prices increase somewhat’ based on Zeithaml et al. (1996)’s study. All six loyalty dimensions were assessed using a 7-point scale (see Appendix 1 for detailed measurement items). Additionally, demographic characteristics of age, gender, education and income level were included in the measurement.

4.4.3 Data Analysis

As the underlying segments were a priori unknown, Latent class analysis (LCA) was utilised to reveal the existence of the segments. LCA has advantages over the traditional segmentation techniques such as cluster analysis as a consequence of its use of advanced statistics and computing power (Vermunt and Magidson, 2005). A key advantage is that researchers can use model fit criteria such as Akaike Information Criterion (AIC) (Akaike, 1973), Bayesian Information Criterion (BIC) (Schwarz, 1978) or Constrained Akaike Information Criterion (CAIC) (Bozdogan, 1987) to determine the optimal number of segments to retain. These values are relative measures, with lower values signalling better fit and parsimony (Wedel and Desarbo, 1994). Whereas the AIC imposes a penalty for each parameter estimated in the model, BIC and CAIC penalize the addition of parameters more heavily (Wedel and DeSarbo, 1994). As tougher criteria, BIC and CAIC perform consistently well even in a large sample size (Andrews and Currim, 2003; Bozdogan, 1987). Therefore, the number of segments was determined using a stopping-rule procedure based on BIC and CAIC.

The LCA procedure was conducted using Latent Gold 4.5 software package (Vermunt and Magidson, 2007). The six loyalty measures were included as core parameters, while gender, age, education and income level were used as the covariates to identify core demographic characteristics of the resulting segments. 177 | Page

4.4.4 Results

The model fit analysis, summarised in Table 5, shows an increase in the BIC and CAIC statistic between segments four and five, indicating four segments to be optimal. Descriptions of resulting segments are provided in Table 6. Segments differ significantly at each loyalty dimension (p < .01).

TABLE 5 (Stage 2) Fit Statistics for Different Segment Solutions in Latent Class Analysis (LCA)

Classes Log Likelihood BIC CAIC 1 -4664.16 9495.84 9522.84 2 -4149.21 8701.72 8766.72 3 -3642.87 7924.81 8027.81 4 -3379.39 7633.63 7774.63 5 -3261.84 7634.30 7813.30 6 -3194.15 7734.70 7951.70 Note: BIC = Bayesian Information Criterion; CAIC = Constrained Akaike Information Criterion. Smallest BIC and CAIC statistics are bolded.

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TABLE 6 (Stage 2) Segment Descriptive Results of Latent Class Analysis (LCA)

Segment 1 Segment 2 Segment 3 Segment 4 Wald p value

Segment Size (%) 31% 27% 24% 18%

Parameters a Behavioural loyalty - Share of wallet 5.87 4.89 4.34 4.89 68.42 0.00 Behavioural loyalty - Use of more services 5.87 5.24 4.09 4.73 100.76 0.00 Behavioural loyalty - Patronage exclusivity 5.87 5.83 4.54 5.16 64.43 0.00 Attitudinal loyalty - Willingness to pay price premium when fee increases 5.81 6.14 4.23 5.29 147.04 0.00 Attitudinal loyalty - Advocacy 5.08 5.34 3.80 4.18 139.45 0.00 Attitudinal loyalty - General positive WOM 6.17 7.00 5.12 6.00 21037.12 0.00

Covariates Gender 25.17 0.00 Male 35% 22% 54% 32% Female 65% 78% 46% 68% Age 20.85 0.01 18-29 yrs 35% 23% 32% 29% 30-39 yrs 26% 20% 27% 34% 40-49 yrs 20% 19% 19% 24% Above 50 yrs 19% 37% 23% 13% Education 8.48 0.49 Under Year 11 16% 17% 10% 11% HSC or VCE qualification 9% 15% 18% 13% TAFE/Trade qualification 31% 28% 25% 23% Undergraduate or above 44% 40% 47% 53% Annual Income 4.75 0.86 less than $10,000 13% 11% 11% 12% $10,000-$30,000 21% 30% 25% 21% $30,000-$80,000 51% 40% 40% 48% over $80,000 15% 19% 24% 20% Note: a. Mean ratings of parameters are reported.

Each segment is now described in detail:

Segment 1 - the Hard-core Loyal. This is the largest segment, comprising 31% of the sample. These customers exhibit loyalty across all dimensions, hence they were labelled as the 'Hard-core Loyal'. Post-hoc analysis indicates that behaviourally, they had spent significantly more on ( = 5.87, p < .01) and used more of the service by the service provider ( = 5.87, p < .01) than customers of other segments. They tended to patronize the service provider exclusively ( = 5.87). Attitudinally, their willingness to pay a price premium and stay with the same service provider even if its service charge increases somewhat in the future was high ( = 5.81) and was only second to that of Segment 2 ( = 6.14, p < .05). Additionally, their attitudinal loyalty was reflected in

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the production of highly positive WOM ( = 6.17) and engagement in advocacy behaviours ( = 5.08). This segment consists of more young adults than older adults, with 18-39 years old accounting for 61% and those above 50 years old accounting for 19%.

Segment 2 – the Overall Loyal. This is the second largest segment, composing 27% of the customers. This segment is characterized by the highest level of attitudinal loyalty. Its level of willingness to stay and pay a price premium ( = 6.14) and positive WOM ( = 7) was significantly higher than other segments (p < .05). Customers of this segment engaged in a greater level of advocacy behaviour than Segment 3 and 4 (p < .01), although their advocacy level was not significantly different from that of Segment 1 ( = 5.34 vs. 5.08, p > .10). They tend to use the service provider exclusively for the service type ( = 5.83) and use more of the service by the service provider ( = 5.24). However, they fell significantly behind the 'hard-core loyal' with respect to the share-of-wallet spending ( = 4.89, p < .01). Nonetheless, they exhibited loyalty in most loyalty outcomes and they can be labelled as the 'Overall Loyal'.

Demographically, this segment has the largest proportion of female (78%) and older customers with 37% of customers over 50 years old. The relatively high percentage of women in both loyal Segment 1 and 2 supports Study 1's finding that women tend to have a higher level of communicative loyalty than men. Additionally, Segment 2 has the lowest education attainment among all segments, with 60% not attaining an undergraduate qualification (i.e. HSC, VCE, under year 11 or Trade qualifications). Customers of this segment had the lowest level of earnings with 41% having an annual income under $30,000. The combination of older age and lower income level may help explain their lower level of share of wallet in comparison to the 'Hard-core Loyal' of Segment 1.

Segment 3 - the Lip-service Loyal. Accounting for 24% of the sample, this segment is the least loyal. Relative to the other three segments, it has the lowest score (p < .05) across all loyalty dimensions. Customers were reluctant to spend more on the service

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provider ( = 4.34). They did not use more services offered by the provider ( = 4.09) or patronize the provider exclusively ( = 4.54). They did not intend to stay with the provider if the service fee increases ( = 4.23) and they were definitely not advocates ( = 3.80). The only loyalty demonstrated was the general positive WOM about the service provider ( = 5.12). This segment is relatively young (59% being 18 to 39 years old) with the largest proportion of male customers (54%) among all segments. Additionally, this segment has the highest income (24% earning more than $80,000 annually).

Segment 4 - the Attitudinally Loyal. Consisting of 18% of the sample, this segment sits somewhere between the 'Overall Loyal' (Segment 2) and the 'Lip-service Loyal' (Segment 3). Their level of patronage exclusivity ( = 5.16) and the willingness to stay and pay a price premium ( = 5.29) were significantly higher than the 'Lip-service Loyal' (Segment 3) (p < .01), but lower than the 'Hard-core Loyal' (Segment 1) and the 'Overall Loyal' (Segment 2) (p < .01). Additionally, their share-of-wallet spending ( = 4.89) and usage rate ( = 4.73) were significantly lower (p < .01) than the 'Hard-core Loyal' (Segment 1) while their usage rate was significantly lower than the 'Overall Loyal' (Segment 2). It is not surprising that they were not advocates of the service provider ( = 4.18). Interestingly, however, their WOM were fairly strong and positive ( = 6). This segment is the youngest across all segments, with 63% being 18 to 39 years old and only 13% above 50 years old. Moreover, this segment has the highest education level as evidenced by 53% of customers having acquired at least an undergraduate qualification.

4.5 Discussion

The empirical evidence in this research indicates that demographic (gender), consumer- related (usage rate, acquisition mode and the number of recommendations made) and psychographic (agreeableness and risk aversion) characteristics are effective in profiling customers with varying degrees of recommendation strength. Data from six services (i.e., hairdressing, beauty salon, personal trainer, education, massage and childcare) 181 | Page

indicate that customers who are female, heavy users and referred by other customers are more likely to produce the strongest level of recommendation. Psychographically, advocates exhibit a greater level of agreeableness and risk aversion than non-advocates. Although WOMers (i.e., those communicate with milder strength of recommendation) are also more likely to be females, agreeable and risk-averse, they have lower service consumption than advocates. Moreover, WOMers are less likely to be referral customers and they recommend a service provider to fewer people than advocates. With the weakest recommendation strength in post-purchase communications, non-WOMers are comparatively the lightest users of the service. They are least likely to be acquired through referrals and they recommend a service provider to the fewest people. They are also distinctly different from advocates and WOMers with the lowest level of agreeableness and risk-aversion.

These findings advance the understanding of the impact of customer characteristics in the context of customer recommendation strength. Women, apart from being more active in seeking WOM and more receptive to WOM influence (Bush, Martin and Bush, 2004; DeAnna and Kay, 2006), are more likely to produce strong recommendations about a service provider. Usage rate also influences the strength of recommendation positively, in addition to its positive effect on customer satisfaction and service quality evaluation (Bolton and Lemon, 1999; Danaher and Rust, 1996). This study also uncovers the significant effect of acquisition mode as referral customers demonstrate greater recommendation strength. This reinforces the earlier finding that referral customers are more valuable both in the short and long run (Schmitt, Skiera and Bulte, 2011; Trusov et al., 2009). In line with the documented connection between the trait of agreeableness and customer communication (Cuperman and Ickes, 2009; Mehl et al., 2006), the level of agreeableness increases with the level of recommendation strength. This confirms the role of this personality trait in relation to customer recommendations.

On the other hand, the study reveals that while certain customer characteristics may impact some loyalty behaviours, they do not appear to affect the recommendation strength. For example, contrary to the higher loyalty (e.g., repurchase) that older

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customers exhibit in a services context (Patterson, 2007), this study shows that older customers are not necessarily predisposed to a greater level of strength of recommendation. Despite the positive effect of relationship length on service evaluation, customer satisfaction and loyalty (e.g., Bolton, 1998; Dagger and Sweeney, 2007; Reinartz and Kumar, 2003), the current research did not find its impact on the strength of recommendation. This is somewhat counter-intuitive, as advocacy is expected to be more common in long-tenure customers if advocacy is associated with customer loyalty. A potential explanation is that if a customer has experienced a substantial number of service providers in the category and therefore has acquired considerable knowledge about the average market performance, a 'stand-out' service performer may leave strong impressions on the customer and therefore promote confidence and subsequently customer advocacy. This suggests that the relative attitude may play an important role in short-term customers' advocacy behaviour, although this assumption requires further investigation. Moreover, previous research records the link between the trait of extraversion/introversion and WOM tendency (Mooradian and Swan, 2006), but this study indicates that despite the higher level of agreeableness, the level of extroversion/introversion and neuroticism are similar across customer groups with varying degrees of recommendation strength. Thus, although high recommendation customers are generally more soft-hearted and warm, they are not more outgoing or emotionally unstable. In fact, their level of risk aversion is higher, which corresponds with the preceding finding that confidence acts as a key determinant of advocacy. Overall, the results demonstrate that in comparison to customers who do not praise a service provider, advocates and WOMers do not differentiate themselves greatly by basic personality traits (i.e., Big Five). Rather, they are cautious communicators who simply have faith in the excellent performance of a service provider.

More importantly, this research reveals that different degrees of recommendation strength are associated with different loyalty outcomes. Customers' heterogeneity in attitudinal and behavioural loyalty can be profiled by four customer segments - the 'Hard-core Loyal', the 'Overall Loyal', the 'Lip-service Loyal' and the 'Attitudinally Loyal'. Customers with the strongest recommendations exhibit consistent behavioural

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(e.g., use of more services, patronage exclusivity) and attitudinal loyalty (e.g., the willingness to stay loyal and pay a premium). This is observed in the segments of the 'Hard-core Loyal' and the 'Overall Loyal'. By contrast, customers of moderate recommendation strength (i.e., general positive WOM) are not necessarily loyal with respect to other loyalty outcomes. For example, the 'Attitudinally Loyal' segment features customers who are high in general positive WOM but are relatively low in share of wallet and use of more services. A similar pattern is exhibited in the 'Lip- service Loyal' who demonstrated loyalty through positive WOM but was low in all other dimensions of loyalty, such as share of wallet or patronage exclusivity. This indicates that highly positive WOM does not necessarily indicate behavioural loyalty. Additionally, general positive WOM may not correspond with high advocacy ratings, which can be best demonstrated by the contrast between the 'Hard-core Loyal' and the 'Attitudinally Loyal' segments.

These additional profiling results enhance the theoretical contribution of the study in several ways. First, the current research highlights the difference between customer advocacy and general positive WOM. Customer advocacy is positively linked with other attitudinal loyalty and behavioural loyalty, while general positive WOM aligns less consistently with other loyalty outcomes. Communication research reports that advocates may behave in the direction of their advocated position (Cialdini, 1971), however, there is also a long-recognized discrepancy between what people say versus what they actually do (Rothwell, 1955). The results of the study indicate that in contrast to customers with moderate recommendation strength (i.e., general positive WOM), customers with the strongest recommendations demonstrate true loyalty by being able to 'walk their talk' in other loyalty dimensions.

Dick and Basu (1994) define loyalty as the favourable correspondence between the attitudinal and behavioural dimensions. Hence unless customers who spread favourable WOM simultaneously display behavioural loyalty, general positive WOM may not indicate a true loyalty. To date, little research examines if positive WOM communicators necessarily purchase more or spend more with the firm. An exception is

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Garnefeld et al. (2010)’s study, however, they only explore WOM communicators' switching intention and affective commitment. The present research challenges previous literature (Bettencourt, 1997; Brown et al., 2005; Hennig-Thurau, Gwinner and Gremler, 2002) and echoes some scholars' scepticism of whether positive WOM is a strong indicator of loyalty (Yim, Tse and Chan, 2008). It may be more appropriate to regard general positive WOM as a manifestation of loyalty, since customers who are loyal are likely to engage in positive WOM (Anderson, 1998; Gremler and Brown, 1999). Customer advocacy, however, tends to be a reliable indicator of loyalty, based on the research findings.

Second, this study enriches the understanding of the heterogeneity of customer loyalty. It marks an initial attempt to include the varied recommendation strength in the investigation of customer loyalty heterogeneity, while previous loyalty heterogeneity research tends to neglect customer post-consumption communication as a loyalty dimension (e.g., Krishnamurthi and Papatla, 2003; Lewis, 2004; Yim and Kannan, 1999). Moreover, the study reveals a common pattern across the four loyalty segments that while customers can be loyal in their patronage exclusivity and continuity, they are less likely to behave equally loyally with respect to using more services and even less so with their share-of-wallet spending. However, there exists a segment of customers (the 'Hard-core Loyals') who exhibit all-around loyalty in both behavioural and attitudinal dimensions.

Third, this study is among the first to systematically examine the potential effect of a comprehensive range of customer characteristics on the strength of recommendation. A vast majority of previous research sheds light on the impact of customer characteristics on the customer decision making process, post-purchase evaluations or customer loyalty dimensions (e.g., Bolton and Lemon, 1999; East et al., 1995; Homburg and Giering, 2001). However, prior research has either placed little attention on the influence of customer characteristics on WOM, or has focused more on the quantity instead of the quality of post-consumption communications such as the strength of recommendation

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(e.g., Brown et al., 2005; Naylor, 1999). This offers promising managerial implications, which will be discussed next.

4.6 Managerial Implications

A number of important managerial implications arise from this research. The results indicate an association between gender and the strength of recommendation, making women an attractive segment to target. Accordingly, to capitalize on female customers' communicative power, service companies need to maximize the quality of service aspects that matter most to women. For example, women care more about the interpersonal components of services (Anderson et al., 2008; Iacobucci and Ostrom, 1993) and expect their voices to be heard during service complaint handling (McColl- Kennedy et al., 2003). Companies should therefore pay special attention to these elements to promote greater strength of recommendation from female customers.

Results show that usage rate and referral status distinguish customers of stronger recommendation from the moderate ones. A firm should therefore make use of a customer database or CRM system to identify heavy users and referred customers, and target them with the knowledge that these customers are more likely to become advocates or positive WOM communicators. On the basis of the commitment to improve service quality, service companies may build on the human nature of reciprocation and gratitude (Becker, 1986; Buck, 2004) to reap additional referrals. For example, the provision of free-consultations or lists of other businesses or even competitors that best suit a customer's needs may generate customer reciprocation and subsequently positive comments. Additionally, company websites should establish a referral page to facilitate customer recommendations. Likewise, email newsletters and other corporate material sent to prospective referrers are advisable in improving the referral volume. Free products and services may be given to customers who introduce new customers. This also serves to increase usage rate of these referrers and potentially their recommendation strength in the future.

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Individuals with high levels of agreeableness tend to promote a service provider with stronger recommendations. These individuals are generally courteous, cooperative and soft-hearted. Service personnel are therefore encouraged to make attempts to recognize, greet and interact with potentially highly agreeable customers, as pleasant service experiences are natural topics of conversations for these customers. It is also important to reduce risks for prospective advocates and positive WOM communicators through quality services, in light of a significantly higher level of risk-aversion among these customers compared to non-WOMers. As a result of social and psychological risks accompanied with strong recommendations, companies should strive to ensure service reliability, which is central to customer confidence and service excellence (Berry and Parasuraman, 1991). Zeithaml et al. (1996) found that customers experiencing no service problems have the strongest level of recommendation intentions. Thus, the association between risk-aversion and customer recommendation strength points out the direction for firms to fully exploit the power of customer communication.

This research reinforces the need for a more nuanced understanding of customer loyalty heterogeneity. Customers are not the same and equal degrees of loyalty on all dimensions are unlikely. For service companies, the prime target is the 'Hard-core Loyal' who demonstrate consistent attitudinal and behavioural loyalty. These customers are mostly female, relatively young with moderate income. An alternative segment worthy of attention is the 'Overall Loyal', who are older with lower income. Although relatively restrained in their share-of-wallet spending, these customers demonstrate superiority in most loyalty outcomes, such as highly praising a service provider, using the service provider exclusively and intending to stay loyal in spite of any fee increase. On the other hand, special attention should be placed on the 'Lip-service Loyal' that features male, high income earners with relatively high educational attainment. Except for general positive WOM, this segment of customers tends to be disloyal attitudinally and behaviourally. Overall, service firms can make use of these findings to deploy their limited resources to prioritize their customer loyalty management activities.

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Lastly, the research indicates that advocates not only recommend a service provider to more than twice the number of people as non-advocates, they also exhibit behavioural (use of more services and patronage exclusivity) and attitudinal loyalty (willingness to stay loyal and pay a premium). By contrast, general positive WOM communicators such as the 'Lip-service Loyal' (Segment 3) and the 'Attitudinally Loyal' (Segment 4) do not appear to be consistently loyal on these non-communicative dimensions. For service companies, this implies that the pursuit of customer advocacy should be an ultimate goal (Urban, 2004) to sustain longevity and competitiveness in the marketplace.

4.7 Limitations and Future Research

This study presents an initial empirical assessment of the profiles of customers with varying degrees of recommendation strength. However, it is subject to a number of limitations most of which may represent interesting opportunities for future research.

The sample size of non-WOMers was relatively small in the examination of customer characteristics among advocates, WOMers and non-WOMers. Future research may further the investigation in larger samples of these customer groups. Although the volume of recommendations from advocates and non-advocates has been measured and compared, it remains unclear to what extent the proportion of advocates is different from that of non-advocates in a population. Furthermore, the usage rate in the study only captures the usage rate for the service provider. Considering the existence of customers who only patronize one service provider, it would be helpful to include the share-of-category measure in future research to capture the usage rate for the service category. Within the research scope of high contact and experience services, the study covers a range of six service products (i.e., hairdressing, beauty salons, personal trainers, education, massage and childcare). This exposes male and female customers to different service products. It is therefore advisable for future studies to replicate the research in a context in which identical products are used by both genders. This assists to eliminate the potential confounding effect of product category on the gender difference in advocacy. Additionally, although behavioural loyalty dimensions have been measured 188 | Page

by past behaviours rather than behavioural intentions, they still rely on customer self- reported information. Hence, future research may measure actual share-of-wallet spending or service usage by taking advantage of the consumption data in a consumer database or CRM system.

The present study focuses on the effectiveness of customer characteristics in discriminating customers of different recommendation strength. Future research may examine the extent to which these characteristics act as moderators in the relationship between extreme recommendation strength (advocacy) and its antecedents such as satisfaction and service quality. Research has indicated that customer characteristics moderate the association between satisfaction and its antecedents (Anderson et al., 2008), as well as that between satisfaction and its loyalty outcomes (Cooil et al., 2007). It is therefore a fruitful line of inquiry to extend the loyalty outcome into the strength of recommendation in exploring the moderating role of customer characteristics.

This research adopts a widely accepted categorization of loyalty outcomes - attitudinal and behavioural dimensions. There exists, however, more elaborate definitions of loyalty. For example, Oliver (1999) introduced a four-stage loyalty model that involves four aspects of loyalty (cognitive, affective, conative and action loyalty) that emerge consecutively over time. Personal characteristics of age, education and income have been found to exert influence on the development of these loyalty stages (Evanschitzky and Wunderlich, 2006). Therefore, an interesting line of research may include advocacy and WOM in the loyalty dimensions and examine the impact of customer characteristics on the development of various loyalty stages.

In profiling customers segments with different recommendation strength and loyalty inclinations, a number of variables are worth closer examinations. For example, productive future research may consider customer characteristics of loyalty card membership, variety seeking (Homburg and Giering, 2001) or price orientation (Kim, Srinivasan and Wilcox, 1999; Mägi, 2003), as well as marketplace characteristics such as the reputation of the service provider and the competitive intensity of the industry.

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This research collected data from six services in Australia (i.e., hairdressing, beauty salons, personal trainers, education, massage and childcare) which are mature markets. These services share the common nature of high contact and experience services. Thus, a replication of this study in other markets and other service types (e.g., low contact or credence services) would assist the generalizability of the results of the research. There are also considerable opportunities to extend the research into a wide range of product and business areas. For example, product sectors may involve consumer durable goods or fast-moving consumer goods, while business-to-business (B2B) and non-profit industries are interesting business sectors for further exploration. Consequently, future research can build upon the findings of this study to provide additional insights into the dynamics of customer loyalty when customer advocacy and general positive WOM are taken into account.

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APPENDIX 1 Questionnaire Items and Measurement Properties Constructs Items Scale Compared to average users of the same service, how would you describe your usage 1 Very light user; 7 Usage rate 1. rate? Very heavy user Relationship length 1. How long have you been with this particular service provider (estimate in months)? About this service provider, how many people have you recommended in the last six Number of people recommended 1. months? Please estimate in number (0, 1, 2 etc) Acquisition mode 1. How do you get to know this service provider? How often do you feel/act this way? 1. Soft-hearted with others. Agreeableness (Licata et al. 2003) 2. Sympathetic. 3. Kind to others. 1. Moody more than others. 2. Temperamental (easily upset and moody) Neuroticism (Licata et al. 2003) 1 Never; 9 Always 3. Emotions go way up and down. 4. Testy (Irritable) more than others 1. Feel bashful (shy and modest) more than others. Introversion (Licata et al. 2003) 2. Quiet when with people. 3. Shy. 1. I would rather be safe than sorry 1 Strongly disagree; Risk aversion (Donthu and Gilliland 1996) 2. I want to be sure before I recommend anything 7 Strongly agree 3. I avoid risky things Behavioural loyalty - Shall of wallet (Oliver 1997; Zeithaml et al. 1996; De Wulf et al. 1. Compared with other insurance providers, I have spent more money on this provider 2001) Behavioural loyalty - Use of more services 1. I have used more of the services offered by this service provider (Zeithaml et al. 1996) 1 Strongly disagree; Behavioural loyalty - Patronage exclusivity 7 Strongly agree 1. When I have a need for this type of service, I buy only from this service provider (Oliver 1997)

Attitudinal loyalty - Willingness to pay price I continue to do business with this service provider even if its prices increase 1. premium (Zeithaml et al. 1996) somewhat

1. I am enthusiastic in my recommendations of this service provider 2. When discussing this service provider, I urge people to consider using it 3. I have told more people about my positive experience with this service provider than I have with most other service providers, regardless of the service category 4. I describe this service provider as the best of its kind 5. I defend this service provider if people raise negative comments about it directly with 1 Does not describe Attitudinal loyalty - Advocacy (Self- me me at all; 7 Describes developed) 6. Even when there is no conversation, but if I think there are people who have an me very well interest in the service category (e.g., dentist, car cleaning, hairdressing or banking), I strongly recommend this service provider, without being asked 7. I take the initiative to act as a 'promoter' of this service provider (e.g., help others have access to this service provider, contact the service provider on behalf of others if needed) Attitudinal loyalty - General positive WOM 1. Say positive things about this service provider to other people 1 Strongly disagree; (Zeithaml et al. 1996) 2. Recommend this service provider to someone who seeks your advice 7 Strongly agree

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Chapter 5: Conclusions

5.1 Synopsis

To date, the WOM and customer recommendation literature has been primarily concerned with the valence (positive or negative nature) and the frequency of WOM activities. The strength of recommendation as an important dimension of these activities has been under-explored. As a result, relatively little is understood about customer advocacy as a vivid form of strong and passionate recommendation. Hence, what contributes to customers' upper level of recommendation strength relies more on intuition than on empirical evidence. Therefore, the aim of this thesis is to advance the understanding of customer advocacy by addressing these three related research objectives:

1. To conceptualize and develop a scale of customer advocacy that captures strongly expressed recommendations.

2. To identify and empirically examine the antecedents and moderators of customer advocacy.

3. To profile customers with varying degrees of recommendation strength on a comprehensive range of customer characteristics and loyalty outcomes.

An insight into these three objectives was achieved through three consecutive studies. These studies were presented in Chapter 2, 3 and 4 respectively and are reviewed as follows.

In addressing the first research objective, Chapter 2 conceptualized and developed a scale of customer advocacy. It is clear that advocacy features intensified explicitness and strong support of a subject that is considered in the best interest of the audience.

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Delivered voluntarily and proactively, advocacy involves highly positive comments and vastly judgmental and forceful opinions in the communication. Advocates are not easily convinced by criticisms from the audience and are not afraid of showing their passionate belief in the focal subject.

Furthermore, results based on three quantitative studies suggest that customer advocacy can be best conceptualized in two-dimensions - 'spontaneous endorsement' and 'proactive promotion'. They are measured by the willingness to be both explicit and passionate in urging people to patronize a service provider. They are also manifested in the tendency to proactively promote a service provider without being asked and to defend the provider in the face of criticisms.

Psychometrically, these two conceptual dimensions of customer advocacy exhibited sound reliability and convergent validity across four major service categories. EFA and CFA analysis provided ample evidence of the scale's discriminant validity at the dimension level. Equally importantly, at the scale level, customer advocacy is distinct from conceptually related measure of general positive WOM. The customer advocacy scale displayed predictive validity as well. It has a positive impact on customers’ exclusive patronage and their willingness to spend more on a service provider.

In summary, customer advocacy and general positive WOM are conceptually related but different. In customer advocacy, there is more emphasis on customers' desire to help a service provider and to exert influence on others as a result of the strong faith in the provider. By contrast, general positive WOM is more concerned with the desire to help other customers and to express the enjoyment of product/service experiences.

Chapter 3 achieved the second research objective based on the newly developed scale. It provided a comprehensive articulation of both the determinants of advocacy and the contingent conditions under which these determinants vary in their impact on customer advocacy. We now understand that both evaluative and motivational dimensions are positively related to customers' strong recommendations. Evaluative factors involve

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service quality and customer expertise that are mediated or partially mediated through confidence in a service provider in affecting customer advocacy. Thus, the certainty of the evaluation of service performance is most important in the generation of advocacy. Apart from the cognitive evaluation, customer advocacy is also driven by customers' willingness to be an opinion leader in the service category (opinion leadership) and the desire to assist a service provider to succeed (altruism towards a service provider).

It was also found that the impact of the evaluative determinants on customer advocacy is subject to the influence of two contingent conditions - situational (service type) and relational factors (relationship quality). Service quality has a stronger effect on confidence in a service provider in high contact and experience services (e.g., hairdressing) than in low/medium contact and experience services (e.g., telecommunications), while the impact of customer expertise on confidence is stronger in low/medium contact and experience services. On the other hand, relationship quality moderates the relationship between customer experience and confidence negatively. When relationship quality is strong, the impact of customer experience on confidence in a service provider becomes relatively low. Conversely, under the condition of weak relationship quality, the influence of customer experience on confidence is stronger. Therefore, strong relationship quality may mitigate the lack of customer expertise and encourage customer confidence. In addition, relationship quality enhances the impact of confidence in a service provider on customer advocacy.

Additionally, the determinants of customer advocacy play a different role in their impact on general positive WOM that typifies moderate strength of recommendation. The influence of service quality on general positive WOM is direct and stronger, while its effect on customer advocacy is indirect. In contrast, the influence of opinion leadership and altruism towards a service provider on general positive WOM is either weak or insignificant. Thus, the relative importance of motivational factors (altruism towards service provider and opinion leadership) is stronger in the production of customer advocacy. Furthermore, general positive WOM is related to the level of involvement, the closeness between the communicator and the audience (tie strength)

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and altruism towards other customers, however, these factors exert no significant impact on customer advocacy.

The third research objective aimed to enhance the understanding of customer advocacy through the profiling characteristics of advocates in comparison to other customers. This was addressed in Chapter 4. The chapter provides additional insights into customer advocacy from two perspectives. The first perspective was associated with the demographic, consumer-related and psychographic characteristics of customers. It was found that advocates tend to be female, heavy users and referral customers. A greater level of agreeableness and risk aversion also distinguishes advocates from non- advocates. WOMers (i.e., those who communicate with milder strength of recommendation) may not be significantly different from advocates in terms of demographic and psychographic characteristics. However, they have lower levels of service consumption and they are less likely to be referral customers. Non-WOMers (i.e., those who do not produce positive WOM) are comparatively the lightest users of the service. They are least likely to be acquired through referrals and they possess the lowest level of agreeableness and risk-aversion.

The second perspective viewed customer recommendation as a loyalty outcome, thus, customers with varying degrees of recommendation strength were profiled on a variety of attitudinal and behavioural loyalty dimensions. It was found that customers can be segmented into four groups, who are the 'Hard-core Loyal', the 'Overall Loyal', the 'Lip- service Loyal' and the 'Attitudinally Loyal'. Advocates tend to exhibit consistent behavioural (e.g., use of more services, patronage exclusivity) and attitudinal loyalty (e.g., the willingness to stay loyal and pay a premium) as described in the segments of the 'Hard-core Loyal' and the 'Overall Loyal'. By contrast, WOMers can be high in general positive WOM but they have a relatively low level of share-of-wallet spending, service usage or patronage exclusivity. These can be seen in the segment of the 'Attitudinally Loyal' and the 'Lip-service Loyal'.

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5.2 Managerial Implications

For service firms, advocates are more valuable than non-advocates as they generate stronger and more recommendations on a voluntary basis. They passionately promote a service provider in a proactive and defensive manner, acting as effective and reliable human resources of a firm. Additionally, advocates bring value to service firms through their loyalty at multiple levels, including the willingness to spend more and use more services, as well as the willingness to stay loyal and pay a price premium in spite of the price increase. Therefore, this study reinforces the importance of customer advocacy and provides a strategic direction for firms to pursue.

For managers, the scale can be widely used in performance tracking and improvement. It can also be used in the identification of advocate segments in the customer base for loyalty maintenance purposes. Further, the findings of this research can be utilized in the design and implementation of core and supporting services. For example, it is found that advocacy is likely only when customers reach a reasonable level of confidence in the evaluation of the service performance, while customers of stronger recommendations tend to be more risk-averse than customers of moderate recommendation strength. These suggest that for companies to enjoy the benefits of strong recommendations from advocates, managers must increase customer confidence through the reliability of the service delivery and the superiority of the service offerings relative to the competition. Companies should designate an after-sale support system that ensures friendly and effective post-purchase communication with customers to eliminate any service-related uncertainty. This also underlines the importance of providing continuous employee training programs and a positive workplace culture. For product brands, customers' evaluations derive predominantly from a product's tangible features. In contrast, customers' perceptions of a service provider are heavily reliant on the interaction with service employees (Hartline and Jones, 1996; Parasuraman, Zeithaml and Berry, 1985). Therefore, if service firms strive to excel in the strength of customer recommendation, they must sustain an organizational culture in which

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employee commitment can flourish. That, in turn, leads to satisfied and committed customers.

Marketers should be aware of the nature of the service industry in which a service firm operates in the process of fostering consumer confidence. For high contact and experience services, the focus in establishing confidence in a service provider lies in the service quality. In comparison, customers of low/medium contact and experience services rely heavily on customer expertise that contributes to confidence and subsequently customer advocacy. Therefore, while every effort should be taken to maximize service quality in all service categories, service industries featured by low levels of employee-customer contact require extra effort to facilitate the growth of customer expertise. This may be achieved through, for example, customer education or customer experience sharing mechanisms (e.g., online forum or events) to disseminate knowledge about service use and market-level performance.

In addition, central to customer advocacy is the role of opinion leadership and altruism towards the service provider. This implies that companies should facilitate opinion leadership through processes and platforms such as customer get-togethers, online chat forums, and recommender systems that allow customers to voice their opinions. Furthermore, customized offerings, efficient return policies and the provision of educational programs will help enhance customers' altruism towards the firm and should be implemented to nourish customer advocacy.

This research has implications for relationship marketing as well. The development of relationship quality helps mitigate the lack of customer expertise and promote customer confidence in a service provider. In the meantime, strong relationship quality enhances the impact of confidence on customer advocacy. As a result, despite the importance of factors associated with service performance (i.e., service quality evaluation and the certainty towards the evaluation) in the generation of customer advocacy, the relational aspect is useful in encouraging customers to 'go the extra mile' to be advocates. Meanwhile, heavy users and referral customers have been found to be more likely to

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engage in advocacy behaviours. Managers may therefore leverage the impact of relationship marketing to promote service use (Liu, 2007) and referral volume. The direct effect of loyalty programs on customer lifetime value (e.g., customer retention and share development) can be small (Verhoef, 2003). Thus, marketers may improve the return on their relationship marketing expenditure indirectly through the development of customer advocacy that brings in more new customers.

The current research also provides managers with a basis for targeting strategies. In light of the role of opinion leadership in advocacy production, opinion leaders should be targeted. As opinion leaders are readily exposed to a specific media vehicle within each product/service category, a thoughtfully designed and executed communication strategy is essential in gaining their attention. Furthermore, advocates are more likely to be female, heavy users and referral customers. These customers should be the prime targets and marketers are encouraged to collect information on customer demographics, usage and acquisition channels to facilitate effective targeting. Additionally, the research reveals segments of customers who exhibit communicative loyalty as well as behavioural loyalty (e.g., the patronage exclusivity). They are either the 'Hard-core Loyal' who are mostly female, relatively young with moderate income, or the 'Overall Loyal' who are older with lower income earnings. Efforts should be aimed at these customers to maximize the benefits of customer loyalty.

5.3 Limitations and Future Research

A number of directions for future research arise from this study. Specifically, empirical, conceptual and methodological extensions represent the directions in enriching and deepening our understandings of customer advocacy.

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5.3.1 Empirical Extensions

The present study examined customer advocacy behaviour in the services context concerning offline customer interactions in Australia. Although offline customer- customer communications affect consumption much more than online interactions (Keller, 2007), the convenience, reach and the transparency of internet communication has led to the surge of customer review websites. This empowers marketers to monitor and even influence customer communications as never before (Kozinets et al., 2010). The differences in customer communication between the online and offline venues have not been extensively explored. However, research has shown that the drivers and the content of WOM may differ between the two channels (Peres, Shachar and Lovett, 2011). This warrants a further examination of customer advocacy in the online customer-to-customer communication environment.

Customer advocacy was investigated in a consumer services setting, based on the consideration that customer recommendation plays a significant role in the selection and evaluation of services (Murray and Schlacter, 1990). However, much of customer- customer communications are driven by the talking about products (Libai et al., 2010). It would be important to explore if the dimensions or the influencing factors of customer advocacy uncovered in this study hold true across various product sectors, for example, fast-moving consumer goods and consumer durable goods. Additionally, industrial services represent the highest growth in services marketing today (Yanamandram and White, 2006). Industrial services are more complex and uncertain (Vickery et al., 2004) and are highly subject to the influence of customer recommendations (Money, 2004). Hence, the need arises to replicate the examination of customer advocacy in the business-to-business (B2B) service area. Moreover, the service categories examined in the present study were based on two dimensions - the degree of customer-employee contact and the experience/credence attribute of services. Further research may extend into a broader array of service characteristics, such as the degree of customization, the degree of discretion, the product/process focus or the extent of membership relationship between the customer and the service provider.

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A cross-cultural perspective marks another overarching empirical extension for the current research. It has been found that the Americans give more recommendations than the British for the same category of services/products (East et al., 2005), whereas the Asians tend to be more susceptible to interpersonal influences (Zou et al., 2009). However, little is known about the role of culture in the production of strong recommendations, although research has suggested some potential effects. For example, the European Americans value self-expression more than the East Asians (Kim and Sherman, 2007). Customers in individualistic cultures tend to be more self-confident (Chelminski and Coulter, 2007). These may be the indication that customer advocacy manifests itself differently in different cultural environments. Moreover, cultural differences exist in the organization of past information and the subsequent service evaluations. East Asians pay attention to a greater amount of past information than Americans as they consider causal factors in a more holistic and complex manner (Ji et al., 2009). This may impact the evaluation of service performance and the certainty of the evaluation. When the service performance is high, Chinese customers perceive significantly higher service quality and express greater customer satisfaction, whereas Western customers are more likely to value tangible cues from the physical environment and the hedonic dimensions of the consumption experience (Mattila, 1999; Ueltschy et al., 2009). These prompt the need to investigate if, and to what extent, the contributing factors of customer advocacy vary with the cultural context.

5.3.2 Conceptual Extensions

This study provides insights into the conceptual dimensions, the determinants and the profiling characteristics of customer advocacy. As an initial attempt to explore this important customer outcome, the present study suggests several valuable conceptual extensions that deserve further investigation.

The scope of this inquiry limits customer advocacy to highly positive customer recommendations. Nonetheless, customers may also communicate strongly negative views about service providers, which is something that most managers would strive to 220 | Page

avoid. Future research may explore if the measurement scale of advocacy can be utilized and adapted in the context of customer extreme hostility. The examination of contributing factors specific to the extremely negative customer communication will be equally important to service firms. The investigation may draw on the expectancy-value framework that underpins Ajzen’s (1991) theory of planned behaviour. The expectancy- value framework deals with the relationship between customer behaviour and the strength of attitude, subjective norms and the perceived behavioural control. It may serve as a starting point to explore strong negative communication.

This research mainly concerns the determinants of customer advocacy, with the consequences of customer advocacy only being briefly assessed in the third study (Chapter 4). Future research may wish to examine the effects of customer advocacy on the firm’s customer equity. Customer equity is an overall long term measure of customer lifetime value and is central to firms' strategic opportunities (Blattberg and Deighton, 1996; Rust, Lemon and Zeithaml, 2004). Thus, it would be beneficial to investigate more consequences associated with customer advocacy.

The current research focuses on customer advocacy from the perspective of the communicators. A substantial number of research opportunities exist from the perspective of the audience and their subsequent behaviours. For example, it would be desirable to understand the persuasiveness of these strong and passionate recommendations, such as the impact of customer advocacy on effective customer acquisitions, purchase accelerations and the switching or retention behaviours of the audience. A central point of investigation would be if and to what extent recommendation from advocates is more persuasive compared with general positive WOM. Another impact of advocacy on the audience is the potential production of a chain reaction. Strong statements from advocates may enhance the audience confidence, especially the confidence in existing users of the same service provider. Advocacy provides arguments for these existing customers to use in their subsequent customer- customer communication. Hence, advocacy may encourage them to generate more

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WOM. This deserves further exploration as it relates to the impact of users on other users that has received relatively little attention in the WOM literature.

A more complete consideration of potential determinants of customer advocacy could be another fruitful line of inquiry. These contributors may be categorized into market- based, customer-based and firm-based factors. Examples of market-based characteristics include the heterogeneity of service offerings in the market, the degree of market competitiveness or the maturity of the market. With regards to consumer-based variables, perceived value is the consumer’s overall evaluation of what is received against what is given (Zeithaml, 1988). Consumers are value-driven (Levy, 1999) and value has been recognized as the key variable associated with customer evaluation, satisfaction and repurchase behaviours (Bolton and Drew, 1991; Dodds, 1991; Patterson and Spreng, 1997). Embodied in perceived value is the comparison between the benefit received and the sacrifice paid by customers. This may add new perspectives to the explanation of advocacy behaviours. Additional individual traits and predispositions may influence customers' engagement in strong levels of recommendations. For example, customer self-enhancement is related to WOM communication (Hennig- Thurau et al., 2004) and may represent an interesting research direction.

Firm-based variables may involve the examination of more relational factors and the impact of brand equity and brand communities. Loyalty rewards programs have been found to have a positive effect on customer value evaluations, spending and repeat purchase intentions (Bolton, Kannan and Bramlett, 2000; Lewis, 2004). It is likely that loyalty programs may impact the strength of recommendation and the extent to which the effectiveness varies with different types of loyalty programs is largely unknown. Additionally, high levels of brand equity are likely to engender high levels of customer commitment and attachment (e.g., Keller, 1998). Customers participating in brand communities are more likely to become connected with the brand and act as promoters for the brand (Muniz Jr and Schau, 2005). Therefore, a branding approach to understanding customer advocacy may provide additional insights into what influences customers' willingness to produce strong recommendations.

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5.3.3 Alternative methodologies

The final point relates to the potential methodological extension. The cross-sectional survey was the main method employed in the present study. This design is suitable in determining the relationship among factors, however, it may not reveal the causal effect of the determinants (Malhotra and Grover, 1998). By contrast, although an experimental design tends to have a lower degree of realism and external validity, it will assist researchers in drawing inferences from the data (Keppel, 1991). As a result, replications of the current research in the form of lab experiments are likely to advance the understandings of the causal factors of customer advocacy.

The dynamic aspect of customer advocacy is an important research direction that requires longitudinal investigation. Prior research has examined how customer satisfaction and customers' responses to marketing activities change and evolve over time (Bolton and Drew, 1991; Bolton and Lemon, 1999). It is conceivable that the passion towards a service provider may change as well, either by reaching its highest level at an early stage or increasing its strength progressively. Empirical evidence has shown that customers' propensity to engage in negative WOM to revenge a company decreases gradually (Gregoire, Tripp and Legoux, 2009). The extent to which this diminishing pattern is consistent or different in customer advocacy necessitates the introduction of longitudinal designs such as a panel study. By tracking the changes in recommendation strength in the same sample, panel studies may yield additional explanations of customer advocacy as a highly desirable customer outcome.

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