International Journal of Service Industry Management (Volume 18 Number 5 2007) Interdisciplinary insights on service activities

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ISSN 0956-4233

Volume 18 Number 5 2007

International Journal of

Service Industry Management Interdisciplinary insights on service activities Guest Editors: Professor Sylvie Llosa and and Professor Chiara Orsingher

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International Journal of

ISSN 0956-4233

Service Industry Management

Volume 18 Number 5 2007

Interdisciplinary insights on service activities Guest Editors Professor Sylvie Llosa and Professor Chiara Orsingher

Access this journal online __________________________ 447 International editorial advisory board _______________ 448 Editorial __________________________________________ 449 Perceived justice and consumption experience evaluations: a qualitative and experimental investigation Philippe Aurier and Be´atrice Siadou-Martin __________________________

450

The validity of the SERVQUAL and SERVPERF scales: a meta-analytic view of 17 years of research across five continents Franc¸ois A. Carrillat, Fernando Jaramillo and Jay P. Mulki _____________

472

Changing roles of customers: consequences for HRM Albert Graf ____________________________________________________

491

Customer switching resistance (CSR): the effects of perceived equity, trust and relationship commitment Gilles N’Goala __________________________________________________

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CONTENTS

CONTENTS continued

Perception and attribution of employees’ effort and abilities: the impact on customer encounter satisfaction Nina Specht, Sina Fichtel and Anton Meyer __________________________

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Call for papers ____________________________________ 555

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IJSIM 18,5

INTERNATIONAL EDITORIAL ADVISORY BOARD

Assistant Professor Lerzan Aksoy Koc University, Turkey Professor Tor W. Andreassen Norwegian School of Management, Norway Professor Colin Armistead Bournemouth University, UK Associate Professor Ravi S. Behara Florida Atlantic University, USA Professor Leonard L. Berry Texas A&M University, USA Professor Mary Jo Bitner Arizona State University, USA Dr Keith Blois Templeton College, Oxford, UK Professor David E. Bowen Thunderbird, The American Graduate School of International Management, Arizona, USA Professor Stephen W. Brown The Center for Services Leadership, Arizona State University, USA Dr Doren D. Chadee University of Auckland, New Zealand Assistant Professor Kah-Hin Chai National University of Singapore, Singapore Professor Richard B. Chase University of Southern California, USA Professor Jean-Charles Chebat HEC Montre´al, Canada Associate Professor Tan Kay Chuan National University of Singapore, Singapore Professor David A. Collier Ohio State University, USA Professor J. Joseph Cronin Jr Florida State University, USA Professor Lawrence F. Cunningham University of Colorado at Denver, USA Professor Pratibha Dabholkar University of Tennessee, USA Professor Mark M. Davis Bentley College, USA Associate Professor Bo Enquist Karlstad University, Sweden Professor Ray Fisk University of New Orleans, USA Professor Sabine Fliess Associate Professor, Fern Universita¨t in Hagen, Germany Margareta Friman Karlstad University, Sweden Professor Dwayne D. Gremler Bowling Green State University, USA Professor Stephen Grove Clemson University, USA Professor Kjell Gro¨nhaug Norwegian School of Economics and Business Administration, Norway Professor Christian Gro¨nroos Swedish School of Economics and Business Administration, Finland International Journal of Service Professor Evert Gummesson Industry Management University of Stockholm, Sweden Vol. 18 No. 5, 2007 Professor Aino Halinen-Kaila p. 448 # Emerald Group Publishing Limited Turku School of Economics and Business Administration, Finland 0956-4233

448

Professor Thorsten Henning-Thurau Bauhaus-University of Weimar, Germany Professor James Heskett Harvard University, USA Dr Railton Hill Swinburne University of Technology, Australia Acting Professor Maria Holmlund Swedish School of Economics, Finland Larry W. Jacobs Northern Illinois University, USA Professor Joby John Bentley College, USA Professor Michael D. Johnson University of Michigan Business School, USA Professor Jay Kandampully Ohio State University, USA Professor Hans Kasper Maastricht University, The Netherlands Timothy Keiningham IPSOS Loyalty, NJ, USA Professor Sheryl E. Kimes Cornell University, USA Per Kristensson Karlstad University, Sweden Dr Annouk Lievens University of Antwerp, Antwerp, Belgium Dr Ian Lings University of Technology, Sydney, Australia Assistant Professor Sylvia J. Long-Tolbert University of Toledo, USA Professor Christopher Lovelock Lovelock & Associates, USA Assistant Professor Peter Magnusson Karlstad University, Sweden Dr Anna Mattila Pennsylvania State University, USA Professor Jan Mattsson Roskilde University, Roskilde, Denmark Dr Bert Meijboom Tilburg University, The Netherlands Professor Anton Meyer Ludwig-Maximilian Universita¨t, Mu¨nchen, Germany Professor Peter Mills University of Oregon, USA Dr Irene C.L. Ng Associate Professor of Marketing, University of Exeter, UK Amy L. Ostrom WP Carey School of Business, Arizona State University, USA Professor A. Parasuraman University of Miami, USA Professor Paul Patterson University of New South Wales, Australia Professor Rajesh Pillania Management Development Institute, India Associate Professor Anat Rafaeli TECHNION – Israel Institute of Technology, Israel

Professor Javier Reynoso EGADE, Nuevo Leon, Mexico Associate Professor Henk Roest Tilburg University, The Netherlands Assistant Professor Inger Roos Karlstad University, Sweden Professor Aleda V. Roth W.P. Carey School of Business, Arizona State University, USA Professor Benjamin Schneider University of Maryland and Personnel Research Associates, USA Associate Professor Magnus So¨derlund Stockholm School of Economics, Sweden Per Ska˚le´n Karlstad University, Sweden Professor Amrik S. Sohal Monash University, Australia Professor Bernd Stauss Katholische Universita¨t Eichsta¨tt, Ingolstadt, Germany Professor Tore Strandvik Swedish School of Economics and Business Administration, Finland Professor Go¨ran Svensson Oslo School of Management, Norway Associate Professor Jill Sweeney University of Western Australia, Australia Acting Professor Jaana Ta¨htinen University of Oulu, Finland Professor David A. Tansik University of Arizona, USA Dr Stephen S. Tax University of Victoria, Canada Dr Gail Ayala Taylor Tuck School of Business at Dartmouth, USA Professor Steven A. Taylor Illinois State University, USA Professor James Teboul INSEAD, France Professor Roland van Dierdonck De Vlerick School voor Management, Belgium Dr Allard van Riel University of Lie`ge, Belgium Professor Stephen L. Vargo University of Hawaii at Manoa, USA Professor Martin Wetzels Technical University of Eindhoven, The Netherlands Professor Dr Celeste Wilderom University of Twente, The Netherlands Professor Jochen Wirtz NUS Business School, National University of Singapore, Singapore Associate Professor Lars Witell Karlstad University, Sweden

Editorial The 9th International Research Seminar in Service Management founded by Pierre Eiglier and Eric Langeard in 1990 was held in La Londe, France, in Spring 2006. It was a success in terms of the quality of the papers presented, and the number of scholars who attended it. The conference formula is unique: only two competitive sessions are opened and each author has 45 minutes to present the paper and to discuss constructively with the room. By enabling thorough and fruitful exchange, between some hundred or so participants from more than 15 different countries, this formula has proved itself. This special issue of the International Journal of Service Industry Management features five stimulating papers that were presented at the Seminar. They reflect the spirit as well as the content of the Seminar. They include a mix of conceptual thinking and field research; a mix of nationalities and a mix of disciplines, marketing, human resources management, strategy, and operations which constitute the heart and the wealth of services as a research area. Aurier and Siadou-Martin address the issue of service recovery; namely the role of perceived justice in service consumption/purchase experiences. Carrilat, Jaramillo and Mulki provide a quantitative overview of the service quality issue: they conduct a meta-analysis to investigate the difference between SERVQUAL and SERVPERF’s predictive validity of service quality. Graf’s paper discusses the implications and consequences of changes in customer roles and involvement on HRM within a service context. Ngoala’s purpose is to better understand why customers resist switching service provider when critical incident occurs. He investigates the role of perceived equity, trust and relationship commitment on customer switching resistance. Specht, Fichtel and Meyer analyze the impact of effort and abilities of employees in a service encounter on customer satisfaction. All these issues are challenging because many solutions are yet to be discovered both from academics and practitioners. Finally, we would like to thank Rosemary Calazel for her continuous support, Ingrid Hansson for guiding us throughout the production of this special issue, and Bo Edvardsson for having encouraged and supported this special issue.

Editorial

449

Sylvie Llosa and Chiara Orsingher Guest Editors

International Journal of Service Industry Management Vol. 18 No. 5, 2007 p. 449 q Emerald Group Publishing Limited 0956-4233

The current issue and full text archive of this journal is available at www.emeraldinsight.com/0956-4233.htm

IJSIM 18,5

Perceived justice and consumption experience evaluations

450

A qualitative and experimental investigation Philippe Aurier

Received 2 January 2006 Revised 1 April 2007 Accepted 1 May 2007

University of Montpellier 2, Montpellier, France, and

Be´atrice Siadou-Martin GSCM-Montpellier Business School University of Montpellier 2, Montpellier, France Abstract Purpose – This paper aims to investigate the role of perceived justice in service consumption/ purchase experiences. Design/methodology/approach – In an initial study, using the critical incident method, the authors show that customers are strongly concerned by perceived injustice. Their judgments involve the three components of justice described in organizational and service marketing literature: distributive, procedural and interactional justice. They also identify a macro-level justice factor which characterizes the perception of collective practices at the industry level. In an experiment applied to the dining experience, the authors manipulate distributive, procedural and interactional justice perception to study their impact on service evaluation (quality, value), satisfaction and relationship quality (trust, commitment). Findings – Contrary to the satisfaction literature, the authors observe a slight direct effect of justice on satisfaction, but rather indirect impacts through perceived quality (outcome and interaction) and value. Moreover, perception of justice has substantial effects on trust (credibility and benevolence) but not on commitment. Originality/value – The paper studies the impact of justice in the context of a customer experience evaluation (service delivery) which is not limited to service recovery. It examines the entire evaluation process, including service evaluation (quality, value), satisfaction and relationship quality (trust, commitment). Keywords Function evaluation, Quality, Customer satisfaction, Consumers, Customer relations Paper type Research paper

International Journal of Service Industry Management Vol. 18 No. 5, 2007 pp. 450-471 q Emerald Group Publishing Limited 0956-4233 DOI 10.1108/09564230710826241

Introduction Firms can achieve profitability if they are able to build well balanced relationships with their clients and meet their needs for self-esteem, justice and security. A large body of research in philosophy and social psychology supports the idea that justice is the starting point to assess the quality of the relationships between organizations and individuals. The literature offers several managerial examples: As fairness helps build customer retention, positive consequences also accrue to the bottom line. A large and impressive body of evidence – from organizations such as MBNA, Banc One, Southwest Airlines, and Taco Bell – now shows that small gains in customer retention,

e.g. 5 percent increases, can lead to 75-100 percent gains in profitability. Fairness reaps profits (Bowen et al., 1999, p. 12). Hampton Inn offers one night’s free stay to customers who are dissatisfied with the hotel’s service. Any hotel employee can honor the guarantee. The guarantee allows Hampton Inn to track customer complaints and make the necessary improvements. Hotel employee retention has improved, and nearly nine out of ten guests who invoke the guarantee indicate that they will stay at Hampton Inn again (Berry et al., 1994, p. 40).

Customer perception of a fair service recovery seems to be a major determinant of complainants’ negative word-of-mouth and future patronage intentions (Blodgett et al., 1993, 1997) and it has a negative effect on intent to complain (Hocutt et al., 1997). It also has a positive effect on satisfaction (Saxby et al., 2000; Sparks and McColl-Kennedy, 1998) and in turn, on trust and commitment (Tax et al., 1998). Chebat and Slusarczyk (2005) investigate the role of emotions associated with a complaining experience and suggest that perceived justice influences retention through positive and negative emotions. Few studies examine the significant relationships between fairness, satisfaction and loyalty (Clemmer and Schneider, 1996; Holbrook and Kulik, 2001). If customers expect a fair treatment, both during service delivery and service recovery (Bowen et al., 1999), it is our belief that most of the research until now has focused on service recovery and complaint management. Thus, our general purpose in this paper is to focus on the role of perceived justice during service purchase/consumption experiences. More specifically, our objective is twofold. Firstly, using a qualitative approach, we want to evaluate the role of perceived justice in everyday service purchase/consumption experiences and to identify its potential components, in comparison with those developed in organizational and service recovery literature. Secondly, as long as the marketing literature emphasizes the role of perceived quality, value, satisfaction, trust and commitment as strong antecedents of loyalty (continuity/repurchase) and long-term cooperation (Garbarino and Johnson, 1999; Henning-Thurau et al., 2002), it is important to investigate the causal role played by perceived justice in these important evaluation processes. Thus, in an experiment where we manipulate distributive, procedural and interactional justice, we study the causal links between perceived justice and: . service evaluation (perceived quality and value); . satisfaction; and . relationship quality (trust and commitment). Study One: identifying the components of injustice in everyday purchase/consumption experiences Theoretical backgrounds Buyers and sellers are not able to foresee and prevent all the potential incidents arising from the exchange process. Each party expects “fair” behavior from its partner and then, places the evaluation process in the domain of perceived justice. Individual beliefs in “paid-for-promise” or “reciprocal obligations” define psychological contracts (Robinson and Rousseau, 1994). Theories of organizational justice have identified three main antecedents of the justice (injustice) perception: distributive justice which focuses on the outcome of the exchange, procedural justice which focuses on the way this

Perceived justice and consumption

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452

outcome is reached and interactional justice which focuses on interpersonal interactions involved during the service delivery process. Distributive justice and the concept of equity. Distributive justice is relative to the customer’s evaluation of the outcome. Three main rules have been discussed in the literature: “needs” (everyone receives according to their own requirements), “equality” (everyone receives the same allocation and treatment) and “equity” (refers to a ratio of rewards vs contributions such as time, money, energy, etc.) (Deutsch, 1975). Building on social exchange theory, Adams (1963, 1965) suggested that individuals establish a judgment in two phases to estimate the equity of an exchange. They first evaluate their own equity ratio of subjective rewards and contributions (Ri for individual i ) and compare it to those obtained by other individuals placed in a similar situation (Rj for individual j ¼ 1 to J). “Inequity exists . . . whenever one perceives that the ratio of his outcomes to inputs and the ratio of other’s outcomes to other’s inputs are unequal” (Adams, 1965, p. 280). Then, distributive justice conforms to the criterion of proportionality. If the ratio (Ri/Rj) is about one for all j, individual i will feel fairness. If it is greater/smaller than one for some j, individual i will, respectively, feel positive or negative inequity and, as a consequence, an emotion of culpability or anger. Deutsch (1985) demonstrates that the equity rule is the most preferred in the economic relationship. Perceived inequity creates a tension that individuals would like to reduce using different strategies, such as seeking tangible elements (refund, exchange), psychological elements (apologies from the service provider) or simply expressing their emotions (anger, joy) and behaving accordingly. Procedural justice. Apart from the fairness of the outcome, people also pay attention to the fairness of procedures. Taking its origins in the study of social justice (Rawls, 1971), procedural justice theory refers to the processes implemented to produce the outcome; essentially rules, policies and procedures chosen by the partner (service provider). Fair procedures must be consistent, unbiased, well-informed, impartial and ethical (Leventhal, 1976, 1980). In the area of conflict resolution procedures, Thibaut and Walker (1978) show that when people have control of decision rules, they more easily accept the outcome, even if it is to their disadvantage. Providers’ responsiveness and flexibility are essential and tied to consumer satisfaction (Bitner et al., 1990; Parasuraman et al., 1985). Interactional justice. Tax et al. (1998, p. 62) define interactional justice as “dealing with interpersonal behavior in the enactment of procedures and the delivery of outcomes”. This component of justice deals with the human aspects of an interaction and refers to characteristics such as honesty, courtesy, respect, politeness and candor. Service delivery studies have demonstrated its relevance and importance: personnel in contact with customers should be polite, responsive and provide useful information not only during the transaction but also after as well, such as during a complain or the giving of simple feed-back (Folger and Cropanzano, 1998). These three components of justice have been studied in organizational research, human resource management and in the service recovery marketing literature (Blodgett et al., 1997; Chebat and Slusarczyk, 2005; Tax et al., 1998). However, except in Clemmer’s (1993) study, they are not specifically studied in the context of the service purchase/consumption experience. Thus, with its qualitative approach, the main objective of this first study is to identify the nature of justice/injustice components experienced by individuals in their daily consumption and purchase experiences.

Methodology We used the critical incident method (Flanagan, 1954), which is particularly useful to explore a rarely studied subject (Bitner et al., 1990). This procedure asks respondents to describe a specific event and the circumstances surrounding it. The narration of the event is a natural exercise for most individuals, since it is comparable to a conversation with friends or acquaintances where participants use their own words (Gremler, 2004). The method has been used in various fields of marketing and, more specifically, in the area of consumer satisfaction with airlines, restaurants and hotels (Bitner et al., 1990) and self-service technology (Meuter et al., 2000). The method was also used to understand the impact of other customers on service encounter (Grove and Fisk, 1997), complaining behavior (Mack et al., 2000) or switching behavior (Keaveney, 1995). Data collection, sample and event codification. We collected the anecdotal evidence (critical incidents) using an open-ended questionnaire. Two scenarios were implemented separately. In Scenario 1, individuals were given instructions to describe a “memorable event” regarding their everyday purchase and consumption experiences: Think of a time, when you lived a particular purchase or consumption experience. Describe the circumstances leading up to this experience. What happened? When? Who was present? What details were memorable for you?

In this (non-conditional) scenario, there was no mention of possible satisfying/dissatisfying or fair/unfair issues. Participants were free to speak about a positive or a negative experience, and in the case of a negative experience they were free to speak about a lack of justice. The objective of Scenario 1 was essentially to appreciate the place of justice/injustice in individuals’ day-to-day purchase/consumption experiences. In Scenario 2, respondents were given the same instructions but, instead of a “memorable event” they had to describe “a negative purchase or consumption experience”. In this scenario, we conditioned the situation as “negative – dissatisfying” in order to identify the possible specific forms of injustice associated with a negative purchase/consumption scenario. In both scenarios, respondents were free to select the product/service of their choice. Subjects’ demographic characteristics were recorded at the end of the questionnaire. Critical incidents were collected from a convenience sample of French consumers. Of each scenario, 130 questionnaires were distributed. After deletion of short, naı¨ve and uncompleted events, 48 and 44 events of Scenario 1 and 2 were recovered, resulting in response rates of 37 and 34 percent, respectively. These two samples (described in Appendix 1) are essentially comprised of young (more than 50 percent are less than 30 years old), well-educated people (about 80 percent holding at least a high school degree) and women (about 75 percent). In both scenarios, all events referred spontaneously to the service industry and deal with service delivery and recovery. In Scenario 1 (48 events), 16 industries were involved: restaurants/hotels (13), hyper/supermarkets (12), hairdressers (6) and automotive repair (5). In Scenario 2 (44 events), 15 service industries are involved: super/hypermarkets (12), automotive repair (6), restaurants/hotels (5) and banks/insurances (5). The other service industries were cited by less than five respondents. The process used in CIM consists of repeated careful readings of events in order to categorize reported experiences into similar categories, and the conclusions heavily depend on the quality of the codification process (Perreault and Leigh, 1989).

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In this perspective, we implemented the following process. First, a Judge A (marketing researcher, but not specialist in the field of justice theory) iteratively established totally inductive categories and then, post-coded the incidents. Her codification grid was developed first on the basis of Scenario 1 and then completed using the negative events of Scenario 2. In a second step, a Judge B post-coded the events using the codification grid developed by Judge A, but extended it when it was necessary to provide a better description of the event, based on justice theory. In a third step, Judge A used the new categories developed by Judge B in order to refine her own codification, if necessary. In the fourth step, Judges A and B had to solve together the remaining discrepancies in the codification grid and the codification of events. In case of disagreement, they were free to rest on their respective positions and the corresponding elements were not categorized. The resulting level of agreement between the two judges is about 90 percent for Scenarios 1 and 2, on the basis of the k coefficient (Cohen, 1960) and 93 percent on basis of the reliability index (Perreault and Leigh, 1989). The following analyses are limited to the data from the codification grid upon which the two judges agreed. Results As events in Scenario 1 (48 non-conditioned events) were coded as “satisfying/dissatisfying” we observe that 60 percent of events are dissatisfying (29 of 48): if consumers feel more concerned with their negative experiences, in 40 percent of cases (19 of 48) they spontaneously refer to a satisfying experience (Table I). Most of the time, dissatisfaction is associated with a service failure (93 percent, 27 of 29 events), an error from the firm (24 percent, 7 of 29 events) or a problem with sales force skills (10 percent, 3 of 29 events). When an event contained at least one unit of analysis coded as “injustice,” it was categorized as an “unjust” event. Conversely, when no unit of analysis referred to injustice, the event was coded as “not unjust”: 50 percent of the events involve one or several forms of injustice (24 of 48), showing that perceived injustice is almost as frequent as dissatisfaction in everyday experiences with services. Moreover, 100 percent of events that refer to any form of injustice involve, at the same time, dissatisfaction: justice perception refers essentially to injustice judgments, associated with negative – unsatisfying events (Table I). At the same time, 83 percent (24 of 29) of unsatisfactory events refer to one or several form(s) of injustice. If we merge unsatisfactory events of Scenario 1 with events of Scenario 2 (73 events) that were controlled as unsatisfactory, we observe again that injustice is strongly associated with dissatisfaction (Table I): 88 percent (64 of 73) of unsatisfying events of Scenarios 1 and 2 involve the perception of an injustice. We can conclude that injustice perception and dissatisfaction are strongly associated, and that any perceived form of injustice is a sufficient condition for dissatisfaction. Presence of injustice Satisfactory

Table I. Satisfaction and perceived injustice

Unsatisfactory Total

No Yes No Yes

Scenario 1

Scenario 2

Total (Scenarios 1 and 2)

19 0 5 24 48

– – 4 40 44

19 0 9 64 92

In the next step, on the basis of the 64 events involving perceived injustice, we examined the arguments underlying injustice judgments. The codification of events allows us to identify four components of injustice (Table II), illustrated in Table III. The first three correspond to the three dimensions of justice presented in our literature review, i.e. distributive justice (56 percent of unsatisfying events), procedural justice (64 percent) and interactional justice (45 percent). All three are micro-level types of injustice, typically associated with a specific transactional service evaluation. However, we can identify a fourth component of injustice (25 percent of events), called “macro-level injustice” because the corresponding judgments do not concern the specific service provider, but rather the service industry considered as a whole, at an aggregated level. Of the events, 34 percent (22 of 64) involve only one component of injustice, 44 percent (28 of 64 events) involve two components, 19 percent (12 of 64) three components and 3 percent (2 of 64) involve all four components.

Distributive injustice Unfavorable quality/price ratio Feeling of “having been cheated” Feeling of wasted time Differential treatment Procedural injustice Misuse of power Dishonesty (laws, contracts or commitments) Absence of commercial favor Retention and misinformation problems Interactional injustice Bad faith Lack of respect Lack of understanding, listening or empathy Macro injustice Lack of choice or competition Unfair managerial practices at the industry level Total

Frequency

Percentage unfair event

44 5 12 17 10 57 20 14 8 15 35 11 9 15 17 3 14 153

36 8 19 27 16 41 31 22 13 23 29 17 14 23 16 5 22

Contrary to other clients, I had to wait more than an hour for my brushing . . . Moreover, they gave me a young and totally inexperienced hairdresser. It was a disaster and I felt frustrated . . . Procedural injustice . . . With this hotel formula, I only had an access to the basic restaurant, but I discovered it too late. It was an injustice because I wasn’t aware of that, they didn’t tell me that before . . . Interactional injustice As I was worried to miss that flight, and this guy was very rude to me . . . I simply regret not having returned their non-professionalism, their incompetence, their non-willingness to help and their bad faith Macro injustice For me, the quarterly commissions should not exist if the account is sufficiently supplied. The global banking system is unfair Retailers are responsible, I know about their bad practices, their tricks with packaging . . .

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Table II. Identified components of perceived injustice

Distributive injustice

Table III. Illustrative sentences of the components of injustice

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Distributive injustice typically involves inequities in the purchase/consumption experience: unfavorable quality/price ratio, wasted time, perception of differential treatment and the feeling of having been cheated. Procedural injustice is concerned with procedures that providers are perceived to adopt: commercial favors in case of shared responsibility, procedures for resolving problems, customer information policies (price), complaint management, after-sales services, etc. We find here stories involving misinformation, unclear contract clauses and abuse from an asymmetric position, to the benefit of the service provider. In the case of perceived interactional injustice, interpersonal and physical interactions are predominant. Customers commonly mention “lack of faith, respect, listening, understanding and empathy” exhibited by employees or managers. This observation underlines the conceptual proximity between this form of injustice and the functional component of perceived quality (Brady and Cronin, 2001; Gro¨nroos, 1984). In the case of perceived macro injustice, consumers attribute the causes of their negative experience to the economic system in the service industry taken as a whole (typically banks, supermarkets, garages, etc.), and not specifically to the focal service provider. Shady practices, abuse of power, asymmetry in the relationship (specifically concerning information about prices) and organized lack of competition at the industry level are common examples. Here, consumers attribute the cause of injustice to the top management blamed for allowing or even organizing unfair practices such as “overbooking,” “charging for fictitious services,” or “selling substandard products, even at low prices.” If the first three components of justice (distributive, procedural, interactional) are transaction specific, macro-justice is more a cumulated-relational judgment of justice which is based on past purchase and consumption experiences with the entire product/service industry. This form of injustice is relatively new in the marketing literature, but has been the subject of theoretical analyses in philosophy and social economics (Kellerhals et al., 1988). Justice, defined by Aristotle as “the virtue of one’s relationship to others” appears when people decide to live together and exchange goods. Macro-level justice theory tries to understand how to create a fair society from a normative perspective. For instance, social economics deals with the question of resource distribution among citizens (Duquenoy and Thimbleby, 1999; Gamel, 1992; Rawls, 1971). Study Two: effects of perceived justice on service evaluation and relationship quality Whereas Study One aimed at identifying the potential components of perceived justice by observing and classifying four dimensions. Study Two focuses on the causal links between the components of perceived justice and service evaluation (including perceived quality, value and satisfaction; Zeithaml et al., 1996), and relationship quality between consumers and the service provider (including trust and commitment; Dwyer et al., 1987; Garbarino and Johnson, 1999). As observed in Study One, justice and satisfaction processes are strongly associated. For this reason, in this second study we set up an experiment where the main components of perceived justice were manipulated. Theoretical development and hypotheses Figure 1 shows the hypothetical relationships between justice, service evaluation and relationship quality.

Justice

Perceived justice and consumption

Service evaluation H2a, b H1a, b, c Relationship Quality

Distributive justice Quality Procedural justice

Interactional justice

Macrojustice

Trust

457

Outcome Quality Interaction quality

Credibility Value

Affective Commitment

Satisfaction Benevolence

Environ. Quality H3a, b, c, d H4a, b, c, d and H5 a, b, c, d H6

Justice and quality. In the context of service experiences, perceived quality covers three components including “outcome quality,” (the core service, such as quality of a meal at a restaurant), “interaction quality” between customers and service personnel, (functional quality, Brady and Cronin, 2001; Gro¨nroos, 1984) and “environment quality” (the interaction of customers with the physical environment in which the service is provided, including atmosphere, ambience, space and other customers frequenting the place; Bitner, 1990; Rust and Oliver, 1994). Consumers evaluate the equity ratio and focus on whether their input is proportional to the outcome received (the perceived quality) by others. As a result perceived justice is expected to have an impact on the service quality-outcome evaluation. For example, in the context of automobile service/repair, Andaleeb and Basu (1994) have demonstrated that perceived fairness is an important determinant of service quality evaluation. Thus: H1a.

Distributive justice has a direct positive impact on outcome quality.

In the context of service experience, there are many employee-customer encounters which customers can evaluate to see whether they have been fairly treated. Moreover, these frequent interactions allow customers to appreciate the service performance and service quality. Because of factors such as technical complexity of the service or expertise, consumers do not evaluate services on the sole basis of the core service, and include formal principles that guarantee “fair” treatment to all customers during the service encounters. From the consumers’ point of view, these principles are to be implemented by service personnel. Bitner et al. (1990) and Parasuraman et al. (1985) have demonstrated the importance of criteria such as responsiveness, effectiveness, and flexibility, in the perception of service quality. We can then hypothesize: H1b.

Procedural justice has a direct positive impact on interaction quality.

H1c.

Interactional justice has a direct positive impact on interaction quality.

Figure 1. Causal relationships and hypotheses

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458

Justice and value. Perceived value refers to the benefits received in the transaction relative to the resources used during the purchase-consumption cycle, such as pre-purchase information search, price paid, time spent (Zeithaml, 1988). As discussed in Oliver and Swan (1989a), distributive justice implies a comparison between inputs and outputs. In other words, customers focus on whether they get what they deserved. Time spent and price paid are compared to the received outcome. The perception of distributive justice may contribute to perceived value. Moreover, some local issues also appear to be particularly relevant in the service evaluation, such as the time needed for delivery of the service and the convenience of the overall process (Gilly and Gelb, 1982; Taylor, 1994). We can hypothesize: H2a.b. Justice (a. Distributive, b. Procedural) has a direct positive impact on value. Justice and satisfaction. The positive relationship between perceptions of distributive justice and consumer satisfaction is well established, specifically in the handling of complaints. Goodwin and Ross (1992) and Tax et al. (1998) report a positive effect of distributive justice on satisfaction with complaint handling. Indeed, Oliver and Swan (1989a, b) and Oliver and De Sarbo (1988) found that equity affects consumer satisfaction. Procedural justice refers to the perceived justice of the means and rules by which the ends are accomplished. Clemmer (1993) identified flexibility, waiting time and efficiency as aspects of procedural justice. It is reasonable to assume that consumers who judge these aspects as fair are satisfied. This argument is supported by several studies (Saxby et al., 2000; Sparks and McColl-Kennedy, 1998; Tax et al., 1998). Interactional justice includes elements such as courtesy, politeness and empathy. There is evidence that contact with service personnel is fundamental for the determination of satisfaction (Bitner et al., 1994; Bitner et al., 1990; Parasuraman et al., 1988). Similarly, in the case of a service failure, interaction quality positively influences satisfaction with how complaints are handled (Blodgett et al., 1997; Tax et al., 1998). Finally, we hypothesize that perceived macro-justice has an influence on satisfaction. If customers have a favorable impression of managerial practices, a good experience reinforces their opinion. We consequently make the following (essentially replication) hypotheses: H3a.b.c.d. Justice (a. Distributive, b. Procedural, c. Interactional, d. Macro) has a direct positive impact on consumer satisfaction. Justice and relationship quality. The literature review discussed earlier leads us to hypothesize that the components of justice might have effects both on trust and commitment. Trust plays an important role in the development of a relationship. It exists “when one party has confidence in an exchange partner’s reliability and integrity” (Morgan and Hunt, 1994, p. 23) and is generally conceptualized as including credibility and benevolence. Credibility reflects the consumer’s perception relative to the provider’s abilities to deal with events, allowing customers to foresee the firm’s behavior. Benevolence, on the other hand, is defined as a belief in the provider’s honesty and integrity. In Study One, we also observed that macro-justice can be an important antecedent of the relationship quality. Accordingly, we can hypothesize: H4a.b.c.d. Justice (a. Distributive, b. Procedural, c. Interactional, d. Macro) has a direct positive impact on trust-credibility.

H5a.b.c.d. Justice (a. Distributive, b. Procedural, c. Interactional, d. Macro) has a direct positive impact on trust-benevolence. Commitment focuses on the enduring desire of parties to maintain a relationship (Morgan and Hunt, 1994). We argue that perceived justice has a positive influence on the relationship quality evaluation. Organizational research has explored causal links between perceived justice and relationship variables, such as affective commitment (Konovsky and Cropanzano, 1991; Summers and Hendrix, 1991) and intention to leave (Summers and Hendrix, 1991). When customers feel injustice, they are not inclined to maintain a relationship. This position is consistent with the findings of Blodgett et al. (1993, 1997), who observed a negative relationship between justice evaluation and negative word-of-mouth. We then posit: H6. Interactional justice has a direct positive effect on affective commitment. Service evaluation – relationship quality. The service marketing literature supports the idea that perceived quality, value, satisfaction, trust and commitment are organized along a causal chain starting in the service evaluation (perceived quality, value) influencing satisfaction, which in turn influences relationship quality (trust, commitment, Figure 1). Product quality is the point of departure, and commitment to the relationship is the ultimate stage where the provider has firm roots (Berry, 2000; Dwyer et al., 1987). Zeithaml (1988), Parasuraman et al. (1988), Oliver (1999) and Slater and Narver (2000) underlined the role of quality as a source of value and satisfaction. The relationship literature has established at the same time that satisfaction influences trust and trust influences commitment (Aurier et al., 2001; Garbarino and Johnson, 1999; Henning-Thurau et al., 2002; Morgan and Hunt, 1994). These relationships are introduced in the model as replication hypotheses. Methodology Experimental design and sample. Our empirical experiment concerns consumer evaluation of a dining experience in a restaurant. Contrary to distributive, procedural and interactional justice, macro-justice is not a transactional evaluation, but concerns more a judgment about managerial practices resulting from cumulated experiences in the service industry taken as a whole. For this reason, macro-justice was not manipulated, but controlled a posteriori, on the basis of its measurement on a scale. To investigate possible interactions between the distributive, procedural and interactional components of justice, we implemented a 2 £ 2 £ 2 full factorial design, involving eight scenarios, where we manipulated distributive justice (positive/negative), procedural justice (positive/negative) and interactional justice (positive/negative). Participants were provided a script describing a dining experience at a restaurant, based on vignettes, along with a written description of the scenario (Alexander and Jay, 1978). From a large French university, 188 undergraduate students participated in the experiment, and were randomly assigned to one of the eight experimental conditions. Every respondent was subjected to a unique scenario evaluation, for instance a dinner situation involving positive distributive justice (the consumer gets a correct meal, compared to others), positive procedural justice (the restaurant demonstrated good service in the serving process, flexibility, etc.) and a negative interactional justice

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460

(the personnel did not show empathy, respect or courtesy). Then, the vignettes centered on the service experience and not on the service recovery. In all scenarios, outcome and environment quality (the meal and restaurant atmosphere) were explicitly described as satisfactory, in order to limit possible halo effects between perceived quality and perceived justice. Measurement procedure. Eight scales were adapted from the literature (Appendix 2). The three components of service quality were measured on the basis of Gro¨nroos (1993), Rust and Oliver (1994) and Brady and Cronin (2001). Perceived global value was measured as a global ratio of received benefits to agreed sacrifices (three items; Zeithaml, 1988). The scale measuring satisfaction (three items) was based on Oliver’s study (1997). Trust scales (credibility and benevolence, three items each) were adapted from Ganesan (1994) and Garbarino and Johnson (1999). The affective commitment scale (four items) refers to the customer’s affiliation/identification with the service provider and was developed on the basis of Garbarino and Johnson (1999), and De Wulf et al. (2001). Scales were specifically developed to measure the four components of perceived justice. This was necessary to check the effectiveness of the three manipulated factors (distributive, procedural and interactional justice) and to control, a posteriori, the effect of perceived macro-justice. Items were developed on the basis of the literature and materials collected in Study One. All constructs were operationalized according to the transaction-specific perspective, except for macro-justice, which relates to the overall relationship with the restaurant sector, considered as a whole, from a cumulated temporal perspective. All items were measured using five-point Likert scales. Exploratory factor analyses were conducted to refine the nine constructs used in the final model: outcome quality, interaction quality, environment quality, value, satisfaction, credibility-trust, benevolence-trust, commitment and macro-justice[1]; taken in isolation, then two by two, three by three, etc. After deleting one item for outcome quality, trust-benevolence and macro-justice scales, the global exploratory factor analysis indicates that the constructs demonstrate a satisfactory degree of convergent and discriminant validity. We then used CFA (Lisrel 8) to test the unidimensionality of each construct separately, two by two, three by three, and finally for all constructs taken together (Anderson et al., 1987). The goodness-of-fit indexes are reasonably satisfactory: RMSEA ¼ 0.054 (prob 0.26), GFI ¼ 0.95, SRMR ¼ 0.055, CFI and IFI ¼ 0.94. The constructs exhibit a good degree of convergent and discriminant validity (Table IV). The reliability coefficients range between 0.77 and 0.92 (percentage of the variance shared by all items measuring a given factor, Table IV). Fornell and Larker’s mean variance indicators, which assess the portion of true variance extracted from the items measuring a construct with respect to measurement error, are between 0.55 and 0.82, which are considered as satisfactory. Manipulation checks. The eight scenarios were first examined in terms of “believability” on the basis of a two-item, five-point scale. All appear to be reasonably believable, with means varying between 3.7 and 4.3. They were also examined on the basis of their evoked mental imagery (Lacher and Mizerski, 1994), on a three-item, five-point scale. The eight scenarios were perceived as reasonably credible and vivid, with means varying between 3.5 and 4. One-way analyses of variance were run to test the

Number of items

Reliability

Shared variance

2 3 3 4 3 3 2 4 2

0.77 0.85 0.77 0.84 0.77 0.75 0.88 0.92 0.78

0.64 0.82 0.58 0.75 0.57 0.55 0.78 0.75 0.65

Outcome quality Interaction quality Environment quality Perceived value Satisfaction Trust-credibility Trust-benevolence Affective commitment Macro justice

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461 Table IV. Reliability and shared variance (CFA)

variations of each manipulated justice component in the negative vs positive experimental conditions. All variations are highly significant (Table V). Moreover, when one justice component is manipulated as “negative,” if we also observe variations of the other components, these variations are quite moderate or insignificant (at the 1 percent level). We can conclude that our manipulations were both effective and discriminant. Model and results In order to validate our hypotheses, we implemented a set of covariance analysis models (the SPSS GLM procedure) using the following hierarchical procedure. Seven factors were systematically introduced as explanatory variables in all the models: the three binary (1/0) manipulated factors (distributive, procedural, interactional justice) and their three second-order interactions were included as six fixed factors, along with perceived macro-justice introduced as a covariate because it was not manipulated but only controlled a posteriori[2]. For significant effects, partial R 2 indicates the size of the direct effect (proportion of variance explained by a factor, in addition to all the others in the model; Table VI). In a first step, we ran three models explaining successively one of the three components of perceived quality (outcome, interaction, and environment) as a function of these seven factors. We observe (Table VI), that distributive justice has an effect on outcome quality, in keeping with the H1a (partial R 2 ¼ 12.3 percent). There is also a significant effect of procedural justice on interaction quality (partial R 2 ¼ 23.1 percent), and a strong effect of interactional justice on interaction quality (partial R 2 ¼ 73.1 percent), in keeping with H1b and H1c. In addition to our hypotheses we observe significant interaction effect, between interactional and procedural justice, on

Distributive justice Distributive justice Procedural justice Interactional justice

a

21.1 (0.000) 20.4 (0.028) 20.1 (0.816)

Procedural justice – 2 0.9 (0.000) 2 0.6 (0.002)

Interactional justice – – 21.7 (0.000)

Notes: Discriminant manipulation check: variation of a justice component (row) when an- other is manipulated as “negative” (column) and (One-way ANOVA test prob); aVariation of the mean scores (difference of the arithmetic means of the corresponding items)

Table V.

462

Table VI. Results of analyses of covariance (partial R 2 – percentage) – 23.1 73.1 – – 10.6 2.4

77

12.3 – – – – – 3.2

16

ns

–

– – – – – –

34.3

– 2.2 3.8 3.2

5.3 3.7 – – – –

60.5

ns 2.3 5.5 ns 32.3

ns 2.1 ns – – –

Note: Values in italic correspond to significant parameters, in addition to our hypotheses

Fixed factors Distributive Just. Procedural Just. Interactional Just. Distri £ Proced Distri £ Intera Proced £ Intera Covariates Macro justice Interact. quality Environ. quality Outcome quality Perceived value Satisfaction Trust-credibility Trust – Benevol. R 2 (percentage) 61.2

ns 2.8 – 6.6 – 16.5

9.6 ns – – – –

74.9

7 10.4 – – – 2.2 9.9

4.7 ns 6.6 4.8 – –

– – – – – 4.6 14.3 5.2 65.6

– – ns – – –

Explained variable Outcome quality Interact. quality Environ. quality Value Satisfaction Trust Credibil. Trust Benevol. Affective Commit.

IJSIM 18,5

interaction quality (partial R 2 ¼ 10.6 percent) and a moderated effect of macro-justice on outcome quality (partial R 2 ¼ 3.2 percent) and on interaction quality (partial R 2 ¼ 2.4 percent). Moreover, there is no significant effect on environment quality, which appears to be independent of perceived justice. In a second step, in following the theoretical model of Figure 1, we ran a model explaining perceived value as a function of justice (the seven factors) and the three components of perceived quality, now included as additional covariates. In keeping with the H2a and H2b, we observe a significant impact of distributive justice and procedural justice on perceived value (partial R 2 ¼ 5.3, 3.7 percent, respectively). At the same time, we observe significant (even moderate) effects of outcome, interaction and environment quality on perceived value (partial R 2 ¼ 3.2, 2.2, 3.8 percent, respectively), which is consistent with the marketing literature. In a third step, we ran a model explaining satisfaction as a function of the seven justice factors, and included perceived quality and value as additional covariates. None of the components of justice have significant effects on satisfaction, except a small effect of procedural justice (R 2 ¼ 2.1), at the 5 percent level. The main factor explaining satisfaction is perceived value (partial R 2 ¼ 32.3 percent), along with the limited impacts of environment quality (partial R 2 ¼ 5.5 percent) and interaction quality (partial R 2 ¼ 2.3 percent). In the fourth step, the model explained trust-credibility and included, along with the seven justice factors, perceived quality, value and satisfaction as additional covariates. Distributive justice has a significant effect on trust-credibility (partial R 2 ¼ 9.6 percent), in keeping with H4a. However, there is no effect of procedural justice, interactional justice or macro-justice, contrary to the H4b, H4c and H4d. Satisfaction remains the main factor to explain trust (partial R 2 ¼ 16.5 percent), along with limited effects of outcome and interaction quality (partial R 2 of, respectively, 6.6 and 2.8 percent), which is consistent with the service marketing literature. In the fifth step, the model explained trust-benevolence, including quality, value, satisfaction and trust-credibility as additional covariates. Distributive justice, interactional justice and macro-justice have significant effects on trust-benevolence (partial R 2 of, respectively, 4.7, 6.6 and 7 percent), in keeping with hypotheses H5a, H5c and H5d. However, contrary to the H5b, there is no effect of procedural justice on benevolence. In addition to our hypotheses, we observe an effect of distributive £ procedural interaction (partial R 2 ¼ 4.8 percent), on trust-benevolence. We observe a significant impact of trust-credibility (partial R 2 ¼ 9.9 percent) and satisfaction (partial R 2 ¼ 2.2 percent) on trust-benevolence, following the service marketing literature. Additionally, we observe an effect of interaction quality (partial R 2 ¼ 10.4 percent). In the last step, the model explained commitment, including quality, value, satisfaction, trust-credibility and trust-benevolence as additional covariates. Contrary to H6 there is no direct effect of interactional justice on commitment. We observe effects of satisfaction (partial R 2 ¼ 4.6 percent), trust-credibility (partial R 2 ¼ 14.3 percent) and trust-benevolence (partial R 2 ¼ 5.2 percent), in keeping with the service marketing literature. Discussion and conclusion We examined how the four components of perceived justice (distributive, procedural, interactional and macro-justice) are related to customer evaluation of

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service experiences. The overriding conclusion from this study is that perceived justice, along with quality and value, is part of the service evaluation process, which in turn influences satisfaction and relationship quality (trust and commitment). Consumers who experience higher levels of perceived justice are more likely to engage in relationship behavior and, as a consequence, to become and remain loyal customers. Our qualitative results regarding the existence of several components of justice are encouraging because we confirm the role of distributive, procedural, and interactional justice in the case of purchase/consumption service experiences. We also identify a new component called “macro-justice,” relative to the perception of practices in the service industry, considered by consumers at a cumulated-aggregate level. In our experimental study, as expected, the components of perceived justice demonstrate impacts on the entire relational structure. Distributive justice has positive effects on outcome quality, value and trust (credibility and benevolence). Procedural justice impacts interaction quality and value. Interactional justice has a strong impact on interaction quality and trust-benevolence, which underlines how prompt, courteous service is important to customers. The macro-justice component has effects on outcome and interaction quality, but also on trust-benevolence. Trust-benevolence is a very important component of the relationship quality evaluation due to its demonstrated impact on loyalty behavior as shown in the literature (Coulter and Coulter, 2003). Another interesting finding concerns the interaction effect between procedural and interactional justice, which has a positive effect on interaction quality, a result consistent with service encounter research highlighting the importance of interpersonal elements (Bitner et al., 1990) and research demonstrating the impact of service personnel on post-complaint behavior (Blodgett et al., 1993; Blodgett et al., 1997). This finding shows the importance (at least in the restaurant industry) of maintaining at the same time a well-organized service delivery system (on which procedural justice perception depends heavily) and the training of retail employees aimed at developing their capacity to satisfy specific and unexpected customer demands. Moreover, we verify in this research the causal links between product evaluation (quality, value), satisfaction and relationship quality (trust and commitment), as observed in the service literature. The three components of service evaluation (outcome, interaction and environment) have direct impacts on perceived value, satisfaction and trust. Perceived value has a strong effect on satisfaction, which in turn impacts trust, which impacts commitment. However, contrary to past research on justice (Blodgett et al., 1997; Szymanski and Henard, 2001; Tax et al., 1998), the justice components have almost no direct impact on customer satisfaction, but only an indirect effect mediated through the service evaluation (quality and value). This points to the need to develop a more complete understanding of the drivers of satisfaction, and the need to consider the role of perceived quality and value, along with that of justice. Limitations and future research Despite the interesting findings and implications that emerge from this study, it is important to recognize its limitations and the need for additional research. One of the most basic limitations in Study Two is the reliance on simulated scenarios and self-reported data from a student sample. Although participants

reported that the scenarios were reasonably realistic and vivid, the external validity of our result is certainly limited. Moreover, our conclusions are limited to the restaurant service industry, and further research is needed for other types of service encounters. Concepts such as involvement, familiarity or expertise are candidates for the study of their potential moderating effects on the impacts of justice. For instance, as noted by Seiders and Berry (1998), when consumers are more expert/confident, they are less likely to rely on justice. Conversely, when customers are novices (typically the case for most consumers with the automotive repair industry), they cannot evaluate intrinsic quality of the core service and thus rely more on fairness issues of dimensions such as procedural, interactional or possibly macro-level justice (Andaleeb and Basu, 1994). Managerial implications Finally, our study provides a number of insights into the management of customer relationships. A better understanding of perceived justice points to four main areas to improve customer satisfaction and relationship management. Firms can take advantage by making their customers perceive more equity in terms of: . Distributive justice, viewed as an input/output ratio such as quality/price which is, at the same time, at the basis of quality and value-based strategies. A better understanding and management of the perceived Ri/Rj ratio and how individuals perceive its variation across consumers constitutes a new direction to improve service and relationship evaluations. Distributive justice represents a great potential to influence credibility and benevolence, and discount the perceived risk associated with purchase (Seiders and Berry, 1998). . Procedural justice, by adopting transparent policies and rules concerning price, information, complaint management, or commercial favors. . Interactional justice, by recruiting and training emphatic contact personnel, with a strong impact on the functional component of quality (Gro¨nroos, 1984). . Macro-justice, by giving consumers the perception that the firm is not playing the same game as a majority of competitors in the industry. In the case where the industry has a negative image in this dimension (such as in the banking or automotive repair sectors in France), this basis of differentiation could become profitable. Our results also emphasize that collective strategies to improve customer perceptions of macro-justice in a service industry can have substantial impacts on perceived quality and trust. Investment in perceived justice management can enhance the long-term orientation in supply chain relationships, as underlined in the justice literature (Griffith et al., 2006). It is important to note that the four components of justice involved in our experiment have significant effects on trust. For instance, distributive justice has a positive impact on both credibility and benevolence, and this is not surprising because this dimension of justice involves a “reciprocity” perspective in the exchange (Schneider and Bowen, 1999).

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Notes 1. As manipulated factors, distributive, procedural and interactional justice will be introduced in the final model as simple binary (1/0) variables. Their scaled measurement was necessary to check the effectiveness of manipulations. 2. Perceived macro-justice, quality, value, satisfaction, trust and commitment were operationalized on the basis of the mean score of their respective items.

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Appendix 1. Sample structure (Study 1)

Scenario 1 Distribution of the sample according to the gender of the respondents

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Table AI.

Scenario 2

Percentage

Male 13 27.1 Female 35 72.9 Total 48 100 Distribution of the sample according to the age of the respondents Less than 20 years 4 8.3 20-29 21 43.8 30-39 5 10.4 40-49 5 10.4 50-59 7 14.6 More than 60 years 6 12.5 Total 48 100 Distribution of the sample according to the profession of the respondents Craftsman or shopkeeper 2 4.2 Executive manager or professional people 11 22.9 Employees 9 18.8 Retired people 7 14.6 Non-worker 19 39.6 Total 48 100

Percentage 10 34 44

22.7 77.3 100

3 18 6 5 6 6 44

6.8 40.9 13.6 11.4 13.6 13.6 100

2 12 6 7 17 44

4.5 27.3 13.6 15.9 38.6 100

Appendix 2. Wording of items (Study 2) Quality The waiter of this restaurant is friendly. I would say the quality of my relationship with employees of this restaurant is good. The waiter of this restaurant is professional. This restaurant is aesthetically pleasing. This restaurant is well decorated and furnished. The atmosphere in this restaurant is really great. The customers frequenting this restaurant are pleasant. The food in this restaurant is of good quality. The presentation of the food is nice. Value I consider this evening’s dinner well worth the effort. This evening’s dinner is worth the sacrifices I made. I consider that having gone to this restaurant well worth the time and money. I consider that this evening was worth more than it cost me. Satisfaction I do appreciate this restaurant. I am satisfied with this dinner. I made a bad decision, when I decided to go to this restaurant. I am happy about the time spent in this restaurant. I am not delighted with this dinner. This evening’s dinner was pleasant.

Trust-confidence This restaurant always meets my expectations. I know I can count on this restaurant for a good dinner. I cannot be sure that the services in this restaurant are good. I am always confident with this restaurant. Trust-benevolence The personnel in this restaurant are always ready to provide me with information. The personnel in this restaurant do the maximum to make customers appreciate their dinner. I consider that going to this restaurant is somewhat like going to a friend’s. I feel this restaurant is totally oriented toward the satisfaction of its customers. Affective commitment I am proud to be a client of this restaurant. I feel attached to this restaurant. I feel comfortable going to this restaurant. I have a great deal of affection toward this restaurant. Spending the evening dining in this restaurant gives me a lot of joy and pleasure. Macro-justice From a general point of view, I would say that restaurants are an industry that respects regulations. On the whole, I find restaurants treat their employees correctly while respecting the regulations. The prices in the restaurants are not clear. I feel that, concerning the foods they prepare to their customers, restaurants respect hygiene and security norms. Believability The scene described is realistic. This kind of scenario is realistic. Mental imagery The scene described created a concrete picture in my mind. The events described reminded me of real things. The scene described conjured up images in my mind.

Corresponding author Be´atrice Siadou-Martin can be contacted at: [email protected]

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The validity of the SERVQUAL and SERVPERF scales A meta-analytic view of 17 years of research across five continents Franc¸ois A. Carrillat

Received 9 January 2006 Revised 4 February 2007 Accepted 17 May 2007

HEC Montre´al, Montre´al, Canada

Fernando Jaramillo Department of Marketing, University of Texas at Arlington, Arlington, Texas, USA, and

Jay P. Mulki Marketing Group, Northeastern University, Boston, Massachusetts, USA Abstract Purpose – The purpose is to investigate, the difference between SERVQUAL and SERVPERF’s predictive validity of service quality. Design/methodology/approach – Data from 17 studies containing 42 effect sizes of the relationships between SERVQUAL or SERVPERF with overall service quality (OSQ) are meta-analyzed. Findings – Overall, SERVQUAL and SERVPERF are equally valid predictors of OSQ. Adapting the SERVQUAL scale to the measurement context improves its predictive validity; conversely, the predictive validity of SERVPERF is not improved by context adjustments. In addition, measures of services quality gain predictive validity when used in: less individualistic cultures, non-English speaking countries, and industries with an intermediate level of customization (hotels, rental cars, or banks). Research limitations/implications – No study, that were using non-adapted scales were conducted outside of the USA making it impossible to disentangle the impact of scale adaptation vs contextual differences on the moderating effect of language and culture. More comparative studies on the usage of adapted vs non-adapted scales outside the USA are needed before settling this issue meta-analytically. Practical implications – SERVQUAL scales require to be adapted to the study context more so than SERVPERF. Owing to their equivalent predictive validity the choice between SERVQUAL or SERVPERF should be dictated by diagnostic purpose (SERVQUAL) vs a shorter instrument (SERVPERF). Originality/value – Because of the high statistical power of meta-analysis, these findings could be considered as a major step toward ending the debate whether SERVPERF is superior to SERVQUAL as an indicator of OSQ. Keywords Services, SERVQUAL, Culture, Quality Paper type Research paper International Journal of Service Industry Management Vol. 18 No. 5, 2007 pp. 472-490 q Emerald Group Publishing Limited 0956-4233 DOI 10.1108/09564230710826250

Over the years, marketing researchers have reached consensuses on several issues related to the domain of services. First, as the economy has become mostly service-based, researchers now consider the marketing discipline as being service dominated.

Consumers in OECD countries spend more on services than for tangible goods (Martin, 1999). Indeed, service activities constitute about 70 percent of OECD (2005) countries GDP, and this trend is expected to continue in the coming decade. The globalization of services marketing has presented both academics and practitioners challenges and opportunities in this area (Javalgi et al., 2006). Reflecting this changing emphasis services marketing has become a well established field of academic inquiry and now represents an alternative paradigm to the marketing of goods (Lovelock and Gummesson, 2004). Researchers also agree that a central topic in service research is service quality (SQ), which is a critical determinant of business performance as well as firms’ long-term viability (Bolton and Drew, 1991; Gale, 1994). This is because SQ leads to customer satisfaction which in turn has a positive impact on customer word-of-mouth, attitudinal loyalty, and purchase intentions (Gremler and Gwinner, 2000). The view that SQ results from customers’ evaluation of the service encounter prevails in the literature (Cronin and Taylor, 1992; Parasuraman et al., 1985). Under this perspective, researchers further agree that SQ is best represented as an aggregate of the discrete elements from the service encounter such as reliability, responsiveness, competence, access, courtesy, communication, credibility, security, understanding, and tangible elements of the service offer (Cronin and Taylor, 1992; Dabholkar et al., 2000; Parasuraman et al., 1985). On the other hand, the question of the operationalization of SQ has continued to evoke discussion. This discussion has been primarily centered on two important issues. The first relates to the debate of whether SERVQUAL or SERVPERF should be used for measuring SQ (Cui et al., 2003; Hudson et al., 2004; Jain and Gupta, 2004; Kettinger and Lee, 1997; Mukherje and Nath, 2005; Quester and Romaniuk, 1997). SERVQUAL, grounded in the Gap model, measures SQ as the calculated difference between customer expectations and performance perceptions of a service encounter (Parasuraman et al., 1988, 1991). Cronin and Taylor (1992) challenged this approach and developed the SERVPERF scale which directly captures customers’ performance perceptions in comparison to their expectations of the service encounter. In spite of recent attempts in the literature toward settling this issue, the SERVQUAL-SERVPERF debate has never been so relevant. In fact, numerous authors have supported the view that SERVPERF is a better alternative than SERVQUAL (Babakus and Boller, 1992; Brady et al., 2002; Brown et al., 1993; Zhou, 2004) while, on the other hand, SERVQUAL has enjoyed and continues to enjoy widespread acceptance as a measure of SQ (Chebat et al., 1995; Furrer et al., 2000; Zeithaml and Bitner, 2003). In addition, the web of science reveals that the original SERVQUAL paper published in 1988, as well as the following 1991 scale refinement paper has both received more than 46 percent of their total citations within the last five years. The same is true of SERVPERF, which also received more than 46 percent of its citations within the last five years. This indicates that Cronin and Taylor’s (1994) conceptual arguments in favor of SERVPERF, while it may have contributed to SERVPERF popularity, have not reduced SERVQUAL’s usage among scholars. In addition, it suggests that the multilevel scale, offered by Brady and Cronin (2001) as a reconciling perspective, has not moved researchers away from either SERVQUAL to SERVPERF. Therefore, shedding light on whether one scale is better than the other remains a very important question to be answered. The second issue centers on the trade-off between the generalizability – and specificity level of the SERVQUAL and SERVPERF scales (Asubonteng et al., 1996).

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A scale can be applied in more diversified contexts as its items become more abstract (Babakus and Boller, 1992; Dabholkar et al., 2000). However, this limits the scale’s ability to capture specific context elements (Babakus and Boller, 1992; Dabholkar et al., 2000). There is a general acceptance of the need to modify scale items to suit study context. However, empirical investigation regarding the impact of item adaptation on scale validity (i.e. when original SERVQUAL/SERVPERF items versus modified items are used) has not been undertaken. In addition, research is needed to assess the appropriateness of the SERVQUAL/SERVPERF scales when they are used outside the USA. This is because differences in national culture or language require not only modification of items but also create distortions in how respondents perceive the construct under investigation (Herk et al., 2005). The above discussion raises several important research questions. First, are SERVQUAL and SERVPERF adequate predictors of SQ? And, as proposed by Cronin and Taylor (1992), is SERVPERF a better predictor of SQ than SERVQUAL? Second, is there an improvement in the predictive validity of the SERVQUAL and SERVPERF measures when the scale items are adapted to the study context? Third, does the predictive power of SERVQUAL and SERVPERF depend on national culture or scale language? Finally, is the predictive validity of SERVQUAL and SERVPERF influenced by the type of industry in which the study is conducted? The current study addresses these research questions by meta-analyzing empirical SQ research. Meta-analysis is appropriate for addressing these research questions because it systematically integrates findings across studies, controls for statistical artifacts, and provides very robust answers about relationships among variables (Arthur et al., 2001; Hunter and Schmidt, 2004). Our meta-analytic framework relies on 42 effect sizes from 17 empirical studies conducted across five continents spanning 17 years. Previous research has already attempted to compare SERVQUAL and SERVPERF (Brady et al., 2002; Cronin and Taylor, 1992; Cui et al., 2003; Hudson et al., 2004; Jain and Gupta, 2004; Kettinger and Lee, 1997; Quester and Romaniuk, 1997). However, considering these studies individually provide dispersed evidence that might add, rather than subtract, ambiguity surrounding the measurement debate. For instance, Jain and Gupta (2004) as well as Kettinger and Lee (1997) found that SERVPERF was more strongly correlated to overall service quality (OSQ) than SERVQUAL whereas Quester and Romaniuk (1997) reported that SERVQUAL exhibited a stronger relationship with OSQ than SERVPERF. In some cases, studies comparing SERVQUAL and SERVPERF focus on dimensionality issues without considering predictive validity (Cui et al., 2003; Hudson et al., 2004). Furthermore, the aforementioned studies rely on one or two samples at most which prevent them from drawing robust conclusions and from testing the impact of contingency factors such as country, language, or industry. Therefore, the current research constitutes a significant contribution to the service literature because it provides answers to the SERVQUAL/SERVPERF validity debate tackled by Cronin and Taylor (1992, 1994), Brady et al. (2002), and Parasuraman et al. (1994). In addition, because meta-analysis is based on the accumulation of empirical evidence over the years, it allows investigating moderating factors by comparing sub-groups of studies that share a similar characteristic – e.g. the country where the sample was drawn (Lipsey and Wilson, 2001). This paper is organized as follows. First, a review of the literature is presented and hypotheses are developed. Second, a description of the meta-analytic

procedure is provided. Third, results, as well as implications and suggestions for further research, are discussed. Conceptual background Both SERVQUAL and SERVPERF’s operationalizations relied on the conceptual definition that SQ is an attitude toward the service offered by a firm resulting from a comparison of expectations with performance (Parasuraman et al., 1985, 1988; Cronin and Taylor, 1992). However, SERVQUAL directly measures both expectations – and performance perceptions whereas SERVPERF only measures performance perceptions. SERVPERF uses only performance data because it assumes that respondents provide their ratings by automatically comparing performance perceptions with performance expectations. Thus, SERVPERF assumes that directly measuring performance expectations is unnecessary. Research comparing the predictive validity of SERVQUAL with SERVPERF has been based on assessing which of the two measures is a better predictor of OSQ. OSQ has been used as the criterion because it is a global representation of the quality of the service offered by an organization (Cronin and Taylor, 1992, 1994; Jain and Gupta, 2004; Kettinger and Lee, 1997; Quester and Romaniuk, 1997). In their comparison of SERVQUAL with SERVPERF, Cronin and Taylor (1992) built their argument for the superiority of SERVPERF over SERVQUAL by empirically showing that SERVPERF is a better predictor of OSQ than SERVQUAL. Also, Parasuraman et al. (1988) assessed the construct validity of SERVQUAL by evaluating whether the scale was an adequate predictor of OSQ. In view of this, the predictive validity of SERVQUAL and SERVPERF is assessed by meta-analyzing extant empirical research on the strength of the relationship between each scale and OSQ. The predictive validity of SERVQUAL and SERVPERF SERVQUAL and SERVPERF are based on rigorous scale development procedures (Parasuraman et al., 1988, 1991) and have been widely used by researchers. Therefore, it is expected that both the SERVQUAL and SERVPERF measures of SQ will be strongly related to OSQ. The literature on scale development does not specifically point to a particular correlation value with a criterion against which the predictive validity of a scale can be assessed. However, it is possible to turn to less formal guidelines formulated by researchers. According to Cohen’s (1992) rule of thumb, a “small” effect size is observed when the correlation is 0.10, a “medium” effect size is obtained when the correlation is 0.30, and a “large” effect size corresponds to a correlation of 0.50. These guidelines have been previously used to qualify the strength of meta-analytic correlations (Jaramillo et al., 2005). Therefore, the following is hypothesized: H1. The correlation between SERVQUAL or SERVPERF and OSQ will be strong and above 0.50. The disconfirmation vs performance-only debate In Parasuraman et al.’s (1985) “disconfirmation” perspective, the SQ construct is seen as an attitude resulting from customers’ comparison of their expectations about the service encounter with their perceptions of the service encounter. The SERVQUAL instrument operationalizes this construct as the difference between expected and actual (perceived) performance (Parasuraman et al., 1988, 1991). Alternatively, SERVPERF is based on the

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“performance only” perspective and operationalizes SQ as customers’ evaluations of the service encounter. As a result, SERVPERF uses only the performance items of the SERVQUAL scale (Brady et al., 2002; Cronin and Taylor, 1992, 1994). In discussing the relative merits of each scale, the debate has been primarily centered on predictive validity and specifically on whether SERVQUAL or SERVPERF better captures SQ. First, some researchers have argued that SERVPERF is a better measure because it does not depend on ambiguous customers’ expectations. Arguments in favor of SERVPERF are based on the notion that performance perceptions are already the result of customers’ comparison of the expected and actual service (Babakus and Boller, 1992; Oliver and DeSarbo, 1988). Therefore, performance only measures should be preferred to avoid redundancy. Second, as Teas (1993) points out, Parasuraman et al.’s (1991) conceptualization of SQ is inconsistent with its operationalization. Teas (1993) argues that, since Parasuraman et al. (1991) define expectations as a type of attitudes, customer expectations must be considered as ideal points. Hence, the Gap model implication that superior perceptions of SQ occur when performance increasingly exceeds expectations is theoretically inconsistent. The classical attitudinal perspective suggests that positive attitudes are formed when evaluations of an object are close to an expected ideal point. Therefore, SQ should peak when perceptions equal expectations (Teas, 1993). Parasuraman et al. (1994) defended SERVQUAL by demonstrating that there was virtually no difference in predictive power between SERVQUAL and SERVPERF. Although, discussions have continued on whether disconfirmation-based measures are superior to performance-only based measures (Dabholkar et al., 2000; Hudson et al., 2004; Jain and Gupta, 2004), the above discussed arguments point toward the superiority of SERVPERF over SERVQUAL. Thus: H2. The relationship between SQ and OSQ is stronger when SQ is measured with SERVPERF than with SERVQUAL. Contextual factors Any scale represents a compromise between relevance and the extent to which it can be applied in a wide array of contexts (Babakus and Boller, 1992). Scale modification is done by adding, deleting or rewording items to ensure suitability for a particular research context. SERVQUAL and SERVPERF scale modifications have led to discussions about: . the universal versus context specific character of the scales; and . whether changes to fit a specific context result in better predictive validity. It is important to mention that in their original development, SERVQUAL and SERVPERF were purported to be universal measures of SQ because the scale development process relied on samples from multiple industries (Cronin and Taylor, 1992; Parasuraman et al., 1988). However, Parasuraman et al. (1988) recognize that SERVQUAL can be adapted to the specific research needs of a particular organization. As Rossiter (2002) indicates, the specificities of the measurement context play an important role in construct validity. Researchers are particularly concerned about the effect of environmental factors on the validity of SQ scales (Babin et al., 2004). In fact, researchers have failed to replicate

the five original dimensions of the SERVQUAL/SERVPERF scales, namely tangibility, reliability, responsiveness, assurance, and empathy (White and Schneider, 2000). Based on this, researchers have noted that SQ scales need to be adapted to the study context (Carman, 1990). For instance, tangibility might not be relevant for a cable company because the customer might never see the facilities of the service provider, whereas it may be critical for a healthcare facility customer. In their study on the photography industry, Dabholkar et al. (2000) dropped items related to physical facilities (tangibility) from the original SERVQUAL because customers did not have to visit the company’s site; however, they added items related to “salespeople pressure” that are absent from SERVQUAL. The above discussion suggests that context adapted versions of SERVQUAL and SERVPERF, hereinafter referred to as MQUAL and MPERF, will have a better predictive validity than non-modified versions (QUAL or PERF, respectively). Thus: H3a. The relationship between SQ and OSQ will be stronger when SQ is measured with MQUAL rather than with QUAL. H3b. The relationship between SQ and OSQ will be stronger when SQ is measured with MPERF rather than with PERF. Country culture Studies using SERVQUAL and SERVPERF have been conducted across more than 17 countries and on each and every continent. The use of these scales in an international context raises a legitimate concern about validity across borders because research has shown that cultural values influence customer responses on measures of SQ (Laroche et al., 2004; Zhou, 2004). According to Herk et al. (2005), research conducted internationally can be affected both by construct bias (i.e. the construct studied differs across countries) and item bias (i.e. items are distorted when used internationally). For instance, Sultan et al. (2000) found significant differences across US and European passengers on their expectations and performance perceptions of airlines SQ. In addition, Mattila (1999) found that Western customers are more likely than their Asian counterparts to rely on tangible cues from the physical environment, which evidences that the tangibility dimension of SERVQUAL is more important for them. Researchers have found that cultural differences can also create item bias. Steenkamp and Baumgartner (1998) show that both: (1) the metric invariance (i.e. the interpretation of the distance between the scale points); and (2) the scalar invariance (i.e. whether scale latent means have systematic biases) of items become uncertain when scales are used across cultures. In fact, Diamantopoulos et al. (2006) found that international differences in response styles (i.e. item wording, type of scale, etc.) generate item bias. Therefore, we propose that SERVQUAL and SERVPERF are likely to be affected by construct and item biases when used in international settings. In order to account for cultural differences, it was decided to rely on Hofstede’s (1997) individualism/collectivism (IDV) measure of national culture. IDV is useful and parsimonious for explaining cross-cultural differences in attitudes and behaviours. Also, IDV has satisfactory reliability and uni-dimensionality (Cano et al., 2004; Triandis, 1995).

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Research indicates that IDV may affect perceptions of OSQ and its dimensions. For instance, Furrer et al. (2000) argue that, in high individualistic cultures, consumers tend to be independent, have an ethic of self-responsibility and demand a higher level of SQ. Furrer et al. (2000) also note that individualistic consumers prefer to maintain a significant distance between themselves and the service provider. In addition, their study results show that consumers with a high degree of individualism considered “responsiveness” and “tangibles” dimensions as more important compared to consumers from collectivistic cultures. Individualistic customers tend focus on their own benefits and interests, and expect the service providers to do the best in catering to their needs (Donthu and Yoo, 1998). Thus, individualistic customers pay careful attention to the service provided and are not likely to accept lower SQ. Donthu and Yoo’s (1998) study showed that individualistic customers have higher OSQ expectations, higher empathy and assurance expectations from their service providers compared to customers from collectivistic societies. SERVQUAL and SERVPERF were developed in the USA, a country with the highest IDV level (Hofstede, 1997). In view of this, the existing dimensions of the SERVQUAL and SERVPERF scales should match more closely with the expectations of consumers from individualistic countries. As a result, it is expected that the predictive validity of SERVQUAL will be diminished in countries with a lower IDV level: H4a. The strength of the relationship between SERVQUAL or SERVPERF and OSQ decreases as the degree of individualism of the country decreases. Country language It is generally known that language translation can be a worsening factor of cultural bias. Even when scales are carefully translated and closely checked by experts (Witkowski and Wolfinbarger, 2002; Zhou, 2004), the absence of a concept in a language does not permit a perfect accuracy in scale translation (Herk et al., 2005). Thus, scale translation can result in higher measurement error which attenuates relationships among constructs (Hunter and Schmidt, 2004). Therefore, the following is hypothesized: H4b. The strength of the relationship between SERVQUAL or SERVPERF and OSQ is stronger when SERVQUAL or SERVPERF is administered in English than when translated. Type of services It is expected that SERVQUAL or SERVPERF will perform differently depending on the industry in which they are used. This is because the relevance of the scale dimensions depends on the study setting (White and Schneider, 2000). Many categorizations of services have been proposed in the literature (Bitner, 1992; Lovelock, 1983; Silvestro et al., 1992). Among the numerous service classifications, Silvestro et al.’s (1992) production perspective has emerged as integrative of other service typologies. Silvestro et al. (1992) divide service providers into the following three groups that range from lower to higher intensity of customer processing: (1) Professional services (PS) (i.e. low customer processing intensity) include services provided by lawyers, business consultants, or field engineering. Some characteristics of this group are: few transactions, highly customized, process-oriented and long customer contact times. Value is added by front office

service employees who rely extensively on their own judgment to perform the service. (2) Service shops (SS) (i.e. intermediate customer processing intensity) such as hotels, rental cars, or banks. This group has an intermediate level of customization and judgment from service employees. Value added is generated in both the back and front offices. (3) Mass services (MS) (i.e. high customer processing intensity) such as provided by retailer, transportation, or confectionery. The group has many customer transactions, few contact opportunities, and limited customization. Value added comes from the back office and service employees use little judgment. According to Silvestro et al. (1992), as the intensity of customer-processing decreases, the emphasis on process rather than product intensifies. The process elements of a service are by nature intangible while the product elements are more tangible (Zeithaml and Bitner, 2003). Therefore, less customer-processing-oriented service industries will have more intangible service offers. Because, SERVQUAL is purported to measure the service aspects of the quality of customer experience, it is expected to perform better when customer-processing intensity decreases while intangibility increases. Thus: H5. The strength of the relationship between SERVQUAL and OSQ decreases as the service category moves from PS to services shop and to MS. Methodology All studies containing an effect size (r) that measures the strength of the relationship between SQ (SERVQUAL, SERVPERF) and OSQ were eligible for inclusion. Valid statistics included Pearson’s correlation coefficients (r) or any other statistics that could be converted to r, such as F-value, t-value, p-value, and x 2. Empirical studies published in 1988 or after and available before May 30, 2005 were included in this meta-analysis. This timeframe is used since SERVQUAL was first published in 1988. Study search The following procedure was used to obtain an ample collection of studies reporting the desired effect sizes. First, an electronic search of the following databases was conducted: Direct Science, Emerald, ProQuest (ABI/INFORM Global and dissertation abstracts). Second, a manual examination of the articles identified from the computer-based searches was carried out. Third, manual searches of leading marketing and service journals were conducted. To contact marketing researchers, a call for working papers, forthcoming articles, conference papers, and unpublished research was posted on ELMAR-AMA (, 5,000 subscribers). The search process yielded a total of 17 studies containing 42 effect sizes resulting from studying 9,880 respondents (Table I). Meta-analytic model Meta-analyses can be conducted using either a fixed-effect (FE) or a random-effect (RE) model (Hunter and Schmidt, 2004). A FE model assumes that the same r value underlies the observed effect sizes in all the studies, whereas the RE model allows for

The validity of the SERVQUAL and SERVPERF 479

Scalea QUAL PERF PERF PERF MPERF PERF QUAL MQUAL MPERF MQUAL MQUAL PERF QUAL PERF MQUAL MQUAL MPERF MPERF MPERF MQUAL MPERF MPERF MQUAL QUAL MQUAL QUAL

Angur et al. (1999) Angur et al. (1999) Babakus and Boller (1992) Bojanic (1991) Brady et al. (2002) Cronin and Taylor (1992) Cronin and Taylor (1992) Dabholkar et al. (2000) Dabholkar et al. (2000) Freeman and Dart (1993) Jabnoun and Al-Tamimi (2003) Lam (1995) Lam (1995) Lam (1997) Lee et al. (2000) Lee et al. (2000) Lee et al. (2000) Lee et al. (2000) Lee et al. (2000) Lee et al. (2000) Mehta et al. (2000) Mehta et al. (2000) Mittal and Lassar (1996) Mittal and Lassar (1996) Mittal and Lassar (1996) Mittal and Lassar (1996)

Table I. Coding of effect sizes included in the meta-analysis

Authors USA USA USA USA USA USA USA USA USA Canada UAI Hong Kong Hong Kong Hong Kong USA USA USA USA USA USA Singapore Singapore USA USA USA USA

Country 91 91 91 91 91 91 91 91 91 80 38 25 25 25 91 91 91 91 91 91 20 20 91 91 91 91

IDV scoreb English English English English English English English English English English Non-English English English English English English English English English English Non-English Non-English English English English English

Language

Mass Mass Pro Mass Mass Mass Pro Shop Pro Mass Pro Shop Mass Shop Shop Pro Pro Pro Pro

* * *

Mass Mass Shop Pro

Servicesc

143 143 520 32 1548 660 660 397 397 217 462 214 214 82 196 128 197 128 196 197 161 161 123 123 110 110

nd

0.70 0.72 0.66 0.57 0.62 0.60 0.54 0.78 0.65 0.63 0.82 0.82 0.69 0.71 0.75 0.59 0.72 0.71 0.81 0.47 0.63 0.75 0.79 0.77 0.86 0.85 (continued)

re

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MQUAL PERF QUAL MQUAL MPERF QUAL MQUAL MQUAL MQUAL MQUAL MQUAL MQUAL MQUAL MQUAL MQUAL MQUAL

Pariseau and McDaniel (1997) Quester and Romaniuk (1997) Quester and Romaniuk (1997) Smith (1999) Smith (1999) Wal et al. (2002) Witkowski and Wolfinbarger (2002) Witkowski and Wolfinbarger (2002) Witkowski and Wolfinbarger (2002) Witkowski and Wolfinbarger (2002) Witkowski and Wolfinbarger (2002) Witkowski and Wolfinbarger (2002) Witkowski and Wolfinbarger (2002) Witkowski and Wolfinbarger (2002) Witkowski and Wolfinbarger (2002) Witkowski and Wolfinbarger (2002)

USA Australia Australia UK UK South Africa Germany USA USA Germany Germany USA USA Germany USA Germany

Country 91 90 90 89 89 65 67 91 91 67 67 91 91 67 91 67

IDV scoreb English English English English English English Non-English English English Non-English Non-English English English Non-English English Non-English

Language Mass Pro Pro Pro Pro Shop Mass Shop Shop Shop Mass Mass Pro Pro Shop Shop

Servicesc

re 0.71 0.55 0.51 0.38 0.36 0.08 0.63 0.62 0.62 0.56 0.54 0.59 0.59 0.58 0.57 0.57

nd 39 182 182 177 177 583 101 86 75 114 132 81 103 105 105 119

Notes: aQUAL ¼ original SERVQUAL, MQUAL ¼ modified SERVQUAL, PERF ¼ original SERVPERF, MPERF ¼ modified SERVPERF; bHofstede’s individualism score; ctype of service industry based on Silvestro et al. (1992); dsample size; eobserved effect size; *these studies relied on multiple industries spanning across service types and were not included in this moderator analysis

Scalea

Authors

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Table I.

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variation of the population parameter r across studies. Credibility intervals (i.e. the distribution of population parameter values) were computed in addition to confidence intervals (i.e. the range of the true population value) (Hunter and Schmidt, 2004). Hunter and Schmidt’s (2004) RE model was used as it accounts for both random and systematic variance and has been shown to yield very accurate credibility intervals in simulation studies (Hall and Brannick, 2002). Also, both the observed mean correlations (r) and the corrected mean correlations (rc) were estimated by following Arthur et al.’s (2001) procedure to account for measurement error. Test of moderators When estimating the significance of nominal moderator variables with two categories, we relied on the “standard method” as advised by Schenker and Gentleman (2001) and implemented in a recent marketing meta-analysis (Jaramillo et al., 2005). The “standard method” consists of building only one interval around the difference between the two-point estimates by adding and subtracting the appropriate z-value multiplied by the square root of the sum of the squared SE of each point estimate. If that interval does not include zero, the difference between the two point estimates is statistically significant. The standard method is preferred to comparisons of confidence intervals since testing of moderating hypotheses has greater statistical power (Jaramillo et al., 2005; Schenker and Gentleman, 2001). Note that since all the moderator hypotheses were directional, the z-value used for computing the interval around the difference between the point estimates corresponded to a 90 percent confidence level to generate an a level of 0.05 as in a one-tailed test (Jaramillo et al., 2005). When testing for continuous moderators, or nominal moderators with more than two categories, the weighted regression approach of Lipsey and Wilson (2001) was adopted. This procedure consists in regressing the disattenuated effect sizes on independent variables (continuous or dummy coded) with wi (the inverse variance component which gives more weight to effect sizes coming from homogeneous distributions) as the weight for each observation. The moderation effect of IDV is tested using weighted regression analysis. Weighted regression analysis is adequate to test the moderating effect of IDV since it is a continuous variable (Cano et al., 2004; Lipsey and Wilson, 2001). Results Table II presents the results of the meta-analysis. The overall strength of the relationship between SERVQUAL and OSQ is larger than 0.50 (r ¼ 0.58; rc ¼ 0.68; CI90percent ¼ 0:50 2 0:66). The average SERVPERF and OSQ correlation is also larger than 0.50 (r ¼ 0.64; rc ¼ 0.75; CI90percent ¼ 0:52 2 0:77). Since, the lower bound values of the 90 percent confidence intervals for both SERVQUAL and SERVPERF are above 0.50, the grand mean correlations can be interpreted as large (Cohen, 1992). This indicates that both SERVQUAL and SERVPERF are valid measures of SQ, thus bringing support for H1. The presence of moderators of the SERVQUAL-OSQ and SERVPERF-OSQ relationships is evidenced in statistically significant Q-statistics (Table II). The Q-statistic is distributed as a x 2 with k 2 1 degrees of freedom and is compared to the corresponding critical x 2 statistic. A significant Q-statistic demonstrates that the effect size distribution is heterogeneous and indicates that the population varies systematically according to some factors other than subject level sampling and measurement errors (Lipsey and Wilson, 2001).

42 27 7 20 15 7 8 34 8

9.880 5.082 2.015 3.067 4.798 1.751 2.965 8.525 1.355

nb 0.61 0.58 0.46 0.66 0.64 0.65 0.64 0.60 0.69

rc 0.71 0.68 0.54 0.77 0.75 0.73 0.75 0.70 0.79

rdc 288 241 56 57 38 11 29 260 19

Q-statistice 0.56-0.66 0.50-0.66 0.27-0.66 0.60-0.72 0.52-0.77 0.59-0.71 0.57-0.70 0.54-0.66 0.60-0.77

Confidence intervalf 0.44-0.98 0.33-1.03 0.11-0.96 0.57-0.97 0.34-1.15 0.62-0.84 0.64-0.87 0.42-0.97 0.63-0.96

Credibility intervalg 67.9 70.9 71.6 64.4 68.7 64.2 42.9 67.8 61.6

Percentage of variance explainedh

2.982 1.836 378 1.540 1.125 511 600 2.380 632

FS N i

Notes: aNumber of effect sizes; bsample size; cattenuated mean effect size; ddisattenuated (i.e. corrected) mean effect size; ecritical values range from 12.59 to 56.93; fat the 95 percent level; gat the 90 percent level; hvariance explained by sample and measurement artifact; ifail-safe N: number of studies with an effect size of zero (ri ¼ 0) needed to reduce the mean effect size (rc) to 0.01

Overall SERVQUAL QUAL MQUAL SERVPERF PERF MPERF English speaking Non English speaking

ka

The validity of the SERVQUAL and SERVPERF 483

Table II. Overall meta-analytic results and categorical moderators for the relationship between SQ and OSQ

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H2 posited that the relationship between SERVPERF and OSQ is stronger than the SERVQUAL-OSQ relationship. However, a comparison of the strength of these relationships reveals no significant difference. As shown in Table II, although the mean SERVPERF-OSQ correlation (rc ¼ 0.75) is larger than the SERVQUAL-OSQ correlation (rc ¼ 0.68), the difference is not statistically significant. In effect, the 90 percent confidence interval for the difference between the two point estimates (rc ¼ 0.75 and rc ¼ 0.68) includes zero (CI90percent ¼ 20:06 to 0:19), indicating that there is no significant difference between the predictive validity of SERVQUAL versus SERVPERF (Schenker and Gentleman, 2001). H3a and H3b stated that the modified SERVQUAL or SERVPERF scales would be more strongly related to OSQ than the original scales. The observed difference between the predictive validity of the original SERVQUAL and its modified version was statistically significant (QUAL rc ¼ 0.54 vs MQUAL rc ¼ 0.77; Drc ¼ 0.23, CI90percent ¼ 0:06 2 0:40). This suggests that the predictive validity of SERVQUAL increases when it is adapted to the study context. However, the observed difference between the predictive validity of the original version of SERVPERF and its modified version was not statistically significant (PERF rc ¼ 0.73 vs MPERF rc ¼ 0.75; Drc ¼ 0.02, CI90percent ¼ 20:04 2 0:09). This suggests that the predictive validity of SERVPERF does not change when the scale is modified. According to H4a and H4b, the predictive validity of SERVQUAL on OSQ decreases: . as the individualism of the country sample decreases; and . when the study is conducted in a non-English speaking country. A weighted regression with the disattenuated correlations between SQ and OSQ as the dependent variable, and IDV as the independent variable, revealed that a country’s individualism negatively impacts the predictive validity of SERVQUAL (B ¼ 2 0.001, p , 0.05), which is contrary to what was hypothesized in H4a (Table III). In addition, the mean effect size for English speaking countries was smaller than the mean effect size for non-English speaking countries (non-English speaking rc ¼ 0.79 vs English speaking rc ¼ 0.70; CI90percent ¼ 0:04 2 0:16); thus, not providing support for H4b (Table II). According to H5, when moving from lower to higher levels of customer processing intensity, the predictive validity of SERVQUAL on OSQ should decrease. H5 implied that the SERVQUAL-OSQ relationships should be strongest for PS followed by SS, and weakest for MS. As shown in Table III, the strongest SERVQUAL-OSQ relationships

Individualism-collectivism Industry typeb Table III. Results for continuous moderators and multimodal categorical moderators

Model

b

Adjusted SEa

z-value

y ¼ b 1 x1 þ 1 y ¼ b 1 x1 þ b 2 x2 þ 1

B1 ¼ 20.001 B1 ¼ 0.096 B2 ¼ 20.12

0.0004 0.035 0.037

2 2.68 * 2.78 * 2 3.28 *

Notes: * Significant at 1 ¼ 0.05; a when applied in a meta-analytic study, although the b coefficient estimates are accurate, their standard errors need to be adjusted; Lipsey and Wilson (2001) indicate that the standard errors of the b coefficients need be divided by the square root of the mean square residuals of the regression model in order to yield z-value used for significance testing; bprofessional services is the base level; B1 corresponds to service shops and B2 to mass services

are for SS (B1 ¼ 0.096, p , 0.05), followed by PS (base line), and then MS (B2 ¼ 2 0.12, p , 0.05). Hence, H5 is not supported. Discussion The study results have important implications because they question isolated findings from earlier studies. In spite of the discussions and several arguments provided by researchers about the superiority of SERVPERF over SERVQUAL (Cronin and Taylor, 1992, 1994), the results of this meta-analysis suggest that both scales are adequate and equally valid predictors of OSQ. Because of the high statistical power of meta-analysis (Cohn and Becker, 2003), these findings could be considered as a major step toward ending the debate whether SERVPERF is superior to SERVQUAL as an indicator of OSQ. As Parasuraman et al. (1994) pointed out, the use of performance-only (SERVPERF) vs the expectation/performance difference scale (SERVQUAL) should be governed by whether the scale is used for a diagnostic purpose or for establishing theoretically sound models. We believe that the SERVQUAL scale would have greater interest for practitioners because of its richer diagnostic value. By comparing customer expectations of service versus perceived service across dimensions, managers can identify service shortfalls and use this information to allocate resources to improve SQ (Parasuraman et al., 1994). Our findings also reveal that the need to adapt the measure to the context of the study is greater when SERVQUAL rather than SERVPERF is used. In effect, the original versions of SERVQUAL had a significantly lower OSQ predictive validity than the modified versions. However, both the original and modified versions of SERVPERF had the same level of OSQ predictive validity. This has important implications for both practitioners and academics. Practitioners using SERVQUAL for OSQ diagnostic purposes need to spend greater effort in modifying the scale for context than SERVPERF users. Our results also show an interesting pattern. Since, SERVQUAL and SERVPERF were originally developed in the USA, we expected that the predictive validity of these instruments would be higher when used in countries with national cultures and languages similar to the US. However, results show that the predictive validity of SERVQUAL and SERVPERF on OSQ was higher for non-English speaking countries and for countries with lower levels of individualism. A closer examination of the sample used in our study revealed that all studies conducted in non-English speaking countries as well as those conducted in less individualistic countries relied on modified versions of the SERVQUAL scale. Hence, scale modification rather than cultural context could be driving the results. Since, there were no studies conducted outside the US using non-modified scales, it was not possible to isolate the effect of national culture and language. Further, research is needed to address this important issue. An interesting avenue would be an experimental design where respondents outside the US, would be given a modified scale (i.e. adapted to the industry context) and others would be given the original items; this would allow teasing apart the effects of culture and scale adaptation on the scale’s validity. Finally, results suggest the predictive validity of SERVQUAL on OSQ is highest in medium customer processing intensity contexts with an intermediate degree of intangibility (SS) followed by low customer processing intensity (PS) and high customer processing intensity (MS).

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A plausible explanation for this finding is that SERVQUAL was developed as a scale generalizable across service contexts. Hence, predictive validity peaks in the category that represents a compromise between the emphasis on process and product (i.e. service shop). Another reason could be the varying degree of importance of the service used in the analysis to the customer. Additional research is needed for a better understanding of this result. With the growing proliferation of technology based self-service (SST) encounters, factors that contribute to satisfaction and dissatisfaction in the SST customer interaction have drawn considerable interest from researchers and practitioners (Meuter et al., 2000). Further, research could explore the degree of predictive validity of SERVQUAL on OSQ in SST customer interactions. Like any other meta-analysis, this study is subject to the file drawer problem which prevents the true effect size from being uncovered (Lipsey and Wilson, 2001). However, as shown in Table II, the fail-safe N statistic reveals that several hundred studies unaccounted for, with an effect size of zero, would be necessary to nullify the effect sizes computed. This strengthens the confidence in the results obtained. Finally, in this study, SERVQUAL and SERVPERF were only assessed through their predictive validity of OSQ. A future meta-analysis could employ additional validation techniques. For example, meta-analysis can be used to construct a broader nomological network that includes constructs related to SQ such as customer satisfaction, customer loyalty, purchase intention, and word-of-mouth (Zeithaml, 2000). Researchers could then assess whether using SERVQUAL or SERVPERF affects the effect of SQ on the above referred constructs. References Angur, M.G., Nataraajan, R. and Jahera, J.S. Jr (1999), “Service quality in the banking industry: an assessment in a developing economy”, The International Journal of Bank Marketing, Vol. 17 No. 3, pp. 116-25. Arthur, W.J., Bennet, W. and Huffcutt, A.I. (2001), Conducting Meta Analysis Using SAS, Lawrence Earlbaum Associates, Mahwah, NJ. Asubonteng, P., McCleary, K.J. and Swan, J.E. (1996), “SERVQUAL revisited: a critical review of service quality”, Journal of Services Marketing, Vol. 10 No. 6, pp. 62-70. Babakus, E. and Boller, G.W. (1992), “An empirical assessment of the SERVQUAL scale”, Journal of Business Research, Vol. 24 No. 3, pp. 253-68. Babin, B., Chebat, J-C. and Michon, R. (2004), “Perceived appropriateness and its effect on quality, affect and behavior”, Journal of Retailing & Consumer Services, Vol. 11 No. 5, pp. 287-98. Bitner, M.J. (1992), “Servicescapes: the impact of physical surroundings on customers and employees”, Journal of Marketing, Vol. 56 No. 2, pp. 57-71. Bojanic, D.C. (1991), “Quality measurement in professional services firms”, Journal of Professional Services Marketing, Vol. 7 No. 2, pp. 27-36. Bolton, R.N. and Drew, J.H. (1991), “A multistage model of customers’ assessments of service quality and value”, Journal of Consumer Research, Vol. 17 No. 4, pp. 375-84. Brady, M.K. and Cronin, J.J. Jr (2001), “Some new thoughts on conceptualizing perceived service quality: a hierarchical approach”, Journal of Marketing, Vol. 65 No. 3, pp. 34-49. Brady, M.K., Cronin, J.J. Jr and Brand, R.R. (2002), “Performance-only measurement of service quality: a replication and extension”, Journal of Business Research, Vol. 55 No. 1, pp. 17-31.

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Zeithaml, V.A. and Bitner, M.J. (2003), Services Marketing: Integrating Customer Focus across the Firm, 3rd ed., Irwin McGraw-Hill, Boston, MA. Zhou, L. (2004), “A dimension-specific analysis of performance-only measurement of service quality and satisfaction in china’s retail banking”, The Journal of Services Marketing, Vol. 18 Nos 6/7, pp. 534-46.

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Corresponding author Franc¸ois A. Carrillat can be contacted at: [email protected]

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Changing roles of customers: consequences for HRM

Changing roles of customers

Albert Graf Institute of Insurance Economics, University of St Gallen, St Gallen, Switzerland Abstract Purpose – The objective of this paper is to provide researchers and practitioners with an understanding of the implications and consequences of changes in customer roles and involvement on human resource management (HRM) within a service context. Design/methodology/approach – This paper is conceptual and the approach adopted is analytical. Extant research and concepts have been used to analyse customer roles and customer involvement and their effects on employees. Based on these insights, managerial and research implications are discussed. Findings – The insights from this study provide conceptual support for including customers as a relevant reference and/or extension of HRM beyond the organisational boundaries. Customers can actually significantly influence the success of a company’s HRM. Research limitations/implications – Analysis of the interrelatedness of customer involvement and HRM is limited to services than encompass emotional and communicative aspects. It is argued that an extension of HRM concepts by considering customers’ influence provides great potential for future research opportunities. Practical implications – The paper discusses the contribution of central HRM functions in increasing the customer orientation of employees and companies, reducing role conflicts and role ambiguity, and creating added value for customers. The aspects described here have the potential to contribute to a more sophisticated understanding of HRM and to increase the added value of the HRM function to the organisation. Originality/value – To date, HRM and customer roles generally have been investigated separately. The analysis of the interrelatedness of these two worlds is likely to trigger and encourage innovative research designs and alternative methodological approaches to new research problems, leading to the added potential of novel research findings with important implications for practice.

491 Received January 2006 Revised February 2007 Accepted May 2007

Keywords Customer orientation, Human resource management, Service levels Paper type Conceptual paper

Introduction New perspectives have emerged over the past decades focusing on intangible resources, the co-creation of value, and relationships. The dominant logic of marketing has been questioned and is under attack from various sides, e.g. by the proposal of Vargo and Lusch (2004). Vargo and Lusch (2004, p. 1) are calling for a new dominant logic in marketing “in which service provision rather than goods is fundamental to economic exchange”. In this context, they also argue the necessity to change how the role of customers in the service provision process is conceived. The customer should always be thought of as co-producer and is always involved in the production of value. This value has an immediate impact on relationships between customers, employees, and companies. In the past, these relationships were generally sales driven, but fundamental changes in customer behaviour have made it necessary for the relationships to become more customer-driven. Changes in framework conditions, customer attitudes, and behavioural patterns result in a new allocation of roles between

International Journal of Service Industry Management Vol. 18 No. 5, 2007 pp. 491-509 q Emerald Group Publishing Limited 0956-4233 DOI 10.1108/09564230710826269

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the company and its customers. In this dynamic context, the forms and the importance of customer involvement in the service provision process are also subject to change. All this has dramatic impact on the relationship between customers, employees, and the company, and can bring about a significant transformation for the organisation, well as its functional components, such as human resource management (HRM). However, to date, HRM and customer involvement generally have been investigated separately. The involvement of customers in the service provision process and, particularly, the interaction between customer and employee has a significant influence on the company: there is a dynamic ongoing process in the relationship between the organisation and its customers (Irons, 1994). Since, customers, employees, and companies constantly exert a mutual influence on each other, they should not be considered as isolated parts of the service management process. Rather, employees, companies, and customers all need to be included – in the form of an integrated service provision management. HRM, which has become a key element in developing and improving organisational effectiveness (Pfeffer, 1995; Ferris et al., 1999), can play a central role in this context. However, until now, HRM research has not yet included factors beyond the organisational boundaries (Lepak and Snell, 1999; Hornsby and Kuratko, 2003). Most HRM research is still concentrated on the more traditional personnel functions such as ability, job satisfaction, attitude, and performance, thus focusing on the individual as the unit of analysis (Hoobler and Johnson, 2004). To add value and improve organisational effectiveness, HRM activities need to be synchronised with the changes mentioned above, moving toward a stronger customer orientation, customer-driven relationships, and the resulting organisational changes. This raises a critical issue: what are the consequences of changes in customer roles and involvement for HRM in practice and research? The purpose of this paper is to address this question within a service context with a focus on customer contact employees in relationship intensive services. Therefore, an analysis is made of changing customer roles and functions and their effect on employees. Subsequently, challenges and implications for HRM in practice and research are discussed. Customers as part of the service process The literature pays particular attention to the characteristics of customer involvement that are essential to a service company’s success, in line with the premise that “the customer is always a co-producer” (Vargo and Lusch, 2004, p. 10). Customer involvement can be defined as “the amount of participation perceived by the consumer to be required to engage in a particular activity or service” (Good, 1990, p. 4). According to this definition, customer involvement is dependent on the type of service and the customer’s needs. It is determined by what role the customer desires to play within the service process (Bitner et al., 1997; Good, 1990). The spectrum of customer involvement ranges from passive customer participation, such as mere physical presence, to a very high degree of customer integration, such as where customers partially or even predominantly take on the service provision themselves. In this latter case, companies cannot effectively deliver the service results without customers’ co-operation. Research into customer involvement has become increasingly sophisticated and a number of important approaches have been developed and enhanced. A short history of the discipline reveals that Lovelock and Young (1979) were the first to argue that an increase in productivity in the service industry can be achieved through a stronger

involvement of customers in the production process. In this context, Mills et al. (1983) speak of customers as “partial” employees, an implicit expansion of the corporate organisational frontier to include customers. Mills and Morris (1986) describe this expansion in such a way that the customer becomes a temporary member of or a participant in the company. The next research developments pointed out that the customer’s involvement in the generation of services is the main source of input uncertainty (Argote, 1982; Bowen and Jones, 1986; Bowen and Schneider, 1988; Larsson and Bowen, 1989). All the above mentioned works attempt to provide an approach that companies can use to deal with this input uncertainty. Customers are seen throughout as a “negative” source that must be managed as efficiently and effectively as possible. Bowen (1986) proposed a different approach, describing how customers can be managed as internal organisational human resources. The use of this approach was intended to lead to an increase in productivity by having customers carrying out certain service operations themselves and thus management’s important strategic task is to define the optimal role for the customer, e.g. as a productive resource or as a contributor to quality, satisfaction, and value (Bowen, 1986; Bitner et al., 1997). The main focus of this research is on providing customers with a contributory role, but only within a field that is defined or limited by the company. For a comprehensive review of the literature on customer participation and associated customer roles, see, Bendapudi and Leone (2003), Chervonnaya (2003), Bitner et al. (1997). In the context of new communication technologies and changing customer attitudes and behavioural patterns, new forms of customer involvement have emerged; customers now want, even demand, new and more sophisticated roles. “Modern” consumers adopt new, active, and multidimensional roles. These changes have changed the focus of how customers are considered in research. Wikstro¨m (1996, p. 360) describes this change as follows: . . . the whole mental map with its view of customer’s role has also altered dramatically. The customer is no longer regarded as a passive receiver but is coming to be seen as an active and knowledgeable participant in a common process.

In the past, customers were mainly considered as a “negative” factor that, perhaps unfortunately, could not be ignored, and that management had to deal with in an effective and efficient way. This view is increasingly being replaced by the insight that customers are a valuable resource and that their involvement offers companies new opportunities. In research various customer roles are discussed in this context, including up to ten different process-specific customer roles as described in the paper of Chervonnaya (2003). Based on an analysis of this literature as well as innovation and community literature, three categories of customer roles can be differentiated with regard to the level and quality of customer participation: the customer as a source of competence, the customer as an innovator, and the customer as an advocate. Customer as source of competence With today’s information and communication technologies, customers now have access to practically the same information as companies do. Eradication of the previously predominant information asymmetry between companies and customers has resulted in a “shift of power” (Prahalad and Ramaswamy, 2000). Customers have become a new source of competence and know-how (Gibbert et al., 2001; Gurgul et al., 2002); they can

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actively co-construct their own consumption experiences through personalised interaction, thereby co-creating unique value for themselves (Prahalad and Ramaswamy, 2003). This co-created experience and the value created through it is defined by and for the customers themselves, as well as by their interactions with a network of companies and communities. This implies that the company, too, may have to perform in a manner that will enable the customer to deal with the service encounter as efficiently and effectively as possible. Owing their know-how and competence, the following types of customers are able to make certain definite contributions to a company (Gibbert et al., 2001): . image-enhancing customers can assist a firm in finding new customers; . organisation-enhancing customers can improve internal structures by demanding state-of-the art solutions; and . competence-enhancing customers improve the level of organisational competencies by challenging a company’s employees with new and demanding projects, enabling (or perhaps forcing) the employees to learn. Customer as innovator Customers can aid in the active development of new products and services. Thus, customers become innovators to the benefit of their providers. In this context, the so-called lead-user plays an important role by participating actively in the supplier’s innovation process. Lead-users are customers having specific needs earlier than the rest of the market who thus benefit strongly from new innovations solving their problems (Urban and von Hippel, 1988; Lilien et al., 2002). von Hippel (2005) distinguishes two general methods by which customers can become innovators for companies. First, companies actively seek innovations developed by (lead)-users that can form the basis for a profitable commercial product or service. Lead-users may invent and develop a novel device for their own application because such innovations cannot be supplied in a needed timeframe or are too specialised or trivial to interest a supplier (Foxall, 1988). Companies can identify such lead-users by the type of support they request and the quality of the questions they ask (Pitta et al., 1996). Lead-users generally have far more sophisticated information needs than do run-of-the-mill customers. Second, companies draw innovative users into joint design interactions by providing them with “toolkits for user innovation” (von Hippel, 2005, p. 74). The principle idea of this method is that companies make a toolkit available to their customers, enabling them to take part in the configuration of new developments or even to create their own designs (von Hippel, 2001a; Thomke and von Hippel, 2002; Fu¨ller et al., 2003). For example, the software game company Electronic Arts ships programming tools to customers and works their creations and modifications into new games. Toolkits for user/customer innovation are most effective and successful when they are made user-friendly by enabling users to use the skills they already have and work in their own customary and well-practised language (von Hippel and Katz, 2002). This method of customer innovation leads to higher customer value as the innovations are adapted to customer needs and ideas and it saves companies costly and time-consuming redesign cycles. Another approach for transforming customer know-how into innovation is proposed by Ulwick (2002): since customers are only familiar with what they have actually experienced, they should not be asked for solutions or features of products

or services but for outcomes. Customers should define what they expect the product or service to do for them. The outcomes described enable the company to uncover opportunities in the areas of product development and market segmentation, and to improve competition analysis (Ulwick, 2002). For a comprehensive literature review on the customer as innovator, see von Hippel (2005).

Changing roles of customers

Customer as advocate Whenever customers are regarded as a source of competence or as innovators, they become involved in new developments under the “supervision” of or in collaboration with a company. In some communities, especially those concerning with “open source” projects, in which leadership and goals are not company driven, customers are primarily the real innovators. Customers become advocates of the value-creation process. Members of such communities can become developers, manufacturers, and consumers of their own products and services. Along with self-organisation and self-management, the community idea is one of the main pillars of open source projects (von Hippel, 2001b; von Krogh, 2003). Well-known examples of open source software communities are the GNU/Linux computer operating system, Apache server software, and the Perl programming language. At SourceForge.net, the world’s largest open source software development web site, more than 100,000 projects are hosted, involving more than 1,000,000 registered users. Innovation communities can be found in other areas as well, for example sporting goods (Shah, 2000) and the medical field (Lettl et al., 2004). Companies need to figure out how they can include and utilise the open source movement in their own business models. Successful examples are Red Hat, which offers support for a variety of open source software, and IBM, which now equips its hardware with the Linux operating system. The increasing contribution of customers, primarily of organised communities fostered by the latest developments in communication technology, has changed the rules for gaining competitive advantage (Prahalad and Ramaswamy, 2000). Sometimes, customers even become manufacturers of their own developed innovations and achieve widespread diffusion of their innovative products (von Hippel, 2005). Lettl et al. (2004) show a pattern in the medical field in which innovating users take over many of the entrepreneurial functions needed to commercialise the new medical products they have developed, but do not themselves abandon their user roles. In this context, research (Hienerth, 2004; Shah and Tripsas, 2004) has started exploring the conditions under which users will become entrepreneurs rather than transfer their innovations to established firms.

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Key elements in the recent development of customer involvement Stronger focus on customer needs: from product to customer orientation. Current developments demonstrate the urgent necessity of a shift toward customer-oriented service generation. To remain competitive and establish the potential for differentiation, it becomes increasingly important to place customers’ needs and expectations in the foreground, which means a change of perspective away from an organisational (inside-out) view toward a customer (outside-in) one, that is, it becomes necessary to define and analyse customer involvement and customer roles from the customer’s perspective, not from the supplier’s point of view. Customer involvement needs to not only create positive results for companies but also create value for customers and satisfy customer requirements.

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Customers assume multidimensional roles. In addition to the traditional customer roles regarded as dominant by companies – those of demander, buyer, user, and cost factor – customers can and want to be engaged in other roles, such as a source of competence and know-how, co-developer, or co-innovator. The customer may take several and changing roles during the value-creation process. To be able to assign an adequate role combination to customers, or allow them to choose their desired combination of roles, it becomes crucial to know what roles certain customers want or are capable of at different points in the value-creation process? The interaction and communication between customers and companies becomes crucial. The communication process and design of the relationship between customers and companies has become a primary challenge. To better recognise customer requests and needs so that customers can exercise new and multiple roles, companies need to become partners in customer systems. Only effective and efficient communication and interaction between customers and employees, and also between customers and companies, can make successful customer involvement possible. Shift of power to customers and communities. More and more often, customers, as well as whole communities, are in the position of having company-relevant power potential. New information and communication technologies enable customers to take on tasks, as well as roles, better and faster than companies could do themselves. New media enables customers to collaborate, exchange, and organise among themselves. Moreover, customers can form cohesive communities in order to interact with companies and exert (positive or negative) influence. Effects of customer involvement on employees As services offered by suppliers vary widely, customer roles do not have the same relevance and importance for all types of services. If and how changing customer roles have an impact on service provider employees depends very much on the kind of service being offered. Figure 1 shows the variety and diversity of service elements, which can be differentiated based on the nature of the interaction (rational, emotional, communicative) and the nature of the service (problem solution, encounter, relationship) (Haller, 2000) (Figure 2). Customer roles

-New communication technologies

Figure 1. Development of customers as part of the service process

-Changing customer attitudes and behaviour -New forms of customer involvement

Major characteristics

Customer as partial employee

Customers are regarded as a main source of input uncertainty and as a negative factor. (Mills et al., 1983)

Customer as human resource

Customers play a contributory role in company defined areas in order to increase productivity. (Bowen 1986)

Customer as source of competence

Customers actively co-create unique value for themselves defined by and for themselves. (Prahalad and Ramaswamy, 2000 and 2003; Gibbert et al., 2001)

Customer as innovator

Customers’ know-how and competence is transformed into new products and services.(von Hippel, 2001a and 2005; Ulwick, 2002)

Customer as advocate

Organization and management of communities are customer driven; companies become part of a customer community. (von Hippel, 2001b; von Krogh, 2003)

• Identity • Integrity

Relationship

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• Interaction • Self-and grouprealisation • Communication form

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• Atmosphere Encounter

• Type of encounter • Social integration • Place of encounter • „Even more“ convenience

Problem solution

• Convenience (Simplicity, elegant solutions) • Purely factual problem solution Objective/rational Technical - economic

Emotional

Communicative Psychological - social

Source: Adopted from Haller (2000, p. 285)

Some services can be primarily thought of as problem solutions and may be purely technical or economic in nature. Examples here include utility and most telephone services or online-shopping services such as eBay. Other types of services such as encounter-based services involve emotional aspects, for example the interaction between customers and employees which takes place in stores, supermarkets, or hotels. If a service consists of several encounters and relational aspects, psychological and social dimensions become increasingly significant, e.g. in areas of education, healthcare and consulting as well as in long-term financial services relationships or those involving B-to-B business. In what follows, these kinds of services are referred to as relationship-based services. In relationship-based services, intensive and personal contacts between customers and employees play a central part and customers can play very powerful roles. The impact of changing customer roles on employees, and also on HRM, is highly relevant for relationship-based services. A change in the role of customers has an immediate impact on the customer’s relationship with company employees, especially the customer contact employees, the so-called boundary spanners. These employees are frequently much closer – physically, geographically, or psychologically – to customers than to their company or fellow employees. The customers’ assessment of the company’s service quality is mainly based on these employees’ performance and these employees are also an important conduit of information about customer demographics and experience to the company, particularly with regard to how often and how well the company succeeds in satisfying customer requirements (Parkington and Schneider, 1979; Chung and Schneider, 2002). Boundary spanners link their company with the outside world – the customers (Chebat and Kollias, 2000; Russ et al., 1998; Singh and Rhoads, 1991). Response to customer or

Figure 2. The service component mix: a great bandwidth of service elements

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community needs, a closer integration of the customer in the service provision process, or even customer-controlled co-operation in open-source projects all have a tremendous impact on the customer-employee relationship. This leads to a number of implications for employees, which are analysed in the following paragraphs: Increasing importance of emotional and communicative aspects. In the context of new and more sophisticated customer roles, encounter and relationship aspects are becoming ever more important. The form of communication and interaction is crucial in enabling customers to share their knowledge and ideas with a company, and also has an impact on customer motivation and “performance” in regard to customers as a source of competence and know-how, or acting as co-developers or co-innovators. Building and maintaining relationships based on trust can be an important prerequisite to getting customers to share information, know-how, and ideas. In communities, the integrity and identity of a company and its employees will be decisive factors in whether a relationship and collaboration can be established and maintained. Extension and change of employees’ tasks. The deeper customers are involved in the value-creation process, the more customer-employee contact there will be. This increase in contact requires organisations to “employ higher level of human capital in order to deal with the heightened information flows and variability resulting from this increased interaction” (Skaggs and Youndt, 2004, p. 94). After all, it is mainly at the emotional and communicative level that employees are able to succeed in generating value for customers or whole communities. Therefore, employees will need to have core competencies not only in technical aspects of the job, but also emotional and communicative skills. Standards for customer service may become quite ambiguous and involve individual judgement (Cardy et al., 2000). Employees will be called upon to perform a wide range of tasks and their performance of them will be the basis for the customers’ assessment of the company’s service quality (Haller, 2000). The range of tasks required of employees is not simply becoming broader; it can also change significantly over time, especially in dynamic service areas. If customers become innovators or even advocates, employees need to develop and maintain relationships with such customers over time. This may require acquiring new knowledge and skills so as to keep up with the changing needs of customers or of the company environment. Increasing role conflicts and role ambiguity. Customer involvement in service production as a co-producer creates a special relationship between customers and employees: The inclusion of customers, as an external constituent in the production of their own service, adds complexity to the service provider’s job (Chung and Schneider, 2002). In this scenario, customer contact employees are subject to a number of role conflicts and role ambiguities (Parkington and Schneider, 1979; Singh and Rhoads, 1991; Schneider, 1994; Chebat and Kollias, 2000). The more sophisticated the customer role, the more complex and challenging the employee’s job and the greater the possibility for role conflict and role ambiguity. In general, role conflict refers to the “degree of incongruity or incompatibility of expectations associated with the role” (House and Rizzo, 1972, p. 475). In the current context, this means discrepancies between what employees believe customers want them to do and what employees believe management wants them to do (Chung and Schneider, 2002). As customers take on multiple roles in the value-creation process – e.g. customer, co-worker, co-developer, or co-innovator – it becomes even more difficult for boundary spanners to cope with all the different expectations

customers have of them. Employees are faced with the question of which customer playing which role should get the most attention. For example, are lead-user customers more important than advocate customers? On very many occasions, the employee has to answer this type of question on his or her own. Add to this dilemma the additional difficulty of trying to satisfy management’s expectations, and the boundary spanner’s job becomes almost impossible. The fact that boundary spanners are typically relatively low in the organisation’s hierarchy adds yet another layer of complexity to the role conflict (Chung and Schneider, 2002). Role ambiguity arises when there is a lack of information about role expectations and what constitutes effective or acceptable performance (Singh, 1998). Role ambiguity is intensified when employees think or suspect that they do not possess the information necessary to function effectively (Singh and Rhoads, 1991; Schneider, 1994). For example, role ambiguity arises when employees do not know whether they have performed adequately (i.e. there is no feedback from a superior or any signal from the customer). Where customers are acting as a source of competence, as innovators, or as advocates, role ambiguity may be caused by management as well as by customers, especially if customer contact responsibilities are poorly defined, assignments are not clear, priorities change, or the appraisal varies over time. Role conflict and role ambiguity result in dissatisfied and frustrated employees who lose confidence in the organisation or even quit the job. A considerable body of research has confirmed that role conflict and role ambiguity have a significant, and mostly negative, influence on employee performance and organisational outcomes (Nygaard and Dahlstrom, 2002). Changes of the psychological contract. The employee challenges outlined above may require important changes in employees’ psychological contracts. The psychological contract comprises the expectations and obligations that employees regard as part of their terms of their employment (Pavlou and Gefen, 2005; Cavanaugh and Noe, 1999; Robinson and Rousseau, 1994). It may include expectations regarding job security, training, financial rewards, and career management issues (Cavanaugh and Noe, 1999). Increasing employer expectations regarding employee skills and behaviour on the one hand, and increasing role conflicts and ambiguity for the employees, on the other hand, increases the potential for serious psychological contract violations. The perceived fairness or unfairness of the employment relationship can be a motivating force for employees to engage in positive or negative behaviours (Pavlou and Gefen, 2005). In their theoretical work, Blancero et al. (1996) linked perceived violations of the psychological contract, feelings of unfairness, and behavioural outcomes. The authors point out that much behaviour in the workplace is discretionary, especially that associated with customer service. Blancero et al. (1996) conclude that violations of psychological contracts can result in either positive or negative discretionary or organisational citizenship behaviours. For example, an employee may choose to ignore a customer or may take the opportunity to bad-mouth the organization. HRM practitioners and researchers need to carefully consider the psychological contracts employees have with their organisations and how violations can be managed, even eliminated (Cardy et al., 2000). Consequences for HRM of changing customer roles In this paper, HRM is considered to be a part of a comprehensive (management) process that is oriented towards and integrated in the corporate strategy. In its broadest sense,

Changing roles of customers

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HRM refers to the policies, procedures and processes involved in the management of people in working organisations (Sisson, 1990). A relatively new HRM research thrust, which is gaining more and more interest, is customer-oriented HRM. Customer-oriented HRM is targeted at meeting the expectations of customers in specific market segments (Schneider and Bowen, 1993; Schneider, 1994; Bowen and Siehl, 1997; Peccei and Rosenthal, 2001; Rogg et al., 2001; Janowski and Huffstadt, 2003). It might also be called strategic HRM, where the strategic focus is the customer (Schneider, 1994). However, until now, HRM research has not yet included much beyond the organisational boundaries (Lepak and Snell, 1999; Hornsby and Kuratko, 2003). This assertion is supported by the extensive literature review by Cardy et al. (2000) on the impact of customer orientation on the HRM functions, job analysis, selection, and performance appraisal. The authors found that customer orientation, in the form of customer input into selection criteria or decisions, was not addressed in the studies examined. In performance appraisals, ratings were primarily based on the evaluations made by internal collaborators, such as supervisors, peers, subordinates, and/or self. However, none included external evaluations made by, for example, customers, as would be expected from in customer-oriented focus (Cardy et al., 2000). These topics are lacking research and a theoretical fundament. Nevertheless, new and more sophisticated customer roles, and the accompanying implications for employees, create various challenges for a strategic and (pro)active HRM. It will become necessary to extend the HRM perspective beyond organisational boundaries, involving further stakeholder especially customers, bearing in mind that customers with their requirements and interests are the most important stakeholder group. Owing to the importance of employees to relationship-based services, management of staff is key to a service company’s success (Russ et al., 1998; Chung and Schneider, 2002). If the (HR) management, especially of boundary spanners, is successful, HRM will be able to make a vital contribution toward a high level of service quality and customer satisfaction, as well as increase customer loyalty and profitability (Heskett, 1987; Schneider and Bowen, 1993; Homburg and Stock, 2001; Rogg et al., 2001; Cook et al., 2002). Based on the above discussion, implications and new challenges are derived for the central HRM functions: recruitment, appraisal, reward and development. The analysis for each HRM function looks in particular at: . How HRM can support and increase the customer orientation of employees and companies so as to achieve customer-oriented service generation. . How HRM can reduce or minimise role conflict and role ambiguity for employees. . How customers can be included within HRM – resulting in “HRM for customers” – in order to optimise customer involvement and create added value for customers. Recruitment HRM has the task of developing, implementing, and evaluating goals, strategies, and instruments that will ensure that the right people with the right motivation and appropriate skills are hired (Hilb, 2002). Therefore, selection criteria should include the increased emotional and interactional skills required from future employees. This may entail inquiring into a prospective employee’s experience with special customers/customer

groups or requesting customer references. It will be important that employees have the skills necessary to meet the expectations of customers or whole communities. In the long run, it is only when employee skills match customer expectations and requirements, that company expectations will be fulfilled. The big challenge for recruitment is, aside from the issue of cost savings, how to involve customer in the hiring process. It is feasible to lessen role ambiguity for boundary spanners by making provisions for consideration, feedback, and autonomy (Singh, 1993). One way of accomplishing this is to involve customer communities or single customers in the job description task so that both (or all) parties will understand the tasks, skills, and expectations required of the employee. Furthermore, when involving customers as innovators, it is essential to select the right customers, for example lead users that correspond to relevant target market. In this context, selection criteria for lead users need to be defined and applied, e.g. concerning personal characteristics, level of experience, technical know-how or knowledge of markets and customer segments. For example, game or software companies like Microsoft use detailed application processes in order to “recruit” new voluntary testers, so-called b testers, for their new programs or applications. Appraisal Employees’ roles and tasks change with the customer’s changing role. This requires an additional dynamic adaptation and/or extension of appraisal. However, so far at least, research in this area supports the use of concrete and tightly defined job-appraisal formats, such as behaviourally anchored rating scales, which may be insufficient for an appropriate appraisal of the skills needed in dynamic employment situations (Ilgen and Pulakos, 1999). Scales that are tied to the technical aspects of a job are inappropriate for relationship-based jobs where emotional and social aspects dominate. It is important that broad skills, including emotional and communicative competence, interpersonal fit, customer service, and customer community issues, be addressed in the appraisal. In this context, it becomes crucial to specify and to adapt the scales utilised, including those aspects necessary to employee performance. Additionally, for a dynamic appraisal system, it is not sufficient to merely expand the content of performance appraisal but also the sources of appraisal. As customers themselves can best evaluate customer-oriented performance criteria, customer performance appraisals should be an important employee evaluation criterion. “However, more than simply expanding the number of sources, it is important that the sources be in the best position to judge the various criteria” (Cardy et al., 2000, p. 182). For this reason it is important to define how, how often, what, and when in the service provision process customers should evaluate the performance of employees, services, and the company. Practice shows that companies, for example, Cisco Systems, that consistently and meticulously evaluate customer satisfaction and use this information in judging and rewarding their employees are highly customer oriented and very successful. The definition and communication of such performance criteria and its impact on individual and team appraisal may substantially increase the customer orientation of employees and the organisation and help avoid role conflict and role ambiguity. If customers are innovators in the business production process or add critical knowledge and skills, it is not only employee performance that needs to be evaluated, but that of the customers as well. Only with a fair evaluation of customer performance can appropriate customer “remuneration” be guaranteed. Employee or HRM customer

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appraisals can be used as the basis for appropriate customer incentive programs. They can also be used to illustrate the potential of customers as innovators or co-producers. Reward If HRM becomes a part of the behaviour alignment concept, it should be tied in with an appropriate remuneration and incentive system. If an individual employee’s contribution creates and adds value to the company, customers, and employees, then it becomes necessary to co-ordinate remuneration with such contribution. This remuneration should be harmonised with the company’s vision, other central HRM functions, the predefined time horizon for reaching goals (short- vs long-term), and periodic evaluation by all groups involved. To achieve this, the following questions need to be addressed: . To what extent does customer satisfaction or customer behaviour play a role in goal definition and the determination of employee compensation? . How can quality and extent of customer involvement or customer recommendations be incorporated into the remuneration system? . What is the basis for the time horizon? Since, customer contact employees also fulfil the important function of supplying and filtering customer information and experiences – especially in case of customers acting as source of competence or as innovators – this aspect of performance also needs to be rewarded. Perhaps, the amount of relevant information these employees feed back into the organisation could become a significant part of their remuneration. A new set of reward-related challenges arises out of the changing roles of customers and their functions. An important requirement for effective customer involvement is their motivation to contribute (Lengnick-Hall et al., 2000). In addition to performing useful and important tasks, customers must also be willing to make direct contributions to various organisational activities (Kelley et al., 1992). This requires a system that not only compensates customers for their efforts, but also motivates them to stay involved in the future. The reward system should secure and develop customer involvement. Customers are more likely to put forth the effort to be an effective co-producer or innovator if they believe their actions and ideas will make a positive difference (Lengnick-Hall et al., 2000). Therefore, it is essential that customers are made aware of the impact of their involvement. Customers are becoming increasingly aware of their own worth and the best and most innovative of them may find more than one company competing for their time and ideas. Customers are starting to ask, “What’s in it for me?” (von Krogh, 2006, p. 46). For very valuable customers, perhaps the company should pay to keep them involved. If the company cannot hire these valuable assets outright, it might be able to negotiate a flat fee or even a share of the royalties in exchange for the customer’s time (von Krogh, 2006). For instance, in 2001, the pharmaceutical giant Eli Lilly founded Innocentive.com, an independent web-based community matching top scientists to relevant R&D challenges, thus enabling major companies to reward scientific innovation with financial incentives. It has signed up more than 90,000 biologists, chemists, and other professionals from more than 175 countries. These “solvers” compete to overcome thorny technical challenges posted by “seeker” companies.

Each challenge has a detailed scientific description, a deadline, and an award, which can run as high as $100,000. Development To enhance employees’ present and future performance, HRM needs to adapt developmental processes. Because changing customer roles necessitate an extension and change of employees’ tasks, training programs need to be constantly updated so that employees will be equipped to cope with these challenges. In addition to its traditional training tasks, HRM now faces the challenges of managing social processes, dealing with conflict management in individual areas, and, at times, managing customer development. Although customer willingness is a prerequisite to their involvement, it may not be a sufficient condition: appropriate know-how and tools will be required in order for them to participate efficiently. For example, the online broker Charles Schwab targets training and empowering customers, with the goal of putting customers in a position to make financial decisions and carry out the appropriate transactions themselves. The company offers a training course, in which its customers can learn to effectively employ the tools it offers. Additionally, if customers as a source of competence or as innovators can become more effective by learning from each other, companies should help them to do so. Companies can provide physical or online sites where the customers can meet, and offer to set the agenda, moderate discussions, and establish communication platforms for follow-ups (von Krogh, 2006). Research implications This analysis has shown that the importance and impact of customer influence on service companies is increasing. Collaborating with customers gives companies deeper insights into customer behaviour and preferences, reduces the cost of developing new and improved products and services, and enhances satisfaction and loyalty as customers become emotionally invested in the value-creation process (von Krogh, 2006). The relationship between customers, employees, and companies has achieved a new level of intensity, confronting each of the parties with new challenges. The present analysis illustrates the widespread and important consequences of this intensified relationship for employees and HRM and shows that customers can significantly influence the success of a company. As a result, the practice of HRM has become increasingly complex and a multitude of new and enhanced HRM approaches have been developed. However, the inclusion of the customer as a relevant reference and/or extension of HRM beyond the organisational boundaries have been very limited so far. The few customer-oriented HRM approaches focus only on the organisational relationships and configurations without actually involving customers. In practice, customers are increasingly becoming a part of the value-creation process by taking on new roles and functions, a situation that has been described as a “shift of power” (Prahalad and Ramaswamy, 2000, p. 81) away from the company and toward the customer. The potential for direct and indirect customer influence on employee behaviour, particularly boundary spanners, has increased dramatically. Owing to this potentially powerful influence, customers can strengthen, complement, or thwart the workings of the organisation, not just HRM but also development, production, and, especially, marketing. Thus, HRM is now faced not only dealing with

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the changing nature of employee functions. HRM should also establish an effective way to deal with customers, negotiating with them as if they were almost employees – a daunting task that far exceeds the type of “customer-oriented HRM” discussed in the literature to date. In the future, the company as a whole, as well as individual functional units, will be forced to meet the challenge of orienting to and aligning with customer needs. HRM, marketing, and leadership will need to figure out how they can create value not just for the organisation and its employees, but also for customers. The numerous issues surrounding the inclusion of customers in HRM opens an entirely new field of research, which requires empirical examination. Below is a list of some of the central questions and issues that will need to be addressed. These items are not limited to HRM research, but are also relevant for other research areas, particularly marketing research, as they deal with customer involvement, and customer satisfaction and/or performance: . In a set of empirical investigations, a positive correlation was found between employee and customer satisfaction (Homburg and Stock, 2001; Schlesinger and Heskett, 1994; Heskett, 1987). The subject of the investigations was the influence of employee satisfaction on customer satisfaction. What effect does this perceived customer satisfaction have on the behaviour of employees? What impact does it have on HRM tasks such as, for example, employee motivation or individual goal setting? . Having customers highly involved in organisational processes and exerting strong influence on employees can cause tensions in various fields at individual and organisational levels. What kind of conflicts can evolve between internal and external sources? How can conflicts between customers, employees, and management be identified and mitigated? A better understanding of customer roles appears to be a viable way of reducing or even avoiding such conflicts. . To date, the psychological contracts of employees have been considered mainly with respect to the exchange with the employer. However, this theory can easily be applied to virtually any reciprocal relationship (Blancero and Ellram, 1997), for example, buyer-seller relationships (Pavlou and Gefen, 2005) or employee-customer relationships (Blancero et al., 1996). For relationship-based services, employees are likely to develop important psychological contracts with customers. This becomes highly relevant when employees are engaged in close relationships with customers who are co-workers, co-developers, or innovators. So far the impact of sophisticated and changing customer roles and a high level of customer involvement on the psychological contracts of employees and customers has not been investigated despite its high importance for the success of employee/company-customer relationships. . HRM research has so far focused on the relationships and configurations within organisations that lead to optimal human resource architecture (Lepak and Snell, 1999). Is there a link – empirical evidence – between HRM practices, customer involvement, and customer satisfaction that would force HRM research to extend its focus beyond organisational boundaries? . Several aspects have been outlined here on how HRM can support and increase customer-oriented service generation. However, what contribution can HRM make in creating and developing a customer-oriented company culture?

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Researchers stress and provided empirical evidence suggests that HRM plays an important role in influencing organisational climate and culture (Rogg et al., 2001; Ferris et al., 2004). Ulrich (1998, p. 133) goes so far as to argue “HR can be the architect of new cultures.” However, how can this be achieved where there is or should be a new customer orientation and in the context of changing customer roles? Recently, there has been some research into “why” HR practices lead to sustainable competitive advantage (Bowen and Ostroff, 2004); however, there is still little HR theory helping to explain “how” HRM practices lead to desired organisational outcomes, especially if the research focus is extended beyond organisational boundaries.

Answering these questions and addressing these issues will require researchers to extend the context of HRM beyond organisational boundaries. This list is intended to be a starting point for new HRM research; especially that research addressing strategic and customer-oriented HRM approaches. Looking at these issues will also enrich other research disciplines, especially marketing research, which is strongly associated with new customer roles and functions. It is important to start thinking (and researching) outside conventional organisational boundaries when looking for ways to achieve performance objectives (Lengnick-Hall et al., 2000). Bringing these two worlds together – the world of the customer and the world of HRM – presents a great opportunity for enhancing progress in both through an integration of perspectives. References Argote, L. (1982), “Input uncertainty and organizational coordination in hospital emergency units”, Administrative Science Quarterly, Vol. 27 No. 3, pp. 420-34. Bendapudi, N. and Leone, R.P. (2003), “Psychological implications of customer participation in co-production”, Journal of Marketing, Vol. 67, pp. 14-28. Bitner, M.J., Faranda, W.T., Hubbert, A.R. and Zeithaml, V.A. (1997), “Customer contributions and roles in service delivery”, International Journal of Service Industry Management, Vol. 8 No. 3, pp. 193-205. Blancero, D. and Ellram, L. (1997), “Strategic supplier partnering: a psychological contract perspective”, International Journal of Physical Distribution & Logistics Management, Vol. 27 Nos 9/10, pp. 616-30. Blancero, D., Johnson, S.A. and Lakshman, C. (1996), “Psychological contracts and fairness: the effect of violations on customer service behaviour”, Journal of Market-Focused Management, Vol. 1, pp. 49-63. Bowen, D.E. (1986), “Managing customers as human resources in service organizations”, Human Resource Management, Vol. 25, pp. 371-83. Bowen, D.E. and Jones, G.J. (1986), “Transaction cost analysis of service organization-customer exchange”, Academy of Management Review, Vol. 11 No. 2, pp. 428-41. Bowen, D.E. and Ostroff, C. (2004), “Understanding HRM-firm performance linkages: the role of the ‘strength’ of the HRM-system”, Academy of Management Review, Vol. 29 No. 2, pp. 203-21. Bowen, D.E. and Schneider, B. (1988), “Services marketing and management: implications for organizational behavior”, Research in Organizational Behavior, Vol. 10, pp. 43-80.

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Bowen, D.E. and Siehl, C. (1997), “The future of human resource management: March and Simon (1958) Revisited”, Human Resource Management, Vol. 36 No. 1, pp. 57-63. Cardy, R.L., Gove, S. and DeMatteo, J. (2000), “Dynamic and customer-oriented workplaces – implications for HRM practice and research”, Journal of Quality Management, Vol. 5, pp. 159-86. Cavanaugh, M.A. and Noe, R.A. (1999), “Antecedents and consequences of relational components of the new psychological contract”, Journal of Organizational Behavior, Vol. 20, pp. 323-40. Chebat, J-C. and Kollias, P. (2000), “The impact of empowerment on customer contact employees’ roles in service organizations”, Journal of Service Research, Vol. 3 No. 1, pp. 66-81. Chervonnaya, O. (2003), “Customer role and skill trajectories in services”, International Journal of Service Industry Management, Vol. 14 No. 3, pp. 347-63. Chung, B.G. and Schneider, B. (2002), “Serving multiple masters: role conflict experienced by service employees”, Journal of Services Marketing, Vol. 16 No. 1, pp. 70-87. Cook, L.S., Bowen, D.E., Chase, R.B., Dasu, S., Stewart, D.M. and Tansik, D.A. (2002), “Human issues in service design”, Journal of Operations Management, Vol. 20, pp. 159-74. Ferris, G.R., Hall, A.T., Royle, M.T. and Martocchio, J.J. (2004), “Theoretical development in the field of human resources management: issues and challenges for the future”, Organizational Analysis, Vol. 12 No. 3, pp. 231-54. Ferris, G.R., Hochwarter, W.A., Buckley, M.R., Harrell-Cook, G. and Frink, D.D. (1999), “Human resources management: some new directions”, Journal of Management, Vol. 25 No. 3, pp. 385-415. Foxall, G.R. (1988), “The industrial user as product innovator: markets, hierarchies and patterns of user-initiated innovation”, Advances in Consumer Research, Vol. 15, pp. 286-91. ¨ Fuller, J., Mu¨hlbacher, H. and Rieder, B. (2003), “An die arbeit lieber kunde!”, Harvard Business Manager, August, pp. 36-45. Gibbert, M., Leibold, M. and Voelpel, S. (2001), “Rejuvenating corporate intellectual capital by co-opting customer competence”, Journal of Intellectual Capital, Vol. 2 No. 2, pp. 109-26. Good, D.J. (1990), “Utilizing consumer involvement to market services”, Review of Business, Vol. 11 No. 4, pp. 3-6. Gurgul, G., Rumyantseva, M. and Enkel, E. (2002), “Customer integration – establish a constant bilateral knowledge flow”, Discussion Paper, No 47, University of St Gallen, St Gallen. Haller, M. (2000), “‘Dienstleistung’ im Produktkonzept fu¨r Financial Services – Konsequenzen fu¨r die Versicherung”, in Belz, C. and Bieger, T. (Eds), Dienstleistungskompetenz und innovative Gescha¨ftsmodelle, Institut fu¨r Versicherungswirtschaft, St Gallen, pp. 268-95. Heskett, J.L. (1987), “Lessons in the service sector”, Harvard Business Review, Vol. 65 No. 2, pp. 118-26. Hienerth, C. (2004), “The commercialization of user innovations: sixteen cases in an extreme sporting industry”, working paper, Vienna University of Economics and Business Administration, Vienna. Hilb, M. (2002), Transnationales Management der Human-Ressourcen, 2nd ed., Luchterhand, Neuwied. Homburg, C. and Stock, R. (2001), “Der Zusammenhang zwischen Mitarbeiter- und Kundenzufriedenheit: Eine dyadische Analyse”, Zeitschrift fu¨r Betriebswirtschaft, Vol. 71, pp. 789-806. Hoobler, J.M. and Johnson, N.B. (2004), “An analysis of current human resource management publications”, Personnel Review, Vol. 33 No. 6, pp. 665-76.

Hornsby, J.S. and Kuratko, D.F. (2003), “Human resource management in US small business: a replication and extension”, Journal of Developmental Entrepreneurship, Vol. 8 No. 1, pp. 73-92. House, R.J. and Rizzo, J.R. (1972), “Role conflict and ambiguity as critical variables in a model of organizational behavior”, Organizational Behavior & Human Performance, Vol. 7 No. 3, pp. 467-505. Ilgen, D.R. and Pulakos, E.D. (1999), The Changing Nature of Performance: Implications for Staffing, Motivation, and Development, Jossey-Bass, San Francisco, CA. Irons, K. (1994), Managing Service Companies: Strategies for Success, Addison-Wesly, Wokingham. Janowski, J. and Huffstadt, K. (2003), “Kundenorientierung als zentrale Fu¨hrungsverhaltensdimension – Anforderungen an das Human Resource Management”, ZfO – Zeitschrift fu¨r Fu¨hrung und Organization, Vol. 6, pp. 330-4. Kelley, S.W., Skinner, S.J. and Donnellly, J.H. Jr (1992), “Organization socialization of customers”, Journal of Business Research, Vol. 25, pp. 197-214. Larsson, R. and Bowen, D.E. (1989), “Organization and customer: managing design and coordination of services”, Academy of Management Review, Vol. 14 No. 2, pp. 213-33. Lengnick-Hall, C.A., Claycomb, V. and Inks, L.W. (2000), “From recipient to contributor: examining customer roles and experienced outcomes”, European Journal of Marketing, Vol. 34 Nos 3/4, pp. 359-83. Lepak, A. and Snell, S. (1999), “The human resource architecture: toward a theory of human capital allocation and development”, Academy of Management Review, Vol. 24 No. 1, pp. 31-48. Lettl, C., Herstatt, C. and Gemu¨nden, H. (2004), “The entrepreneurial role of innovative users: evidence from radical innovations in the field of medical technology”, working paper, Technical University, Berlin. Lilien, G., Morrison, P.D., Searls, K., Sonnak, M. and von Hippel, E. (2002), “Performance assessment of the lead user generation process for new product development”, Management Science, Vol. 48, pp. 1042-59. Lovelock, C.H. and Young, R.F. (1979), “Look to consumers to increase productivity”, Harvard Business Review, Vol. 57, pp. 168-78. Mills, P.K., Chase, R.B. and Margulies, N. (1983), “Motivating the client/employee system as a service production strategy”, Academy of Management Review, Vol. 8 No. 2, pp. 301-10. Mills, P.K. and Morris, J.H. (1986), “Client as ‘partial’ employees of service organizations: role development in client participation”, Academy of Management Review, Vol. 11 No. 4, pp. 726-35. Nygaard, A. and Dahlstrom, R. (2002), “Role stress and effectiveness in horizontal alliances”, Journal of Marketing, Vol. 66, pp. 61-82. Parkington, J.J. and Schneider, B. (1979), “Some correlates of experienced job stress: a boundary-role study”, Academy of Management Journal, Vol. 22, pp. 270-81. Pavlou, P.A. and Gefen, D. (2005), “Psychological contract violation in online marketplaces: antecedents, consequences, and moderating roles”, Information Systems Research, Vol. 16 No. 4, pp. 372-99. Peccei, R. and Rosenthal, P. (2001), “Delivering customer-oriented through empowerment: an empirical test of HRM assumptions”, Journal of Management Studies, Vol. 38 No. 6, pp. 831-57. Pfeffer, J. (1995), “Producing sustainable competitive advantage through the effective management of people”, Academy of Management Executive, Vol. 9 No. 1, pp. 55-72.

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Pitta, D., Franzak, F. and Katanis, L. (1996), “Redefining new product development teams”, Learning to actualize consumer contributions. Journal of Product & Brand Management, Vol. 5 No. 6, pp. 48-60. Prahalad, C.K. and Ramaswamy, V. (2000), “Co-opting customer competence”, Harvard Business Review, January/February, pp. 79-87. Prahalad, C.K. and Ramaswamy, V. (2003), “The new frontier of experience innovation”, MIT Sloan Management Review, Summer, pp. 12-18. Robinson, S.L. and Rousseau, D.M. (1994), “Violating the psychological contract: not the exception but the norm”, Journal of Organizational Behavior, Vol. 15, pp. 245-59. Rogg, K.L., Schmidt, D.B., Shull, C. and Schmitt, N. (2001), “Human resource practices, organizational climate and customer satisfaction”, Journal of Management, Vol. 27, pp. 431-49. Russ, G.S., Galang, M.C. and Ferris, G.R. (1998), “Power and influence of the human resources function through boundary spanning and information management”, Human Resource Management Review, Vol. 8 No. 2, pp. 125-48. Schlesinger, L.A. and Heskett, J.L. (1994), “Putting the service profit chain to work”, Harvard Business Review, Vol. 72 No. 2, pp. 164-74. Schneider, B. (1994), “HRM – a service perspective: towards a customer-focused HRM”, International Journal of Service Industry Management, Vol. 5 No. 1, pp. 64-76. Schneider, B. and Bowen, D.E. (1993), “The service organization: human resources management is crucial”, Organizational Dynamics, Vol. 21 No. 4, pp. 39-52. Shah, S. (2000), “Sources and patterns of innovation in a consumer products field: innovations in sporting equipment”, Working Paper No. 4105, MIT Sloan School of Management, Cambridge, MA. Shah, S. and Tripsas, M. (2004), “When do user-innovators start firms? Towards a theory of user entrepreneurship”, Working Paper No. 04-0106, University of Illinois, Chicago, IL. Singh, J. (1993), “Boundary role ambiguity: facets, determinants, and impacts”, Journal of Marketing, Vol. 57, pp. 11-31. Singh, J. (1998), “Striking a balance in boundary-spanning positions: an investigation of some unconventional influences of role stressors and job characteristics on job outcomes of salespeople”, Journal of Marketing, Vol. 62, pp. 69-86. Singh, J. and Rhoads, G.K. (1991), “Boundary role ambiguity in marketing-oriented positions: a multidimensional, multifaceted operationalization”, Journal of Marketing Research, Vol. 28 No. 3, pp. 328-38. Sisson, K. (1990), “Introducing the human resource management journal”, Human Resource Management Journal, Vol. 1 No. 1, pp. 1-11. Skaggs, B.C. and Youndt, M. (2004), “Strategic positioning, human capital, and performance in service organizations: a customer interaction approach”, Strategic Management Journal, Vol. 25, pp. 85-99. Thomke, S. and von Hippel, E. (2002), “Kunden zu Erfindern machen”, Harvard Business Manager, No. 5, pp. 51-60. Ulrich, D. (1998), “A new mandate for human resources”, Harvard Business Review, January-February, pp. 124-34. Ulwick, A.W. (2002), “Turn customer input into innovation”, Harvard Business Review, January, pp. 91-7. Urban, G.L. and von Hippel, E. (1988), “Lead user analyses for the development of new industrial products”, Management Science, Vol. 34, pp. 569-82.

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Changing roles of customers

509

The current issue and full text archive of this journal is available at www.emeraldinsight.com/0956-4233.htm

IJSIM 18,5

510

Customer switching resistance (CSR) The effects of perceived equity, trust and relationship commitment Gilles N’Goala

Received 29 January 2006 Revised 27 April 2007 Accepted 15 May 2007

GSCM – Montpellier Business School, Universite´ Montpellier, Montpellier, France Abstract Purpose – This research attempts to understand why – or why not – customers resist switching service providers when a critical incident occurs. The paper examines how service relationship perceptions, such as perceived equity, trust (perceived reliability and benevolence) and relationship commitment (affective and calculative), enhance relationship maintenance and CSR in many critical situations. Design/methodology/approach – A survey was conducted in the financial service industry on a sample of 1,999 consumers (retail banking) and then conceptualized and measured CSR in several critical situations. Findings – The paper demonstrates that perceived equity, perceived reliability, perceived benevolence, affective commitment, and calculative commitment do not influence CSR the same way. CSR mainly depends on the type of critical incident which occurs. For instance, calculative commitment, which is an evaluation of the costs associated with leaving the service provider, enhances CSR in three critical situations (service encounter failures, employee responses to service failures, pricing problems), whereas it leads to relationship disengagement in two other critical situations (inconvenience, changes in the consumer or service provider situation). Research limitations/implications – This research highlights the need to better take into account the different types of critical incident discussed in the relationship marketing literature and to better consider the complementary roles of perceived equity, trust and relationship commitment in the service switching literature. Originality/value – This research implies that service companies have to anticipate the critical incidents and to develop specific “shock absorbers” to continue doing business with their current customers. Keywords Relationship marketing, Trust, Customer loyalty Paper type Research paper

International Journal of Service Industry Management Vol. 18 No. 5, 2007 pp. 510-533 q Emerald Group Publishing Limited 0956-4233 DOI 10.1108/09564230710826278

Introduction Customer loyalty has become a top priority in service industries, since it has been proven to strongly affect profitability (Reichheld and Sasser, 1990; Rust and Zahorik, 1993; Rust et al., 2000; Verhoef, 2003). However, preventing current customers from switching to other service providers is a very difficult task. During their lifetime, customers have many opportunities to switch service providers (competitor offers, sales promotions, etc.), and many events within the established relationship are likely to cause service relationship deterioration and dissolution (Bitner et al., 1990; Gustafsson et al., 2005). For Keaveney (1995), service switching may be due to critical

incidents, such as attraction by competitors, inappropriate employee responses to service failures, pricing problems, core service failures, service encounter failures, lack of convenience, ethical problems or changes in the consumer’s or service provider’s situation (involuntary switching). Switching does not necessarily refer to immediate business relationship dissolution. According to Zeithaml et al. (1996, p. 38), switching means “doing less business with the current service provider in the next few years.” In many service situations, switching is a progressive process by which customers disengage from the established relationship and allocate more and more of their expenses to competitors (banking, insurance, telecommunications, utilities, etc.). Since, “long life customers are not necessarily profitable customers” (Reinartz and Kumar, 2000, p. 17), companies also aim to develop customer share, which is: . . . the ratio of a customer’s purchases of a particular category of products or services from supplier X to the customer’s total purchases of that category of products or services from all suppliers (Verhoef, 2003, p. 30).

How can customer retention and customer share be maintained? How can customers be prevented from switching service providers when they are attracted by competitors, dissatisfied with contact persons, or disappointed by service pricing? What should service companies do to maintain the business relationship with their customers despite the several critical incidents, which may occur during a service relationship? How will their customers react, when they are attracted by competitors, when they experience service failures (core service, service encounters, and ethical problems), when the service prices are unfair or too high, or when service recovery by employees is inappropriate? How can service companies make customers absorb these critical incidents and assure that these customers keep doing business with them? To deal with these problems, the relationship marketing literature often suggests that service providers improve the quality of the service relationship and/or develop switching costs (Dwyer et al., 1987; Morgan and Hunt, 1994; Bendapudi and Berry, 1997; Garbarino and Johnson, 1999). Indeed, business relationship maintenance depends on the way the customer perceives the service relationship. In particular, it should be strongly linked to key relational processes, such as: . perceived equity, or the fair distribution of inputs and outcomes between the customer and the service provider (Johnson et al., 2001a, b; Feinberg et al., 2002; Musa et al., 2005); . trust, or the service provider’s perceived reliability and benevolence (Ganesan, 1994; Ganesan and Hess, 1997); . affective commitment, or the customer’s relative intensity of identification and affiliation with the service provider and the involvement in the service relationship (Garbarino and Johnson, 1999); and . calculative commitment or the awareness of the costs associated with leaving the service provider (Geyskens et al., 1996; Verhoef et al., 2002). Thus, prior service switching and relationship marketing literature has provided us with very useful contributions concerning customer loyalty (why consumers decide to stay with their current service provider) and switching behaviors (why they decide to switch service providers). However, different questions remain unresolved: why do

Customer switching resistance 511

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512

customers switch when they have reasons to stay with the same service provider (trust, perceived equity, affective and calculative commitment)? Another is why do customers stay when they have reasons to switch to another service provider (core service failures, pricing problems, lack of convenience, etc.)? Indeed, a gap in the literature needs to be filled. Empirical studies, which examine the respective roles of perceived equity, trust, affective and calculative commitment in preventing customers from switching service providers in various critical situations (service encounter failures, lack of convenience, ethical problems, etc.) are still lacking. On the one hand, service switching literature focuses on critical incidents, i.e. on the “event, combination of events, or series of events between the customer and one or more service firms that causes the customer to switch service providers” (Keaveney, 1995). It also strongly emphasizes the effects of customer dissatisfaction on service switching (Ganesh et al., 2000; Gremler, 2004). However, empirical studies which examine the respective roles of relational constructs, such as perceived equity, trust, affective commitment and calculative commitment, in each and every critical situation need to be carried out. How to explain that, for the same critical incident, some customers leave while others stay? Does a service provider’s perceived reliability and benevolence lead customers to maintain the relationship despite these problems? Would a customer be more reluctant to leave a service provider if the exchange with the service provider has always been fair? Do customer identification and affiliation with the service provider enhance relationship maintenance in each and every critical situation? Should service companies implement switching costs and develop calculative commitment to prevent customers from switching even though they have reasons to switch? And do perceived equity, trust, affective commitment, and calculative commitment have the same role in each and every critical situation? On the other hand, relationship marketing literature demonstrates that perceived equity, trust, affective commitment, and calculative commitment positively influence the customers’ general intentions to maintain the business relationship, to repurchase the provider’s products and services and/or to recommend its products (Morgan and Hunt, 1994; Garbarino and Johnson, 1999). As a result, most studies do not take into account the various critical incidents which are likely to cause customers to switch service providers. Instead of measuring general intentions or favorable attitudes, we suggest measuring customer switching resistance (CSR), which reflects customers’ reluctance to switch service providers, even though they face a critical incident in the service relationship. Indeed, we suggest measuring customer loyalty when consumers have a reason to switch service providers and not when there is no reason or opportunity for them to do so. This approach is consistent with composite approaches of loyalty which were developed in packaged goods settings (Pessemier, 1959; Cunningham, 1967; Jacoby and Kyner, 1973; Jacoby and Chestnut, 1978). It is also consistent with Oliver’s definition which considers loyalty as: . . . a deeply held commitment to rebuy or repatronize a preferred product/service consistently in the future, thereby causing repetitive same-brand or same brand-set purchasing, despite situational influences and marketing efforts having the potential to cause switching behavior (Oliver, 1997, p. 392).

The more customers resist switching service providers, the more loyal they should be. To our knowledge, relatively few studies have developed this approach in service settings. Most of them have simply focused on consumers’ competitive resistance or

price tolerance (Parasuraman et al., 1991; Fornell et al., 1996; Reynolds and Arnold, 2000). As a result, most problems which occur in the service relationship (core service failures, ethical problems, etc.), tend to be underestimated, although they are supposed to be the main drivers of switching behaviors in service industries (Bitner et al., 1990; Keaveney, 1995; Gremler, 2004). We propose examining CSR in more varied situations. Using the critical incident technique, Keaveney (1995) identified and classified most of the critical incidents which occur in service settings: offers by competitors, inappropriate employee responses to service failures, pricing problems, core service failures, service encounter failures, lack of convenience, ethical problems or changes in the consumer or service provider situation (involuntary switching). Even though her study is not necessarily exhaustive, we will examine CSR in the eight main critical situations which were identified by Keaveney (1995) in service industries. Therefore, this paper aims at shedding light on how the service relationship perceptions (perceived equity, trust, affective commitment and calculative commitment) affect CSR in various critical situations. In the first part, we present a conceptual framework that connects perceived equity, trust and relationship commitment with CSR. In the second part, we test the model in the financial services industry on a sample of 1,999 bank customers. Conceptual framework Relationship maintenance and development should depend on relational constructs such as perceived equity, trust, affective commitment and calculative commitment. Below we develop a composite approach of customer loyalty and, as a consequence, reexamine the effects of service relationship perceptions on CSR. Customer switching resistance In consumer goods settings, consumer loyalty has often been measured as a consumer’s switching resistance when faced with competitors’ counter persuasion, promotions, price decreases and/or stock shortage problems (Pessemier, 1959; Cunningham, 1967; Day, 1969; Jacoby and Kyner, 1973; Jacoby and Chestnut, 1978; Oliver, 1997). We suggest applying a similar composite approach to the service industry. The composite measurements of customer loyalty should have better predictive validity than the attitudinal measurements and better trait validity than the behavioral measurements (Jacoby and Kyner, 1973; Jacoby and Chestnut, 1978; Dick and Basu, 1994). Our approach differs from attitudinal measurements. which have often assessed customers’ general intentions to maintain the service relationship, to repurchase the provider’s services, and/or to recommend its products (Zeithaml et al., 1996; Garbarino and Johnson, 1999; De Wulf et al., 2001; Hennig-Thurau et al., 2002). Our approach also differs from behavioral measurements, which have recently emerged in the literature to capture the customers’ repeat purchase behaviors: service usage, frequency of purchase, proportion of purchase, and/or service relationship duration (Verhoef, 2003; Gustafsson et al., 2005). In this research, according to Oliver (1997, p. 392), we examine how customers respond to “situational influences and marketing efforts” which have the potential to cause switching behavior. Our purpose is to assess a customer’s likelihood to stay or to leave a service provider if a critical incident occurs. In a consumer goods setting, Fournier (1998) has considered different kinds of stressors that can lead to the relationship dissolution: situational factors

Customer switching resistance 513

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(geographical situation), intrusion of alternatives, change in the consumer’s situation, managerial decisions that change the exchange relationship, failure to keep a promise or perception of neglect. She suggests that relationship maintenance is then based on psychological processes, such as accommodation, tolerance/forgiveness, biased partner perceptions, devaluation of alternatives and attribution biases. In service settings, we also have to consider a wide variety of critical incidents. Most studies in service research apply the critical incident technique, which was previously developed by Flanagan (1954) (for a literature review, see Gremler, 2004). Bitner et al. (1990) classified three major groups of employee behavior that account for all unsatisfactory incidents: employee response to service delivery system failures, employee response to customer needs and requests, and unprompted and unsolicited employee actions. In 1995, Keaveney completed this classification and considered eight main categories of critical incidents: attraction by competitors, employee responses to service failures, pricing, core service failures, service encounter failures, involuntary switching, inconvenience, and ethical problems. This approach is consistent with the research of Gustafsson et al. (2005) who have recently distinguished situational triggers (changes in consumers’ lives) from reactional triggers (deterioration in perceived performance of the service provider). Building on Keaveney’s results, we will consider eight main critical situations, which are likely to occur in service settings. Then, we will assess customer reluctance to switch service providers, even though they face a critical incident in the service relationship. The more customers resist switching in various critical situations, the more they should be truly loyal to the focal service provider. This research represents an extension of the service switching literature. We do not use the critical incident methodology at all, since we do attempt to identify the events, which cause service relationship dissolution. It has been done many times (Gremler, 2004). We will hereafter measure customer resistance to service switching. To achieve this objective, we will create “what if” hypothetical scenarios based on Keaveney’s (1995) service switching categories to assess to what extent customers resist switching service providers in various critical situations. The effects of relationship commitment on CSR In the relationship marketing literature, the concept of relationship commitment has been often viewed as one-dimensional and refers to customers’ general intentions to maintain the business relationship. Morgan and Hunt (1994, p. 23) define commitment to the relationship as: . . . an exchange partner believing that an ongoing relationship with another is so important as to warrant maximum efforts at maintaining it; that is, the committed party believes the relationship is worth working on to ensure that it endures indefinitely.

However, a multidimensional view of relationship commitment has also been developed to highlight the customer motivations (Meyer and Allen, 1991; Gundlach et al., 1995). Customers maintain a business relationship because they want (affective commitment), they need (calculative or continuance commitment) or they ought (normative commitment) to do so. Affective commitment is defined as the relative intensity of identification and affiliation with the service provider and the involvement in the service relationship

(Crosby et al., 1990; Garbarino and Johnson, 1999; De Wulf et al., 2001). As a result, affective commitment is not similar to a positive attitude towards the service provider. It refers to an identification process (congruence of values, affiliation, and belongingness) rather than to an evaluation process. Calculative commitment refers to an awareness of the costs associated with leaving the service provider (Geyskens et al., 1996; Verhoef et al., 2002). Calculative commitment – also called continuance commitment – results from an accumulation of “side bets” which would be lost if the relationship were discontinued (Meyer and Allen, 1991). The perceived costs can be either monetary or non-monetary (time, effort, risk taking, etc.). As Bendapudi and Berry (1997) note: . . . a customer that closes bank accounts due to poor service typically would open new accounts with another financial institution; the time, effort, and money required to identify an alternative supplier and establish new accounts illustrate relationship termination costs.

Calculative commitment represents a global calculus of the switching costs. Normative commitment has received much less attention in marketing, except for the examination of membership behaviors in professional associations (Gruen et al., 2000). In most service settings, consumers do not feel a moral obligation to continue the business relationship. In this study, following Gustafsson et al. (2005), we will focus only on affective and calculative commitment. As a result, the concept of relationship commitment (calculative/affective) has to be distinguished from the notion of brand commitment (CSR). Calculative and affective commitment, refer to customer motivations to maintain a relationship (awareness of termination costs versus identification and affiliation), whereas CSR refers to behavioral intentions. Therefore, it becomes critical to estimate the effects of each relationship commitment facet (calculative/affective) on consumers’ intentions to resist switching to another service provider. Both affective commitment and calculative commitment should affect customer loyalty and lead customers to accept efforts and sacrifices in the short-term (Pritchard et al., 1999; Johnson et al., 2001a, b; De Wulf et al., 2001; Verhoef et al., 2002; Verhoef, 2003; Fullerton, 2005; Gustafsson et al., 2005). However, the effects of calculative and affective commitment on CSR might not be the same, depending on the critical incident, which is likely to occur. Therefore, we will consider CSR in the eight critical situations which have been identified by Keaveney (1995). We can then hypothesize (Figure 1): H1. Calculative commitment has a direct and positive effect on CSR in critical situations, such as: H1a. Attraction by competitors. H1b. Inappropriate employee responses to service failures. H1c. Pricing problems. H1d. Core service failures. H1e. Service encounter failures. H1f. Involuntary switching.

Customer switching resistance 515

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Calculative commitment TRUST

516

H3

H2

Perceived Reliability H4

Figure 1. The effects of reliability, benevolence, perceived equity, and relationship commitment dimensions on CSR

H1

Affective commitment

H5 H6

Perceived Benevolence H7 Perceived Equity

Customer Switching Resistance

H8

H1g. Inconvenience. H1h. Ethical problems. H2. Affective commitment has a direct and positive effect on CSR in critical situations, such as: H2a. Attraction by competitors. H2b. Inappropriate employee responses to service failures. H2c. Pricing problems. H2d. Core service failures. H2e. Service encounter failures. H2f. Involuntary switching. H2g. Inconvenience. H2h. Ethical problems. The effects of trust on CSR Customers would probably be reluctant to commit to a service relationship unless they have confidence in the service provider’s ability to constantly meet their expectations in the future (reliability) and in its willingness to avoid any behavior that could be detrimental to them (benevolence) (Morgan and Hunt, 1994; Garbarino and Johnson, 1999). For Ganesan (1994) and Ganesan and Hess (1997), trust has two main dimensions: . Reliability concerns the service provider’s perceived ability to perform a service that conforms to the consumer’s expectations over time. It is related to the service provider’s competence, expertise, know-how and general reputation.

.

Benevolence designates perceived willingness to consistently meet consumer expectations and to avoid doing anything that might be detrimental to customers. It is inversely related to the partner’s opportunistic behavior.

Prior work suggests that trust reinforces affective commitment (Morgan and Hunt, 1994; Garbarino and Johnson, 1999; Hennig-Thurau et al., 2002). Bitner et al. (1998) show that trust affects consumer perception of congruence in values with the provider (identification/affiliation). We can hypothesize (Figure 1): H3. The service provider’s perceived reliability has a direct and positive effect on affective commitment. H4. The service provider’s perceived benevolence has a direct and positive effect on affective commitment. Trust should also have a direct effect on consumer resistance to switch to another service provider when a critical incident occurs (Garbarino and Johnson, 1999; Singh and Sirdesmukh, 2000; Sirdesmukh et al., 2002; Harris and Goode, 2004). Trust implies uncertainty and vulnerability, and as such, is critical when services are intangible, difficult to evaluate, complex and technical (as with financial and insurance products, for instance). Therefore, once a critical incident occurs, a consumer’s responses should depend on the level of confidence the customer has in the service provider. If a customer perceives deterioration in the service provider’s performance, that customer would believe that the service provider has the ability and the willingness to solve the problem in the customer’s interests (Ganesan, 1994; Morgan and Hunt, 1994). The customer would expect compensation or a recovery from the service provider over time and would then be reluctant to switch to another service provider when a critical incident occurs. In contrast, if one does not trust a service provider, (s)he would be more sensitive to the critical incidents and would strive to decrease vulnerability to the service provider. Moreover, consumers expect confidence benefits from a long lasting relationship: maintaining a business relationship permits consumers to decrease the perceived risk associated with each specific transaction (Bitner et al., 1998). When a critical incident occurs, trust enables the consumer to make confident predictions about the provider’s future behavior (Morgan and Hunt, 1994; Sirdesmukh et al., 2002): H5. Perceived reliability has a direct and positive effect on CSR in critical situations, such as: H5a. Attraction by competitors. H5b. Inappropriate employee responses to service failures. H5c. Pricing problems. H5d. Core service failures. H5e. Service encounter failures. H5f. Involuntary switching. H5g. Inconvenience. H5h. Ethical problems.

Customer switching resistance 517

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H6. Perceived benevolence has a direct and positive effect on CSR in critical situations, such as: H6a. Attraction by competitors. H6b. Inappropriate employee responses to service failures.

518

H6c. Pricing problems. H6d. Core service failures. H6e. Service encounter failures. H6f. Involuntary switching. H6g. Inconvenience. H6h. Ethical problems. The effects of perceived equity on CSR Perceived equity concerns the extent to which the consumer determines that there is fair distribution of inputs and outcomes between the service provider and himself (or herself) (Oliver and Swan, 1989). Therefore, perceived equity refers to fair treatment received by customers and many researchers view equity as being similar to distributive fairness (Johnson et al., 2001a, b; Feinberg et al., 2002; Musa et al., 2005). Perceived equity is said to have a positive effect on affective commitment (Dwyer et al., 1987; Anderson and Weitz, 1992; Tax et al., 1998; Johnson et al., 2001a, b): consumers should be more involved in the service relationship if the relational exchange is perceived to be fair (Gundlach and Murphy, 1993; Reynolds and Arnold, 2000; Ganesh et al., 2000). Thus, we can hypothesize: H7. Perceived equity has a direct and positive effect on affective commitment. Moreover, relational exchanges involve reciprocity and there are reciprocal commitments between the partners, which may be explicit or tacit. Dwyer et al. (1987) and Gundlach and Murphy (1993) have underlined the role of this norm of reciprocity in relationship maintenance and development. If a service provider has always been fair with its customers, as a tradeoff the customers will tend to be fair with it and thus be reluctant to leave for a competitor’s offer of better prices or products. On the other hand, if the service exchanges are not perceived as fair, if they have mainly benefited the company, the customers will switch to other service providers as soon as they have the opportunity to do so. As Johnson et al. (2001a, b, p. 124) note, “Relationships viewed by a buyer as inequitable are at greater risk of customer defection than those perceived as equitable”. Thus, we can hypothesize: H8. Perceived equity has a direct and positive effect on CSR in critical situations, such as: H8a. Attraction by competitors. H8b. Inappropriate employee responses to service failures.

H8c. Pricing problems. H8d. Core service failures. H8e. Service encounter failures.

Customer switching resistance

H8f. Involuntary switching. H8g. Inconvenience. H8h. Ethical problems. An empirical study in the financial service industry The financial service industry is an interesting field for studying the service provider-consumer relationship (Berry, 1995; Verhoef, 2003; Ryals, 2005). Today, leading European banks strive to strengthen CSR since they still stand to lose business when their competitors launch new, better performing products, when they increase their service prices or when they do not completely succeed in a zero defect strategy (Datamonitor, 2003). They need to prevent switching behavior and enhance short-term tolerance, given the fact that all critical incidents cannot always be stopped before they happen. Determining the drivers of CSR is thus a key issue in this industry. Methodology In the summer of 2003, 30,000 questionnaires were sent by mail in France to a convenience sample comprised of respondents in the age group of 18-75. The sample was randomly drawn from a mailing list of people. The questionnaires were mailed by the university (Edhec Business School). A total of 1,999 completed questionnaires were returned. They were asked to reply about their main bank in terms of turnover. More than 12 European banks were considered by the respondents. The respondents were often committed to long-lasting banking relationships (22 years on average). Respondents first indicated their level of trust and relationship commitment towards their main bank. The rating scales used were borrowed or adapted from the literature on reliability and benevolence (Ganesan and Hess, 1997) and perceived equity (Tax et al., 1998; Johnson et al., 2001a, b). For affective commitment, we used the scale developed by Garbarino and Johnson (1999). For calculative commitment, we used the scale of Gruen et al. (2000). All scales had been adapted and validated previously in a French retail-banking context. We used five-point Likert scales (strongly disagree to strongly agree). Then, later in the questionnaire, the respondents were then offered a list of 40 critical situations elaborated from the previous work of Keaveney (1995) in service industries (see Appendix for examples). For each of the 40 critical incidents (“high prices in comparison with those of competitors” the incompetence of banking personnel, bad management service of the account, etc.), the respondents had to indicate their propensity or likelihood to “do more business with competitors” on a five-point Likert scale, which goes from very unlikely (1) to very likely (5). As a consequence, we used the reverse score to estimate the CSR. To sum up, the CSR scale creates “what if” hypothetical scenarios based on Keaveney’s (1995) service switching categories.

519

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In our study, switching does not refer to customer churn (Gustafsson et al., 2005) or to service relationship dissolution (Keaveney, 1995). It is more consistent with the approach of Zeithaml et al. (1996, p. 38) who consider an exit dimension in their research and estimate the customers’ propensity to “do less business with XYZ in the next few years”. In the financial service industry, most customers tend to avoid breaking the service relationship (retention rate ¼ 95 percent on average in France). They prefer to patronize different suppliers and allocate their resources to other service providers. This leads to a drop in the customer share for the bank concerned (Verhoef, 2003). Exploratory and confirmatory factor analysis First, we performed a factor analysis from the items, which was used to estimate the CSR in 40 critical situations. The purpose was to reduce the number of variables (CSR in 40 critical situations), by combining two or more variables into a single factor, and to identify groups of inter-related variables which are strongly related to each other since they refer to the same category of critical incident. As a result, we were able to check if the respondents classified the 40 critical incidents we had formulated into the previous eight categories, which were identified by Keaveney (1995). The exploratory factor analysis gave most of the results we expected. However, we found out that ethical problems and inappropriate employee responses to service failures fall into the same category – same factor – in the exploratory factor analysis. Those items concern CSR when employees appear not to be benevolent and fair with their customers, especially when they complain. Finally, thanks to this result, we decided to keep only seven factors or components (and not eight) and to consider CSR in seven critical situations: (1) CSR – attraction by competitors; (2) CSR – employee responses to service failures (also including the items which refer to ethical problems); (3) CSR – pricing; (4) CSR – core service failures; (5) CSR – service encounter failures; (6) CSR – involuntary switching; and (7) CSR – inconvenience. About 17 variables (out of 40) were not strongly correlated with any of the seven factors which were extracted (loadings , 0.50). We decided to remove them from our measurement scales to improve their psychometric qualities (reliability and validity). After measurement scale purification, Cronbach’s a coefficients ranged from 0.76 to 0.92, which can be considered satisfactory (Table I). We then tested in a confirmatory factor analysis the one-dimensionality (in the sense defined by Anderson et al., 1987) of each of the seven constructs separately, then two by two, three by three, etc. and finally for all the seven constructs taken together (CSR in seven critical situations). The confirmatory factor analysis was performed with Amos 4 software. The estimation was performed using the maximum likelihood method because of its robustness for large sample sizes (N ¼ 1,999). The final 23 items we used are presented in Appendix.

Number of items

Reliability (r) (CFA)a

AVEb

pffiffiffiffiffiffiffiffiffiffi AVE

Reliability 4 0.91 0.71 0.84 Benevolence 4 0.89 0.68 0.83 Perceived equity 3 0.80 0.58 0.77 Affective commitment 3 0.78 0.54 0.74 Calculative commitment 3 0.76 0.52 0.72 CSR – attraction by competitors 4 0.90 0.70 0.84 CSR – employee responses to service failures 4 0.90 0.69 0.83 CSR – pricing 4 0.86 0.61 0.78 CSR – core service failures 4 0.81 0.52 0.71 CSR – service encounter failures 3 0.86 0.67 0.82 CSR – involuntary switching 2 0.79 0.65 0.80 CSR – inconvenience 2 0.92 0.85 0.92 P 2 P 2 P a 2 b Notes: Werts et al. (1974),P r ¼ ð Plyi Þ =½ðP lyi Þ þ Varð1iÞ
; with Varð1iÞ ¼ 1 2 lyi ; Fornell and Larcker (1981): AVE ¼ l2yi =½ l2yi þ Varð1i Þ


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Table I. Psychometric qualities of measurement scales

According to our conceptual framework, trust was divided into two constructs (reliability and benevolence). We tested a one-dimensional (trust) model of trust that did not fit the data. The two-dimensional (reliability þ benevolence) model of trust exhibited an acceptable fit (RMSEA ¼ 0.05). We did the same for relationship commitment dimensions (calculative and affective commitment). The one-dimensional model resulted in a bad fit (RMSEA ¼ 0.266), whereas the two-dimensional model exhibited a satisfactory fit (RMSEA ¼ 0.044). According to the exploratory factor analysis results, CSR is estimated in seven categories of critical situations. The seven component model exhibits a satisfactory fit (RMSEA ¼ 0.05). We noted an unacceptable fit when we consider together some of the most tightly correlated constructs. Taken together, the constructs demonstrated a satisfying degree of reliability and convergent validity (Table I). The reliability coefficients are between 0.76 and 0.92 (r coefficients). The average variances extracted are between 0.52 and 0.85, which can be considered satisfactory. Moreover, latent variable discriminant validity was checked using the Fornell and Larcker (1981) criterion. As shown in Tables II and III, the square root of the average variance extracted (AVE) exceeds the correlations between every pair of latent variables. This indicates a satisfactory level of discriminant validity. The respondents 1 1 2 3 4 5

Reliability Benevolence Perceived equity Affective commitment Calculative commitment pffiffiffiffiffiffiffiffiffiffi Note: *p , 0.001; a AVE

2

3

4

5

0.83 0.69 0.55 0.07

0.77 0.66 0.18

0.74 0.35

0.72

a

0.84 0.78 * 0.63 0.52 0.09

Table II. Correlation matrix between perceived reliability, benevolence, perceived equity, and relationship commitment dimensions (standardized correlation coefficients)

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were able to clearly dissociate their intentions to switch to another service provider according to the critical incidents that are likely to occur in the near future. Results of the model Structural equation modeling was used to test the model (Figure 1). We examined successively the effects of trust and perceived equity (direct and indirect) and relationship commitment (direct) on CSR in seven critical situations: (1) CSR attraction by competitors; (2) CSR inappropriate employee responses to service failures; (3) CSR pricing problems; (4) CSR core service failures; (5) CSR service encounter failures; (6) CSR involuntary switching; and (7) CSR inconvenience. CSR has been successively examined in these seven critical situations. All the seven models fit the data well, since we obtained a RMSEA ranking between 0.038 and 0.053 and fit indexes (NFI, RFI, CFI) were all higher than 0.99 (Bollen and Scott, 1993). The path coefficients are indicated below (Table IV). Perceived equity, trust and relationship commitment do not have exactly the same role in each and every critical situation. Affective commitment has a positive, direct and significant effect on CSR. According to our H1a-H1d and H1f-H1g, the more the consumer is affectively committed, the more he (or she) will resist switching when a reason to change occurs. However, contrary to our H1e, affective commitment does not lead the consumer to resist switching when he (or she) faces a service encounter failure. Contrary to calculative commitment, affective commitment is insufficient to limit the negative effects of bank employee impoliteness, incompetence or lack of attention. Calculative commitment has a limited influence. According to our H2b-H2c and H2e, calculative commitment leads consumers to resist switching when there are inappropriate employee responses to service failures (0.12), a pricing problem (0.075) or a service encounter failure (0.14). But it has no significant effect on their switching resistance when competitors offer better performing products or when the core service fails. When the deterioration in perceived performance concerns the core service or

Table III. Correlation matrix between the CSR’s facets

1 2 3 4 5 6 7

Attraction by competitors Employee responses to service failures Pricing Core service failures Service encounter failures Involuntary switching Inconvenience pffiffiffiffiffiffiffiffiffiffi Note: *p , 0.001; a AVE

1

2

3

4

5

6

7

0.84 a 0.39 * 0.61 0.45 0.17 0.24 0.37

0.83 0.64 0.66 0.57 0.30 0.25

0.78 0.62 0.33 0.25 0.36

0.71 0.43 0.27 0.33

0.82 0.17 0.10

0.80 0.42

0.92

Independent variables

Dependent variables

Perceived equity Benevolence Reliability

Affective commitment Affective commitment Affective commitment CSR – attraction by competitors CSR attraction by competitors CSR attraction by competitors CSR attraction by competitors CSR attraction by competitors CSR attraction by competitors CSR – employees’ responses to service failures CSR employees responses to service failures CSR employees responses to service failures CSR employees responses to service failures CSR employees responses to service failures CSR employees responses to service failures CSR – pricing CSR pricing CSR pricing CSR pricing CSR pricing CSR pricing CSR – core service failures CSR core service_ failures CSR core service_ failures CSR core service_ failures CSR core service_ failures CSR core service_ failures CSR – service encounters failures CSR service encounter failures CSR service encounter failures CSR service encounter failures CSR Service encounter failures CSR service encounter failures CSR – involuntary switching CSR involuntary switching CSR involuntary switching CSR involuntary switching CSR involuntary switching CSR involuntary switching CSR – inconvenience CSR inconvenience CSR inconvenience CSR inconvenience CSR inconvenience CSR inconvenience

Reliability Benevolence Perceived equity Affective commitment Calculative commitment Reliability Benevolence Perceived equity Affective commitment Calculative commitment Reliability Benevolence Perceived equity Affective commitment Calculative commitment Reliability Benevolence Perceived equity Affective commitment Calculative commitment Reliability Benevolence Perceived equity Affective commitment Calculative commitment Reliability Benevolence Perceived equity Affective commitment Calculative commitment Reliability Benevolence Perceived equity Affective commitment Calculative commitment

Path coefficients 0.551 0.094 0.094 0.098 ns * 0.127 0.126 ns

Customer switching resistance 523

ns ns 0.143 0.099 0.118 ns ns 0.124 0.237 0.075 ns ns ns 0.217 ns ns ns ns ns 0.139 0.096 ns ns 0.143 20.093 ns ns 0.109 0.117 20.110

Note: *ns: non significant at p , 0.05

product (technical quality), calculative commitment has no more influence on CSR. But it slows down the switching process when employee attitudes and behaviors are seen as sources of failure or when pricing policy does not meet their expectations. Furthermore, a surprising result appears: calculative commitment tends to lower CSR

Table IV. The effects of the service provider’s perceived reliability, benevolence, perceived equity, and customers’ affective and calculative commitment on CSR (standardized regression weights)

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when an event apart from the service relationship occurs, such as involuntary switching and inconvenience. A change in the bank (closing of a branch) or customer (moving to a new locale) situation or the opening of new competitor branches closer to customers’ homes or workplaces are seen as opportunities to switch to another service provider and to recover more freedom and independence, as previously demonstrated in the resource dependence theory (Pfeffer and Salancik, 1978). As a consequence, the H2a, H2d and H2f-H2g have to be rejected. As expected, trust has a direct and positive effect on affective commitment. Perceived reliability and perceived benevolence have a slight, but significant positive influence (0.09). Thus, our H3 and H4 are confirmed. According to previous relationship marketing literature, affective commitment is a key-mediating construct between trust and behavioral intentions (Morgan and Hunt, 1994; Garbarino and Johnson, 1999). Most effects of trust on CSR are mediated by affective commitment. Reliability has only an indirect and positive effect on CSR when customers face inappropriate employee responses to service failures, pricing problems, core service failures, service encounter failures and inconvenience problems. As a result, our H5b-H5e and H5g are rejected. But, according to our H5a and H5f, reliability has a direct and positive effect on CSR when customers are exposed to competitors’ offers. Banking products are quite complex, difficult to evaluate and risky. Thus, consumers tend to resist switching if their focal service provider is credible and competent enough to deliver similar products and services (0.10). Perceptions of service provider reliability also lead the consumer to resist switching when there is a change in the customer or bank situation (0.10). Consumers will strive to maintain the service relationship if they are dealing with a competent financial advisor. Benevolence has no direct effect on CSR. All its effects are mediated by affective commitment. Our H6a-H6g have to be rejected. This result is surprising because benevolence has always been described as a key concept when a critical incident occurs (Ganesan, 1994; Ganesan and Hess, 1997). Perceived equity has a stronger effect on CSR. According to H7, perceived equity has a strong impact on affective commitment (0.55). As a result, it has an indirect influence on consumer resistance to switch to another service provider in several critical situations (six out of seven critical situations). According to our H8a-H8c and H8g, it also has a direct influence on CSR in critical situations, such as attraction by competitors (0.13), employee responses to service failures (0.14), pricing problems (0.12) and inconvenience (0.11). However, the H8d-H8f have to be rejected since perceived equity does not directly influence CSR in situations, such as core service failures, service encounter failures and involuntary switching. Nevertheless, perceived equity remains a key process that strongly affects consumer responses to critical incidents. We note that consumer responses to competitors’ products, prices and points of sale (opening of another branch) depend strongly on perceived equity. If the customer has always been treated fairly by the service provider, (s)he will be more reluctant to leave it as soon as (s)he has a better opportunity offered by competitors. On the other hand, when there is an unfair distribution of inputs and outcomes between the service provider and the customer, there is a higher risk of customer defection.

Discussion To sum up, affective commitment has the strongest impact on CSR. Thus, financial service companies should enhance identification and affiliation processes, communication on their corporate and brand values, as well as their identity and personality in order to develop customer feelings of belonging to the service organization. Customer clubs or brand communities represent solutions for strengthening relationships with customers (Roos et al., 2005). There is only one exception: when a service encounter with contact persons fails, affective commitment does not allow for the maintenance of the service relationship over time. On the other hand, calculative commitment, which refers to an evaluation of the termination costs, has three positive influences on CSR (service encounter failures, employee responses to service failures, pricing problems) and two negative effects (inconvenience, involuntary switching). As a consequence, in the long run, companies cannot count on calculative commitment and termination costs to keep their customers. Once consumers have an opportunity to switch to another service provider, they will tend to do so (Bendapudi and Berry, 1997). In the financial service industry for instance, customers will often tend to do more business with competitors: on the short-term, there will be no effect on customer retention rate but in the long run, it will result in a drop in customer share and in company profits. Trust (reliability and benevolence) mainly has an indirect effect on CSR – via affective commitment. Benevolence and reliability are key components of trust in the marketing literature (Ganesan and Hess, 1997; Sirdesmukh et al., 2002). However, their respective influence – both direct and indirect – on CSR is rather low. Perceived equity is also a key factor of affective commitment (0.55) and of CSR in four situations: attraction by competitors (0.13), employee responses to service failures (0.14), pricing problems (0.12), and inconvenience (0.11). Companies should measure and manage the perceived equity of the relationships from the customers’ point of view. An unfair exchange relationship will lead customers to switch to another service provider once a critical incident occurs. As different authors have already postulated (Dwyer et al., 1987; Gundlach and Murphy, 1993; Sirdesmukh et al., 2002), reciprocity is important in developing enduring relationships. If companies want their customers to be truly loyal and their customer relationship management to be effective, they should get back to basics: consumers will resist switching to another service provider if they obtain a fair return on their sacrifices and efforts (Mac Neil, 1978; Dwyer et al., 1987; Bitner et al., 1998; Johnson et al., 2001a, b; Feinberg et al., 2002; Musa et al., 2005). Managerial implications While between 35 and 75 percent of customer relationship management projects are considered to be failures, it is important to better understand why customers are doing even more business with competitors (Zablah et al., 2004). The critical incidents we considered in this research can be seen as precipitating events, which have to be effectively managed. Of course, managers can prevent service performance deterioration from occurring (service quality programs). They can also develop procedures to solve the problems (service recovery management). However, several critical incidents cannot be predicted nor avoided. Therefore, managers have to anticipate several critical incidents that will come from the company (pricing policy, complaint management, service quality management, core service production

Customer switching resistance 525

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and delivery), from its environment (competitors’ products and new branches) or from its customers (change in his (her) personal situation). Some events are more critical than others, i.e. they are more likely to cause the customer to switch service providers. Below we rank CSR according to the mean scores presented in Appendix (from the lowest to the strongest CSR ¼ from 0 “very likely to do more business with competitors” to 4 “very unlikely to do more business with competitors”). Moreover, according to our findings, we examine for each critical situation what the most effective “shock absorbers” are: affective commitment, calculative commitment, reliability, benevolence and/or perceived equity. CSR – service encounter failures (mean score ¼ 0.89) This represents the most important critical incident in the financial service industry (i.e. lowest CSR). If contact persons (financial advisors, branch employees, etc.) are incompetent or impolite or do not pay sufficient attention to customers, only calculative commitment can help to maintain the service relationship. As a result, if a service organization is unable to train and control its employees so that they become customer-oriented, they will be obliged to implement high switching costs. However, as Bendapudi and Berry (1997, p. 28) note: . . . when customers stay in relationships because of the constraints against leaving, the relationship tends to last only as long as the constraints do; when the constraints no longer apply, the customer feels no compelling reason to continue in the relationship.

CSR – employee responses to service failures (mean score ¼ 1.09) If a service provider does not succeed in managing customer complaints, it should pay attention to perceived equity as Tax et al. (1998) have already demonstrated. Affective commitment also has a positive influence (0.10): affectively committed customers would probably attribute their dissatisfaction to the employees’ personality or incompetence instead of switching to another service provider. If they perceive high switching costs, they would also tolerate the situation (0.12). CSR – pricing (mean score ¼ 1.25) If the provider increases service prices, the customers who have affective commitment are less likely than other customers to switch to a competitor. They would probably reduce the value of the alternatives considering that lower prices are signs of poor quality. Or they would re-evaluate the service quality delivered by a favorite supplier (biased partner perception). If customers perceive that the exchange relationship is fair, they would also be more tolerant and accept to pay more. In the reverse situation, they would have a higher propensity to switch to another service provider. Calculative commitment also has a slight but significant effect in this situation (0.07). Switching costs lead customers to accept to pay a price premium. CSR – core service failures (mean score ¼ 1.51) If a service provider faces some problems in delivering core services, affective commitment will be the only “shock absorber” from the customer’s point of view. Some customers identify with the service provider (identification and affiliation) and will tend to tolerate a core service failure on the short-term. As Dwyer et al. (1987) or

Morgan and Hunt (1994) demonstrated in other business settings, affective commitment leads customers to accept sacrifices on the short run. CSR – attraction by competitors (mean score ¼ 1.53) Whereas most studies focus on consumers’ competitive resistance (Zeithaml et al., 1996; Fornell et al., 1996; Reynolds and Arnold, 2000), we demonstrate that attraction by competitors is not the most critical factor of switching in the financial service industry (rank: 5). Nevertheless, if a competitor launches a new offer, the service provider can prevent customer defection by improving the competence of its personnel (recruitment, training, incentives, etc.). Financial services are often intangible and difficult to evaluate. Thus, if the customers rely on their financial advisors, they will try to avoid the risks associated with dealing with another exchange partner. Perceived equity also has a positive effect (0.13): the customers will accept opportunity costs (miss out on a good deal) because they believe that their partners will find a fair solution in the long run (reciprocity in the exchange). Affective commitment will also lead customers to accept sacrifices (opportunity costs) in the short run because they expect more long-term benefits from their suppliers (Bitner et al., 1998). CSR – involuntary switching (mean score ¼ 1.66) If there is a change in the consumer (change of address) or the bank (closure) situation, the competence of bank personnel will directly slow down the switching process (0.09). Nevertheless, affective commitment is once again the main factor in enhancing CSR (0.14). Calculative commitment has the opposite effect (-0.09). When consumers have to move to another country or when a bank closes its branches, this represents a good opportunity for consumers to recover more freedom (Pfeffer and Salancik, 1978). CSR – inconvenience (mean score ¼ 2.46) If consumers face convenience problems (distance from home or workplace), calculative commitment also has the same negative effect: consumers will be likely to switch to another service provider to recover their independence. Affective commitment has a more positive effect on CSR. The feeling of belonging to a specific bank community leads consumers to make efforts (time, distance, money) in order to maintain the relationship. Perceived equity also has a positive influence (0.11). Reciprocity within the exchange relationship will lead individuals to make efforts in return for what they obtain from the service provider. Ranking the critical incidents according to their adverse impacts, we note – as in previous literature (Bitner et al., 1990) – that the main drivers of switching intentions are closely related to employee behaviors. As a result, human resource management – i.e. employee satisfaction and involvement, and contact person recruitment and training, etc. – remains extremely important in maintaining long lasting-relationships with customers. Furthermore, this research allows managers to determine what the drivers of CSR are. As such, it enables managers to maintain customer retention and customer share, even though they face difficulties in applying a zero defect strategy or if new entrants arrive on the market with new better performing products and low prices. It is quite impossible to prevent a critical incident from occurring throughout the business relationship. But our results demonstrate that managers can slow down the switching

Customer switching resistance 527

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process by managing the service provider’s perceived reliability, benevolence and perceived equity and by developing affective and/or calculative commitment. It depends in great part on the type of critical incident which is likely to occur. To sum up, managers should: . anticipate the critical incidents that are likely to occur (core service failures, inconvenience problems, competitor offers, price increases, etc.); . identify the customers who will be the most likely to switch to another service provider in such a situation (age, occupation, lifetime value); and . develop specific “shock absorbers”: reliability, benevolence, perceived equity, calculative commitment and/or affective commitment. Contributions, limits and future areas of research This research has two main contributions. First, we measure customer loyalty when consumers experience a reason to switch service providers and not when there is no reason or opportunity to do so. As Jacoby and Kyner (1973) and Jacoby and Chestnut (1978) note, the composite measurements of loyalty should have better trait validity than behavioral indicators and better predictive validity than attitudinal indicators. Most studies tend to estimate customer attitudes and intentions (Zeithaml et al., 1996) or to capture behavioral loyalty (Verhoef, 2003; Gustafsson et al., 2005). We propose a composite approach that aims at considering true customer loyalty. The more customers resist doing more business with competitors, the more they should be loyal to their service provider. Moreover, we consider CSR according to the main types of critical incident customers are likely to experience in their service relationship (Bitner et al., 1990; Keaveney, 1995; Gremler, 2004). To our knowledge, no research in the service industry has examined CSR in seven critical situations. Most studies focus on consumers’ competitive resistance or price tolerance (Parasuraman et al., 1991; Fornell et al., 1996; Reynolds and Arnold, 2000), although they do not represent the main drivers of switching behaviors – especially in the financial service industry. The critical incidents that occur within the established service relationship (service encounter failure, employee responses to service failures, etc.) are more likely to cause the customer to switch service providers (Keaveney, 1995; Bitner et al., 1990; Gremler, 2004). Second, we examine the roles of perceived equity, trust and relationship commitment and their direct and indirect effects on CSR. We explain why customers switch when they have some reasons to stay with the same service provider. We demonstrate that perceived equity, trust, affective and calculative commitment – which are key constructs in the relationship marketing literature – do not always lead to the service relationship maintenance. In various critical situations (service encounter failure for instance), the service relationship quality, do not permit to maintain the business relationship. Furthermore, some constructs, such as calculative commitment, may have a positive, a negative or a non-significant effect on relationship maintenance: it depends on the situation. Thus, this research complements the relationship marketing literature and underlines the need to better take into account critical incidents. We also explain why customers stay when they have some reasons to switch to another service provider (core service failures, pricing problems, lack of convenience, etc.). This research highlights the need to consider the service relationship evaluations to better understand service-switching behaviors. We show that customers will often

be reluctant to switch service providers if they feel a sort of identification and affiliation with the service provider and if they believe that there is fair distribution of inputs and outcomes between the customer and the service provider. Thus, this research extends the service switching literature, which is often focused on customer satisfaction/dissatisfaction (Ganesh et al., 2000) and often applies the critical incident technique to identify the most frequent critical incidents (Bitner et al., 1990; Keaveney, 1995; Gremler, 2004). In our empirical study, we tested a model on a sample of 1,999 bank customers and measured the effects of key relational constructs (perceived equity, trust, relationship commitment) on service switching intentions. However, this research has limitations, which can be overcome in future research. Firstly, this research concerns a contractual service setting: the long-term relationship between a bank and its customers. It will be necessary to explore other industries, which are usually non-contractual, such as transportation, restaurant or health services. Moreover, the financial service industry delivers an essentially “utilitarian” service and it will be interesting to test the model for more hedonic consumption experiences, such as sports, leisure activities, or artistic pursuits. Finally, we note a low response rate (1,999/30,000 respondents ¼ 6.66 percent) which is due to the data collection process: mail survey and long questionnaire. Further, research has to be done to strengthen the external validity of our results. Secondly, the effects of perceived equity, trust and relationship commitment might be moderated by the relationship phase (awareness, exploration, expansion, commitment and dissolution), (Dwyer et al., 1987). For example, as shown by Verhoef et al. (2002), the role of trust could be stronger in the relationship’s first stage, when clients do not have enough experience or expertise. Future research should also take into consideration the characteristics of the service relationship (length, breadth and depth) and of the individuals (age, occupation, lifetime value, etc.). Thirdly, we only measure behavioral intentions and not actual behaviors. As a consequence, we cannot check the predictive validity of the CSR’s indicators. Gustafsson et al. (2005) have examined the role of a few “reactional and situational triggers” on customer churn during nine months in the telecommunication industry, and they did not find any impact on customer churn. However, Gustafsson et al. (2005) suggest doing more research on the switching path (length, immediate or progressive dissolution of the relationship) and to consider several critical incidents that occur within a relationship (change in the consumer situation, competitor offers, core service failures, etc.). Finally, in consumer goods settings, Fournier (1998) suggest that CSR depends on psychological processes, such as accommodation, tolerance/forgiveness, biased partner perceptions, devaluation of alternatives and attribution biases. In the service industry, we need also to develop a deeper analysis of how consumers resist switching to another service provider. How do they really cope with the critical incidents that occur? What happens in the minds of consumers? References Anderson, E. and Weitz, B. (1992), “The use of pledges to built and sustain commitment in distribution channels”, Journal of Marketing Research, Vol. 24, pp. 18-34. Anderson, J.C., Gerbing, D. and Hunter, J.E. (1987), “On the assessment of unidimensional measurement: internal and external consistency and overall consistency criteria”, Journal of Marketing Research, Vol. 24 No. 4, pp. 432-7.

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Oliver, R.L. (1997), Satisfaction: A Behavioral Perspective on the Consumer, MacGraw Hill, New York, NY. Oliver, R.L. and Swan, J.E. (1989), “Consumer perceptions of interpersonal equity and satisfaction in transactions: a field survey approach”, Journal of Marketing, Vol. 53 No. 2, pp. 21-35. Parasuraman, A., Berry, L.L. and Zeithaml, V.A. (1991), “Understanding customer expectations of service”, Sloan Management Review, Vol. 32 No. 3, pp. 39-48. Pessemier, E.A. (1959), “A new way to determine buying decisions”, Journal of Marketing, Vol. 24 No. 2, pp. 41-6. Pfeffer, J. and Salancik, G.R. (1978), The External Control of Organizations: A Resource Dependence Perspective, Harper and Row, New York, NY. Pritchard, M.P., Havitz, M.E. and Howard, D.R. (1999), “Analyzing the commitment-loyalty link in service contexts”, Journal of the Academy of Marketing Science, Vol. 27 No. 3, pp. 333-48. Reichheld, F.F. and Sasser, W.E. Jr (1990), “Zero defections: quality comes to services”, Harvard Business Review, Vol. 68 No. 5, pp. 105-11. Reinartz, W.J. and Kumar, V. (2000), “On the profitability of long-life customers in a noncontractual setting: an empirical investigation and implications for marketing”, Journal of Marketing, Vol. 64 No. 4, pp. 17-35. Reynolds, K. and Arnold, M.J. (2000), “Customer loyalty to the salesperson and the store: examining relationship customers in an upscale retail context”, Journal of Personal Selling & Sales Management, Vol. 20 No. 2, pp. 89-98. Roos, I., Gustafsson, A. and Edvardsson, B. (2005), “The role of customer clubs in recent telecom relationships”, International Journal of Service Industry Management, Vol. 16 No. 5, pp. 436-54. Rust, R.T. and Zahorik, A.J. (1993), “Customer satisfaction, customer retention, and market share”, Journal of Retailing, Vol. 69 No. 2, pp. 193-215. Rust, R.T., Zeithaml, V. and Lemon, K. (2000), Driving Customer Equity, The Free Press, New York, NY. Ryals, L. (2005), “Making customer relationship management work: the measurement and profitable management of customer relationships”, Journal of Marketing, Vol. 69, pp. 252-61. Singh, J. and Sirdesmukh, D. (2000), “Agency and trust mechanisms in consumer satisfaction and loyalty judgments”, Journal of the Academy of Marketing Science, Vol. 28 No. 1, pp. 150-67. Sirdesmukh, D., Singh, J. and Sabol, B. (2002), “Consumer trust, value, and loyalty in relational exchanges”, Journal of Marketing, Vol. 66 No. 1, pp. 15-37. Tax, S.S., Brown, S.W. and Chandrashekaran, M. (1998), “Customer evaluations of service complaint experiences: implications for relationship marketing”, Journal of Marketing, Vol. 60/62 No. 2, pp. 60-76. Verhoef, P.C. (2003), “Understanding the effect of customer relationship management efforts on customer retention and customer share development”, Journal of Marketing Research, Vol. 67 No. 4, pp. 30-45. Verhoef, P.C., Franses, P.H. and Oekstra, J.C. (2002), “The effect of relational constructs on customer referrals and number of services purchased from a multiservice provider: does age of relationship matter?”, Journal of the Academy of Marketing Science, Vol. 30 No. 3, pp. 202-16. Werts, C.E., Linn, R.L. and Jo¨reskog, K.G. (1974), “Intraclass reliability estimates: testing structural assumptions”, Educational and Psychological Measurement, Vol. 34 No. 1, pp. 25-33.

Zablah, A.R., Bellenger, D.N. and Johnston, W.J. (2004), “Customer relationship management implementation gaps”, Journal of Personal Selling & Sales Management, Vol. 24 No. 4, pp. 279-95. Zeithaml, V.A., Berry, L.L. and Parasuraman, A. (1996), “The behavioral consequences of service quality”, Journal of Marketing, Vol. 60 No. 2, pp. 31-46.

533

Appendix. Customer switching resistance measurement scales and statistics

CSR – attraction by competitors More productive competitor open-end investment trust A more effective competitor banking investment A more effective competitor investment or savings product A better competitor property management CSR – employee responses to service failures The bank personnel’s inflexibility to deal with my requests The lack of good faith bank employees in the case of a claim The lack of kindness lack on the part of the bank toward me The bank personnel’s refusal to favorably respond to my claim CSR – pricing More competitive rates for everyday transactions (checking, credit card) High prices in comparison with those of competitors Disappointing service prices in comparison with my expectations An unjustified variation of practiced banking prices CSR – core service failures The impoliteness of bank personnel towards me Bank personnel lack of concern toward my requests The incompetence of bank personnel CSR – service encounter failures Bank oversight to carry out a transaction The slow response of the bank in case of credit request Bad management service of my account A mistake in banking service prices CSR – involuntary switching Moving to another country The closure of my banking agency CSR – inconvenience The opening of a new branch of another bank near my workplace The opening of a new branch of another bank near my home

Mean score

SD

1.53 1.75 1.45 1.45 1.45 1.09 1.07 1.01 1.12 1.15 1.25 1.34 1.16 1.4 1.06 1.51 1.1 0.88 0.67 0.89 1.57 1.51 1.21 1.74 1.66 1.76 1.56 2.46 2.46 2.45

0.90 1.036 1.042 1.038 1.014 0.84 0.961 0.942 0.968 0.952 0.80 1.032 0.964 0.909 0.916 0.78 1.115 0.910 0.963 0.88 1.021 0.933 0.955 1.024 1.12 1.215 1.242 1.02 1.054 1.070

Corresponding author Gilles N’Goala can be contacted at: [email protected]

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Customer switching resistance

Table AI.

The current issue and full text archive of this journal is available at www.emeraldinsight.com/0956-4233.htm

IJSIM 18,5

Perception and attribution of employees’ effort and abilities The impact on customer encounter satisfaction

534 Received 29 January 2006 Revised 26 February 2007 Accepted 15 May 2007

Nina Specht, Sina Fichtel and Anton Meyer Institute of Marketing, Ludwig-Maximilians-University Munich, Munich, Germany Abstract Purpose – Do customers recognize the effort and abilities of employees in service encounters? If so, to what extent do their perceptions influence customer satisfaction? The paper seeks to answer these questions. Design/methodology/approach – Two empirical studies, including a critical incident study and a video-based experiment. Theoretically, this paper builds on motivation theory, naı¨ve psychology, and attribution theory. Findings – Customers spontaneously and explicitly judge service encounters on the basis of service employees’ effort and abilities, perceived through certain behavioral cues. The specific, direct impact of perceived effort and abilities on customer satisfaction varies for different service types. Research limitations/implications – Taking different dependent variables into account (e.g. customer emotions, customer loyalty and brand perceptions) might offer a valuable contribution to the fields of service or brand research. Practical implications – Companies must examine customers’ perceptions of their employees’ encounter behavior in depth to evaluate and effectively and efficiently manage perceived effort and abilities as the main determinants of customer satisfaction. They should acknowledge behavioral training represents a significant satisfaction management approach. Originality/value – The paper offers interdisciplinary theoretical foundation, brings in innovative research methods and combines content and methodology to a new scientific framework for the field of service research as well as practical application for companies. Keywords Customer satisfaction, Services, Service levels, Employees Paper type Research paper

International Journal of Service Industry Management Vol. 18 No. 5, 2007 pp. 534-554 q Emerald Group Publishing Limited 0956-4233 DOI 10.1108/09564230710826287

Introduction Facing strong competition, businesses now recognize customer satisfaction as a primary goal on the path to profitability, especially as a result of research suggesting the significant influence of customer satisfaction on retention (Cronin and Taylor, 1992; Oliver, 1980) and, in turn, profitability (Reichheld and Sasser, 1990). Within this context, “moments of truth” reflect a fundamental means of quality management. Both service companies and manufacturing firms that offer customer service therefore must understand which aspects of a service encounter determine customer satisfaction. Research and management note the relevance of the employee in customer – employee interactions as a crucial determinant of satisfaction, to the point of claiming that “The offering is the employee” (Zeithaml et al., 1996). Thus, extensive theoretical and empirical work in personal selection, human resource, and service research literature focuses on the employee, his or her service

orientation, and service competence during customer interaction processes. Whereas some research concentrates primarily on the personality dimension of service orientation, and therefore on an employee’s predisposition to engage in service-oriented behavior (Brown et al., 2002; Cran, 1994; Frei and McDaniel, 1998; Hogan et al., 1984; Hollenbeck and Whitner, 1988), other studies focus more on employee behavior itself and its impact on quality and customer satisfaction (Bettencourt and Gwinner, 1996; Bitner et al., 1990, 1994; Chandon et al., 1997; Coenen, 2001; Cronin et al., 2000; Farrell et al., 2001; Gro¨nroos, 1982; Hartline and Ferrell, 1996; Meyer and Mattmu¨ller, 1987; Mohr and Bitner, 1995; Parasuraman et al., 1985, 1988; Winsted, 2000). On a meta-level, two behavioral drivers provide important determinants of customer satisfaction in customer – employee interactions: the employee’s motivation, as perceived by his or her effort, and competence, as perceived by his or her abilities. The importance of these drivers appears in basic definitions of service that take customers’ point of view (Meyer, 1991, 1998): Services refer to the offered effort and abilities of employees applied directly to the customer or an object he or she possesses. This definition clearly states that the employee and his or her perceived effort and abilities represent the service from a customer’s point of view. Despite the broad consensus about the importance of effort and abilities, no research provides empirical evidence for their simultaneous perception by customers, their relationship, or their influence on customer satisfaction. This study attempts to close this research gap by investigating, through two studies (one exploratory study and one experiment) that use three types of data (critical incident descriptions, survey responses and experimental data): . whether customers perceive service providers’ effort and abilities during service encounters; . the behavioral cues customers use to assess effort and abilities; . whether perceived effort and abilities influence customer satisfaction; and . whether perceived effort and abilities influence customer satisfaction when service outcome is statistically controlled. Our results thus contribute to a deeper understanding of customer satisfaction in interpersonal service encounters because they explore the nature and role of perceptions of service providers’ effort and abilities as dominant process factors, as well as their relevance in direct comparison with the perceived service outcome. Research is based on naı¨ve psychology (Heider, 1958), attributional research developed in Weiner’s (1985, 1986) attributional theory of motivation and emotion’ and motivation theory. Conceptual background and theoretical foundation Service research Customer satisfaction with services depends not only on the service outcome (what the customer receives during the exchange) but also on the process of service delivery, or the quality of the interaction itself (how the customer receives value) (Czepiel, 1990; Gro¨nroos, 1990). Moreover, traditional research conceptually and empirically analyzes specific service quality and satisfaction dimensions, which relate closely to service providers’ behavior. Such research was triggered by the well-known SERVQUAL studies (Parasuraman et al., 1985, 1988), which state that in addition to tangible elements, the reliability, responsiveness, assurance, and empathy associated with

Perception and attribution

535

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536

service provision determine a customer’s service evaluation. Therefore, disregarding situational facilitators and inhibitors, perceived performance strongly depends on the individual employee and his or her behavior. However, the multi-item measurement applied by SERVQUAL has been subject to the criticism that it cannot capture the specific nature of service transactions as personal and individual social interaction processes (Babakus and Boller, 1992; Carman, 1990; Lee et al., 2000). Inspired by such criticism, Bitner et al. (1990) published a well-respected research study in which they apply critical incident methods to capture central dimensions of service encounter satisfaction. Compared with close-ended surveys, their approach generates inductive and detailed information about customer satisfaction with a service encounter and places employees’ behavior at the center of interest. However, no research theoretically investigates and empirically tests both perceived effort and perceived abilities as core behavioral representatives of employees’ performance, determines their impact on customers’ encounter satisfaction, or analyzes their possible interaction effects. Therefore, to obtain a deeper understanding of effort and abilities as behavioral drivers of human performance, their relationship, and their perception and psychological anchoring in the mind of observers (i.e. customers), we move from the field of service research to draw on motivation theory, naı¨ve psychology, and attribution theory. Motivation theory Motivation literature offers insight into the relationships among performance, motivation, and ability and assumes outcomes are direct functions of motivation multiplied by ability (Lawler, 1966; Vroom, 1960, 1964). According to this approach, “to achieve a high level of performance a person must have both the ability and the motivation to perform effectively” (Baldwin, 1958). Although some authors regard motivation as the predominant factor (French, 1957), others assert the equality of both attributes (Fleishman, 1958; Locke et al., 1978; Vroom, 1964). Moreover, motivation literature links these constructs to concrete descriptions of perceptions; for example, motivation perceptions rely on effort, or the displayed energy put into a behavior (Locke et al., 1978). Because ability represents a task-related, relative construct however, it is hard to specify a perceptual definition. For example, Vroom (1964) defines a person’s ability to perform a task as “the degree to which he possesses all of the psychological attributes necessary for a high level of performance excluding those of a motivational nature” but ignores to describe how abilities are perceived. Naı¨ve psychology With his naı¨ve analysis of action, Heider (1958) demonstrates that the outcome of behavioral action depends on two components: a personal and an environmental force. Behavioral outcome is a function of both personal and environmental components and result from their additive relation. The personal component consists of a motivational factor, determined by a person’s intention and effort, and a multiplicative enabling power factor that reflects a person’s ability, temperament, etc. In contrast, the environmental component is characterized by the degree of task difficulty, chance, and luck. To capture common situations, Heider (1958) suggests a regrouping of these components that leads to the concepts of trying (personal motivational factor) and being enabled (personal power factor plus environmental factor). Thus, personal inputs

to behavioral action comprise one motivational component represented by intention and effort, which determine the trying for the outcome, and one non-motivational component, which reflects the concept of being enabled and encompasses the ability of a person and the situational factors. In the context of our research, Heider’s research suggests effort refers to the strength of motivation, which can be perceived explicitly by observing the behavior of a person, whereas ability involves the general aptitude of a person (physically and mentally), though his work provides no guidance about perceiving abilities. Attribution theory On the basis of Heider’s (1958) naı¨ve psychology theory, attribution theory analyses the relationship between personal perceptions and interpersonal behavior (Weinert, 1998) to take into account people’s perceptions of human interactions. We focus on the attributional research developed by Weiner (1985, 1986) in his attributional theory of motivation and emotion. Weiner finds that perceived abilities and effort represent the dominant perceptual causes of human performance (and its outcome) that determine the perceiver’s cognitive, affective, and intentional reactions. This process of attribution occurs particularly in response to negative, unexpected, or important outcomes (Weiner, 1985, 1986; Folkes, 1982; Weiner and Handel, 1985; Wong and Weiner, 1981). To incorporate Weiner’s attribution theory into our research task, we must address causal ascriptions of achievement-related actions and outcomes. In this regard and on the basis of Heider’s work, Weiner identifies four decisive causes: perceived effort and abilities, perceived luck, and task difficulty (Weiner and Kukla, 1970). However, empirical investigations show effort and abilities dominate perception, such that “In nearly all reported investigations, how competent we are and how hard we try are the most frequently given explanations of success and failure” (Weiner, 1986). The impact of effort and abilities as the perceived causes of actions and outcomes appears in discussions of the so-called “fundamental error of attribution” (Gilbert and Malone, 1995; Jones and Davis, 1965; Kelley, 1973; Miller et al., 1981, 1990; Miller and Rorer, 1982), which argues that people tend to overestimate internal causes (e.g. effort, abilities) but underestimate external causes (e.g. fate, luck) when evaluating the behavior and behavioral consequences of others (Werth, 2004). Even when perceivers know about constraining situational circumstances, they still attribute actions and outcomes to internal determinants. Therefore, service customers likely attribute a negative outcome to an employee’s effort and abilities, even when they know that something else, such as a computer system error, bad weather, or just bad luck, was responsible for it. Jones and Nisbett (1971) show such internal attributions take place particularly when the attributor is an observer; if he or she is an actor, the person instead tends to attribute his or her own actions to situational requirements and therefore makes external attributions. In our context, employees as actors likely therefore attribute a negative outcome not to their own effort and abilities but rather to external circumstances. These three theories underline the central position of effort and ability as behavioral drivers of human performance and suggest their multiplicative connection. They further explain the psychological consequences (e.g. customer satisfaction) and intentions (e.g. word-of-mouth) that can result from observers’ perceptions of these drivers. In turn, these theories could make important contributions to service encounter research by suggesting perceived effort and abilities as behavioral drivers for

Perception and attribution

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encounter satisfaction. However, none of the theories can provide in-depth insights into perceived effort and perceived abilities, because they fail to provide concrete behavioral cues for representing these two constructs. Therefore, the first stage of our research is explorative in nature and addresses the following research questions: RQ1. Do customers actually perceive service providers’ effort and abilities during service encounters? RQ2. What behavioral cues do customers use to determine employees’ effort and abilities? RQ3. Do perceived effort and abilities influence encounter satisfaction? RQ4. Do perceived effort and abilities influence customer satisfaction when perceived service outcome is statistically controlled? Whereas the first two research questions focus on exploring the existence and nature of effort and abilities in a service context from the customer’s point of view, the latter two examine their influence on encounter satisfaction and thus represent cognitive and emotional evaluations of an interaction (Stauss, 1999). They further take into consideration perceived service outcome as an additional relevant determinant of satisfaction (Czepiel et al., 1985; Gro¨nroos, 1990; Mohr and Bitner, 1995; Parasuraman et al., 1985; Zeithaml et al., 1996). Study 1: critical incident study To explore in depth the constructs of perceived effort and abilities and answer our four research questions in Study 1, we apply a mix of methods. Study 1 therefore combines the explorative critical incident technique (CIT) with a questionnaire approach before conducting a formal experiment in Study 2. Survey instrument The survey instrument asks respondents to write a two-page description of a critical incident with a service provider they had experienced in the past six months. This first part of the survey instrument does not contain any information about our two constructs of interest. After writing about the incident, subjects completed a questionnaire that contains a combination of scales and open-ended questions to probe their perceptions of the described encounter, with a focus on their perceptions of employees’ effort and abilities and their satisfaction with the interaction. We combine critical incident descriptions and survey data for several reasons. First, the CIT, developed by Flanagan (1954) and since then applied in different marketing contexts (Pollay, 1985; Swan and Jones Combs, 1976; Zimmer and Golden, 1988), captures the individual and socially interactive nature of a service encounter experience particularly well (Bitner et al., 1990, 1994; Mohr and Bitner, 1995; Nyquist and Booms, 1987; Stauss and Weinlich, 1995). The incident reporting reflects subjects’ original thoughts, including their cognitive, affective, and intentional reactions, but does not force them into a certain pattern, which results in “‘pure’ customer data. CIT allows marketers to see how customers think” (Nyquist and Booms, 1987). Therefore, if respondents mention the employee’s effort and abilities in their reported critical

incidents without being asked for it, they freely provide evidence of the relevance and perception of these constructs (RQ1). Second, open- and close-ended questions about the reported incidents capture and operationalize the nature of perceived effort and abilities (RQ2). Through a combined analysis of each critical incident and the questionnaire, we can analyze the relationship of perceived effort and abilities with satisfaction and outcome measures (RQ3 and RQ4). Third, the detailed incident descriptions refresh subjects’ memory, which prompts them to answer the questionnaire accurately and thus improves the data quality. Sample We administered the survey to 150 people with university affiliations (e.g. students, personnel). This sample size is sufficient for the exploratory nature of this study. We required that respondents have experience with service consumption and writing skills that would enable them to express their experience. Measures Critical incidents. The CIT employs the methodology recommended by several service researchers (Bitner et al., 1990, 1994; Meuter et al., 2000; Mohr and Bitner, 1995; Stauss, 1994). Respondents report the details of a service encounter incident that fulfills the following criteria: The incident: . involved a concise employee interaction; . was especially satisfying or dissatisfying; . represented a discrete episode; . allowed for a detailed description; . was not more than six months in the past; and . showed unambiguous (not alternating) employee behavior. We place no restrictions on the type of service. Perceived effort and abilities (open-ended). The two open-ended questions ask respondents to specify the behavioral cues they used to perceive the service provider’s effort and abilities. These questions indicate, from a customer perspective, which behavioral cues form the focal constructs. Perceived effort and abilities (closed-ended). After responding to the open-end questions, respondents evaluated 25 items on a seven-point Likert scale, on which 1 represents full agreement. To measure perceived effort, we adapt six items from Mohr and Bitner (1995): attentiveness, energy put into behavior, endeavor, time spent, endurance, and effort itself. To measure perceived abilities, we derive 19 items from the literature to measure all facets of perceived abilities, which includes items for perceived methodical, professional, and social competence (Delhees, 1994; Goleman, 1999; Nerdinger, 1998; Schuler and Barthelme, 1995): knowledge, competence, ability to organize, self-assurance, credibility, reliability, conscientiousness, self-confidence, eye contact, communication skills, capability, tolerance, patience, put oneself into the customer’s place, understanding, friendliness, empathy, professionalism, and abilities. With this question, we develop scales for both perceived effort and perceived abilities.

Perception and attribution

539

IJSIM 18,5

540

Customer satisfaction. For the context of this study, we define satisfaction as an attitude-like construct that comprises both cognitive and emotional components (Herrmann and Johnson, 1999; Johnson et al., 1995; Meyer and Ertl, 1996; Oliver, 1993, 1994; Stauss, 1999). We gather our measurement items from Mohr and Bitner (1995) and Bitner et al. (1990) and ask respondents to report their: . overall satisfaction; . satisfaction with the employee; and . satisfaction with the transaction process on seven-point scales anchored by completely satisfied and dissatisfied. Perceived service outcome. To measure whether customers obtained their preferred service outcome during the encounter, we include one item with a seven-point Likert scale that questions whether, by the end of the incident, respondents received the service they wanted when they approached the firm (adapted from Mohr and Bitner, 1995; a score of 1 represents full agreement). Similar to the measures of perceived effort and abilities, this outcome measure represents a quality judgment and thus differs from satisfaction judgments, which include a stronger component of individual value in the evaluation. Demographics. Finally, the questionnaire includes items related to information about the respondents, such as gender, age, and educational level. Results Sample. Of the 150 research respondents, 130 correctly completed the critical incident study and the questionnaire. In terms of demographics, 54.6 percent were female, and subjects’ ages ranged from 18 to 70 years with a mean of 30 years of age. One-third of all subjects were students, whereas the other two-thirds already had university degrees. Respondents could write about any service firm, so the selected service types include retail (28 respondents), railway transportation (18), telecommunication (16), financial institutions (14), restaurants (14), hairdressers (10), airlines (4), medical institutions (3), car repair (3), and other (20). Scales. The satisfaction measure (three items) reveals a mean of 4.23 and a standard deviation of 2.66. For our reliability check, we compute Cronbach’s coefficient a as a measure of internal consistency. The resulting reliability estimate is quite high for satisfaction (0.96), and our explorative factor analysis confirms the convergent validity of the scale. The perceived service outcome measure indicates a mean of 3.57 and a standard deviation of 2.45. As we expected, the measures of satisfaction and service outcome evince a strong positive correlation (r ¼ 0.767): RQ1. Do customers perceive the service provider’s effort and abilities during service encounters? We use a deductive content analysis to examine the critical incident descriptions and determine whether respondents make spontaneous references to employees’ effort and abilities. As a starting point for analyzing the critical incidents, we use the 25 items that represent perceived effort and abilities (see Measures) as the categories for a coding scheme applied by three expert teams. We adjust this coding scheme on the basis of a pretest of 20 incidents. Furthermore, we apply a two-step approach to the content analysis. First, two well-trained, two-student expert teams coded the

incidents independently. Second, a third expert team (first and second author) compared any deviant results from the preceding step and arrived at a final estimate. The percentage of agreement (PoA) (Kolbe and Burnett, 1991) and measure of interjudge reliability (index of Perreault and Leigh, 1989) are very good, with PoA ¼ 0.87 and Ir ¼ 0.908 (Gremler, 2004). No additional categories needed to be added to the coding scheme, which reveals the high-content validity of our research (Keaveney, 1995). The results from the content analysis expose that all 130 incidents contain spontaneous descriptions of perceived effort and abilities that refer to items found in the literature for both constructs, such as “he was very helpful,” “he was obviously overcharged,” “the employee spent a lot of time on my concern,” and “he had excellent professional competence.” Thus, the critical incidents clearly confirm that customers notice employees’ effort and abilities:

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541

RQ2. What behavioral cues do customers use to determine employees’ effort and abilities? After describing their critical incidents, respondents answered two open-ended questions about the behavioral cues they used to perceive that the service provider had shown or not shown effort and abilities. We apply the CIT coding scheme and a subsequent frequency analysis to analyze these answers. The respondents perceive our theoretically derived items for perceived effort and abilities but do not always assign them to their respective theoretical construct. For example, they assign many items to both perceived effort and abilities, which suggests customers have difficulty discriminating between constructs. However, for some items, the assignment frequency differed enough (i.e. frequency difference of 10) that we can clearly assign them to either perceived effort or perceived abilities (Table I). In particular, respondents generally perceive abilities on the basis of displayed technical knowledge/competence, abilities themselves, and self-confidence/self-assurance. In contrast, they consider the displayed effort itself, endeavor, energy put into the behavior, empathy, friendliness, attentiveness, time spent, conscientiousness, and flexibility as indicators of effort. This cue assignment indicates that customer perceptions Assignment frequency to perceived effort

Assignment frequency to perceived abilities

Effort/endeavor/energy put into behavior Empathy Friendliness Attentiveness Time spent Conscientiousness Flexibility Technical knowledge/competency

90 39 33 28 22 16 15 22

41 29 20 12 1 6 5 66

Abilities Self-confidence/self-assurance

0 10

25 20

Behavioral cues/items

Cues clearly assigned to perceived effort

Cues clearly assigned to perceived abilities

Table I. Frequency analysis of behavioral categories and their assignment to perceived effort and perceived abilities

IJSIM 18,5

542

do not completely reflect the theoretical definitions of perceived abilities and effort; rather, some behavioral cues of abilities appear to represent the effort dimension for customers. In practice, customers almost always perceive abilities on the basis of cues that represent professional competence. Furthermore, professionalism, which the literature considers a clear synonym for perceived abilities, gets assigned equally to perceived effort and abilities by customers, so from customers’ perspective, professionalism reflects both constructs. We also conduct a factor analysis (principal component analysis, Varimax rotation) to analyze the responses to the 25-item battery. After eliminating cross-loading items, we extract a two-factor result, namely, perceived effort and perceived abilities, which confirms our preceding findings. The effort factor comprises not only cues derived from effort literature but also behavioral cues from social competence literature (e.g. empathy, friendliness). These findings offer an empirically tested scale for perceived effort and abilities that conflicts with the current view in the literature. Our final scale of perceived effort contains 11 items (effort, patience, energy put into behavior, understandability, endurance, time spent, empathy, friendliness, tolerance, credibility, and endeavor) and shows very-high reliability (Cronbach’s a ¼ 0.97). The mean value of perceived effort is 3.98, with a standard deviation of 2.46. The final three-item scale of perceived abilities (knowledge, self-assurance, abilities) also reveals good reliability (a ¼ 0.81), a mean value of 2.98, and a standard deviation of 2.25: RQ3. Do perceived effort and abilities influence encounter satisfaction? To analyze the influence of perceived effort and abilities on customer satisfaction, we regress satisfaction on these factors. The resulting ß coefficients of both independent components indicate a positive and significant impact on satisfaction (perceived effort b ¼ 0.752, perceived abilities ß ¼ 0.186, p , 0.001). However, the impact of perceived effort is significantly greater than that of perceived abilities. The adjusted R 2 of 0.78 indicates that 78 percent of the variance in satisfaction may be explained by perceived effort and abilities. Because our theoretical foundation suggests a contingent relationship between perceived effort and abilities, we integrate the interaction effect into our regression analysis and find a significant interaction (ß ¼ 2 0.253, p , 0.001) that explains

dissatisfied 21.00 low perceived effort

Figure 1. Profile plots indicating the interaction of perceived effort and abilities on satisfaction

satisfaction

18.00 15.00 12.00

high perceived effort

9.00

low perceived abilities

18.00

satisfaction

dissatisfied 21.00

15.00 12.00 9.00 6.00

6.00 completely satisfied 3.00

high perceived abilities

completely satisfied 3.00 high

perceived abilities

low

high

perceived effort

low

3 percent more of the variance (R 2 ¼ 0.81). As Figure 1 shows, the hybrid interaction suggests perceived effort moderates the impact of perceived abilities on satisfaction:

Perception and attribution

RQ4. Do perceived effort and abilities influence customer satisfaction when perceived service outcome is statistically controlled? Finally, our combined regression of the three quality dimensions of perceived effort, perceived abilities, and perceived outcome (R 2 ¼ 0.81) shows that perceived outcome (ß ¼ 0.236, p , 0.001) is more important for explaining satisfaction than are perceived abilities (ß ¼ 0.120, p , 0.05) but less important than perceived effort (ß ¼ 0.649, p , 0.001). When we test for the significance of the differences in the ß values (Howell, 2004), we find no significant results for the three variables; therefore, we interpret the hierarchy with caution. To examine the influence of perceived effort and abilities on customer satisfaction independently from perceived service outcome, we perform regression analyses on two subsamples defined by a median split: subjects who perceived a positive outcome and those who perceived a negative outcome. In both cases, we find a significant and strong impact of perceived effort on satisfaction (positive ß ¼ 0.736, p , 0.001; negative ß ¼ 0.667, p , 0.001). However, perceived abilities have a significant impact on satisfaction only for those subjects who perceive a negative service outcome (negative ß ¼ 0.231, p , 0.05; positive ß ¼ 0.126, p , 0.5). Conclusions from study 1 Study 1 clearly reveals that customers perceive employees’ abilities and effort and spontaneously evaluate whether and how an employee exerts effort or abilities (RQ1). Our sample judges both perceived effort and abilities positively. However, the specific behavioral cues customers use to assign perceived employee effort and abilities differ from those provided in current literature. Therefore, we develop two reliable scales for use in further analyses (RQ2). When we apply these scales, we find that both behavioral constructs have a positive and significant impact on customer satisfaction and largely explain satisfaction, though perceived effort has more relevance than perceived abilities. We also uncover a significant interaction effect, which shows that perceived effort acts as a moderator for the impact of perceived abilities on satisfaction (RQ3). Finally, when we consider perceived service outcome, we find that perceived effort has a strong and positive impact, independent of the outcome, whereas perceived abilities are significant only in the case of negative outcome (RQ4). These results show that subjects who report a positive outcome think of employees’ abilities only accidentally. This mirrors Weiner’s (1986) attributional theory of motivation and emotion which suggests that the process of attribution and therefore the causal search for perceived effort and abilities of the employee (fundamental error of attribution) is especially instigated by a negative outcome. Study 2: experiment Because we reveal the importance of perceived effort, abilities, and outcome for encounter satisfaction in Study 1, we conduct Study 2 to develop a deeper understanding of how customers perceive effort, abilities, and outcome vis-a`-vis. Therefore, we use a discriminant manipulation of the three variables to examine the

543

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impact of perceived abilities, perceived effort, and perceived service outcome on customer satisfaction with a laboratory experiment. Hypotheses Our experimental study relies on five hypotheses, which are based on our conceptual and theoretical work, as well as the empirical findings from Study 1. Specifically, we argue that: H1. Perceived effort and abilities relate positively to customer satisfaction. This hypothesis is mainly based on Weiner’s attributional theory of motivation and emotion, naı¨ve psychology and motivation theory as well as on service research literature (Mohr and Bitner, 1995; Parasuraman et al., 1988; Meyer and Mattmu¨ller, 1987) and the results of Study 1: H2. The strength of the relationship between perceived abilities and customer satisfaction relates positively to perceived effort. Naı¨ve psychology and motivation theory discussed the multiplicative interaction of effort and ability (Heider, 1958; Vroom, 1960, 1964; Lawler, 1966); the hypothesized strength of effort in this interaction draws back on the results from Study 1 as well as suggestions coming from motivation theory (French, 1957): H3. Perceived service outcome has positive influence on customer satisfaction. In the service marketing field it is assumed that service outcome is crucial for customer satisfaction (Parasuraman et al., 1985; Czepiel et al., 1985): H4. Perceived effort relates positively to customer satisfaction when the service outcome is statistically controlled. With this hypothesis it is suggested that effort as the dominant process variable has an independent impact on satisfaction and therefore influences satisfaction beyond its effect on service outcome. It is based on service research literature (Parasuraman et al., 1985; Czepiel et al., 1985; Gro¨nroos, 1990) as well as results from Study 1: H5. The influence of perceived abilities on customer satisfaction depends on service outcome. Weiner’s (2000) attributional theory of motivation and emotion suggests that the attribution process and therefore causal search is particularly initiated when service outcome is negative. H5 combines this insight with results from Study 1; it suggests that perceived abilities, being a less dominant process variable in comparison to perceived effort, only impact satisfaction in case of a negative outcome and therefore are outcome dependent. Research design and procedures To investigate these five hypotheses, we develop a factorial design, with effort (high/low) and abilities (high/low) as the independent variables and service outcome (positive/negative) and service type (retail/financial services) as the supplementary variables. Therefore, we apply a 2 £ 2 £ 2 £ 2 design with 16 treatment cells.

Stimuli We recognize that laboratory experiments are appropriate only when: . we can create realistic service settings; and . the simulated service settings can produce the same experience as an actual service setting (Bateson and Hui, 1992).

Perception and attribution

Therefore, we develop audio-visual stimuli to account for the multisensory nature of a service encounter to ensure ecological validity. This realistic presentation of a service encounter through a video stimulus offers control over the independent variables; it also considers any interfering variables and combines internal and external validity (Sparks et al., 1997). We create the video stimuli using a scenario technique and writing specific stage directions for the actors. Respondents watched one scenario and were to put themselves in the customer’s position. To enhance realism and minimize the influence of stereotype-related bias in the two service settings, we hired one male and one female actor and had the videos produced professionally. To manipulate perceived effort, the employee/actor displayed two different levels of effort itself, energy put into behavior, endurance, and time spent with the customer during the interactions. The two-level manipulation of the employee’s perceived abilities is based on the displayed knowledge, self-assurance, and abilities themselves. Finally, we manipulate perceived outcome in the retail setting according to whether the customer left the shop with the pullover he or she wished to buy (positive outcome) or if the pullover was not in stock and therefore the customer had to leave without it (negative outcome). In a financial service setting, we manipulate perceived outcome by whether the customer could make a decision about a specific financial investment (positive outcome) or not (negative outcome). The critical incidents from Study 1 serve as examples that we use to transform the abstract behavioral items into concrete behavioral directions for the actors and develop realistic scenarios in the two service settings. Each video stimulus had a playing time of approximately three minutes.

545

Sample In accordance with experimental research quality requirements (Calder et al., 1981, 1982, 1983; Friedrichs, 1990; Kruglanski, 1973; Webster and Kevin, 1971), we administered the survey to a homogenous student convenience sample with experience with the two service types. Our total sample size includes 420 students. Procedures We ran the experiment for five days, between 9:00 am and 6:00 pm, during a two-week period. We randomly assigned participants to one of the 16 film scenarios (between-subjects design). Participants watched the film on a big screen (maximum of 15-20 people at a time) and received no information about the purpose of the study; their instructions only indicated they were to concentrate on the video stimuli, not talk with other participants, and fill out the questionnaire after watching the video. At the beginning of each film, a recorded announcer gave an introduction (service type-specific) and told participants to take over the customer’s role and concentrate on how they would think and feel in the position of the customer in this situation. After having watched the film, they received questionnaires, which they answered at their own speed.

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Measures We apply a seven-point Likert scale for all measures, on which 1 represents full agreement. The items measuring satisfaction come from Study 1, and we measure service outcome with three items: (1) overall, I got the expected outcome; (2) I got the outcome I intended; and (3) the aim of my visit was achieved. We measure perceived effort in accordance with the manipulation of the four items, that is, displayed energy put into behavior, endurance, time spent, and effort itself. Our measure of perceived abilities includes three items: displayed knowledge, self-assurance, and abilities. Finally, we include additional measures of the realism of the scenario and the ease with which the respondent imagined him- or herself in the role of the customer, to determine whether the experimental procedures worked as intended. Results Sample. Of the 420 initial questionnaires, we retain 400 for the final analyses (25 per treatment group). Of all respondents, 45.9 percent are male, and their ages varied between 19 and 50 years with a mean of 24 years of age. Scales and validity of the experimental procedure. We conduct reliability and validity analyses for each scale in both scenarios and achieve satisfying results. Participants found the scenarios realistic (means of 2.71 for retail and 2.93 for financial service) and the role playing easy (means of 2.43 for retail and 2.38 for financial service). Manipulation checks. To test whether our manipulations of perceived effort, abilities, and outcome were perceived as intended, we ran a one-way ANOVA for each industry grouping. The findings indicate that the manipulations were successful (effort retail: F(1,191) ¼ 532.27, p , 0.001; effort financial services: F(1,192) ¼ 158.03, p , 0.001; abilities retail: F(1,192) ¼ 429.97, p , 0.001; abilities financial services: F(1,191) ¼ 470.77, p , 0.001; outcome retail: F(1,191) ¼ 713.11, p , 0.001; outcome financial services: F(1,192) ¼ 144.97, p , 0.001). Hypotheses testing. We test all five hypotheses using regression analysis and present the results in Table II. The relationship between perceived effort and abilities and satisfaction is positive and significant for both the retail setting (perceived effort ß ¼ 0.519, p , 0.001; perceived abilities ß ¼ 0.487, p , 0.001) and the financial services setting (perceived effort ß ¼ 0.340, p , 0.001; perceived abilities ß ¼ 0.594, p , 0.001), in support of H1. However, the results do not support H2; in the retail setting, our analysis of the interaction effects shows a significant but ordinal interaction, which means that the main effects of both perceived effort and abilities can be interpreted and no moderator emerges. In the financial setting, the interaction is also significant, and both main effects are interpretable. Diagrams even show the visible tendency of the employee’s perceived abilities to moderate perceived effort. Our analysis of H3 shows that perceived service outcome significantly influences customer satisfaction in both settings (perceived outcome retail ß ¼ 0.358, p , 0.001; perceived outcome financial services ß ¼ 0.706, p , 0.001). The independent influence of perceived effort and abilities on satisfaction, compared with perceived outcome, suggests support for H4 but not H5. In these experimental conditions, both variables have a significant, positive, and strong influence on customer satisfaction, independent

Satisfaction, when perceived outcome is negativea

Satisfaction Satisfaction, when perceived outcome is positivea

Satisfaction

Satisfaction

Dependent variable

Satisfaction, when perceived outcome is negativea

Satisfaction Satisfaction, when perceived outcome is positivea

11.270 10.583

T

0.297 0.527

3.588 6.367

4.812 8.364

p , 0.005 p , 0,001

p , 0.001 p , 0.001

0.384

0.617

0.620 0.495

p , 0.005 p , 0.001

20.132 20.3055 0.706 14.010 0.339 0.590

0.603

p , 0.001 p , 0.001

7.142 12.468

0.629

0.621

0.340 0.594

p , 0.001 p , 0.001

p , 0.001 p , 0.001

0.124

0.637

0.594

9.028 7.327

7.850 8.614

p , 0.001 p , 0.001

p , 0.001 p , 0.001

Result

H5 Not supported for financial services

H2 Not supported for financial services since moderation direction is not as assumed H3 Supported for financial services H4 Supported for financial services

H1 Supported for financial services

H5 Not supported for retail

H2 Not supported for retail since moderation direction is not as assumed H3 Supported for retail H4 Supported for retail

H1 Supported for retain

Significance Adjusted T R2 H

0.569 0.462

0.497 0.545

20.206 24.891 0.358 5.378

0.519 0.487

b

Note: aA median-split of the sample was conducted to classify subjects in groups of positive/negative perceived service outcome

Perceived abilities

Perceived abilities Perceived effort

Perceived outcome Perceived effort

Perceived abilities Financial Service (n ¼ 200) Perceived effort Satisfaction Perceived abilities Perceived effort £ Satisfaction perceived abilities

Perceived abilities Perceived effort

Perceived outcome Perceived effort

Retail (n ¼ 200) Perceived effort Perceived abilities Perceived effort £ perceived abilities

Independent variables

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547

Table II. Summary of hypothesis test results

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of perceived service outcome. For the financial service setting, we find that with statistical control of the outcome, the perceived abilities of the employee have an even stronger influence on satisfaction than does perceived effort; in the retail setting, the influence of both determinants remains nearly equal. Overall, the results support H1, H3, and H4 for both service types. In experimental conditions, we thus show that the impact of perceived abilities on satisfaction increases and even exceeds that of perceived effort in a financial services setting. Conclusion and implications Our research provides interesting and fundamental insights into customer – employee interactions during service encounters, which represent moments of truth for companies of almost any kind, because they are manifold in nature and not only apply to pure service companies. Our results clarify how customer satisfaction during service encounters develops across and within different service settings and thereby provide implications for human resources, service, and sales managers. In addition, the results clearly reinforce the importance of service delivery process quality for achieving customer satisfaction, as well as that of employee behavior for influencing perceptions of effort and abilities, which represent direct determinants of customer satisfaction. With Study 1, we demonstrate that perceived employee effort is more important for customer satisfaction than are perceived abilities, suggesting that managers should focus predominantly on effort, including performance elements that represent social competence. However, Study 2 indicates that the relative importance of perceived effort and abilities depends on the service type, which implies differentiated management conclusions. Companies thus must examine customers’ perceptions of their employees’ encounter behavior in depth to evaluate and effectively and efficiently manage perceived effort and abilities as the main determinants of customer satisfaction. In turn, practitioners should acknowledge behavioral training represents a significant satisfaction management approach. The continuous auditing of perceived effort and abilities should be a focus of regular satisfaction surveys; our operationalization and development of generic scales for perceived effort and abilities offer a valuable contribution to this. Also, human resource management should include self-evaluation tests for designated encounter employees regarding the display of effort and abilities in customer-contact situations. In addition to the content-related implications of this study, we note the importance of our methodological approach for managers. In combining CIT with a video experiment, we suggest ways that firms can: . analyze critical encounter interactions in depth from a customer’s point of view; and . effectively and efficiently optimize and manage these interactions by manipulating the relevant behavioral cues. The video stimuli rated by customers (such as in Study 2) can help firms to consequently implement our results in everyday encounter situations. First, they provide a means to evaluate hypothetical behavioral changes before the company undertakes cost-intensive employee training. Second, they serve as training videos

with great didactic value, which help close the organizational and procedural gap between market research and frontline implementations. Limitations and suggestions for further research Similar to any empirical study, our research suffers from several limitations. With regard to the general applicability of our findings, we note restrictions imposed by the sample characteristics. Because we mainly use university students or persons with university degrees, our sample does not represent the broad scope of German service customers. Moreover, our data were collected in Germany, which raises the question of the transferability of our findings to other cultural regions. Furthermore, we acknowledge the limitation of the methodical approaches of our two studies. The explorative study (Study 1) samples particularly satisfying and dissatisfying encounters and eliminates more typical, middle-of the road encounters, so the results cannot represent the impact of perceived effort and abilities in normal encounters, which might be of less strength. In addition, the isolated manipulation of the three variables in Study 2 might overestimate the influence of perceived abilities on satisfaction in the two scenarios. In the financial service scenario, the recorded introductions at the start of the movie stimulus might have overemphasised the impact of perceived outcome on customer satisfaction. Further, research should transfer this study to different cultures. Taking different dependent variables into account (e.g. customer emotions, customer loyalty and brand perceptions) might offer a valuable contribution to the fields of service or brand research. In addition, it seems advisable that further research should consider social stereotypes and their impact on perceptions of interpersonal behavior and social judgments. Attractiveness, race, age, and gender can cause stereotypic reactions (Schneider, 2004), so employee appearance might represent a separate determinant, along with employee behavior, for customers’ effort and abilities perceptions and thus affect satisfaction with a service encounter. Finally, as we noted from the start, we investigate behavioral cues that represent the constructs of perceived effort and abilities from a customer’s point of view, which means we disregard the potential influence of the employee’s personality as a behavioral predisposition. Additional research therefore might investigate if and how personality traits influence employees’ behavior and, in turn, the perceived effort and abilities of an employee. References Babakus, E. and Boller, G.W. (1992), “An empirical assessment of the SERVQUAL scale”, Journal of Business Research, Vol. 24 No. 3, pp. 253-68. Baldwin, A.L. (1958), “The role of an ’ability’ construct in a theory of behavior”, in McClelland, D.C., Baldwin, A.L., Bronfenbrenner, U. and Strodtbeck, F.L. (Eds), Talent and Society, Princeton University Press, Princeton, NJ, pp. 195-232. Bateson, J.E.G. and Hui, M.K. (1992), “The ecological validity of photographic slides and videotapes in simulating the service setting”, Journal of Consumer Research, Vol. 19, pp. 271-81. Bettencourt, L.A. and Gwinner, K. (1996), “Customization of the service experience: the role of the frontline employee”, International Journal of Service Industry Management, Vol. 7 No. 2, pp. 3-20.

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Call for papers

International Journal of Service Industry Management (IJSIM) Special Issue Research and practice in marketing, human resources and operations management in services – perspectives from Asia Many Asian economies are highly competitive and can offer alternative perspectives on many aspects of marketing, human resources and operations management in the services sector. Furthermore, Asia is arguably the fastest growing region in the world and is likely to soon be home to three of the largest economies in the world – Japan, China and India. In view of the large customer base and immense market potential in Asia, service organisations searching for growth are increasingly realising that they must cater to these markets. The services literature has been largely derived from research in the ‘‘western’’ world. Managing service businesses in many Asian countries requires a set of complexities less common in the developed economies of the West. Research and practice in services in Asia is at the exploratory stage. It requires new knowledge and is likely to need departures from existing tracks. This call for papers seeks to highlight research and practices surrounding services management in Asia. Manuscripts are therefore invited from both research and practice. Conceptual, empirical, case-study and industry-based research is welcome. The primary criterion for consideration of publication is that manuscripts be on research and application, and be grounded in the Asian context. Researchers with a cultural or contextual connection with Asian economies are particularly encouraged. Contributed manuscripts may deal with, but are not limited to:

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service excellence;

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service branding;

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transactional marketing;

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performance measurements and management systems;

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managing people for service advantage;

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innovation and creativity in service processes;

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business/knowledge process outsourcing services; and

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bottom of the pyramid services.

The submission and review process for this special issue will adopt the author guidelines and review procedures as stipulated in the International Journal of Service Industry Management (IJSIM), where the special issue will be published. The deadline for submission of manuscripts is the 31 May 2008. Please e-mail manuscripts for consideration to Christopher Seow at: [email protected]

Guest editors Jochen Wirtz, NUS Business School, National University of Singapore, Singapore Robert Johnston, Warwick Business School, University of Warwick, United Kingdom Christopher Seow, Business School, University of East London, United Kingdom