Co-production and customer loyalty in financial services

Transcription

1 Journal of Retailing 83 (3, 2007) Co-production and customer loyalty in financial services Seigyoung Auh a,, Simon J. Bell b,1, Colin S. McLeod c,2, Eric Shih d a Yonsei School of Business, Yonsei University, 134 Shinchon-dong, Seodaemun-gu, Seoul , South Korea b Judge Business School, University of Cambridge, Trumpington Street, Cambridge CB2 1AG, United Kingdom c Australian Football League, 140 Harbour Esp Docklands, Melbourne 3008, Australia d Graduate School of Business, Sungkyunkwan University, Seoul , South Korea Abstract Recent developments in marketing thought and practice highlight the opportunities that co-production of services provide for creating customer value. The authors propose a model of co-production with which they investigate the links between co-production and customer loyalty and the factors likely to increase the level of co-production in a financial services context, with support from an investigation in the medical services context. Further, the authors consider the relationships of customer expertise, customer advisor communication, customer affective commitment, and interactional justice with the level of co-production. On the basis of testing with a sample of 1,197 customers of a large multinational financial services organization and 100 patients of medical services, the model is partially supported. Therefore, the authors suggest that co-production may have an important role as a basis for competition in the financial services industry New York University. Published by Elsevier Inc. All rights reserved. Keywords: Financial services; Customer loyalty; Co-production; Medical services; Customer value Introduction Managers have undertaken varied and creative approaches to draw customers closer to their organizations in the pursuit of longer, more profitable relationships. Recent literature on customer firm co-production has focused on the merits of increasing the extent of active customer involvement in service production and delivery. In most cases, the literature unequivocally supports increasing opportunities for co-production between the firm and its customers as a means to achieve competitive advantage (e.g., Prahalad and Ramaswamy 2004). Co-production even has become a central tenet of a proposed service-centered logic for marketing (Vargo and Lusch 2004) and could be the next frontier in competitive effectiveness (Bendapudi and Leone 2003, p. 14). Corresponding author. Tel.: addresses: sauh@yonsei.ac.kr (S. Auh), s.bell@jbs.cam.ac.uk (S.J. Bell), colinm@afl.com.au (C.S. McLeod), e shih@yahoo.com (E. Shih). 1 Tel.: ; fax: Tel.: The shift toward co-production as a means to enhance perceptions of value seems entirely reasonable, particularly because the notion of customers creating value with the firm as opposed to the firm creating value for customers (Lengnick-Hall 1996; Prahalad and Ramaswamy 2004; Vargo and Lusch 2004) appears consistent with a market-oriented organization. Organizations may find that they have no choice but to embrace co-production to organize their service delivery. Increasingly empowered customers can elect which element and how much of a service they want to produce for themselves, which means that the previously clear-cut producer customer divide has become far more blurred. Thus, it seems a hallmark of the postmodern marketing age is that the product is likely to become less and less a finished object and more and more a process into which the customer can immerse oneself and can provide inputs (Firat et al. 1995, p. 51). Services marketing literature has long recognized the role of customers as participants in the service delivery process (e.g., Lovelock and Young 1979), but until recently, customer participation remained something service organizations simply endured. Some researchers have argued that less direct contact between the customer and the production system /$ see front matter 2007 New York University. Published by Elsevier Inc. All rights reserved. doi: /j.jretai

2 360 S. Auh et al. / Journal of Retailing 83 (3, 2007) provides a greater chance for maximum efficiency (Chase 1978). Co-production requires a shift to a buyer-centric business model, through which customer preferences can be expressed in real time and products customized accordingly. The emphasis on real-time customization and involvement introduces additional uncertainty to the service process that may lead to increased workloads and greater role conflict for service employees (Hsieh et al. 2004). So, is this recent and seemingly inexorable march to closer, more participative service relationships desirable for service organizations? Co-production, we contend, must have a meaningful impact on customers loyalty to the organization for the benefits to outweigh the costs. However, few empirical studies investigate the relationship between customer firm co-production and loyalty. This study therefore investigates the role of co-production in financial and medical services contexts and clarifies its impact on customer loyalty. The remainder of this article is organized as follows: First, we review co-production literature and consider how co-production contributes to customer perceptions of value. Second, we consider some factors that determine the extent to which customers engage in co-production with the firm, specifically, the antecedent conditions widely accepted in the conceptual literature: task clarity, customer ability, and customer motivation (Lengnick-Hall 1996; Schneider and Bowen 1995). In addition, we consider the outcomes of coproduction, namely, its impact on attitudinal and behavioral loyalty. Third, we test our hypothesized model using a sample of 1,197 clients of a global financial services firm and validate the model with a sample of 100 patients in a medical services context. Fourth, we discuss our findings, as well as some implications for management and future research directions. Background We adopt the view of co-production as engaging customers as active participants in the organization s work (Lengnick-Hall et al. 2000, p. 359). The prevailing view is that co-production has two primary benefits. First, involving the customer in co-production can lower costs for organizations, and to the extent that customers participate in the production of the goods (e.g., assembling furniture from IKEA) or services (e.g., arranging airplane tickets over the Internet), the consumer can expect a reduction in price. This economic rationale provides the focus for most early literature on co-production (e.g., Lovelock and Young 1979), and its benefits are especially significant for services organizations with labor-intensive production processes and an inability to stockpile. In such contexts, coproduction can lead to dramatic increases in productivity. Although this viewpoint has its critics (e.g., Chase 1978), the consensus has indicated that increased co-production leads to productivity gains for organizations (Mills et al. 1983). Second, co-production enables the firm to customize its offerings to customers needs. The personal interface between the client and the firm thus represents a critical component of a service delivery process in which the customer has direct input into the production of the final service. High-contact services, such as financial, legal, and medical services, are defined by a high degree of coupling (i.e., continuous, immediate impact of contact), interdependence (i.e., service quality is contingent on inputs from each party), and information richness (i.e., the value of information passed between parties) (Kellogg and Chase 1995). The benefits of co-production are not limited solely to high-involvement services though; even in low- and moderate-involvement services, customers may find co-production attractive because they enjoy increased perceived control over the service delivery process (Bateson 1985) and additional opportunities to make choices, which offers them higher levels of customization (Schneider and Bowen 1995). These customer benefits form the basis of our understanding of co-production and its relationship with client loyalty. We suggest that co-production leads to stronger perceptions of customization and cost reductions, which in turn lead to more favorable assessments of the organization and increase customers intentions to spend and actual spending with the organization over time. To the extent that co-production proves desirable for a firm, the firm must come to understand its facilitating factors. Three customer factors are key to effective co-production: perceived clarity of the task, ability or competence, and motivation (Bettencourt et al. 2002; Lengnick-Hall 1996; Lovelock and Young 1979; Meuter et al. 2005). Task clarity refers to the extent to which customers understand what is required of them in service production. The clearer a customer s role expectations, the greater is the likelihood that their contributions will lead to improved service outcomes (Mills et al. 1983). Because communications between service employees and customers help improve task clarity (Lengnick-Hall et al. 2000), we investigate client advisor communication as an antecedent of co-production. Fig. 1 illustrates our conceptual model. Customer ability refers to the quality of input the customer provides to the service production process. Useful and timely customer contributions lead to higher quality co-produced output (Schneider and Bowen, 1995). We employ a measure of client expertise to indicate ability or competence, because expertise facilitates efficient and effective service transactions, in that clients are better able to provide accurate and pertinent information to advisors (Kelley et al. 1990). Finally, customers motivation to participate is critical for effective co-production. In addition to being able to contribute, customers must be willing to get involved (Lengnick-Hall et al. 2000). Motivated clients are more likely to respond to advisor requests for information in a timely manner and read documentation provided by the advisors prior to face-to-face meetings. Therefore, advisors do not need to repeat themselves, and clients can ask more incisive

3 S. Auh et al. / Journal of Retailing 83 (3, 2007) Fig. 1. Proposed theoretical model. questions. To capture clients motivation to become involved in co-production, we measure their affective commitment to the firm and their perceptions of interactional justice. Social exchange theory (Blau 1964) supports the view that clients who like an organization (i.e., affective commitment) and perceive interaction processes as consistent, timely, open, and fair (i.e., interactional justice) reciprocate with efforts that help the organization. These customers therefore should be more motivated to participate in the service process. Hypothesized model Co-production and client loyalty We define co-production as constructive customer participation in the service creation and delivery process and clarify that it requires meaningful, cooperative contributions to the service process. In the delivery of financial services, such contributions might involve client preparation prior to meetings with advisors, client provision of input to decisionmaking processes, and timely and accurate responses to advisor requests for information. Co-production likely yields several benefits for customers, including lower prices, more opportunities to make choices, and greater discretion about the configuration of the final product. Moreover, clients likely experience shorter waiting times and enjoy a greater likelihood of customization. The relationship between co-production and perceived value, however, may not be unequivocally positive (Lengnick-Hall 1996). Firms have less control over the training of customers to participate effectively in the production process than they do with service employees. In turn, customer involvement may raise the overall level of uncertainty in the transaction and thereby affect outputs. Furthermore, customers may dislike the level of effort required to coproduce a service and therefore abdicate their role in the service process (Solomon 1986). On balance, however, customers likely consider being involved in a service creation process as a source of value, especially in the emerging era of connected, informed, empowered, active consumers (Prahalad and Ramaswamy 2004). Dellande et al. (2004) recently demonstrated that customer compliance one facet of co-production relates significantly to customer satisfaction, and efforts by organizations to involve clients in co-production lead to increased mutual understanding (Mohr and Bitner 1991). Such mutual understanding results in positive emotional responses to the service (Price et al. 1995). Moreover, according to the view that satisfaction results from both cognitive and affective assessments of service experiences (Westbrook 1987), repeated positive emotional experiences ultimately contribute to the sort of customer satisfaction that underpins customers decisions to be loyal to an organization (Lam et al. 2004). Therefore, we incorporate measures of attitudinal and behavioral loyalty (Dick and Basu 1994; Wallace et al. 2004). Attitudinal loyalty refers to a measure of clients intentions to stay with and level of commitment to the organization, whereas behavioral loyalty, in the financial services context, is an objective measure of the amount of brokerage the client paid to the firm in the year following the administration of the questionnaire. Taking these measures of loyalty into account and on the basis of the preceding arguments, we hypothesize: H1a. H1b. Co-production relates positively to attitudinal loyalty. Co-production relates positively to behavioral loyalty. Role of communication We adapt Sharma and Patterson s (1999) definition of communication as the formal and informal sharing of mean-

4 362 S. Auh et al. / Journal of Retailing 83 (3, 2007) ingful and timely information between a client and advisor in an empathetic manner. The content of communication, in the financial context of our study, focuses on keeping clients informed of investment strategies and portfolio performance, explaining financial concepts and trade-offs, and responding to client requests for information. Communication flow is central to the establishment of strong relationships; it builds trust by resolving clients queries and concerns and managing their expectations (Sharma and Patterson 1999). Accordingly, we expect communication between the advisor and the client to increase the incidence of co-production because of their greater willingness to share (potentially sensitive) information. Communication also socializes customers with regard to the procedures and norms of the organization by helping them identify with a particular role within the organization, which also is important for effective co-production (Mills et al. 1983). The extent to which customers identify themselves as customers, as opposed to co-producers, affects their willingness to adapt to co-production and participation. Communication therefore leads to greater clarity in terms of both the tasks required of customers and involvement and participation norms. Accordingly, we hypothesize that H2. Client advisor communication relates positively to coproduction. Role of client expertise Expertise represents a customer s accrued knowledge about how a product should perform and general understanding of the average performance of similar brands in a product category (Sharma and Patterson 2000). Expertise likely increases client involvement in co-production for several reasons. First, as customers gain more expertise in the product category, they can better assess where they might make a contribution. Moorthy et al. (1997) similarly argue that as customers gain experience, they are better able to evaluate the various attributes of different service offerings. In other words, customers who have a high level of expertise are better equipped to make valuable contributions to the production of a service and hence more likely to do so. Second, inexperienced customers typically perceive higher decision-making risk (Heilman et al. 2000). These customers are less likely to involve themselves in co-production because they fear producing a suboptimal outcome. Third, expert customers likely have a greater need for control in the service delivery process (O Connor and Siomkos 1994). Therefore, the need to control or influence the outcome of a service will increase expert customers likelihood of getting involved in co-production. Accordingly, we hypothesize: H3. Client expertise relates positively to co-production. Role of affective commitment Consistent with the view of services clients as partial employees (Kelley et al. 1990), we adapt an employeefocused definition of affective commitment (Meyer and Allen 1997). That is, we define affective commitment as the customer s attachment to, identification with, and involvement in the organization. Therefore, affective commitment is based on a sense of liking and emotional attachment to the partnership and leads to a sense of belonging or being part of the family. The critical feature of this component of commitment is that the customer stays with the firm because he or she wants to. Compared with continuance ( I have no choice but to be committed ) or normative ( I should be committed ) forms, affective commitment is far more likely to be associated with increased involvement in co-production. Human resources management literature also suggests that employees with strong affective commitment feel greater emotional attachment to the organization and more motivation to contribute meaningfully to organizational outcomes than do those who have low affective commitment (Meyer and Allen 1997). Furthermore, affective commitment is associated positively with work effort (e.g., Randall et al. 1990) and job performance (Babakus et al. 2003). Similarly, customers who regard themselves as partial employees should be more engaged in co-production. The related notion of customer company identification that is, perceived oneness with an organization (Bhattacharya and Sen 2003) suggests that customers with high affective commitment to an organization are more likely to become involved in co-production. Customers who strongly identify with a firm experience a more positive sense of self when they, or others, perceive the firm as more successful (Dutton et al. 1994). Experiencing the organization s successes or failures as their own, these customers are motivated to participate in the service delivery to ensure the best possible outcome for both themselves and the firm. In addition, they are more likely to understand the rationale behind the firm s internal processes, rules, and norms and participate to the full extent that these allow. Consequently, we hypothesize: H4. Client affective commitment to the firm relates positively to co-production. Role of interactional justice Interactional justice refers to fairness judgments based on the quality of the interpersonal treatment a customer receives during a service encounter (Bies and Moag 1986; Masterson et al. 2000). In an interpersonal relationship, perceptions of interactional justice influence attitudes and behaviors toward the person (i.e., advisor) who provides the treatment (Masterson et al. 2000). According to the notions of social exchange (Blau 1964), the degree to which clients perceive their treatment as fair influences the extent to which they reciprocate with cooperative behaviors. In other words, fairness perceptions increase the level of co-production, to the extent that such co-production helps the advisor perform his or her job more effectively. Accordingly, we hypothesize:

5 S. Auh et al. / Journal of Retailing 83 (3, 2007) H5. Client perceptions of interactional justice relates positively to co-production. Sample and data collection Method A global financial services firm provided a list of 4,244 clients, classified as high value, from its retail advisory services division. We sampled high-value clients because they have greater frequency of contact with advisors and an increased likelihood of being able to recall the nature of the service interactions. These financial advisory services comprise both core service elements (e.g., investment advice, research, investment planning) and various associated support products (e.g., margin lending, cash accounts, settlement services). A high degree of product complexity exists within this sector of the financial services industry, affording significant opportunities for customization in terms of service bundling and modifications of offerings to suit customers needs. Furthermore, the service delivery occurs in a climate of high customer and environmental uncertainty (Sharma and Patterson 1999). The importance of transparency in the service process, the prospect for ongoing customer advisor interactions, and the opportunities for customer control make it an ideal context for assessing the importance of co-production as a potential source of value. We collected data through a self-administered questionnaire sent by mail to each respondent. We received 1,197 usable responses, for a response rate of 28 percent. Although we used only one mailing, those who responded are representative of the total population according to demographic criteria. Respondent demographic characteristics show that the majority are men (87 percent) and older than 45 years of age (92 percent). In addition, most respondents had transacted with the organization for more than 2 years (96 percent). Measures For the measures used in this study, we adapt existing scales to suit the financial services sector. The constructs employ seven-point, Likert-type scales, with anchors ranging from strongly disagree (1) to strongly agree (7). We provide the complete wording of each scale item, along with their measurement properties, in Tables 1 and 2. We measure co-production, defined as constructive customer participation in the service creation and delivery Table 1 Confirmatory factor analysis, reliability, and validity (client advisor relationship) Constructs Factor loadings t-values Affective commitment (α =.95, CR =.96, AVE =.84) I feel like part of the family at [Business Name].86 a [Business Name] has a great deal of personal meaning for me I feel emotionally attached to [Business Name] I feel a strong sense of belonging to [Business Name] Investment expertise b (r =.84, CR = na, AVE = na) I possess good knowledge of financial planning services and products.98 a I am quite experienced in this area Interactional justice (α =.93, CR =.93, AVE =.78) The length of time taken to resolve any concerns I have is adequate.81 a My advisor is flexible when dealing with any concerns I have My advisor takes seriously any concerns I have My advisor puts the appropriate amount of effort into resolving any concerns I have Communication (α =.91, CR =.91, AVE =.72) My advisor keeps me very well informed about what is going on with my investments.83 a My advisor explains financial concepts and recommendations in a meaningful way My advisor always offers me as much information as I need My advisor always explains to me the pros and cons of the investment he/she recommends to me Co-production (α =.80, CR =.77, AVE =.53) I try to work cooperatively with my advisor.75 a I do things to make my advisor s job easier I prepare my queries before contacting my advisor Attitudinal loyalty (α =.86, CR =.88, AVE =.65) I will invest more funds through [Business Name] in the future.65 a The chances of me staying in this relationship are very good The likelihood of me trying other [Business Name] services is very good I try to use [Business Name] for all my investment needs Notes. na: not available, α: Cronbach s alpha, CR: composite reliability, and AVE: average variance extracted. a Initial loading is fixed to 1 to set the scale of the construct. b For a two-item scale, we report the interitem correlation (Cortina 1993).

6 364 S. Auh et al. / Journal of Retailing 83 (3, 2007) Table 2 Confirmatory factor analysis, reliability, and validity (physician patient relationship) Constructs Factor loadings t-values Affective commitment (α =.92, CR =.93, AVE =.76) I feel like part of the family at my doctor s office when I visit.87 a The doctor s office I go to has a great deal of personal meaning for me I feel emotionally attached to my doctor I feel a strong sense of belonging to my doctor s office Medical expertise (α =.83, CR =.84, AVE =.56) I possess good knowledge of medical and health-related issues.75 a I am quite experienced in doctor patient consultations I can understand my doctor s techniques and treatments d I understand almost all the aspects of the services provided by my doctor d Interactional justice (α =.89, CR =.89, AVE =.66) The length of time my doctor takes to resolve any concerns I have is adequate.78 a My doctor is flexible when dealing with any concerns I have My doctor takes seriously any concerns I have My doctor puts the appropriate amount of effort into resolving any concerns I have Communication (α =.87, CR =.88, AVE =.64) My doctor keeps me very well informed about what is going on with my health.81 a My doctor explains medical and treatment options in a meaningful way My doctor always offers me as much information as I need My doctor always explains to me the pros and cons of a particular treatment he/she recommends to me Co-production (α =.76, CR =.78, AVE =.49) I try to work cooperatively with my doctor.85 a I do things to make my doctor s job easier I prepare my questions before going to an appointment with my doctor I openly discuss my needs with my doctor to help him/her deliver the best possible treatment d Attitudinal loyalty b (r =.92, CR = na, AVE = na) I will continue to see my current doctor for my medical needs in the future.99 a The chances of me staying with my current doctor are very good The likelihood of me trying other services from my doctor is very good c I try to use my current doctor for most of my medical needs c Notes. na: not available, α: Cronbach s alpha, CR: composite reliability, and AVE: average variance extracted. Initial loading is fixed to 1 to set the scale of the construct. For a two-item scale, we report the interitem correlation (Cortina 1993). c Deleted after scale purification. d Newly added items. process and the extent to which customers are engaged as active participants in the organization s work (Lengnick-Hall et al. 2000), at the client/advisor level with a three-item scale adapted from Bettencourt s (1997) measures of customer cooperation and participation. For attitudinal loyalty, or clients intentions to stay with and level of commitment to the organization, we use a four-item scale adapted from Boulding et al. (1993) behavioral intentions scale and the loyalty dimension of the behavioral intentions scale used by Zeithaml et al. (1996). We calculate behavioral loyalty as the amount of brokerage paid by the client to the firm in the year following the administration of the questionnaire. To capture communication, defined as the sharing of meaningful and timely information between a client and an advisor in an empathetic manner, we use a four-item scale that measures the extent to which advisors communicate information relevant to the core service, adapted from Sharma and Patterson (1999). We define investment expertise as the extent of a customer s prior product knowledge and ability to assess product performance and measure it by capturing clients market-related investment expertise, rather than firm-specific expertise, through a four-item scale developed by Sharma and Patterson (2000) and adapted to fit the context of the study. Because respondents self-assess their level of investment expertise, the measure is subjective. However, previous research indicates that subjective and objective knowledge have similar relationships with customer loyalty (e.g., Capraro et al. 2003). For affective commitment, or the extent to which clients feel identification with and attachment to the organization, we use a four-item scale adapted from Meyer and Allen (1984). Finally, for interactional justice, which we define as fairness judgments based on the quality of the interpersonal treatment received by a customer during a service encounter (Bies and Moag 1986), we adapt a four-item scale from Smith et al. (1999) to suit the financial services context.

7 S. Auh et al. / Journal of Retailing 83 (3, 2007) Table 3 Descriptive statistics and intercorrelations (n = 1,197) Variables Affective commitment 2. Expertise Interactional justice Communication Co-production Attitudinal loyalty Behavioral loyalty Relationship length Gender Age group Mean , Standard deviation , Note. Correlations of.06 or greater are significant at p <.05 (two-tailed test). Control variables We control for relationship length, client gender, and client age. Client age and gender moderate the relationship between satisfaction and loyalty (e.g., Mittal and Kamakura 2001). Controlling for age also accounts for the potential that older customers may demonstrate differences in their loyalty because of their greater accumulation of wealth over time. Relationship length also might serve as a proxy for customer inertia. Inert customers naturally are more likely to be loyal (Colgate and Lang 2001). We measure relationship length as the length of time (in years) that a customer has held an account with the service provider. Finally, respondents indicated their gender, as well as their age group according to five categories: 18 30, 31 45, 46 65, 66 80, and >81 years of age. Measurement model Results We first assess the measurement model, then test the hypotheses according to the guidelines suggested by Anderson and Gerbing (1988). For scale purification, we delete measures from the initial battery of items on the basis of the item-total correlation and low factor loadings (<.50) from the initial confirmatory factor analysis (CFA). As a result, we delete one item from the original co-production scale and two items from the initial investment expertise scale. We take extra caution to verify that the deletion of these items does not change or harm the intended meaning of the constructs. In Table 1, we illustrate the final set of items for each construct used in the financial services study. After we determined the final pool of items, we subjected the remaining pool of items, which correspond to affective commitment, investment expertise, interactional justice, communication, co-production, and attitudinal loyalty, to a CFA using Amos 6.0. Our measurement model suggests acceptable fit to the data (χ(174) 2 = , p <.001, goodness-of-fit index =.92, Tucker Lewis index =.95, confirmatory fit index =.96, root mean squared error of approximation =.07). In Table 1, we also provide the results of the CFA (i.e., factor loadings, t-values, and reliability estimates). The convergent validity of the scales is supported because the estimated coefficients of the indicators are all significant (t > 2.0) (Gerbing and Anderson 1988) and all estimates for the average variance extracted (AVE) are equal to or greater than.50 (Bagozzi and Yi 1988). Furthermore, we find support for discriminant validity by observing that (1) the difference between the constrained model in which the correlation between the two constructs is set to 1 and the unconstrained model is significant and (2) the squared correlation between any pair of constructs is less than the respective AVE of each construct in the pair (Fornell and Larcker 1981). Considering our reliability, convergent, and discriminant validity tests collectively, we conclude that our measurement model satisfies all the psychometric property requirements. In Table 3, we show the means, standard deviations, and intercorrelations for the constructs operationalized in this study. Hypothesis testing We use structural equation modeling to test our hypotheses because our constructs are linked within a system of equations. Also, because our independent and dependent variables come from the same source, we recognize the potential for common method bias. To minimize this possibility, we control for such biases by following the recommendations of Podsakoff et al. (2003), such as including a common method factor along with the theoretical constructs in the conceptual model. As we show in Table 4, model 1 does not include any control for common method bias, whereas models 2 and 3 do (model 3 is the result of a second study in the medical services context, which we discuss subsequently). Our results show the presence of common method variance, as exemplified by the chisquare difference between models 1 and 2 ( χ 2 M1 M2 (2) = , p <.001). However, when we compare model 2 with model 1, all the fit indices improve, which suggests our theoretical model is robust, even after we control for common method bias and include objective data. Also, we observe few changes in the significance of paths across the models, which imply that the common source does not adversely affect our results. During hypotheses testing, we use a one-tailed test for significance testing because our hypotheses are directional, such that we propose either a positive or a negative effect on the basis of strong

8 366 S. Auh et al. / Journal of Retailing 83 (3, 2007) Table 4 Model testing Paths Model 1 Model 2 Model 3 Attitudinal loyalty as DV, without controlling for common method factor (n = 1,197) Behavioral loyalty as DV controlling for common method factor (n = 1,197) Physician patient co-production model with attitudinal loyalty as DV (n = 100) β t-value β t-value β t-value DV: co-production Communication *** *** *** Client expertise *** *** *** Affective commitment ** *** Interactional justice *** *** Age *** *** Gender * Relationship length DV: attitudinal loyalty Co-production *** * *** Age Gender Relationship length * * DV: behavioral loyalty Attitudinal loyalty * Co-production Age * Gender Relationship length Squared multiple correlations, R 2 Co-production Attitudinal loyalty Behavioral loyalty.01 Model fit χ 2 1, df GFI TLI CFI RMSEA Notes. Table includes standardized coefficients. DV: dependent variable. * p <.05 (one-tailed test for hypothesized relationships; two-tailed test for controls). ** p <.01 (one-tailed test for hypothesized relationships; two-tailed test for controls). *** p <.001 (one-tailed test for hypothesized relationships; two-tailed test for controls). theoretical grounds from extant literature. However, we use a twotailed test for nondirectional associations, such as those between the control and dependent variables (Robinson 1988). In reporting our results, we focus on the results obtained from model 2, which controls for common method bias. We find support for H1a, in that co-production is positively and significantly related to attitudinal loyalty (β =.06; p <.001). However, H1b is not supported, because co-production is positively but not significantly related to behavioral loyalty (β =.04; p >.10). Regarding the antecedents of co-production, we find that H2 H5 are all supported; client advisor communication (β =.23; p <.001), client expertise (β =.19; p <.001), client affective commitment (β =.13; p <.001), and client-perceived interactional justice (β =.32; p <.001) are positively and significantly related to co-production. With regard to the control variables, older clients are more likely to participate in coproduction, and younger clients are more likely to spend more in brokerage. Finally, clients in longer relationships are more likely to display attitudinal loyalty. Follow-up study Because we obtained the results of our first study from a single firm, we conduct a second study in a different context (patient physician co-production) to enhance the generalizability of our findings. For this follow-up study, we collect survey data using the mall intercept method. That is, we set up a booth at a large mall in the southeast region of the United States and approached random shoppers to participate in a study of people s impressions of their primary health care provider. If a respondent agreed to participate, he or she was led to a private table to complete the survey and compensated $5 for his or her time. To preserve space, we do not elaborate on the measurement model; however, we report the items, reliability, and AVE in Table 2. Using the same criteria as in our previous study, we find support for convergent and discriminant validity. We use path analysis because of the many parameters to be estimated relative to the small sample (n = 100). Therefore, we combine

9 S. Auh et al. / Journal of Retailing 83 (3, 2007) the items of each construct into a single indicator and set the measurement path estimates to 1 (Bollen 1989) and the error variance to the scale variance (1 reliability) to account for measurement error (Hayduk 1987). A summary of our hypotheses testing appears as model 3 in Table 4. We could not test H1b because we lack a measure of behavioral loyalty in our second data set; however, we find support for H1a, in that co-production is positively and significantly related to attitudinal loyalty (β =.59; p <.001). In addition, H2 and H3 are supported, because communication (β =.50; p <.001) and expertise (β =.29; p <.001) are positively and significantly related to co-production. However, H4 and H5 are not supported; affective commitment (β =.09; p>.10) and interactional justice (β =.10; p>.10) are positively but not significantly related to co-production. Discussion We find mixed results pertaining to the relationships between co-production and both attitudinal and behavioral loyalty. Co-production is significantly associated with attitudinal loyalty but not with behavioral loyalty. However, attitudinal loyalty is positively and significantly related to behavioral loyalty, which suggests that attitudinal loyalty may have mediated the effect of co-production on behavioral loyalty, consistent with theories of reasoned action (Sheppard et al. 1988), according to which attitude formation precedes behavioral intentions and actual behavior. This finding also suggests that the financial benefits to the firm of changing the service process to embrace co-production might not be immediate. In the patient physician context, we again find a positive and significant relationship between co-production and attitudinal loyalty. Thus, we tentatively conclude that a more seamless integration of the customer into the valuecreation processes of firms is warranted (Vargo and Lusch 2004). That is, the co-creation experience may yet offer an opportunity for value creation for service organizations (Prahalad and Ramaswamy 2004). Our results also provide some empirical support for the many conceptual frameworks that incorporate motivation, ability, and task clarity as determinants of co-production (Bettencourt et al. 2002; Lengnick-Hall 1996; Lovelock and Young 1979). Client advisor communication has a significant positive association with co-production, which implies that clarity of expectations regarding customers roles leads to more effective co-production outcomes and therefore a more loyal customer base. Communication delineates the roles that each participant in the service delivery process should have and thereby avoids the potential that overzealous customers will become involved in aspects of service production for which they are unqualified. Similarly, role theory as applied to service relationships (e.g., Solomon et al. 1985) suggests that service quality will be perceived as high when customers and service providers stick to their predefined roles and responsibilities. We confirm our expectation that client expertise or ability would have a significant and positive association with coproduction. Expert customers have greater ability to make meaningful contributions to service delivery, which, again, leads to an increase in loyalty. The positive and significant associations between both affective commitment and interactional justice and co-production in the financial services context provide support for the expectation that customers must be sufficiently motivated to take part in joint service production. Apparently, a commitment to the organization and a perception of being treated fairly by advisors increases the likelihood that customers will reciprocate with behaviors that help the advisor deliver a more customized, satisfactory product (Bettencourt 1997). Thus, both interactional justice and commitment indicate relationship quality and encourage a deepening of the relationship through co-production initiatives. In the physician patient study, we could not confirm these results; neither affective commitment nor interactional justice relates significantly to co-production. This finding may be a reflection of the historically strong paternalistic model of physician patient interactions (Gafni et al. 1998), in which patients play a more passive role. In financial services, and certainly among the high-value clients surveyed in our primary study, there may be a greater acceptance that committed clients would be more proactive in shaping the service they receive. Managerial implications Various implications for managers emerge from this study. First, to the extent that firms pursue co-production, managers may need to institute cultural changes in their organizations. The notion of drawing customers into the production and delivery processes of high-credence services such as financial advice and medical services may be an anathema to conservative financial service firms and medical practices. Therefore, managers might experiment with new service delivery methods on a limited basis to demonstrate the value of co-production. Second, our study has implications for recruitment, job design, and employee reward structures. An increase in coproduction initiatives will require more flexible, capable, and responsive employees to cope with the uncertainty inherent in additional customer involvement. Increased customer contact likely will give rise to more emotional labor and concordant increases in role stress and emotional exhaustion (Grayson 1998). In general, these demands speak to the importance of highly trained employees who are rewarded accordingly (Beatty et al. 1996). Third, increasing customer ability (i.e., expertise) and task clarity (i.e., communication) increases the incidence of co-production, so managers should work to improve communications between advisors and clients, which can serve the dual purpose of improving task clarity and developing customer expertise. Various means of communication might be employed. Advisors, for example, offer an obvious source of information and expertise and should be prepared to answer clients queries in nontechnical language. Other less personal

10 368 S. Auh et al. / Journal of Retailing 83 (3, 2007) forms of communication also might assist in building client expertise and improving task clarity. For example, financial service firms have long made analyst reports available to clients, but these reports often are highly technical and inaccessible to novice investors. Materials with various levels of complexity, ranging from introductory materials that include definitions and nontechnical language and analyses to highlevel reports, would enable customer investment expertise to develop gradually. Fourth, increasing customers motivation to co-produce involves managing the service experience such that customers perceive the relationship as equitable. Those with a stronger affective commitment to the organization will be more motivated to make the most of their co-production opportunities, but building customers affective bonds with an organization is no simple task. Providing advisors with the latitude and resources to forge stronger personal links with clients might be a good place to start. Furthermore, comprehensive service recovery processes likely will increase customers perceptions of interactional justice (Maxham and Netemeyer 2003), though our physician patient study indicates that resources should be dedicated first to increasing patients expertise and improving communication. Nonetheless, improving patients affective commitment and perceptions of interactional justice could have a bearing on the likelihood of co-production in the long term, according to the mounting evidence that patients are becoming more proactive in their choices of medical services and how they are delivered (Campbell 2003). Study limitations The veracity of our findings should be considered in light of some of our study s limitations. First, and perhaps most important, we study customers from only two industries financial services and medical. Although our primary goal has been to investigate the role of co-production in a financial services context and validate those findings in another industry, the strength of the various relationships we test in our model may differ in other service industries. Financial and medical services are high in credence properties and, for most customers, are high involvement (Sharma and Patterson 1999), so co-production initiatives are more likely to be perceived by customers as sources of value. However, in low-involvement service situations (e.g., grocery shopping), fewer opportunities for co-production may exist to create additional value, which may cause customers to perceive coproduction as a chore. Many customers in such contexts may adopt a cynical view that co-production is simply a means of shifting the workload onto customers shoulders. Second, our sample maintains a business-to-customer focus. In business-to-business relationships, co-production is far more likely to be an accepted part of the relationship; many firms consider it a job requirement of buyers. To the extent that co-production goes with the territory in business-tobusiness relationships, it may be associated less strongly with loyalty. In addition to studies of other industries, researchers might consider a broader range of factors that determine customer loyalty. Clearly, our model is limited in its ability to explain behavioral loyalty, so further studies might yield additional insights, particularly into organizations for which the service employee is less critical in creating customer value. Third, employing scales that measure our focal constructs with more fine-grained precision might generate some additional insights. Other conceptual and empirical studies provide some indication of how a measure of co-production could be expanded to incorporate a broader range of dimensions (see Bettencourt et al. 2002; Lengnick-Hall et al. 2000). For example, the inclusion of less personal, remote coproduction technologies (e.g., the Internet) might affect our model (Hsieh et al. 2005) if co-production activities that drive customers to more widely available Internet resources, after repeated experience, equip the customer with the expertise and knowledge to switch firms (e.g., Bell et al. 2005). Fourth, it would be worthwhile to explore firms transaction costs associated with integrating customers more fully into the production process. 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