Understanding Customers' service

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Understanding customers’ satisfaction and repurchase intentions
An integration of IS success model, trust, and justice
Yu-Hui Fang
Tamkang University, Tamsui, Taiwan

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Received 22 August 2010 Revised 4 May 2011 Accepted 7 May 2011

Chao-Min Chiu
National Sun Yat-sen University, Kaohsiung City, Taiwan, and

Eric T.G. Wang
National Central University, Jhongli City, Taiwan
Abstract
Purpose – The aim of this study is to extend DeLone and McLean’s IS success model by introducing justice – fair treatments received from the exchanging party – and trust into a theoretical model for studying customers’ repurchase intentions in the context of online shopping. Design/methodology/approach – The research model was tested with data from 219 of PCHome’s online shopping customers using a web survey. PLS (partial least squares) was used to analyze the measurement and structural models. Findings – Data collected from 219 valid respondents provided support for all but one hypotheses (with a p-value of less than 0.05). The unsupported hypothesis regards the relationship between service quality and satisfaction (H4). The study shows that trust, net benefits, and satisfaction are significant positive predictors of customers’ repurchase intentions toward online shopping. Information quality, system quality, trust, and net benefits, are significant determinants of customer satisfaction. Besides, online trust is built through distributive, procedural, and interactional justice. Overall, the research model accounted for 79 percent of the variance of repurchase intention. Originality/value – An endeavor to extend the updated IS success model in terms of the peculiar nature of e-commerce is needed. The study complements the updated IS success model with justice trust perspectives, considering them a more comprehensive measure of online shopping satisfaction and repurchase intention in an e-commerce context. Keywords IS success model, Justice, Online shopping, Repurchase intention, Online catalogues, Satisfaction, Home shopping, Service quality assurance, Trust, Taiwan Paper type Research paper

1. Introduction The business-to-consumer (B2C) e-commerce or online shopping market is growing rapidly and has become one of the most interesting developments in e-commerce. According to a market survey by ComScore, online sales outperform offline retail sales in certain key holiday categories in 2008 despite the 3 percent decline in overall sales (including online and offline sales) during the holiday season[1]. Clearly, online shopping market provides an avenue for struggling to survive in the turbulent markets

Internet Research Vol. 21 No. 4, 2011 pp. 479-503 q Emerald Group Publishing Limited 1066-2243 DOI 10.1108/10662241111158335

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of the global weak economy. As with any transaction mode, repurchase is critical to the success of online stores. What, then, keeps buyers loyal to an online store? E-commerce research has addressed this issue from different aspects, including explanations based on service quality, benefits of online shopping, trust, and satisfaction (Childers et al., 2001; Gefen et al., 2003). Customer satisfaction is particularly important to the success of online stores as it is posited as a major driver of post-purchase phenomena, such as repurchase intentions. In early online shopping, a web presence and low prices were believed to be key drivers of success. More recently, web site quality has become essential for improving customer satisfaction and creating customer loyalty (Parasuraman et al., 2005). In traditional service research and in emerging research on electronic service (e-service) (Collier and Bienstock, 2006), several antecedents of customer satisfaction have been proposed. Among these, web site quality figures prominently. Several researchers have developed conceptual models for measuring B2C web site success (Liu and Arnett, 2000). They identified three major quality constructs that are critical to web site success in e-commerce: information quality, system quality, and service quality. Those models are consistent with the updated information systems (IS) success model (DeLone and McLean, 2003), a research framework theorizing that information quality, system quality, and service quality are fundamental determinants of an individual’s satisfaction, which in turn is the determinant of repurchase intention. DeLone and McLean (2004) argue that IS success model can be applied to study e-commerce success. Accordingly, the study uses IS success model as the theoretical foundation for explaining customer repurchase intention. Trust in the seller is a vital key to building customer loyalty and maintaining continuity in buyer-seller relationships (Anderson and Weitz, 1989). The spatial and temporal separation between online buyers and sellers leads to asymmetry problems. A typical type of asymmetry is information asymmetry, which refers to a situation where one party to a transaction has more or better information than the other party (Akerlof, 1970). Many researchers have argued that trust is a crucial enabling factor in relations where there is uncertainty, information asymmetry, and fear of opportunism (Pavlou et al., 2007), as is the case in online shopping (e.g. Lee et al., 2011). Accordingly, the first objective of this research is to integrate IS success model variables with trust and examine their relative influences on customers’ satisfaction and repurchase intentions toward online shopping. Justice is a fundamental basis for relationship maintainability in social exchange (Lind et al., 1993). Justice refers to perceptions of fairness and assessment concerning the appropriateness of performance outcomes or processes (Cropanzano and Greenberg, 1997). According to uncertainty management theory, justice is important for people because justice judgments are an effective and readily available device for handling various uncertain conditions (Van den Bos and Lind, 2002). Justice can remove trust-related uncertainty and alleviate much of the discomfort that uncertainty would otherwise generate. Accordingly, justice theory is a framework through which to explain and understand individuals’ feelings of trust or mistrust more fully (Saunders and Thornhill, 2003). This study proposes an extension of justice to buyer-seller relationships in online shopping. The logic behind the proposed extension is that as with organizational employment relationships, the online buyer-seller relationship also involves information or power asymmetry, and thus online

transactions are also governed by justice. A vulnerable buyer, unable to avail him or herself of traditional safeguards against seller opportunism, must rely on the powerful seller’s sense of justice and restraint to avoid mistreatment (Anderson and Weitz, 1989). Consequently, examining the impact of justice on customers’ trust in online vendors is the second objective of this research. In sum, this study complements the updated IS success model with justice and trust perspectives, considering them more comprehensive measures of online shopping satisfaction and repurchase intention in an e-commerce context. 2. Theoretical background 2.1 IS success model and e-commerce DeLone and McLean’s (1992) model of IS success is one of the widely used models for explaining the impact of quality on individual’s satisfaction and use of IS. The IS success model consists of six interrelated dimensions of success: (1) system quality; (2) information quality; (3) use; (4) user satisfaction; (5) individual impact; and (6) organizational impact. The model posits that system quality and information quality, individually and jointly, affect user satisfaction and system use. Additionally, system use affects user satisfaction with the reverse being true. Based on their evaluation of some important research on IS success of the last decade, DeLone and McLean (2003) proposed an updated IS success model as a foundation for empirical e-commerce research. The model adds service quality, intention to use, and net benefits. While the updated IS success model is currently regarded as a major breakthrough in this field, there are several challenges facing it as applied to e-commerce context. An endeavor to refine and extend the updated IS success model in terms of the peculiar nature of e-commerce is still needed (DeLone and McLean, 2004). Therefore, this study attempts to illuminate the challenges and to develop the e-commerce success model. First, service quality was added to the original IS success model to reflect the importance of the services of the IS function. Service quality is commonly defined as how well a delivered service level matches customer expectation. The SERVQUAL instrument[2] (Parasuraman et al., 1988) has been widely tested as a means of measuring customer perceptions of service quality. DeLone and McLean (2003) adopted three dimensions of SERVQUAL (i.e. responsiveness, empathy, assurance) as the metrics for the service quality construct. However, the SERVQUAL instrument does not embrace the unique facets of e-commerce service quality (e.g. the interactions between customers and the web sites). Therefore, Parasuraman et al. (2005) proposed the E-S-QUAL scale and identified seven dimensions for assessing electronic service quality[3]. Given the unique nature of e-commerce, the measures for service quality in the e-commerce success model should adopt the E-S-QUAL scale, rather than the SERVQUAL instrument.

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Second, the net benefits are the most important success measure as they capture the balance of the positive and negative impacts of e-commerce on customers (DeLone and McLean, 2003). DeLone and McLean (2004) identify improved customer experience, entertainment, reduced shopping cost, and real-time marketing offers as individual benefits from e-commerce. These are in line with recent online shopping research that convenience, price savings, extensive information, enjoyment, and broad product selection are considered as major benefits of online shopping (Childers et al., 2001). In addition, according to DeLone and McLean (2003), use and user satisfaction will lead to net benefits. If repurchase is to occur, it is assumed that the net benefits from the perspective of the customer are positive, thus influencing re-purchase intention and satisfaction. Therefore, this study reconciles the net benefits measures with the e-commerce context and considers them as antecedents of repurchase intention and satisfaction, instead of as dependent variable in the updated IS success model. Third, the updated IS success model is originally developed in the traditional setting where the level of uncertainty is lower than that in the online environment (Grabner-Kraeuter, 2002) and does not involve trust construct based on that the need for trust only arises in uncertain environments (Mayer et al., 1995). In e-commerce, the transaction-specific uncertainty is elicited by an asymmetric distribution of information between the transaction partners (Grabner-Kraeuter, 2002). Therefore, two of the main obstacles to directly apply the updated IS success model to measure e-commerce success are the lack of deliberating the inherent uncertainty of e-commerce and the exclusion of other critical factors (e.g. trust). These difficulties, however, could be alleviated by investigating IS success along with trust. Trust is especially critical in online transaction because trust absorbs transaction-specific uncertainty through mitigating the negative effect of perceived information asymmetry and the resulting possibility of encountering opportunistic behavior (Pavlou et al., 2007). Furthermore, two important deficiencies of the updated IS success model are that it excludes justice theory as a basis for any of its scales and its incapability to deal with the imbalance of power and information in online transaction settings. E-commerce has been described as the conduct of business among consumers and e-businesses, which enable them to exchange value electronically (e.g. money, goods, services, and information). Given the hidden information and hidden action problems in the e-commerce context (Pavlou et al., 2007), there are power and information asymmetries between online buyers and sellers. Justice evaluations are more likely to arise in any exchange of value (Adams, 1965) and in asymmetrical power relationships (Lind, 2001). Consequently, justice should not be ignored due to its valuable framework for explaining customers’ reactions to a variety of situations. 2.2 The importance of trust in online shopping and antecedents of trust According to Blau (1964), trust is a key element in the emergence and maintenance of social exchange relationships. Bradach and Eccles (1989) view trust as a control mechanism that facilitates exchange relationships characterized by uncertainty, vulnerability, and dependence. These characteristics are reflected in the online shopping environment, where customers are unable to personally scrutinize the vendor, physically examine the merchandise, or collect the merchandise upon payment. Customers have limited information and cognitive resources available, and thus seek to reduce the uncertainty and complexity of online transactions by applying

mental shortcuts (Grabner-Kraeuter, 2002). One effective mental short cut is trust, which can serve as a mechanism to reduce the complexity of human conduct in situations where people have to cope with uncertainty (Luhmann, 1989). Because of limited control over the vendor and the absence of proven guarantees that the vendor will not engage in undesirable opportunistic behaviors, trust is a critical aspect of online shopping (Gefen et al., 2003). Such behaviors include sale of fake or defective products, fake photos and misleading descriptions, failure of the vendor to deliver merchandise, failure to deliver in a timely manner, sending something of lesser value than advertised, and high handling and shipping costs. Since the key to successful economic transactions is avoiding opportunistic behavior, online customers in general stay away from online vendors whom they do not trust (or trust to be bad) (Gefen et al., 2003). On the other hand, trust needs to be promoted between buyers and sellers if commerce over the web is to continue to success (Gefen et al., 2003). Trust has been defined in various ways, in terms of the context in which it appears. Some definitions have concentrated on the facet of risk involved, while others on the vulnerability of one of the parties’ concerned (Everard and Galletta, 2005; Mayer et al., 1995). Trust refers to “the willingness of the party to be vulnerable to the actions of another party based on the expectation that the other will perform a particular action important to the trustor, irrespective of the ability to monitor or control that other party” (Mayer et al., 1995, p. 712). Our research considers trust as a set of specific beliefs dealing primarily with the benevolence, competence, and integrity of the seller/vendor. According to previous studies dealing with buyer-seller and business interactions, this set of specific beliefs comprises the most widely used specific beliefs in trust literature (Gefen et al., 2003). The same argument also holds with the Internet (e.g. Gefen et al., 2003; Pavlou and Fygenson, 2006). Benevolence is the belief that the trustee will not act opportunistically against the trustor, even given the opportunity. Competence is the belief in the trustee’s ability to fulfill its obligations as expected by the trustor. Integrity is the belief that the trustee will be honest and keep its commitments. In addition, trust and trustworthiness are related constructs. Trustworthiness refers to the perceived accuracy and goodness of the source (Everard and Galletta, 2005). Although there are differences between these two constructs, some scholars have viewed trust as synonymous with trustworthiness (e.g. McKnight et al., 1998). McKnight et al. (1998) have suggested that trustworthiness is a multifaceted construct that captures the competence of the trustee. Trustworthiness can be considered as one component of trust (i.e. competence). If trust is indeed an important aspect of online shopping, then understanding antecedents of trust should be a prime concern of the online vendors. Recently, increasing attention has been devoted toward justice as an antecedent of trust in online contexts. For instance, Turel et al. (2008) have applied justice notions to customer-service provider relationships and examined their impact on trust in the e-service context. Chiu et al. (2010) have considered bidding justice as an important antecedent of trust in online auctions. Fang and Chiu (2010) have extended the relationship between justice and trust to the virtual communities of practice. Given power and information asymmetries between buyers and sellers in the context of e-commerce, justice has the potential to provide deeper insights into trust in such context.

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2.3 Justice theory Before 1975, the study of justice was primarily concerned with distributive justice, i.e. the fairness of outcome distributions. Homans’ (1961) simple formula for distributive justice stressed that “a man’s rewards in exchange with others should be proportional to his investments.” Adams (1965) used a social exchange theory framework to evaluate fairness. According to Adams’s (1965) equity theory, an individual’s perception of the fairness of exchange relationships is determined by comparing the output/input ratio for oneself with that of referent others. A fair balance between input and outcome leads to feelings of fairness or justice[4]. Thibaut and Walker’s (1975) studies of disputant reactions to legal procedures led to the development of their theory of procedural justice. Procedural justice is concerned with the processes by which outcomes are distributed among parties to an exchange. Bies and Moag (1986) separated out the interpersonal aspect of procedural justice, labeled as interactional justice – the quality of the interpersonal treatment people receive during the enactment of formal procedures. Recently, justice theory has been applied to the IS service context (Carr, 2007) and to buyer-seller relationships, hence we have seen a shift in patterns of justice research. As with organizational employment relationships, buyer-seller information asymmetry is commonplace in online marketplaces and occurs when one party to a transaction has pertinent information that the other party lacks. Two information problems – hidden information and hidden action (Pavlou et al., 2007) – breed the online buyer-seller power asymmetry (imbalance). The seller is in a position of power of whether to provide its true characteristics, deliver the product, keep the promised product quality, comply with transaction rules, provide accurate information about products and transaction policies, etc. Consequently, to smooth a transaction, buyers are, of necessity, concerned about the powerful seller’s justice, and a typical question includes: will the seller misuse his/her power to not deliver the product that a buyer paid for? According to justice theory, when humans are engaged in any exchange of value (e.g. a transaction), they estimate the equity of the exchange (Adams, 1965). Any injustice treatment may stimulate a psychological contract violation between exchange (transaction) parties (Morrison and Robinson, 1997). Injustice is not only the absence of justice (Simon, 1995). Injustice is an active event that can cause harm in many different ways such as material harm and personal harm to individuals (Wolgast, 1987). Besides, injustice may imply that the potential trustee is malevolent or has a hidden agenda (Turel et al., 2008). This psychological contract violation has a destructive impact on the trusting relationships between exchange parties. Trust is especially critical when information or power asymmetry is present in online transactions (Pavlou et al., 2007). Correspondingly, justice theory offers a means through which to explain and understand buyers’ trust in the sellers in e-commerce context. 3. Research model and hypotheses Figure 1 presents the proposed model, referred to as an e-commerce success model. The dependent variable – repurchase intention – is posited as the primary construct to determine customers’ repurchase behaviors. Repurchase intention refers to the subjective probability that an individual will continue to purchase products from the online vendor or store in the future. All key variables are explained, and their relationship with repurchase intention is proposed as follows.

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Figure 1. E-commerce success model

3.1 Satisfaction According to Kolter (2000), satisfaction is an individual’s feelings of pleasure or disappointment resulting from comparing the perceived performance (or outcomes) of online shopping in relation to his or her expectations. Oliver (1980) theorizes that satisfaction is positively associated with future intention, both directly and indirectly via its impact on attitude. In the final step of satisfaction formation processes, satisfaction determines intentions to patronize or not to patronize the store in the future (Tsai and Huang, 2007). Therefore: H1. Customers’ satisfaction positively affects their repurchase intentions.

3.2 Net benefits Net benefits refer to the benefits of online shopping to customers against the costs (e.g. time, effort, and money). Given the costs of online shopping, this study focuses on benefits such as convenience, enjoyment, broad product selection, flexibility, and effectiveness in product searching and buying (usefulness). Research supports the notion that online shopping involves hedonic and utilitarian value (net benefits) (Childers et al., 2001). Hedonic shopping value reflects the entertainment and emotional worth of the shopping, while utilitarian shopping value reflects a more task-oriented, cognitive, and non-emotional benefits of the shopping (Babin et al., 1994). Mano and Oliver (1993) posit that affective responses arising from evaluation of the outcomes of product/service usage and cognitive interpretation lead to satisfaction. Online shopping gives a customer the opportunity to economize on time and effort by making it easy to locate merchants, find items, and procure offerings (Szymanski and Hise, 2000). Prior research shows that positive perceptions of convenience, extensive product information, and enjoyment (Bauer et al., 2006) have significant effects on customer satisfaction with online shopping. According to self-determination theory (Deci and Ryan, 1985), individuals are self-determining and intrinsically motivated in online shopping when they are interested in it. According to Davis et al. (1989), customers form intentions toward online shopping based largely on a cognitive appraisal of how it will improve their shopping performance, i.e. perceived usefulness. Customers who accomplished the

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shopping task of product acquisition in an efficient manner will be more likely to exhibit stronger repurchase intentions (Babin and Babin, 2001). Childers et al. (2001) consider perceived usefulness as a utilitarian benefit and enjoyment as a hedonic benefit of online shopping and showed that they are important motivations for individuals to engage in online shopping. Support for the role of net benefits on customers’ satisfaction and repurchase intentions is provided by Forst et al. (2010) and Jones et al. (2006). Therefore: H2. H3. Net benefits positively affect customers’ satisfaction. Net benefits positively affect customers’ repurchase intentions.

3.3 Information quality Information quality refers to customers’ perceptions of the characteristics and presentation of information in the online shopping web site. It deals with attributes such as relevance, understandability, accuracy, completeness, and timeliness. Since a primary role of an online store is to provide information about product, transaction, and service, higher quality information leads to better buying decisions and higher levels of customer satisfaction (Peterson et al., 1997). Inaccurate and out-of-date information cause customers to become dissatisfied with an online vendor (Collier and Bienstock, 2006). McKinney et al. (2002) posited that satisfaction with the quality of web site’s information content is one of the two sources of web-customer satisfaction. Therefore: H4. Information quality positively affects customer satisfaction.

3.4 System quality System quality refers to customers’ perceptions of the online shopping web site’s performance in information retrieval and delivery. It measures the functionality of a web site: ease of navigation, availability, layout, appearance, and page load speed. The technology acceptance model (TAM) (Davis et al., 1989) implies that, other things being equal, an online shopping web site perceived to be easier to use is more likely to induce a positive feeling toward it. Szymanski and Hise (2000) argue that the functionality of a web site plays an important role in shaping customers’ satisfaction with online shopping. When consumers use a web site for browsing or purchasing, function problems (e.g. system crash) lead to unsatisfying shopping experience (Collier and Bienstock, 2006). Prior studies (Bauer et al., 2006) have provided support for the notion that system quality positively affects customer satisfaction. Therefore: H5. System quality positively affects customer satisfaction.

3.5 Service quality Service quality refers to the perception of the degree to which the service provided by the online store meets the customer’s expectations. It includes responsiveness, contact, and privacy. Responsiveness concerns the efficiency of handling problems and returns through the e-commerce web site (Parasuraman et al., 2005). The concept of contact concerns the availability of assistance through telephone and online representatives. Providing numerous methods for customers to contact the online vendor to get assistance is essential to improving the quality of the vendor’s online service operation,

which could prevent or minimize customers’ dissatisfaction (Collier and Bienstock, 2006). Consumers will hesitate to shop online if they do not feel assured that credit card information is secure and protected from potential hackers. Support for the role of service quality on customer satisfaction is provided by Bauer et al. (2006). Therefore: H6. Service quality positively affects customer satisfaction.

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3.6 Trust Following Pavlou and Fygenson (2006), trust is defined as the buyer’s beliefs that the seller will behave benevolently, capably, and ethically. According to TPB (Ajzen, 1991), trust beliefs create favorable feelings toward the online vendor that are likely to increase a customer’s intention to purchase products from the vendor. Lack of trust prevents buyers from engaging in online shopping because they are unlikely to transact with a vendor that fails to convey a sense of its trustworthiness, mainly because of fears of seller opportunism (Hoffman et al., 1999). According to Gefen et al. (2003), online customers in general will avoid purchasing from the online vendor whom they do not trust, or they assume that the online vendor will not be ethical and behave in a socially suitable manner (i.e. trust to be bad). Indeed, prior research shows that trust plays a pivotal role in driving customer satisfaction (Lin and Wang, 2006) and repurchase intention (Weisberg et al., 2011; Zboja and Voorhees, 2006). Therefore: H7. H8. Customer trust in the online vendor positively affects customer satisfaction. Customer trust in the online vendor positively affects repurchase intention.

3.7 Distributive justice In this study, distributive justice refers to the extent to which the customer’s investments (e.g. invested money, time, and efforts) are fairly rewarded. Distributive justice contains the concept of order fulfillment. According to Colquitt et al. (2006), distributive justice is judged by gauging whether rewards are proportional to investments (Homans, 1961), whether returns adhere to expectation (Blau, 1964), and whether outcome/input ratios match those of a referent other (Adams, 1965). When outcome distributions are considered fair, higher levels of trust are likely to ensue (Pillai et al., 2001). In other words, customers’ trust in the vendor will be built when the products they received are proportional to their investments. Support for the role of distributive justice on trust is provided by Hubbell and Chory-Assad (2005). Therefore: H9. Distributive justice positively affects customer trust in the online vendor.

3.8 Procedural justice Procedural fairness refers to the perceived fairness of policies and procedures in the online shopping process. The transaction process is an integral part of online shopping, thus an online vendor can enhance customers’ trust by engaging activities that enhance their perceptions of procedural justice, such as providing detailed information about shopping policy and procedure, applying policies consistently, clarifying decisions about any change in the web site, and handling problems fairly. According to Cohen-Charash and Spector (2001), procedural justice perceptions are associated with

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trust because procedural justice indicates that the exchange party acts fairly as a rule and hence can be trusted. Support for the role of procedural justice on trust is provided by Pillai et al. (2001). Therefore: H10. Procedural justice positively affects customer trust in the online vendor.

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3.9 Interactional justice Interactional justice refers to the quality of the interpersonal treatment a customer received during the online shopping process. Attitudes of treating people with dignity and respect are effective communication for increasing people’s feelings of perceived justice (Bies and Moag, 1986). Lind (2001, p. 65) noted that “people use overall impressions of fair treatment as a surrogate for interpersonal trust”, and interpersonal communications that express social sensitivity can facilitate the establishment of trust among them. Support for the role of interactional justice on trust is provided by Cohen-Charash and Spector (2001). Therefore: H11. Interactional justice positively affects customer trust in the online vendor.

4. Research methodology 4.1. Measurement development Measurement items were adapted from the literature wherever possible (see Appendix). A small-scale pretest of the questionnaire was conducted using 20 graduate students with online shopping experience to assess its logical consistencies, ease of understanding, and contextual relevance. Finally, a large-scale pretest with 195 customers of the target online shopping store was also conducted to confirm the measurement properties of the final items and provide preliminary evidence for the proposed model. The results indicated that the measurement model fulfills the criteria of reliability, convergent validity, and discriminant validity, with composite reliability values ranging from 0.87 to 0.95, AVE ranging from 0.61 to 0.87, and factor loadings ranging from 0.68 to 0.95. The results of the structural path analysis indicated that 9 of 11 hypotheses were supported. The relationship between service quality and satisfaction ðH4; t ¼ 0:72Þ was insignificant, while the relationship between trust and repurchase intention was marginal ðH8; t ¼ 1:82Þ: Items for measuring three justice dimensions were adapted from Anderson and Srinivasan (2003) and Folger and Konovsky (1989) to fit the context of online shopping. Items for measuring trust were based on Gefen et al. (2003). Items for measuring three quality dimensions were adapted from DeLone and McLean (2003), McKinney et al. (2002), and Parasuraman et al. (2005). Among the seven dimensions of the E-S-QUAL scale proposed by Parasuraman et al. (2005), efficiency and system availability, however, could be classified into the measures of system quality in this e-commerce success model; while fulfillment could be replaced by the measures of distributive justice. Overall, this study retained responsiveness, contact, and privacy as the measures of service quality and did not include the compensation measure because very few customers had compensation experience. Items for measuring net benefits were based on Anderson and Srinivasan (2003), Childers et al. (2001), DeLone and McLean (2003), and Devaraj et al. (2002). Items for measuring satisfaction were adapted from McKinney et al. (2002) and Oliver (1980). Repurchase intention was adapted from Parasuraman et al. (2005) and Pavlou and Fygenson (2006). For all the measures, a

seven-point Likert scale was adopted with anchors ranging from strongly disagree (1) to strongly agree (7). 4.2. Survey administration Given that our research aimed at understanding online customers’ satisfaction and repurchase intentions, the research model was tested with data from PCHome’s online shopping customers. PCHome was chosen because it is the most widely used online shopping store in Taiwan. A banner with a hyperlink connecting to our web survey was published on a number of bulletin board systems (BBS), chat rooms and virtual communities and individuals with online shopping experience with PCHome were cordially invited to support this survey. Given that the questionnaire items of service quality and interactional justice constructs involved issues regarding interactions with service representatives and problem handling such as product return, for survey results to be valid, respondents had to experience online service and contact with service representatives of PCHome to evaluate both constructs (so-called purposive sampling or judgment sampling). In this sampling plan, sample elements were selected because they are believed to be representatives of the population of interest and were expected to serve the research purpose of our study (Churchill, 1991). Therefore, in the demographic information of our survey web page, we required respondents to indicate whether they had experience in contacting customer service representatives and returning products. Initially, 2,072 online respondents voluntarily completed the survey. Since very few respondents have experience in contacting customer service representatives and returning products, after eliminating invalid respondents (e.g. those without service representative contacting and product-return experience), 219 valid ones remained for our data analysis. The promise of an incentive significantly enhanced the probability that a respondent would more fully complete the questionnaire and make fewer errors in the responses to survey questions (Godwin, 1979). Only 50 respondents were randomly selected from these 219 valid ones due to our limited budget. Table I lists the demographic information of the respondents. 4.3. Data analysis Data analysis utilized a two-step approach as recommended by Anderson and Gerbing (1988). The first step involves the analysis of the measurement model, while the second
Measure Gender Age Items Freq. Percent Measure Male , 20 20-24 25-29 30 , 1-2 3-5 6-10 11 , 104 9 74 92 44 87 79 35 18 47.5 4.1 33.8 42.0 20.1 39.7 36.1 16.0 8.2 Gender Education Items Female High school College University Graduate school Freq. Percent 115 13 16 133 57 8 47 63 101 52.5 6.0 7.3 60.7 26.0 3.6 21.5 28.8 46.1

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Buys in the past 6 months

Internet experience (in years) ,5 5-6 7-8 9,

Table I. Demographic information of respondents (N ¼ 219)

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step tests the structural relationships among latent constructs. The aim of the two-step approach is to assess the reliability and validity of the measures before their use in the full model. Given that our research model has involved a set of metric independent variables and one or more metric dependent variable, structural equation modeling (SEM) analysis and multiple regression analysis are the appropriate multivariate techniques. SEM analysis was chosen over regression analysis, because SEM can simultaneously analyze all of the paths in one analysis (Chin and Newsted, 1999). SEM can provide fuller information about the extent to which the research model is supported by the data than in regression techniques (see Gefen et al., 2000). Within SEM, PLS (partial least squares) is partial-least-squares based, while LISREL represents covariance-based SEM. PLS is more suited for exploratory research, predictive applications, and theory building, in contrast to LISREL. PLS (PLS-Graph version 3.0) was chosen over LISREL because this study aims at theory development instead of theory testing. Besides, PLS places minimal restrictions on measurement scales, sample size, and residual distribution (Chin and Newsted, 1999). According to Tanaka’s (1984) guideline that a sample size of at least 400 or 500 is needed for SEM, our sample size of 219 was insufficient to obtain a proper solution if we used other SEM approaches. The PLS bootstrap technique – a resampling procedure – is a useful strategy for evaluating replicability. Because the analysis considers so many configurations of subjects, one use of such techniques informs the researcher concerning the extent to which results generalize across different types of subjects (Thompson, 1993). PLS also provides the analysis of both a measurement model and a structural model. 4.3.1 Measurement model. The adequacy of the measurement model was evaluated on the criteria of reliability, convergent validity, and discriminant validity. Reliability was examined using the composite reliability values. Table II shows that all the values were above 0.7, which is the commonly acceptable level for explanatory research. Additionally, the convergent validity of the scales was verified by using two criteria suggested by Fornell and Larcker (1981): (1) all indicator loadings should be significant and exceed 0.7; and (2) average variance extracted (AVE) by each construct should exceed the variance due to measurement error for that construct (i.e. AVE should exceed 0.50).

Constructs Distributive justice (DJ) Procedural justice (PJ) Interactional justice (IJ) Trust (TR) Information quality (IQ) System quality (SQ) Service quality (SEQ) Net benefits (NB) Satisfaction (SA) Repurchase intention (RI)

Composite reliability 0.91 0.92 0.95 0.93 0.89 0.91 0.89 0.90 0.96 0.96

Mean 4.79 4.70 5.05 4.90 4.83 4.93 4.86 5.48 4.93 5.20

AVE 0.67 0.70 0.87 0.74 0.61 0.65 0.64 0.65 0.85 0.88

Table II. Descriptive statistics of constructs

As shown in Table III, all items exhibited loading higher than 0.7 on their respective construct, providing evidence of acceptable item convergence on the intended constructs. AVE ranged from 0.61 to 0.88 (see Table II). Hence, both conditions for convergent validity were met.
DJ DJ1 DJ2 DJ3 DJ4 DJ5 PJ1 PJ2 PJ3 PJ4 PJ5 IJ1 IJ2 IJ3 TR1 TR2 TR3 TR4 TR5 IQ1 IQ2 IQ3 IQ4 IQ5 SQ1 SQ2 SQ3 SQ4 SQ5 SQ6 SEQ1 SEQ2 SEQ3 SEQ4 NB1 NB2 NB3 NB4 NB5 SA1 SA2 SA3 SA4 RI1 RI2 RI3 0.85 0.86 0.86 0.78 0.72 0.51 0.50 0.53 0.53 0.51 0.61 0.62 0.63 0.63 0.68 0.55 0.60 0.51 0.47 0.47 0.60 0.61 0.54 0.38 0.38 0.43 0.41 0.36 0.36 0.41 0.56 0.56 0.39 0.48 0.40 0.49 0.47 0.48 0.66 0.65 0.67 0.66 0.59 0.58 0.63 PJ 0.47 0.54 0.52 0.52 0.47 0.87 0.77 0.85 0.87 0.80 0.70 0.68 0.69 0.64 0.66 0.66 0.67 0.58 0.38 0.45 0.50 0.54 0.44 0.39 0.42 0.50 0.47 0.39 0.44 0.46 0.71 0.56 0.65 0.49 0.43 0.51 0.42 0.45 0.61 0.63 0.66 0.62 0.60 0.56 0.59 IJ 0.50 0.63 0.52 0.50 0.53 0.68 0.59 0.61 0.63 0.56 0.93 0.94 0.93 0.68 0.67 0.71 0.70 0.69 0.49 0.52 0.53 0.48 0.38 0.39 0.42 0.43 0.44 0.36 0.41 0.54 0.72 0.65 0.60 0.55 0.50 0.59 0.51 0.56 0.70 0.71 0.65 0.71 0.67 0.67 0.73 TR 0.53 0.67 0.53 0.48 0.57 0.71 0.58 0.65 0.61 0.56 0.74 0.76 0.74 0.85 0.86 0.87 0.91 0.79 0.47 0.50 0.61 0.52 0.48 0.46 0.48 0.51 0.52 0.42 0.48 0.52 0.67 0.62 0.61 0.58 0.50 0.58 0.53 0.58 0.75 0.78 0.74 0.77 0.73 0.71 0.72 IQ 0.58 0.65 0.56 0.48 0.54 0.53 0.46 0.52 0.49 0.44 0.56 0.60 0.57 0.57 0.61 0.58 0.56 0.52 0.75 0.80 0.80 0.81 0.74 0.44 0.48 0.47 0.48 0.40 0.50 0.40 0.56 0.51 0.45 0.50 0.45 0.56 0.53 0.49 0.63 0.65 0.65 0.64 0.57 0.56 0.60 SQ 0.34 0.50 0.33 0.30 0.45 0.48 0.47 0.46 0.45 0.39 0.48 0.48 0.46 0.50 0.54 0.49 0.50 0.54 0.46 0.50 0.48 0.43 0.37 0.82 0.85 0.77 0.89 0.72 0.76 0.49 0.45 0.48 0.46 0.50 0.43 0.54 0.45 0.52 0.59 0.64 0.59 0.62 0.55 0.59 0.53 SEQ 0.45 0.58 0.49 0.46 0.46 0.74 0.59 0.62 0.61 0.51 0.73 0.74 0.74 0.64 0.66 0.66 0.67 0.62 0.43 0.49 0.54 0.54 0.35 0.40 0.47 0.50 0.49 0.42 0.52 0.75 0.87 0.78 0.80 0.50 0.45 0.53 0.49 0.57 0.64 0.67 0.63 0.65 0.59 0.59 0.61 NB 0.45 0.61 0.45 0.36 0.43 0.49 0.57 0.48 0.47 0.37 0.59 0.66 0.63 0.50 0.55 0.56 0.60 0.73 0.57 0.57 0.48 0.42 0.42 0.44 0.51 0.42 0.49 0.55 0.51 0.58 0.55 0.44 0.46 0.79 0.81 0.85 0.84 0.74 0.68 0.70 0.64 0.66 0.74 0.72 0.78 SA 0.54 0.66 0.55 0.54 0.59 0.65 0.52 0.62 0.55 0.50 0.70 0.72 0.68 0.70 0.74 0.66 0.76 0.66 0.50 0.54 0.60 0.54 0.53 0.52 0.54 0.54 0.58 0.49 0.51 0.51 0.64 0.56 0.52 0.55 0.53 0.68 0.60 0.55 0.92 0.94 0.90 0.92 0.82 0.77 0.78 RI 0.53 0.64 0.52 0.47 0.53 0.57 0.57 0.58 0.53 0.43 0.67 0.71 0.70 0.64 0.65 0.61 0.73 0.70 0.48 0.50 0.51 0.48 0.44 0.48 0.48 0.45 0.56 0.50 0.47 0.52 0.60 0.51 0.48 0.67 0.56 0.70 0.69 0.55 0.84 0.80 0.75 0.79 0.94 0.93 0.95

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Table III. PLS confirmatory factor analysis and cross-loadings

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Discriminant validity was assessed by examining cross-loadings and the relationship between correlations among constructs and the square root of AVEs (Fornell and Larcker, 1981). An examination of cross-factor loadings (Table III) indicates good discriminant validity, because the loading of each measurement item on its assigned latent variable is larger than its loading on any other constructs. The other criterion is that the square root of the AVE from the construct should be greater than the correlation shared between the construct and other constructs in the model (Fornell and Larcker, 1981). Table IV lists the correlations among the constructs, with the square root of the AVE on the diagonal. All the diagonal values exceed the inter-construct correlations, indicating satisfactory discriminant validity of all constructs. Therefore, we conclude that the scales should have sufficient construct validity. 4.3.2 Structural model. In PLS analysis, examining the structural paths and the R-square scores of endogenous variables assesses the explanatory power of a structural model. The results of structural path analysis are depicted in Figure 2. Data collected from 219 valid respondents provided support for all but one of eleven hypotheses, exhibiting a p-value less than 0.05. The unsupported hypothesis, the relationship between service quality and satisfaction (H4), was not significant at the 0.05 level. Tests of significance of all paths were performed using the bootstrap
DJ DJ PJ IJ TR IQ SQ SEQ NB SA RI 0.82 0.62 0.66 0.69 0.69 0.48 0.60 0.58 0.71 0.64 PJ 0.84 0.74 0.75 0.59 0.54 0.74 0.57 0.69 0.62 IJ TR IQ SQ SEQ NB SA RI

Table IV. AVE and correlation among constructs

0.93 0.80 0.62 0.51 0.79 0.67 0.75 0.73

0.86 0.67 0.60 0.76 0.69 0.83 0.77

0.78 0.57 0.61 0.63 0.70 0.61

0.81 0.58 0.61 0.66 0.59

0.80 0.63 0.70 0.64

0.81 0.72 0.80

0.92 0.84

0.94

Figure 2. SEM analysis of the research model

resampling procedure. In addition, the model accounts for 72 to 79 percent of the variance (R-square scores). Overall, the research model accounted for 79 percent of the variance of repurchase intention (Figure 2). 5. Discussion and implications Drawing on the IS success model, trust, justice, management and marketing literature, the study theoretically develops and empirically tests a model that explains and predicts customers’ repurchase intentions toward online shopping. 5.1 Summary of results Data from our survey suggest support for the proposed model of e-commerce success. Results indicate that repurchase intention is most dominantly influenced by satisfaction ðb ¼ 0:47Þ: This suggests that satisfaction is a powerful mediator between quality perceptions and trust, and repurchase intention. The results confirm that the significant positive impacts of net benefits on customers’ satisfaction and repurchase intentions, validating our proposition that net benefits perception is a major enabler for online exchange relationships. Information quality and system quality have significant effects on satisfaction, whereas service quality does not affect satisfaction. A possible explanation for the insignificant relationship is that customers with limited experience in contacting service representatives were not sufficient to evaluate service quality. Usually, e-service quality is established through accumulated experience of interaction or contact with service representatives (Devaraj et al., 2002). Another possible explanation is that service quality is a hygiene factor. According to Herzberg et al. (1959), some factors (called motivational factors) influenced satisfaction but not dissatisfaction, while others (called hygiene factor) only influenced dissatisfaction but not satisfaction. Similar to this line of reasoning, service quality may negatively affect dissatisfaction towards online shopping, but may not positively affect satisfaction towards online shopping. Results indicate that trust has a strong effect on satisfaction ðb ¼ 0:47Þ but its effect on repurchase intention ðb ¼ 0:13Þ is marginally significant. A possible explanation for the relatively weak effect of trust on repurchase intention, is that trust also acts indirectly on repurchase intention through the mediating effect of satisfaction. The partial mediating effects of satisfaction on the relationship between trust and repurchase intention was assessed following Baron and Kenny’s (1986) procedures: . trust has a significant effect on repurchase intention ðb ¼ 0:42Þ; . trust has a significant effect on satisfaction ðb ¼ 0:47Þ; and . satisfaction has a significant effect on repurchase intention ðb ¼ 0:47Þbut the effect of trust on repurchase intention ðb ¼ 0:13Þ decreases to a marginally significant level. 5.2 Implications for theory From a theoretical perspective, our findings imply that perceptions of quality by themselves are not sufficient in increasing customers’ satisfaction. For example, service quality is necessary but not sufficient to create customer satisfaction. Service quality may act as a hygiene (satisfaction maintaining) factor. That is, a customer may

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or may not be satisfied with an online store providing good service, but he/she will definitely be dissatisfied with an online store providing poor service. Information quality and system quality can contribute customers’ satisfaction to some extent, but it is trust and net benefits that lead to greater level of satisfaction. In addition, justice represents an additional key element of buyer-seller relationships in online shopping that has been ignored in the literature. The integration of the three distinct dimensions of justice also results in a more descriptive model that better explains customers’ repurchase intentions toward online shopping. Besides, the extent of explained variance in trust ðR 2 ¼ 0:72Þ implies that the three dimensions of justice are possibly among the most important antecedents of customers’ trust in online vendors. Furthermore, a major finding of the study is the dominant role of interactional justice in building customers’ trust. However, some research has found that interactional justice has less of an effect than procedural justice on trust in the organizational context (Hubbell and Chory-Assad, 2005). Our findings imply that the relative importance of each of the justice dimensions may be context specific. Overall, the study extends the justice literature from employee-organization relationships to customer-vendor relationships, shedding light on the trust-building potential of the three dimensions of justice. 5.3 Implications for practice Regarding the drivers of repurchase intention, the results suggest that online stores may need to employ a combined strategy aimed at increasing satisfaction, trust, and net benefits of online shopping. To enhance customer satisfaction, online stores can devote valuable corporate resources to information quality and system quality of the web sites. A successful e-commerce web site starts with good content. The information provided in the web site has to be easy to understand, accurate, complete, timely, and relevant to customers’ purchase decisions. From a vendor’s perspective, it would be especially unfortunate to interpret our results to imply that service quality is not important. The appropriate interpretation is that providing good service is not sufficient to create customers’ satisfaction and loyalty. However, bad service is deemed to elicit customers’ dissatisfaction. According to Desatnick (1987), each of those unsatisfied customers will tell his or her bad experience to at least nine other people, i.e. spreading negative word of mouth (NWOM). NWOM is likely to dissuade potential customers from placing an order from the vendor, thus damaging the vendor’s reputation and financial position (Holmes and Lett, 1977). For example, among 2,072 questionnaires collected from our web survey, although a majority of respondents (89 percent)[5] had not contacted service representatives due to their satisfying shopping experience with PCHome, 219 respondents (11 percent) had such experience due to the problem-handling issues. For a vendor, losing the opportunity to rectify unsatisfied service or quality problems is likely to generate customers’ NWOM, thus driving customers away and jeopardizing vendor profitability (McCollough et al., 2000). Thus, providing good service is vital to a vendor. An important way of increasing trust is to treat customers with respect, friendliness, and politeness during the interaction with them. The quality of interpersonal treatment might signal to customers that the vendor cares for their wellbeing. This is good news for vendors, because the economic costs of interacting in

a manner that raises the dignity of customers are not likely to be as high as the costs associated with satisfying either procedural or distributive justice. The online vendor should provide some training to customer service representatives to ensure that they have good communication skills to provide an adequate level of service or help with customers’ concerns. Besides, the strong relationship between net benefits and repurchase intention suggests that online vendors should allocate more attention and resources to elements that enhance customer convenience, merchandise variety and assortment, the richness of product information and fun and entertainment of online shopping. 5.4 Limitations and future research We note that our findings must be interpreted in light of the study’s limitation. First, the data were collected from a single online shopping store, PCHome. Nonetheless, the generality of the findings to other online stores (e.g. Amazon) requires additional research. Second, our results may have been impacted by self-selection bias. Our sample comprises only active online customers. Individuals who had already ceased to purchase products from PChome might have different perceptions about the influence of IS success model variables, trust, and the three dimensions of justice, and so could have been differently affected by them. Therefore, the results should be interpreted as only explaining repurchase intentions of current online shopping customers. Thus, further research is needed to examine whether the results can be generalized to non-customers, disaffected customers, first-purchase customers, or those customers with multiple contact experience with service representatives. Although our web survey may have been affected by self-selection bias, Hayslett and Wildemuth (2004) have indicated that there are no significant differences between the demographic backgrounds of self-selected respondents and a random sample. Self-selected respondents also gave higher-quality responses. In summary, the influence of self-selection bias could be minor in this study. Third, as the data are cross-sectional and not longitudinal, the posited causal relationships could only be inferred rather than proven. While a longitudinal analysis would be a desired approach, solid cross sectional models must first be conducted before future research can confirm their viability over time. Fourth, the influences of quality dimensions on satisfaction are either insignificant or relatively weak, therefore future research is necessary to verify whether quality dimensions exerts the influences on repurchase intention indirectly through other mediators (e.g. value) instead of satisfaction. Furthermore, although several factors have been considered as antecedents of repurchase intention in our research model, further research is encouraged to investigate whether other possible factors (e.g. laziness, habit, and/or familiarity) affect repurchase intention.
Notes 1. www.comscore.com/press/release.asp?press ¼ 2658 2. The SERVQUAL instrument contains five dimensions: reliability, responsiveness, empathy, assurance, and tangibility (Parasuraman et al., 1988). 3. These seven dimensions for assessing electronic service quality are efficiency, fulfillment, system availability, privacy, responsiveness, compensation, and contact (Parasuraman et al., 2005).

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Turel, O., Yuan, Y. and Connelly, C.E. (2008), “In justice we trust: predicting user acceptance of e-customer services”, Journal of Management Information Systems, Vol. 24 No. 4, pp. 123-51. Van den Bos, K. and Lind, E.A. (2002), “Uncertainty management by means of fairness judgments”, in Zanna, M.P. (Ed.), Advances in Experimental Social Psychology, Academic Press, San Diego, CA, pp. 1-60. Weisberg, J., Te’eni, D. and Arman, L. (2011), “Past purchase and intention to purchase in e-commerce: the mediation of social presence and trust”, Internet Research, Vol. 21 No. 1, pp. 82-96. Wolgast, E.H. (1987), The Grammar of Justice, Cornell University Press, Ithaca, NY. Zboja, J.J. and Voorhees, C.M. (2006), “An empirical examination of the impact of brand trust and satisfaction on retailer repurchase intentions”, Journal of Services Marketing, Vol. 20 No. 5, pp. 381-90. Further reading Kernan, M.C. and Hanges, P.J. (2002), “Survivor reactions to reorganization: antecedents and consequences of procedural, interpersonal, and informational justice”, Journal of Applied Psychology, Vol. 87 No. 5, pp. 916-28. Shipley, B. (2000), Cause and Correlation in Biology: A User’s Guide to Path Analysis, Structural Equations and Causal Inference, Cambridge University Press, Port Chester, NY. Teo, T.S.H. and Liu, J. (2007), “Consumer trust in e-commerce in the United States, Singapore and China”, Omega-International Journal of Management Science, Vol. 35 No. 1, pp. 22-38. Van der Heijden, H., Verhagen, T. and Creemers, M. (2003), “Understanding online purchase intentions: contributions from technology and trust perspectives”, European Journal of Information Systems, Vol. 12 No. 1, pp. 41-8. Appendix. Questionnaire items Distributive justice (DJ) DJ1 DJ2 DJ3 DJ4 DJ5 I think what I got is fair compared to the price I paid. I think I got what I paid for from PChome. I think the value of the products that I received from PChome is proportional to the price I paid. I think the products that I purchased at PChome are considered to be a good buy. I think the products that I received from PChome are the same quality as advertised.

Procedural justice (PJ) PJ1 PJ2 PJ3 PJ4 I think the procedures used by PChome for handling problems occurred in the shopping process are fair. I think PCHome allows customers to complain and state their views. I think the policies of PChome are applied consistently across all affected customers. I think PChome would clarify decisions about any change in the Web site and provide additional information when requested by customers.

PJ5

I think PChome provide detailed information about shopping policies and procedures.

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Interactional justice (IPJ) IPJ1 IPJ2 IPJ3 Customer service representatives of PChome treat me with respect. Customer service representatives of PChome treat me with friendliness. Customer service representatives of PChome treat me with politeness.

Trust (TR) TR1 TR2 TR3 TR4 TR5 Based on my experience with PChome in the past, I know it is honest. Based on my experience with PChome in the past, I know it is not opportunistic. Based on my experience with PChome in the past, I know it keeps its promises to customers. Based on my experience with PChome in the past, I know it is trustworthy. Based on my experience with PChome in the past, I know it has the ability to complete transactions.

Information quality (IQ) IQ1 IQ2 IQ3 IQ4 IQ5 Information provided by the PChome Web site is relevant to my purchase decisions. Information provided by the PChome Web site is easy to comprehend. Information provided by the PChome Web site is accurate. Information provided by the PChome Web site is complete. Information provided by the PChome Web site is timely.

System quality (SQ) SQ1 SQ2 SQ3 SQ4 SQ5 SQ6 The PChome Web site has a simple layout for its contents. The organization and layout of the PChome Web site facilitate searching for products. The appearance of PChome Web site is appealing. The PChome Web site is easy to navigate. The PChome Web site is always available. The PChome Web site loads its pages fast.

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Service quality (SEQ) SEQ1 SEQ2 SEQ3 PChome provides me with convenient options for returning products. PChome takes care of my problems promptly. PChome does not share my personal information with other vendors. PChome offers the ability to speak to a live person if there is a problem.

502

SEQ4

Net benefits (NB) NB1 NB2 NB3 NB4 NB5 I think PChome offers a broad selection of products. I think purchasing products from PChome is flexible. I think purchasing products from PChome is interesting. I think purchasing products from PChome is convenient. I think PCHome enhances my effectiveness in product searching and buying.

Satisfaction (SA) SA1 SA2 SA3 SA4 I like to purchase products from PChome. I am pleased with the experience of purchasing products from PChome. I think purchasing products from PChome is a good idea. Overall, I am satisfied with the experience of purchasing products from PChome.

Repurchase intention (LI) CI1 CI2 CI3 If I could, I would like to continue using PChome to purchase products. It is likely that I will continue purchasing products from PChome in the future. I intend to continue purchasing products from PChome in the future.

About the authors Yu-Hui Fang is an Assistant Professor in the Department of Accounting at the Tamkang University, Taiwan. She gained her PhD degree in Information Management from National Central University and her Masters degree in Accounting from University of Houston. Her research interests include electronic commerce, virtual communities and knowledge management. Her research has appeared in Computers in Human Behavior, Online Information Review, and others. Yu-Hui Fang can be contacted at: [email protected] Chao-Min Chiu is a Professor in the Department of Information Management at the National Sun Yat-sen University, Taiwan. He holds a PhD in Management from the Rutgers University. His research interests include electronic commerce, virtual communities, and knowledge management. His research has appeared in Decision Support Systems, Information & Management, Information Systems Journal, International Journal of Human-Computer Studies, Computers & Education, Computers in Human Behavior, Electronic Commerce Research

and Applications, Behaviour & Information Technology, Information and Software Technology, Information Systems Management, Information Technology and Management, Journal of Information Science, Online Information Review, and others. Chao-Min Chiu can be contacted at: [email protected] Eric T.G. Wang is Information Management Chair Professor at National Central University, Taiwan (ROC). He gained his PhD degree in Business Administration, specialized in computer & information systems, from the William E. Simon Graduate School of Business Administration, University of Rochester. His research interests include electronic commerce, outsourcing, organizational economics, and organizational impact of information technology. His research has appeared in Management Science, Information Systems Research, Journal of Management Information Systems, Decision Sciences, Decision Support Systems, Information & Management, Information Systems Journal, Omega, European Journal of Information Systems, European Journal of Operational Research, International Journal of Information Management, and others. Eric T.G. Wang can be contacted at: [email protected]

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