Understanding Customers' service

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Understanding customerssatisfaction and repurchase

intentionsAn integration of IS success model, trust,

and justice

Yu-Hui FangTamkang University, Tamsui, Taiwan

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

Eric T.G. WangNational Central University, Jhongli City, Taiwan

Abstract

Purpose The aim of this study is to extend DeLone and McLeans IS success model by introducingjustice fair treatments received from the exchanging party and trust into a theoretical model forstudying customers repurchase intentions in the context of online shopping.

Design/methodology/approach The research model was tested with data from 219 of PCHomesonline shopping customers using a web survey. PLS (partial least squares) was used to analyze themeasurement and structural models.

Findings Data collected from 219 valid respondents provided support for all but one hypotheses

(with ap-value of less than 0.05). The unsupported hypothesis regards the relationship between servicequality and satisfaction (H4). The study shows that trust, net benefits, and satisfaction are significantpositive 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 researchmodel 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 peculiarnature of e-commerce is needed. The study complements the updated IS success model with justicetrust perspectives, considering them a more comprehensive measure of online shopping satisfactionand 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. IntroductionThe business-to-consumer (B2C) e-commerce or online shopping market is growingrapidly and has become one of the most interesting developments in e-commerce.According to a market survey by ComScore, online sales outperform offline retail salesin 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, onlineshopping market provides an avenue for struggling to survive in the turbulent markets

The current issue and full text archive of this journal is available at

www.emeraldinsight.com/1066-2243.htm

Understandingcustomers

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Received 22 August 2010Revised 4 May 2011

Accepted 7 May 2011

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 thesuccess of online stores. What, then, keeps buyers loyal to an online store? E-commerceresearch has addressed this issue from different aspects, including explanations basedon 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 driversof success. More recently, web site quality has become essential for improvingcustomer satisfaction and creating customer loyalty (Parasuraman et al., 2005). Intraditional service research and in emerging research on electronic service (e-service)(Collier and Bienstock, 2006), several antecedents of customer satisfaction have beenproposed. Among these, web site quality figures prominently. Several researchers havedeveloped conceptual models for measuring B2C web site success (Liu and Arnett,2000). They identified three major quality constructs that are critical to web sitesuccess in e-commerce: information quality, system quality, and service quality. Thosemodels 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 individualssatisfaction, which in turn is the determinant of repurchase intention. DeLone andMcLean (2004) argue that IS success model can be applied to study e-commercesuccess. Accordingly, the study uses IS success model as the theoretical foundation forexplaining customer repurchase intention.

Trust in the seller is a vital key to building customer loyalty and maintainingcontinuity in buyer-seller relationships (Anderson and Weitz, 1989). The spatial andtemporal 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 inrelations 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 trustand examine their relative influences on customers satisfaction and repurchaseintentions 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 concerningthe appropriateness of performance outcomes or processes (Cropanzano andGreenberg, 1997). According to uncertainty management theory, justice is importantfor people because justice judgments are an effective and readily available device for

handling various uncertain conditions (Van den Bos and Lind, 2002). Justice canremove trust-related uncertainty and alleviate much of the discomfort that uncertaintywould otherwise generate. Accordingly, justice theory is a framework through whichto explain and understand individuals feelings of trust or mistrust more fully(Saunders and Thornhill, 2003). This study proposes an extension of justice tobuyer-seller relationships in online shopping. The logic behind the proposed extensionis that as with organizational employment relationships, the online buyer-sellerrelationship also involves information or power asymmetry, and thus online

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transactions are also governed by justice. A vulnerable buyer, unable to avail him orherself of traditional safeguards against seller opportunism, must rely on the powerfulsellers 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 theupdated IS success model with justice and trust perspectives, considering them morecomprehensive measures of online shopping satisfaction and repurchase intention inan e-commerce context.

2. Theoretical background2.1 IS success model and e-commerceDeLone and McLeans (1992) model of IS success is one of the widely used models forexplaining the impact of quality on individuals satisfaction and use of IS. The ISsuccess 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 usersatisfaction with the reverse being true. Based on their evaluation of some importantresearch 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. Themodel adds service quality, intention to use, and net benefits.

While the updated IS success model is currently regarded as a major breakthroughin this field, there are several challenges facing it as applied to e-commerce context. Anendeavor to refine and extend the updated IS success model in terms of the peculiarnature of e-commerce is still needed (DeLone and McLean, 2004). Therefore, this studyattempts 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 theimportance of the services of the IS function. Service quality is commonly defined ashow well a delivered service level matches customer expectation. The SERVQUALinstrument[2] (Parasuraman et al., 1988) has been widely tested as a means ofmeasuring customer perceptions of service quality. DeLone and McLean (2003)

adopted three dimensions of SERVQUAL (i.e. responsiveness, empathy, assurance) asthe metrics for the service quality construct. However, the SERVQUAL instrumentdoes not embrace the unique facets of e-commerce service quality (e.g. the interactionsbetween customers and the web sites). Therefore, Parasuraman et al. (2005) proposedthe E-S-QUAL scale and identified seven dimensions for assessing electronic servicequality[3]. Given the unique nature of e-commerce, the measures for service quality inthe e-commerce success model should adopt the E-S-QUAL scale, rather than theSERVQUAL instrument.


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Second, the net benefits are the most important success measure as they capture thebalance of the positive and negative impacts of e-commerce on customers (DeLone andMcLean, 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 thatconvenience, price savings, extensive information, enjoyment, and broad productselection are considered as major benefits of online shopping (Childers et al., 2001). Inaddition, according to DeLone and McLean (2003), use and user satisfaction will lead tonet benefits. If repurchase is to occur, it is assumed that the net benefits from theperspective of the customer are positive, thus influencing re-purchase intention andsatisfaction. Therefore, this study reconciles the net benefits measures with thee-commerce context and considers them as antecedents of repurchase intention andsatisfaction, instead of as dependent variable in the updated IS success model.

Third, the updated IS success model is originally developed in the traditional settingwhere 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 needfor trust only arises in uncertain environments (Mayer et al., 1995). In e-commerce, thetransaction-specific uncertainty is elicited by an asymmetric distribution ofinformation between the transaction partners (Grabner-Kraeuter, 2002). Therefore,two of the main obstacles to directly apply the updated IS success model to measuree-commerce success are the lack of deliberating the inherent uncertainty of e-commerceand the exclusion of other critical factors (e.g. trust). These difficulties, however, couldbe alleviated by investigating IS success along with trust. Trust is especially critical inonline transaction because trust absorbs transaction-specific uncertainty throughmitigating the negative effect of perceived information asymmetry and the resultingpossibility 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 withthe imbalance of power and information in online transaction settings. E-commerce hasbeen described as the conduct of business among consumers and e-businesses, whichenable them to exchange value electronically (e.g. money, goods, services, andinformation). Given the hidden information and hidden action problems in thee-commerce context (Pavlou et al., 2007), there are power and information asymmetriesbetween online buyers and sellers. Justice evaluations are more likely to arise in anyexchange of value (Adams, 1965) and in asymmetrical power relationships (Lind, 2001).Consequently, justice should not be ignored due to its valuable framework forexplaining 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 ofsocial exchange relationships. Bradach and Eccles (1989) view trust as a controlmechanism that facilitates exchange relationships characterized by uncertainty,vulnerability, and dependence. These characteristics are reflected in the onlineshopping environment, where customers are unable to personally scrutinize thevendor, physically examine the merchandise, or collect the merchandise uponpayment. Customers have limited information and cognitive resources available, andthus seek to reduce the uncertainty and complexity of online transactions by applying

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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 insituations 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 ofonline shopping (Gefen et al., 2003). Such behaviors include sale of fake or defectiveproducts, fake photos and misleading descriptions, failure of the vendor to delivermerchandise, failure to deliver in a timely manner, sending something of lesser valuethan advertised, and high handling and shipping costs. Since the key to successfuleconomic transactions is avoiding opportunistic behavior, online customers in generalstay 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 ifcommerce 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 ofanother party based on the expectation that the other will perform a particular actionimportant 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 specificbeliefs dealing primarily with the benevolence, competence, and integrity of theseller/vendor. According to previous studies dealing with buyer-seller and businessinteractions, this set of specific beliefs comprises the most widely used specific beliefsin 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 thetrustee will not act opportunistically against the trustor, even given the opportunity.Competence is the belief in the trustees ability to fulfill its obligations as expected bythe trustor. Integrity is the belief that the trustee will be honest and keep itscommitments. 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 twoconstructs, some scholars have viewed trust as synonymous with trustworthiness (e.g.McKnight et al., 1998). McKnight et al. (1998) have suggested that trustworthiness is amultifaceted construct that captures the competence of the trustee. Trustworthinesscan be considered as one component of trust (i.e. competence).

If trust is indeed an important aspect of online shopping, then understandingantecedents 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 tocustomer-service provider relationships and examined their impact on trust in thee-service context. Chiu et al. (2010) have considered bidding justice as an importantantecedent of trust in online auctions. Fang and Chiu (2010) have extended therelationship between justice and trust to the virtual communities of practice. Givenpower and information asymmetries between buyers and sellers in the context ofe-commerce, justice has the potential to provide deeper insights into trust in suchcontext.


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2.3 Justice theoryBefore 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 mans rewards in exchange with others should be proportional

to his investments. Adams (1965) used a social exchange theory framework toevaluate fairness. According to Adamss (1965) equity theory, an individualsperception of the fairness of exchange relationships is determined by comparing theoutput/input ratio for oneself with that of referent others. A fair balance between inputand outcome leads to feelings of fairness or justice[4]. Thibaut and Walkers (1975)studies of disputant reactions to legal procedures led to the development of their theoryof procedural justice. Procedural justice is concerned with the processes by whichoutcomes 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 enactmentof formal procedures.

Recently, justice theory has been applied to the IS service context (Carr, 2007) and tobuyer-seller relationships, hence we have seen a shift in patterns of justice research. Aswith organizational employment relationships, buyer-seller information asymmetry iscommonplace in online marketplaces and occurs when one party to a transaction haspertinent information that the other party lacks. Two information problems hiddeninformation and hidden action (Pavlou et al., 2007) breed the online buyer-sellerpower asymmetry (imbalance). The seller is in a position of power of whether toprovide its true characteristics, deliver the product, keep the promised product quality,comply with transaction rules, provide accurate information about products andtransaction policies, etc. Consequently, to smooth a transaction, buyers are, ofnecessity, concerned about the powerful sellers 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). Anyinjustice 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 differentways 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 onthe trusting relationships between exchange parties. Trust is especially critical wheninformation or power asymmetry is present in online transactions (Pavlou et al., 2007).Correspondingly, justice theory offers a means through which to explain andunderstand buyers trust in the sellers in e-commerce context.

3. Research model and hypothesesFigure 1 presents the proposed model, referred to as an e-commerce success model. Thedependent variable repurchase intention is posited as the primary construct todetermine customers repurchase behaviors. Repurchase intention refers to thesubjective probability that an individual will continue to purchase products from theonline vendor or store in the future. All key variables are explained, and theirrelationship with repurchase intention is proposed as follows.

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3.1 SatisfactionAccording to Kolter (2000), satisfaction is an individuals feelings of pleasure ordisappointment resulting from comparing the perceived performance (or outcomes) ofonline shopping in relation to his or her expectations. Oliver (1980) theorizes thatsatisfaction is positively associated with future intention, both directly and indirectlyvia 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 onbenefits such as convenience, enjoyment, broad product selection, flexibility, andeffectiveness in product searching and buying (usefulness). Research supports thenotion that online shopping involves hedonic and utilitarian value (net benefits)(Childers et al., 2001). Hedonic shopping value reflects the entertainment and emotionalworth 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 andOliver (1993) posit that affective responses arising from evaluation of the outcomes ofproduct/service usage and cognitive interpretation lead to satisfaction. Onlineshopping gives a customer the opportunity to economize on time and effort by makingit easy to locate merchants, find items, and procure offerings (Szymanski and Hise,2000). Prior research shows that positive perceptions of convenience, extensive productinformation, and enjoyment (Bauer et al., 2006) have significant effects on customersatisfaction with online shopping.

According to self-determination theory (Deci and Ryan, 1985), individuals areself-determining and intrinsically motivated in online shopping when they areinterested in it. According to Davis et al. (1989), customers form intentions towardonline shopping based largely on a cognitive appraisal of how it will improve theirshopping performance, i.e. perceived usefulness. Customers who accomplished the

Figure 1.E-commerce success

model


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shopping task of product acquisition in an efficient manner will be more likely toexhibit stronger repurchase intentions (Babin and Babin, 2001). Childers et al. (2001)consider perceived usefulness as a utilitarian benefit and enjoyment as a hedonicbenefit of online shopping and showed that they are important motivations for

individuals to engage in online shopping. Support for the role of net benefits oncustomers satisfaction and repurchase intentions is provided by Forst et al. (2010) and

Jones et al. (2006). Therefore:

H2. Net benefits positively affect customers satisfaction.

H3. Net benefits positively affect customers repurchase intentions.

3.3 Information qualityInformation quality refers to customers perceptions of the characteristics andpresentation of information in the online shopping web site. It deals with attributessuch as relevance, understandability, accuracy, completeness, and timeliness. Since aprimary role of an online store is to provide information about product, transaction,and service, higher quality information leads to better buying decisions and higherlevels of customer satisfaction (Peterson et al., 1997). Inaccurate and out-of-dateinformation cause customers to become dissatisfied with an online vendor (Collier andBienstock, 2006). McKinney et al. (2002) posited that satisfaction with the quality ofweb sites information content is one of the two sources of web-customer satisfaction.Therefore:

H4. Information quality positively affects customer satisfaction.

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

H5. System quality positively affects customer satisfaction.

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

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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 cardinformation is secure and protected from potential hackers. Support for the role ofservice quality on customer satisfaction is provided by Bauer et al. (2006). Therefore:

H6. Service quality positively affects customer satisfaction.

3.6 TrustFollowing Pavlou and Fygenson (2006), trust is defined as the buyers beliefs thatthe seller will behave benevolently, capably, and ethically. According to TPB(Ajzen, 1991), trust beliefs create favorable feelings toward the online vendor thatare likely to increase a customers intention to purchase products from the vendor.Lack of trust prevents buyers from engaging in online shopping because they areunlikely 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 toGefen 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 willnot 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 andVoorhees, 2006). Therefore:

H7. Customer trust in the online vendor positively affects customer satisfaction.

H8. Customer trust in the online vendor positively affects repurchase intention.

3.7 Distributive justiceIn this study, distributive justice refers to the extent to which the customersinvestments (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 toinvestments (Homans, 1961), whether returns adhere to expectation (Blau, 1964), andwhether outcome/input ratios match those of a referent other (Adams, 1965). Whenoutcome 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 whenthe products they received are proportional to their investments. Support for the roleof 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 justiceProcedural fairness refers to the perceived fairness of policies and procedures in theonline shopping process. The transaction process is an integral part of online shopping,thus an online vendor can enhance customers trust by engaging activities thatenhance their perceptions of procedural justice, such as providing detailed informationabout shopping policy and procedure, applying policies consistently, clarifyingdecisions about any change in the web site, and handling problems fairly. According toCohen-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 ruleand hence can be trusted. Support for the role of procedural justice on trust is providedby Pillai et al. (2001). Therefore:

H10. Procedural justice positively affects customer trust in the online vendor.

3.9 Interactional justiceInteractional justice refers to the quality of the interpersonal treatment a customerreceived during the online shopping process. Attitudes of treating people with dignityand respect are effective communication for increasing peoples feelings of perceived

justice (Bies and Moag, 1986). Lind (2001, p. 65) noted that people use overallimpressions of fair treatment as a surrogate for interpersonal trust, and interpersonalcommunications that express social sensitivity can facilitate the establishment of trustamong them. Support for the role of interactional justice on trust is provided byCohen-Charash and Spector (2001). Therefore:

H11. Interactional justice positively affects customer trust in the online vendor.

4. Research methodology4.1. Measurement developmentMeasurement items were adapted from the literature wherever possible (see Appendix).A small-scale pretest of the questionnaire was conducted using 20 graduate studentswith online shopping experience to assess its logical consistencies, ease ofunderstanding, and contextual relevance. Finally, a large-scale pretest with 195customers of the target online shopping store was also conducted to confirm themeasurement properties of the final items and provide preliminary evidence for theproposed model. The results indicated that the measurement model fulfills the criteria ofreliability, 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 loadingsranging from 0.68 to 0.95. The results of the structural path analysis indicated that 9 of11 hypotheses were supported. The relationship between service quality and satisfactionH4; t 0:72 was insignificant, while the relationship between trust and repurchaseintention was marginal H8; t 1:82:

Items for measuring three justice dimensions were adapted from Anderson andSrinivasan (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 threequality 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-QUALscale 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 distributivejustice. Overall, this study retained responsiveness, contact, and privacy as themeasures of service quality and did not include the compensation measure becausevery few customers had compensation experience. Items for measuring net benefitswere based on Anderson and Srinivasan (2003), Childers et al. (2001), DeLone andMcLean (2003), and Devaraj et al. (2002). Items for measuring satisfaction were adaptedfrom McKinney et al. (2002) and Oliver (1980). Repurchase intention was adapted fromParasuraman et al. (2005) and Pavlou and Fygenson (2006). For all the measures, a

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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 andrepurchase intentions, the research model was tested with data from PCHomes onlineshopping customers. PCHome was chosen because it is the most widely used onlineshopping store in Taiwan. A banner with a hyperlink connecting to our web surveywas published on a number of bulletin board systems (BBS), chat rooms and virtualcommunities and individuals with online shopping experience with PCHome werecordially invited to support this survey. Given that the questionnaire items of servicequality and interactional justice constructs involved issues regarding interactionswith service representatives and problem handling such as product return, for surveyresults to be valid, respondents had to experience online service and contact withservice representatives of PCHome to evaluate both constructs (so-called purposivesampling or judgment sampling). In this sampling plan, sample elements were

selected because they are believed to be representatives of the population of interestand were expected to serve the research purpose of our study (Churchill, 1991).Therefore, in the demographic information of our survey web page, we requiredrespondents to indicate whether they had experience in contacting customer servicerepresentatives and returning products. Initially, 2,072 online respondents voluntarilycompleted the survey. Since very few respondents have experience in contactingcustomer service representatives and returning products, after eliminating invalidrespondents (e.g. those without service representative contacting and product-returnexperience), 219 valid ones remained for our data analysis. The promise of anincentive significantly enhanced the probability that a respondent would more fullycomplete the questionnaire and make fewer errors in the responses to surveyquestions (Godwin, 1979). Only 50 respondents were randomly selected from these219 valid ones due to our limited budget. Table I lists the demographic information ofthe respondents.

4.3. Data analysisData 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 Items Freq. Percent Measure Items Freq. Percent

Gender Male 104 47.5 Gender Female 115 52.5

Age , 20 9 4.1 Education High school 13 6.0

20-24 74 33.8 College 16 7.325-29 92 42.0 University 133 60.730 , 44 20.1 Graduate school 57 26.0

Buys in the past 6months 1-2 87 39.7

Internet experience(in years) , 5 8 3.6

3-5 79 36.1 5-6 47 21.56-10 35 16.0 7-8 63 28.811 , 18 8.2 9 , 101 46.1

Table I.Demographic informationof respondents (N 219)


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step tests the structural relationships among latent constructs. The aim of the two-stepapproach is to assess the reliability and validity of the measures before their use in thefull 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 simultaneouslyanalyze all of the paths in one analysis (Chin and Newsted, 1999). SEM can providefuller information about the extent to which the research model is supported by thedata than in regression techniques (see Gefen et al., 2000). Within SEM, PLS (partialleast squares) is partial-least-squares based, while LISREL representscovariance-based SEM. PLS is more suited for exploratory research, predictiveapplications, 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 oftheory testing. Besides, PLS places minimal restrictions on measurement scales,sample size, and residual distribution (Chin and Newsted, 1999). According toTanakas (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 SEMapproaches. The PLS bootstrap technique a resampling procedure is a usefulstrategy for evaluating replicability. Because the analysis considers so manyconfigurations of subjects, one use of such techniques informs the researcherconcerning the extent to which results generalize across different types of subjects(Thompson, 1993). PLS also provides the analysis of both a measurement model and astructural model.

4.3.1 Measurement model. The adequacy of the measurement model was evaluatedon the criteria of reliability, convergent validity, and discriminant validity. Reliabilitywas 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 criteriasuggested 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 variancedue to measurement error for that construct (i.e. AVE should exceed 0.50).

Constructs Composite reliability Mean AVE

Distributive justice (DJ) 0.91 4.79 0.67

Procedural justice (PJ) 0.92 4.70 0.70Interactional justice (IJ) 0.95 5.05 0.87Trust (TR) 0.93 4.90 0.74Information quality (IQ) 0.89 4.83 0.61System quality (SQ) 0.91 4.93 0.65Service quality (SEQ) 0.89 4.86 0.64Net benefits (NB) 0.90 5.48 0.65Satisfaction (SA) 0.96 4.93 0.85Repurchase intention (RI) 0.96 5.20 0.88

Table II.Descriptive statistics ofconstructs

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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 PJ IJ TR IQ SQ SEQ NB SA RI

DJ1 0.85 0.47 0.50 0.53 0.58 0.34 0.45 0.45 0.54 0.53DJ2 0.86 0.54 0.63 0.67 0.65 0.50 0.58 0.61 0.66 0.64DJ3 0.86 0.52 0.52 0.53 0.56 0.33 0.49 0.45 0.55 0.52DJ4 0.78 0.52 0.50 0.48 0.48 0.30 0.46 0.36 0.54 0.47DJ5 0.72 0.47 0.53 0.57 0.54 0.45 0.46 0.43 0.59 0.53PJ1 0.51 0.87 0.68 0.71 0.53 0.48 0.74 0.49 0.65 0.57PJ2 0.50 0.77 0.59 0.58 0.46 0.47 0.59 0.57 0.52 0.57PJ3 0.53 0.85 0.61 0.65 0.52 0.46 0.62 0.48 0.62 0.58PJ4 0.53 0.87 0.63 0.61 0.49 0.45 0.61 0.47 0.55 0.53

PJ5 0.51 0.80 0.56 0.56 0.44 0.39 0.51 0.37 0.50 0.43IJ1 0.61 0.70 0.93 0.74 0.56 0.48 0.73 0.59 0.70 0.67IJ2 0.62 0.68 0.94 0.76 0.60 0.48 0.74 0.66 0.72 0.71IJ3 0.63 0.69 0.93 0.74 0.57 0.46 0.74 0.63 0.68 0.70TR1 0.63 0.64 0.68 0.85 0.57 0.50 0.64 0.50 0.70 0.64TR2 0.68 0.66 0.67 0.86 0.61 0.54 0.66 0.55 0.74 0.65TR3 0.55 0.66 0.71 0.87 0.58 0.49 0.66 0.56 0.66 0.61TR4 0.60 0.67 0.70 0.91 0.56 0.50 0.67 0.60 0.76 0.73TR5 0.51 0.58 0.69 0.79 0.52 0.54 0.62 0.73 0.66 0.70IQ1 0.47 0.38 0.49 0.47 0.75 0.46 0.43 0.57 0.50 0.48IQ2 0.47 0.45 0.52 0.50 0.80 0.50 0.49 0.57 0.54 0.50IQ3 0.60 0.50 0.53 0.61 0.80 0.48 0.54 0.48 0.60 0.51IQ4 0.61 0.54 0.48 0.52 0.81 0.43 0.54 0.42 0.54 0.48IQ5 0.54 0.44 0.38 0.48 0.74 0.37 0.35 0.42 0.53 0.44

SQ1 0.38 0.39 0.39 0.46 0.44 0.82 0.40 0.44 0.52 0.48SQ2 0.38 0.42 0.42 0.48 0.48 0.85 0.47 0.51 0.54 0.48SQ3 0.43 0.50 0.43 0.51 0.47 0.77 0.50 0.42 0.54 0.45SQ4 0.41 0.47 0.44 0.52 0.48 0.89 0.49 0.49 0.58 0.56SQ5 0.36 0.39 0.36 0.42 0.40 0.72 0.42 0.55 0.49 0.50SQ6 0.36 0.44 0.41 0.48 0.50 0.76 0.52 0.51 0.51 0.47SEQ1 0.41 0.46 0.54 0.52 0.40 0.49 0.75 0.58 0.51 0.52SEQ2 0.56 0.71 0.72 0.67 0.56 0.45 0.87 0.55 0.64 0.60SEQ3 0.56 0.56 0.65 0.62 0.51 0.48 0.78 0.44 0.56 0.51SEQ4 0.39 0.65 0.60 0.61 0.45 0.46 0.80 0.46 0.52 0.48NB1 0.48 0.49 0.55 0.58 0.50 0.50 0.50 0.79 0.55 0.67NB2 0.40 0.43 0.50 0.50 0.45 0.43 0.45 0.81 0.53 0.56NB3 0.49 0.51 0.59 0.58 0.56 0.54 0.53 0.85 0.68 0.70

NB4 0.47 0.42 0.51 0.53 0.53 0.45 0.49 0.84 0.60 0.69NB5 0.48 0.45 0.56 0.58 0.49 0.52 0.57 0.74 0.55 0.55SA1 0.66 0.61 0.70 0.75 0.63 0.59 0.64 0.68 0.92 0.84SA2 0.65 0.63 0.71 0.78 0.65 0.64 0.67 0.70 0.94 0.80SA3 0.67 0.66 0.65 0.74 0.65 0.59 0.63 0.64 0.90 0.75SA4 0.66 0.62 0.71 0.77 0.64 0.62 0.65 0.66 0.92 0.79RI1 0.59 0.60 0.67 0.73 0.57 0.55 0.59 0.74 0.82 0.94RI2 0.58 0.56 0.67 0.71 0.56 0.59 0.59 0.72 0.77 0.93RI3 0.63 0.59 0.73 0.72 0.60 0.53 0.61 0.78 0.78 0.95

Table III.PLS confirmatory factor

analysis andcross-loadings


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Discriminant validity was assessed by examining cross-loadings and the relationshipbetween correlations among constructs and the square root of AVEs (Fornell andLarcker, 1981). An examination of cross-factor loadings (Table III) indicates gooddiscriminant 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 isthat the square root of the AVE from the construct should be greater than thecorrelation shared between the construct and other constructs in the model (Fornell andLarcker, 1981). Table IV lists the correlations among the constructs, with the squareroot of the AVE on the diagonal. All the diagonal values exceed the inter-constructcorrelations, 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 theR-square scores of endogenous variables assesses the explanatory power of astructural model. The results of structural path analysis are depicted in Figure 2. Datacollected from 219 valid respondents provided support for all but one of elevenhypotheses, exhibiting a p-value less than 0.05. The unsupported hypothesis, therelationship between service quality and satisfaction (H4), was not significant at the0.05 level. Tests of significance of all paths were performed using the bootstrap

Figure 2.SEM analysis of theresearch model

DJ PJ IJ TR IQ SQ SEQ NB SA RI

DJ 0.82PJ 0.62 0.84IJ 0.66 0.74 0.93TR 0.69 0.75 0.80 0.86IQ 0.69 0.59 0.62 0.67 0.78SQ 0.48 0.54 0.51 0.60 0.57 0.81SEQ 0.60 0.74 0.79 0.76 0.61 0.58 0.80

NB 0.58 0.57 0.67 0.69 0.63 0.61 0.63 0.81SA 0.71 0.69 0.75 0.83 0.70 0.66 0.70 0.72 0.92RI 0.64 0.62 0.73 0.77 0.61 0.59 0.64 0.80 0.84 0.94

Table IV.AVE and correlationamong constructs

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resampling procedure. In addition, the model accounts for 72 to 79 percent of thevariance (R-square scores). Overall, the research model accounted for 79 percent of thevariance of repurchase intention (Figure 2).

5. Discussion and implicationsDrawing on the IS success model, trust, justice, management and marketing literature,the study theoretically develops and empirically tests a model that explains andpredicts customers repurchase intentions toward online shopping.

5.1 Summary of resultsData from our survey suggest support for the proposed model of e-commerce success.Results indicate that repurchase intention is most dominantly influenced bysatisfaction b 0:47: This suggests that satisfaction is a powerful mediatorbetween quality perceptions and trust, and repurchase intention. The results confirmthat the significant positive impacts of net benefits on customers satisfaction and

repurchase intentions, validating our proposition that net benefits perception is a majorenabler 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 theinsignificant relationship is that customers with limited experience in contactingservice representatives were not sufficient to evaluate service quality. Usually,e-service quality is established through accumulated experience of interaction orcontact with service representatives (Devaraj et al., 2002). Another possibleexplanation is that service quality is a hygiene factor. According to Herzberg et al.(1959), some factors (called motivational factors) influenced satisfaction but notdissatisfaction, while others (called hygiene factor) only influenced dissatisfaction butnot satisfaction. Similar to this line of reasoning, service quality may negatively affect

dissatisfaction towards online shopping, but may not positively affect satisfactiontowards online shopping.

Results indicate that trust has a strong effect on satisfaction b 0:47 but its effecton repurchase intention b 0:13 is marginally significant. A possible explanationfor the relatively weak effect of trust on repurchase intention, is that trust also actsindirectly on repurchase intention through the mediating effect of satisfaction. Thepartial mediating effects of satisfaction on the relationship between trust andrepurchase intention was assessed following Baron and Kennys (1986) procedures:

. trust has a significant effect on repurchase intention b 0:42;

. trust has a significant effect on satisfaction b 0:47; and

. satisfaction has a significant effect on repurchase intention b 0:47but the

effect of trust on repurchase intention b 0:

13 decreases to a marginallysignificant level.

5.2 Implications for theoryFrom a theoretical perspective, our findings imply that perceptions of quality bythemselves are not sufficient in increasing customers satisfaction. For example,service quality is necessary but not sufficient to create customer satisfaction. Servicequality 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 willdefinitely be dissatisfied with an online store providing poor service. Informationquality and system quality can contribute customers satisfaction to some extent, but itis trust and net benefits that lead to greater level of satisfaction.

In addition, justice represents an additional key element of buyer-sellerrelationships in online shopping that has been ignored in the literature. Theintegration of the three distinct dimensions of justice also results in a more descriptivemodel that better explains customers repurchase intentions toward online shopping.Besides, the extent of explained variance in trust R2 0:72 implies that the threedimensions of justice are possibly among the most important antecedents of customerstrust in online vendors.

Furthermore, a major finding of the study is the dominant role of interactionaljustice in building customers trust. However, some research has found thatinteractional justice has less of an effect than procedural justice on trust in theorganizational context (Hubbell and Chory-Assad, 2005). Our findings imply that therelative importance of each of the justice dimensions may be context specific. Overall,the study extends the justice literature from employee-organization relationships tocustomer-vendor relationships, shedding light on the trust-building potential of thethree dimensions of justice.

5.3 Implications for practiceRegarding the drivers of repurchase intention, the results suggest that online storesmay need to employ a combined strategy aimed at increasing satisfaction, trust, andnet benefits of online shopping. To enhance customer satisfaction, online stores candevote valuable corporate resources to information quality and system quality of theweb sites. A successful e-commerce web site starts with good content. The informationprovided in the web site has to be easy to understand, accurate, complete, timely, and

relevant to customers purchase decisions. From a vendors perspective, it would beespecially unfortunate to interpret our results to imply that service quality is notimportant. The appropriate interpretation is that providing good service is notsufficient to create customers satisfaction and loyalty. However, bad service is deemedto elicit customers dissatisfaction. According to Desatnick (1987), each of thoseunsatisfied 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 potentialcustomers from placing an order from the vendor, thus damaging the vendorsreputation and financial position (Holmes and Lett, 1977). For example, among 2,072questionnaires collected from our web survey, although a majority of respondents (89percent)[5] had not contacted service representatives due to their satisfying shoppingexperience with PCHome, 219 respondents (11 percent) had such experience due to the

problem-handling issues. For a vendor, losing the opportunity to rectify unsatisfiedservice or quality problems is likely to generate customers NWOM, thus drivingcustomers 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 ofinterpersonal treatment might signal to customers that the vendor cares for theirwellbeing. This is good news for vendors, because the economic costs of interacting in

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a manner that raises the dignity of customers are not likely to be as high as the costsassociated with satisfying either procedural or distributive justice. The online vendorshould provide some training to customer service representatives to ensure that theyhave 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 elementsthat enhance customer convenience, merchandise variety and assortment, the richnessof product information and fun and entertainment of online shopping.

5.4 Limitations and future researchWe note that our findings must be interpreted in light of the studys limitation. First,the data were collected from a single online shopping store, PCHome. Nonetheless, thegenerality of the findings to other online stores (e.g. Amazon) requires additionalresearch. Second, our results may have been impacted by self-selection bias. Oursample comprises only active online customers. Individuals who had already ceased topurchase products from PChome might have different perceptions about the influenceof IS success model variables, trust, and the three dimensions of justice, and so couldhave been differently affected by them. Therefore, the results should be interpreted asonly explaining repurchase intentions of current online shopping customers. Thus,further research is needed to examine whether the results can be generalized tonon-customers, disaffected customers, first-purchase customers, or those customerswith multiple contact experience with service representatives. Although our websurvey may have been affected by self-selection bias, Hayslett and Wildemuth (2004)have indicated that there are no significant differences between the demographicbackgrounds of self-selected respondents and a random sample. Self-selectedrespondents 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 causalrelationships could only be inferred rather than proven. While a longitudinal analysiswould be a desired approach, solid cross sectional models must first be conductedbefore future research can confirm their viability over time. Fourth, the influences ofquality dimensions on satisfaction are either insignificant or relatively weak, thereforefuture research is necessary to verify whether quality dimensions exerts the influenceson repurchase intention indirectly through other mediators (e.g. value) instead ofsatisfaction. Furthermore, although several factors have been considered asantecedents of repurchase intention in our research model, further research isencouraged to investigate whether other possible factors (e.g. laziness, habit, and/orfamiliarity) affect repurchase intention.

Notes

1. www.comscore.com/press/release.asp?press 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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4. Justice can be considered as a set of fairness perceptions (Cropanzano and Greenberg, 1997).Justice researcher s generally have accepte d the terms fairness and justice asinterchangeable, both implicitly and explicitly (e.g. Greenberg and Colquitt, 2005), and wefollow this tradition.

5. Percentage of respondents without contact and return experience: (2072 2 219)/2072 89percent; Percentage of respondents with contact and return experience: 219/2072 11percent

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Appendix. Questionnaire itemsDistributive justice (DJ)

DJ1 I think what I got is fair compared to the price I paid.

DJ2 I think I got what I paid for from PChome.

DJ3 I think the value of the products that I received from PChome is proportional to the

price I paid.

DJ4 I think the products that I purchased at PChome are considered to be a good buy.

DJ5 I think the products that I received from PChome are the same quality as advertised.

Procedural justice (PJ)

PJ1 I think the procedures used by PChome for handling problems occurred in theshopping process are fair.

PJ2 I think PCHome allows customers to complain and state their views.

PJ3 I think the policies of PChome are applied consistently across all affected customers.

PJ4 I think PChome would clarify decisions about any change in the Web site and

provide additional information when requested by customers.

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PJ5 I think PChome provide detailed information about shopping policies and

procedures.

Interactional justice (IPJ)

IPJ1 Customer service representatives of PChome treat me with respect.

IPJ2 Customer service representatives of PChome treat me with friendliness.

IPJ3 Customer service representatives of PChome treat me with politeness.

Trust (TR)

TR1 Based on my experience with PChome in the past, I know it is honest.

TR2 Based on my experience with PChome in the past, I know it is not opportunistic.

TR3 Based on my experience with PChome in the past, I know it keeps its promises to

customers.

TR4 Based on my experience with PChome in the past, I know it is trustworthy.

TR5 Based on my experience with PChome in the past, I know it has the ability to

complete transactions.

Information quality (IQ)

IQ1 Information provided by the PChome Web site is relevant to my purchase decisions.

IQ2 Information provided by the PChome Web site is easy to comprehend.

IQ3 Information provided by the PChome Web site is accurate.

IQ4 Information provided by the PChome Web site is complete.

IQ5 Information provided by the PChome Web site is timely.

System quality (SQ)

SQ1 The PChome Web site has a simple layout for its contents.

SQ2 The organization and layout of the PChome Web site facilitate searching forproducts.

SQ3 The appearance of PChome Web site is appealing.

SQ4 The PChome Web site is easy to navigate.

SQ5 The PChome Web site is always available.

SQ6 The PChome Web site loads its pages fast.


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Service quality (SEQ)

SEQ1 PChome provides me with convenient options for returning products.

SEQ2 PChome takes care of my problems promptly.

SEQ3 PChome does not share my personal information with other vendors.

SEQ4 PChome offers the ability to speak to a live person if there is a problem.

Net benefits (NB)

NB1 I think PChome offers a broad selection of products.

NB2 I think purchasing products from PChome is flexible.

NB3 I think purchasing products from PChome is interesting.

NB4 I think purchasing products from PChome is convenient.

NB5 I think PCHome enhances my effectiveness in product searching and buying.

Satisfaction (SA)

SA1 I like to purchase products from PChome.

SA2 I am pleased with the experience of purchasing products from PChome.

SA3 I think purchasing products from PChome is a good idea.

SA4 Overall, I am satisfied with the experience of purchasing products from PChome.

Repurchase intention (LI)

CI1 If I could, I would like to continue using PChome to purchase products.

CI2 It is likely that I will continue purchasing products from PChome in the future.

CI3 I intend to continue purchasing products from PChome in the future.

About the authorsYu-Hui Fang is an Assistant Professor in the Department of Accounting at the TamkangUniversity, Taiwan. She gained her PhD degree in Information Management from NationalCentral University and her Masters degree in Accounting from University of Houston. Her

research interests include electronic commerce, virtual communities and knowledgemanagement. Her research has appeared in Computers in Human Behavior, Online

Information Review, and others. Yu-Hui Fang can be contacted at: [email protected] 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 knowledgemanagement. His research has appeared in Decision Support Systems, Information& Management, Information Systems Journal, International Journal of Human-ComputerStudies, Computers & Education, Computers in Human Behavior, Electronic Commerce Research

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and Applications, Behaviour & Information Technology, Information and Software Technology,Information Systems Management, Information Technology and Management, Journal ofInformation 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 hasappeared 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, EuropeanJournal of Operational Research, International Journal of Information Management, and others.Eric T.G. Wang can be contacted at: [email protected]


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