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EJM 44,6

874 Received April 2008 Revised August 2008, November 2008 Accepted November 2008

Alternative explanations of online repurchasing behavioral intentions A comparison study of Korean and UK young customers Hong-Youl Ha Department of Business Administration, Kangwon National University, Chuncheon, South Korea

Siva K. Muthaly Swinburne University of Technology, Hawthorn, Australia, and

Raphae¨l K. Akamavi University of Hull, Hull, UK Abstract

European Journal of Marketing Vol. 44 No. 6, 2010 pp. 874-904 q Emerald Group Publishing Limited 0309-0566 DOI 10.1108/03090561011032757

Purpose – This study sets out to employ a cross-cultural perspective to explore alternative explanations in the development of the online repurchase intentions model. Design/methodology/approach – The proposed constructs of alternative models deal with key online consumer behavior variables such as customized information, web use applications, online service and perceived interactivity which lead to the formation of purchase intentions. These variables form the theoretical foundation for four alternative models. To test these four alternative models 448 online consumers were surveyed in 2007. Structural equation modeling is used to test these models. Findings – The results demonstrate that the power of customized information lies in its ability to influence consumer satisfaction and perceived interactivity that are proximate to repurchase intentions. The results also show that the link of satisfaction ! repurchase intentions provides a meaningful empirical representation of the hierarchical sequence in which the cycle of customer repurchasing process (satisfaction ! attitude ! repurchase intentions) is related. Research limitations/implications – One may examine applications of the findings in the travel industry in practice and reflect upon potential pitfalls when applying the findings of this piece of the present research. Although previous studies reveal the significance between the two constructs, further research can investigate the issue to be generalizable to the many types of e-B2C services. Second, particularly in terms of the ability to generalize the model, the study did not provide fairly consistent results for different service categories. Studies on other service classes, such as online bookstore and online banking services, may reveal findings that extend the authors’ approach. Practical implications – Similarity of purchasing attitudes between Korean and UK consumers may allow e-tailors to design a systematic strategy for generating favorable attitudes toward their web sites, even though standardized online treatment of consumers is not the golden path of internet marketing. Further, the proposed research model (PRM) provides a means of identifying the underlying dispositions associated with the mediating variable. Originality/value – This is the first study to examine online repurchasing models using alternative explanations. By structuring such alternative models, McKenzie argues that researchers may be better able to judge how the evidence relates to each alternative view. The present approach can be viewed as a significant early step on the path toward a comprehensive understanding of online consumer behaviors in new information communication technology. Keywords Internet marketing, Purchasing techniques, Internet shopping, Korea, Consumer behaviour, United Kingdom Paper type Research paper

Introduction Consumers who engage in online transactions can find competitive providers to meet their needs and wants through the click of a mouse (Singh, 2002). They are increasingly making purchase decisions based upon their peer recommendation and endorsement rather than on their experience of interaction with the supplier (Smith et al., 2005). This is because there are informational methods of online interaction such as blogs, social networks (e.g. facebook, myspace, typad, wordpress) and other community-based resources (e.g. telephone call center, e-mail, peer-to-peer file transfer) which are having a huge impact upon consumers’ buying decisions (Herring et al., 2005; Bernoff and Li, 2008). However, some research using international samples suggest that there are consumer differences in shopping attitudes toward internet stores between relatively collectivist cultures and individualist cultures (Ja¨rvenpa¨a and Tractinsky, 1999; Lynch and Beck, 2001). To overcome the consternation of the differences, this is critical because these methods are likely to vary depending on users’ attitudes toward a particular web site from the global market perspective. Thus, a complete understanding of how shopping attitudes impact online purchasing behavior could benefit marketers in their pursuit of penetrating any market. This approach would be highly resorted by researchers and practitioners in their quest for comparisons of online shopping behavior between Asia-Pacific consumers and Western consumers across borders. In this study, we propose a model on Online Repurchase Intentions (ORI), which investigates the predictors of intentions to purchase offerings (e.g., services, products). As shown in Table I, we have distinguished three main constructs such as consumer satisfaction, consumer attitude, and repurchase intentions. In particular, both satisfaction and repurchase intentions refer to a customer’s evaluation of a specific transaction as outcomes of marketing variables (Agustin and Singh, 2005; Oliver, 1999; Rust and Oliver, 2000). In contrast, a consumer’s attitude corresponds to a global evaluation of the product or service, rather than to an evaluation of a specific transaction (Holbrook and Corfman, 1985; Schlosser, 2003). Scholars have begun gaining an understanding of e-marketing strategies in order to attract buyers to web sites (Hoffman et al., 2000; Park and Fader, 2004; Schlosser et al., 2006; Shim et al., 2001). However, these strategic approaches are linked to the abovementioned relationships, which are rarely highlighted in online consumer research (Schlosser, 2003; Urban et al., 2000). In their seminal Marketing Science article titled “Consumer decision making in online shopping environments: a structural modeling approach” Hoffman et al. (2000) argue that investigating the relationship between customer experience and online marketing outcome variables as didactic. Consequently, the development of an integrated model between three main constructs and other online-based constructs (e.g. customized information and perceived interactivity) is pivotal in better comprehending online purchase behavior. For example, both customized information and perceived interactivity might be beneficial if they are incorporated into the online repurchasing mechanism (e.g., Alba et al., 1997; Schlosser, 2003). Customized information has become important in internet based applications due to the multitude of choices that are available to customers on the internet (Simonson, 2005; Ha, 2002). Firms have added value by providing appropriate information to simplify the customer’s decision process (Murthi and

Alternative explanations

875

Interactivity, trust, reputation, attitude

Information, satisfaction, commitment

Heijden et al. (2003)

Park and Kim (2003)

Park and Fader (2004) Information, visiting time, expectations

Attitude, information search

Shim et al. (2001)

Koufaris et al. (2001)

Interactivity, flow, skills, challenges

Hoffman and Novak (2000)

Study

Positive attitudes can increase the intention of web customers to purchase. Also, information can have a significant impact on their online experience which will result in future online purchase Intention to use the internet to search for Determines whether intent to search the information not only is the strongest internet for product information is a key element for marketing researchers to employ predictor of online purchase intention but also mediates relationships between in predicting consumers’ internet purchasing intention and other predictors purchasing intentions (i.e. attitude toward online shopping and previous online purchase experience) Online purchase intention at the web site is Investigate the antecedents of online purchase intention for B2C web sites with strongly determined by attitude toward online shopping at the web site. In two competing models particular, trust-antecedent interactivity directly influence the attitude towards purchasing online Information affects information satisfaction Investigates the relationship between and relational benefit that, in turn, are various characteristics of online shopping significantly related to each consumer’s site and consumer purchase behavior commitment and actual purchase behavior Develop a stochastic timing model of cross- Summary information (i.e. frequency) from past visit patterns at a competing site can site visit behavior to understand how to leverage information from one site to help make accurate statements about the future behavior (i.e. previous nonvisitors to a given explain customer behavior at another site) (continued)

Consumers who experience the flow state in a hypermedia CME exhibit exploratory behaviors (e.g. shopping behavior) compared with those who do not

Main findings

876

Focuses on the marketing implications of commercializing hypermedia computermediated environments, of which the world wide web on the internet is the first and current networked global implementation Attitude, involvement, information Investigates the impact of consumer experience and attitudes on intention to return and purchase online

Key variables

Table I. Summary of research relevant to online consumer behavior

Author(s)

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Interactivity, attitude

Interactivity, satisfaction, attitude

Satisfaction, service quality service Examine the relationships between value behavioral intentions and its antecedent factors in online service settings

Wu (2005)

Fiore et al. (2005a)

Hackman et al. (2006)

Focuses on consumer characteristics that may influence the importance of hedonic value from a web site design feature

Investigates the mediating role of interactivity on communication outcomes such as attitude toward the web site

Examines the impact of three key components of TPB (i.e. attitude, subject norm, control beliefs) on e-consumers’ purchase intentions Easy of use (interactivity), attitude Examines four key constructs that have indirect influences on online purchase intentions through the mediation of attitudes

Chiu et al. (2005)

Consumer attitude

Lim and Dubinsky (2005)

Study

Key variables

Author(s)

Attitude toward online shopping is reinforced to the extent to which consumers think their relevant others support their online purchase behavior Consumer attitudes play a significant role in facilitating their purchase intentions. Also, the influences of perceived ease of purchasing on both attitudes and online purchase intentions are stronger for females than for males The research has proven the mediating role of web interactivity in affecting the effect of web interactivity on attitude toward the web site. Such an attitude will play an important role in making consumers’ final behavior Their model reveals significant paths between emotional variables and attitudes. Their findings also show that interactivity has a direct impact on satisfaction, attitudes, and willingness to buy Online buyers’ behavioral intentions are directly influenced by online service quality and satisfaction. In particular, the strongest direct effect on behavioral intentions comes from online service satisfaction

Main findings

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Table I.

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Sarkar, 2003). Similarly, a unique characteristic of online shopping environments is the implementation of high degrees of interactivity (Ha¨ubl and Trifts, 2000). Thus, the culmination of these two constructs provides a basis for substantial understanding of e-purchasing behavior. In order to justify the model, this study compares four alternative models for predicting e-purchase behavioral intentions. These alternative models have been utilized in better understanding relationships among our proposed constructs (Ferrer and McArdle, 2003). By structuring such alternative models, McKenzie (1998) argues that researchers have a better leverage on how evidence relates to each alternative view. The availability of competing models involving the constructs of interest in this study allows us to interpret alternative explanations of consumer purchase behavior. Therefore, the aim of this study is to develop and test alternative models, which can assist in the comprehensive understanding of young consumer behaviors from an information communication technology perspective (e.g. internet). Cronin et al. (2000) postulated the importance of the alternative models and their significance to repurchase intentions. Although it is evident that few studies have investigated multiple direct links among information, interactivity, consumer satisfaction, and consumer attitudes, there has been no reported investigation carried out on these variables’ level of influence on online repurchase intentions and its effects of all four alternative models. Thus, this study proposes to ascertain if the four alternative constructs could address their true relationships in selecting the best model for online repurchase intention model. The remainder of this paper is as follows. The first two sections provide the underpinning theoretical background for four alternative models that examine the effect of key antecedents on purchasing intentions. The third section describes the methodological approach and provides the justification to authenticate the proposed conceptual model. Results from a cross-sectional survey are presented, followed by discussion of empirical findings which lead to managerial and academic implications. Finally, limitations for this study and area for future research are discussed. Potential cultural differences of Korea and UK We conducted our study in South Korea and the UK because they represent nearly opposite positions on four important cultural dimensions proposed by Hofstede (1980). According to Hofstede’s (2003) cultural dimensions, both Korea and UK are significantly different for four cultural dimensions: Power Distance Index (Korea 60 v. UK 38); Individualism Index (Korea 18 v. UK 83); Masculinity (Korea 39 v. UK 61); and Uncertainty Avoidance (Korea 85 v. UK 39). Collectivist societies have been shown to display differences from individualist societies in information seeking behavior (Dawar et al., 1996, p. 506. For example, Korean online consumers utilize, search for, and depend on, service information quality (Park and Kim, 2003) more than their Western counterparts in the UK. However Westerners had less directed search and depended more on their internal knowledge and personal experience with products or services (Doran, 2002). These differences indicate that there are significant differences between Korean and UK consumers when researchers develop an overall repurchasing model in a Korean-UK context.

Definitions and primary links of constructs Customized information Numerous studies have examined over 30 years the consumer information search related to conventional pre-purchase phase (e.g., Dawar et al., 1996; Klein and Ford, 2003). The internet’s vast global network provides buyers and sellers unprecedented reach and access to consumers, products, services, and information (Chiang and Dholakia, 2003). The information-rich nature of the online environment can easily become a trap for information overload to occur, as more and more consumers become part of the internet population (e.g. Roos et al., 2004). Web sites emerge as the key carrier of information for business transactions and marketing communication (Poel and Buckinx, 2005). Thus, online providers can communicate with their consumers directly. Shoppers compare notes on suppliers’ product information, and these consumer trends can be tracked. This networked communication makes the e-marketplace unlike conventional markets. In the context of online purchasing behavior, Ha (2002) defines customized information as “optimal self-relevance information for each segmented customer based on experiences of existing or membership customers.” After reviewing the literature on customized information in relation to online marketing (Ansari and Mela, 2003; Ha, 2002; Simonson, 2005; Srinivasan et al., 2002), it can be conceptualized as personalized data, which have been organized or given structure for subsequent purchases. The conceptual approach is appropriate because researchers investigating consumer behavior from an information-processing perspective claim that the attention aspect in message processing controls a substantial portion of the variability in consumer decisions (Bettman, 1979; Lee et al., 2007).

Perceived interactivity The term “perceived interactivity” from a communicator’s perspective is usually taken to refer to design features of the web site, e.g. the inclusion of polls, customization, videos, a contact address, or the provision of a chat room, blogs (Coyle and Thorson, 2001; Sillence et al., 2006; Herring et al., 2005; Bernoff and Li, 2008). As outlined by Wu (2005), the potential for perceived interactivity can be realized by the audience. The perceived interactivity as a two-way communication is almost equivalent to the blended form of interpersonal and computer-mediated interpersonal communication (Rafaeli and Sudweeks, 1997; Sillence et al., 2006). Hoffman et al. (2000) indicate that interactivity is the interaction between a particular site and its users. The interactivity may go to the core of a computer-mediated communication environment. Researchers describe that perceived interactivity offers benefits such as facilitated communication, customization of presented information, image manipulation, and entertainment for the customer (Chang et al., 2005; Fiore et al., 2005a). The leading role of a perceived interactivity is a series of repeated exchanges between parties known to each other as they evolve in response to these interactions and to fluctuations in the contextual environment (McMillan, 2002). In this study, a perceived interactivity is conceptualized as psychological state experienced by a site-visitor during the interaction process (Wu, 2005).

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Consumer satisfaction with web site Over the years, numerous definitions of satisfaction have been used by marketing scholars (Giese and Cote, 2000). Although the marketing literature recognizes the importance of satisfaction, there is no general agreement on how the concept should be defined (Rogers et al., 1992). This lack of consensus implies that satisfaction may not mean the same thing to everyone (Oliver, 1980). Based on this reasoning, in this study regards consumer satisfaction as the perceived degree of contentment with regard to a customer’s prior purchase experience with a given electronic commerce firm (Anderson and Srinivasan, 2003; Boyer and Hult, 2006; Kim and Stoe, 2004). According to O’Cass (2001) who argues that satisfied consumers with past purchasing experiences tend to influence their brand loyal behavior. Generally, the relationship between e-satisfaction and purchase intentions is assumed to be positive (Hackman et al., 2006). Few empirical studies have tested the relationship between satisfaction and actual buying behavior in the traditional markets, and there has been weak associations (Mittal and Kamakura, 2001; Szymanski and Henard, 2001). These studies have been focused on the moderating effect to be linked to low relationship between consumer satisfaction and purchase intentions, whereas a few published studies have examined the mediating effect of the relationship in an online shopping context (Yoon, 2002). Although researchers considered commitment or involvement as a mediator between satisfaction and behavioral intentions (Johnson et al., 2001; Olsen, 2007), an important finding from Yoon (2002) study is that other constructs (e.g., consumer attitudes) may serve as a complete mediator. Consumer attitude toward web site Generally, consumer attitude (CA) is defined as “a psychological tendency that is expressed by evaluating a particular entity with some degree of favor or disfavor” (Eagly and Chaiken, 1993, p. 1). Consumer attitudes are considered as a summary of hypothetical constructs representing overall feelings towards or evaluative judgments about a person, object or issue (Zajonc and Markus, 1982). This study conceptualizes consumer attitude toward web site as overall feelings towards a particular web site with some degree of favor. In the e-purchasing context, it is argued that the mediating effect of CA may be considered. For example, evidence is supported by Yoh et al. (2003) who found that the relationship between trust and purchase intentions is mediated by consumer attitudes. Based on this reasoning this study focuses on the mediating effect of consumer attitudes toward web site between consumer satisfaction and repurchase intention. Table I summarizes key studies related to these above mentioned constructs. Repurchase intentions Generally, customer loyalty can be measured as a behavior (e.g. repeat purchase probability, choice probability for a brand) and as an attitude (e.g. brand preference, commitment, intention-to-buy) (Raju et al., 1990; Dekimpe et al., 1997; Jeuland, 1979). Numerous definitions of brand loyalty focus on different parameters such as behavioral and attitudinal brand loyalty (East, 1990; Sheth et al., 1999; Kahn et al., 1986), repurchase intention (Anderson and Sullivan, 1993), and willingness to buy, but e-loyalty remains underexplored (Gommans et al., 2001; Reichheld and Schefter, 2000;

Ribbink et al., 2004). Thus, this study examines the framework of on-line repurchase intention (ORI). Thus, the final conceptual element of the framework is online repurchase intentions. This construct is considered as to why online customers remain with a particular web site. A key argument in the marketing literature is that e-customers are motivated to remain with a provider due to one or two attachments: desired-based and customized-based. (Bansal et al., 2004; Tsai and Hunag, 2007). In line with this observation, online repurchase intentions are finally defined as a consumer’s willingness to repurchase offerings in a particular web site. Generally, the direct relationship between satisfaction, attitude toward web site and repurchase intentions is assumed to be positive, but the indirect effect through attitude toward web site may be also valuable for a better understanding of online purchasing behavior. Four alternative models of online purchase behavior Marketers who investigate the marketing literature as a means of business performance are likely to look to the literature with different angles. Such approaches may not be fully supported by the literature. In so doing, this study undertakes to develop an appropriate theoretical model to predict e-shopping intentions; the process of selection involves a full study of alternative models in order to select the one providing the best fit. Thompson (2004, p. 115) states that “the fit of a single tested model may always be an artifact of having tested too few models.” As shown in Figure 1, alternative explanations are utilized amongst customized information, perceived interactivity, consumer satisfaction, attitude toward web site and repurchase intentions. Figure 1 shows four possible specifications of the causal role of consumer attitudes, each based on prior theoretical work. All of the competing explanations are couched within a general hierarchy-of-effects framework: for example, in all four explanations, consumer satisfaction is seen as determining consumer attitudes, and two individual variables stimulated by a company are posited as causal antecedents of attitudes. The latter relationship has been documented repeatedly in both the multiattribute attitude (Simonson, 2005) and behavioral response (Srinivasan et al., 2002). In addition, Sundar and Kim (2005) observed a positive linear relationship between information and perceived interactivity. In the case of competing models shown in Figure 1, competition is represented within the design frame in three stages: the mediating role of consumer attitudes via the indirect effect, the role of perceived interactivity, and the direct effects of customized information on repurchase intentions through attitude toward web site. Taking the direct attitude model as the essential hypotheses of this research, the implication is that constructs focus on the function of attitude toward web site in predicting e-shopping behavior. Direct Attitude Model (DAM) To provide a base comparison, this study posits a direct causal relationship from attitude toward web site to repurchase intentions. Each of these variables has been shown to have a relevant linkage on attitude toward web site. As a primary framework, considerable evidence in support of the linkage of consumer satisfaction ! consumer attitude ! repurchase intentions has been accumulated under the

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Figure 1. Four competing models of online purchasing intentions

consumer satisfaction theory. The first consumer post-purchase evaluation stage reflects on consumer’s reactions which follow the basic judgment, whereby satisfaction is a function of prior purchase experience (Oliver, 1980, 1997). Similarly, the model derived from Dick and Basu (1994) also suggests that attitude mediates the effects of satisfaction on purchase intentions. Consistent with the cycle of satisfaction proposed by Oliver (1997), the framework suggests further that the resulting level of satisfaction is a major influence on the consumer’s updated attitude toward web site, which also affects repurchase intentions. The first model also allows one-way indirect effect for the independent constructs on the dependent construct – attitude toward web site, and is called the direct attitude model (DAM). In light of the conceptual and empirical evidence in support of the perceived interactivity ! attitude toward web site (Lim and Dubinsky, 2004; Teo et al., 2003) and customized information ! attitude toward web site (Eroglu et al., 2003;

Simonson, 2005) linkages, they are all incorporated into each of the competing specification of the role of attitude toward web site. Real Mediation Model (RMM) The sequences start with experience (customized information ! perceived interactivity) and progress to attitude toward web site and then to intentions. More specifically, one link that is added in the RMM is a direct link between perceived interactivity and repurchase intentions. This link suggests that perceived interactivity can have a direct impact on repurchase decisions independent of attitude (Chang and Cheon, 2005; Fiore et al., 2005b). The additional link between customized information and perceived interactivity in RMM also means that customized information can have an impact on repurchase intentions through perceived interactivity, again independent of consumer attitudes. The “real mediation model” is the second model that allows the effect of customized information on repurchase intentions through the mediating effect of perceived interactivity. When consumers are interested in customized information, interactivity is the condition of communication in which simultaneous and continuous exchanges occur (Rafaeli, 1988). Given that consumers involve interactivity, fully interactive communication requires that later messages in any sequence take into account not just messages that preceded them, but also the manner in which previous messages were reactive (Rafaeli and Sudweeks, 1997). It is theorized that propensity to interactivity may be possible when customized information meets their demands in the basis of previous messages, particularly in a repurchasing context (Ramani and Kumar, 2008). In this model we incorporate these indirect links into the online purchasing model. Further theoretical justification for the link can be attributed to Wu (2005) information ! perceived interactivity ! attitudes. From the literature based on the “uses-and-gratifications theory”, an individual’s needs, desires, and motives may determine in part his/her patterns of media usage and attitude (Katz et al., 1973). The theory is linked to the level of interactivity. Sohn and Lee (2005) and Wu (2005) demonstrate that the initial evaluation of information affects the level of perceived responsiveness that, in turn, engages in perceived interactivity which leads to form consumer attitudes. Information Effects Model (IEM) Since studies have shown both direct and indirect effects of information on repurchase intentions, this study attempts to also test a model that allows the direct effects of customized information on consumer satisfaction. More specifically, an additional link that is added in the IEM is a direct link of customized information to satisfaction. This link suggests that customized information can have direct impacts on three important constructs (perceived interactivity, consumer satisfaction, and attitude toward web site) which influence repurchase decisions. In situations where the customer is able to conduct a detailed analysis, research identifies the importance of customized information as a vehicle for human motivation because there are motives about information quality in the firm’s offerings (e.g., Murthi and Sarkar, 2003; Simonson, 2005). Expanding this theoretical point, Cook and Coupey (1998) theorize that high levels of information quality on the web have the potential to

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result in more knowledgeable consumers, who are then able to make better quality decisions, who will then experience greater satisfaction with any purchases they make. The IEM model could be enhanced by the theory of reasoned action (Fishbein and Ajzen, 1975). Two hypothesized paths such as customized information-perceived interactivity-attitude and customized information-satisfaction-attitude are partially supported by the acceptance-yielding-impact model of attitude formation (Fishbein and Ajzen, 1981). More specifically, Eagly and Chaiken (1993, p. 239) point out that “a message can exert direct effects by producing acceptance of and yielding to the arguments it contains, but it can also exert indirect effects by its impact on primary beliefs that are not explicitly mentioned in the message”. Customized information is the key for understanding current firm’s strategy which will reinforce repurchase intentions at the final stage of online shopping. It indicates that the model allows the additional effect of customized information on consumer satisfaction. Thus, this third model “information effects model” with three direct effects of customized information on repurchase intentions through other variables is developed. Proposed Research Model (PRM) Past research has largely supported the hypotheses of the IEM in that a direct positive relationship between information and satisfaction variables has been observed. Therefore, the IEM must be regarded as a plausible structure for the mediating role of consumer attitudes. The final model, the most plausible one on conceptual grounds, specifies an additional mediation, and is called the proposed research model. The model focuses on the direct relationship between satisfaction and repurchase intentions. The conceptualization on the link of the satisfaction ! repurchase intentions is similar to models that position satisfaction as the key determinant of behavioral intentions (e.g., Brady et al., 2005; Kim et al., 2006; Oliver, 1997). Since the linkage of satisfaction-repurchase intentions has been well documented, the linkage of customized information-consumer satisfaction-repurchase intentions might be critical for a better understanding of online purchasing process. Because satisfaction may be merely a judgment with cognitive and affective dimensions (Mittal et al., 1998), performance on a certain attribute of customized information may become crucial for repurchase intentions through satisfaction. In environments where consumers evaluate amount of information, the elaboration likelihood model offers an extended view of persuasion as it specifies the conditions under which persuasion should be mediated by message-related thinking (Eagly and Chaiken, 1993; Petty and Cacioppo, 1981). In this case, the elaboration likelihood model could incorporate this viewpoint by positing a certain attribute of customized information to persuasion. Customized information is an example of central cues that directly relate to central issues of internet shopping. For the central route, it is argued that the quality of customized information may affect consumer satisfaction when a firm’s demonstrability is clear (e.g., Yang et al., 2006). As ELM claims, the extent and nature of people’s processing of persuasive argument depends upon motivation and ability (Petty and Cacioppo, 1986). Motives about customized information in the firm’s offerings should be linked to message-related thinking. For example, if the probability that recipients follow the specific attribute of customized information is high, they are

satisfied from the message source or argument, illustrating that they are more likely to engage in their purchasing activity. It can be seen in this final model that the framework indicates that the theoretical juxtaposition should be relatively accurate or consistent with the previous models. The important feature of the PRM is that it provides an experience-oriented view of attitude formation, which is formed from an ongoing experience and marketing stimuli that are adjusted on the basis of currently available information. Under that specification, perceptions of the web navigation are seen as leading to an attitude toward the e-shopping intentions, which in turn governs cognitive and affective reactions to the behavioral actions. In terms of the final model, a customer may visit a store to check out a product, then go online to review blog postings, check out the reviews of earlier purchasers, then telephone a call center with some technical queries, then go online again to check price comparison sites and find out where to purchase it at the lowest price. The proposed model of purchase intention may be narrow in its scope and assumes that the customer proceeds only through an online decision to purchase. However, the model is important for a better understanding of online purchasing behavior from the psychological perspective. In his best-seller book titled Why They Don’t Buy McKeown (2002) argue that this requires enhanced understanding of the psychology of the online customer because shopping behavior comes from feeling. Although this mechanism (e.g., relationships among proposed constructs) may be difficult to assess, our approach is acceptable because alternative models is useful for a complete understanding of systematic relationship among model constructs (Armstrong et al., 2001). Method Measures The five constructs were measured by 23 questions using a five-point Likert scale (1 ¼ strongly agree and 5 ¼ strongly disagree) adapted from the literature. Table II shows the operationalisation of the data collection instrument illustrating the four antecedent facets of repurchase intentions. Data collection In order to guard against possible sample selection bias, cross-cultural research usually requires comparable samples which involve drawing matched samples from identifiable subgroups of the population (Madden et al., 2000). Studies reveal that students represent appropriate samples to examine e-purchasing behavior (Heijden et al., 2001). Based on this reasoning, we used student samples from Korea and the UK of actual users of online travel services. As generally prescribed, we employed the back-translation procedure to ensure conceptual equivalency in the questionnaire (Douglas and Craig, 1983, 2007). Participants were recruited via verbal invitation which was monitored by researchers at two large universities. Our piloted questionnaire was distributed to 568 subjects by three researchers in Korea (n ¼ 348) and the UK (n ¼ 220). We obtained responses from 539 respondents (334 in Korea and 205 in the UK). Owing to missing information and careless response patterns, a total of 448 questionnaires were used for analysis purposes which comprise 284 Korean cases with a usable response rate of 85 percent and 164 British cases with usable response rate of 80.0 percent. In the Korean sample,

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Constructs

Items

Sources

Customized information

This web site makes purchase recommendations that match my needs The web site enables me to order products or services that are tailormade for me I believe that this web site is customized to my needs

Ha¨ubl and Trifts (2000); Klein and Ford (2003); Roos et al. (2004) Ha (2002); Srinivasan et al. (2002); Murthi and Sarkar (2003),

886 Attitudes

Perceived interactivity

Satisfaction

Table II. Operationalisation of the data collection instrument

Repurchase intentions

Ansari and Mela (003); Ha (2002); Simonson (2005); Alba et al. (1997); Sch;osser (2003) This web site makes it easy for me to Yoh et al. (2003); Simon and Peppas build a relationship with this company. (2005). I feel comfortable in surfing this web Srinivasan et al. (2002); Hoffman et al. site (2000) I feel surfing this web site is a good way Koufaris et al. (2001); Hoffman et al. for me to spend time (2000) Shim et al. (2001) Shim et al. (2001) I could communicate with the company Wu (2005) and Srinivasan et al. (2002); directly for further questions about the company or its products if I wanted to This web site does not have a tool that Srinivasan et al. (2002) makes product comparisons easy The site had the ability to respond to Yang et al. 2006); Hoffman et al. (2000) my specific questions quickly and efficiently I was in control of my navigation Hackman et al. 2006) through this web site I feel that this is a very engaging web McMillan (2002) site I am satisfied with this web site Park and Fader (2004); Anderson and Srinivasan (2003) This web site offers what I expect from Mittal and Kamakura (2001); Anderson a good web site and Srinivasan (2003) Kim and Stoe (2004) Kim and Stoe (2004); Boyer and Hult (2006) I think that I made the correct decision Yoon (2002) to use this web site I will repurchase other products/ Taylor and Hunter (2002). services at this web site Kim et al. (2006) Kim et al. (2006) I would like to visit this web site again Brady et al. (2005) in the future

162 respondents had made just one purchase at the particular e-commerce web site, as compared with 98 respondents from the UK sample. Similarly, 122 respondents in the Korean sample had made multiple purchases from the e-commerce web site compared to 66 respondents from the UK sample. The groups had similar profiles except for one difference: the Korean group has almost three more years of higher education relative to the UK group. Respondents were asked to think about the web site from which they purchased their most recent travel item (e.g., flight, hotel, rent car, etc.). As outlined by Wang and Waller (2006), the decision to use university students as a subject population was motivated by considerations of ease of recruitment and

administration, as well as the desire to maximize the equivalence of the sample across the two cultures. Furthermore, because most students are familiar with the online travel service, their use would not appear to compromise the validity of the study (Alford and Sherrell, 1996; Ueltschy et al., 2004). Metric equivalence In testing equivalence across two or more groups, the recommended procedure is to verify metric equivalence (Mullen, 1995). Metric equivalence is firstly best assessed through back translation (Douglas and Craig, 2007) and secondly via confirmatory factor analysis (Mullen, 1995; Steenkamp and Baumgartner, 1998). As the purpose of the present research is to test the cultural differences of shopping behavior, the construct measures have to exhibit at least partial scalar invariance across data (Steenkamp and Baumgartner, 1998). Without evidence of at least partial scalar invariance, comparing construct or factor means across countries is meaningless (Wang and Waller, 2006). As shown in Table III, RMSEA for the configural invariance model (the first level of measurement invariance) across the countries was 0.055, indicating a good fit. The two incremental fit indices were also above the commonly recommend 0.9 level (TLI ¼ 0:938, CFI ¼ 0:950). The normed chi-square (x 2/df) was 1.508, below the recommended cut-off point of 3. These results, coupled with the fact that all factor loadings were highly significant in both countries, suggest that the construct measures exhibited adequate configural invariance across the countries. Also, it should be noted that the x 2 value for the configural invariance model is the sum of the x 2 values obtained for the two sub-samples (South Korea and UK). After the configural invariance model was established, a test was conducted for the full metric invariance model in which the factor loadings were set to be invariant across countries. The results showed that there was a significant increase in chi-square between the configural invariance model and the full metric invariance model (Dx 2 (2) ¼ 6.474, p , 0.05). The final test was to compare the configural invariance model and the partial scalar invariance model. The chi-square difference test was not significant ðDx 2 ð6Þ ¼ 8:745, p . 0.05) and the other fit indices were either close or slightly better. Thus, we can conclude that partial scalar invariance has been achieved across the samples. Non-response bias We examined non-response bias between the two periods (early response v. late response). One viable check for non-response bias is to split the sample into early (n ¼ 212 for the Korean sample and n ¼ 89 for the UK sample) and late respondents (n ¼ 72 for the Korean sample and n ¼ 75 for the UK sample). No significant differences among the two samples were found on any of the study variables. Statistical analysis Three separate approaches were required to examine the research questions. The first goal was to check validity of each construct. To do so, a confirmatory factor analysis with use of AMOS 6.0 was conducted. Table IV exhibits the reliability results. The second stage of the analysis used structural equation modeling to test the proposed and rival structural models by maximum likelihood estimation in AMOS 6.0.

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Table III. Assessment of measurement invariance and latent mean difference in Korea and the UK 428.269 434.743 437.014

x 2(df) (284) (286) (290)

Notes: *Is significant at p , 0.05; * *is not significant at p , 0.05

Configural invariance model (M1) Full metric invariance model (M2) Partial scalar invariance model (M3)

Model specification N/A M2 v. M1 M3 v. M1

Model compared

N/A 6.474 * 8.745 * *

Dx 2

0.055 0.061 0.053

RMSEA

0.938 0.906 0.941

TLI

0.950 0.928 0.952

CFI

1.508 1.523 1.507

x 2/df

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888

Factor

Items

Scale reliability *

Customized

This web site makes purchase recommendations that match my needs The web site enables me to order products or services that are tailor-made for me I believe that this web site is customized to my needs This web site makes it easy for me to build a relationship with this company I feel comfortable in surfing this web site I feel surfing this web site is a good way for me to spend time Compared with other web sites, I would rate this one as (one of the best/one of the worst) I could communicate with the company directly for further questions about the company or its products it I wanted This web site does not have a tool that makes product comparisons easy The site had the ability to respond to my specific questions quickly and efficiently I was in control of my navigation through this web site I feel that this is a very engaging web site I am satisfied with this web site This web site offers what I expect from a good web site This web site gives me a feeling of satisfaction I think that I made the correct decision to use this web site I will repurchase other products/services at this web site I would like to buy new service products at the site I would like to visit this web site again in the future

0.761 (Korea)

Information Consumer Attitudes

Perceived Interactivity

Consumer Satisfaction

Repurchase Intention

Alternative explanations

0.722 (UK) 0.822 (Korea)

889

0.814 (UK)

0.743 (Korea) 0.802 (UK)

0.870 (Korea) 0.882 (UK)

0.793 (Korea) 0.793 (UK)

Note: *Scale reliability is Cronbach’s alpha

At the final stage, the appropriateness of each model was examined using several fit indices. The appropriate test for nested models is to test whether any reduction in the overall chi-square by estimating additional parameter is significant (Punj and Hillyer, 2004). Further statistical significances vary according to sample size. In response to this issue, Bentler and Bonett (1980) proposed a nonnormed fit index (NNFI; r) that compares the fit of a model with that of a null model, taking into account the number of degrees of freedom used in moving from one model to the other. In comparing two models that are nested, both a substantial reduction in chi-square and a large increase in rho are required to accept a model with a great number of parameters. Confirmatory factor analysis As shown in Table V, the CFA model provided good fits to the data. The X 2 of the measurement models was 254.149 for the Korean sample and 174.905 for the UK

Table IV. Measurement items and reliability (n ¼ 284, Korea and n ¼ 164, UK)

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Table V. Factor loadings using CFA

Loading Factor items

Korea

UK

Customized information Item 1 Item 2 Item 3 Composite reliability AVE

0.754 0.763 0.694 0.83 0.53

0.626 0.843 0.757 0.87 0.62

Perceived interactivity Item 1 Item 2 Item 3 Item 4 Item 5 Composite reliability AVE

0.674 0.633 0.543 0.673 0.674 0.83 0.54

0.652 0.690 0.715 0.735 0.845 0.81 0.56

Consumer satisfaction Item 1 Item 2 Item 3 Item 4 Composite reliability AVE

0.778 0.815 0.785 0.789 0.91 0.70

0.784 0.842 0.778 0.827 0.94 0.71

Consumer attitude toward website Item 1 Item 2 Item 3 Item 4 Composite reliability AVE

0.654 0.652 0.712 0.692 0.88 0.58

0.674 0.768 0.645 0.691 0.87 0.58

Repurchase intentions Item 1 Item 2 Item 3 Composite reliability AVE

0.855 0.769 0.757 0.90 0.75

0.781 0.843 0.859 0.91 0.77

Notes: Korea: X 2 ð142Þ ¼ 254:149; IFI ¼ 0:954; TLI ¼ 0:944; CFI ¼ 0:954; RMSEA ¼ 0:053. UK: X 2 ð142Þ ¼ 174:905; IFI ¼ 0:953; TLI ¼ 0:941; CFI ¼ 0:951; RMSEA ¼ 0:052

sample with 142 degree of freedom. While the chi-square statistics were significant ( p , 0.05), it is known to be highly sensitive to sample sizes, such as the ones used here (Thompson, 2004). Relative to the other indices, the TLI (the Tucker-Lewis index) performs the best followed by RMSEA (the root-mean-square-error-of-approximation) (Sharma et al., 2005). Sharma et al. (2005) recommend that TLI was used to evaluate model fit because TLI performs the best as long as the size of factor loadings is 0.5. The

TLI estimates were 0.944 for the Korean sample and 0.941 for the UK sample, and the RMSEA estimates were, respectively: 0.053 and 0.052. Composite reliability was calculated using the procedures outlined by Fornell and Larcker (1981). The parameter estimates and the average variance extracted for each construct were carried out (Anderson and Gerbing, 1988). The composite reliabilities for the five constructs ranged from 0.83 to 0.91 in the Korean sample and from 0.81 to 94 in the UK sample. The factor loadings ranged from 0.54 to 0.85 (Korean sample, p , 0.01) and 0.62 to 0.86 (UK sample, p , 0.01). All loadings are significant and in the predicted direction ( p , 0.01), in support of convergent validity (Byrne, 1998). The average variance extracted (AVE) ranged from 0.53 to 0.75 in the Korean sample and 0.56 to 0.77 in the UK sample. On the basis of the validation sample, we assessed discriminant validity with Fornell and Larcker’s (1981) criterion. Table VI shows that the smallest AVE exceeds the squared correlation between each pair of the relationship value dimensions. This indicates a satisfactory level of discriminant validity.

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891

Results The appropriateness of each model was examined using several fit indices. The overall fit of a measurement model to the data has most commonly been tested using the chi-square test statistic ( Jackson et al., 1993). As shown in Table VII, we checked alternative model fits to select the best model. Because three competing models (DAM, RMM, and IEM) are sequentially nested within PRM, the x 2 value for the larger model is subtracted from the x 2 value for the smaller nested model and the difference, also, a x 2, is evaluated with degrees of freedom equal to the difference between the degrees of freedom in the two models (Tabachnick and Fidell, 2001). Comparison of alternative models The fit indices for the alternative models within each sample are summarized in Table VII. The direct effects model (DAM) provides a poor fit in both samples: for the Korean sample X 2 (148, n ¼ 284Þ ¼ 520:242, r ¼ 0:82 and for the UK sample X 2 (148,

Mean

SD

1

2

3

4

5

Korea 1. 2. 3. 4. 5.

Customized information Perceived interactivity Satisfaction Attitude toward web site Repurchase intentions

20.58 20.71 20.64 20.68 20.60

0.910 0.839 0.850 0.831 0.856

0.53 0.28 0.38 0.30 0.39

0.54 0.39 0.31 0.40

0.70 0.41 0.52

0.58 0.43

0.75

UK 1. 2. 3. 4. 5.

Customized information Perceived interactivity Satisfaction Attitude toward web site Repurchase intentions

20.43 20.47 2.29 20.44 20.18

0.912 0.840 0.805 0.702 0.785

0.62 0.34 0.44 0.36 0.47

0.56 0.39 0.32 0.43

0.71 0.41 0.54

0.58 0.44

0.77

Note: Italic numbers on the diagonal show the AVE

Table VI. Discriminant validity

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Chi-square

DF

a 2/df

IFI

TLI

CFI

RMSEA

892

Korea sample DAM RMM IEM PRM

520.242 450.176 293.705 259.403

148 146 145 144

3.516 3.083 2.026 1.801

0.847 0.875 0.939 0.942

0.822 0.852 0.927 0.929

0.846 0.874 0.938 0.941

0.094 0.086 0.060 0.058

Table VII. Comparison of four alternative models

UK sample DAM RMM IEM PRM

234.806 214.598 187.389 180.319

148 146 145 144

1.587 1.470 1.292 1.252

0.876 0.902 0.940 0.945

0.852 0.881 0.926 0.932

0.872 0.899 0.937 0.943

0.085 0.076 0.060 0.056

n ¼ 164Þ ¼ 234:806, r ¼ 0:85. However, the results are further supported by the much improved fit given by the real mediation model as shown by the decrease in chi-square. Comparisons between the direct attitude model (DAM) and the real mediation model (RMM) show a further substantial drop in chi-square in two samples: for the Korean sample, Dx 2 (2, n ¼ 284) ¼ 70.066 and for the UK sample, Dx 2 (2, n ¼ 164Þ ¼ 20:208. A reasonable explanation for this is that the model has a number of potential paths. Comparisons between RMM and IEM are further supported by the much improved fit given by the model solution with an additional path as shown by the decrease in chi-square: for the Korean sample, Dx 2 (1, n ¼ 284Þ ¼ 156:471; ps , 0.001 and for the UK sample, Dx 2 (1, n ¼ 164Þ ¼ 27:209; ps , 0.001. This figure indicates that the real mediation model is acceptable. Further support for the proposed research model with an additional path comes from comparison between IEM and PRM: for the Korean sample, Dx 2 (1, n ¼ 284) ¼ 34.302; ps , 0.001, r ¼ 0.93 and for the UK sample, Dx 2 (1, n ¼ 164Þ ¼ 7:070; ps , 20.01, r ¼ 0:93. For example, the critical value of a chi-square difference for 1 degree of freedom at p ¼ 0:01 is 6.63. Finally, the two step process (Anderson and Gerbing, 1988) suggests the fit of the models in Table V should be compared with the measurement models in Table III. Because the goal was to select a best model that fits the data, a nonsignificant chi-square is desired. The comparison between PRM and measurement model is: for the Korean sample, Dx 2 (2, n ¼ 284Þ ¼ 5:254; ps . 0.05 and for the UK sample, Dx 2 (1, n ¼ 164Þ ¼ 5:914; ps . 0.05. Thus, this x 2 is nonsignificant so we conclude that PRM fits the data, rather than other competing models. When the other fit indices did not indicate a strong difference between the two models (IEM and PRM), a chi-square difference allows researchers to select the best one if two models are nested (Tabachnick and Fidell, 2001). The chi-square difference between these models is significant, indicating that the proposed research model fits the data significantly better than the IEM, RMM, and DAM. Therefore, it is concluded that the proposed research model is the best of all the competing models in representing the role of customized information, perceived interactivity, consumer satisfaction, and consumer attitudes in the development of the online consumer purchase model.

Best model selection among alternative models The proposed research model is superior to the competing models most clearly with all data sets as the competing models DAM, RMM, and IEM do not provide a better fit. It is arguable that the current data sets provide strong supporting evidence because despite having more parameters of interest estimated, the proposed model does significantly fit the data better than other competing model. However, the improved fit of the proposed model occurred because the extra parameters enabled the proposed model to absorb random error in the data (e.g., Pitt et al., 2002). To get around this problem, the chi-square difference among competing explanations is useful for the model selection ( Jackson et al., 1993; Punj and Hillyer, 2004). Accordingly, chi-square difference tests were employed to determine whether one of these structures performed better than the other (Bagozzi and Yi, 1988). In this study, the chi-square differences between the proposed model and competing models from two different countries are also statistically supported at p , 0.05. The proposed research model, therefore, is at least the accurate model in two different data, Korea and the UK (see Table VIII). Discussion This study empirically examined the simultaneous effects of variables of interest, namely, customized information, perceived interactivity, consumer satisfaction, and consumer attitudes on online purchase intentions in two different countries. Overall, there is strong empirical evidence in support of the proposed research model. Such an approach is linked to managerial implications: that is, marketers’ awareness of consumer motivation strategies may be restructured from the mediating effects of consumer attitudes with the shared constructs. This research takes a step of incorporating the indirect effect in four competing models. Our paper contributes to the literature in several ways. First, customized information is the only driver for satisfaction in any of alternative models. It can be viewed as an indicator of customized informative-based two-way communication tool. Our analysis of the cross-cultural setting shows that customized information is strongly linked to satisfaction. The direct effect of customized information plays a significant role in predicting further activities, indicating that consumers are likely to engage in online shopping behavior. The impact of customized information leads to the direct effect of satisfaction on repurchase intentions in both countries. This finding supports Bechwait and Xia’s (2003) study on Americans’ on-line shopping perspective. In line with this observation, we can conclude that there are no significant differences when consumers evaluate levels of customized information from a Korean-UK context. Second, non-significance between the two consumer groups is further supported by the relationship between customized information and attitude toward web site. Although Anderson (1981) established a theoretical framework known as “information integration theory”, which presumes that consumer attitudes are formed and modified as people receive and interpret new information, our findings reveal that the linkage between customized information and attitude toward web site is not supported in both data sets. Since consumer attitudes are considered as an indicator of decision-making, young consumers in these countries are less likely to generate favorable attitudes through customized information. These findings suggest that the effect of customized information is completely mediated by the other constructs such as interactivity and consumer satisfaction.

Alternative explanations

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Notes: *p , 0.05; * *p , 0.01; * * *p , 0.001

520.242 148 0.846 0.094

234.806 148 0.872 0.085

0.434 * * 0.703 * * * 0.130 (ns) 0.508 * * *

0.661 * * * 0.683 * * * 0.211 * * * 0.441 * * *

! ! ! ! ! ! ! !

SA AT CI PI CI PI CI SA Model fit Chi-square Degree of freedom CFI RMSEM

AT RI AT AT PI RI SA RI

UK

DAM Korea

Table VIII. Standardized estimates of path coefficients for measuring the structural equation modeling

To

450.176 146 0.874 0.086

0.421 * * * 0.460 * * * 0.099 (ns) 0.433 * * 0.498 * 0.385 * *

UK

214.598 146 0.899 0.076

RMM 0.644 * * * 0.553 * * * 0.138 (ns) 0.405 * * * 0.587 * * * 0.223 * *

Korea

293.705 145 0.938 0.060

0.258 (ns) 0.464 * * 0.123 (ns) 0.595 * * * 0.815 * * * 0.335 (ns) 0.900 * * *

UK

187.389 145 0.937 0.060

IEM 0.595 * * * 0.553 * * * 0.011 (ns) 0.365 * * * 0.695 * * * 0.243 * 0.796 * * *

Korea

287.403 144 0.941 0.059

0.367 * 0.294 * 0.057 (ns) 0.425 * * 0.562 * * 0.340 * 0.646 * * * 0.316 * *

UK

182.557 144 0.943 0.057

PRM 0.560 * * * 0.280 * 0.022 (ns) 0.382 * * * 0.688 * * * 0.257 * * 0.792 * * * 0.299 * *

Korea

894

From

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Third, the direct link between satisfaction and repurchase intentions might enlighten a practitioner of customer relationship management (CRM) as to the alternative methods of appealing to young online customers. The linkage is clearly supported statistically and theoretically in both the Korean and the UK sample. The link of satisfaction ! repurchase intentions provides a meaningful empirical representation of the hierarchical sequence in which the cycle of customer repurchasing process (consumer satisfaction ! consumer attitude ! repurchase) is related. Fourth, one of the key drivers of the e-purchase model is to capture the significance of perceived interactivity. Since interactivity reflects a firm’s ability to interact with its customers (Ramani and Kumar, 2008), it is the major consideration of consumers’ repurchase decision in an online store. The motivating principle underlying their decision process is that intensive messages such as customized information, has the potential for implementing direct interaction with customers and has the advantages of forming favorable attitude toward web site for repurchase. The results of our study indicate that use of perceived interactivity has a substantial impact on shoppers’ purchasing decisions. Finally, our findings show that some difference exists between the two groups. While the four alternative models did not make a huge difference for the UK sample, there was a big difference for the Korean sample (see, Table V). The DAM and RMM models seem to have blended well with fit the Korean data. These findings suggest that the framework may be problematic in relation to the Korean culture. However, when customized information is directly linked to satisfaction the gap between Korean and UK samples were dramatically reduced. This distinction highlights the importance of the link of customized information-consumer satisfaction for consumers that visit web sites their online shopping. Limitations and future research It is essential to acknowledge some of the limitations of this study. Although previous studies reveal the significance between the two constructs, further research can investigate the issue as generalizable to the many types of e-B2C services. Similarly, the university students tend to be more familiar with the use of online services than the overall satisfaction. This indicates that student samples may influence the findings or their generalizability. In order to reduce this gap, further research should consider more general populations to enhance their findings. Second, particularly in terms of the ability to generalize the model, this study did not provide fairly consistent results for different service categories. Studies on other service classes, such as online bookstore and online banking services, might reveal findings that extend our approach. Thus, future study could assess alternative models using different data settings including banking and retail services. Third, increasingly sophisticated (and inexpensive) analytics software is now available to highlight areas of inefficiency within web sites and pinpoint where and why shopping carts are abandoned, but this aspect is not considered in the paper. In a consumer context, making a purchase involves action while deciding not to purchase constitutes inaction (Abendroth and Diehl, 2006). As this pattern exists in limited purchase opportunities, further research is desirable to assess consumer inaction as a moderating role of the proposed model. Thus, it is hoped that these issues of potential future research encourage other scholars to undertake alternative studies to further disentangle the effects in this research.

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Conclusion and implications The outcome of this study contributes to the existing e-purchasing literature by identifying that there are different effects between customized information and web interactivity when people revisit web sites to purchase. The results have significant implications for commercial web sites. It appears that web interactivity has the capability of impacting on attitude toward a web site and therefore can be interesting and have potentially powerful outcomes for consumer purchasing behavior (Fortin and Dholakia, 2005). E-retailers may be interested in developing and setting up weblog in order to drive Google traffic to their businesses, and for customer peer interaction with the business or to create alternatives to time spent on social networking sites such as myspace, facebook, typepad etc. Customers could be able to use an open-access comment section. The main observation is that marketers should make an effort to trigger customers’ web interactivity. Therefore, managers should make blog marketing efforts in terms of time and commitment to update sites, consideration of blog as part of a marketing plan, a form of conversations. Blogs should be transparent, authentic and focused. This would include providing customized information for partnering in activities in which the customer is personally engaged. To facilitate their behavioral intentions, a company could demonstrate relevance for customers by being involved in various activities or supporting online customers in activities in optimizing their purchase decision. However, managers should be aware that the term blog has more negative implications in terms of credibility, time consumption or material of interest. Managers should not assume that setting a blog means that customers will automatically read it. A more focused development of consumer attitudes is crucial to understand the reason for different evaluations on the mediating effects of positive attitude in different service categories. Marketers require a more complete understanding of the role of favorable attitude in any project where the consumer purchase process in e-B2C environments should be expected to have higher returns. If practitioners believe that their firm’s performance is not satisfied, then they should be likely to change the direction of the e-purchasing process with a better understanding of favorable attitude toward the web site. Similarity of purchasing attitudes between Korean and UK consumers may allow e-tailors to design a systematic strategy for generating favorable attitudes toward their web sites. However, standardized online treatment of consumers is not the golden path of internet marketing (Barnes et al., 2007). Further the proposed research model (PRM) provides a means of identifying the underlying dispositions associated with the mediating variable. References Abendroth, L.J. and Diehl, K. (2006), “Now or never: effects of limited purchase opportunities on patterns of regret over time”, Journal of Consumer Research, Vol. 33 No. 3, pp. 342-51. Agustin, C. and Singh, J. (2005), “Curvilinear effects of consumer loyalty determinants in relational exchanges”, Journal of Marketing Research, Vol. 42 No. 1, pp. 96-108. Alba, J., Lynch, J., Weitz, B., Janiszewski, C., Lutz, R., Sawyer, A. and Wood, S. (1997), “Interactive home shopping: consumer, retailer, and manufacturer incentives to participate in electronic marketplaces”, Journal of Marketing, Vol. 61 No. 3, pp. 38-53. Alford, B.L. and Sherrell, D.L. (1996), “The role of affect in consumer satisfaction judgments of credence-based services”, Journal of Business Research, Vol. 37 No. 1, pp. 71-84.

Anderson, E. and Sullivan, M.W. (1993), “The antecedents and consequences of customer satisfaction for firms”, Marketing Science, Vol. 12 No. 2, pp. 125-43. Anderson, J.C. and Gerbing, D.W. (1988), “Structural equation modeling in practice: a review and recommended two-step approach”, Psychological Bulletin, Vol. 103, pp. 411-23. Anderson, R.E. and Srinivasan, S.S. (2003), “E-satisfaction and e-loyalty: a contingency framework”, Psychology & Marketing, Vol. 20 No. 2, pp. 123-38. Ansari, A. and Mela, C.F. (2003), “E-customization”, Journal of Marketing Research, Vol. 40 No. 2, pp. 131-45. Armstrong, J.S., Brodie, R.J. and Parsons, A.G. (2001), “Hypotheses in marketing science: literature review and publication”, Marketing Letters, Vol. 12 No. 2, pp. 171-87. Bagozzi, R.P. and Yi, Y. (1988), “On the evaluation of structural equation models”, Journal of the Academy of Marketing Science, Vol. 16, pp. 74-94. Bansal, H.S., Irving, P.G. and Taylor, S.F. (2004), “A three-component model of customer commitment to service providers”, Journal of the Academy of Marketing Science, Vol. 32 No. 3, pp. 234-50. Barnes, S.J., Bauer, H.H., Neumann, M.M. and Huber, F. (2007), “Segmenting cyberspace: a customer typology for the internet”, European Journal of Marketing, Vol. 41 Nos 1/2, pp. 71-93. Bechwait, N.N. and Xia, L. (2003), “Do computers sweat? The impact of perceived effort of online decision aids on consumers’ satisfaction with the decision process”, Journal of Consumer Psychology, Vol. 13 Nos 1/2, pp. 139-48. Bentler, P.M. and Bonett, D.G. (1980), “Significance tests and goodness of fit in the analysis of covariance structures”, Psychological Bulletin, Vol. 88 No. 3, pp. 588-606. Bernoff, J. and Li, C. (2008), “Harnessing the power of the oh-so-social web”, MIT Sloan Management Review, Vol. 49 No. 3, pp. 35-43. Bettman, J.R. (1979), An Information Processing Theory of Consumer Choice, Addison-Wesley, Reading, MA. Boyer, K.K. and Hult, G.T.M. (2006), “Customer behavioral intentions for online purchases: an examination of fulfilment method and customer experience level”, Journal of Operations Management, Vol. 24 No. 2, pp. 124-47. Brady, M.K., Knight, G.A., Cronin, J.J., Hult, G.T.M. and Keillor, B.D. (2005), “Removing the contextual lens: a multinational, multi-setting comparison of service evaluation models”, Journal of Retailing, Vol. 81 No. 3, pp. 215-30. Byrne, B. (1998), Structural Equation Modeling with LISREL, PRELIS, and SIMPLIS: Basis Concepts, Applications, and Programming, Lawrence Erlbaum, Mahwah, NJ. Chang, C-H. and Cheon, H.J. (2005), “Cross-cultural comparisons of interactivity on corporate web sites: the United States, the United Kingdom, Japan, and South Korea”, Journal of Advertising, Vol. 34 No. 2, pp. 99-115. Chang, M.K., Cheung, W. and Lai, V.S. (2005), “Literature-derived reference models for the adoption on online shopping”, Information & Management, Vol. 42 No. 4, pp. 543-59. Chiang, K. and Dholakia, R.R. (2003), “Factors driving consumer intention to shop online: an empirical investigation”, Journal of Consumer Psychology, Vol. 13 Nos 1/2, pp. 177-83. Chiu, Y., Lin, C. and Tang, L. (2005), “Gender differs: assessing a model of online purchase intentions in e-tail service”, International Journal of Service Industry Management, Vol. 16 No. 5, pp. 416-35.

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EJM 44,6

898

Cook, D. and Coupey, E. (1998), “Consumer behavior and unresolved regulatory issues in electronic marketing”, Journal of Business Research, Vol. 41 No. 3, pp. 231-8. Coyle, J.R. and Thorson, E. (2001), “The effects of progressive levels of interactivity and vividness in web marketing sites”, Journal of Advertising, Vol. 30 No. 3, pp. 65-77. Cronin, J.J., Brady, M.K. and Hult, G.T.M. (2000), “Assessing the effects of quality, value, and customer satisfaction on consumer behavioral intentions in service environments”, Journal of Retailing, Vol. 76 No. 2, pp. 193-218. Dawar, N., Parker, P.M. and Price, L.J. (1996), “A cross-cultural study of interpersonal information exchange”, Journal of International Business Studies, Vol. 27 No. 3, pp. 497-516. Dekimpe, M.G., Steenkamp, J.E.M. and Mellens, M. (1997), “Decline and variability in brand loyalty”, International Journal of Research in Marketing, Vol. 14, pp. 405-20. Dick, A.S. and Basu, K. (1994), “Customer loyalty: toward an integrated conceptual framework”, Journal of the Academy of Marketing Science, Vol. 22 No. 2, pp. 99-113. Doran, K.B. (2002), “Lessons learned in cross-cultural research of Chinese and North American consumers”, Journal of Business Research, Vol. 55 No. 10, pp. 823-9. Douglas, S.P. and Craig, C.S. (1983), International Marketing Research, Prentice-Hall, Englewood Cliffs, NJ. Douglas, S.P. and Craig, C.S. (2007), “Collaborative and iterative translation: an alternative approach to back translation”, Journal of International Marketing, Vol. 15 No. 1, pp. 30-43. Eagly, A.H. and Chaiken, S. (1993), The Psychology of Attitudes, Harcourt College Publishers, Fort Worth, TX. East, R. (1990), Changing Consumer Behaviour, Cassell, London. Eroglu, S.A., Machleit, K.A. and Davis, L.M. (2003), “Empirical testing of a model of online store atmospherics and shopper responses”, Psychology & Marketing, Vol. 20 No. 2, pp. 139-50. Ferrer, E. and McArdle, J. (2003), “Alternative structural models for multivariate longitudinal data analysis”, Structural Equation Modeling, Vol. 10 No. 4, pp. 493-524. Fiore, A.M., Jin, H. and Kim, J. (2005a), “For fun and profit: hedonic value from image interactivity and responses toward an online store”, Psychology & Marketing, Vol. 22 No. 8, pp. 669-94. Fiore, A.M., Kim, J. and Lee, H-H. (2005b), “Effect of image interactivity technology on consumer responses toward the online retailer”, Journal of Interactive Marketing, Vol. 19 No. 3, pp. 38-53. Fishbein, M. and Ajzen, I. (1975), Belief, Attitude, Intention, and Behavior: An Introduction to Theory and Research, Addison-Wesley, Reading, MA. Fishbein, M. and Ajzen, I. (1981), “Acceptance, yielding and impact: cognitive processes in persuasion”, in Petty, R.E., Ostrom, T.M. and Brock, T.C. (Eds), Cognitive Responses in Persuasion, Erlbaum, Hillsdale, NJ, pp. 339-59. Fornell, C. and Larcker, D.F. (1981), “Evaluating structural equations models with unobservable variables and measurement error”, Journal of Marketing Research, Vol. 18 No. 1, pp. 39-50. Fortin, D.R. and Dholakia, R.R. (2005), “Interactivity and vividness effects on social presence and involvement with a web-based advertisement”, Journal of Business Research, Vol. 58 No. 3, pp. 387-96. Giese, J.L. and Cote, J.A. (2000), “Defining customer satisfaction”, Academy of Marketing Science Review (online), pp. 1-59.

Gommans, M., Krishnan, K.S. and Scheffold, K.B. (2001), “From brand loyalty to e-loyalty a conceptual framework”, Journal of Economic and Social Research, Vol. 3 No. 1, pp. 43-58. Ha, H. (2002), “The effects of consumer risk perception on pre-purchase information in online auctions: brand, word-of-mouth, and customized information”, Journal of Computer-Mediated Communication, Vol. 8 No. 1, available at: http://jcmc.indiana.edu/ vol8/issue1/ha.html Hackman, D., Gundergan, S.P., Wang, P. and Daniel, K. (2006), “A service perspective on modeling intentions of online purchasing”, Journal of Services Marketing, Vol. 20 No. 7, pp. 459-70. Ha¨ubl, G. and Trifts, V. (2000), “Consumer decision making in online shopping environments: the effects of interactive decision aids”, Marketing Science, Vol. 19 No. 1, pp. 4-21. Heijden, H., Verhagen, T. and Creemers, M. (2001), “Predicting online purchase behaviour: replications and tests of competing models”, Proceedings of the 34th Hawaii International Conference on Systems Sciences. Heijden, H., Verhagen, T. and Creemers, M. (2003), “Understanding online purchase intentions: contribution from technology and trust perspectives”, European Journal of Information Systems, Vol. 12 No. 1, pp. 41-8. Herring, C.S., Scheidt, A.L., Wright, E. and Bonus, S. (2005), “Weblogs as bridging genre”, Information Technology & People, Vol. 18 No. 2, pp. 142-71. Hoffman, D.L., Novak, T.P. and Yiu-Fat, Y. (2000), “Consumer decision making in online shopping environments: a structural modeling approach”, Marketing Science, Vol. 19 No. 1, pp. 22-41. Hofstede, G. (1980), Culture’s Consequences, Sage, Beverly Hills, CA. Hofstede, G. (2003), Cultural Dimensions, available at: www.geer-hofstede.com Holbrook, M.B. and Corfman, K.P. (1985), “Quality and value in the consumption experience: Phaedrus rides again”, in Jacoby, J. and Olson, J. (Eds), Perceived Quality, Lexington Books, Lexington, MA, pp. 31-57. Jackson, P.R., Wall, T., Martin, R. and Davis, K. (1993), “New measures of job control, cognitive demand, and production responsibility”, Journal of Applied Psychology, Vol. 78 No. 5, pp. 753-62. Ja¨rvenpa¨a, S.L. and Tractinsky, N. (1999), “Consumer trust in an internet store: a cross-cultural validation”, Journal of Computer-Mediated Communication, Vol. 5 No. 2, available at: www.jcmc.indiana.edu/vol5/issue2/jarvenpaa.html Jeuland, A.D. (1979), “Brand choice inertia as one aspect of the notion of brand loyalty”, Management Science, Vol. 25 No. 7, pp. 671-82. Johnson, M.D., Gustafsson, A., Andreassen, T.W., Lervik, L. and Cha, J. (2001), “The evolution and future of National Customer Satisfaction Index models”, Journal of Economic Psychology, Vol. 22 No. 2, pp. 217-45. Kahn, B.E., Kalmwani, M.U. and Morrison, D.G. (1986), “Measuring variety seeking and reinforcing behaviours using panel data”, Journal of Marketing Research, Vol. 23, pp. 89-100. Katz, E., Blumler, J.G. and Gurevitch, M. (1973), “Uses and gratifications research”, Public Opinion Quarterly, Vol. 37 No. 4, pp. 509-23. Kim, S. and Stoe, L. (2004), “Dimensional hierarchy of retail web site quality”, Information & Management, Vol. 41 No. 5, pp. 619-33.

Alternative explanations

899

EJM 44,6

Kim, W.G., Ma, X. and Kim, D.J. (2006), “Determinants of Chinese hotel customers’ e-satisfaction and purchase intentions”, Tourism Management, Vol. 27 No. 5, pp. 890-900.

900

Koufaris, M., Kambil, A. and LaBarbera, P.A. (2001), “Consumer behavior in web-based commerce: an empirical study”, International Journal of Electronic Commerce, Vol. 6 No. 2, pp. 115-38.

Klein, L.R. and Ford, G.T. (2003), “Consumer search for information in the digital age: an empirical study of prepurchase search for automobiles”, Journal of Interactive Marketing, Vol. 17 No. 3, pp. 29-49.

Lee, J., Park, D. and Han, I. (2007), “The effect of negative online consumer reviews on product attitude: an information-processing view”, Electronic Commerce Research and Applications, Vol. 7 No. 3, pp. 341-52. Lim, H. and Dubinsky, A.J. (2004), “Consumers’ perceptions of e-shopping characteristics: an expectancy-value approach”, Journal of Services Marketing, Vol. 18 No. 7, pp. 500-13. Lim, H. and Dubinsky, A.J. (2005), “The theory of planned behavior in e-commerce: making a case for interdependencies between salient beliefs”, Psychology & Marketing, Vol. 22 No. 10, pp. 833-55. Lynch, P.D. and Beck, J.C. (2001), “Profiles of internet buyers in 20 countries: evidence for region-specific strategies”, International Journal of Business Studies, Vol. 32 No. 4, pp. 725-48. McKenzie, C.R.M. (1998), “Taking into account the strength of an alternative hypothesis”, Journal of Experimental Psychology, Vol. 24 No. 3, pp. 771-92. McKeown, M. (2002), Why They Don’t Buy: The Science of Selling Online, Prentice-Hall, London. McMillan, S.J. (2002), “A four-part model of cyber-interactivity: some cyber-places are more interactive than others”, New Media & Society, Vol. 4 No. 2, pp. 271-91. Madden, T.J., Hewett, K. and Roth, M.S. (2000), “Managing images in different cultures: a cross-national study of color meanings and preferences”, Journal of International Marketing, Vol. 8 No. 4, pp. 90-107. Mittal, V. and Kamakura, W.A. (2001), “Satisfaction, repurchase intent, and repurchase behavior: investigating the moderating effect of customer characteristics”, Journal of Marketing Research, Vol. 38 No. 1, pp. 131-42. Mittal, V., Ross, W.T. and Baldasare, P.M. (1998), “The asymmetric impact of negative and positive attribute-level performance on overall satisfaction and repurchase intentions”, Journal of Marketing, Vol. 62 No. 1, pp. 33-47. Mullen, M.R. (1995), “Diagnosing measurement equivalence in cross-national research”, Journal of International Business Studies, Vol. 26 No. 3, pp. 573-96. Murthi, B.P.S. and Sarkar, S. (2003), “The role of the management sciences in research on personalization”, Management Science, Vol. 49 No. 10, pp. 1344-62. O’Cass, A. (2001), “Consumer self-monitoring, materialism and involvement in fashion clothing”, Australian Journal of Marketing, Vol. 9 No. 1, pp. 46-60. Oliver, R.L. (1980), “A cognitive model of the antecedents and consequences of satisfaction decisions”, Journal of Marketing Research, Vol. 17 No. 4, pp. 460-9. Oliver, R.L. (1997), Satisfaction: A Behavioral Perspective on the Consumer, Irwin McGraw-Hill, Boston, MA. Oliver, R.L. (1999), “Whence consumer loyalty?”, Journal of Marketing, Vol. 63 No. 4, pp. 33-44.

Olsen, S.O. (2007), “Repurchase loyalty: the role of involvement and satisfaction”, Psychology & Marketing, Vol. 24 No. 4, pp. 315-41. Park, C. and Kim, Y. (2003), “Identifying key factors affecting consumer purchase behavior in an online shopping context”, International Journal of Retail & Distribution Management, Vol. 31 No. 1, pp. 16-29. Park, Y. and Fader, P.S. (2004), “Modeling browsing behavior at multiple web sites”, Marketing Science, Vol. 23 No. 3, pp. 280-303. Petty, R.E. and Cacioppo, J.T. (1981), Attitudes and Persuasion: Classic and Contemporary Approaches, Brown, Dubuque, IA. Petty, R.E. and Cacioppo, J.T. (1986), Communication and Persuasion: Central and Peripheral Routes to Attitude Change, Springer-Verlag, New York, NY. Pitt, M.A., Myung, I. and Zhang, S. (2002), “Toward a method of selection among computational models of cognition”, Psychological Review, Vol. 109 No. 3, pp. 472-91. Poel, D. and Buckinx, W. (2005), “Predicting online purchasing behavior”, European Journal of Operational Research, Vol. 166 No. 2, pp. 557-75. Punj, G.N. and Hillyer, C.L. (2004), “A cognitive model of customer-based brand equity for frequently purchased products: conceptual framework and empirical results”, Journal of Consumer Psychology, Vol. 14 Nos 1/2, pp. 124-31. Rafaeli, S. (1988), “Interactivity: from new media to communication”, in Hawkins, R.P., Wiemann, J.M. and Pingree, S. (Eds), Sage Annual Review of Communication Research: Advancing Communication Science, Sage, Beverly Hills, CA, pp. 110-34. Rafaeli, S. and Sudweeks, F. (1997), “Networked interactivity”, Journal of Computer-Mediated Communication, Vol. 2, available at: www.jcmc.indiana.edu/vol2/issue4/rafaeli.sudweeks. html Raju, J.S., Srinivasan, V. and Lal, R. (1990), “The effects of brand loyalty on competitive price promotional strategies”, Management Science, Vol. 36 No. 3, pp. 276-304. Ramani, G. and Kumar, V. (2008), “Interaction orientation and firm performance”, Journal of Marketing, Vol. 72 No. 1, pp. 27-45. Reichheld, F. and Schefter, P. (2000), “E-loyalty: your secret weapon on the web”, Harvard Business Review, Vol. 78 No. 4, pp. 105-14. Ribbink, D., van Riel, A., Liljander, V. and Steukens, S. (2004), “Comfort your online customer: quality, trust and loyalty on the internet”, Managing Service Quality, Vol. 14 No. 6, pp. 446-56. Rogers, H.P., Peyton, R. and Berl, R.L. (1992), “Measurement and evaluation of satisfaction processes in a dyadic”, Journal of Consumer Satisfaction, Dissatisfaction, and Complaining Behavior, Vol. 5, pp. 12-23. Roos, L.L., Menec, V. and Currie, R.J. (2004), “Policy analysis in an information-rich environment”, Social Science & Medicine, Vol. 58 No. 11, pp. 2231-41. Rust, R.T. and Oliver, R.L. (2000), “Should we delight the customer?”, Journal of the Academy of Marketing Science, Vol. 28 No. 1, pp. 86-94. Schlosser, A.E. (2003), “Experiencing products in the virtual world: the role of goal and imagery in influencing attitudes versus purchase intentions”, Journal of Consumer Research, Vol. 30 No. 2, pp. 184-98.

Alternative explanations

901

EJM 44,6

902

Schlosser, A.E., White, T.B. and Lloyd, S. (2006), “Converting web visitors into buyers: how web site investment increases consumer trusting beliefs and online purchase intentions”, Journal of Marketing, Vol. 70 No. 2, pp. 133-48. Sharma, S., Mukherjee, S., Kumar, A. and Dillon, W.R. (2005), “A simulation study to investigate the use of cut-off values for assessing model fit in covariance structure models”, Journal of Business Research, Vol. 58 No. 7, pp. 935-43. Sheth, N.J., Mittal, B. and Newman, I.B. (1999), Customer Behavior, Consumer Behavior and Beyond, Dryden Press, New York, NY. Shim, S., Eastlick, M.A., Lotz, S.L. and Warrington, P. (2001), “An online prepurchase intention model: the role of intention to search”, Journal of Retailing, Vol. 77 No. 3, pp. 397-416. Sillence, E., Briggs, P., Harris, P. and Fishwick, L. (2006), “A framework for understanding trust factors in web-based health advice”, International Journal of Human-Computer Studies, Vol. 64 No. 8, pp. 697-713. Simon, S.J. and Peppas, S.C. (2005), “Attitudes towards product web site design: a study of the effects of gender”, Journal of Marketing Communications, Vol. 11 No. 2, pp. 129-44. Simonson, I. (2005), “Determinants of customers’ responses to customized offers: conceptual framework and research propositions”, Journal of Marketing, Vol. 69 No. 1, pp. 32-45. Singh, M. (2002), “E-services and their role in B2C e-commerce”, Managing Service Quality, Vol. 12 No. 6, pp. 434-46. Smith, D., Menon, S. and Sivakumar, K. (2005), “Online peer and editorial recommendations, trust, and choice in virtual markets”, Journal of Interactive Marketing, Vol. 19 No. 3, pp. 15-37. Sohn, D. and Lee, B. (2005), “Dimensions of interactivity: differential effects of social and psychological factors”, Journal of Computer-Mediated Communication, Vol. 10 No. 3, available at: www.jcmc.indiana.edu/vol10/issue3/sohn.htlm Srinivasan, S.S., Anderson, R. and Ponnavolu, K. (2002), “Customer loyalty in e-commerce: an exploration of its antecedents and consequences”, Journal of Retailing, Vol. 78, pp. 41-50. Steenkamp, J.E.M. and Baumgartner, H. (1998), “Assessing measurement invariance in cross-national consumer research”, Journal of Consumer Research, Vol. 25, pp. 78-90. Sundar, S.S. and Kim, J. (2005), “Interactivity and persuasion: influencing attitudes with information and involvement“, Journal of Interactive Advertising, Vol. 5 No. 2, available at: www.jiad.org/vol5/no2/sundar Szymanski, D.M. and Henard, D.H. (2001), “Customer satisfaction: a meta-analysis of the empirical evidence”, Journal of the Academy of Marketing Science, Vol. 29 No. 1, pp. 16-35. Tabachnick, B.G. and Fidell, L.S. (2001), Using Multivariate Statistics, Allyn & Bacon, Boston, MA. Taylor, S.A. and Hunter, G.L. (2002), “The impact of loyalty with e-CRM software and e-services”, International Journal of Service Industry Management, Vol. 13 No. 5, pp. 452-78. Teo, H., Oh, L., Lin, C. and Wei, K. (2003), “An empirical study of the effects of interactivity on web user attitude”, International Journal of Human-Computer Studies, Vol. 58 No. 3, pp. 281-305. Thompson, B. (2004), Exploratory and Confirmatory Factor Analysis, American Psychological Association, Washington, DC. Tsai, H. and Hunag, H. (2007), “Determinants of e-repurchase intentions: an integrative model of quadruple retention drivers”, Information & Management, Vol. 44, pp. 231-9.

Ueltschy, L.C., Laroche, M., Tamilia, R.D. and Yannopoulos, P. (2004), “Cross-cultural invariance of measures of satisfaction and perceived quality”, Journal of Business Research, Vol. 57 No. 8, pp. 901-12. Urban, G.L., Sultan, F. and Qualla, W.J. (2000), “Placing trust at the center of your internet strategy”, Sloan Management Review, Vol. 42 No. 1, pp. 39-48. Wang, P.Z. and Waller, D.S. (2006), “Measuring consumer vanity: a cross-cultural validation”, Psychology & Marketing, Vol. 23 No. 8, pp. 665-87. Wu, G. (2005), “The mediating role of perceived interactivity in the effect of actual interactivity on attitude toward the web site”, Journal of Interactive Advertising, Vol. 5 No. 2, available at: www.jiad.org/vol5/no2 Yang, S., Hung, W., Sung, K. and Farn, C. (2006), “Investigating initial trust toward e-tailers from the elaboration likelihood model perspective”, Psychology & Marketing, Vol. 23 No. 5, pp. 429-45. Yoh, E., Damhorst, M.L., Sapp, S. and Laczniak, R. (2003), “Consumer adoption of the internet: the case of apparel shopping”, Psychology & Marketing, Vol. 20 No. 12, pp. 1095-118. Yoon, S. (2002), “The antecedents and consequences of trust in online purchase decisions”, Journal of Interactive Marketing, Vol. 16 No. 2, pp. 47-63. Zajonc, R.B. and Markus, H. (1982), “Affective and cognitive factors in preferences”, Journal of Consumer Research, Vol. 9 No. 2, pp. 123-31. Further reading Anderson, E. and Weitz, E. (1989), “Determinants of continuity in conventional industrial channel dyads”, Marketing Science, Vol. 8, pp. 310-23. Brady, M.K. and Robertson, C.J. (2001), “Searching for a consensus on the antecedent role of service quality and satisfaction: an exploratory cross-national study”, Journal of Business Research, Vol. 51, pp. 53-60. Brengman, M., Geuens, M., Weijters, B., Smith, S.M. and Swinyard, W.R. (2005), “Segmenting internet shoppers based on their web-usage-related lifestyle: a cross-cultural validation”, Journal of Business Research, Vol. 58, pp. 79-88. E-marketer (2007), Asia-Pacific B2C e-Commerce, available at: www.emarketer.com/Reports/All/ Em_b2c_ecom_asia_feb07 Huang, E. (2007), “Entry to the e-commerce markets of China and Taiwan: an application of content analysis”, International Journal of Management, Vol. 24 No. 1, pp. 82-91. Jowell, R., Roberts, C., Fitzgerald, R. and Eva, G. (2007), Measuring Cross-nationally, Sage Publications, London. Krishnan, H.S. and Smith, R.E. (1998), “The relative endurance of attitudes, confidence, and attitude-behavior consistency: the role of information source and delay”, Journal of Consumer Psychology, Vol. 7 No. 3, pp. 273-98. Lee, J., More, A.J. and Cotiwan, B.S. (1999), “Problems translating a questionnaire in a cross-cultural setting”, Preventive Veterinary Medicine, Vol. 41, pp. 187-94. Lubbe, B. (2007), “The effect of internet apprehension and web site satisfaction on air travelers’ adoption of an airline’s web site”, Journal of Air Transport Management, Vol. 13 No. 2, pp. 75-80. Maurer, T.J. and Pierce, H.R. (1998), “A comparison of Likert scale and traditional measures of self-efficacy”, Journal of Applied Psychology, Vol. 83 No. 2, pp. 324-9.

Alternative explanations

903

EJM 44,6

904

Merrilees, B. and Fry, M. (2002), “Corporate branding: a framework for e-retailers”, Corporate Reputation Review, Vol. 5 Nos 2/3, pp. 213-25. Mitchell, A.A. and Olson, J.C. (1981), “Are product attribute beliefs the only mediator of advertising effects on brand attitude?”, Journal of Marketing Research, Vol. 18, pp. 318-32. Richins, M.L. and Dawson, S.A. (1992), “A consumer value orientation for materialism and its measurement: scale development and validation”, Journal of Consumer Research, Vol. 19, pp. 303-16. Schweizer, K., Moosbrugger, H. and Schermeller-Engel, K. (2003), “Models for hierarchical structures in differential psychology”, Methods of Psychological Research Online, Vol. 8 No. 2, pp. 159-80. Thatcher, S., Foster, W. and Zhu, L. (2006), “B2B e-commerce adoption decision in Taiwan: the interaction of cultural and other institutional factors”, Electronic Commerce Research and Applications, Vol. 5, pp. 92-104. Corresponding author Hong-Youl Ha can be contacted at: [email protected]

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