Trust Transfer

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Transfer From Offline Trust to Key Online Perceptions: An Empirical Study Kun Chang Lee,* In Won Kang** and D. Harrison McKnight*** * Professor of MIS School of Business Administration Sungkyunkwan University Seoul 110-745, Korea [email protected] ** Assistant Professor School of Economics & International Trade Kyunghee University Seoul 130-701, Korea [email protected] ***Assistant Professor Eli Broad College of Business Michigan State University East Lansing, MI 48824-1121 [email protected] Forthcoming IEEE-TEM Index Terms: Trust, trust transfer, structural assurance, online banking, online trust, marketing channel, flow, website satisfaction, extent of use, coherence, cognitive overhead.  Professor McKnight has served as a corresponding author. Inquiries about this study can be directed to any author. 0 Transfer From Offline Trust to Key Online Perceptions: An Empirical Study Abstract- Research has provided little evidence that trust in an offline bank can encourage adoption of the bank’s online business. Yet more and more brick-andmortar banks and other businesses are investing in online websites that supposedly “leverage” positive consumer impressions of their offline business. The main purpose of this study is to test empirically whether or not trust in an offline bank transfers (i.e., influences) perceptions about that company’s online bank. In order to do so, we analyze how trust in an offline bank influences four perceptions about its online banking counterpart (flow, structural assurance, perceived website satisfaction and perceived extent of future use). The study tests the hypothesized influence of offline trust using a sample of 199 South Korean consumers responding about offline and online banking. Results show that offline trust influences all four online perceptions, just as proposed. These effects were especially prominent among respondents new to online banking. Thus, offline-toonline transfer should be considered when designing strategies for online channels. This study fills a key research gap by examining transfer of cognitive beliefs from an offline to an online setting. 1 I. INTRODUCTION Trust is a central construct in the study of commercial transactions, both in information systems (IS) and such reference disciplines as marketing, sociology, and organizational behavior [27, 31, 73, 75, 95]. Trust plays an important role in both offline and online commercial transactions [106]. Several IS studies on the role of trust in electronic commerce (e-commerce) have been conducted in recent years [5, 33, 34, 36, 43, 50, 58, 71, 80, 81, 96, 103]. Especially, with the advent of online marketplaces on the Internet as a new business paradigm, trust has been dealt with as a crucial enabling factor for businessto-consumer relationships [33, 50] as well for business-to-business marketplaces [80]. A number of trust constructs including institution-based trust [70, 81, 108] have been defined [34, 72]. Interpersonal or inter-party trust definitions largely fall into two categories:  Willingness to become vulnerable to another [67, 71], such as “a willingness to rely on an exchange partner in whom one has confidence” [74].  Positive perceptions or beliefs about the attributes of another. This includes the perception of “confidence in the exchange partner’s reliability and integrity” [75], and the perceived credibility (honesty) and benevolence of a particular target [30, 56]. Several have listed integrity, benevolence, and ability as attributes [8, 34, 71]. In buyer-seller relational exchanges, which are the most popular forms of transactions between persons or companies, two dimensions of trust are perhaps most pertinent. The first, credibility/honesty, is the expectation that words or written statements can be relied upon or depended upon [61]. The second, benevolence, is the extent to which one partner is genuinely interested in the other’s welfare and is motivated to seek mutual gain [57]. Trust is important in relational exchanges because it allows partners to transcend short-run inequities or risks to concentrate on long-term profits or gains [73]. Personal relational exchanges are typified by qualities such as intensity, interaction frequency, relationship duration, and future relational expectations [48]. These qualities inherent in offline 2 relational exchange facilitate trust building. By contrast, trust is more difficult to create online because parties do not typically have intense, face-to-face contact that enables trust to be built through tangible cues. One can enter a bank, for example, and learn to trust the bank tellers through repeated personal interactions that provide interpersonal cues. One can also become secure quite rapidly with the bank institution itself by entering the bank and observing the look-and-feel of the building, its furnishings, and the banking procedures —physical cues on which to base trust [71]. By contrast, the online bank has no such physical cues for trust building, although its website provides certain online cues. One way to solve this trust building problem is to transfer trust from a known brick-andmortar entity to positive perceptions in the unknown online entity. We postulate that trust gained through experience with an offline company positively influences key customer perceptions of the same company’s online division, such as website satisfaction or intention to use its site. Stewart [96], who called this phenomenon “trust transfer,” applied it specifically to business-to-consumer e-commerce in terms of buying computers online. The results revealed that websites that had a picture and street address of their landbased store generated a higher consumer intention to buy from the store. Stewart [96] did not use specific trust-related perceptions about the offline store to predict online perceptions, a gap this paper addresses. Today’s Internet-oriented world has made it even more important for marketers to study the interplay between offline and online channels when analyzing customer behavior. Those who do so are rewarded with a level of predictive power that affords them a distinct advantage over their competitors. Potentially, this study contributes by testing an instance of the effects of offline bank trust on four specific consumer perceptions regarding the bank’s online counterpart. II. LITERATURE REVIEW AND RESEARCH MODEL A. Trust Transfer Process—Four Types Narrowly defined, trust transfer means transfer of trust from one domain, such as offline, to another, such as online. We define trust transfer more broadly to include the influence of 3 trust in one domain on attitudes and perceptions in another domain. For example, consumer trust of an offline bank can affect perceptions about the same firm’s online bank. Figure 1 depicts several types of trust transfer. The Type 1 TTP shows how trust (or other perceptions) can be transferred from offline to offline channels. For example, the trust that customers perceive in an offline company, like General Electric (GE), tends to extend easily to favorable perceptions about all other GE products or services. Trust for a certain company means consumer brand knowledge which relates to the cognitive representation of the brand [83]. Consumer brand knowledge can be defined in terms of the personal meaning about a brand stored in consumer memory. Therefore, if customers possess a sound memory about an offline company’s brand, then such memory affects consumer purchase behavior about the product and/or service offered by the same or similar offline brand. Figure 1. Trust Transfer Process (TTP) In the mid-1990s, many firms began performing marketing activities both offline and online (Type 2). As the Internet has proven an ever more effective global communication medium, marketing strategies have shifted from offline to online channels. One key issue has been whether consumer trust, something offline firms had worked for years to accumulate, 4 would translate into favorable perceptions of online products. Examples both support and cast doubt on such trust transfer. Offline-turned-online channels such as Barnes & Noble Company (http://www.barnesandnoble.com) and Samsung Electronics Corporation (http://www.sec.co.kr) have maintained offline trust and brand loyalty online as well as offline. On the other hand, FTD (Florists’ Trans world Delivery) and Hallmark have had trouble leveraging their offline business into online successes [15]. Although many pure online companies have fizzled out, some continue to succeed (e.g., Dell Computer). Continuing questions about Dell’s survival prospects are based on whether the online trust it has built thus far will remain stable in the future, when stronger competition is expected. It follows, then (Type 3), that if existing online companies wish to expand their online products and service offerings, they will have to maintain or increase their level of consumer trust. Stewart [96] suggests that, in online B2C, trust transfers from trusted websites to unfamiliar websites based on hyperlinks between them. There are rare cases of Type 4 trust transfer, in which customers’ trust in online channels move to offline channels. In South Korea, which has a very high Internet adoption rate due to its highly advanced Internet infrastructure, a number of successful online companies have expanded their business into offline channels. In the home shopping industry, two companies, CJ Home Shopping (www.cjmall.com) and GS Home Shopping (www.gseshop.co.kr), have successfully opened offline shops, taking advantage of their online reputation. MISSHA (www.beautynet.co.kr), a quality-based and low-priced cosmetics company that originally started as a pure online company and accumulated a solid reputation by innovating a process of developing new cosmetic products on the basis of online polls, is recently receiving a great deal of attention from the Korean cosmetics industry and mass media, due to its fast and successful expansion into offline markets. In Figure 1, TTP is categorized as either intra-channel (Types 1 and 3) or inter-channel (Types 2 and 4). Regardless of type, TTP indicates that trust amassed over time in one channel (online or offline) may influence the evaluation of a product or service in the same channel or in another channel. 5 A customer’s evaluation of a new brand is based on the schema developed from experience and knowledge of it [66]. Customers evaluate products and services in order to reduce transaction risks [12], and it has been established that customers use their experience to determine their purchase intentions [86]. The consumer schema remains in memory and is converted into trust in the vendor. Knowledge of TTP can help marketing strategists enhance consumer loyalty and expand it into different channels. This study fills a research gap by focusing on Type 2 (Offline-to-Online) TTP, which has received almost no attention. Offline to online TTP is a pervasive phenomenon, since most businesses and individual customers are already involved in some form of e-commerce or Internet business. Type 1 (Offline-to-Offline) TTP has already been studied by numerous researchers [14, 54, 82]. Once an offline brand (or company) has succeeded in acquiring a certain level of consumer trust, it can extend easily into another offline business under the same brand category. By contrast, real cases of Type 3 (Online-to-Online) and Type 4 (Online-to-Offline) TTP are relatively rare, although Stewart [96] has addressed Type 3. B. Offline Trust and Online Mental Models Figure 2 represents the general interaction of offline trust with a mental model of online banking. We begin with this general model before proceeding to the specific research model. A mental model is the representation of objects and semantic relations that a user constructs when he or she is processing new information [38]. Customers who view hyperdocuments on an online banking website must be able to form quick and clear mental models before attempting to perform online banking transactions. Coherence positively influences the ability to form mental models, while cognitive overhead inhibits it. By definition, a document is coherent if a customer can construct a 6 mental model from it that approximates reality [52]. A website that combines design factors to create a user-friendly website design improves customer coherence [63]. Figure 2. Relationships between Offline Trust and Online Customers’ Mental Model Note: Dotted line box indicates constructs not in this study Cognitive overhead, on the other hand, is the additional effort and concentration users need to perform one or more complex tasks at once [17]. Cognitive overhead is influenced by orientation, navigation and interface design [101]. For example, multimedia overkill in a Web interface (e.g., excessive graphics, color, animation, sound) can cause excessive cognitive overhead because the capacity for human information processing is limited. We thus argue that various website design factors affect both coherence and cognitive overhead. Based on Thuring et al. [101], this study argues that flow and structural assurance affect perceived website satisfaction and extent of use through coherence and cognitive overhead. While this study does not measure coherence and cognitive overhead, it argues that flow and structural assurance partly influence the Figure 2 outcomes by their effects on coherence and cognitive overhead. Flow means an optimal experience with a task [20]. 7 Structural assurance means the belief that situational factors like contracts, regulations and guarantees support the likelihood of success [71]. Using both coherence/cognitive overhead and their causes (flow and structural assurance) in the model seemed like overkill in terms of model complexity. Also, because it is viable to test a subset of a larger theoretical model [99], we chose to test a model that only included flow and structural assurance. C. Offline Trust and Flow Arguments are now made for why trust will transfer in general, and then reasons for why trust will transfer to online flow. Trust in an offline business should transfer to perceptions about an online business because of brand knowledge. Since brand knowledge is the cognitive representation of a particular brand [83], it is also defined as the personal meaning that is stored in consumer memory: descriptive and evaluative brand-related information [54]. Brand knowledge involves a synthesis of multiple factors such as awareness, attributes, benefits, images, thoughts, feelings, attitudes, and experiences [54]. Among those factors, trustworthiness perceptions about the brand are implicitly included in attitude, which is believed to transfer to the other entity (refer to Figure 2 in [54]). Thus, consumer trust in a certain brand will transfer to products and services related to that brand, regardless of channel. The concept of flow involves the optimized processing of experiences that occur when customers navigate or browse the Internet. Online interactivity between companies and their customers is carried out through flow [44, 101]. Flow helps explain certain aspects of human-computer interaction [20]. Figure 3 shows the research model that will now be justified. A common measure of flow is the level of intrinsic enjoyment of an activity, not unlike the emotional response of pleasure in environmental psychology [55]. In an online environment, flow is based on three aspects: shopping enjoyment, perceived control, and concentration/attention. This study, accordingly, defines flow using these three aspects. Just as shopping enjoyment is 8 important in offline markets, it is important online, where it can have a significant impact on customers’ attitudes and intentions about online shopping [49, 55]. Internet shopping does not always provide the emotionally fulfilling experience of conventional shopping because it is mostly limited to two-dimensional text and pictures [55]. Figure 3. Research Model One aspect of online shopping enjoyment is the perception that the website has interesting content. The second aspect of flow, perceived system user control (i.e., not being controlled by the system), concerns the information environment on the Internet. A high level of consumer control should be guaranteed by companies that wish their customers to conduct online transactions [6, 44]. The third aspect of flow, concentration/attention, is not always easy to maintain on a website. In an online banking situation, customers do not engage with tellers personally. Such face-to-face interaction can be very engaging. Online interaction can also be engaging [20]. However, online concentration/attention may be discouraged by limited time and lack of information processing resources [86], lack of a 9 real space where people can meet each other face to face [55], and other distractions, such as email, pop-ups, and instant messaging. Trust in an offline bank probably reduces user hesitance to give attention to the system, because without trust, the perceived risk of using the system would interfere. Trust therefore obviates uncertainty or concern and facilitates the development of flow. It is hard for the user to experience flow when the mind is bogged down in uncertainty or risk. That is, flow means a feeling of total involvement [20]. It is hard to feel total involvement using the online system of a bank one does not trust. Trust has long been seen as a way to address uncertainty [64]. Trust can act as a substitute for control because one feels more in control and less uncertain with a person one trusts [64]. Offline bank trust would thus positively influence flow in terms of perceived control because one who trusts the offline bank would feel more in control and less uncertain when using the bank’s system. H1: Trust in an offline bank positively influences perceived user flow with the online bank system. D. Offline Trust and Structural Assurance While Internet customers can access products and services efficiently regardless of location or time, they have limited means of assessing the characteristics of the products and services they purchase [22, 49, 87]. They can only view products on-screen. This is one reason many customers perceive transaction risk when shopping on the Internet [86]. Risky situations result in customer uncertainty: unexpected worries, concerns and psychological misgivings at the time of purchase or adoption [7, 26, 100]. Structural assurance, a form of institution-based trust particularly key for minimizing perceived risk in online transactions, means consumer projections of success due to such safety nets as legal recourse, guarantees and regulations that exist in the context [23, 71]. Trust in the e-vendor results from the security customers feel due to these safety nets [34]. 10 Online banking requires a high level of security, since private information must be completely protected against computer hacking attacks or technical failures that would damage customers’ financial status. Online banking transactions should therefore result in positive consequences with regard to the amount of money and financial conditions involved. In this study, structural assurance focuses on situations in which customers contemplate online banking transactions in a known service class. If a consumer trusts the offline business, this trust should transfer to beliefs that the firm’s online system is safe and secure, i.e., to structural assurance beliefs. If an offline bank is trusted, it should also be perceived as having adequate online safeguards. This is as natural as believing a trusted retail store will back its product guarantees in a new line of business, just as it already does for existing lines of business. H2: Trust in an offline bank positively influences structural assurance of the bank’s online system. E. Offline Trust and Perceived Website Satisfaction User satisfaction with a specific web-based system depends on numerous factors, including web design [62, 79, 105], content [85], user interface [94], navigation and information structure [69, 90], and contextual marketing [65]. User satisfaction was initially promoted as a proxy for the success of an information system. Such systems were evaluated in an attempt to specify their quality from the user’s point of view [32], and many instruments were developed that successfully measured different aspects of user experiences and opinions [21]. From a marketing perspective, user satisfaction depends largely on performance. Product experiences alone do not determine overall satisfaction, however; a large body of research has shown that the level of performance the customer expects is also important [16, 78], as is knowledge of outcomes that were not experienced. When people evaluate outcomes they compare their actual results with the results that might have occurred had they chosen differently [11, 53]. Similarly, trust development has been depicted as the process 11 of setting expectations of another’s behavior and then evaluating whether or not those actions are confirmed [89]. Trust expectations can also act as cognitive filtering devices by predisposing one to interpret the other’s behavior as consistent with the original expectations. For example, Holmes [46] found that trusting marriage partners tended to block out or reinterpret (positively) actions by their partner that did not match their positive trusting expectations. Likewise, one who trusts has expectations that will likely be confirmed in terms of perceived website satisfaction. If one has found an offline bank to be trustworthy, then one is likely to project positive satisfaction towards its online counterpart. Thus trust in the offline bank should positively affect perceived satisfaction with the website. H3: Trust in an offline bank positively influences perceived online bank website satisfaction. F. Offline Trust and Perceived Extent of Use Extent of use means expected or planned future behavior — the probability that a customer will translate beliefs and attitudes into user-related actions. Different factors can influence extent of use in online transactions, including product perceptions, purchasing experiences, customer service and transaction risk [49]. Online products are perceived through interactions between people and machines, while products in offline transactions are perceived directly. Thus, online transactions highlight customer risk [45], since customers are unable to directly experience products and services. The level of risk that customers perceive in online transactions influences their level of satisfaction with a website. Extent of use depends on the level of satisfaction with several factors about the website. So far, research on extent of use in offline and online transactions has assumed that every extent of use applies only to one channel [8, 103, 106]. However, Ramaswami et al. [88], applying to both offline and online transactions, suggests two factors influence purchase of online financial products: 1) prior clues a consumer gains in the offline channel; and 2) use of the online channel for information search. 12 Therefore, consumer online behavior is often determined by prior clues in the offline channel, which are translated into the mental model in the online channel [88]. If a customer is satisfied with a product in the offline channel, he or she is more likely to favor that product in an online channel. When a consumer’s experience with an offline vendor is satisfactory, the consumer will trust the offline vendor. Trust in the offline vendor should then transfer into positive attitudes towards the vendor’s online products. Gefen et al. [35] found that trust in an e-commerce vendor leads to intentions to purchase from that vendor. Similarly, trust in the offline bank should positively influence extent of intended use of the bank’s online system. H4: Trust in an offline bank positively influences perceived extent of use of an online bank. G. Online to Online Model Relationships Perceived structural assurance also relates to website satisfaction. Depending upon the extent of customers’ misgivings regarding the purchasing environment, they may be more committed to the website (and thus more satisfied), or less committed to it (and thus less satisfied). It is hard to feel satisfied with a website about which one is concerned in terms of safeguards. Such concerns may increase cognitive overhead. Hence, lack of structural assurance may interfere with the development of website satisfaction. Security signs or logos indicating the existence of website security systems have also been shown to lessen the psychological burden of online customers [34], which should facilitate website satisfaction. H5: Online bank structural assurance positively influences perceived online bank website satisfaction. Web satisfaction is dependent on both information quality and system quality [69]. Information quality encompasses web design factors and hypertext document quality. System quality embraces user interface, navigation, and information structure. To the extent that a site user experiences flow, the user will feel positive towards the system and 13 information quality of the website, both of which affect web satisfaction. The user experiencing flow will: a) devote attention to the site, b) experience greater coherence, and c) enjoy using the site because of its good content and design. Therefore, flow will be positively related to perceived website satisfaction. H6: Online bank system user flow positively influences perceived online bank website satisfaction. Enjoyment of the online shopping experience is an important determinant of customer loyalty [28, 49]. Concentration, a measure of flow, has also been found to positively influence the overall experience of computer users and their intention to use a system again [104]. Flow should improve customers’ perceived coherence, which will lead users to want to use the system. As flow becomes greater, customers tend to become more absorbed in the website. Flow is a pleasurable experience, something users desire. Thus, the pleasure behind flow will increase the likelihood that users will utilize the website again. H7: Online bank system user flow positively influences perceived extent of use of an online banking system. Meanwhile, structural assurance can influence website extent of use. Because online banking activity is especially vulnerable to financial risks [13, 86], trustworthy structural assurance relieves the tension of customers and lowers cognitive overhead, both of which will improve their perceived extent of use. Structural assurance lowers perceived transaction risks [34, 72], which allows customers a more active interaction with online banking websites. Suh and Han [98] found that such structural assurances as nonrepudiation, privacy protection, and data integrity indirectly influenced both intention to use a website and actual use through trust in the website. H8: Online bank structural assurance positively influences perceived extent of use of an online banking system. 14 Feelings of interactivity (indicating flow [44, 55]) will be influenced by the perceived safety of online banking transactions (indicating structural assurance [34, 72]). One may have trouble reaching a state of flow unless one feels safe about the online vendor’s website. Once the online banking environment is considered safe, one can use the website without concern, making flow more likely. H9: Online bank structural assurance positively influences online bank system user flow. Many researchers believe that the extent of use varies with the level of satisfaction with the technology [8, 19, 23]. It has been well documented that customer satisfaction affects repeat purchases, product return rates, brand loyalty and the prevalence of word-of-mouth communications [18]. Satisfaction and trust in websites affects purchasing intentions [8, 19, 23, 103]. Greater satisfaction with a website means that customers favor those products, leading to increased intent to use. H10: Perceived online bank website satisfaction positively influences perceived extent of use of an online banking system. A boundary condition should be noted for H1-H4: the effects of offline trust will likely be strongest when consumers have little experience with online banking. After some period of online bank experience, online banking variables, such as website satisfaction, will play a more important role than will offline bank trust. This boundary condition will be tested using two subsets of customers—first-time users and experienced users. Results will be presented after the hypothesis tests. III. RESEARCH METHODOLOGY To examine trust transfer across offline and online banking channels, this study employed a field study method, using questionnaire techniques to measure each construct in the 15 model. Questionnaires enable researchers to obtain the beliefs of prospective website users, and those beliefs are especially important to trust-related research, in which perceptions are more important than objective measures [46]. A. Customer Online Banking Studies Online banking transactions have emerged as an important topic in domestic and international financial circles [60] because online banking allows financial transactions to be carried out conveniently in virtual space, without regard for time or geographical location [103]. Previous studies of online banking have focused on four kinds of research: (1) general description and success factors [2, 4], (2) analysis of behavioral issues such as customer adoption [76, 92] and customer attitudes toward the usefulness and willingness to use online banking [59, 60], (3) regional characteristics [51, 91] and (4) other issues, such as security [40, 107]. Banks are attracted to online banking because it reduces costs and can provide a competitive edge [47]. These factors have led to growth in automated banking such that more than half of banking transactions now take place via telephone, ATM, or the Internet [47]. Internet banking is expected to grow strongly in the future, such that banks that do not offer Internet services are likely to lose significant portions of their customers [100]. Internet banking is growing fast in such Asian countries as South Korea, Singapore, and Thailand [91]. B. Research Setting This study used South Korean subjects responding to questions about online banking. Banking is an appropriate test domain for a trust model because of the potential financial risk to customers an online bank presents [98] and because of the need for online banks to keep transactions and sensitive personal/financial information secure. Trust only makes a difference in an environment of uncertainty and risk, and it is difficult for a consumer to control or monitor an online vendor in order to eliminate risk [34]. Banking also makes a good test because its online component is relatively new compared to offline banking and because of the natural link between offline and online banking. South Korea makes an 16 interesting place to test the model because of its rapid adoption of Internet. Besides, South Koreans are processing about 3 banking transactions out of 10 through the online banking system, or 30.9 % rate for online banking usage according to the Bank of Korea’s survey data on September 2005. This indicates that South Koreans are aware of online banking because most South Korean offline banks encourage customers to use online banking so that they can focus on more profitable banking services. C. Instrument Development A questionnaire was designed based on the research model (Figure 3). Respondents answered questions based on a five-point Likert-type scale ranging from “strongly disagree” (=1) to “strongly agree” (=5). This study’s trust measure combines trust in the bank itself, the bank’s services, and the bank’s tellers together so that the concept of offline trust is not limited to a single object. (See Table 2 for items.) The first item reflects the honesty/integrity of the offline bank and the second item reflects the bank’s benevolence, in terms of how the bank’s services meet the consumer’s needs [25]. The third item reflects general trustworthiness [84]. Thus, our measures cover two of the three most commonly researched aspects of trust, benevolence and honesty/integrity, while not covering competence, the third aspect [67, 71]. Flow is measured by the most applicable online aspects of the flow concept: consumer enjoyment, concentration/attention, and perceived control [37, 55]. These are key aspects of flow and follow the theory base on which the hypotheses were developed. They form a unity in terms of being closely related to the psychological aspects of flow. Customers who use the Internet for financial transactions have been known to feel threatened by potential online risks [13, 86], such as possible loss of personal property or identity. In light of the general information available about security for Internet-based financial transactions, we adapted three structural assurance items from [71] to measure perceived safety in the vendor’s bank in the midst of perceived online banking risks. 17 Several factors determining website satisfaction have been identified, including web design [62], content [85], user interface [94], navigation and information structure [69]. This study adopts three measures representing online banking website satisfaction: information quality, system quality, and overall satisfaction with a website (as noted in [69]). This study measures perceived extent of use based on the items in [23]. Among them, two seem most relevant to perceived extent of use of online banking – intention to reuse, and frequency of use – in order to apply to an online banking case (see Table 2). The instruments were translated from English into Korean by bi-lingual (Korean and English) authors. To assure the translation was correct, the authors had 10 MIS doctoral students review the translated instruments and suggest adjustments for subtle nuances in Korean expression. Then, to obtain further translation equivalence, the authors compared the Korean-translated instruments with the same instruments published in Korean in a toptier South Korean journal, to assure they would convey the original English meaning of the instruments precisely. Translation equivalence from English to Korean language was in this way accomplished in line with other cross-cultural research [77]. D. Pretest To test the psychometric properties of the constructs [97], a questionnaire pretest was given to 68 graduate students enrolled in classes administered by the study authors. The respondents were volunteers and were not told the study’s objective. Respondents were asked to visit their bank’s website to gauge the convenience and services it offered. The convergent validity and unidimensionality of each construct was verified using a principal component factor analysis for factors with eigenvalues above 1, using a varimax rotation. Each item loaded on the intended construct and each Cronbach’s alpha exceeded 0.80. The highest cross-loadings were in the 0.4 range. Since the exploratory factor analysis results appear acceptable for a pretest, we decided to use these measurements (Table 2). E. Respondents, Primary Data Analysis and Descriptive Statistics 18 Respondents included 325 company workers, housewives and college students. 300 were selected from the Seoul metropolitan area in South Korea, using a stratified quota sampling method. Seoul was first divided into two parts-Southern area and Northern area in order to consider economic status. Then five districts were selected from each group on a random basis, totaling 10 sampling districts. 30 customers were chosen randomly from each district, totaling 300 respondents. Another 25 respondents were MBA students enrolled in the first author’s university. In order to assure the sample was representative, we tried to obtain 165 males and 160 females. We also targeted 125 in the 20s age group, 110 in the 30s age group, and 90 in the 40s age group in order to cover those most interested in online banking. The questionnaire was divided into two parts. The first asks questions about offline banking experience, and the second about online banking experience. Before respondents answered the questions, they were asked to select one bank with which they did offline (but not online) business, and to visit its website to check out its basic banking functions and services. Respondents then answered the questionnaire. The entire exercise took approximately 20 minutes to complete. Among the 325 distributed questionnaires, 85 were not completed validly, and 41 were not returned, resulting in 199 valid responses for a 61.2 % valid return rate. The demographic characteristics of the 199 respondents are shown in Table 1. IV. EMPIRICAL RESULTS A. Confirmatory Factor Analysis The research model has five constructs with interrelated dependence relationships or causal paths, requiring a structural equation model (SEM) analysis [9, 41]. SEM analysis requires constructs to be assessed rigorously by confirmatory factor analysis (CFA) [1, 29, 41, 93], to examine convergent and discriminant validity. CFA results were obtained using software package LISREL 8.30. (1) Sample Size: 19 Experts consider the minimum sample size for reliable SEM analysis to be from 100 [9], to 150 [3] to over 200 [10]. Because our research model is relatively simple, with only five constructs, the sample size of 199 is considered adequate. (2) Item Reliability: Item reliability denotes the amount of variance in an item due to its underlying construct. Table 2 shows that t-values for all of the standardized factor loadings of items are significant at p<.01, indicating item reliability. Additional evidence was found using average SMC (squared multiple correlation), which denotes the explanatory power of items related to latent variables. SMC figures in Table 2 lie between .73 and .84, indicating item reliability. Table 1. Descriptive Statistics (n=199) Construct Mean Standard Deviation Trust in Offline Bank 3.13 .66 2.77 .95 Structural Assurance 3.05 1.02 Perceived Website Satisfaction 2.83 1.02 Perceived Extent of Use 3.03 .83 Male Female 107 (54%) 92 (46%) Flow Sex Job Demographic Characteristics Age Online Banking Experience Company Workers Housewives College Students 109 (55%) 54 (27%) 36 (18%) 20-29 30-39 Over 40 46 (23%) 85 (43%) 68 (34%) Below 1 month including first-time user 1-12 months Over 1 year 113 (57%) 22 (11%) 64 (32%) Table 2. CFA Results 20 Item Reliability Measurement Items for Each Construct Factor Loading Std. Errors Std. Loading t-value Average SMCa Cronbach’s alpha .79 .91 .84 .94 .73 .88 .81 .92 .76 .85 Trust in Offline Bank (adapted from Doney and Cannon 1997 [items 1, 3]; Plank et al. 1999 [item 2]) This bank keeps the promises it makes to me. 1.00 - .88 - This bank’s services meet my needs. 1.06 .05 .94 19.62** This bank’s teller is trustworthy. .98 .06 .86 16.76** Flow (adapted from Ghani et al. 1991 [item 1]; Koufaris 2002 [items 2, 3]) During my visit to this online banking website, I found a lot of interesting content. 1.00 - .91 - During my visit to this online banking website, my attention was focused on online banking activity. 1.02 .05 .92 21.06** During my visit to this online banking website, I felt in control. 1.02 .05 .93 21.29** Structural Assurance (adapted from McKnight et al. 2002) This online banking website has enough safeguards to make me feel comfortable using it for my personal business. 1.00 - .88 - I feel assured that the legal and technological structures of this online banking website adequately protect me from Internet problems. .97 .07 .85 15.59 In general, this online banking website is a robust and safe environment in which to transact business. .95 .06 .84 15.21** ** Perceived Website Satisfaction (adapted from McKinney et al. 2002) I feel satisfied with the information quality offered by this online banking website. 1.00 - .91 - I feel satisfied with the system quality by this online banking website. .98 .05 .89 19.02** After using this online banking website, I feel very satisfied. .99 .05 .90 19.78** Perceived Extent of Use (adapted from Devaraj et al. 2002) I intend to use the services offered by this online banking site again. 1.00 - .90 - I intend to visit this online banking website as often as possible. .93 .08 .84 12.13** ** p <0.01, a SMC = Squared Multiple Correlation (3) Construct Reliability, Convergent Validity, and Average Variance Extracted (AVE): 21 Table 3 shows construct reliability and AVE figures. Reliability is a necessary condition for evaluating convergent validity. Construct reliability estimates range from .86 to .94, and all are greater than .70. The AVEs, which should meet a .50 standard, fall between .75 and . 84, indicating convergent validity. Table 3. Correlations, Construct Reliability, and Average Variance Extracted Intercorrelations between Constructs Flow Structural Assurance Perceived Website Satisfaction Perceived Extent of Use Trust in Offline Bank Construct Reliability (>.70) Average Variance Extracted (>.50) Flow Structural Assurance Perceived Website Satisfaction Perceived Extent of Use Trust in Offline Bank 1.00 .72 1.00 .73 .79 1.00 .58 .56 .52 1.00 .69 .79 .76 .69 1.00 .94 .89 .93 .86 .92 .84 .75 .81 .76 .80 (4) Discriminant Validity: Since intercorrelations between constructs are relatively high (refer to Table 3), common method bias may exist. In order to detect this, a discriminant validity test was performed in accordance with [29], one of the more statistically rigorous methods of doing so. In this test, the squared correlations between two constructs must be lower than the corresponding AVE. Table 3 shows that the AVE figures, ranging from .75 to .84, all exceed the squared correlations between the five constructs, the highest of which is .63, confirming discriminant validity of the proposed constructs. Thus, the five constructs possess adequate convergent and discriminant validity for further SEM analysis. 22 B. Structural Equation Modeling (SEM) Analysis The SEM results depicted in Figure 4 show that all the fit indices are successfully met. For 2 example,  value divided by degree of freedom is less than 3, and GFI is over .90, AGFI over .80, NFI over .90, NNFI over .90, and SRMR below .05 [34, 41]. Other fit indices also meet the theoretical threshold: CFI=.99, IFI=.99. This model, while very parsimonious, explains a significant portion of the variance in perceived extent of use (R 2 = .51), perceived website satisfaction (R2 = .71), flow (R2 = .56), and structural assurance (R2 = . 63). It can be concluded, then, that the proposed structural model is statistically sound. The structural model SEM results are as follows (Figure 4). First, offline trust impacts online banking constructs directly, with a significant effect on flow (.72, t=10.49**), structural assurance (.57, t=7.18**), perceived website satisfaction (.27, t=2.88**) and perceived extent of use (.64, t=5.03**). Second, structural assurance influences flow (.32, t=4.38**) and perceived website satisfaction (.41, t=4.00**), but not extent of use. Third, flow significantly influences perceived website satisfaction (.27, t=3.52**) and extent of use (.26, t=2.60**). Finally, perceived satisfaction did not influence extent of use. ** p < 0.01 (t>1.96) Figure 4. Structural Equation Model Results 23 The off-to-on portion of these results can be summarized by stating that in support of H1H4, offline trust has a strong and positive influence on the online variables proposed. This supports the existence of Type 2 (Offline-to-Online) TTP. We may therefore conclude that Type 2 TTP exists and applies to real world banking e-commerce activities. The questionnaire respondents include those who had never before used online banking (first-time users) and those who had (experienced users). While the experienced users reported based on online banking experience, the first-time users reported more based on cue-based trust [24, 72], in that they supplemented their offline bank trust with cues from the online website of the bank. To examine the differences between first-time users and experienced users, we split the sample into first time (n= 111) and experienced (n=88) and re-ran the model. Table 4 shows the model results split by first-time user data and experienced user data. For first-time users, offline trust influences all four online constructs significantly, whereas for experienced users, offline trust affects only three online constructs significantly—flow, structural assurance, and perceived extent of use, but not perceived website satisfaction. This likely means that since cue-based trust is replaced with experience-based trust over time, the experienced users’ perceived website satisfaction is not affected by offline trust itself. We also observe from Table 4 that the path coefficients from offline trust to the online constructs for first-time users are significantly greater (p<.01) than those for experienced users, except for perceived extent of use. This finding conforms with theory about cue-based trust and experience-based trust, in that first-time users are influenced more than experienced users by their trust in the brick-andmortar bank to form their site flow, structural assurance, and satisfaction impressions. Experienced users know how online banking works, so they are less influenced by their offline bank trust. However, offline trust had a greater effect on perceived extent of use among experienced users, perhaps because these users have developed the skill set needed to effectively use the product. Thus, they could more clearly apply their offline trust to a projected use of the service. 1 We project that over time with repeated interactions using the online banking systems, experience-based trust will play a greater role than cuebased trust for all the dependent variables. 1 We are indebted to an anonymous reviewer for this insight. 24 Table 4. Empirical results for first-time users and experienced users First-time user model χ2=98.152(p=0.007), df=67, GFI=0.89, AGFI=0.83, NFI=0.92, NNFI=0.96 RFI=0.89, IFI=0.97, CFI=0.97, SRMR=0.04, RMSEA=0.06 Experienced user model χ2=82.983(p=0.090), df=67, GFI=0.89, AGFI=0.83, NFI=0.89, NNFI=0.97 RFI=0.85, IFI=0.98, CFI=0.98, SRMR=0.05, RMSEA=0.04 R2 .33 .59 .72 .29 R2 .28 .32 .43 .46 TrustFlow 0.55** 0.34** a TrustStructural Assurance 0.72** 0.40** a Trust  Perceived Website Satisfaction 0.54** n.s a Trust Perceived Extent of Use 0.40* 0.58** a Structural Assurance Flow n.s 0.36** a n.s 0.63** a n.s. n.s 0.25** n.s Flow Perceived Extent of Use n.s 0.35** a Perceived Website Satisfaction  Perceived Extent of Use n.s n.s Indexes of adjustment of the model Flow: Structural Assurance Perceived Website Satisfaction Perceived Extent of Use Structural Assurance  Perceived Website Satisfaction Structural Assurance  Perceived Extent of Use Flow  Perceived Website Satisfaction ** p <0.01 (t>1.96), * p <0.1 (t>1.282) a : t-tests showed significant (p<.01) differences for these coefficients between first-time and experienced groups, using the formula: t = (PC1-PC2)/[ Spooled x SQRT(1/N1+1/ N2)]; Spooled = SQRT{[( N1-1)/( N1+ N2 -2)] x SE12 +[( N2-1)/( N1+ N2 -2)] x SE22}; SE =Standard error of path in structural model; PC =Path Coefficient in structural model V. Study Limitations 25 This study captures primarily a cross-sectional view of model constructs. Thus, a longitudinal study would be helpful. Because of the lack of longitudinal data, causality of the model is not proven. Reverse linkages or bi-directional linkages among the constructs are possible over time. For example, online satisfaction should lead to online trust over time, just as Harris and Goode [42] found about satisfaction and trust in the offline world. This constitutes a boundary condition for our model, in that the model works best when consumers are relatively new to online banking, such as those in our sample (see Table 1). Sample size is another limitation, although the size is adequate for the tests conducted. Note that while model fit degraded somewhat when the sample was split by experience level, yet the RMSEA remained in an acceptable range. Although our scales displayed acceptable psychometric properties, the items we used are adapted from (and subsets of) the items used in other studies. Using alternative items, the results may vary somewhat from ours. Generalizability of results is another weakness. The study results may be different if the model were tested in other offline/online domains or in other cultures. For comparison purposes, it is noteworthy that South Korea is regarded as a rapid adopter of the Internet. The model results may also differ depending on whether the websites serve an information-intensive or fulfillment-risk function. Our study’s results do not apply directly to those website vendors who attract customers through sources other than their offline business. Since offline trust cannot be used by these vendors to build online trust, they should build trust through other methods, such as through institutional assurances, website ease of use, and website design quality that signals to consumers the trustworthy attributes of the web vendor [34, 70]. VI. Managerial Implications Based on the empirical results, this study arrives at the following implications. First, the TTP (trust transfer process) provides a unified view for understanding the effects of offline trust on online perceptions of flow and structural assurance. The empirical results show that consumer trust in an offline channel transfers rather easily to positive online channel perceptions, suggesting that vendors can leverage their offline trust to produce online flow and structural assurance. Hence, marketing strategies are best organized so that online perceptions like these are considered. For example, marketing materials should 26 emphasize the good content and user control of the online system (to elevate flow perceptions) and the safeguards and protections of the system, such as SSL / sophisticated encryption (to elevate structural asurance). Marketing materials could subtly connect these positive online system attributes to aspects of the quality offline service the consumer already receives to increase the offline-to-online transference effect. Second and related, offline trust is important in triggering positive online outcomes. Figure 4 shows that offline trust can trigger positive perceived website satisfaction and perceived extent of use. After the dot com bubble burst in 1998, financial analysts understood the importance of companies maintaining some offline activity. This confirms our proposed TTP: offline trust enables or facilitates the transfer process across channels. The result that offline trust positively influences these variables such as perceived website satisfaction and perceived extent of use of the website indicates that offline trust can be used as an enabling factor by which an online company that started from an offline channel can attract customers and make them more loyal to its website. Then an online company with strong offline trust can build up a high level of trust and reputation among online customers for a certain period, eventually triggering other types of TTP, for example, Type 3 (Online-to-Online) and Type 4 (Online-to-Offline). Since offline trust has the impacts we found, several actions should be taken to increase offline trust in the bank. a) Banks should use marketing campaigns to try to improve perceptions about the reputation and size of the firm, because these have proven to increase trust [25, 50]. b) Banks should improve actual and perceived offline customer service, which should improve the benevolence and competence aspects of trust. c) Customer service employees should be trained to be more likable, which has been shown to influence offline trust [25]. d) Marketing should emphasize the values the bank shares with customers [75]. Third, the results not supportive of two hypotheses—H8 and H10—also have an important implication. Although structural assurance and perceived extent of use were significantly correlated (r = .56**), structural assurance did not predict perceived extent of use. This is largely because more powerful factors (i.e., offline trust and flow) outweighed the effects of 27 structural assurance. Similarly, perceived website satisfaction correlated with perceived extent of use (r = .52**), but did not predict it because of the more dominant effects of flow and offline trust. This may also be because in the initial phase, website satisfaction is tentative and therefore is not heavily relied on as input for whether or not to use the online banking site. Offline trust, developed and reinforced over time, was relied on instead. These findings reinforce how important offline trust is to online outcomes. Fourth, the questionnaire data confirmed the existence of Type 2 TTP, which was not studied previously in an explicit manner. Offline trust was undeniably transferred to online channels, in that it had a significant effect on online consumer perceptions. Further, though offline trust was empirically proven here to be usually a starting point for TTP initiator, we believe from Figure 1 that TTP can be triggered not only from offline trust, but from online customer trust. TTP Types 3 and 4 in TTP are examples. As discussed in the previous section, among the four cells in TTP, Type 4 is very rare to find in real ecommerce applications. However, as e-commerce becomes omnipresent in business world and the Internet-infrastructure is getting more advanced and high-speed, Type 4related businesses emerge in the market as a new trend in e-commerce to attract more customers regardless of offline and online. For example, WASSADA (http://www.wassada.com) is another typical Type 4-related company based in South Korea, which started from a pure Internet company selling audio electronic items and expanded into offline stores, successfully taking advantage of the high level of trust among online customers. MISSHA (http://www.beautynet.co.kr), mentioned earlier, is a case of another Type 4 business which is very successful and highly praised in various massmedia in South Korea. The authors feel that Type 4-related successes will most likely be found in those societies having high speed and broadband Internet infrastructure, and showing a mature stage of e-commerce, like South Korea, Hong Kong, etc. Further implications of the empirical results of this study include the following two issues. Issue 1: The Role of Offline Trust in Determining a Customer’s Online Behavior 28 As already discussed, offline trust plays a crucial role in determining consumer online behavior. In the case of online banking, offline trust directly affects four key related online constructs. The flow customers experience on banking websites is especially influenced by offline trust, as it has a 0.72 path coefficient. Customers’ website satisfaction increases when offline trust is greater. Similarly, customers’ perceived extent of use is affected by offline trust, with a 0.64 path coefficient. The latter is especially striking since neither structural assurance nor perceived satisfaction affected perceived extent of use. Offline trust also influences customers’ structural assurance beliefs about the safety and security of the bank’s online website. Perceived website satisfaction is relatively less affected by issues of offline trust than other online constructs. As noted in [69], many factors influence perceived website satisfaction. Offline trust, therefore, is only part of the answer. Likewise, many factors affect the adoption of Internet banking [39], which indicates a need for much additional research. Internet marketing strategists have struggled to understand why various strategies prove effective on some websites and ineffective on others. Obviously, the transfer of offline trust to online channels should be given more attention by researchers and marketers before online marketing strategies are developed. Research should explore factors that facilitate transfer of offline trust to online website adoption. Issue 2: How to secure trust in man-machine website interactions One characteristic of the online channel is the tendency for customers to perform many human-computer interactions (“click-and-see” activities). Websites offer a hypermedia environment which is made up of text, images, voice, and animation, leading to an enriched environment for human-machine interactions. Nevertheless, the issue is whether consumers trust the information they get from the websites. Conventional wisdom is that the more users understand how the information originates, the more online trust increases [68]. Since websites provide a rather enriched hypermedia environment for customers, using the websites would probably secure online trust to some extent. However, we still need to turn to the importance of offline trust to understand how consumers feel secure about a website. There have been no studies thus far to clearly argue that offline trust is the key to predicting online behavior. Although a certain level of human-computer interaction is necessary to navigate a website, the results of this study suggest that mere 29 management of the websites will not lead to the hoped-for results without the proper degree of offline trust. VII. CONCLUDING REMARKS This study’s results show that trust in an offline bank influences key factors in an online bank environment. Specifically, this paper contributes by showing that trust in an offline bank influences structural assurance, flow, consumer satisfaction and extent of use of the bank’s online system. In an era in which many companies are turning to the Internet as a way to expand their business, this study indicates that firms can leverage customer trust in their brick-and-mortar business to provide a similar customer-satisfying product line on the Internet. The extent to which offline trust affected online perceptions suggests that trust in the offline business may be a key factor of online business success. There are further research issues that need to be addressed, including the link between offline trust in the bank and trust in its online banking system. This study addressed offline/online banking. Other types of businesses should be studied to see the extent to which TTP works in other domains besides banking. Also, it is important to understand the psychological mechanisms by which the offline to online trust transfer process operates. It is hoped that this study will provide a stepping stone to building more effective marketing strategies for e-commerce. ACKNOWLEDGMENT This paper was supported by Samsung Research Fund, Sungkyunkwan University, 2003. REFERENCES [1] S. L. Ahire, D. Y. Golhar and M. A. Waller, "Development and valuation of quality management implementation constructs," Decision Sciences, vol. 27, no. 1, pp. 3-56, 1996. [2] A. M. Aladwani, "Internet banking: A field study of drivers, development challenges, and expectations," International Journal of Information Management, vol. 21, no. 3, pp. 213-225, 2001. 30 [3] J. C. Anderson and D. W. Gerbing, "Some methods for respecifying measurement models to obtain unidimensional construct measures," Academy of Management Review, vol. 19, no. 3, pp. 47-59, 1982. [4] E. M. Awad, "The structure of e-commerce in the banking industry," Proceedings of the 2000 ACM SIGCPR Conference on Computer Personnel Research, pp. 144-150, 2000. [5] S. Ba and P. A. Pavlou, "Evidence of the effect of trust building technology in electronic markets: price premiums and buyer behavior," MIS Quarterly, vol. 26, no. 3, pp. 243-268, 2002. [6] J. B. Baty and R. M. Lee, "Intershop: enhancing the vendor/customer dialectic in electronic shopping," Journal of Management Information Systems, vol. 11, no. 4, pp. 9-31, 1995. [7] J. R. Bettman, "Relationship of information-processing attitude structures to private brand purchasing behavior," Journal of Applied Psychology, vol. 59, pp. 79-83, February 1974. [8] A. Bhattacherjee, "An empirical analysis of the antecedents of electronic commerce service continuance," Decision Support Systems, vol. 32, no. 2, pp. 201-214, 2001. [9] K. A. Bollen, Structural Equations with Latent Variables, New York: John Wiley, and Sons, 1989. [10] A. Boomsma, K. G. In, Jorekog and H. Wold (eds.), The robustness of LISREL against Small Sample Sizes in Factor Analysis Models, in Systems under Indirect Observation: Causality, Structure, Prediction, Amsterdam, North Holland, pp. 149-173, 1982. [11] W. Boulding, A. Kalra, R. Staelin and V. A. Zeithaml, "A dynamic process model of service quality: From expectations to behavioral intentions," Journal of Marketing Research, vol. 30, pp. 7-27, February 1993. [12] M. C. Campbell and R. C. Goodstein, "The moderating effect of perceived risk on consumers’evaluations of product incongruity: Preference for the norm," Journal of Consumer Research, vol. 28, pp. 439-449, December 2001. [13] G. Chakraborty, V. Lala and D. Warren, "An empirical investigation of antecedents of B2B websites’ effectiveness," Journal of Interactive Marketing, vol. 16, no. 4, pp. 51-72, 2002. [14] A. Chaudhuri and M. B. Holbrook, "The chain of effects from brand trust and brand affect to brand performance: The role of brand loyalty," Journal of Marketing, vol. 65, pp. 81-93, April 2001. [15] Cheskin Research, eCommerce Trust Study, January 1999. [16] G. A. Jr. Churchill and C. Surprenant, “An investigation into the determinants of customer satisfaction,” Journal of Marketing Research, vol. 19, pp. 491-504, November 1982. 31 [17] J. Conklin, "Hypertext: An introduction and survey," IEEE Computer, vol. 20, no. 9, pp. 1741, 1987. [18] A. D. J. Cooke, T. Meyvis and A. Schwartz, "Avoiding future regret in purchase-timing decisions," Journal of Consumer Research, vol. 27, pp. 447-459, March 2001. [19] A. T. Coughlan, E. Anderson, L. Stern and A. El-Ansary, Marketing Channels. Prentice Hall, NJ: Englewood Cliffs, 2001. [20] M. Csikszentimihalyi, Flow: The Psychology of Optimal Experience, New York: Harper & Row Publishers, 1990. [21] W. DeLone and E. McLean, "Information systems success: The quest for the dependent variable," Information Systems Research, vol. 3, no. 1, pp. 60-95, 1992. [22] A. R. Dennis, "Lessons from three years of web development," Communications of the ACM, vol. 4, no. 7, pp.112-113, 1998. [23] S. Devaraj, M. Fan and R. Kohli, "Antecedents of B2C channel satisfaction and preference: Validating e-commerce metrics," Information Systems Research, vol. 13, no. 2, pp. 316-333, September 2002. [24] R. R. Dholakia and B. Sternthal, “Highly Credible Sources: Persuasive Facilitators or Persuasive Liabilities?” Journal of Consumer Research, vol. 3, pp. 223–232, March 1977. [25] P. M. Doney and J. P. Cannon, "An examination of the nature of trust in buyer-seller relationships," Journal of Marketing, vol. 61, pp. 35-51, April 1997. [26] G. R. Dowling and R. A. Staelin, "Model of perceived risk and intended risk activity," Journal of Consumer Research, vol. 21, pp. 119-134, June 1994. [27] R. F. Dwyer, P. H. Schurr and S. J. Oh, "Developing buyer seller relationships," Journal of Marketing, vol. 51, pp. 11-27, April 1987. [28] J. Eighmey and L. McCord, "Adding value in the information age: Uses and gratifications of sites on the world wide web," Journal of Business Research, vol. 41, no. 3, pp. 187-194, 1998. [29] C. R. Fornell and D. F. Lacker, "Two structural equation models with unobservable variables and measurement error," Journal of Marketing Research, vol. 18, no. 1, pp. 3950, 1981. [30] S. Ganesan, "Determinants of long-term orientation in buyer-seller relationships," Journal of Marketing, vol. 58, pp. 1-19, April 1994. [31] E. Garbarino and M. S. Johnson, "The different roles of satisfaction, trust, and commitment in customer relationships," Journal of Marketing, vol. 63, pp. 70-87, April 1999. 32 [32] A. W. Gatian, "Is user satisfaction a valid measure of system effectiveness?," Information & Management, vol. 26, pp. 119-131, 1994. [33] D. Gefen, "Customer loyalty in e-commerce," Journal of the AIS, vol. 3, pp. 27-51, 2002. [34] D. Gefen, E. Karahanna and D. W. Straub, "Trust and TAM in online shopping: an integrated model," MIS Quarterly, vol. 27, no. 1, pp. 51-90, 2003. [35] D. Gefen, E. Karahanna and D. W. Straub, "Inexperience and experience with online stores: The importance of TAM and trust," IEEE Transactions on Engineering Management, vol. 50, no. 3, pp. 307-321, 2003. [36] D. Gefen, D. W. Straub and M. C. Boudreau, "Structural equation modeling and regression: Guidelines for research practice," Communications of the Association for Information Systems, vol. 4, no. 7, pp. 1-70, 2000. [37] J. A. Ghani, R. Supnick and P. Rooney, "The experience of flow in computer-mediated and in face-to-face groups," Proceedings of the 12th International Conference on Information Systems, pp. 229-327, 1991. [38] A. M. Glenberg and W. E. Langston, "Comprehension of illustrated text: Pictures help to build mental models," Journal of Memory and Language, vol. 31, no. 2, pp. 129-151, 1992. [39] S. Gopalakrishnan, J. D. Wischnevsky and F. Damanpour, "A multilevel analysis of factors influencing the adoption of Internet banking," IEEE Transactions on Engineering Management, vol. 50, no. 4, pp. 413-426, November 2003. [40] M. Gupta, A. R. Chaturvedi, S. Mehta and L. Valeri, "The experimental analysis of information security management issues for online financial services," Proceedings of the 21st International Conference on Information Systems, pp. 667-675, 2000. [41] J. F. J. Hair, R. E. Anderson, R. L. Tatham and W. C. Black, Multivariate Data Analysis with Readings, Prentice Hall, NJ: Englewood Cliffs, 1998. [42] L. C. Harris and M. M. H. Goode, “The four levels of loyalty and the pivotal role of trust: a study of online service dynamics,” Journal of Retailing, vol. 80, pp. 139-158, 2004. [43] P. Hart and C. Saunders, "Power and trust: critical factors in the adoption and use of electronic data interchange," Organizational Science, vol. 8, no. 1, pp. 23-42, 1997. [44] D. L. Hoffman and T. P. Novak, "Marketing in hypermedia computer-mediated environments: Conceptual foundations," Journal of Marketing, vol. 60, pp. 50-68, July 1996. [45] D. L. Hoffman, T. P. Novak and M. Peralta, "Building consumer trust online," Communications of the ACM, vol. 42, no. 4, pp. 80-85, 1999. 33 [46] J. G. Holmes, "Trust and the appraisal process in close relationships," In W. H. Jones and D. Perlman (eds.) Advances in Personal Relationships, London: Jessica Kingsley, vol. 2, pp. 57-104, 1991. [47] D. Hutchinson and M. Warren, “Security for Internet banking: A framework,” Logistics Information Management, vol. 16, no. 1, pp. 64-73, 2003. [48] D. Iacobucci and J. D. Hibbard, "Toward an encompassing theory of business marketing relationships (BMRs) and interpersonal commercial relationships (ICRs): An empirical generalization," Journal of Interactive Marketing, vol. 13, no. 3, pp. 13-33, 1999. [49] S. L. Jarvenpaa and P. A. Todd, "Consumer reactions to electronic shopping on the world wide web," International Journal of Electronic Commerce, vol. 1, no. 2, pp. 59-88, 1997. [50] S. L. Jarvenpaa, N. Tractinsky and M. Vitale, "Consumer trust in an Internet store," Information Technology and Management, vol. 1, no. 12, pp. 45-71, 2000. [51] C. Jayawardhena and P. Foley, "Changes in the banking sector - The case of internet banking in the UK," Internet Research, vol. 10, no. 1, pp. 19-31, 2000. [52] P. N. Johnson-Laird, Mental Models, In Foundations of Cognitive Science, Posner, M. I. (ed.), Cambridge, MA: MIT Press, pp. 469-499, 1989. [53] D. Kahneman and D. T. Miller, "Norm theory: Comparing reality to its alternatives," Psychological Review, vol. 93, pp. 136-153, April 1986. [54] K. L. Keller, "Brand synthesis: The multidimensionality of brand knowledge," Journal of Consumer Research, vol. 29, pp. 595-600, March 2003. [55] M. Koufaris, "Applying the technology acceptance model and flow theory to online consumer behavior," Information Systems Research, vol. 13, no. 2, pp. 205-223, 2002. [56] N. Kumar, "The power of trust in manufacturer-retailer relationships," Harvard Business Review, vol. 74, no. 6, pp. 93-106, 1996. [57] N. Kumar, L. K. Scheer and J. E. M. Steenkamp, "The effects of perceived interdependence on dealer attitudes," Journal of Marketing Research, vol. 32, pp. 348-356, August 1995. [58] M. K. O. Lee and E. Turban, "A trust model for consumer internet shopping," International Journal of Electronic Commerce, vol. 6, no. 1, pp. 75-91, 2001. [59] S. Liao, Y. P. Shao, H. Wang and A. Chen, "The adoption of virtual banking: An empirical study," International Journal of Information Management, vol. 19, no. 1, pp. 63-74, 1999. [60] Z. Liao and M. T. Cheung, "Internet-based e-banking and consumer attitudes: An empirical study," Information & Management, vol. 39, pp. 283-295, 2002. 34 [61] S. Lindskold, "Trust development, the GRIP proposal and the effects of conciliatory acts on conflict and cooperation," Psychological Bulletin, vol. 85, no. 4, pp. 772-793, 1978. [62] G. L. Lohse and P. Spiller, "Electronic shopping," Communications of the ACM, vol. 41, no. 7, pp. 81-87, 1998. [63] R. F. Lorch and E. O'Brien, Sources of coherence in reading, NJ: Erlbaum, 1995. 10. [64] N. Luhmann, Trust and Power, New York: Wiley, 1979. [65] X. Luo, "The performance implications of contextual marketing for electronic commerce," Journal of Database Marketing, vol. 10, no. 3, pp. 231-239, 2003. [66] A. J. Lurigio and J. S. Carroll, "Probation officers’ Schemata of offenders: content, development, and Impact on Treatment Decisions," Journal of Personality and Social Psychology, vol. 48, no. 5, pp. 1112-1126, 1985. [67] R. C. Mayer, J. H. Davis and F. D. Schoorman, “An integrative model of organizational trust,” Academy of Management Review, vol. 20, no. 3, pp.709-734, July 1995. [68] D. L. McGuinness and P. P. da Silva, Trusting answers on the web. Working Paper, Knowledge Systems Laboratory, Stanford University, 2003. (http://www.ksl.stanford.edu/ people/pp/papers/McGuinness_QA_2003.pdf) [69] V. McKinney, K. Yoon and F. M. Zahedi, “The measurement of web-customer satisfaction: An expectation and disconfirmation approach,” Information Systems Research, vol. 13, no. 3, pp. 296-315, September 2002. [70] D. H. McKnight and N. L.Chervany, “What trust means in e-commerce customer relationships: an interdisciplinary conceptual typology,” International Journal of Electronic Commerce, vol. 6, no. 2, pp. 35-53, 2002. [71] D. H. McKnight, V. Choudhury and C. Kacmar, “Developing and validating trust measures for e-commerce: An integrative typology,” Information Systems Research, vol. 13, no. 3, pp. 334-359, 2002. [72] D. H. McKnight, L. L. Cummings and N. L. Chervany, “Initial trust formation in new organizational relationships,” Academy of Management Review, vol. 23, no. 3, pp. 472490, 1998. [73] G. R. Milne and M. Boza, “Trust and concern in consumers’ perceptions of marketing information management practices,” Journal of Interactive Marketing, vol. 13, no. 1, pp. 524, 1999. [74] C. Moorman, R. Deshpande and G. Zaltman, “Factors affecting trust in market research relationship,” Journal of Marketing, vol. 57, pp. 81-101, January 1993. 35 [75] R. M. Morgan and S. D. Hunt, “The commitment-trust theory of relationship marketing,” Journal of Marketing, vol. 58, pp. 20-38, July 1994. [76] L. Moutinho and A. Smith, “Modeling bank customer satisfaction through mediation of attitudes towards human and automated banking,” International Journal of Bank Marketing, vol. 18, no. 3, pp. 124-134, 2000. [77] Mullen, M.R., “Diagnosing measurement equivalence in cross-national research”, Journal of International Business Studies, 3rd Quarter, 1995, pp. 573-596. [78] R. L. Oliver, “A cognitive model of the antecedents and consequences of satisfaction decision,” Journal of Marketing Research, vol. 17, pp. 460-469, November 1980. [79] J. W. Palmer and D. A. Griffith, “An emerging model of web site design for marketing,” Communications of the ACM, vol. 41, no. 3, pp. 45-51, 1998. [80] P.A. Pavlou, “Institution-based trust in interorganizational exchange relationships: the role of online B2B marketplaces on trust formation,” Journal of Strategic Information Systems, vol. 11, pp. 215-243, March/April 2002. [81] P.A. Pavlou and D. Gefen, “Building effective online marketplaces with institution-based trust,” 23rd International Conference on Information Systems, pp. 667-675, 2002. [82] H. Perks and S. V. Halliday, “Sources, signs and signaling for fast trust creation in organisational relationships,” European Management Journal, vol. 21, no. 3, pp. 338-350, June 2003. [83] J. P. Peter and J. C. Olson, Consumer Behavior, Chicago: Irwin, 2001. [84] R. E. Plank, D. A. Reid and E. B. Pullins, “Perceived trust in business-to-business sales: A new measure,” Journal of Personal Selling & Sales Management, vol. 19, pp. 61-71, Summer 1999. [85] T. A. Powell, Web Site Engineering: Beyond Web Page Design, Prentice-Hall, NJ: Englewood Cliffs, 1998. [86] J. A. Quelch and L. R. Klein, “The internet and international marketing,” Sloan Management Review, vol. 37, pp. 60-75, Spring 1996. [87] J. A. Quelch and H. Takeguchi, “Nonstore marketing: Fast track or slow ?,” Harvard Business Review, vol. 59, no. 4, pp. 75-84, 1981. [88] S. N. Ramaswami, T. J. Strader and K. Brett, “Determinants of on-line channel use for purchasing financial products,” International Journal of Electronic Commerce, vol. 5, no. 2, pp. 95-118, Winter 2000-2001. 36 [89] P. S. Ring and A. H. Van de Ven, “Developmental processes of cooperative interorganizational relationships,” Academy of Management Review, vol. 19, pp. 90-118. 1994. [90] L. Rosenfeld and P. Morville, Information Architecture for the World Wide Web, Sebastopol, CA: O’Reilly, 1998. [91] S. Rotchanakitumnuai and M. Speece, “Barriers to Internet banking adoption: a qualitative study among corporate customers in Thailand,” International Journal of Bank Marketing, vol. 21, no. 6/7, pp. 312-323, 2003. [92] M. Sathye, “Adoption of internet banking by Australian consumers: An empirical investigation,” International Journal of Bank Marketing, vol. 17, no. 7, pp. 324-334, 1999. [93] A. H. Segars and V. Grover, “Re-examining perceived ease of use and usefulness: A confirmatory factor analysis,” MIS Quarterly, vol. 17, no. 4, pp. 517-525, 1993. [94] B. Shneiderman, Designing the User Interface: Strategies for Effective Human-Computer Interaction, 3rd edition, Addison Wesley, 1998. [95] J. B. Smith and D. W. Barclay, “The effects of organizational differences and trust on the effectiveness of selling partner relationships,” Journal of Marketing, vol. 61, pp. 3-21, January 1997. [96] K. J. Stewart, “Trust transfer on the world wide web,” Organization Science, vol. 14, pp. 513, January/February 2003. [97] D. W. Straub, “Validating instruments in MIS research,” MIS Quarterly, vol. 13, no. 2, pp. 147-169, 1989. [98] B. Suh and I. Han, “The impact of customer trust and perception of security control on the acceptance of electronic commerce,” International Journal of Electronic Commerce, vol. 7, no. 3, pp. 135-161, 2003. [99] Sutton, R. I. and Staw, B. M. “What theory is not,” Administrative Science Quarterly (40), 1995, pp. 371-384. [100] M. Tan and T. S. H. Teo, “Factors influencing the adoption of Internet banking,” Journal of the Association for Information Systems, vol. 1, no. 5, pp. 1-42, 2000. [101] M. Thuring, J. Hanneman and J. M. Haake, “Hypermedia and cognition: Designing for comprehension,” Communications of the ACM, vol. 38, no. 8, pp. 57-66, 1995. [102] L. K. Trevino and J. Webster, “Flow in computer-mediated communication-electronic mail and voice mail evaluation and impacts,” Communication Research, vol. 19, no. 5, pp. 539573, 1992. 37 [103] G. L. Urban, F. Sultan and W. J. Qualls, “Placing trust at the center of your internet strategy,” Sloan Management Review, vol. 42, no. 1, pp. 39-48, Fall 2000. [104] J. Webster, L. K. Trevino and L. Ryan, “The dimensionality and correlates of flow in humancomputer interaction,” Computer. Human Behavior, vol. 9, no. 4, pp. 411-426. 1993. [105] M. Wolfinbarger and M. C. Gilly, “Shopping online for freedom, control, and fun,” California Management Review, vol. 43, no. 2, pp. 34-55, 2001. [106] S. J. Yoon, “The antecedents and consequences of trust in online-purchase decisions,” Journal of Interactive Marketing, vol. 16, no. 2, pp. 47-63, 2002. [107] D. Zhu, “Security control in inter-bank fund transfer,” Journal of Electronic Commerce Research, vol. 3, no. 1, pp. 15-22, 2002. [108] L.G. Zucker, “Production of trust: institutional sources of economic structure, 1840-1920,” Research in Organizational Behavior, vol. 8, pp. 53-111, 1986. 38