Examining the Channels to Form Initial Online Trust 1)

Transcription

1 Asian Journal of Information and Communications 2014, Vol. 6, No. 2, ) Examining the Channels to Form Initial Online Trust 1) Yunjie (Calvin) Xu Fudan University Shun Cai Xiamen University Hee-Woong Kim* Yonsei University Abstract Customers trust in an online seller is considered to be an important factor for e- commerce success. The extant research has addressed the question of what online trust is and how some factors such as website usability are helpful to trust building. However, the question of where customers collect information about an online firm to base their trust belief and intention on has not been addressed. In this study, we investigate various information channels through which evidences are collected and trust belief is formed. A framework of four information channels is proposed based on social learning theory. We apply this framework to the initial online trust building for brick-and-click firms. Our results suggest that the social learning theory is a viable tool to understand different channels through which a customer s trust is built. Keywords: Initial online trust, trust building process, social learning theory, brick and click strategy 1. Introduction According to U.S department of commerce, the third quarter e-commerce sales for 2005 reached $957.9 billion, an increase of 26.7 percent from the same quarter of For e-commerce to continually succeed, buyer s trust in the seller is argued to play a pivotal role (Gefen, 2003; Kim et al. 2012). Evidence has confirmed that trust is a significant predictor of the customer s online purchase intention, personal information disclosure, and use of services offered by the vendor (Bhattacherjee, 2002; Gefen, 2003; Grazioli & Jarvenpaa, 2000; McKnight et al., 2002a). Although trust in vendors has been extensively studied since the start of e-commerce, recently, initial online trust (IOT) has gained increasing interest in information systems (IS) research community (e.g., Gefen, 2003). IOT is the trust a potential customer places on the online vendor before she engages in any trust behavior such as online transaction. IOT is crucial because many claimed e-commerce benefits such as reaching customers in a region where a company does not have local presence are arguably dependent on IOT. Previous research (Gefen, 2003; Grazioli & Jarvenpaa, 2000; McKnight et al., 2002a, 2002b) has identified several factors (e.g., reputation, size, the look of the website) as antecedents of online trust. Nonetheless, one question remains: What are the channels through which customers get to know the online vendor and build their trust? The extant research has recognized that trust building is a psychological process. In this process, evidence about a firm, the trustee, is interpreted with respect to the goodwill of the firm, probability of opportunistic * Author for Correspondence, kimhw@yonsei.ac.kr 1) This work was supported by the National Research Foundation of Korea Grant funded by the Korean Government(NRF-2012 S1A3A )

2 Asian Journal of Information and Communications 7 behavior by the firm, and its ability to fulfill obligations (Doney & Cannon, 1997, 1998). If such reasoning favors the conclusion that a firm can and will fulfill its obligations, the customer s trust is formed. But where does the evidence come from, especially in the case of IOT? Bigley and Pearce (1998) assert that trust evidence is context-dependent and varies at different stages of the dyadic relationship. For example, trust in government and trust in family members are based on different evidence (Barber, 1983). Likewise, trust in a stranger and trust in a long-standing friend manifest for different reasons (Bigley & Pearce 1998). However, it is not clear where a trustor collects such evidence. The lack of a systematic approach to identifying trust antecedents is reflected in online trust studies. With a focus on the pure-play online company alone, many trust building antecedents such as reputation-like signs (Pavlou & Gefen, 2004), web design factors (Belanger et al., 2002), and online service quality (Gefen, 2002) have been identified. However, these antecedents may not generalize to other contexts. For example, in the brick-and-click context, customers who have prior experience with the firm s offline channel and those who do not will have different knowledge of the web store, and consequently, different reasons to trust. It is important to have a systematic way to identify trust antecedents in various contexts. Such systematic way should be relatively context-independent. To this end, we propose a trust building framework based on social learning theory (SLT). We consider trust building to be a learning process. Four channels are identified through which a trustor collects evidence. To demonstrate the utility of the framework, we apply it to the IOT building for brick-and-click firms because it is an important yet under-investigated area. In search for online-offline synergy, the transference of trust from the offline brand or the company namesake to the online establishment is identified as one of the potential benefits (Willcocks & Plant, 2001). However, few empirical studies have investigated this phenomenon. Applying our proposed SLT-based framework to this context, our research question is: What are the different channels through which customers IOT is formed for a brick-and-click firm and what are their impacts? Answers to these questions can help a firm to better manage their trustworthiness through the management of customers learning channel. 2. TRUST AND TRUST BUILDING 2.1 Trust and Initial Online Trust (IOT) Trust is generally defined as the willingness to be vulnerable when the trustor is dependent on the trustee for some resources or actions (Mayer et al., 1995). Willingness to be vulnerable however does not reflect actual behavior, but rather the intention. McKnight et al. (2002a) further delineate the differences among trust belief, trust intention, and trust behavior. It is the trust intention that Mayer et al. define as the willingness, while the trust belief is really the perceived trustworthiness of the trustee, i.e., the ability, benevolence and integrity, as Mayer et al. (1995) summarize them. Ability, benevolence, and integrity constitute the three major dimensions of trust (Mayer et al., 1995, McKnight et al., 2002a). Accordingly, we define trustworthiness as trust belief, i.e., the perceived ability, benevolence, and integrity of the trustee, and trust intention the willingness to be vulnerable when a trustor depends on a trustee for certain resource or action. We use the term trust as an overarching concept that covers both belief and intention. Trust is context-dependent and dynamic. As Barber (1983, pp.16-17) points out, whether we have in mind expectations of the persistence of the moral social order, expectations of technically competent performance, or expectations of fiduciary responsibility, we must always specify the social relationship or social system of reference. The context-dependent nature of trust implies not only the emphasis placed on different aspect of

3 8 Yunjie (Calvin) Xu, Shun Cai, Hee-Woong Kim trustworthiness, but also different evidences people use to construe their trust. Furthermore, trust is by no means a static concept (Tyler & Kramer, 1995; Bigley & Pierce, 1998). A theory of trust presupposes a theory of time (Luhmann, 1979, p. 10). Accordingly, the antecedents of trust may also vary at different stages of the relationship. IOT is the initial stage of a customer s trust in a web store. It inherits the essential nature of general trust. Nevertheless, materializing at this specific stage, and in such a specific Internet context, IOT has its special properties as well. Surprisingly, no robust definition of IOT has emerged. In the context of an offline firm, McKnight et al. (1998, p. 473) define initial trust as the trust that surface when parties first meet or interact. McKnight et al. (1998, p. 474) further argue that initial trust between parties will not be based on any kind of experience with, or firsthand knowledge of, the other party but rather on an individual s disposition to trust or on institutional cues that enable one person to trust another without firsthand knowledge. Likewise, Bigley and Pierce (1998, p. 410) refer to initial trust as the trust in an unfamiliar trustee, a relationship in which the actors not yet have credible, meaningful information about, or affective bonds with, each other. In the online environment, IOT is characterized as the trust a potential customer has on an online vendor when she has no prior purchase experience with that vender (McKnight et al., 2002a). Nonetheless, extant literature does not clearly divulge when initial trust starts or what kind of knowledge a trustor may have before this interaction. These issues are relevant because in the online context, several scenarios could emerge. For instance, some customers of a brick-and-click firm may have offline experience with it, while other may not. Winning trust of a first time online customer without offline experience may require a different strategy than convincing an existing offline customer to buy online. Differentiating customers based on their knowledge is the first step to understand their trust. Against this backdrop, in this study, we define IOT as the trust belief and intension that a potential customer holds towards an online vendor before the first purchase; hence, IOT is pre-purchase and has both a belief and an intention component. We recognize that before purchase, customers can have different types of knowledge of the vendor. Because of such difference, we resort to the social learning theory for a systematic way to categorize different trust building processes of trustors. 2.2 Trust Building Processes How is trust belief, and especially initial trust, formed? Our review of the literature reveals at least two different perspectives on trust development: the context-dependent perspective and the psychological process perspective. In considering the context-dependent perspective, Bigley and Pearce (1998) argue that trust can differ between unfamiliar/familiar actors, and in the organizations of different nature. The signs and evidence used to build trust are assumed to be different across these situations. The most systematic treatment of trust context is probably the conceptualization of institution-based trust that highlights the importance of institutional context on trust building. Built on the history of the U.S. economy, Zucker (1986) suggests that institutionalization is both a product of trust and a platform to develop trust. Such a view is widely echoed by other researchers in both theoretical discussion and empirical studies (Barber, 1983; McKnight et al., 1998, Mayer et al., 1995). Besides institution-based trust, strategies for identifying context-specific trust antecedents have not emerged. In summary, the context-dependent view tries to answer the question what are the context-specific factors that affect trust building? Nonetheless, this current perspective does not offer a systematic approach to identifying those factors for different contexts. In highlighting the importance of the psychological trust building perspective, Doney and Cannon (1997, 1998) identify five cognitive processes of trust belief formation. In the calculus process, a trustor calculates the costs and/or rewards for another party to be opportunistic. In the prediction process, the trustor forecasts

4 Asian Journal of Information and Communications 9 another party s behavior from historical data. The capability process gauges another party s ability to meet its obligations while the intentionality process assesses the other party s goodwill in the exchange. Finally, in the transference process, the trustor relies on a third party s definition of another as a basis for defining trust. Most of these processes attempt to explain the formation of trust belief after some individuating evidences have been collected and to answer the question how is the trust evidence interpreted? Figure 1: The overall trust building process Channel A Evidences Psychological reasoning Trust belief Different contexts Channel B Integration Overall trust Evidences Psychological reasoning Trust belief The strength of the prior trust building research is the recognition of the context-dependent nature of trust and the psychological reasoning behind trust formation. However, they have some limitations. While the psychological perspective explains how certain signals are processed to predict trustworthiness, it is silent on where such signals are obtained. The context-dependent perspective recognizes the specific nature of each context, but derives no commonalities among different contexts, and therefore offers no guidelines to analyze new contexts. In summary, the current literature does not answer a key question, through which channels do the trustor get to know the trustee? Answering this question is in fact critical if we are to offer practitioners guidance on how to manage their trustworthiness in the eyes of trustors. To this end, this study proposes a social learning theory (SLT)-based framework to studying trust building. The SLT proposes a set of general learning channels through which one forms belief of an object. Based on it, for a given context, the evidence-collecting channels can be identified for trust building. The evidences so collected then undergo psychological reasoning as suggested by Doney and Cannon (1997) to produce trust beliefs specific to that channel. Such trust beliefs then collectively affect the overall trust belief (Figure 1). Therefore, the proposed learning channels complement the trust building processes identified in the literature to produce an integrated picture of trust building. 3. TRUST BUILDING FROM THE SOCIAL LEARNING PERSPECTIVE From the social learning theory (SLT) perspective, perceived trustworthiness, including trust belief in IOT, can be regarded as expectancy. Rotter s (1954) SLT defines expectancy as probability held by the individual that a particular reinforcement will occur as a function of a specific behavior on this part in a specific situation (Rotter, 1954, p.107). Rotter (1980a) characterizes interpersonal trust belief as a kind of general expectancy held by an individual that the word, promise, oral or written statement of another individual or group can be relied upon. General expectancy is formed based on experience from many similar situations. Trust belief in

5 10 Yunjie (Calvin) Xu, Shun Cai, Hee-Woong Kim a specific situation is recognized and regarded as a kind of specific expectancy (Rotter, 1980a). The view of trustworthiness as expectancy is echoed in the trust literature. According to Mayer et al. (1995, p.712), trust belief is the expectation that other will perform a particular action. Likewise, many others (Doney & Cannon, 1997; Schurr & Ozanne, 1985) typify trust belief as the expectation that an individual can be relied on to fulfill obligations. Treating trustworthiness as expectancy enables us to examine trust building in general, and IOT building in particular from the SLT perspective. Because SLT is a well-established theory in psychology, and because the expectancy formation is one of its main themes, many insights can be drawn from it when we examine trustworthiness. Rotter s SLT explains why a person chooses a certain course of action when she has a number of possible alternatives available. There are four basic concepts in Rotter s theory (Rotter, 1954): 1. Behavior potential is the likelihood for any given behavior to occur. 2. Expectancy considered the central concept of SLT, is the probability that a particular reinforcement will occur as a function of a specific behavior in a particular situation. 3. Reinforcement value-is the degree of preference for various reinforcements, if the probability of their occurring were all equal. 4. Psychological situation-emphasizes that all the above factors are subjective perceptions. Rotter (1960) goes on to treat behavior potential as a function of expectancy and reward (i.e., reinforcement value). Although Rotter (1971, 1980a) subsequently applies this theory to trust propensity, further application of this theory in the trust domain has not been explored. Thus, we extend SLT to study the trust relationship between a buyer and a seller. In doing so, we consider the behavior potential as either to-buy or not-to-buy. The reward is the outcome of the purchase (e.g., product value, convenience, peace in mind). The corresponding expectancy can be regarded as the perceived trustworthiness of the seller in fulfilling its obligations. From SLT perspective, trust building process is essentially an expectancy formation process. While Rotter s SLT does not propose any specific channels for expectancy formation, Bandura s SLT addresses this issue (Bandura, 1977; Bandura, 1986). In Bandura s SLT, human behavior and its associated expectation can be formed through two ways: direct experience and modeling. Learning through direct experience requires the subject to be personally involved in the activity, and realize the consequences of her response, successful or punitive. Modeling is the process of learning by observing others responding to an environment and experiencing certain consequence. Modeling includes both vicarious learning (i.e., observing others) and symbolic learning (e.g. reading printed material). With respect to people s learning behavior, Bandura (1977) highlights that: By observing the different outcomes of their action, they develop hypotheses about which responses are most appropriate in which settings. This acquired information then serves as a guide for future action (p.17) Performance accomplishments provides the most dependable source of efficacy expectations because they are based on one s own personal experiences. Successes raise mastery expectations; repeated failures lower them, especially if the mishaps occur early in the course of events. (p.81) According to social learning theory, modeling influences produce learning principally through their informative function (p.23) Many expectations are derived from vicarious experience. Seeing others perform threatening activities without adverse consequences can create expectations in observers that they too will eventually succeed if they intensify and persist in their efforts (p.81) italics in original.

6 Asian Journal of Information and Communications 11 SLT is in fact not the only theory that proposes the dichotomy of direct and indirect learning. In referring to the social exchange theory, Blau (1964, p.143) comments that these expectations of social rewards are based on the past social experiences of individuals and on the reference standards they have acquired, partly as the result of the benefits they themselves have obtained in the past and partly as the result of learning what benefits others in comparable situations obtain. Relating to trust building, the trust transference (Doney & Cannon, 1997) might be regarded as a type of learning by modeling. Besides direct experience and modeling, learning can also occur from similar experiences. Rotter (1980a, p.2) posits that in social learning theory expectancy in each situation is determined not only by specific experiences in that situation but also, to some varying degree, by experiences in other situations that the individual perceives as similar italics in original. Experience with a specific situation produces specific expectancy, and the experiences with similar situations collectively form the general expectancy. Both specific expectancy and general expectancy can affect behavior. As Rotter (1971, p.445) highlights, in social learning theory, expectancy is a function of a specific expectancy, and a generalized expectancy resulting from the generalization from related experience. If we cross-combine these two aspects, namely the directness of experience and the specificity of situation, we can have four combinations: direct experience in the same situation, direct experience is a similar situation, modeling in the same situation, and modeling in a similar situation. These four learning channels constitute the SLT-based framework which we use to explain trust building. Based on these four channels, two types of learning outcomes emerge. First, some detailed beliefs about the situation (e.g. service quality, store environment etc.), which Rotter termed cognitions, can be learned in each channel. Such cognitions constitute what Rotter calls the psychological situation (Rotter, 1980b). Second, outcome expectancy is formed for each channel (e.g., Based on my offline experience, the offline store is trustworthy. ). Such expectancy can be vicarious when the experience is indirect (e.g., Based on his experience, my friend regards the online store as trustworthy. ). These two types of learning outcomes are related. The learned simple cognitions are evidences to from a more general expectancy of the channel. Such expectancies formed for each channel can be integrated to form a more general expectancy of the focal situation. Furthermore, the SLT posits that the expectancies from individual channel will be combined when one needs to form the expectancy of the focal object. This learning and integration process is illustrated in figure 1 for the formation of trust belief. 4. INITIAL ONLINE TRUST BUILDING FOR BRICK-AND-CLICK COMPANIES An advantage of the above framework is that it is content-independent. To demonstrate its utility, we apply it to IOT building for potential first-time customers of a brick-and-click firm. While it is possible to apply this framework to other contexts such as offline trust, the current context is interesting because past IOT studies have almost exclusively focused on pure-play online companies. Brick-and-click companies were largely neglected. The SLT-based framework implies that we should differentiate customers based on the learning channels available to them. Corresponding to the four learning channels, for potential online customers of a brick-and-click firm, the types of direct and vicarious experiences and their outcomes can be summarized as in Table 1.

7 12 Yunjie (Calvin) Xu, Shun Cai, Hee-Woong Kim Table 1: Initial online trust building Same situation Direct experience Browsing experience with the website Trust belief of the online store based on website Modeling Other s experience web store Other s experience with the online store, i.e., word-of-mouth of the online store Similar situation Purchase experience with the offline establishment Offline trust belief Purchase experience with other companies online Situational normality based on personal experience, structural assurance Other s evaluation of the offline establishment Word-of-mouth of the offline store Other s evaluation of the Internet environment Situational normality as perceived by others Structural assurance as perceived by others The SLT suggests that direct and/or vicarious trust beliefs can be produced through these channels, which collectively form an overall trust belief in the online store. Based on this framework, we propose the IOT building model for brick-and-click firms as in Figure 2. Potential customers, first of all, can have partial direct experience with the online firm. These customers could have browsed the website and searched for a product without actual purchase. In other words, their direct experience is limited to window shopping (Gefen, 2003). Based on such window shopping experience, customers can form cognitions of the website system quality, ease of use, information quality, sales policy, and product variety. Figure 2: Research model for brick-and-click firms Website-based trust belief WoM-based trust belief of online store Online trustworthiness Offline trust belief Situational normality We defined website-based trust belief as trust belief based the information and partial experience with a website. It is the initial learning outcome of the online channel. Albeit it is a partial experience, partial trust belief of the web store can be formed. First, the website quality as reflected in downloading speed, professional design, robust security setup, rich and high quality information, and ease of use requires substantial investment by the firm. Such investment can be regarded as a sunk-cost-type of signal from the trustee (Kirmani & Rao 2000). Regardless of future sales, the cost is incurred upfront. A confident store expects to recover this cost in future sales, while a less confident one may be better off not making such an investment, as suggested by the signaling

8 Asian Journal of Information and Communications 13 theory. Therefore, based on the calculus process as suggested by Doney and Cannon (1997), website quality provides a signal of the store s trustworthiness. Prior studies have found empirical support that such evidences contribute to online trust (Gefen, 2003; McKnight et al., 2002a; Kim et al., 2004). Besides the investment in systems, sales policies, privacy policies, and customer service promises are other clues that signal firm s trustworthiness. Such policies can be regarded as contracts between the seller and the buyer. Although formal contracts enforced by law serve as substitute of trust and may inhibit trust rather than enforcing it, informal contract, when initiated by one party, suggests good-will and future cooperation (Malhotra & Murnighan 2002). Most retail polices can be regarded as informal contracts, because they are not jointly constructed and legally binding. Overall, evidences collected from different aspects of a website help form a partial trust belief and such trust belief can contribute to the overall belief of the trustworthiness of the web store. We hypothesize: Hypothesis 1 (H1): For potential online customers, website-based trust belief is positively related to their overall trust belief in the firm s online presence. Potential customers can have personal experiences with the focal firm offline, which constitutes direct experience in a similar purchase situation. A satisfactory offline experience brings high offline trust (Ganesan, 1994; Garbarino & Johnson 1999). Such trust belief of the offline store is expected to be generalized to the online contexts because of the similarity of the situations. The SLT posits that the similarity of the problem provides the dimension for the generalization of expectances (Rotter, 1980b, p.304). Moreover, in their cases studies, Steinfield et al. (2002) find that for brick-and-click companies one key spillover effect is the improved online trust if the goals and the coordination between online and offline establishments can be aligned. Online trust is boosted because experience with local company reduces customer s risk perception and because of the embeddedness of company in the local social and business networks. Steinfield et al. (2002) give examples that illustrate when a local bookstore leverages its expertise to provide better services offline, its website attracts more users. Therefore, we posit that: Hypothesis 2 (H2): For potential online customers, offline trust belief is positively related to their overall trust belief in the firm s online presence. A major means for consumer learning is word-of-mouth. Word of mouth affects consumer purchase decision making and choice (Senecal & Nantel, 2004). Word of mouth affects consumer s choice for two reasons. First, word-of-mouth provides information of the seller. In addition to price information, the major content of word of mouth also include special sales, quality of merchandise, variety of products, availability of particular brands, helpfulness and friendliness of employees, and return policy in offline shopping. In online shopping, in addition to the above information, delivery speed, customer service, hidden charges, and web site quality are also covered in many online feedbacks. Obviously, these are detailed cognitions that manifest a seller s ability, benevolence, and integrity. Second, word-of-mouth also affects risk perception. It is well known that the purpose of consumer information seeking is to reduce risk (Senecal & Nantel, 2004). Word-of-mouth is more likely to be employed when the purchase is associated with un-contractible aspect which can only be evaluated through experience, or when the consumer has little prior knowledge of the seller (Sundaram & Webster, 1999). Therefore, word-of-mouth is a channel to collection information to form trust belief. For a brick-and-click company, word-of-mouth of the online store and of the offline store can be distinguished. Word-of-mouth of the offline store is likely to affect offline trust belief which in turn affects online trust. Since it is mediated, we focus on word-of-mouth of the online store only, which is the degree of positive evaluation of online shopping experience. Word-of-mouth of the online store can contribute to the overall online trust belief in two possible ways. First, other peoples comments about the website quality and

9 14 Yunjie (Calvin) Xu, Shun Cai, Hee-Woong Kim information quality might affect a customer s evaluation of the website, hence affect her website-based trust belief. This denotes a mediated relationship between word-of-mouth and the ultimate online trust belief. Second, other people may comment on product delivery, customer service, product quality and other aspects that might not directly relate to the evaluation of website, yet relate to the risk of online shopping in general. Therefore, word-of-mouth might have a direct impact to online trust belief directly. In either case, according to SLT, observation of other s behavioral consequence can serve as an antecedent of the observer s expectation when facing the same stimulus (Bandura, 1977). Word-of-mouth incorporates other s behavioral consequence. Akin to the transference process of trust building (Doney & Cannon, 1997), such vicarious experience is internalized by the observer and affects the observer s perception and behavior when facing the same situation. Therefore, when customers have heard of the firm s online practice, we posit that: Hypothesis 3 (H3): For potential online customers, the word-of-mouth of the online store is partially mediated by website-based trust belief in affecting their overall trust belief in the firm s online presence. Still another learning scenario can be potential customers purchase experience with other online firms, but not the focal one. Such experience with online stores in general can form what Rotter (1971) terms as the generalized expectancy in a context. McKnight et al. (2002a) define such generalized expectancy as situational normality which means the trustworthiness of online stores in general. In addition to the formation of perceived situational normality, online purchase experience helps build the perceived structural assurance which means that one believes that structures like guarantee, regulations, promises, legal resources, or other procedures are in place to promote success (McKnight et al., 2002b). Zucker (1986) considers situational normality and structural assurance to be two main aspects of institution-based trust. Prior empirical studies have shown that situational normality and structural assurance foster trust development (McKnight et al., 2002a, 2002b). However, in this study, we consider only the impact of situational normality. This is because structural assurance is a general perception of online environment, it affects both one s situational normality and the trust in a specific online store; it is therefore their common cause, or a confounding variable, as Cohen et al. (2003) term it. A confounding variable s impact on the dependent variable (online trust belief) is less predictable, because part of its impact is mediated by other variable (situational normality). According to SLT, situational normality is a belief based on trustor s experience with the environment beyond trustee, hence the channel of direct learning in similar situations. Although situational normality can also be learned in vicarious ways, i.e., it can be formed based on others online experience and others perception of situational normality, such indirect experience is likely incorporated into and modified by one s own experience with the internet. Because generalized expectancy can affect specific expectancy when the situation is novel (Rotter, 1980a), situational normality are expected to affect trust belief of a specific firm. Therefore: Hypothesis 4 (H4): For potential online customers, the situational normality based on direct experience is positively related to their overall trust belief in the firm s online presence. 5. METHODOLOGY 5.1 Instrument Development To empirically test the proposed model and hypotheses, we adopted the survey methodology. For all constructs in the model, we generated multiple measurement items either through reusing tested items in the

10 Asian Journal of Information and Communications 15 literature or creating new ones based on the conceptual discussion in the literature. Based on the hypotheses, subjects need to evaluate the trustworthiness of an online store (online trust belief) and its offline store (offline trust belief). Similar items were used for these two constructs. We based our items on the (Bhattacherjee, 2002) with six items that cover ability, benevolence and integrity of a store (refer to Appendix A for detailed items). The reliability of these items as reflected in Cronbach s alpha was 0.93 for offline trust and 0.94 for online trust in our study. Because situational normality is defined as the trustworthiness of online stores in general (McKnight et al., 2002a), we used the same set of trust items for situational normality. The reliability of situational normality was 0.85 in our study. Website-based trust belief is a new construct. We developed five new items that measure the benevolence, integrity, ability and general trustworthiness perception of the online store based on the experience of website alone. For example, to capture the ability aspect, we used The web site of store.com provides enough information and functions to facilitate smooth online purchase. The reliability of website-based trust belief was Finally, word-of-mouth effect captures the successfulness of other s experience with the online store. We developed five new items that capture other s satisfaction, confidence, and trust in the online store (e.g., She/he is happy with the online shopping experience, She/he seems to trust the online store. ). However, we anticipated that some subjects might not have heard about an online store from other sources, therefore, a binary choice was also given to indicate whether they had heard about or aware of the online store. Only those who were aware of the online store needed to answer the word-of-mouth items. The reliability of word-of-mouth was 0.93 for those who responded to the items. 5.2 Data Collection The survey was conducted in a major shopping mall in Singapore. A table with eight seats was set up at the main hallway of the shopping mall. When there were empty seats in the table, three assistants randomly invited passengers to participate. Posters were used to attract passengers. Participants were paid SGD$15 as an incentive, which was about an hour s wage for an average employee. The interested participants were screened to ensure that they had offline experience with the stores we used for the survey, and they used Internet at least for window shopping. When participants were invited, we first checked their knowledge of some local brick-and-click stores. We included two grocery stores, two book stores, a flower and gift store, an electronics store, and a free space for them to indicate other brick-and-click stores they were familiar with. They were assigned to the store that they had offline experience, but not online experience, and preferably had heard about it. After that, they were asked to evaluate the store with the questionnaire. Eight notebook computers were set up for them to brows the online stores, and the links to product categories, service policy and company information were explicitly given in a starting page so that they could easily locate necessary information even if they had no prior browsing experience. To avoid common method variance, two versions of the questionnaires were used, with one version starting with the browsing of the website and items for website-based trust belief, the other starting with offline trust, situational normality, structural assurance, and then the browsing and evaluation of the website. Participants took about 20 to 30 minutes to finish the survey. 5.3 Data Analysis In total, 183 responses were collected with 182 responses complete and usable. Table 2 reports the demographics of the sample. Overall, there were 74 (41%) males and 108 (59%) females in the survey, with an average age of 25 (SD=6.4). Although young, this sample has extensive Internet experience (Mean=7.3 yrs)

11 16 Yunjie (Calvin) Xu, Shun Cai, Hee-Woong Kim and online shopping experience (Mean=5.4 times). They also used Internet for window shopping or production comparison (Mean=4 times per week). Most of them evaluated a grocery store (83, 46%) or bookstore (86, 47%); only 13(7%) subjects evaluated other stores. Because there was no statistics available for the non-response subjects, sampling bias could not be evaluated. Table 2: Demographics Age Mean Age =25 (SD=6.4) Years using Internet 7.3 yrs (SD = 2.6) Gender M = 74 (41%) F = 108 (59%) Stores Grocery = 83 (67%) Bookstore = 86 (57%) Other = 13 (7%) Education Post Graduate = 22 (12%) Undergraduate = 52 (29%) Polytechnic = 74 (41%) High School = 15 (8%) Other = 19 (10%) Number of online purchases in the past year 5.4 (SD = 5.1) Window shopping per week 4 (SD = 4) Heard of the online store Yes = 125 (69%) No = 57 (31%) Browsed the online store before Yes = 113 (62%) No = 69 (38%) Fifty seven subjects had not heard about the online store they were to evaluate. If they were excluded in the analysis, the remaining sample would not only be much smaller, but also unrepresentative, because in reality, potential online customers of an online store are a mix of customers of different levels of knowledge. Therefore, we decided to keep those subjects, and use a dummy variable to indicate whether a subject had heard of the online store. Consequently, the existence of missing values for some subjects prevented us from using structural equation modeling. We adopted regression approach instead, with construct scores calculated as averages of items. Methodologist Cohen et al. (2003, p. 444) suggest that two options are available when a quantitative scale measure has missing value. The first option is to substitute in the average of observations for the missing ones. Such substitution will not change the regression coefficient for that variable, compared to the one produced with only observations with no missing value. However, it deflates the standard deviation of that variable because it has more homogeneous values now, that is, the significance of regression coefficient is inflated artificially. The second method is to substitute in that average and employ a dummy variable at the same time. In our case, it is to employ a dummy variable indicating whether a participant had heard about the website from other people. Including it [the dummy variable] in the regression equation then provides appropriate adjustments so that differences between the average value of X the variable with missing values and the estimated value given the other variables are taken into account (Cohen et al., 2003, p.444). In other words, the dummy variable helps to compensate for the deflation in the variance of the focal independent variable. Another advantage of using a dummy variable is that it can be informative of why an observation has missing data. Cohen et al. (2003) suggest using dummy variable when there is the slightest reason to suspect that the cases with absent data may be different from other cases. Therefore, we adopted the second method in our data analysis. Online trust belief (ONT) was regressed on offline trust belief (OFFT), situational normality (SN), website based trust (WBT), whether the customer had heard about and was aware of the website (AWARE), and the online store s word-of-mouth evaluation (WOM). ONT = β 0 + β 1 OFFT + β 2 SN + β 3 WBT + β 4 HO + β 5 WOM + ε The descriptive statistics of the main variables are reported in Table 3.

12 Asian Journal of Information and Communications 17 Table 3: Descriptive statistics and correlation Mean Stdev ONT WBT ** 3. SN ** 0.55** 4. AWARE WOM (a) ** 0.56** 0.55** OFFT ** 0.56** 0.49** ** (a) The correlation and standard deviation of word-of-mouth is based on observations without missing data. Hierarchical regression was employed to test the hypotheses. We started with the demographic and other control variables (the control variables model) including years using Internet, number of online purchases, number of times browsing Internet for window shopping per week, age, gender, education, store under evaluation, previous browsing of the online store, and structural assurance. These variables explained 24.4% variance of online trust. The main variables were then entered into the model (the full model). Regression coefficients and their significance are reported in Table 4. Website-based trust belief (b=0.33, p=0.00) and offline trust belief (b=0.19, p=0.01) were found to be significant to online trust, so was situational normality (b=0.20, p=0.03). But awareness (b=0.00, p=0.99) and word-of-mouth (b=0.15, p=0.13) were insignificant. When using only observations with no missing data, the significance of the variables (excluding awareness) remained the same. Table 4: Regression analysis Control Variables Only (Constant) (25.42) (20.81) Yeas of using Internet 0.04(0.03) 0.04(0.02) Purchases 0.02(0.01) 0.01(0.01) Window shopping per week 0.01(0.02) 0.00(0.01) Store1-0.32(0.41) -0.25(0.33) Store2-0.63(0.40) -0.43(0.32) Store3-0.48(0.40) -0.38(0.32) Store4-1.13(0.42)** -0.67(0.34)* Store5-0.06(0.56) -0.24(0.45) Browsed the website before 0.06(0.16) 0.06(0.13) Gender 0.11(0.15) -0.01(0.12) Birth 0.01(0.01) 0.01(0.01) Education Postgraduate -0.01(0.32) 0.04(0.26) Education Undergraduate 0.37(0.27) 0.25(0.22) Education Poly 0.07(0.26) 0.03(0.22) Full Model

13 18 Yunjie (Calvin) Xu, Shun Cai, Hee-Woong Kim Control Variables Only Education High school -0.08(0.36) -0.06(0.29) Structural Assurance 0.28(0.07)** -0.03(0.06) Website-basted trust (WBT) 0.33(0.07)** Situational normality (SN) 0.20(0.09)* Awareness (AWARE) 0.00(0.13) Word-of-mouth (WOM) 0.15(0.10) Offline trust (OFFT) 0.19(0.07)* R * Significant at p<0.05, **p< Full Model In the full model, since the sign of structural assurance was opposite to what was typically hypothesized, and the correlations among constructs were relative high, multicollinearity was a concern. We employed both variance inflation factor and condition index to check for multicollinearity (Cohen et al., 2003). However, no serious violation was found. Therefore, multicollinearity was unlikely to threat the validity of hypothesis testing. The nonsignificance of structural assurance in the full model seemed to be consistent with the confounding factor argument. Because structural assurance could affect situational normality too, its impact on online trust belief seemed to be mediated by situational normality. Based on the regression result, our hypotheses on website-based trust belief (H1), offline trust belief (H2), and situational normality (H4) were supported. For hypothesis 3, that word-of-mouth is partially mediated by website-based trust belief, a different testing procedure is required (Frazier et al., 2004). Table 5: Test of mediator effect for hypothesis 3 Steps Regression Coefficient Standardized Deviation Standardized Coefficient Step 1: Outcome = f(x) Outcome: Online Trust Predictor: Word-of-Mouth 0.29** ** Step 2: Mediator = f(x) Outcome: Website-based trust Predictor: Word-of-Mouth 0.43** ** Step 3: Outcome = f(x, Z) Outcome: Online Trust Mediator: Website-based trust 0.33** ** Predictor: Word-of-Mouth * Coefficients for other variables are omitted. Frazier et al. (2004) summarized a three-step procedure to test mediator effect. Basically, if X (e.g., word-of-mouth) is mediated by Z (e.g., website-based trust belief) in affecting Y (e.g., online trust), step 1 is to regress the outcome variable Y on X; step 2 is to regress the mediator on X, and step 3 is to regress the outcome variable Y on both X and Z. A mediator is required to be significantly affected by X (step 2). If the impact of

14 Asian Journal of Information and Communications 19 X on Y (as reflected in regression coefficient) in step 1 is significantly larger than when the mediator is introduced (step 3), then the mediation relationship is said to be supported. In step 3, if the impact of X on Y is reduced to zero (or non-significant), the mediation relation is a complete one, otherwise partial. Cohen et al. (2003) suggested incorporate other potential variables in the above three regression to avoid spurious mediator effect. Following the above suggestion, we fitted the above three regression models with all control variables and other independent variables included. Table 5 reports the changes in regression coefficients for these variables. The testing result indicates that word-of-mouth was completely rather than partially mediated by website-based trust, therefore, the partial mediation relationship (H3) was not supported, but a complete mediation relationship was found. If the level of word-of-mouth did not have a direct effect on online trust, would the awareness of the online store make a difference? We tested the combined effect of online store awareness and word-of-mouth in the full model by examining their extra contribution to the model R-square. The R2 introduced by these two variables is The F-test of total effect was insignificant (F 2, 160 = 1.22, p=0.30). 6. Discussion and Implication 6.1 Discussion of Findings Based on SLT, we set out to test the contribution of different learning channels on formation of online trust belief for brick-and-click firms. We hypothesized that website-based trust belief, offline trust belief, and situation normality would have a direct impact on online trust belief, and word-of-mouth would have both direct and indirect impact. Our empirical study of 182 subjects supported the effect of website-based trust belief, offline trust belief and situation normality, and found the effect of word-of-mouth was completely mediated by website-based trust. In total, 54% of the variance in online trust belief was explained by the model, indicating a satisfactory model fit. The result is consistent with SLT. People learn from their direct experience in the focal situation as well as their experience in related and similar situations. In the context of IOT formation, experience with the physical stores of the online store and experience with other online stores contribute to trust in an online store. The impact of word-of-mouth, however, is fully mediated by people s experience with the website. Because other people s comments of online shopping with the focal store and one s own experience with it pertain to the same situation, the content of information are likely to be very similar. The SLT suggests direct experience is stronger than vicarious experience in belief formation; our finding suggests that when the content of information is very similar, direct experience is affected by preceding indirect experience. When the final belief is to be formed, direct experience could incorporate information learned from indirect experience and dominate it. 6.2 Limitations and Future Research Before discussing the implications of the study, the limitations of the study should be noted. First, SLT-based trust building framework suggests a process model is more desirable to understand belief formation, although it does not exclude cross-sectional studies. Our application to brick-and-click firms is based on the outcomes of each learning channel rather than the processes themselves. It is prudent to directly investigate the learning processes customers use to further verify the utility of the process model. In consumer research, the consumer information searching studies which investigate how different information sources are utilized (e.g., Beatty and

15 20 Yunjie (Calvin) Xu, Shun Cai, Hee-Woong Kim Smith 1987) might give us some insight into how to investigate a process model directly in future studies. Second, our use of Singaporean sample might limit the generalizability of this study to other contexts. Finally, although anticipated and prepared for, common method variance might still affect the validity of the findings. 6.3 Implications for Research and Practice This study has several implications for research. First, this study provides an SLT-based framework to examine trust building processes; based on it, the major learning processes are identified and their outcomes organized and analyzed. It complements the prior research, which identifies the psychological process of trust building and answers why evidence leads to trust, by providing an answer to how such evidence is obtained by the trustor. Together with prior literature, it provides a comprehensive picture of the overall process. Second, this framework offers a tool to identify and examine the important trust antecedents. We demonstrated its application with the IOT building for brick-and-click companies. Hence, it is possible to apply the same framework to other trust contexts to uncover important antecedents. Finally, this study suggests that when studying IOT formation, trustors with different levels of experience with the trustee should be differentiated. For potential online customers of brick-and-click firm, those with offline experience and those without have different set of learning processes for initial online trust. So are those who had heard about the online store and those who had not. Such segmentation of trustors was largely neglected in prior empirical research of buyer-seller trust relationship both online and offline. Studies that explicitly compares different customer segments are scant (one such exception is Gefen et al. 2003). Our study represents an effort in this direction to capture the impact of different customer knowledge levels. With respect to practical implications of this study, we offer a statistical evidence for the hypothesized brick-and-click synergy in the case of trust transference. Reputation spillover is one of the major mechanisms that lead to such synergy. This confirms many earlier speculation of the benefit of having offline presence for online firms (e.g. Willcocks and Plant 2001). Hence, offline-established firms can leverage customers trust in their offline stores and compete with newly established online companies who have little name recognition yet. Such an edge allows them to enjoy the second mover advantage by learning from the mistakes of earlier players in online market, yet still be able to catch up with reputation as a weapon. In this regard, online competition is essentially the extension of offline competition, not an independently new one. In addition to the offline trust, online operation has its distinct element, the website. It is possible to take a lead with a better website. Willcocks and Plant (2001) suggested several strategic routes that a company can take to win the online competition: brand strategy, technology leadership, and service leadership. The technology leadership, however, is not the sheer use of the latest technology, but rather the management of it (Willcocks and Plant 2001). The leadership comes with wise use of technology to better serve the customers need. By offering a high quality and easy to use website that facilitate customers searching and buying process, a company can achieve greater customer trust in its online operations. This is especially important for companies which have only online presence or for those companies reaching to a new market with little or no name recognition. Customers perception of online operation is also significantly affected by word-of-mouth of the online store. Therefore, in order to stay in a self-reinforcing cycle, a firm should not publish a website pre-maturely and should actively manage the word-of-mouth of a working website.