Untangling the Antecedents and Covariates of E-Commerce Trust: Institutional Trust vs. Knowledge-Based Trust

Transcription

1 RESEARCH Untangling the Antecedents and Covariates of E-Commerce Trust: Institutional Trust vs. Knowledge-Based Trust STEPHEN C. WINGREEN AND STEPHEN L. BAGLIONE A b s t r a c t Through experimentation, we establish a causal relationship between structural assurance and both vendor trustworthiness (knowledge-based trust) and technology trustworthiness (institution-based trust). We expand the definitions of structural assurance and institution-based trust to include the element of security measures employed by the marketplace technology. Situational normality demonstrates a causal relationship with technology trustworthiness, and with vendor trustworthiness, the latter only when the covariates of familiarity with the Internet and a person s initial trust are controlled. Keywords: institution-based trust, knowledgebased trust, structural assurance, situational normality, controlled experiment INTRODUCTION Consumer trust in electronic commerce has received a great deal of attention in recent years among both researchers and practitioners. Although still small in absolute dollars to more traditional industries, its growth rate has been phenomenal. From 2003 to 2004, its growth rate was 26%, and online sales were estimated at $145 billion for 2004, which is 7% of US retail sales (Mullaney and Hof 2004). Clearly, it is an important retail channel that should continue to grow in importance and size. This channel also presents challenges for researchers and practitioners. Without a physical location to visit or a product to touch, where transactions occur mostly between strangers, online purchases foster greater uncertainty about trading partners being opportunistic (Ba et al. 1999; Brynjolfsson and Smith 2000; Gefen 2002; Gefen et al. 2003a, 2003b; Mayer et al. 1995). Trust is of greater import in the ethereal online environment than the physical environment (Bailey and Bakos 1997), because trust reduces uncertainty or expectations of opportunistic behavior (Pavlou and Gefen 2004: 45). It also increases purchase intentions both directly (Gefen 2002), as it does in other buyer seller relationships (Ganesan 1994), and through reduced perceived risk (Jarvenpaa and Tractinsky 1999; Kollock 1999). In the words of Reichheld and Schefter (2000: 107): Price does not rule the Web; trust does. For sellers, a lack of trust portends market failure (Granovetter 1985). Recent conceptualizations of trust have proposed, for instance, that trust beliefs are comprised of benevolence, ability and integrity (Chen and Dhillon 2003; Gefen 2002; Mayer et al. 1995; McKnight et al. 1998; McKnight et al. 2002), and is related to disposition to trust, institution-based trust and trusting intentions (McKnight et al. 1998, 2002). Pavlou and Gefen (2004) showed that institution-based trust is equally important in the online and traditional business environments. Some forms of trust are also related to other forms of trust, and some forms of trust are theorized to have antecedents in the marketplace (Gefen 2002; McKnight et al. 1998, 2002, 2004; Pavlou and Gefen 2004), but experimental work has yet to verify whether many of the relationships are causal or merely correlational. Furthermore, where transactions between buyer and A u t h o r s Stephen C. Wingreen (wingreen@bellsouth.net) is an Associate Professor of Information Systems and Decision Sciences at Southeastern University in Lakeland, Florida. Beside e-commerce trust, his research interests include IT workforce management, IT skill portfolios, IT professionals motivation to update, and IT professionals person organization fit. Stephen L. Baglione (stephen.baglione@saintleo.edu) is an Associate Professor of Marketing and Quantitative Methods at Saint Leo University in Florida. His research focuses on active learning, ethics, e-commerce, and sport. Copyright ß 2005 Electronic Markets Volume 15 (3): DOI: /

2 Electronic Markets Vol. 15 No seller may occur only once, consumers place a premium on vendor honesty, and the reliability of the technological infrastructure plays an enhanced role in the development of consumer trust (Pavlou and Gefen 2004; McKnight et al. 2002). Interest in the antecedents of trust in electronic environments has begun to swell (Gefen et al. 2003a, 2003b). Prior research has also established that the institutional factors of structural assurance and situational normality are related both to institution-based trust (Gefen et al. 2003b; McKnight et al. 2002) and to the more traditional vendor-benevolence concept of consumer trust, which Gefen et al. (2003b) refer to as knowledge-based trust. Although institution-based trust considers technological components of the institution, prior theoretical (McKnight et al. 1998) and operational definitions have been very general with respect to technological factors present in the electronic marketplace (McKnight et al. 2002), and recent research suggest that some refinement of the construct may be necessary (McKnight et al. 2004). A structural assurance is defined as a guarantee or endorsement of the institution itself which assures the consumer that the transaction will be a success, and is theorized to be an antecedent to the formation of institution-based trust (Gefen et al. 2003b; McKnight et al. 1998). Examples of structural assurances are statements of legal recourse, website icons representing endorsements of third parties such as the Better Business Bureau, the etrust TRUSTe seal, a statement of privacy policy (Gefen et al. 2003b), or online vendor reputation mechanisms (Ba and Pavlou 2002). Recent research, however, has shown that web icons and seals, as such, have no significant effect on trust (McKnight et al. 2004), thus necessitating a revisitation of whether the trust is derived from the presence of web icons and seals or knowledge of the security measures used by the e- commerce technology itself. Situational normality is defined as the extent to which the marketplace resembles a normal or customary situation (Gefen et al. 2003b). In the case of web vendors, situational normality is exemplified by an environment where the vendors have the attributes of competence, benevolence and integrity (McKnight et al. 2002), and perhaps also various other positive vendor characteristics to the extent that any given characteristic is perceived to be normal or customary by the consumer. Although individual perceptions of structural assurance and situational normality and conceptualizations of e-commerce trust have been observed and measured in field studies and surveys of e-commerce (Gefen et al. 2003b; McKnight et al. 2002), to our knowledge they have never been directly manipulated experimentally. The goal of this study is to employ an experimental design to examine the cause-and-effect relationships between structural assurances and situational normality, which are two previously-theorized antecedents to e-commerce trust (Gefen et al. 2003b; McKnight et al. 1998, 2002), and institution-based trust (beliefs in technological trustworthiness) and knowledge-based trust (beliefs in vendor trustworthiness), which are two previously-theorized dimensions of e-commerce trust (Gefen et al. 2003b; McKnight et al. 1998, 2002). Field studies and surveys excel when the goal of research is to formulate generalizations about a population, but they are less adequate with respect to resolving cause-and-effect relationships. Generally, causation is shown through research design (Pedhazur and Schmelkin 1991). In the case of e-commerce trust, more experimental work is required in order to untangle the cause-and-effect relationships between its antecedents and covariates and the multiple dimensions and conceptualizations of e-commerce trust currently being considered by researchers. Following the failure of McKnight et al. (2004) to support certain elements of the existing concept of institution-based trust, this study will investigate whether the concept should be revised to include security measures of the marketplace technology itself. Furthermore, the relationships between initial trust and familiarity with e-commerce, and institution-based and knowledge-based e-commerce trust will also be explored. Since initial trust and familiarity with e- commerce are difficult to control experimentally, their effects will be controlled by entering them as covariates in the causal relationship between structural assurance and situational normality, and institution-based trust and knowledge-based trust as part of a supplementary experimental design. Therefore, the study will seek to answer the questions: RQ1: Do structural assurances and situational normality form differential causes between consumer beliefs in vendor trustworthiness and consumer beliefs in the trustworthiness of the marketplace technology? RQ2: Does the institution-based trust dimension of e-commerce trust extend to consumer beliefs in the trustworthiness of marketplace technology? This study will use an experimental method to triangulate aspects of previous field research on the antecedents to e-commerce trust (Gefen et al. 2003b; McKnight et al. 2002) in the environment of a university campustextbook market. The study will also extend the existing research on structural assurance and institution-based trust by operationalizing them with information about system reliability and security measures employed by the online marketplace technology. RQ1 will be answered by employing a MANOVA experimental design with consumer beliefs in vendor trustworthiness (knowledgebased trust) and consumer beliefs in technology trustworthiness (institution-based trust) as the dependent

3 248 Stephen C. Wingreen and Stephen L. Baglione & Untangling the Antecedents and Covariates of E-Commerce Trust variables and randomly-assigned treatments of situational normality and structural assurance as the independent variables. RQ2 will be answered by adapting an existing institution-based trust scale (McKnight et al. 2002) to include content about technological characteristics of the marketplace technology (e.g., technological security, data encryption, security of personal information, technology reliability) and operationalizing structural assurance with information about technological security measures employed by the marketplace technology. REVIEW OF LITERATURE AND RESEARCH MODEL The context of online textbook markets Online textbook markets have been the subject of prior e-commerce research (Carlson 2002; Gefen 2002; Gefen et al. 2003a, 2003b; Mutter and Zeitchik 2000; O Neill 2002; Pavlou and Gefen 2004), perhaps due to the accessibility of college students to researchers. However, online textbook markets are one of the few areas where the use of students as experimental subjects may be externally valid, based primarily on increasing levels of computer skill and Internet use among college students, resulting in more students using the Internet as a purchase avenue (Yang and Lester 2000). Beside their computer and Internet skills, the increased level of sophistication among college students as textbook buyers appears also to be partly driven by skyrocketing textbook prices (Lewin 2003). Research and practitioner interests have included both Internet bookstores, such as VarsityBooks.com (Carlson 2002), Wal- Mart (O Neill 2002), and Amazon.com (Gefen 2002), as well as online college bookstores (Mutter and Zeitchik 2000). Consumer trust in the electronic commerce environment There is a multiplicity of theories about how trust should be conceptualized in the e-commerce environment. Some theories treat trust as a single construct with multiple dimensions (Gefen 2002), while others propose that there are different types of trust beliefs (Doney and Cannon 1997; McKnight et al. 1998, 2002). For instance, some studies propose that trust is a three-dimensional construct consisting of benevolence, integrity and ability dimensions (Chen and Dhillon 2003; Gefen 2002). Benevolence or caring is the belief that the vendor has the customer s best interests in mind as a business practice (Gefen 2002). Integrity is the belief that the vendor is honest and ethical in his or her conduct of business. Ability is the belief about vendor skills and competence. Marketing literature, on the other hand, argues that there are two distinct types of trust: benevolence and credibility (Doney and Cannon 1997; Ganesan 1994). Benevolence is the belief that one partner is genuinely interested in and has beneficial motives for the other partner s welfare. This requires familiarity and prior interaction. Credibility, defined similarly to integrity (Gefen 2002), is the belief that the other party is honest, reliable and competent. Zucker proposes that in business relations there are three types of trust: process-based, characteristic-based and institution-based. Process-based trust is based on successful transactions in the past, which assumes an ongoing relationship. Characteristic trust assumes a common culture (i.e., lifestyles, values, appearance). For our purposes, neither is examined. We assume no ongoing relationship between the online vendor and customers, and the e-commerce environment precludes physical interaction between the vendor s employees and purchasers (i.e., students). Institution-based trust includes independent intermediaries (e.g., regulators, certifying bodies, credit-card companies, companies to run another s website). Following prior research (McKnight et al. 1998, 2002), institution-based trust in a community of sellers is defined as a buyer s perception that appropriate conditions are in place to facilitate a successful transaction. It exists when trust is tied to the existence of third-party structures that are independent of dyadic actions (Pavlou and Gefen 2004: 41). Trust beliefs are the consumer s beliefs that the vendor has beneficial attributes such as competence, benevolence and integrity. Trusting intentions refer to the consumer s willingness to depend on the vendor or his or her purchase intentions (Gefen 2002; McKnight et al. 1998, 2002). Vendor trustworthiness incorporates benevolence, integrity/credibility and ability. In the virtual environment, trust is critical to success because buyers must rely on sellers to accurately portray products and fulfil transactions (Lee 1998). Following Gambetta (1988); Bhattacharya et al. (1998); and McKnight et al. (1998, 2002), we define trust as the subjective assessment of one party that another party will perform a particular transaction according to his or her confident expectations in an environment characterized by uncertainty. It means the seller s intention and behaviour will be dependable, ethical and socially appropriate (Gefen 2002; Hosmer 1995; Kumar et al. 1995; Zucker 1986). In accordance with the goals of this study to investigate whether the existing concept of institutionbased trust (Gefen et al. 2003b; McKnight et al. 1998, 2002) extends to the consumer s trust in the technological security of a marketplace and a third-party host for

4 Electronic Markets Vol. 15 No the website, and whether previously studied antecedents of trust differentiate between consumer beliefs in vendor trustworthiness (knowledge-based trust) and technology trustworthiness (institution-based trust), trust will be operationalized with two scales developed in prior research. A knowledge-based trust scale that incorporates benevolence, integrity/credibility and ability (Gefen et al. 2003b) was adopted to measure the consumer s trust in the online vendor. Consumer trust in marketplace technology (institution-based trust) is operationalized by adapting an institution-based trust subscale for structural assurance-based trust that was developed in prior research as part of a larger instrument for institution-based trust (McKnight et al. 2002). The scale was adapted also to include statements about website security, encryption and related technological safety features that fit the general definition of institution-based trust, but have not been a part of either the formal theory of institution-based trust (McKnight et al. 1998) or the instrumentation that was subsequently developed. This is, in fact, one of the contributions of this study (per RQ2). Antecedents to e-commerce trust and hypotheses Previous research has identified structural assurances and situational normality as two very important antecedents to e-commerce trust (Gefen et al. 2003b; McKnight et al. 1998, 2002). Situational normality. Situational normality is the extent to which a transaction appears to be occurring in a normal or customary situation (Gefen et al. 2003b; McKnight et al. 1998). By this definition, situational normality is how normal an online vendor may appear with respect to the buyer s idea of what a vendor should be. In the context of this study, situational normality refers to how similar the online textbook-buying experience is to the students expectations of what online textbook-buying ought to be. In this study, situational normality will be operationalized consistently with prior research (McKnight et al. 2002) by providing the subjects with a positive description and information about the online vendor that is worded so as to make the vendor appear as reliable, competent and experienced as any other reputable vendor known by the subject. Based on these considerations of situational normality, the following hypotheses are proposed: H1: Situational normality will have a positive effect on beliefs of technological trustworthiness. H2: Situational normality will have a positive effect on beliefs in vendor trustworthiness. Structural assurance. Structural assurances refer to the various safety features of the online marketplace, such as guarantees, legal recourse for failed transactions, seals of approval, and similar features aimed at minimizing consumer anxiety over marketplace safety and security (Gefen et al. 2003b; McKnight et al. 1998). The importance of structural assurance is confirmed in other research (Ba and Pavlou 2002; Lee and Turban 2000). One study (Ba and Pavlou 2002) found trust developed through the feedback of prior buyers. With greater trust came a price premium. The major difference between the two studies is the impact of negative feedback: one study found an impact, the other reported little negative feedback and no impact. Although structural assurances may be built into the website, and include product guarantees and legal recourse if problems arise (e.g., Better Business Bureau membership), or reputation mechanisms such as feedback from prior buyers (Ba and Pavlou 2002), its definition is broad and may encompass assurances about the reliability of the marketplace technology itself, such as technological security measures, use of encryption, and secure personal information. In this study, structural assurances will be operationalized by providing the subjects with a positive description and information about the online vendor s website that makes the website appear to be a technologically reliable, secure and a safe place to conduct business (e.g., rigorous security for credit card and personal information). The considerations of structural assurances and the question of whether trust, including the consumer s beliefs about the technological trustworthiness of the e-commerce environment, leads to the formulation of the following hypotheses: H3: Structural assurances will have a positive effect on beliefs of technological trustworthiness. H4: Structural assurances will have a positive effect on beliefs in vendor trustworthiness. Interaction of structural assurances and situational normality. There is no reason to believe that the relationship between structural assurances and either dependent variable will depend on the level of situational normality, or vice-versa, since previous research has suggested that their effects are independent (cf, Gefen et al. 2003b; McKnight et al. 2002). Therefore, this study expects to find no interaction between structural assurances and situational normality and either dependent variable. Merely to confirm prior research, we did include an interaction among the treatments. Based on hypotheses one through four, the mathematical models that will be tested are: Eðy 1 Þ~b 0 zb 1 x 1 zb 2 x 2 and Eðy 2 Þ~b 0 zb 1 x 1 zb 2 x 2

5 250 Stephen C. Wingreen and Stephen L. Baglione & Untangling the Antecedents and Covariates of E-Commerce Trust where y 1 is consumer beliefs in vendor trustworthiness, y 2 is consumer beliefs in technology trustworthiness, x 1 is structural assurances, and x 2 is situational normality. Both x 1 and x 2 are categorical variables with two levels: 05no treatment, 15treatment of either structural assurance or situational normality. The hypotheses predict that b 1 and b 2 will be significant for both dependent variables. Covariates As it is with experimental effects of any size, however, statistical significance is determined by the size of the effect, the number of subjects participating in the experiment, the chosen level of statistical significance, and the existence of effects from other [uncontrolled] variables. With this latter point in mind, this study will adopt an exploratory position with respect to certain covariates. Based on previous research, certain variables are expected to influence the dependent variables in this study and are uncontrollable by the researchers. Therefore, these variables will be accounted for as covariates. Initial trust beliefs. Prior research has also demonstrated that some consumers are innately more prone to trust an e-vendor than others, or that any given consumer comes to a situation with an initial trust (McKnight et al. 1998, 2002). Initial trust is defined as the trust a consumer has in an unfamiliar stimulus. It is one s general propensity to trust (Lee and Turban 2000). Therefore, this study will control for the consumer s initial level of trust as a covariate. Initial trusting belief was operationalized in this study as the subject s response to a summated general-trust scale composed of Likert-type items (Cheung and Lee 2001; Lee and Turban 2000). Familiarity with online shopping. Previous research has identified consumer familiarity as an antecedent both to the development of trust in general (Kumar 1996) and also specifically to e-commerce trust (Gefen et al. 2003b). As such, consumer familiarity with online shopping will be controlled in this study as a covariate. In this study, familiarity or comfort with the Internet and online shopping was operationalized as the subjects response to a summated scale of Likert-type items that asks the subjects to indicate their level of comfort with the Internet and various aspects of the online purchasing experience as a whole. Research model Figure 1 is the research model derived from the hypotheses and covariates proposed above. The model hypothesizes a positive relationship between structural assurances and situational normality, and vendor trustworthiness and technology trustworthiness. Furthermore, the effects of initial trust and familiarity with Internet shopping (comfort scale) on the dependent variables vendor trustworthiness and technology trustworthiness will be controlled as covariates. METHODS Procedure Pilot test. First, a three-phase pilot procedure was conducted to determine both the instrumentation (Appendix A) to be adopted so as to meet the goals of the study and how to design and administer the treatments. The overall goal of the pilot procedure was to ensure that both the treatment scenarios and the instruments were understandable by the target population, and demonstrated preliminary evidence of reliability and validity. In phase one, the primary investigators collaboratively wrote the treatment scenarios and evaluated existing instrumentation to determine its suitability to address the goals of the study. Once selected, the instruments were adapted to fit within the context of the stated research questions and hypotheses. Phase two involved several cycles of treatment and instrument revision involving student focus groups. Manipulation validity was addressed in this phase by initiating a guided discussion among the focus groups over whether they believed that the information provided in the treatment scenarios corresponded with their expectations of both a normal textbook buying situation for the situational normality scenario, and guarantees and assurances aimed at minimizing consumer anxiety about the safety and security of the online marketplace for the structural assurance scenario. The feedback obtained from the focus groups resulted in the scenarios being revised until there was unanimous agreement both within and between the groups over whether the scenarios accurately reflected instances of the theoretical definitions of situational normality and structural assurance. The focus groups also identified some concerns that resulted in the adoption of control language to be written into each scenario to minimize the threat that the treatments may be confounded through the consideration of extraneous factors. For instance, the focus groups belief that subjects differential perceptions of familiarity between the campus bookstore s brickand-mortar store and an unspecified third party selling books in an electronic market would probably lead to a systematic preference for the familiar brick-and-mortar vendor, and thus vendor trustworthiness (knowledgebased trust). This discovery led to the adoption of language describing the campus bookstore as operating

6 Electronic Markets Vol. 15 No Figure 1. Research model of the antecedents and covariates of e-commerce trust both a brick-and-mortar and an online store that is hosted by a well-known online textbook market in order to control the effect. Other potentially confounding effects, such as total cost and source of referral, were similarly controlled. In phase three, the treatments and instruments were examined for preliminary evidence of reliability and validity based on feedback from a small sample (n,30). The small sample provided sufficient evidence that both the treatments and instrumentation were adequate for experimental purposes. The participants in phases two and three were students in various classes supervised by the study s authors. None of the participants in phases two or three were allowed to participate in the study s final experimental procedure, and were asked not to discuss their knowledge of the study with any of their classmates elsewhere on campus to preserve the internal validity of the experimental treatments by eliminating a potential source of contamination. Data were also gathered from the experimental population to perform a manipulation check to assess whether the subjects were aware that the treatment had been administered, and the extent to which respondents believed that the scenarios corresponded to the definitions of situational normality and structural assurance. Approximately 67% of the subjects responded affirmatively about whether they were aware that the questionnaire instructions included the information provided in the treatment, which well exceeds the general rule of thumb that more than 50% of experimental subjects should be aware that a treatment had been administered (Gefen et al. 2004). Furthermore, tests of hypotheses that were performed on questions pertaining to whether the subjects believed that the treatment scenarios represented instances of situational normality and structural assurances were supported at p,0.003 and p,0.01, respectively, thus indicating that members of the experimental population believed that the treatment scenarios adequately represented the constructs of interest. Based on these results, the criteria for manipulation validity appear to be satisfied. Experimental procedure. The treatment scenarios were printed on the questionnaire in the instructions at the top of the section containing the instrumentation for beliefs in vendor trustworthiness (knowledge-based trust), technology trustworthiness (institution-based trust), and initial trust. The familiarity or comfort scale preceded the treatment manipulations. The questionnaires were then randomly assigned and administered to the subjects, who then completed and returned the questionnaires on the spot so as to control the various threats to internal validity that may result from information about the experiment being shared between the subjects. In order to reinforce manipulation validity, the subjects were encouraged to carefully read the questionnaire instructions, which included the treatment scenario, before providing their responses to the trust items. Experiment design and subjects A completely randomized experimental design with covariates was employed as a prelude to the use of the MANCOVA procedure. The main effects were tested using the MANOVA procedure to address hypotheses one to four by means of a purely experimental design. The effects of the covariates were explored separately using the MANCOVA procedure, a total of three: each covariate separately and then both covariates together. The goal of the exploratory phase of the study was to determine whether either or both of the covariates allow the detection of additional effects that are otherwise not discernible by means of the experimental procedure employed by this study, thus raising the question of whether a refinement of theory should be considered in future research. Thus, the results of the MANCOVA were interpreted as one would an experiment.

7 252 Stephen C. Wingreen and Stephen L. Baglione & Untangling the Antecedents and Covariates of E-Commerce Trust A total of 188 subjects participated in the experiment. Of those, 33 were unusable because of missing data or identification that the respondent did not take the exercise seriously (e.g., circling the same scale value for the entire survey, despite the inclusion of reverse-coded scale items). Eight surveys were also invalidated as statistical outliers. Therefore, the final experiment size includes 147 subjects. All subjects were undergraduate students at two southeastern universities. All subjects had prior experience in the campus textbook market, 73% of the subjects have prior Internet purchasing experience, and 40% have purchased a textbook over the Internet. Treatments A between-subjects design was employed by randomly assigning experimental subjects to treatments consisting of written scenarios about electronic marketplaces (Appendix B). The first treatment operationalizes structural assurances by offering information about the website s security features for credit card and personal information. The second treatment operationalizes situational normality by offering information about seller reliability, competence, experience and integrity. The third treatment represents an interaction between situational normality and structural assurance by offering the same information about both the seller and the website. The final condition is the control group, which received no treatment. Instrumentation Belief in vendor trustworthiness is a knowledge-based trust scale (Gefen et al. 2003b), an eight-item sevenpoint summed Likert-type scale. This includes questions on whether the vendor is: honest, cares about customers, has the ability to handle sales transactions on the Internet, is not opportunistic, and knows the market. The scale has demonstrated acceptable reliability from previous research (Gefen 2002). Belief in technology trustworthiness was measured by adapting and extending an institution-based trust scale to include beliefs about trustworthiness of marketplace technology (McKnight et al. 2002). It focused on the electronic security measures for transactions, security of personal information, encryption, and general website safety beliefs. It is a four-item seven-point Likert-type summed scale with demonstrated reliability from previous research (McKnight et al. 2002). Initial trust was measured using a four-item sevenpoint summed Likert-type scale. This disposition to trust is the individual s general propensity to trust, and may also affect beliefs and intentions in an e-commerce environment. The initial trust instrument has demonstrated acceptable reliability in previous research (Cheung and Lee 2001; Lee and Turban 2000). Familiarity or comfort with Internet shopping is a four-item six-point summed Likert-type scale. The comfort scale was administered prior to the treatments. It includes questions on comfort with using the Internet, with buying products over the Internet, sending personal information via the Internet, and buying textbooks online. The familiarity scale was developed through the pilot procedure described previously. ANALYSIS Factor analysis Table 1 reports the results of a factor analysis that was performed to determine the discriminant validity of the instrumentation that was used in the study. The Oblimin factor rotation method was used, rather than Varimax, because several of the scales used represent constructs that are correlated in theory. The factors represented in Table 1 account for 72% of the variation among all the measured variables. The four eigenvalues were acceptable, and all items loaded on the predicted factors with no noteworthy cross-loadings. Scale reliability, inter-scale correlations, descriptive statistics MANOVA and MANCOVA are sensitive to departures from multivariate normality, therefore it is necessary to examine and verify the statistical assumptions of the variables in the study. An examination of the assumptions revealed no violations. Table 2 reports Cronbach s alpha for each scale, as well as n, mean, standard deviation, skewness, kurtosis, and inter-scale correlations for each construct. Using Box s M test, we fail to reject the null hypothesis of equal covariance matrices of the dependent variable across all groups of the independent variable (p,0.44). Levene s Test of Equality of Error Variances, which tests the null hypothesis of equal error variance of the dependent variable across all groups of the independent variable, is not statistically significant for either variable (p,0.2 and 0.8, for vendor trust and technology trust, respectively). Table 3 reports the n, means, and standard deviations by experimental group for both dependent variables. All scales demonstrate strong evidence of internal consistency as evidenced by Cronbach s alpha, and thus support the formation of summated scales. Furthermore, the statistical assumptions appear to be satisfied for all variables. Given the positive results of the factor analysis and the reliability of the study s dependent variables and

8 Electronic Markets Vol. 15 No Table 1. Results of factor analysis with Oblimin Rotation Items Factor 1 (knowledge-based trust) Factor 2 (initial trust) Factor 3 (comfort/familiarity) Factor 4 (institution-based trust) Comf Comf Comf Comf Trust Trust Trust Trust Trust Trust Trust Trust Trust Trust Trust Trust Trust Trust Trust Trust covariates, there is sufficient evidence to suggest the validity of the measurements employed in the study. Because the results of MANOVA and MANCOVA are sensitive to outliers, tests for both univariate and multivariate outliers were also conducted. Using univariate and studentized residuals, eight observations were invalidated as statistical outliers. Therefore, the study proceeds to the specification of a MANOVA to test the purely experimental effects of hypotheses one to four, and three MANCOVA (controlling the effects of familiarity/comfort, initial trust, and familiarity/comfort plus initial trust, respectively) models to examine the experimental effects of the covariates, and thus accomplish the exploratory component of the study. Statistically, when a covariate is required to resolve the effect of an experimental treatment, it is because the confidence interval of the sampling distribution of group mean differences has been narrowed to a significant range of values by extracting the variance related to the covariate. In each model, Wilks Lambda, Pillai s Trace, and Hotelling Lawley Trace are used to assess the overall MANOVA and MANCOVA models (i.e., main and interaction effects). Homogeneity of variance is tested for each cell of the design to allow for a single estimate of error. The results for Type III sum of squares are examined on account of unequal group sizes between the experimental treatments. RESULTS Global F-tests for the independent variable (treatment groups) were uniformly significant for each dependent Table 2. Scale reliability, descriptive statistics, and inter-scale correlations Descriptives Correlations Alpha n Mean Std. Skew. Kurt. Vend. trust Tech. trust Fam./comf. Vendor trust Technology trust Familiarity/comfort Initial trust The values reported represent summated scales

9 254 Stephen C. Wingreen and Stephen L. Baglione & Untangling the Antecedents and Covariates of E-Commerce Trust Table 3. N, mean, and standard deviation by experimental group Vendor trustworthiness Technology trustworthiness Control Struct. assur. Sit. norm. Interact Control Struct. assur. Sit. norm. Interact. N Mean Std. dev The values reported represent summated scales Table 4. Experimental tests of hypotheses and differences between group means Vendor trustworthiness Technology trustworthiness Global F (p-value) p p R-square Structural assurance - Control (w/95% CI) 1.15/5.70/10.26 * (H3) 1.56/4.26/6.95 * (H4) Situational normality - Control (w/95% CI) 20.56/4.10/8.76 (H2) 0.459/3.21/5.97 * (H1) Interaction - Control (w/95% CI) 23.13/1.59/ /1.38/4.18 Effects significant experiment-wise at p,0.05 are marked in bold with an * variable, thus indicating the effectiveness of at least one of the treatments for each dependent variable. Furthermore, Wilks Lambda (p,0.0045), Pillai s Trace (p,0.0053), and the Hotelling Lawley Trace (p,0.0041) indicate that the combined dependent variables were significantly affected by the treatments. For each dependent variable, Table 4 reports the global F tests, the variance explained by the treatments, the differences between group means for each hypothesis with its respective 95% confidence interval (lower 95%/ mean/upper 95%), and an indication of which hypotheses are supported (in bold with an *). Experiment-wise error was controlled by setting Tukey s Studentized Range to a value of 0.05 for all pairwise comparisons. It should be noted that this is a conservative approach to testing the study s hypotheses, since Tukey s SR will also control error for several pairwise comparisons between group means that are not being examined by this study (such as the difference between the structural assurance and situational normality groups). Differences between groups are significant if the confidence interval of the sampling distribution of differences between group means does not include the zero value. The data support H1, specifically that situational normality will affect beliefs about technological trustworthiness, although the data fail to support H2, that situational normality will affect belief in vendor trustworthiness. H3 and H4, which predict that structural assurance will affect both belief in technological trustworthiness and vendor trustworthiness, are also supported. Overall, the experimental treatments explain 7% of the variance in beliefs in vendor trustworthiness and 10% of the variance in beliefs in technological trustworthiness. The results indicate that the test values for both interaction effects are negative, as expected. For each dependent variable, Table 5 reports the global F test, the variance explained by the treatments, the differences between group means for each hypothesis Table 5. Experimental tests with familiarity/comfort as covariate Vendor trustworthiness Technology trustworthiness Global F (p-value) p, p, Partial F Test for treatment p, p Partial F Test for familiarity/comfort p p, R-square Structural assurance - Control (w/95% CI) 1.53/5.70/9.87 * 2.03/4.26/6.48 * Situational normality - Control (w/95% CI) 20.18/4.12/ /3.22/5.51 * Interaction - Control (w/95% CI) 22.73/1.59/ /1.38/3.69 Effects significant experiment-wise at p,0.05 are marked in bold with an *

10 Electronic Markets Vol. 15 No Table 6. Experimental tests with initial trust as covariate Vendor trustworthiness Technology trustworthiness Global F (p-value) p, p, Partial F Test for treatment p p Partial F Test for initial trust p, p, R-square Structural assurance Control (w/95% CI) 1.48/5.70/9.93 * 1.76/4.26/6.75 * Situational normality Control (w/95% CI) 20.22/4.10/ /3.21/5.77 * Interaction - Control (w/95% CI) 22.79/1.59/ /1.38/3.97 Effects significant experiment-wise at p,0.05 are marked in bold with an * with its respective 95% confidence interval (lower 95%/ mean/upper 95%), an indication of which hypotheses are supported (marked in bold with an *), and the Type- III partial F test for the familiarity/comfort covariate. Again, Wilks Lambda (0.0019), Pillai s Trace (0.0023), and the Hotelling Lawley Trace (0.0017) universally confirm that the experiment as a whole has a significant effect on the combined dependent variable. In the exploratory spirit of this phase of the study, it is worth noting that the addition of the familiarity/comfort covariate did not allow the resolution of H2 (situational normality affects belief in vendor trustworthiness), which was previously rejected, nor did it elevate the interaction effect to statistical significance. The addition of the familiarity/comfort covariate, together with the experimental treatments explains 23% of the variance in beliefs in vendor trustworthiness and 40% of the variance in beliefs in technological trustworthiness, which is an improvement over the variance explained by the treatments alone, especially with respect to beliefs in technological trustworthiness. For each dependent variable, Table 6 reports the global F test, the variance explained by the treatments, the differences between group means for each hypothesis with its respective 95% confidence interval (lower 95%/ mean/upper 95%), an indication of which hypotheses are supported (marked in bold with an *), and the Type-III partial F test for the initial trust covariate. Wilks Lambda (0.0075), Pillai s Trace (0.0086), and the Hotelling Lawley Trace (0.0068) universally confirm once again that the experiment as a whole has a significant effect on the combined dependent variable. As it was with familiarity/comfort, so also the addition of the initial trust as a covariate did not allow the resolution of H2 (situational normality affects belief in vendor trustworthiness), which previously we failed to reject, nor did it elevate either interaction effect to statistical significance. The addition of the initial trust covariate, together with the experimental treatments explains 20% of the variance in beliefs in vendor trustworthiness and 24% of the variance in beliefs in technological trustworthiness, which is an improvement over the variance explained by the treatments alone, but not quite as substantial as the contribution of the familiarity/comfort covariate. For each dependent variable, Table 7 reports the global F test, the variance explained by the treatments, the differences between group means for each hypothesis with its respective 95% confidence interval (lower 95%/ mean/upper 95%), an indication of which hypotheses are supported (marked in bold with an *), and the Type- III partial F tests for the familiarity/comfort and initial trust covariates. Wilks Lambda (0.0022), Pillai s Trace (0.0027), and the Hotelling Lawley Trace (0.0019) Table 7. Experimental tests of significance with both familiarity/comfort and initial trust as covariates Vendor trustworthiness Technology trustworthiness Global F (p-value) p, p, Partial F Test for treatment p p Partial F Test for familiarity/comfort p, p Partial F Test for initial trust p, p, R-square Structural assurance Control (w/95% CI) 1.72/5.70/9.69 * 2.12/4.26/6.39 * Situational normality Control (w/95% CI) 0.02/4.12/8.22 * 1.02/3.22/5.42 * Interaction Control (w/95% CI) 22.54/1.59/ /1.38/3.60 Effects significant experiment-wise at p,0.05 are marked in bold with an *

11 256 Stephen C. Wingreen and Stephen L. Baglione & Untangling the Antecedents and Covariates of E-Commerce Trust universally confirm that the experiment as a whole has a significant effect on the combined dependent variable. Although the addition of both covariates did not elevate either interaction effect to statistical significance, the effect predicted for H2 (situational normality affects beliefs in vendor trustworthiness) is now significant. Both covariates, together with the experimental treatments explain 30% of the variance in beliefs in vendor trustworthiness and 45% of the variance in beliefs in technological trustworthiness. In summary, all hypotheses were supported with the exception of H2 specifically, that situational normality causes a positive effect on beliefs in vendor trustworthiness although the effect predicted by H2 is observable when controlling the effects of both initial trust and familiarity/comfort with e-commerce. In other words, structural assurances that include information about the technological infrastructure itself cause significant effects on both beliefs in technological trustworthiness and beliefs in vendor trustworthiness. Situational normality causes a significant effect on beliefs in technological trustworthiness but not beliefs in vendor trustworthiness unless the effects of familiarity/comfort with e-commerce and initial trust are controlled. Finally, interaction effects between structural assurances, situational normality and the dependent variables were not discernible either by this experiment alone, or by controlling initial trust and familiarity/comfort in an experiment. DISCUSSION Confirming the work of others (Gefen et al. 2003a, 2003b; McKnight et al. 2002), trust is a multidimensional construct: vendor trustworthiness (knowledge-based) and technological trustworthiness (institution-based) that makes Internet transactions possible. An online vendor may be honest and knowledgeable, but be technologically unreliable. Situational normality and structural assurances are causal antecedents to establishing trust in the online environment, thereby affirming the research questions that structural assurances and situational normality do form differential causes between consumer beliefs in vendor trustworthiness and consumer beliefs in the trustworthiness of marketplace technology. Our positive description and information about the campus bookstore established situational normality (i.e., transaction appears to be occurring in a normal or customary situation), but only for beliefs in technological trustworthiness. Situational normality included that the bookstore has years of experience, good prices, fair policies on returns and buybacks, and, in general, good customer service. From this description, respondents felt safe conveying personal information including credit card information with the online vendor. Only when both familiarity and initial trust are included as covariates, both are positively correlated with the vendor trustworthiness and technology trustworthiness, did situational normality increase beliefs that the vendor was honest, caring, predictable, knowledgeable, and possessed the expertise and resources to conduct business online. In the online environment, it is customary and expected that the website will safeguard personal information transported through the Internet by the buyer, and therefore enhance perceived technology trustworthiness. Our association of the bookstore with a well-known online textbook seller partly accounts for this relationship, although situational normality also appears to transfer from the brick-and-mortar environment to the online, a finding supported by previous research (Stewart 2003). For those familiar or comfortable with the Internet and with high initial trust, the online environment for situational normality is similar to the brick-and-mortar environment. Does the vendor have good prices? Does the vendor have fair policies on returns and buy backs? In general, is customer satisfaction paramount in the buyer s strategy for success? Once established in the brick-and-mortar environment, they readily transfer to the online environment. Selling the same product means that a proficient and honest brick-and-mortar vendor is believed to have the same capabilities and trust online. Expertise and reputation are brand equity measures that transcend the distribution channel. This bodes well for companies operating in both environments; although our study worked from the brick-and-mortar environment to the online, we could hypothesize that the relationship is bi-directional. Our description of the technological aspects of the vendor s website infrastructure established structural assurance by focusing on technological reliability, security and whether it was a safe place to conduct business. From this, consumers developed positive beliefs about both technological trustworthiness and vendor trustworthiness for the online vendor. Using safe and reliable technology enhances beliefs on technological trustworthiness and on general vendor trustworthiness. In other words, the vendor s ability to handle the transaction in a non-opportunistic fashion shows the vendor cares about the customer. This relationship holds with or without the covariates of familiarity (comfort with online shopping) and initial trust, unlike situational normality which only effects vendor trustworthiness, structural assurance effects trust in both. Expanding from the brick-and-mortar environment into the e- commerce realm, a vendor can engender buyer beliefs in vendor and technology trust by focusing on ensuring technology to make transactions safe. In a society where billions of bytes of personal data flow freely daily and computer hackers spend countless hours and resources trying to steal that precious data, people, not just buyers, are fearful of being victimized, especially through