Business-to-consumer electronic commerce (e- commerce), one form of which is Web-based shopping, is defined as electronic-based economic transactions.
Factors Influencing the Adoption of Web-Based Shopping: The Impact of Trust Craig Van Slyke University of Central Florida France Belanger Virginia Polytechnic Institute and State University Christie L. Comunale Long Island University, C.W. Post Campus
Abstract Business-to-consumer electronic commerce (ecommerce), one form of which is Web-based shopping, is defined as electronic-based economic transactions conducted between individual consumers and organizations. While this form of ecommerce is forecast to grow rapidly for the foreseeable future, it still represents only a small fraction of total consumer spending. To better take advantage of and be prepared for this economic phenomenon, organizations need to identify and understand factors that may impact consumers' decisions to engage in Web-based e-commerce. Recently, the importance of trust has been discussed in both the academic and practitioner press. The impact of trust on the use of e-commerce has been established empirically. The research reported here builds on those findings by establishing that not only is trust in Web merchants significantly related to purchase intentions via the Web, but this significance holds even when other, more traditional perceptions are considered. A survey of consumers was conducted and results indicate that trust in Web merchants is positively related to intentions to make purchases from Web merchants, even when the impact of other perceived innovation characteristics are considered. The research also contributes to the literature on technology adoption by verifying the impact of perceived innovation characteristics on adoption intentions. ACM Categories: H.1.2, K.4.4 Keywords: Electronic Commerce, Diffusion Innovations, Trust, Web-based Shopping.
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Introduction and Objectives Broadly defined, electronic commerce can be viewed as "any form of economic activity conducted via electronic connections" (Wigand, 1997, p. 2). There are several forms of electronic commerce, such as business-to-business, business-to-consumer, or government-to-constituent. The focus of this research is consumer purchasing of retail goods and services over the Web, an area of business-to-consumer electronic commerce. The efficiencies of Internet-enabled business have spurred growth and prosperity in the global economy. Gartner (www.gartner.com) predicts that worldwide Internet commerce is expected to reach $8.5 trillion in 2005. This growth is no more evident than in online retail sales. Forrester Research Inc., of Cambridge, suggests that US consumers spent $51.3 billion
online in 2001 and $72.1 billion in 2002, and projects that US consumers will spend approximately $217.8 billion online in 2007. In addition, it is estimated that approximately 36.5 million US households currently shop online and by 2007, 63 million households, or two-thirds of the US total, should be shopping online (Reidy, 2002). However, not all forecasts are so positive. A recent study by the Wharton School of Business suggests that although total online retail spending is increasing, per person online retail spending is quickly declining (Pastore, 2000). It seems that there is a significant dropout rate among online shoppers, for example, 15 percent of online buyers from 1997 did not buy online in 1998. Indeed, forecasts for e-commerce spending and market growth assume linear increases in per person online spending. Why the decline? Interestingly, concerns about privacy and trust are among the most important factors that reportedly distinguish buyers from non-buyers online. A study by Jupiter Communications finds that 64 percent of online consumers are unlikely to trust a Web site, even if the site prominently features a privacy policy (Pastore, 2000). This study is not alone. The issue of trust is often raised by practitioners with statements like “trusting a website is like following a helpful stranger in Morrocco who offers to take you to the best rug store,” (New York Times, as reported in Nielsen, 1999). Finally, researchers suggest that the level of trust consumers are willing to place in Web merchants is considered a key factor for the continued growth of e-commerce (Ba, 2001; Houston, 2001; Jarvenpaa et al., 2000). One limitation of extant research into the importance of trust in consumer-oriented e-commerce is that it has considered trust somewhat in isolation from other factors. It is therefore important to identify trust in Web merchants in relation to other factors that may influence adoption of business-to-consumer electronic commerce. This research attempts to address this issue by answering the following research question: Is a consumer’s trust in Web merchants an important determinant of the consumer’s intentions to make purchases over the Web, even when the influence of other perceived innovation characteristics is taken into consideration? Using research on trust in electronic commerce and innovation adoption, a research model is proposed. We then investigate whether trust in Web merchants and perceptions of the characteristics of Web-based shopping are related to intentions to shop over the Web. The answer to this question helps extend the emerging research area of trust in electronic commerce, and also contributes to the literature on
technology adoption by determining whether the widely accepted role of perceived innovation characteristics holds in the context of Web-based shopping.
Research Model and Theoretical Background This study uses literature on trust and diffusion of innovation (Rogers, 1995) to identify and understand factors that may influence consumers’ intention to use business-to-consumer electronic commerce. Overviews of the salient aspects of this literature are provided below. Trust in Web Merchants Web-based shopping removes many geographic barriers between customers and merchants, allowing new distant merchant-customer relationships to emerge. In these distant relationships, trust of the merchant, the technology, the product, and the service-provider are central to the transaction. In this paper, we examine trust in the merchant, and consistent with Doney and Cannon, we define trust in Web merchants as the “trustor’s expectations about the motives and behaviors of a trustee” (Doney & Cannon 1997, p.37). Trust is an important factor in determining whether an individual chooses to, or not to, acquire goods or services via the Web (Quelch & Klein, 1996). Whenever an exchange relationship is characterized by uncertainty, vulnerability, and dependence, the issue of trust arises (Bradach & Eccles, 1989). In Web-based shopping, there may be increased uncertainty with respect to the merchant performing the contract since the consumer has no direct face-toface interaction with the merchant, and often has no physical store to refer to. Vulnerability for consumers in purely electronic exchanges stems from the fact that consumers must typically pay for a transaction ahead of time (usually with credit cards). This puts them, and not the Web merchant, at risk should anything go wrong with the transaction. Finally, the consumer depends on the electronic system to conduct the transaction, on the safety of the payment system implemented by the Web merchant, and on the merchant itself for ensuring that the exchange is complete (e.g., delivery, etc.). Prior research in this area has established that trust in an Internet store depends partly on the size and reputation of the organization (Jarvenpaa et al., 2000). Familiarity with an Internet vendor also impacts trust in that vendor (Gefen, 2000). It is not surprising then that 39% of consumers indicate that they would use the Web to buy from merchants they
already know or have bought from in the past (Kane, 1999). One limitation of extant research into the importance of trust in consumer-oriented e-commerce is that it has considered trust in Web merchants somewhat in isolation from other factors that may influence use. For example, Jarvenpaa et al. (1999) investigated whether perceived size and reputation impact trust in an Internet store. In addition, they established that trust impacts willingness to buy from an Internet store through attitudes and risk perception. Gefen (2000) found that both familiarity with an Internet vendor and the consumer’s disposition to trust impact trust, which in turn impacts purchase intention. These studies establish that trust is related to purchase intention. However, they do not consider other factors that may account for these intentions. There is an extensive body of literature on factors that impact adoption and use intentions. For example, perceptions of the relative advantage of an innovation often impact adoption decisions. A number of factors other than trust in Web merchants that impact individual adoption decisions are discussed in the next section. The study reported here extends the findings of previous research by investigating whether the relationship between perceptions of trust in Web merchants and use intentions holds even when the influence of other factors is considered. If so, then the importance of trust in Web merchants is reinforced. If not, then perhaps Web merchants would be better served by addressing first perceptions other than trust. Diffusion of Innovation Theory Diffusion of Innovation Theory, a theory widely applied in studies related to information technologies, suggests that many factors contribute to an individual’s adoption of and intention to use an innovation (Prescott & Conger, 1995). The theory highlights that it is the potential adopters’ perceptions of the characteristics of an innovation that impact the diffusion rate, not experts’ predictions or assessments of the characteristics that matter (Lancaster & Taylor, 1986; Rogers, 1995). Some of the most well-known and researched characteristics thought to influence adoption, as proposed by Rogers (1995), include 1) relative advantage, 2) compatibility, 3) complexity, 4) trialability, and 5) observability. In a meta-analysis of research on the relationship between perceptions of innovation characteristics and adoptionimplementation, Tornatzky and Klein (1982) conclude that relative advantage, compatibility, complexity are the most relevant to adoption-diffusion research, thus we use them in this study. Additional factors
proposed to influence adoption and use of innovations include image, result demonstrability, visibility, and voluntariness (Moore & Benbasat, 1991). Result demonstrability and visibility are two sub-components of observability that have been demonstrated empirically to be separate constructs (Moore & Benbasat, 1991). This might explain why observability was omitted from Tornatzky and Klein's (1982) list of most relevant characteristics (observability being more properly considered as two separate constructs). In addition, given the amount of coverage Web-based electronic commerce has received in the popular press, we include image in our study. We do not explore the constructs of voluntariness or trialability. Voluntariness is the degree to which individuals feel they have the option to use the innovation or not. Since Web-based shopping is an individual choice and is not likely to be mandated, voluntariness would be unlikely to show any variability, and is therefore inappropriate to include in this study. A similar argument can be made for excluding trialability from the study. Trialability is the degree to which potential adopters feel that they can use the innovation before actual adoption. The primary requirements for Web-based shopping are 1) access to a Web-capable computer, 2) access to an acceptable payment method such as a credit card, and 3) sufficient knowledge to use the Web. The researchers believed, and the demographic data confirm, that the vast majority of the subjects were able to meet these requirements. Over 99% of the subjects reported that they had convenient access to a Web-capable computer, 82% reported having access to a credit card, and approximately 95% reported using the Web at least once a week. Since it was unlikely that perceived trialability would display sufficient variance to offer explanatory power and in the interest of parsimony, this construct was not included in the study. Table 1 provides definitions and references of the characteristics discussed in this section. Research Model The constructs described in the previous section are presented graphically in the research model, which is shown in Figure 1. Given the importance of trust in Web merchants as a potential determinant of Webbased shopping, and given the supporting prior research, trust in Web merchants is added to the traditional perceived innovation characteristics to make up the independent variables for this research. The independent variables are predicted to impact the intention to use Web-based shopping, which is the dependent variable.
Innovation Characteristic Relative advantage Complexity
Compatibility
Trialability
Result demonstrability Visibility Image Voluntariness
Description Degree to which an innovation is seen as being superior to its predecessor Degree to which an innovation is seen by the potential adopter as being relatively difficult to use and understand Degree to which an innovation is seen to be compatible with existing values, beliefs, experiences and needs of adopters Based on adopters' perceptions of the degree to which an innovation can be used on a trial basis before confirmation of the adoption must occur Degree to which the results of using an innovation are perceived to be tangible The perception of the actual visibility of the innovation itself as opposed to the visibility of outputs Degree to which the use of the innovation is seen as enhancing to an individual's image or social status Degree to which use of an innovation is perceived as being of free will
References Hebert & Benbasat, 1994; Kwon & Zmud, 1987; Premkumar et al., 1994; Teo et al., 1995; Torknatzky & Klein, 1982; Van Slyke et al., 2002 Cooper & Zmud, 1990; Grover, 1993; Rogers, 1995; Teo et al., 1995; Torknatzky & Klein, 1982; Van Slyke et al., 2002 Eastlick, 1993; Gatignon & Robertson, 1985; Grover, 1993; Lancaster & Taylor, 1986; Nedovic-Budic & Godschalk, 1996; Rogers, 1995; Taylor & Todd, 1995; Van Slyke et al., 2002 Lancaster & Taylor, 1986; Rogers, 1995; Teo et al., 1995
Hebert & Benbasat, 1994; Moore & Benbasat, 1991; Van Slyke et al., 2002 Agarwal & Prasad, 1997; Moore & Benbasat, 1991 Moore & Benbasat, 1991, Van Slyke et al., 2002. Agarwal & Prasad, 1997; Moore & Benbasat, 1991
Table 1. Summary of Perceived Innovation Characteristics It is appropriate to examine use intentions, particularly when an innovation is in the early stages of its development (Hu et al., 1999). There is significant empirical evidence of a causal relationship between intentions and subsequent behavior (Sheppard et al., 1988). This evidence extends to the context of information technologies (Davis et al., 1989; Mathieson, 1991). With the exception of perceived complexity, all constructs are expected to have a positive relationship with use intention. The expected relationships between the independent variables and intention to use represent the hypotheses tested in this study (which are presented in more detail in the following section). Hypotheses As discussed previously, trust tends to influence consumers' general buying decisions (Hosmer, 1995). Empirical evidence indicates that trust impacts decisions to engage in business-to-consumer ecommerce (Gefen, 2000; Jarvenpaa et al., 1999; Jarvenpaa et al., 2000). It is expected that perceived
trust of Web merchants will positively affect consumers’ intent to conduct electronic commerce transactions with these vendors. H1:
Higher trust in Web merchants will be associated with higher intention to purchase goods or services over the Web.
The importance of potential adopters' perceptions of innovation characteristics has been well documented in previous research, as can be seen from Table 1. Since Web-based shopping is considered an innovation (using new technologies or new uses of existing technologies), the expected directions of relationships between these independent variables and the dependent variable are hypothesized to be consistent with previous work. Studies have generally shown a positive relationship between relative advantage, which is the degree to which an innovation is seen as being superior to its predecessor, and innovation adoption (Agarwal & Prasad, 1997; Kwon & Zmud, 1987; Teo et al., 1995;
Relative Advantage
Trust in Web Merchants
H2a H1
Complexity H2b
Compatibility
Image
H2c
Use Intention
H2c: Higher perceived compatibility of purchasing goods or services over the Web will be associated with higher intention to purchase goods or services over the Web.
H2d H2f H2e
Result Demon.
Compatibility, or the degree to which an innovation is compatible with existing values, beliefs, experiences, and needs of adopters is thought to affect adoption decisions (Gatignon & Robertson, 1985; Lancaster & Taylor, 1986; Moore & Benbasat, 1991; Rogers, 1995; Van Slyke et al., 2002b). For example, in another context, farmers place a high social value on increasing crop production. As a result, innovations aimed at conserving soil may not be adopted if they are viewed as being at odds with increasing production (Rogers, 1995). Similarly speaking, if individuals place a high social value on time management, an innovation that conserves time, as does Web-based shopping, is consistent with the social value of time management and thus will be positively related to adoption.
Visibility
Figure 1. Intentions to Use Web-Based Shopping Research Model Tornatzky & Klein, 1982; Van Slyke et al., 2002b). We expect that one motivation behind an individual’s decision to shop on the Web is the degree to which it is perceived as superior to alternative shopping experiences such as catalogue order and malls. We posit the following. H2a: Higher perceived relative advantage of purchasing goods or services over the Web will be associated with higher intention to purchase goods or services over the Web. Complexity, or the degree to which an innovation is seen by the potential adopter as being relatively difficult to use and understand, has been shown to be an inhibitor to adoption (Rogers, 1995; Tornatzky & Klein, 1982). For example, Rogers (1995) notes the increase in the adoption rate of personal computers when they became user friendly. We suspect that such a phenomenon holds true in Web-based shopping and thus posit the following: H2b: Higher perceived complexity of purchasing goods or services over the Web will be associated with lower intention to purchase goods or services over the Web.
Rogers (1995) notes that the status-conferring aspects of adopting an innovation may lead some to adopt even when objective experts feel that the innovation should be rejected. Perceived image (sometimes called status) has been included in studies of IT-based innovation adoption (Agarwal & Prasad, 1997; Van Slyke et al., 2002), although there has been little empirical support for its relationship to use intentions. However, the growing use of technology and the amount of coverage Web-based electronic commerce has received in the popular press suggest that perceptions of image-enhancing impacts of an innovation may be particularly important in the context of Web-based electronic commerce. We posit the following: H2d: Higher perceived image of purchasing goods or services over the Web will be associated with higher intention to purchase goods or services over the Web. Rogers defines observability as “the degree to which the results of an innovation are visible to others (Rogers, 1995). Moore and Benbasat (1991) find that observability consists of two aspects – the visibility of the innovation itself and the tangibility of results, including observability and communicability. They call this second aspect result demonstrability. We posit that both of these aspects will be positively related to adoption. H2e: Higher perceived result demonstrability of purchasing goods or services over the Web will be associated with higher intention to purchase goods or services over the Web. H2f: Higher perceived visibility of purchasing goods or services over the Web will be
associated with higher intention to purchase goods or services over the Web.
Methodology Overview To evaluate the research model a survey was administered to a sample of consumers enrolled in courses at three public North American universities. Subjects were asked to respond to items related to their perceptions of Web-based shopping and Web merchants. Following the methods used by Cheung and Lee (2001), Lee and Turban (2001), and Van Slyke, Comunale & Belanger (2002) questions were worded to refer to Web shopping and merchants in general, rather than any specific Web-based merchant. A pilot study was conducted to refine the measurement scales included in the survey. Regression analysis was used to test hypotheses related to the research model. Instrument Development The survey instrument included a combination of items derived from earlier studies and newly developed items. Items were measured on seven-point Likert-type scales, except for items intended to collect demographic data. All constructs shown in the research model (Figure 1), with the exception of trust in Web merchants (TR), and intention to use (USE), were measured using items derived from Moore and Benbasat’s (1991) instrument. Slight modifications were made to the original scale items to reflect the business-to-consumer electronic commerce nature of this study as opposed to the personal workstation wording of the original items. New scales were constructed for TRUST and USE. A pool of items was developed for each of these scales, and then pre-tested. The pre-test indicated that there were serious problems with the trust in Web merchants scale, so the wording of the items were modified, retested, and then included in the final survey. (The individual measurement items are presented in Appendix A.) Instrument Validation A pilot study with 84 subjects was conducted to refine measurement scales and data collection procedures. Analysis of pilot study data demonstrated that all scales except trust in Web merchants displayed acceptable reliability to warrant inclusion in the full study without further refinement. The revised items for the trust in Web merchants scale demonstrate acceptable psychometric properties, as discussed in this section. Note that the pilot data were not included as part of the full data set.
The data from the full data collection (n=507) was used to compute Cronbach's statistic alpha (Cronbach, 1970) to evaluate the reliability of the scales. SPSS 8.0.0 was used for this computation. Table 2 shows the alpha reliability statistics for each scale as computed from the full data set. Where applicable, the table also provides the alphas reported by Moore and Benbasat (1991). Scale Relative advantage (RA) Complexity (CX) Compatibility (CT) Image (IM) Result demonstrability (RD) Visibility (VI) Use intention (USE) Trust in Web merchants (TR)
#items (pilot) 3
Alpha 0.76
Moore & Benbasat 0.90
4 3 3 4
0.78 0.90 0.71 0.58
0.84 0.86 0.79 0.79
2 3
0.18 0.95
0.83 N/A
3
0.86
N/A
Table 2. Reliability Statistics (n=507) The reliabilities of all scales except result demonstrability and visibility exhibit acceptable reliability with alpha values over 0.70. The result demonstrability and visibility scales are below this cutoff and are also noticeably lower than the reliabilities found by Moore and Benbasat (1991). For this reason, result demonstrability and visibility are excluded from further analysis. The unidimensionality and convergent validity of each scale was also analyzed using confirmatory factor analysis (CFA) with maximum likelihood estimation. Unidimensionality was analyzed by specifying a measurement model for each scale with each indicator item included as an observed variable for the underlying latent construct. If the measurement model for a scale exhibits acceptable fit, then unidimensionality is established (Ravichandran & Rai, 1999-2000). Three measurement models were evaluated, one for the complexity scale, one for the relative advantage, compatibility and image scales, and one for the trust in Web merchants and use intention scales. The groupings of scales were done because individual measurement models for these scales lacked sufficient degrees of freedom. Table 3 shows two fit indices for each of these measurement models. The Goodness of Fit Index (GFI) is a widely used measure of fit. A GFI exceeding 0.900 is generally considered to indicate an acceptable fit (Ravichandran & Rai, 1999-2000). The root mean square residual (RMR) is an alternate fit measure. An
RMR below 0.090 is an indication of acceptable fit (Ahire et al., 1996). Note that the fit indices for all scales are acceptable, indicating unidimensionality. Scale Items GFI RMR Delta RA* 3 0.961 0.087 0.962 CT* 3 0.961 0.087 0.962 CX 4 0.994 0.035 0.990 IM* 3 0.961 0.087 0.962 TR** 3 0.990 0.034 0.994 USE** 3 0.990 0.034 0.994 *, ** These items were combined for the analysis. Table 3. Scale Unidimensionality and Convergent Validity (n=507) One approach to establishing convergent validity is to view each indicator item as being a different approach to measuring the underlying construct (Ahire et al., 1996). Using this approach, the fit of the measurement model of a scale can be compared to the fit of a null model using the Bentler-Bonnet coefficient delta, which is the ratio of the difference between the chi-square value of the two models to the chi-square of the null model. Values exceeding 0.900 indicate acceptable fit, which is an indication of convergent validity (Ahire et al., 1996). As can be seen in Table 3, the delta values for all scales indicate convergent validity. Note that the scales (other than complexity) derived from Moore and Benbasat (1991) were grouped together as were the newly developed scales. As a further check, other groupings were examined; results of these alternate groupings exhibited similar results to those shown in Table 3.
The discriminant validity of each scale was evaluated following the method recommended by Venkatraman (1989). A series of CFAs are performed with each analysis considering the measurement model for a pair of factors. For each pair of factors, two models are compared. One model allows the covariance between the two factors to be unconstrained, while the other model constrains the covariance at unity. The constrained model is the equivalent of considering both factors to be, in actuality, one factor. The unconstrained model considers the factors to be separate. If the fit of the unconstrained model is superior to the constrained model, there is evidence that there are two factors present, indicating discriminant validity. The fit of the two models is compared by computing a chi-square statistic for each model. If the difference in fit between the two models is significant, then the fit of the unconstrained model is superior, which establishes discriminant validity (Ahire et al., 1996). In this research, there are fifteen possible pairings of factors, as shown in Table 4. As can be seen from Table 4, in all cases, the unconstrained model is superior (at p < 0.05), indicating that the scales display discriminant validity. Sample Characteristics The instrument was administered to 511 subjects who were enrolled in courses at three large, North American Universities. Four partially-completed surveys were discarded resulting in a final sample size of 507. Completion of the survey was voluntary. The respondents ranged in age from 17 to 48 years old. Of the responses, 295 were from males, and 212 were from females.
Chi-square Chi-square Factors Unconstrain. Constrained Trust– relative advantage 30.136 37.988 Trust – compatibility 34.013 60.094 Trust – complexity 50.976 82.227 Trust – image 12.076 183.778 Trust – use intention 13.772 17.998 Relative advantage - compatibility 52.880 100.371 Relative advantage – complexity 53.208 57.170 Relative advantage – image 34.447 159.487 Relative advantage – use intention 26.402 35.895 Compatibility – complexity 53.164 60.987 Compatibility – image 13.612 105.102 Compatibility – use intention 40.055 69.404 Complexity – image 36.381 278.612 Complexity – use intention 57.028 62.604 Image – use intention 10.568 147.631 Note: where significance is shown as 0.000, actual significance is < 0.001 Table 4. Discriminant Validity (n=507)
Chi-square Difference 7.852 26.081 31.252 171.702 4.276 47.490 3.962 125.040 9.493 7.823 91.491 29.348 242.232 5.576 137.063
Significance 0.005 0.000 0.000 0.000 0.039 0.000 0.047 0.000 0.002 0.005 0.000 0.000 0.000 0.018 0.000
The median computer experience in the sample was five years. Almost all respondents (504) reported prior Web experience, and the vast majority used email (493), the Web (483) and word processing (437) at least once a week. The subjects were also asked whether they had ever purchased anything over the Web. Responses to this item split almost evenly with 259 subjects indicating previous Web purchasing experience and 248 indicating that they had never made a purchase over the Web. Information related to the subjects' ability to purchase goods or services over the Web was also gathered. We asked subjects whether they had 1) a credit card, and 2) convenient access to a computer that they could use to access the Web. Virtually all (505) of the subjects had convenient access to a Web-capable computer. However, a smaller number (415) reported having access to a major credit card, which is typically a requirement for making Web purchases. Table 5 summarizes some of the characteristics of the subjects. A Note on Generalizability. This study investigates factors believed to impact intention to adopt businessto-consumer electronic commerce. We used samples from three universities. When students are used as the sample for a study, there is often concern for the generalizability of the results. When students are asked to perform contrived tasks that are not of direct relevance to them, this concern is often valid. However, when the domain area of study is of relevance to these subjects, the negative effects of the use of students are mitigated (Gopal et al., 19921993). In this research, students were surveyed about a topic that held direct relevance to them, the use of the Internet to acquire goods or services. Students were not asked to project themselves into an artificial role; they were asked their perceptions as individuals. Thus, it is reasonable to expect that any associations found between their perceptions and their adoptions are valid. In addition, several studies of technology acceptance and usage employed students as their subjects (Chin & Gopal, 1995; Davis, 1989; Taylor & Todd, 1995). Data Analysis Multiple regression analyses were used for hypothesis testing. The purpose of performing a regression analysis is to relate a response, or dependent variable, to a set of independent variables (Mendenhal & Sincich, 1993). Since the goal of this analysis was to determine the relationship between use intention (dependent variable) and the perceptions of the characteristics of Web shopping (independent variables), regression analysis was seen as the most appropriate analytical technique.
Prior to testing hypotheses, a regression analysis was performed to assess the significance of the demographic characteristics. The demographic characteristics were used as independent variables and USE as the dependent variable. Prior Web purchasing experience, availability of a credit card, prior Web experience, access to a computer, and gender emerged as significant factors in the equation. These variables were included in subsequent analyses, while the other demographic variables were dropped. A number of assumptions about the residual or error term (ε) underlie the statistical tests used in regression analysis, and they should be evaluated in order to know how much confidence to place in the inferences drawn from the analysis when certain assumptions are violated (Mendenhal & Sincich, 1993). Prior to conducting these analyses, assumptions of multivariate normal distribution, independence of errors, and equality of variance were tested. The USE variable was slightly skewed. There were no violations of the other assumptions. Pearson correlation coefficients revealed that some of the independent variables were correlated, and the main effect regression models with variance inflation factors (VIF) confirmed that multicollinearity was not a concern with this data set (VIF range from 1.01 to 2.63). Outlier influential observations were identified with leverage, studentized residuals, and Cook’s D-statistic. This analysis indicated that there were no problems with respect to influential outliers. Hypothesis Testing A stepwise multiple regression analysis was performed on a parsimonious main effects model with the significant demographic variables and the hypothesized variables included as independent variables (except result demonstrability and visibility, which were dropped due to reliability concerns) and USE as the dependent variable.
Results The regression analysis results in a model with an 2 adjusted R of 0.698, indicating that the independent variables account for 69.8% of the variation in USE. 2 The adjusted R statistic takes both sample size and the number of terms in the regression model into account; it is a more conservative statistic than the 2 unadjusted R . The F statistic for the regression equation (F=169.389, 7/502 df) is significant at p
