Browsers or Buyers in Cyberspace an Investigation of Factors Influencing Electronic Exchange

Browsers or Buyers in Cyberspace? An Investigation of Factors Influencing Electronic Exchange 1. Vanitha Swaminathan1,*, 2. Elzbieta Lepkowska-White2,*, 3. Bharat P. Rao3,* Article first published online: 23 JUN 2006 DOI: 10.1111/j.1083-6101.1999.tb00335.x Issue

Journal of Computer-Mediated Communication Volume 5, Issue 2, page 0,December 1999

Abstract In its current form, the Internet is primarily a source of communication, information and entertainment but increasingly also a vehicle for commercial transactions. An understanding of reasons for purchasing on the World Wide Web is particularly relevant in the context of predictions made regarding electronic shopping in the future. In the paper, we focus on some of the antecedents to electronic exchange in the online context. In particular, what are some of the factors influencing online purchasing behavior? What is the role of privacy and security concerns in influencing actual purchase behavior? How do vendor and customer characteristics influence consumers' propensity to engage in transactions on the Internet? We analyze secondary data from an e-mail survey. The study has implications for both theory and practice. The findings extend our knowledge of factors influencing marketing exchange from the traditional setting to the internet context. In addition, the findings regarding factors enhancing the propensity to shop online have implications for internet retailers seeking to enlarge their online customer base.

Introduction The Internet has emerged in the recent past as a dynamic medium for channeling transactions between customers and firms in a virtual marketplace. The growth of the Web has been phenomenal, and there has been a corresponding growth in commerce on this robust platform. Varying estimates of its impact on online retail and shopping abound, and the estimates point to dramatic growth. It is projected that online shopping will grow from

$11 billion in 1999 to $41 billion in 2002 (National Retail Federation, 1999). The US Commerce Department citing a study by Forrester Research suggests that online retail trade ranging from $7 to $15 billion in 1998 will reach anywhere from $40 billion to $80 billion by 2002 (US Department of Commerce, 1999).(1) Many companies are already online, exploring and shaping this new opportunity. Amazon.Com offering more than two million titles of books and CDNow are prime examples of companies that are successful in the online space as evidenced by their stock market capitalizations (Zwass, 1999). According to the Travel Industry Association of America (TIA), the number of Americans using the Internet to plan vacation or travel grew from 10% in 1996 to more than 25% in 1998 (Kate, 1998). The rapid growth of this new medium poses intriguing questions for academic research. To date, researchers have focused on the role of the Web as an information and communications medium (Hoque and Lohse 1999; Lynch and Ariely 1998; Alba et. al 1997; Hoffman, Novak and Chatterjee 1996; Berthon, Pitt and Watson 1996; Hoffman and

Novak 1995). Berthon, Pitt and Watson (1996) introduce the concept of conversion efficiencies of a Web site, which refers to the rate at which browsers are converted into buyers. While a number of authors have examined factors that may influence shopping on the internet, (e.g., Alba et al. 1997; Palmer 1997), much of this research is primarily conceptual in nature. Hoque and Lohse (1999) examine the impact of user interface design on information search costs in electronic media. Very little empirical research exists on issues relating to shopping on the internet. In order to examine the various alternative shopping formats, a brief comparison of Web retailing with traditional retail, TV in-home shopping, catalog shopping on various dimensions of variety, trialability, asynchrony and interactivity is enclosed in Table I. As can be seen in the Table, the internet retailer offers the benefit of asynchrony, i.e., the internet retailer is available for shopping any time of the day or night, and the benefit of interactivity. The disadvantages are the inability to sample the product and the limited variety in terms of merchandise.(2) Variety

Trialability

Asynchrony

Full Retail

High

High

Low

TV

Moderate

Low

Moderate

Catalogs

Moderate

Low

Moderate

Web

Moderate

Low

High

Table 1. Transaction Types Compared

Scant research exists which examines factors influencing purchasing over the internet, an issue which is particularly relevant in the context of predictions regarding electronic shopping in the future. In particular, the following questions are of interest to the researchers: (1) What factors influence online purchasing behavior?; (2) What is the role of perceived risk in online purchasing on actual purchase behavior?; (3) How do differences in customer characteristics influence their decisions to shop online? Our primary objective of this study is to investigate factors influencing commercial transactions in the online environment. To address this issue, we develop a model examining the factors influencing electronic exchange. The model will be tested on a sample of internet users. Some of these internet users are likely to exhibit a greater propensity to shop online. Therefore, the purpose of this study will be to examine factors influencing buying among internet browsers. In the following section, the model used in this study will be described and the hypothesis for each antecedent developed. Following that, the data analysis is presented. Finally, results, conclusions and directions for future research are presented.

THE CONCEPTUAL MODEL It has been long known that the exchange process is central to the concept of marketing (Sheth and Parvatiyar 1995; Morgan and Hunt 1994; Dwyer, Schurr and Oh 1987; Bagozzi 1975, 1974). The exchange system has been conceptualized as a set of social actors and

their relationships to each other, and as the endogenous and exogenous variables affecting the behavior of the social actors in those relationships (Bagozzi 1974). The theory of exchange has evolved into the theory of relational exchange. Relational exchange theory or relationship marketing has a number of proponents and has been frequently used in the past studies (Morgan and Hunt 1994; Sheth and Parvatiyar 1995). Although the relational exchange literature primarily focuses on the determinants of long-term buyer-seller relationships, some of the concepts from the earlier literature on exchange ( Bagozzi 1975, 1974) and the recent work in the relationship marketing area can be drawn upon in

identifying factors influencing the likelihood of electronic exchange. The fundamental exchange model (Bagozzi 1974) views the exchange process as a social influence process. Among the characteristics identified in the social exchange model as key antecedents of exchange are social influence, social characteristics of actors and third party

effects. Thus, in the context of electronic exchange, the characteristics of the consumers and the vendors should affect the propensity to engage in a transaction. For instance, in regards to consumers, Sheth and Parvatiyar (1995) suggest that consumers' sociological orientations may play an important role in increasing the propensity to engage in relationships. We investigate the role of consumer characteristics by examining the consumers' shopping orientations as an antecedent to the likelihood of electronic exchange. In regards to vendors, literature on relationship marketing recognizes the role played by trust (Morgan and Hunt 1994; Moorman, Deshpande and Zaltman 1993). Trust is defined as confidence on the part of the trusting party that the trustworthy party is reliable, has high integrity and is associated with such qualities as consistency, competency, honesty, fairness, responsibility, helpfulness and benevolence (Morgan and Hunt 1994). Vendor characteristics were chosenbased on these factors. Finally, perceived risk has been identified as a key antecedent to relationship commitment in past studies (Sheth and Parvatiyar 1995). In this research, the perceived risk is referred to as the overall perceived security of transactions in an online environment and it is not specifically related to a single vendor. The notion of perceived risk as a key antecedent to consumer behavior has been established in the past and may be a primary factor influencing the conversion of browsers to buyers (Bauer 1960). Connected with this issue is consumers' concern for privacy, e.g., Bloom, Milne and Adler (1994). This research suggests that consumers may not be willing to give out information on the Internet since they may be afraid that their private information may be sold to someone else and this may prevent them from engaging in e-commerce. We posit that perceived security of transactions and concern for privacy are two other antecedents to electronic exchanges. The model of antecedents to electronic exchange is presented in Figure I. The model shows the likelihood of electronic exchange as the focal construct of interest influenced by consumer and vendor characteristics, concern for privacy and perceived security of transactions. For the purpose of this study, we define electronic exchange as past purchasing behavior measured in two ways: (1) number of occasions when a WWW user makes an electronic purchase and (2) the total amount spent online in the last six months.(3) The data in this study was analyzed at the individual level. In the next section, each antecedent presented in the model is described and formal hypotheses are developed. Figure I. Factors Influencing Likelihood of Electronic Exchange

Vendor Characteristics We define a “vendor” as any seller who seeks commercial electronic exchange with an Internet user. This should not be confused with electronic service providers like Netcom or America Online who provide computer time for a fee. A retailer with a home page on the WWW, like JC Penney, which provides users with the opportunity to shop over the computer is therefore classified as a vendor. Consumers evaluate these vendors before they enter into electronic exchanges and therefore, the characteristics of these vendors play an important role in facilitating an exchange. These vendors have to be superior to other vendors in alternative shopping modes in order to be noticed and contacted by consumers. We identify the following vendor characteristics as important in the context of electronic transactions: (1) reliability, (2) convenience in terms of services offered, and (3) the perceived price competitiveness and easy access of information offered by Web vendors in comparison to alternative shopping modes. Reliability is related to the construct of trust. Trust is defined as, “a willingness to rely on an exchange partner in whom one has confidence,” (Moorman et al. 1993, p.82) and as confidence that the other party is reliable, honest, consistent, competent, fair, responsible, helpful and altruistic (Morgan and Hunt 1994). Luedi (1997, p.22) argues that vendors should “„…fulfill transactions by reliably and securely supporting the full spectrum of electronic commerce from promotional pricing to secure payment handling.‟” The trust in a vendor is likely to affect the consumers' perception of vendor's reliability and is therefore identified as an antecedent of an electronic exchange. H1a: The greater the perceived reliability of Web vendors compared to other vendors, the

greater the likelihood of electronic exchange. The perceived convenience offered by Web vendors is a significant factor in influencing the decision to purchase at home. Shopping convenience is acknowledged to be the primary motivating factor in consumer decisions to buy at home (Gillett 1976). Studies of catalog and telephone shoppinghave indicated the role of convenience-orientation as a significant predictor of in-home shopping behavior (Gillett 1976; Reynolds 1974). Shopping convenience includes the time, space and effort saved by a consumer and it includes aspects such as an ease of placing and canceling orders, returns and refunds, timely delivery of orders (Gehrt, Yale and Lawson 1996). H1b: The greater the perceived convenience of using Web vendors compared to other

vendors, the greater the likelihood of electronic exchange.

Price competitiveness of an online vendor in comparison to other online vendors should promote Internet purchases. Previous research suggests that the Internet provides consumers with information that allows for price comparisons (Zellweger 1997). Alba et al (1997)states that the Internet increases price comparisons and intensifies competition

among the online vendors who try to attract potential buyers. H1c: The greater the perceived price competitiveness of Web vendors compared to other

vendors, the greater the likelihood of electronic exchange. Finally, the wealth of useful information that is readily provided on the Internet by a vendor is likely to enhance electronic transactions (Zellweger 1997). Zellweger (1997, p. 13) states that buyers become extremely frustrated “especially when pages contain irrelevant information.”Luedi (1997 p.22) argues that successful Internet marketers should “attract and retain consumers by providing personalized and compelling content coupled with a sense of community relevant to them.” This suggests that buying might be the result of encouraging browsers to repeat visit the site. Consumers might consider richness of information as a vendor-specific characteristic. This information may in itself be a reason to return to that vendor. Further, with technologies like personalization used in conjunction with detailed product information, the switching costs of moving to another vendor are increased after the first positive shopping experience at the vendor. Therefore, it is proposed that: H1d: The greater the perceived usefulness of information of Web vendors compared to

other vendors, the greater the likelihood of electronic exchange.

Perceived Security of Transactions and Concern for Privacy One of the most important and pressing concerns for businesses on to the Internet deals with the level of security in transactions. Many companies are going online, not because of strategic reasons, but due to strong lobbies that push for such an interface with the outside „computer‟ world. Most current commercial Web Pages provide consumers with various options to place orders for the products advertised. These include addresses, toll-free numbers, and in many cases, a provision for sending credit card information. Unscrupulous use of such sensitive information cannot be ruled out. Despite advances in Internet security mechanisms like SHTTP, cryptography, and authentication, customers are still concerned about using an impersonal transaction medium for secure transactions. Online retailers have to make concerted efforts to allay these fears by offering clear guidelines to consumers on their online security and privacy policies, limits of consumer liability in the

case of fraudulent transactions, and offer alternate payment mechanisms through toll-free phone numbers, customer representatives, etc. if necessary. Still, perceptions of unsatisfactory security on the Internet is one of the primary reasons hindering online purchasing (Zellweger 1997; Communications of the ACM, April 1999, p.80). Risk is faced by individuals when a decision, action or behavior leads to different outcomes (Bem 1980). When an individual's action produces social and economic consequences that cannot be estimated with certainty, the individual encounters risk (Zinkhan and Karande 1991). Risk relates to situations or problems (Bem 1980; Dowling 1986), overall product

categories or brands (Dowling and Staelin 1994) or persons' attitude to risk (Zinkhan and Karande 1991). In our context, two types of risk are especially relevant:(1) person's overall

risk taking propensity and (2) the perceived risk of online transactions. The perceived security of online transactions varies based on the specific payment procedure. For instance, the perceived security of giving credit card information directly over the Web is likely to be different from the risk of setting up a third party account and using that account number in transactions. H2: The greater the perceived security of transactions in an online medium, the greater the

likelihood of electronic exchange. Connected with this issue are consumers' concerns about the use of their private information by organizations when engaging in Internet activities (Business Week, April 5, 1999). Various surveys show that online shoppers are concerned about privacy (Communications of the ACM, April 1999, p. 80). Rohm and Milne (1999) confirmed the finding that privacy is an important issue to the Internet users although other research on direct mail suggests that privacy may not be of such great importance to consumers ( Milne and Gordon, 1993).

Alternative payment procedures might offer convenience, but offer a limitation to customers who are concerned about transmitting personal information, e.g., credit card numbers, online. P3P and other privacy protocols also represent the initial steps in the evolution of technical standards to combat privacy abuse. We refer the reader to Cranor, et al (1999) for a comprehensive analysis of consumer attitudes to online privacy, technological solutions, and the current debate on privacy issues. (Cranor et al, 1999).Given the current recognition of privacy as a major issue in electronic commerce, we propose that consumers propensity to engage in shopping over the internet is lower if they are concerned about privacy of information. H3: The greater the concern for privacy, the lower the likelihood of electronic exchange.

Consumer Characteristics The relationship marketing literature suggests that consumer characteristics, eg., sociological orientation, plays an important role in a consumers' propensity to engage in the Internet transactions (Sheth and Parvatiyar 1995). The retailing literature also suggests that consumer characteristics are important indicators of the probability of making purchase decisions on the Internet. In his pioneering study,Stone (1954) suggested that shopping behavior has social-psychological origin and classified shoppers into four types: economic shopper, the personalizing shopper, the ethical shopper and the apathetic shopper. Another typology was identified by Stephenson and Willett (1969) who grouped consumers into recreational, convenience and price oriented shoppers. Two additional categories that is psychosocializing and name-conscious shoppers were added by Moschis (1976). Bellenger and Korgaonkar (1980) suggest that consumers can be classified into recreational and

convenience shoppers. They suggest that the recreational shopper is motivated by the social aspects of shopping. Past research suggests that the Internet is less attractive to consumers who value social interactions since it allows for very limited interactions relative to other retail formats such as department stores (Alba et al 1997).(4) Therefore, it is hypothesized that those consumers who are primarily convenience shoppers are more likely to shop online than those that seek social interaction. H4a: The likelihood of electronic exchange will be greater among convenience shoppers. H4b: The likelihood of electronic exchange will be lower among shoppers seeking social

interaction.

DATA This study uses secondary data based on an e-mail survey conducted by the Georgia Visualization and Usability Center at Georgia Tech of approximately 5000 respondents. The respondents were invited to participate in the e-mail survey through announcements on Internet related newsgroups (e.g. comp.infosystem, www.announce, comp.internet.nethappenings, etc.), banners randomly rotated though high-exposure sites (e.g. Yahoo, CNN, Excite, Webcrawler, etc.), banners rotated through advertising networks (e.g., DoubleClick), announcements made to the www-surveying mailing list, a list maintained by GVU's WWW User Surveys composed of people interested in the surveys, and announcements made in the popular media, e.g., newspapers, trade magazines, (http://www.gvu.gatech.edu/user_surveys/survey-1998-10/#methodology). A $ 100 cash incentive was given to approximately ten randomly chosen respondents. One of the

limitations of this survey is that respondents are not chosen in a random manner. In order to ensure a random sample, it is essential to have a list of all users of the Internet such that respondents may be chosen randomly using probability sampling. Since such a list is not available, a non-probabilistic sampling procedure described above is used. This may result in self-selection bias and reduce our ability to generalize to the population at large. However, most surveys have some element of self-selection bias due to the refusal by certain respondents to participate in a survey. It is acknowledged that the sampling procedure may be a limitation of the current study but also believe that the insights derived from an empirical analysis of the topic outweigh the limitation imposed by the sampling procedure. The survey, conducted in 1998, involved data collected in questionnaires addressing each topic: vendor characteristics, security of transactions, concern for privacy, customer characteristics and purchasing behavior. The data available in various databases was matched using an ID number given to each consumer. Of the 5000 respondents that responded to the individual questionnaires, only 428 had completed responses to all the topics. In other words, the final sample size after merging data sets was 428. A description of the respondents in the sample in terms of their age, education, income and gender is presented in Table II. The respondents were qualified to include those who are users of the internet for collecting information and browsing. Some of these respondents use the internet for purchasing. Approximately 15% of the respondents hardly ever purchased anything online. Variable

Percentage(Sample Size=428)

Gender

28.3%

Female

71.7%

Education

7%

High School

2%

Vocational

27%

Some College

39%

College

19%

Masters

4%

Doctoral

2%

Variable

Percentage(Sample Size=428)

Other Age

4.4%

16–20

12.4%

21–25

16.1%

26–30

15.9%

31–35

12.9%

36–40

10.3%

41–45

9.6%

46–50

8.9%

51–55

9.5%

>55 Income

2.3%

Under $10,000

5.1%

$10,000–$19,000

7.2%

$20,000–$29,000

13.3%

$30,000–$39,000

11.4%

$40,000–$49,000

21.7%

$50,000–$74,000

12.9%

$75,000–$99,000

13.3%

Over $100,000

12.6%

No Answer Table 2. Descriptive Statistics Assessing Discriminant Validity of the Scale: In order to assess the discriminant validity of

the entire scale, a factor analysis was conducted and those factors with an eigenvalue greater than 1.0 retained. This analysis resulted in eight factors. Items with loadings greater than .50 were identified and used in naming the factors. The results from this analysis

showed that the factors that were identified corresponded with the various constructs that were measured, e.g., vendor characteristics, privacy, security and customer characteristics. The results of this are presented in Table III. A detailed analysis of each of these factors follows. Vendor

Privacy1

Privacy2

Security

Privacy3

Privacy4

Vendor(Q1)

0.64

−0.09

−0.16

−0.07

0.00

0.08

Vendor(Q2)

0.73

−0.05

−0.03

0.00

0.00

0.10

Vendor(Q3)

0.73

−0.08

0.13

−0.05

0.07

−0.01

Vendor(Q4)

0.53

−0.09

−0.13

−0.06

0.02

−0.08

Vendor(Q5)

0.66

0.04

0.12

0.05

−0.19

−0.10

Security(Q6)

0.01

0.14

−0.07

0.85

0.15

−0.03

Security(Q7)

−0.08

0.15

0.03

0.85

−0.03

−0.04

Privacy(Q8)

−0.13

0.64

−0.02

0.13

−0.09

0.08

Privacy(Q9)

−0.01

0.82

−0.05

0.02

0.15

−0.09

Privacy(Q10)

−0.03

0.74

−0.06

0.10

0.39

0.07

Privacy(Q11)

−0.12

0.61

−0.12

0.15

0.19

−0.04

Privacy(Q12)

−0.07

−0.07

0.69

0.08

0.17

0.01

Privacy(Q13)

0.00

−0.08

0.74

−0.04

−0.12

−0.02

Privacy(Q14)

0.00

0.07

0.59

−0.17

−0.10

−0.15

Privacy(Q15)

−0.10

0.34

0.02

0.15

0.72

0.10

Privacy(Q16)

0.01

0.11

−0.04

0.00

0.86

−0.11

Privacy(Q17)

0.08

0.06

0.15

−0.31

−0.24

0.57

Privacy(Q18)

−0.05

0.01

−0.09

0.05

0.08

0.85

Privacy(Q19)

0.04

−0.07

−0.07

0.01

0.05

0.25

Privacy(Q20)

0.08

−0.18

0.48

−0.05

−0.10

0.31

Social(Q21)

−0.06

0.04

−0.12

−0.01

0.00

−0.03

Vendor Convenience(Q22)

0.25

Privacy1 −0.08

Privacy2 0.06

Security −0.05

Privacy3 −0.07

Table 3. Factor Analysis of Scale Items Vendor Characteristics (VENDOR)a. Vendor characteristics were operationalized using a 5-

point agree-disagree scale 2. To evaluate vendors characteristics, the following scale items were used: (1) vendor perceived reliability - respondents expressed their opinion to the statement „World Wide Web vendors are more reliable‟, (2) perceived convenience of using Web vendors - respondents expressed their opinion to the two statements'(a) It is easier to place orders placed with World Wide Web vendors, and (b) It is easier to contact World Wide Web vendors, (3) price competitiveness - respondents expressed their opinion to the statement „World Wide Web vendors offer better prices‟, and (4) access to information – respondents expressed their opinion to the statement' World Wide Web vendors offer more useful information about the choices available. The factor analysis of the scales showed that all vendor characteristics load on one factor with an eigenvalue greater than one. The reliability of the entire scale was 0.69. The vendor characteristics of respondents were averaged to form one score. Since all the characteristics of the vendor related to the vendors' superiority over others (in regards to reliability, price, information provision or convenience) the construct was named „superiority over other vendors‟. Perceived Security of Transactions (SECURITY)b. Theperceived security of transactions

was operationalized using a 4-point scale with the following two items: (1) In general, how concerned are you about security on the Internet? and (2) How concerned are you about security in relation to making purchases or banking over the Internet? The correlation between these two items was 0.57. Concern for Privacy (PRIVACY1-PRIVACY5)c. The privacy scale items used in the survey

consisted of 13 items. A factor analysis of the scale items used in the survey indicated that the privacy items loaded on five factors. These five factors emerged which seemed to map onto various aspects of privacy, i.e., use of information, anonymity, perception of direct marketing, privacy laws and control over information. The items which had factor loadings of greater than 0.5 were retained. The reliability coefficients for the first two subscales were .727 and .536. The correlation between items in the last two subscales were .499 and .411. Customer Characteristics (SOCIAL INTERACTION and CONVENIENCE)d. In order to

assess whether customers are motivated by convenience or the social interaction associated with shopping, two questions were posed. The first question asked the

Privacy4 0.11

respondents whether they preferred dealing with people during shopping (One of the reasons I have not shopped on the Web is that I prefer to deal with people). The second question asked whether convenience affected their choice of the shopping mode (One of the reasons I shop on the Web is convenience). Both were dummy variables that were coded 1 if the response was a „yes‟ and 0 otherwise.

Likelihood of Electronic Exchange (PUR) Likelihood of electronic exchange was based on past purchasing behavior on the Web and measured in two ways (1) as number of electronic purchases and (2) the total amount spent online in the last six months. The means and standard deviations of the scale items along with detailed descriptions are presented in Table IV. Factors and Scale Items Vendor Characteristics (a) (5-point scale ranging from strongly disagree to strongly agree) 1. World Wide Web vendors are more reliable 2. World Wide Web vendors offer better prices 3. World Wide Web vendors offer more useful information about the choices available 4. It is easier to cancel orders placed with World Wide Web vendors. 5. It is easier to contact World Wide Web vendors Perceived Security of Transactions and Concern for Privacy (b)Perceived Security of Transactions (SECURITY) 6. In general, how concerned are you about security on the Internet? 7. How concerned are you about security in relation to making purchases or banking over the internet? (4-point scale ranging from not at all concerned to very concerned) Concern for Privacy (c) (5-point scale ranging from disagree strongly to agree strongly) USE OF INFORMATION (PRIVACY1) 8. Web sites need information about their users to market their site to advertisers 9. Content providers have the right to resell information about its users to other companies 10. A user ought to have complete control over which sites get what demographic information

Factors and Scale Items 11. Third party advertising agencies should be able to compile my usage behavior across different web sites for direct marketing ANONYMITY (PRIVACY2) 12. I value being able to visit sites on the Internet in an anonymous manner 13. I ought to be able to visit sites on the internet in an anonymous manner 14. I would prefer internet payment systems that are anonymous to those that are user-identified DIRECT MARKETING (PRIVACY3) 15. I like receiving mass postal mailings that were specifically targeted to my demographics 16. I like receiving mass electronic mailings PRIVACY LAWS (PRIVACY4) 17. There should be new laws to protect privacy on the Internet 18. There should be laws to protect children's privacy CONTROL OVER INFORMATION (PRIVACY5) 19. I ought to be able to communicate over the Internet without people being able to read the content. 20. I support the establishment of key escrow encryption where a trusted party keeps a key that can read encrypted messages Customer Characteristics (d) 21. SOCIAL INTERACTION: One of the reasons I have not shopped on the Web is that I prefer to deal with people (Yes=1/No=0) 22. CONVENIENCE: One of the reasons I shop on the Web is convenience (Yes=1/No=0) (5-point scale ranging from very uncomfortable to very comfortable) Table 4. Variables and Scale Items Variables and Scale Items 1.

Num

a the complete questionnaire available at: http://www.gvu.gatech.edu/user_surveys/survey-1998-10/graphs

Variables and Scale Items

Num

2.

b and c the complete questionnaire available at: http://www.gvu.gatech.edu/user_surveys/survey-1998-10/g

3.

d the complete questionnaire available at: http://www.gvu.gatech.edu/user_surveys/survey-1998-10/graphs

4.

1 reliability is assessed using Cronbach's Alpha when more than 2 scale items are present; otherwise the nu

correlation coefficient. Purchase Behavior (PUR 1) (d) 23. On average, how often do you make online purchases from Web-based vendors? (5-point scale ranging from hardly ever to at least once a day) Purchase Behavior (PUR 2) (d) 24. What is the total amount you spent on purchases through vendors on the World Wide Web during the past six months? (4-point scale ranging from $0 to $500 or more)

MODEL DEVELOPMENT The constructs described above were measured using various scale items that were reduced to various dimensions using factor analysis. The variables were averaged for each factor and the averages were used as input for each construct. We use multiple regression analysis to estimate the model. The model to be tested is of the following form:



RESULTS Main Model Two models for each indicator of likelihood of electronic exchange were estimated. The results with frequency of Web shopping (Model 1) the total amount spent online in the last six months (Model 2) as the dependent variables are presented in Table V. The explanatory power of the models, as indicated by adjusted R2 for Models 1 and 2 is 10% and 13% respectively. Variable 1.

* significant at p