The Internet and Consumer Buying Behavior: A Research Framework and Analysis

Vanitha Swaminathan* Elzbieta Lepkowska-White**

Bharat P. Rao***

Published as a book chapter in Advances in Electronic Commerce, Charles Steinfeld, Ed., Purdue University Press.

*Vanitha Swaminathan is Assistant Professor of Marketing, Isenberg School of Management, University of Massachusetts-

Amherst, MA 01003. Tel: (413) 545-5665. e-mail: vanitha@mktg.umass.edu. ** Elzbieta Lepkowska-White is Assistant Professor of Marketing, Department of Management and Business, Skidmore

College, 815 North Broadway, Saratoga Springs, NY 12866. Tel: (518) 580-5113. E-mail: elepkows@skidmore.edu. *** Bharat P. Rao is Assistant Professor of Management, Institute for Technology and Enterprise, Polytechnic University, 55

Broad Street Suite 13B, New York, NY 11201. Tel: 212-547-7030 Ext. 205. e-mail: brao@usa.net

The authors would like to thank the Georgia Tech Research Corporation and the Graphic, Visualization, and Usability Center for making the data available for academic research.

The Internet and Consumer Buying Behavior: A Research Framework and Alysis

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, the antecedents to electronic exchange in the online context are examined. 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? Secondary data from an e-mail survey are

analyzed. 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

Online retail is attracting an increasing numbers of consumers as well as companies. Every year more

businesses set up their own web sites to reach internet users. By the year 2005, revenues from just managing

web sites are predicted to reach $35 billion in the US (Cimillica and Bliss, 2000). At present, Amazon.Com

and CDNow are among most successful online companies as reflected by their stock market capitalizations

(Zwass, 1999). On the consumer side, Travel Industry Association of America (TIA) reports that in 1998,

25% of Americans used the internet to plan their travel or a vacation versus 10% in 1996 (Kate, 1998).

Overall, future internet sales forecasts are very encouraging. The Forrester Research study predicts that in the

US e-commerce will grow from between $7 to $15 billion in 1998 to approximately between $40 to $80 billion

by 2002 (US Department of Commerce, 1999) 1. The National Retail Federation (1999) forecasts that internet

purchases will grow by almost 400% in the next few years to 41 billion by 2002 from $11 billion in 1999

(National Retail Federation, 1999).

Some researchers as well as practitioners suggest that “true visionaries of the Internet commerce expect

this medium to fundamentally transform business” (Clark, 1997). Bill Gates, cofounder of Microsoft, refers to

the Internet as to “friction free capitalism” and thus emphasizes the free flow of one-to-one interactions that is

possible only with the Internet use (Gates et al., 1995).

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 influence shopping on the internet, (e.g., Alba et al. 1997;

Palmer 1997), there is limited empirical work in this area (e.g., Li et al. 1999; Donthu, 1999). Hoque and Lohse

(1999) examine the impact of user interface design on information search costs in electronic media. Rohm and

Swaminathan (2000) create a typology of internet shoppers based on shopping motivations based on a survey

of online shoppers. Their work identifies four shopping types based on a survey of online shoppers. These

types include habitual shoppers, cherry pickers, traditionalists and balanced buyers. These shopping types are

then contrasted with a typology from a matched offline survey. Swaminathan (2000) examines the role of

computer-mediated environment on shopping behavior with specific reference to brand switching behavior. The

paper identifies factors that enhance or weaken the role of the shopping medium on brand switching behavior.

However, none of the above studies focus on examining factors influencing shopping in an online environment.

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.1 _____________________ Insert Table I About Here

_____________________

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

1 This is however, dependent upon the product category. Amazon.Com offers a vast selection of books. In other cases, e.g., online grocery stores, there are limitations in the variety of products that can be offered.

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 chosen based 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.2 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. _____________________ Insert Figure I About Here _____________________

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

2 Note that the emphasis here is on commercial transactions implying monetary exchange. This definition precludes those transactions where information is collected online but the transaction is completed offline.

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 shopping have 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, e g., 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).3 Therefore, it is hypothesized that those consumers who are primarily convenience shoppers are more

likely to shop online than those that seek social interaction.

3 We acknowledge that online communities, discussion groups, and review boards (like the one on Amazon.com), are mechanisms that are being employed by marketers to encourage social interaction among consumers. These allow consumers

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.net-happenings, 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.

to interact as they scan for information, make buying decisions, or report feedback on their purchases. They definitely provide incentives to customers to visit sites, and, if implemented properly, might do a better job of turning browsers into shoppers.

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.

_____________________

Insert Table II About Here _____________________

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. _____________________ Insert Table III About Here

_____ ________________

Vendor Characteristics (VENDOR)a. Vendor characteristics were operationalized using a 5-point

agree-disagree scale2. 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. The perceived 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 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. _____________________

Insert Table IV About Here _____________________

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:

Y(1,2) = b0 +b1 X1 + b2 X2 + b3 X3 + b4 X4 + e

Y(1,2) = likelihood of electronic exchange

(operationalized as (1) number of electronic purchases and (2) the total amount spent online in the last six months)

X1 = vendor characteristics (measured on reliability, convenience; price competitiveness and access to information)

X2 = perceived security of transactions

X3 = concern for privacy

X4 = customer characteristics (convenience and social interaction)

e = residual term

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.

Model 1 shows that perceived superiority of Web vendors positively affects frequency of consumer

shopping on the Internet (b1 = 0.22, p<0.01). Thus, reliability of a vendor, convenience of placing orders and

contacting vendors, price competitiveness and access to information, have a positive influence on the number of

purchases on the Internet.4 However, Model 2 shows that these characteristics do not influence the amount of

money spent on the Internet.

The results of the study show that social interaction as a shopping motivation deters consumers from

shopping frequently (b1 = 0.48, p<0.01) and from spending money on the Internet (b1 = 0.64, p<0.01).5

These consumers are likely to treat the shopping experience as a social experience. On the other hand,

consumers who value convenience tend to use the Internet to purchase goods frequently (b1 = 0.55, p<0.01)

and they seem to spend more money (b1 = 0.55, p<0.01) in the electronic transactions. Thus, the need for

social interaction negatively affects the propensity to engage in Internet transactions, and convenience orientation

positively affects the frequency and the size of purchases on the Internet.

Interestingly, perceived security of transactions had a negative marginal effect on the frequency of

shopping on the Internet (Model 1) which means that consumers seem less concerned about the security of

electronic exchanges (p<0.1). The analysis of Model 2 shows that consumers are concerned about some

aspects of information privacy. Consumers who purchased more on the Internet seemed to be more concerned

about the creation of laws protecting privacy on the Internet (b1 = 0.13, p<0.01). Another dimension of

privacy, i.e., consumers’ beliefs that marketers need information about them for marketing purposes, had a

marginal negative effect on the amount of money spend on the Internet (p<0.1). Thus, it seems that consumers

who spend more on the Internet have a tendency to believe that marketers do not need more information about

them to market their products. _____________________

Insert Table V About Here _____________________

4 Additional analyses were conducted to examine which of the vendor characteristics such as reliability of a vendor, convenience of placing orders and contacting vendors, price competitiveness and access to information had a greater influence. Results of these are discussed in the next section. 5 Please note that social interaction was reverse-coded such that a positive coefficient indicates a lower propensity to shop online.

Additional Analyses

To assess the relative contribution of each of the four variables, a stepwise regression analysis was

conducted on each of the dependent measures. The incremental R2 for the model with each additional variable

included provides an assessment of the relative contribution of each variable. According to this, in Model 1,

convenience as a shopping motive accounted for 7% of the variance explained, vendor characteristics

accounted for 3%, social interaction accounted for 2% and security for 1% of the variance explained. In the

case of Model 2, with the total amount spent online as a dependent measure, convenience as a shopping motive

accounted for 7% of the variance explained, recreation accounted for 4%, and privacy concerns such as use of

information and privacy laws accounted for 3% and 1% of the variance explained respectively. Thus, across

both models, with both frequency of shopping and amount spent online as dependent measures, it appears that

customer characteristics dominate all other variables in terms of variance explained. ______________________ Insert Table VI About Here ______________________

One of the factors that emerges significant in explaining the frequency of shopping on the Web is vendor

characteristics. Vendor characteristics is a summated scale including reliability of a vendor, convenience of

placing orders and contacting vendors, price competitiveness and access to information. One important issue is

the relative role of each of these aspects in influencing frequency of shopping. In order to examine this issue, we

estimate five regressions with frequency of shopping as the dependent measure and each vendor characteristic,

e.g., reliability of a vendor, price competitiveness, access to information, ease of canceling orders and contacting

vendors. Each of these is incorporated as an independent variable in a separate regression in the presence of

other independent variables such as security, privacy, social interaction and convenience. The results of these

regressions suggest that price competitiveness and ease of canceling orders were both significant at the 1% level

while reliability and access to information were significant at the 10% level. The only variable that did not

emerge significant was the ease of contacting vendors on the Web. In other words, all but one of the vendor

characteristics was significant in explaining the frequency of shopping on the Web. It is possible that the ease of

contacting vendors on the Web is similar across all vendors in the online environment.

The impact of customer characteristics suggests the possibility that there are distinct segments of

consumers who place emphasis on convenience versus the social aspects of shopping. In order to investigate

the characteristics of these segments, a logistic regression was estimated. The results of these estimates are

presented in Table VI. As shown in the table, Model 1 and 2 included gender, age, household income and

education as independent variables. These variables were included primarily on the basis of previous research

which suggests that demographic variables that have an impact on internet usage patterns (e.g., Hoffman,

Kalsbeek and Novak 1996; Burke 1997) and on the basis of the previous research relating demographic

variables to shopping motivations in traditional retailing contexts (Bellenger and Kargoankar 1980). Although

some of these demographic differences appear to be declining as internet usage is becoming more mainstream, it

is still interesting to examine whether these variables might account for differences in motivations. In Model 1,

the dependent variable was whether or not convenience was mentioned as a reason for shopping or considering

shopping on the Internet. Model 2 included the same independent variables but the dependent variable was

whether or not preference for dealing with people was mentioned as a reason for not shopping or not

considering shopping on the Internet.

The results of the logistic regression of these two models indicate that there is a significant relationship

between gender and social interaction as a shopping motive (b1 =-1.450, p<0.05). Gender was a dummy

variable with females being coded as a zero and males as a one. The result suggests that males might be less

motivated than females by social interaction, a finding that confirms what is known about gender differences

even in the bricks-and-mortar setting. For example, the study by Bellenger and Kargoankar (1980) suggests

that the typical recreational shopper tends to be a female head-of-household. The results from Model 2 suggest

that convenience as a shopping motivation is related to gender, education and income. The positive coefficient

on gender suggests that males are more convenience-oriented than females. The positive coefficient on

education (b1 =.524, p<0.01) and income (b1 =.203, p<0.01) suggests that convenience is a greater motivator

at higher levels of education and income. It is likely that these variables are proxies for time-poverty. More

research on profiling segments of consumers with different shopping motives is necessary in order to promote

online shopping as an alternative shopping medium to various segments of consumers.

Implications of the Study and Future Research

This study has both theoretical and managerial implications. The study examines a model of electronic

exchange based on a theoretical framework proposed by Bagozzi (1975). The factors examined in this study

include vendor characteristics, security and privacy and customer characteristics. By examining a sample of

internet users, we are able to examine the impact of these factors in converting existing users from browsers to

buyers. The study supports previous theoretical propositions that vendors should be reliable, offer competitive

prices, provide useful information on the Internet and easy to conduct services (Zellweger, 1997; Alba et al.,

1997; Luedi, 1997; Palmer, 1997). The empirical findings suggest that perceived vendor characteristics,

particularly price competitiveness and ease of canceling orders, affect the frequency of purchases on the

Internet. The result regarding vendor characteristics provides empirical support for the previous research

regarding reliability of an exchange partner (Morgan and Hunt 1994). It highlights the role of trustworthiness of

an exchange partner (Moorman et al 1993), thereby extending our knowledge of the importance of vendor

characteristics from the traditional to the internet domain.

Interestingly, this study shows an average consumer is not as concerned about the security of electronic

exchanges or privacy issues. The concern over security has decreased over the years particularly with

developments in internet payment systems that ensure confidentiality. This finding supports previous research,

that focused on privacy issues in the context of direct mail, which indicated that consumers are not concerned

about privacy issues (Milne and Gordon, 1993). However, this study does show that consumers who purchase

frequently on the Internet are interested in creation of new laws protecting privacy on the Internet. They also do

not believe that marketers need more information about them to successfully market products on the Internet.

This has important implications both from a theoretical and a managerial standpoint. From the theoretical

standpoint, this result serves to resolve the debate regarding the level of consumer concern regarding privacy

issues (Milne and Gordon (1993) versus Rohm and Milne (1999)). From the managerial standpoint, marketers

should be sensitive about privacy issues particularly the perception that information is not necessary to enhance

marketing of products on the internet.

Finally, this study shows that consumers who are primarily motivated by convenience are more likely to

make purchases online. Those who value social interactions are less interested in the Internet use for shopping

and thus shop less frequently on the Internet and spend less money on e-commerce. This in itself is valuable

because it suggests that retailers have to fine tune their offerings, and provide very specific solutions to each

segment of customers at the aggregate level, and individual customers if possible. With advances in

personalization and customization, the notion of one-to-one marketing can be realized. These findings should be

valuable for marketers for the purpose of segmentation and targeting their prospective buyers.

The result regarding the importance of convenience as a motivator of internet shopping is interesting

from the perspective of enhancing our understanding of shopping motivations in the internet context. In addition,

it also provides a basis for marketers to differentiate themselves from competitors. The role of social interaction

as a deterrent of internet shopping is also an important result. While it may not be possible to mimic all the

features of a physical store and the ability of the physical world to provide unique shopping experiences (Rao

1999), marketers should take advantage of developments such as discussion groups, chat forums etc. in order

to enhance the level of social interaction at various sites. This is being achieved by some internet retailers who

provide links to various discussion forums, thereby tying in aspects of social interaction with visits to their Web

sites. These developments may go a long way in enhancing the level of social interaction during internet

shopping. Future research should focus on creating a typology of internet shoppers and linking various

demographic and psychographic variables with types of internet shoppers. Such a typology along with a profile

of the various internet shopper types may provide actionable guidelines to marketers interested in targeting

various segments of consumers.

It is important to note the limitations of this study. This research is based on an e-mail survey and

therefore a selection bias might have affected our findings. Only online respondents participated in the study.

Therefore, the self-selection bias may limit the generalizability of the findings. Extensions of this study in other

settings and using other data collection methods should provide additional evidence to support and expand our

findings. An interesting future research could focus on the empirical comparison of factors that affect online

shopping versus more traditional formats of retailing. The results we present here are an important step forward

in enhancing our understanding of the future of electronic commerce.

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Table I

Transaction Types Compared

Variety Trialability Asynchrony Interactivity Full Retail High High Low High TV Moderate Low Moderate Low Catalogs Moderate Low Moderate Low Web Moderate Low High High

TABLE II Descriptive Statistics

Variable Percentage

(Sample Size=428) Gender Female Male Education High School Vocational Some College College Masters Doctoral Other

28.3% 71.7% 7% 2% 27% 39% 19% 4% 2%

Age 16-20 21-25 26-30 31-35 36-40 41-45 46-50 51-55 >55

4.4% 12.4% 16.1% 15.9% 12.9% 10.3% 9.6% 8.9% 9.5%

Income Under $10,000 $10,000-$19,000 $20,000-$29,000 $30,000-$39,000 $40,000-$49,000 $50,000-$74,000 $75,000-$99,000 Over $100,000 No Answer

2.3% 5.1% 7.2% 13.3% 11.4% 21.7% 12.9% 13.3% 12.6%

TABLE III

Factor Analysis of Scale Items

Vendor Privacy1 Privacy 2 Security Privacy3 Privacy4 Customer Privacy 5

Vendor (Q1) 0.64 -0.09 -0.16 -0.07 0.00 0.08 -0.15 -0.14 Vendor (Q2) 0.73 -0.05 -0.03 0.00 0.00 0.10 -0.13 0.00 Vendor (Q3) 0.73 -0.08 0.13 -0.05 0.07 -0.01 0.19 0.11 Vendor (Q4) 0.53 -0.09 -0.13 -0.06 0.02 -0.08 -0.26 0.03 Vendor (Q5) 0.66 0.04 0.12 0.05 -0.19 -0.10 -0.01 0.02 Security (Q6) 0.01 0.14 -0.07 0.85 0.15 -0.03 -0.03 0.04 Security (Q7) -0.08 0.15 0.03 0.85 -0.03 -0.04 0.05 -0.05 Privacy (Q8) -0.13 0.64 -0.02 0.13 -0.09 0.08 0.16 0.07 Privacy (Q9) -0.01 0.82 -0.05 0.02 0.15 -0.09 0.02 -0.14 Privacy (Q10) -0.03 0.74 -0.06 0.10 0.39 0.07 0.03 -0.16 Privacy (Q11) -0.12 0.61 -0.12 0.15 0.19 -0.04 -0.12 0.30 Privacy (Q12) -0.07 -0.07 0.69 0.08 0.17 0.01 -0.16 -0.05 Privacy (Q13) 0.00 -0.08 0.74 -0.04 -0.12 -0.02 -0.03 -0.06 Privacy (Q14) 0.00 0.07 0.59 -0.17 -0.10 -0.15 -0.07 0.43 Privacy (Q15) -0.10 0.34 0.02 0.15 0.72 0.10 0.11 -0.02 Privacy (Q16) 0.01 0.11 -0.04 0.00 0.86 -0.11 -0.04 0.07 Privacy (Q17) 0.08 0.06 0.15 -0.31 -0.24 0.57 -0.19 0.18 Privacy (Q18) -0.05 0.01 -0.09 0.05 0.08 0.85 -0.03 0.09 Privacy (Q19) 0.04 -0.07 -0.07 0.01 0.05 0.25 0.07 0.80 Privacy (Q20) 0.08 -0.18 0.48 -0.05 -0.10 0.31 0.30 -0.32 Social (Q21) -0.06 0.04 -0.12 -0.01 0.00 -0.03 0.79 0.16 Convenience (Q22) 0.25 -0.08 0.06 -0.05 -0.07 0.11 -0.49 0.15 Note: Items with loadings greater than .50 are highlighted

Table IV Variables and Scale Items Factors and Scale Items Means and

Standard Deviations

Reliability

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

3.380 (1.037) 3.666 (0.986) 2.764 (0.993) 3.387 (0.965) 3.357 (1.137)

.690

Perceived Security of Transactions and Concern for Privacyb 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 Privacyc (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 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 elect ronic 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)

3.030 (1.016) 3.156 (0.959) 2.631 (1.286) 4.332 (1.055) 4.191 (1.145) 4.393 (1.015) 2.049 (1.207) 1.489 (0.782) 1.979 (1.182) 3.990 (1.197) 4.666 (0.699) 2.324 (1.370) 2.462 (1.466) 2.806 (1.707) 1.224 (0.578) .105 (.307) .825 (.380)

.567 .727 .536 .499 .411 .090 - -

a the complete questionnaire available at: http://www.gvu.gatech.edu/user_surveys/survey-1998-10/graphs/graphs.html#shopping. b and c the complete questionnaire available at: http://www.gvu.gatech.edu/user_surveys/survey-1998-10/graphs/graphs.html#privacy d the complete questionnaire available at: http://www.gvu.gatech.edu/user_surveys/survey-1998-10/graphs/graphs.html#general 1 reliability is assessed using Cronbach’s Alpha when more than 2 scale items are present; otherwise the numbers reported refer to a correlation coefficient.

Table IV (cont..) Variables and Scale Items

d the complete questionnaire available at: http://www.gvu.gatech.edu/user_surveys/survey-1998-10/graphs/graphs.html#shopping note: the pearson correlation between PUR1 and PUR2 is .566

Variables and Scale Items Number of Scale Items Mean S.D. 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)

1

2.593

0.983

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)

1

2.960

1.091

Table V Regression Statistics

Variable Model 1 Model 2 INTERCEPT VENDOR SECURITY PRIVACY1 PRIVACY2 PRIVACY3 PRIVACY4 PRIVACY5 SOCIAL INTERACTION CONVENIENCE

0.724 (.459) .219* (.069) -.089# (.055) .018 (.063) .003 (.061) .067 (.060) .037 (.041) .066 (.081) .476* (.150) .552* (.123)

2.066* (.526) .097 (.079) -.024 (.063) -.130# (.072) .064 (.070) -.064 (.070) .126* (.047) .040 (.093) .639* (.172) .552* (.143)

Dependent Variable PUR 1 PUR2

* *

Sample Size 427 423 R2 .12 .15 Adjusted R2 .10 .13

* significant at p < .01 ** significant at p < .05 # significant at p < .10 note: total sample size was 428;

TABLE VI Logistic Regression

Variable Model 1 Model 2

Dependent Variable Convenience Social Interaction

*

*

Intercept

4.356* (1.525)

-3.485* (1.001)

Gender

-1.450** (.619) .235

.737** (.310) 2.090

Education

.039 (.163) 1.039

.524* (.130) 1.689

Income

-.102 (.098) 1.107

0.203* (.078) 1.224

Age

-.030 (.079) .970

.038 (.067) 1.039

-2 Log L Sample Size

9.320** 371

32.229* 371

a numbers in italics refer to odds ratios * p <.01 ** p< .05

Figure I Factors Influencing Likelihood of Electronic Exchange

VendorCharacteristics

Security ofTransactions

Concern forPrivacy

CustomerCharacteristics

Likelihood ofElectronicExchange

H1a,b,c,d

H2

H3

H4a and H4b