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New information and communication technologies (ICTs) have had profound and pervasive impacts on modern society impacts that are still evolving with the ongoing development of new technologies and the continued adoption/adaptation of older ones. ICT applications and services are changing how and where we work, shop, play, travel, and in other ways live our lives. In particular, the emergence and spread of electronic commerce (e-commerce) have greatly influenced the behavior of both retailers and consumers. For example, the Association of National Advertisers stated that 44% of US companies were selling online, and an additional 36% would participate in e-selling by 2000 according to Nua Internet Surveys (the Internet Economy Indicators, 2004); in their sample of more than 2,000 households across the U.S., Cole et al. (2003) found that about 66% of internet purchasers have reduced their purchasing in traditional retail stores somewhat or a lot in 2002.

E-commerce refers to “the buying, selling, marketing, and servicing of products or services over computer networks” (Wikipedia, 2004). E-commerce consists of several components: business to business (B2B), business to consumer (B2C), and consumer to consumer (C2C) (such as eBay). Although the B2B segment dominates e-commerce in terms of the dollar value of transactions made, B2C remains important for its potential impacts on consumers’ shopping behavior. As a flexible, interactive, efficient medium, e-shopping is likely to be a substitute for traditional shopping media, and may well dominate the exchange of certain products (e.g., digital assets such as music, software, movies, and the written word) in the future. The review presented here focuses on the B2C segment of e-commerce.

B2C e-commerce has grown rapidly worldwide over the past few years. In Singapore, about 30% of internet users aged 15 years and above have purchased online as of 2004, an 18-percentage point increase since 2000; and the average amount spent online per internet shopper has increased from $336 in 2003 to $539 in 2004 (IDA, 2005). In South Korea, the B2C volume of the e-commerce market increased from $0.61 billion in 2000 to $2.15 billion in 2001. In the UK, one in ten adults has frequently purchased online; 25% of adults were frequent online purchasers in some areas; and total e-commerce revenues are expected reach 20 billion pounds by 2005, accounting for 7.6% of retail sales (CACI, 2000). According to the US Census Bureau (2004), adjusted retail sales through e-commerce reached $16.1 billion in the first quarter of 2004 (compared to $5.9 billion in the first quarter of 2000), or 1.8% of total retail sales.

However, this percentage has yet to reach the starting point (2.5%) of the classical sigmoid diffusion curve presented by Rogers (1983). On the other hand, the development of e-commerce is still in a volatile stage. One survey of American internet users and non-users found that fewer adult internet users bought online in 2002 than in the previous two years, and that more than half of internet purchasers spent less than they intended when shopping online (Cole et al., 2003). The Boston Consulting Group estimated that 65% of internet buyers, accounting for 80% of the dollar value of prospective purchases, terminated their transaction before checking out (Maravilla, 2001). This evidence highlights the importance of understanding the behavior of online
purchasers.

A growing number of studies have explored the determinants of consumers’ electronic-shopping (e-shopping) behavior during recent years. Most studies either explicitly or implicitly embedded online purchasing behavior into various theoretical frameworks, including the theory of reasoned action, the theory of planned behavior, the technology acceptance model, transaction cost theory, innovation diffusion theory, and others. According to the dependent variables of interest, previous studies can be generally classified into three categories: online purchasing intention, online purchasing adoption, or both.

Further, these studies used different sampling approaches such as internet-based survey, paper and pencil survey, telephone interview, or using an available database, and recruited their samples from different populations, including students, computer/internet/email users, online purchasers, and the general population. With respect to the product, some studies dealt with all products sold online, while others chose particular products (or product categories). In addition, a variety of statistical methodologies (including descriptive as well as multivariate analyses) has been applied, to answer different kinds of questions.

Previous works identified various antecedent factors of online purchasing intention and usage, and hence greatly improve our understanding of the behavior of online purchasers. This understanding offers a useful foundation to a number of different actors. To retailers, it provides a basis for implementing e-commerce promotion policies and better designing e-retail sites; to academics it serves as an important example of the impact of ICT in a key realm of everyday life; and to transportation and urban planners it presents a critical background from which to assess the follow-on implications for travel and land use (Mokhtarian, 2004). This report represents an effort to summarize previous e-shopping research in a systematic way. Specifically, this report covers topics related to consumer theories, dependent variables of interest, sampling approaches, product classification, methodologies, and the determinants of e-shopping intention and adoption.

Marketing activities can occur through three types of channels: communication, transaction, and distribution. Communication channels enable the exchange of information between vendors and consumers; transaction channels generate sales activities between vendors and consumers; and the function of distribution channels is to facilitate the physical exchange of products (Peterson et al., 1997). Although the internet will play a role in all three channels, in this review, we are interested primarily in the potential for efficiency improvement and other benefits, as well as drawbacks, that the internet offers in the transaction channel. Thus, we focus on studies of actual or intended online purchase behavior, not just information-gathering activities.

The report is organized as follows: the next chapter summarizes the theories employed by researchers; Chapter 3 discusses the dependent variables (the phenomena of interest typically measured); Chapter 4 presents various approaches used by previous studies to recruit their samples; Chapter 5 highlights various product classification systems that have been employed; Chapter 6 reviews the statistical methodologies used to describe and explain the phenomena of interest; Chapter 7 recapitulates the statistically significant determinants of e-shopping intention and actual adoption; and Chapter 8 summarizes the report. An appendix tabulates key facts with respect to 65 empirical studies reviewed for this report.

Contents

List of Tables
List of Figures
Executive Summary
1. Introduction
2. Theoretical Frameworks

2.1 Theory of Reasoned Action (TRA)
2.2 Theory of Planned Behavior (TPB)
2.3 Technology Acceptance Model (TAM)
2.4 Transaction Cost Theory (TCT)
2.5 Innovation Diffusion Theory (IDT)
2.6 Other theories
2.7 Summary

3. Dependent Variables of Interest

3.1 E-shopping intention
3.2 Actual e-shopping behavior
3.3 Summary

4. Sampling Approaches
5. Product Classifications
6. Methodologies

6.1 Descriptive analysis
6.2 Correlational analysis
6.3 Multivariate analysis
6.4 Summary

7. Determinants of E-shopping Behavior

7.1 Perceived characteristics of the web as a shopping channel
7.2 Vendor and product characteristics
7.3 Online consumer characteristics
7.4 Summary

8. Summary
References
Appendix: Overview of Previous Research on E-shopping Intention and Adoption

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