For years, primary channels for electronic sales have been enabled through technologies such as telephone, fax, ATM, credit cards, or television. Recently, e-commerce (EC) has emerged as a growing force in the world economy, both in terms of communication opportunities and in terms of sales channels. Here, we define e-commerce as “the use of information technology to enhance communications and transactions with all of an organization’s stakeholders” (Watson et al., 2000, p. 1).
Shopping is increasingly popular on the Internet. For instance, consumers increased their online shopping by 46% in 2000, with sales for the year totaling $56 billion, according to a recent report from ActivMedia Research, an e-business information company. ActivMedia estimates that, as e-tailers continue to fine-tune their marketing and order processing, online sales for B-to-C marketers will reach one trillion dollars by 2010 (Scott, 2001). Almost 40 million Americans, or about 17% of the population, have made purchases online, and by 2010 B2C e-commerce may account for 15 to 20 percent of all United States retail sales (Peet, 2000).
Emerging technologies have touched off a revolution among conventional retailers who are now faced with a new transaction channel. In line with evolution in the marketplace, a recent Journal of Retailing editorial calls for papers that examine this increasingly important topic (Levy and Grewal, 2001). Marketing academicians have probed the topic of online shopping from different perspectives (see Table 3-1 for a summary of these studies). Previous research reveals some useful insights on the demographic, socioeconomic, and psychographic profiles of Internet shoppers. Some studies on consumer online choice behavior are descriptive in nature. Some have set forth explicit hypotheses regarding factors that influence online buying. Fewer attempts have been made to develop models of online buying. Few studies, if any, have made an elaborate attempt to relate the emerging e-tailing research to the extant research on traditional retailing. The current study seeks to conduct an extensive review of the relevant literature and to address such intuitively appealing issues as: What is the state of the art for the study of online patronage behavior? What are the relevant variables for explaining online shopping? To what extent are key theories about bricks-and-mortar retailing relevant for exploring patronage behavior in an online setting?
|
Author |
Publication |
Study Design/Data Source |
Main Findings |
|---|---|---|---|
|
Donthu and Garcia, 1999 |
Journal of Advertising Research |
Telephone survey of 790 people |
Internet shoppers are older and make more money than non-shoppers. Internet shoppers are more convenience seekers, innovative, impulsive, variety seekers, and less risk averse than non-shoppers are. Internet shoppers are also less brand and price conscious than nonshoppers are. Internet shoppers have a more positive attitude toward advertising and direct marketing than non-shoppers do. |
|
Lohse and Spiller, 1999 |
Journal of Computer-Mediated Communication |
28 online retail stores |
The study analyzes the impact of some Web site features (e.g., FAQ section, browsing and navigation capabilities, number of store entrances, appetizer information) on store traffic and sales. |
|
Lin, 1999 |
Journal of Advertising Research |
A telephone survey using random digit dialing with 348 participants |
Motives for online service use (i.e., surveillance, entertainment, and escape/companionship/identity) are significant predictors for likely online-service adoption. |
|
Li, Kuo, and Russell, 1999 |
Journal of Computer-Mediated Communication |
Online survey of 999 US Internet users |
Channel knowledge, perceptions of channel utilities, convenience orientation, experiential orientation, income, education, and gender are predictors in the model of online buying behavior. |
|
Jarvenpaa et al., 1999 |
Journal of Computer-Mediated Communication |
Survey |
Favorable attitudes towards an Internet store and reduced perceived risks associated with buying from an Internet store will increase the consumer’s willingness to purchase from that Internet store. |
|
Korgaonkar and Wolin, 1999 |
Journal of Advertising Research |
Interviews of Web users |
Online shoppers and non-shoppers are compared with respect to their motivations and demographics. |
|
Swaminathan, Lepkowska-White, and Rao, 1999 |
Journal of Computer-Mediated Communication |
Georgia Tech GVU online survey |
Perceived superiority of Web vendors (e.g., reliability of a vendor, convenience of placing order and contacting vendors, price competitiveness and access to information) positively affects frequency of consumer shopping on the Internet. |
|
Lohse, Bellman, and Johnson, 2000 |
Journal of Interactive Marketing |
9738 panelists from Wharton Virtual Test Market survey panel |
The demographics and Internet usage are used to predict who are buying online, and how much they spend. |
|
Degeratu, Rangaswamy, and Wu, 2000 |
International Journal of Research in Marketing |
Datasets from Peapod (where about 300 subscribers were tracked) and IRI for 1039 panelists |
The impact of brand names and sensory search attributes on choices online is examined. |
|
Haubl and Trifts, 2000 |
Marketing Science |
Experiment |
The study analyzes the effects of two decision aids on purchase decision making in an online store. |
|
Miyazaki and Fernandez, 2001 |
Journal of Consumer Affairs |
Survey with a sample size of 160 Internet users |
Perceived risk, security concerns, Internet experiences, and the adoption of established methods for remote retail purchase transactions are found to be associated with the online purchase rate. |
|
Shim et al., 2001 |
Journal of Retailing |
Mail survey to computer users |
Intention to use the Internet to search for information is the strongest predictor of Internet purchase intention. It mediates relationships between purchasing intention and other predictors (i.e., attitude toward Internet shopping, perceived behavioral control, and previous Internet purchase experience). |
|
Childers et al., 2001 |
Journal of Retailing |
Experiments |
Motivations to engage in retail shopping include both utilitarian and hedonic dimensions. |
|
Levy, 2001 |
ACR |
Case study featuring one online shopper |
The superiority of online shopping is shown in savings of time, money, and in providing a variety of other consumer satisfaction. |
|
Lynch, Kent, and Srinivasan, 2001 |
Journal of Advertising Research |
Experiments conducted in 12 countries with a total of 299 subjects |
Three characteristics (site quality, affect, and trust) significantly affect consumers’ purchase behavior. |
|
Mathwick, Malhotra, and Rigdon, 2001 |
Journal of Retailing |
Mail survey of 213 Internet shoppers |
An experiential value scale reflecting the benefits derived from perceptions of playfulness, aesthetics, customer “return on investment” and service excellence is developed in the Internet shopping context. |
|
Liao and Cheung, 2001 |
Information & Management |
Survey |
The life content of products, transactions security, price, vendor quality, IT education and Internet usage significantly affect the initial willingness of Singaporeans to e-shop on the Internet. |
|
Mathwick, 2002 |
Journal of Interactive Marketing |
GVU online survey data |
The study classifies online shoppers with relational norms (e.g., behavior loyalty, contract barriers, continuity barriers, effort, enjoyment, entertainment, escapism). |
|
Goldsmith, 2002 |
Journal of Marketing Theory and Practice |
Survey of 107 undergraduates |
Frequency of online buying and intent to buy online in the future are predicted by general innovativeness, an innovative predisposition toward buying online, and involvement with the Internet. |
|
Menon and Kahn, 2002 |
Journal of Retailing |
Experiment |
The characteristics of products and Web sites encountered earlier can significantly influence consumers’ later shopping behavior. |
|
Menon and Kahn, 2002 |
Journal of Retailing |
Experiment |
The effect of arousal and pleasure on shopping behavior was examined. |
|
Forsythe and Shi, 2003 |
Journal of Business Research |
GVU online survey data |
This study examined the nature of perceived risks associated with Internet shopping and the relationship between types of risk perceived by Internet shoppers and their online patronage behaviors. |
The study is stimulated by the managerial needs to understand why consumers buy online so that marketers can develop effective strategies to increase store traffic and online sales. The study has implications for marketing academics as well. Previous researchers have been investigating the antecedents of choice behavior in a traditional retailing context for decades. The effect of various correlates of retail patronage is well researched theoretically as well as empirically. Compared to its bricks-and-mortar counterpart, e-tailing research is relatively new and awaits more rigorous studies. It is of intuitive appeal to close the gap between what we know about online patronage from two streams of research: the emerging e-commerce research and the extant traditional retailing research. We propose in this study that many of the “old” variables under study for decades may shed light on our current research on e-tailing.
The overall objective of this chapter is to develop a better understanding of Internet shopping, based on a review of the extant retailing literature and the emerging EC literature. We are interested to see the extent to which the two research streams generate alternative (or complementary) models, and we attempt to determine the extent to which existing retailing theories can be adapted to cyberspace.