AccessMyLibrary provides FREE access to millions of articles from top publications available through your library.
The recent failure of a large number of e-tail companies epitomizes the challenges of operating through virtual channels and underscores the need to better understand key drivers of online consumer behavior The objective of this study is to provide a comprehensive review of the extant information systems (IS) literature related to online consumer behavior and integrate the literature in order to enhance our knowledge of consumer behavior in electronic markets and provide clear directions for future research. This paper introduces a framework that integrates research findings across studies to develop a coherent and comprehensive picture of the online consumer behavior research conducted in the IS field. The integrative framework proposes system quality, information quality, service quality, and vendor and channel characteristics as key factors that impact online consumer behavior, achieving their effects by altering the perceptions of usefulness, ease of use, trust, and shopping enjoyment.
Keywords: electronic commerce, online consumer behavior, meta-analysis, system quality, information quality, service quality, TAM, trust
The Internet offers immense opportunities for companies to reach a wide base of consumers and efficiently market their products through an electronic channel. According to the Boston Consulting Group, online retailing will reach $168 billion by 2005 (Mark, 2001). Such estimates substantiate that the Internet has emerged as a powerful alternative channel for selling products and services. However, the recent failure of a large number of e-tail companies exemplifies the challenges of operating through virtual channels and also highlights the need to better understand key factors that drive consumer behavior in online market channels. The infusion of the Internet technology into customer-supplier interactions requires a reconsideration of existing theories and frameworks regarding consumer behavior. Explicit attention should focus on understanding the factors that can explain a consumer's interaction with the technology, their purchase behavior in electronic channels, and their preference to transact with an electronic vendor on a repeat basis.
Although online consumer behavior has been the subject of considerable research in the last few years, there is a paucity of research that attempts to integrate research findings across studies. Online consumer behavior research is a young and dynamic academic domain that is characterized by a diverse set of variables studied from multiple theoretical perspectives. Researchers have relied on the Technology Acceptance Model (Davis, 1989; Davis et al., 1989), the Theory of Reasoned Action (Fishbein & Ajzen, 1975), the Theory of Planned Behavior (Ajzen, 1991), Innovation Diffusion Theory (Rogers, 1983, 1995), and Flow Theory (Csikszentmihalyi, 1988) in investigating consumers' adoption and use of electronic commerce. Studies have examined various aspects of consumer behavior such as Web site use, future use, purchase, future purchase, unplanned purchase, channel preference, and satisfaction. In terms of explanatory factors that drive such behavior, researchers have explored the role of attributes of the Web site, attributes of the vendor, consumer characteristics, individual perceptions, and the social context (Agarwal & Karahanna, 2000; Agarwal & Venkatesh, 2002; Gefen & Straub, 1997, 2000; Jarvenpaa et al., 2000; Koufaris, 2002; Limayem et al., 2000; Moon & Kim, 2001; Torkzadeh & Dhillon, 2002). While these studies individually provide meaningful insights, a single study does not resolve a major issue (Hunter & Schmidt, 1990). By integrating research findings across multiple studies, we can accumulate knowledge, develop a comprehensive understanding of the phenomena, and identify remaining research issues.
The objective of this study is to provide a comprehensive review of the extant information systems (IS) literature related to online consumer behavior and rigorously integrate the literature in order to enhance our knowledge of consumer behavior in electronic markets and provide clear directions for future research. To that end, we not only review and analyze studies that have been published in the major IS journals, but also propose an integrative framework that describes the relationships between key variables that predict and determine consumer behavior in electronic channels. Such an approach should provide insights on factors that need to be carefully considered by companies starting or operating electronic businesses, as well as researchers developing and testing models to further understand online consumer behavior.
A set of sampling criteria was initially determined in order to identify the studies that formed the foundation for our research endeavor. First, we decided to include only those studies that have been published in major journals within the IS domain. Second, only studies published between 1995 and 2002 were included for further consideration. Third, we limited our focus to those electronic commerce studies that were conducted at the individual level unit of analysis. Hence, consumers or users of Web technologies were the main subjects in these studies. Fourth, for a study to be included, it had to be based on empirical (quantitative) analysis. This allowed us to focus on empirically tested constructs and relationships rather than those that have only been conceptualized.
Based on the stated criteria, we conducted a thorough search of the following major IS journals: Communications of the ACM; Decision Sciences; Decision Support Systems; IEEE Transactions on Systems, Man, and Cybernetics; Information Systems Research: Information Technology and Management; Information and Management; International Journal of Electronic Commerce; Journal of End User Computing; Journal of Management Information Systems; and MIS Quarterly. These journals were considered to be mainstream IS journals that are appropriate outlets for research on online consumer behavior. Studies were located via computer searches of large bibliographic databases (UMI-Proquest and ScienceDirect) and by manually scanning the journals. Upon completion, a total of 42 nonredundant papers were identified for inclusion.
As shown in Table 1, the most popular outlets for online consumer behavior research were Information Systems Research (11 articles), International Journal of Electronic Commerce (11 articles), and Information and Management (nine articles). Two recent special issues on ecommerce metrics were the main sources of the Information Systems Research articles. While the number of articles published each year was increasing over time, most articles were published in 2000 and thereafter (seven articles before 2000, seven articles in 2000, seven articles in 2001, and 21 articles in 2002).
Two researchers read each of the papers and independently coded and tabulated the following items in independent tables: methodology, sample size, sample source, independent and dependent variables, task, theory basis, and study findings. The coders then met to compare the tables and resolve the discrepant cases in order to reach a consensus in their categorization and tabulation as shown in Table 2. The overall inter-rater agreement between the two coders for the categorization of study methodology, sample source, theory basis, and task was 94%. Analysis showed that the most common research method is survey (23 studies), followed by laboratory experiments (15 studies), combined approaches (three studies), and secondary data analysis (one study). Half of the studies used consumers and the other half used student (including undergraduate and graduate) subjects as the source of samples. A total number of 27,202 individuals participated in the studies that were included in the final set. Laboratory experiment-based studies either used actual Web sites (Web site for books, airline tickets, legal services, automotives, car rental, etc.) or resorted to simulated replicas of actual Web sites.
Books were the most popular product type used in the studies. Other product types included CDs, airline tickets, used laptop computers, videos, and flowers. In terms of virtual products, legal services, e-banking services, financial products, and news services were employed by the studies. Subjects were typically asked to respond to the instrument based on their immediate prior experience or their general impression regarding behavior in an online environment. The tasks ranged from rating Web site attributes that may influence their behavior to making purchases for a specific product.
REVIEW OF STUDY FINDINGS
Our review of the 42 studies focused on understanding the interrelationships between the study variables. We first present our review of the study findings organized around three related but distinct categories of the dependent variables of online consumer behavior research: Web use, online purchase, and post-purchase. The Web use category included variables such as current Web site use, future intention to use a Web site, and satisfaction with the use of the Web or Internet-based services. However, if the underlying purpose of use was to "purchase", that behavior was classified in the second category called online purchase. Post-purchase behaviors such as future purchase and satisfaction with purchase were classified in the third category. Following the review, we present the results of our quantitative analysis conducted for the theoretic models and variable relationships commonly found across studies. Table 3 summarizes the list of study variables for the dependent variables of online consumer behavior research.
Studies on Web Use
The Internet has evolved to become a technology that serves multiple needs. Users can access various types of services (such as news, e-banking, information search, etc.). Studies that evaluated use behavior focused on actual use or willingness to use these services. Some studies assessed use of the Internet in general, without contextualizing use for a specific service. The predictors of the use behavior can be segmented into user characteristics, user perceptions, and the social context of the user (Table 3).
Two dominant aspects within user characteristics that have been subjected to empirical analysis are demographic variables and psychographic variables. The demographic variables investigated by studies as predictors of Internet use included race, gender, generation, and culture. The findings supported the notion that the white population used the Internet more than minorities, males were marginally heavier users than females, and subjects younger than 19 years of age displayed a much higher usage behavior (Kraut et al., 1999). Culture (subjects in the U.S. and Hung Kong) not only impacted the use behavior but also influenced the underlying purpose of the use (Chau et al., 2002). The subjects in the U.S. were found to be more oriented toward using the Internet for commerce and entertainment, while subjects in Hong Kong primarily used the Internet for hobbies and social communication. In terms of psychographics, researchers have found that personal innovativeness, playfulness, and computer skill were distal determinants of use, achieving their effects through ease of use and usefulness (Agarwal & Karahanna, 2000; Agarwal & Prasad, 1998; Kraut et al., 1999; Moon & Kim, 2001).
User perceptions were widely used as the main variables of interest in a variety of studies. User perceptions regarding lack of data security, instability of the system, information content and accuracy, responsiveness, download delay, navigation, interactivity, system design quality, ease of use, and usefulness were found to be significant predictors of use behavior (Agarwal & Venkatesh, 2002; Han & Noh, 2000; Liao & Cheung, 2002; Liu & Arnett, 2000; Moon & Kim, 2001; Palmer, 2002). In addition, it was found that the difference between expectation and perceived performance regarding Web information quality and service quality significantly explained Web customer satisfaction (McKinney et al., 2002). Factors such as control, curiosity, heightened enjoyment, focused immersion and temporal dissociation collectively proposed as cognitive absorption were also found to influence perceptions such as ease of use and usefulness, which subsequently impacted use (Agarwal & Karahanna, 2000).
A limited number of studies has investigated the impact of social context on Web use behavior. Use of the Internet by other family members, external influence (articles, reviews, and promotion of the Web site), and interpersonal influence (relatives and colleagues) were identified as significant predictors of Web use (Agarwal & Venkatesh, 2002; Kruat et al., 1999; Parthasarathy & Bhattacherjee, 1998).
Studies on Online Purchase
The studies within this category focused on identifying factors that impacted the intention to purchase or the actual purchase behavior. The variables used as predictors of purchase behavior are categorized into consumer characteristics, consumer perceptions, technology attributes, and social context (Table 3).
Studies found that the higher a person's income, education, and age, the more likely he or she was to buy online (Bellman et al., 1999; Liao & Cheung, 2001). Gender was found to significantly impact perceptions toward shopping through the Web. Women view shopping as a social activity and were found to be less technology oriented compared to men (Slyke et al., 2002). However, researchers have cautioned that demographic variables alone explain a very low percentage of variance in the purchase decision (Bellman et al., 1999). An interesting result that emerged was that consumers that are more likely to buy online have a "wired lifestyle". Such consumers have used the Internet for a long time, received a large number of emails everyday, believed the Internet improves productivity at work, and used the Internet for most of their other activities such as reading news and searching for information (Bellman et al., 1999). Other consumer characteristics, such as personal innovativeness, discretionary time, search for product information, Web skill, Internet self-efficacy, email use, and prior Web use were also found to be predictors of willingness …