Discussion

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Online consumer behavior is a complex phenomenon. By taking a meta-analytic approach and integrating previous findings across studies, this study seeks to overcome the inherent limitation of a single study and provide a comprehensive overview of the current status of knowledge within the domain of online consumer behavior research in the IS field. Furthermore, this study offers an integrative framework that is comprehensive enough to facilitate an understanding of how key variables fit together and detailed enough to allow investigation into sub-domains of online consumer behavior. The framework proposes that system quality, information quality, service quality, and vendor and channel characteristics are central variables that predict and determine online consumer behavior, achieving their effects primarily by altering the perceptions of ease of use, usefulness, trust, and shopping enjoyment. In addition, the framework includes individual difference variables and social context variables to further account for their potential influence on mediating perceptual variables and online consumer behavior variables.

Several major avenues for future research emerge from the analysis presented in this paper. First, the relationships between web site use, online purchase, and post-purchase have not been explicitly investigated. Research projects that focus on this issue can provide meaningful insights into what factors contribute toward converting users to customers and customers to repeat customers. An interesting extension in this regard could also be to investigate the role of services (such as buy online and subsequently pick up or return to the physical store) that leverage both virtual and physical channels in determining purchase behavior, and also the linkage between that purchase experience and post-purchase satisfaction.

Second, the integrative model proposes some new relationships between predicting and mediating variables and between mediating and dependent variables that have not been subjected to empirical examination, and thus provide areas toward which future research can be directed. For example, future research is needed to systematically vary the system quality factors, such as navigation efficiency, interactivity, value added mechanisms, and assurance mechanisms, and trace their effects on ease of use, usefulness, trust, shopping enjoyment, and consumer behavior variables. While there are studies that examined the effects of system quality on consumer behavior (e.g., Koufaris, 2002; Liang & Lai, 2002; Limayem et al., 2000), and the effects of perceptual variables on consumer behavior (e.g., Agarwal & Karahanna, 2000; Agarwal & Prasad, 1998; Chen et al., 2002; Devaraj et al., 2002; Grazioli & Jarvenpaa, 2000; Koufaris, 2000; Stafford & Stern, 2002), only one study (Koufaris, 2000) examined the linkages between system quality factors and online consumer behavior via the perceptual variables.

Finally, future research can examine whether task and product characteristics play a role in the factors that determine online consumer behavior. For example, task type can include information search, purchase, and online service use, while task complexity can range from programmed (routine, structured) task to non-programmed (novel, unstructured) task. Based on this categorization, a matrix for different types of tasks can be developed and subsequently used to identify important variables within a specific task context.

The business to consumer (B2C) segment within electronic commerce is under enormous pressure due to the frequent failures of a large number of e-tailers. By identifying specific variables that impact web use, online purchase, and post-purchase behaviors of customers, this study provides useful guidance to managers seeking to wisely use limited resources to improve online transactions. Given that system quality, information quality, service quality, and vendor and channel characteristics are important drivers of consumer perceptions and subsequent online behaviors, companies should focus on providing consumers with a well-designed web site, accurate and useful content, high-quality service, and low cost advantage.

Some limitations should be noted. The results and analysis presented in the study were limited to the pool of journals that satisfied our selection criteria. The scope of the study did not include other areas, such as Marketing and Human Computer Interaction, which have also examined online consumer behavior. In this study, our intention was to understand online consumer behavior from an IS perspective. It should also be noted that the number of studies included in the quantitative meta-analysis performed for the common relationships was rather small. Although we included all the variable relationships examined by more than one study, due to the lack of the correlation table and incomparable sets of variables, there were only eight relationships based on a limited number of studies. Thus, the results of the meta-analysis should be interpreted with caution and should be expanded by future research.



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Advanced Topics in End User Computing (Vol. 3)
Advanced Topics in End User Computing, Vol. 3
ISBN: 1591402573
EAN: 2147483647
Year: 2003
Pages: 191

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