Cao, Xinyu and Patricia L. Mokhtarian (2005) The Intended and Actual Adoption of Online Purchasing: A Brief Review of Recent Literature. Institute of Transportation Studies, University of California, Davis, Research Report UCD-ITS-RR-05-07
A number of studies have investigated e-shopping behavior, and reviewing them is valuable for further improving our understanding. This report aims to summarize previous e-shopping research in a systematic way. In this review, we are interested primarily in the potential benefits and costs that the internet offers for the business-to-consumer segment of e-commerce in the transaction (purchase) channel. An overview of the 65 empirical studies analyzed in this report is provided in the Appendix.
Most previous studies fall into one or more of several 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. Among them, social psychological theories (the theory of reasoned action, the theory of planned behavior, the technology acceptance model) were widely applied. As shown in the applications of different theories, e-shopping behavior is not a simple decision process, and thus an integration of various theories is necessary to deal with its complexities. We suggest synthesizing these theories through the development of a comprehensive list of benefits and costs, using each of the key constructs of the pertinent theories as a guide to identifying the nature of those benefits and costs.
The dependent variables mainly include e-shopping intention and actual e-shopping behavior (a few studies used attitudes toward e-shopping). E-shopping intention was measured by various dimensions. Among them, the directly-stated intention to purchase online is the most frequently used measure. Although some studies used a unidimensional measure, most adopted a latent construct to assess consumers' e-shopping intentions. Actual e-shopping behavior mainly includes three dimensions: adoption, spending, and frequency. Most studies examined one or more of these three dimensions directly, while a few studies constructed a latent variable to measure actual e-shopping behavior. When both behavioral intention and actual behavior are included in model development, attention should be paid to the time precedence between intention and behavior.
With respect to sampling, a choice-based sampling approach is probably preferable given that online shopping activity accounts for a minor proportion of all consumers, and a far smaller proportion of total retail sales. In previous studies, most chose internet/computer/email users or students as their subjects. Generally, a student sample is a natural choice for some particular products such as books. However, parameter estimates developed from a student sample lack generalizability to a larger population because of its homogeneity. By contrast, a more general sample of internet/computer/email users is more applicable for e-shopping behavior research.
The suitability of the internet as a shopping medium depends to a large extent on the characteristics of the products. Mixing product categories in e-shopping behavior research tends to yield vague or inconsistent results. It is therefore necessary to explicitly consider product characteristics when exploring consumers' e-shopping behavior. However, relatively little effort has been invested into product classification in the context of e-shopping. Although Nelson's dichotomized system (search and experience goods) and Peterson et al.'s three-dimensional system (cost, tangibility, and differentiability) provide useful guides for product type classification, each has some shortcomings. Therefore, more research should focus on the construction of product classification systems.
Different methodologies have been applied in previous research. Generally, descriptive analysis is used to describe consumers' e-shopping behavior; correlational analysis goes beyond descriptive analysis and attempts to analyze how two variables are related; and multivariate analysis is mainly used to explain consumers' behavior using many variables considered together. Therefore, although descriptive and correlational analyses are important steps in helping to construct multivariate analyses, multivariate studies provide more information than these other two types of analyses. Multivariate analysis is ideal to study e-shopping behavior in depth. Among multivariate analysis techniques, multiple regression, structural equations modeling, and discrete choice models were most frequently used.
Previous studies have identified various determinants of consumers' e-shopping behavior. These determinants mainly cover three essential elements: characteristics of e-shopping as a shopping channel, consumer characteristics, and vendor and product characteristics. Among these characteristics, the former two have been examined extensively in previous research, confirming their importance in understanding e-shopping behavior. Specifically, the dimensions of channel characteristics of e-shopping include e-shopping service quality, relative advantages, perceived risk of and confidence in e-shopping, and trust. Consumer characteristics include their shopping orientations, personality, social and psychological characteristics, computer/internet experience, in-home shopping experience, and socio-demographics.