Measuring Customer Satisfaction on the Internet

Based on the Expectancy Disconfirmation Model as the underlying construct, methods to measure customer satisfaction with products and the steps to be undertaken in the research process are investigated. The measurement of Derived Satisfaction using (dis)confirmation was identified to be the appropriate approach to CS measurement. Prior research has also shown that during the research process, several points specific to CS measurements need to be accounted for. The Internet services currently used by marketing and social researchers include E-mail, mailinglists, newsgroups, Internet Chat, the World Wide Web (WWW) and Virtual Worlds. Virtual Worlds, being most advantageous for observational studies, are not useful for customer satisfaction research. Virtual Communities, in turn, have some promising characteristics for future use. Internet research methods based on these services include E-mail surveys and WWW-surveys. Common advantages of E-mail- and WWW-surveys include administrative and response speed, cost savings and global reach of respondents. Their greatest common disadvantage is the non-representativeness of the respondents for the larger population as well as their self-selection. Unless access is restricted to a known population, probability sampling is impossible when using the World Wide Web. Based on these insights, the Internet was found to be an advantageous medium for customer satisfaction studies only if specific conditions are met. Companies need to investigate on a case-by-case basis if the online measurement of customer satisfaction is possible in their specific situation. The recommendations were summarized in a decision-making framework. The results of a survey among market research agencies show that practitioners are to a large extent aware of the limitations within which the Internet can be used for customer satisfaction surveys. However, especially WWW-surveys sometimes are conducted in a way that does not lead to representative results.management information;

Psychological reasons for consumer trust in e-retailing

This research in progress expands on existing research on e-retailing by examining the psychological factors that influence consumer trust in e-retailing. The psychological factors expected to influence trust are personality-based factors, perception-based factors, experience-based factors, knowledge-based factors and attitude. Hypotheses have been developed based on a thorough review of the trust literature. A pilot study has been conducted in the Netherlands and the results hereof are included in this paper.management information;

An Empirical Comparison of Consumer Innovation Adoption Models: Implications for Subsistence Marketplaces

So called “pro-poor” innovations may improve consumer wellbeing in subsistence marketplaces. However, there is little research that integrates the area with the vast literature on innovation adoption. Using a questionnaire where respondents were asked to provide their evaluations about a mobile banking innovation, this research fills this gap by providing empirical evidence of the applicability of existing innovation adoption models in subsistence marketplaces. The study was conducted in Bangladesh among a geographically dispersed sample. The data collected allowed an empirical comparison of models in a subsistence context. The research reveals the most useful models in this context to be the Value Based Adoption Model and the Consumer Acceptance of Technology model. In light of these findings and further examination of the model comparison results the research also shows that consumers in subsistence marketplaces are not just motivated by functionality and economic needs. If organizations cannot enhance the hedonic attributes of a pro-poor innovation, and reduce the internal/external constraints related to adoption of that pro-poor innovation, then adoption intention by consumers will be lower

Analyzing Group Data in MIS research: Including the Effect of the Group

A review of recent group experimental research in three major MIS research journals indicates a lack of understanding of how to analyze group data measured at the individual level. This paper draws on statistical analysis literature from various disciplines to explain why current analysis of group data in MIS research is often incorrect and demonstrates how to analyze such data correctly