Logical Rules Characterization Of Online Consumer Trust

In this study, we address the discriminant factors of website trust. We specifically build sets of propositional rules that can be used to predict the level of trustworthiness of a site. Focusing on initial trust, a survey was designed to assess site characteristics observed by the respondent and his/her perceptions around appearance, reputation, fulfillment, and security. By exploring data, we look for the most favorable rules classifiers among decision trees as well as classical and dominance-based rough sets. A heuristic aiming to derive simpler classifiers is also proposed. The experimental setup considers diverse groups of attributes (predictors) for the extraction of rules. Results obtained are compared by taking into account predictive ability and parsimony of rules' sets. Finally, the selected sets help bring light on how consumers process site information and suggest specific recommendations for e-commerce vendors.