In this study, we address the discriminant factors of website trust. We specifically build sets of propositionalrules that can be used to predict the level of trustworthiness of a site. Focusing on initial trust, a survey wasdesigned 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 amongdecision trees as well as classical and dominance-based rough sets. A heuristic aiming to derive simplerclassifiers is also proposed. The experimental setup considers diverse groups of attributes (predictors) for theextraction of rules. Results obtained are compared by taking into account predictive ability and parsimony ofrules’ sets. Finally, the selected sets help bring light on how consumers process site information and suggestspecific recommendations for e-commerce vendors.

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Trust,E-commerce,B2C,Information theory,Rough sets,Tree algorithms,Knowledge-based systems

• Mathematics - Natural Sciences
• Computer and Information Sciences - Natural Sciences
• Economics and Business - Social Sciences
• Other Social Sciences - Social Sciences