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Résumé
Purpose: The site selection for a restaurant is one of the most critical pre-opening choices a restaurateur faces. The restaurant's site can influence all other pre- and post-opening decisions from concept through marketing; however, the locational choices are often made by intuition, as there is limited research on the topic. This study investigates the significance of site characteristics and the surrounding population's attributes to create two prediction models for future site selection decisions. Design/methodology/approach: Two prediction models, logistic regression and an artificial neural network (ANN) are utilized and compared. Findings: The logistic regression had a prediction accuracy of 71.267%, and the artificial neural network predicted restaurant site success with an accuracy of 72.55%. One hundred eighty-eight new restaurants were placed into the ANN model; the model provided an overall accuracy rate of 82.447% in predicting first-year success or failure. Originality: These are the first models that allow an independent restaurateur to predict if a site will provide potential success. Selecting a site with the highest potential for restaurant success will minimize failed restaurants' social and economic losses.