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Recommender systems, which suggest relevant products to internet users, have become an integral part of our daily lives. The factors responsible for their success from the different stakeholder perspectives, however, have never been thoroughly investigated. This study proposes a novel model for measuring the success of recommender systems that consolidates different success factors. The model is a modified version of the DeLone and McLean Information Systems Success Model with trust as an additional latent variable. The model was evaluated in an empirical study with PLS-SEM. The proposed model exhibits a high predictive power and all structural paths were significant. The integration of trust is an important contribution as the path between information quality and trust yielded the highest path coefficient. The proposed model can be used by recommendation system providers to explain and predict the successful use of the systems and to improve business processes.

Measuring the Success of Recommender Systems: A PLS-SEM Approach