Customer Evaluation of Interactive Recommendation Systems: the Leveraging Role of Perceived Personalization
(A2022-107465)

AUTHORS

Claire Deventer, University of Namur; Pietro Zidda, University of Namur

ABSTRACT

Interactive recommendation systems (IRS) advice customers about what to buy based upon a personalized dialog. Despite the observed general impact of perceived IRS personalization on customer attitudes and behaviors, little is known about how personalization interacts with perceived benefits, costs and risks associated with IRS usage in driving satisfaction with the IRS. To answer this question, we develop and test a model with 913 customers of a well- known food supplements actor. Our results show that perceived personalization is not only the most important driver of IRS satisfaction but also acts as a lever for all perceived benefits and as a reducer for perceived risks and costs. This research deepens the understanding of the role of personalization on customer attitude towards IRS and offer managerial insights on how to successfully design IRS.