Recommendation Systems and Sales Concentration: The Moderating Effects of Consumers´ Product Awareness and Acceptance to Recommendations

This research investigates how recommendation systems affect sales concentration in the e-market. In the literature, there have been two opposite theories accounting for this issue: the winner-take-all theory and the long tail theory. The former predicts that the market will be homogenized by recommendation systems, whereas the later predicts that the market will be fragmented. We argue that both directions of influence are possible, depending on the types of products that recommendation systems favor. Furthermore, we argue that the effects of recommendation systems are moderated by consumers´ cognitive states before and after they encounter the systems, namely their awareness of products and their acceptance level to recommendations. We used the simulation method on the MovieLens dataset, which consists of 100,000 ratings from 943 users on 1682 movies. The simulation results support our hypotheses, showing that, relative to the condition of no recommendation, the content-based recommendation systems tend to decrease sales concentration, whereas the collaborative filtering tends to increase it. Moreover, the magnitudes of increase and decrease are moderated by consumers´ awareness type and acceptance level.