When E-Commerce Personalization Systems Show and Tell: Investigating the Relative Persuasive Appeal of Content-Based versus Collaborative Filtering

Abstract

In the e-commerce context, are we persuaded more by a product recommendation that matches our preferences (content filtering) or by one that is endorsed by others like us (collaborative filtering)? We addressed this question by conceptualizing these two filtering types as cues that trigger cognitive heuristics (mental shortcuts), following the heuristic-systematic model in social psychology. In addition, we investigated whether the degree to which the recommendation matches user preferences (or other users’ endorsements) provides an argument for systematic processing, especially for those who need deeper insights into the accuracy of the algorithm, particularly in product categories where quality is subjective. Data from a 2 (algorithm type: content vs. collaborative filtering) x 3 (percentage match: low vs. medium vs. high) x 2 (product category: search vs. experience) + 2 (control: search and experience) between-subjects experiment (N = 469) reveal that for experience products, consumers prefer content-based filtering with higher percentage matches, because it is perceived as offering more transparency. This is especially true for individuals with high need for cognition. For search products, however, collaborative filtering leads to more positive evaluations by triggering the “bandwagon effect.” These findings have implications for theory pertaining to the use of artificial intelligence in strategic communications and design of algorithms for e-commerce recommender systems.