Comparing the effectiveness of recommendation agents across devices

Abstract

This research empirically studies whether recommendation agents are as effective on newer, mobile devices (i.e., tablets, smartphones) as they are on older, stationary ones (i.e., desktop computers). We analyze clickstream data from Airbnb with a novel econometric model based on beta and logit regressions and estimated within the Bayesian framework. The model controls for self-selectivity bias. Our empirical findings show that recommendation agents are less effective on mobile devices; the number of recommended alternatives clicked by smartphone (desktop computer) users is smaller (larger) than that of tablet users. This is managerially important as we also show that a consumer’s purchase likelihood is directly related to the number of recommended alternatives evaluated by them. Furthermore, we found that men and younger consumers rely less on recommendation agents. Our results highlight the importance of redesigning recommendation agents for mobile devices as well as identifying consumer segments that need stronger incentives to shop online.