  In this paper, we examine the effectiveness of various recommendation
strategies in the mobile channel and their impact on consumers' utility and
demand levels for individual products. We find significant differences in
effectiveness among various recommendation strategies. Interestingly,
recommendation strategies that directly embed social proofs for the recommended
alternatives outperform other recommendations. Besides, recommendation
strategies combining social proofs with higher levels of induced awareness due
to the prescribed temporal diversity have an even stronger effect on the mobile
channel. In addition, we examine the heterogeneity of the demand effect across
items, users, and contextual settings, further verifying empirically the
aforementioned information and persuasion mechanisms and generating rich
insights. We also facilitate the estimation of causal effects in the presence
of endogeneity using machine-learning methods. Specifically, we develop novel
econometric instruments that capture product differentiation (isolation) based
on deep-learning models of user-generated reviews. Our empirical findings
extend the current knowledge regarding the heterogeneous impact of recommender
systems, reconcile contradictory prior results in the related literature, and
have significant business implications.

Computers and Society