Recommender System Rethink: Implications for an Electronic Marketplace with Competing Manufacturers

Lusi Li (), Jianqing Chen () and Srinivasan Raghunathan ()
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Lusi Li: College of Business and Economics, California State University, Los Angeles, Los Angeles, California 90032
Jianqing Chen: Jindal School of Management, The University of Texas at Dallas, Richardson, Texas 75080
Srinivasan Raghunathan: Jindal School of Management, The University of Texas at Dallas, Richardson, Texas 75080

Information Systems Research, 2018, vol. 29, issue 4, 1003-1023

Abstract: Recommender systems that inform consumers about their likely ideal products have become the cornerstone of e-commerce platforms that sell products from competing manufacturers. Using a model of an electronic marketplace in which two competing manufacturers sell their products through a common retail platform, we study the effect of recommender systems on the retail platform, manufacturers, consumer surplus, and social welfare. In our setting, consumers are differentiated with respect to their preference for the two products (locational differentiation) and awareness about the two products (informational differentiation). A recommender system selects the recommendation based on a recommendation score, which is a weighted sum of expected retailer profit and expected consumer value. We find that the recommender system may benefit or hurt the retailer and the manufacturers depending on the signs and magnitudes of the substitution effect and demand effect of the recommender system. The substitution effect of the recommender system either intensifies or softens the price competition between two manufacturers through two forces—its direct influence alters the informational differentiation of consumers (which affects the markup that manufacturers can charge), and its strategic influence motivates the manufacturers to use price as a lever to attract more recommendations in their favor. The demand effect of the recommender system increases overall consumer awareness, but, depending on the substitution effect, may increase or decrease the demand. The recommendation strategy, namely, the relative weight assigned to retailer profit vis-á-vis consumer value in computing the recommendation score, along with recommender system precision and the relative sizes of segments of consumers with different awareness levels, determines whether the retailer benefits from the recommender system and by how much. We find that the retailer’s optimal recommendation strategy is mildly profit oriented in the sense that it assigns a larger, but not too large, weight to retailer profit compared to consumer value, and that under the optimal strategy, the price competition is less intense and the retailer profit is higher compared to when there is no recommender system. Furthermore, an increase in either the recommender system precision or the fraction of consumers that are aware of at least one product induces the retailer to adopt a more profit-oriented recommendation strategy.

Keywords: analytical modeling; economics of IS; recommender system; electronic commerce (search for similar items in EconPapers)
Date: 2018
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Citations: View citations in EconPapers (11)

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