The Economics of Bestsellers: Consumer Search, Sales Ranking, and Social Learning

• 成果类型: Article
• 署名作者: Lu, Wentao; Yu, Man
• 署名单位: Chinese Academy of Sciences; University of Science & Technology of China, CAS; Hong Kong University of Science & Technology
• 刊物名称: M&SOM-MANUFACTURING & SERVICE OPERATIONS MANAGEMENT
• ISSN/ISSBN: 1523-4614
• DOI: 10.1287/msom.2023.0583
• 发表日期: 2026
• 关键词: information

摘要

Problem definition: Motivated by major e-commerce platforms' diverse practices in bestseller information provision, this paper examines consumers' learning, searching, and purchasing behavior under uncertainty about products' values, whereas a revenue-maximizing platform strategically decides whether and, if so, how to disclose products' past sales information to consumers. Methodology/results: We analyze a two-period Bayesian learning model that embeds consumers' sequential product search in a social learning framework and shows how the interaction between bestseller information and consumer search impacts sales and welfare. We find that a bestseller list constitutes an informative yet noisy signal about the products' values. The informativeness of the signal is determined by the granularity of the bestseller information. By evaluating bestseller information of two levels of granularity, sales ranking and sales volume, we discover that, although consumers benefit more from information of a higher granularity (i.e., sales volume), the platform may prefer providing information of a lower granularity (i.e., sales ranking), suggesting that the platform may withhold information at the cost of consumers. In particular, an inference effect unique to multiproduct Bayesian learning gives rise to the possibility that disclosure of sales volume backfires and hurts the platform. We demonstrate significant sales implications of bestseller information granularity and show that concave distribution functions for consumers' search cost, a stochastic increase in product values, or a growth in consumer population can tilt the platform's preference toward displaying bestseller rankings without revealing sales volumes. Furthermore, we show that bestseller information may lead to lower purchased value or higher search cost, the latter implying that public learning may stimulate rather than substitute private learning. Managerial implications: The paper cautions retail platform practitioners about a pitfall associated with disclosing bestseller sales volume and presents guidelines on the timing and granularity of sales information provision. The findings also suggest e-commerce platforms with consumer-centric goals enhance bestseller information transparency on their marketplaces.