Information Transparency With Targeting Technology for Online Service Operations Platform
研究在线服务平台如何通过个性化推荐影响客户对服务质量的认知，发现推荐能提升匹配效率、减少拥堵，但平台有时应故意不推荐部分客户以增加整体福利，这对利润导向平台更明显。
Social technologies have enabled the emergence of online platforms that provide offline service consultations and recommendations. In this environment, economic inefficiency arises when customers are not fully aware of their horizontally differentiated preferences. With its expertise or data dominance, a platform can be more informed about customers’ hidden preferences. We focus on an instrumental social technology, that is, targeting, which is a type of data-driven personalized information provision to manipulate customers’ beliefs about service quality. We propose a Hotelling model wherein customers are sensitive to the delays for service while making Bayesian belief updates based on a platform’s recommendations. When customers self-select their favorite service, their choices impose negative externalities through congestion and welfare loss. Our results indicate that service recommendations allow customers to navigate toward the more appropriate service, thus improving matching efficiency, reducing congestion costs, and enhancing aggregate customer welfare. We further identify the role of “information transparency” and study how the platform should strategically release information by making personalized service recommendations to customers. Interestingly, when a customer-centric platform maximizes aggregate customer welfare, we identify the “value of opaqueness” by strategically withholding service recommendations from a subset of customers and notice that this effect is more pronounced for a profit-seeking platform. Our results offer a better understanding of information transparency policies in the joint design of service recommendation systems and pricing mechanisms.