Frontiers of Information and Platform Design in Operations Management

This dissertation revolves around the intricate domains of information and mechanism design in operations management, presented in two essays. The first essay delves into the concerns surrounding customer apprehensions about price discrimination and explores the optimal utilization of information to alleviate such concerns. Employing a correlated Bayesian persuasion framework, we uncover the conditions under which a binary inventory signal complements pricing strategies, concurrently enhancing firm revenue and customer welfare. In the second essay, we study the design of rating platforms in the presence of disconfirmation effects, i.e., when customers incorporate their prior belief of the product into their ratings. The study elucidates the pivotal role of reference effects in shaping rating convergence to true product quality. In the first essay, we focus on the strategic information transmission between firms and strategic customers. Customers have heterogeneous valuations for the products and services, and firms use various price discrimination tactics where they charge different prices to different customers. This practice, i.e., customizing the price for individual customers, is known as personalized pricing (PP) and is implemented in various industries. We investigate whether pricing can informatively signal PP to customers and how firms should adjust pricing strategies in response to customer reactions. We also investigate whether disclosing inventory information can benefit firms and customers, ultimately advocating for increased transparency in PP practices. By modeling dynamic interactions between firms and a continuum of heterogeneous strategic customers over two periods, we unveil nuanced insights. We find that firms reduce the first-period price to persuade high-valuation customers to purchase in the first period, even when they do not intend to implement PP. This is because the mere presence of PP risk makes customers reluctant to reveal their identity. The firm then must “compensate” customers to persuade them to reveal their valuations. We show that the price alone cannot perfectly signal the firm’s PP intention when the firm takes the strategic customer behavior into account. We next consider whether customers can infer the implementation of PP from the inventory availability information. To study this, we focus on a class of binary signals where the firm marks the inventory low when it is below a threshold. Such inventory signals are commonly adopted by retailers such as IKEA and ZARA. We show that an inventory signal can improve the firm revenue only when customers believe the firm conducts PP with a sufficiently low probability. In this case, an inventory signal alleviates customer PP concerns and allows the firm to set higher prices. Additionally, we demonstrate that disclosing inventory availability information is a strategic complement to the prices when alleviating the customer PP concerns. Furthermore, an inventory signal, in addition to the firm, can benefit all customers. With the growing interest in PP regulation, requiring firms to disclose inventory availability information could be a viable policy to make PP more transparent and credibly reduce customer concerns. In the second essay, we study the customers’ social learning problem upon observing the product ratings. Customers and platforms increasingly rely on online ratings to assess the quality of products and services. However, customer ratings are susceptible to various biases. Disconfirmation bias is a specific form where customers incorporate the discrepancy between their prior expectations and post purchase experiences into their ratings. We study the asymptotic behavior of ratings in the presence of disconfirmation bias in three rating systems: (i) complete system, where customers observe the entire rating history; (ii) aggregate system, where only the frequency of each rating option is available; and (iii) average ratings, where customers solely use the average of past ratings. Customers are Bayesian and update their quality beliefs upon observing the ratings. After experiencing the product, they rate it according to their heterogeneous ex-post utility and disconfirmation bias. In complete and aggregate systems, we show that customer beliefs converge to the intrinsic quality when disconfirmation bias is small. When this bias is large, there will be a discrepancy between converged beliefs and the intrinsic quality, although this discrepancy could be arbitrarily small. When the disconfirmation bias is intermediate, beliefs may diverge significantly from the intrinsic quality or not converge. However, we establish that the platform can guarantee correct learning by designing a sufficiently granular rating system, i.e., a system with more rating options. We confirm all these results in the system with average ratings, albeit with a bias-correcting rule. Finally, we characterize the learning speed in the aggregate system. In summary, this thesis contributes to the literature on the interface of information, platform, and mechanism design in operations management, unraveling the intricacies of pricing strategies, information revelation mechanisms, and rating platforms.