2026年第63期(总第1207期)
演讲主题：Dynamic Pricing and Product Quality with Review–Driven Learning
主讲人：Sandun Perera 内华达大学教授
主持人：关旭 教授、供应链管理与系统工程系主任
活动时间：2026年07月16日（周四）14:00-15:30
活动地址：管院大楼105教室
主讲人简介：
Sandun Perera is a Professor of Business Analytics, Supply Chain, and AI in the College of Business at the University of Nevada, Reno. He received his Ph.D. in Operations Management, MBA, and M.S. in Supply Chain Management from the Jindal School of Management at the University of Texas at Dallas, and also holds a Ph.D. in Financial Mathematics.
His research spans Supply Chain Management, Disruptive Technologies in Operations Management, Healthcare Operations Management, and the interfaces between Operations and other functional business areas. His work has appeared in journals on the Financial Times Top 50 and UTD Top 24 lists. He serves as a Senior Editor for Production and Operations Management, Department Editor for IEEE Transactions on Engineering Management. He is also the Executive Director for Initiatives and Outreach of the Human-Centered AI Society and holds key leadership roles within the Production and Operations Management Society (POMS), where he is the Program Chair for the POMS Annual Conference 2026 and Vice President of Membership.
活动简介：
Online product reviews greatly reduce consumers’ purchase uncertainty, and existing research mostly analyzes reviews from the consumer demand side, while few studies explore supply-side applications, especially how enterprises formulate integrated pricing and quality strategies based on reviews. This paper investigates firms’ dynamic pricing and quality decisions across two sales periods for new experience goods, in which consumer reviews dominate public quality perception. Both firms and consumers lack initial quality information and update judgements via early reviews. This study identifies review volume and valence as two core indicators determining corporate optimal strategies. They jointly generate the learning precision effect, helping enterprises infer product quality accurately and adjust operational decisions dynamically. If firms cannot modify post-launch product quality, they tend to adopt high initial quality and low launch prices to attract reviews and optimize information learning. Allowing post-launch quality adjustment strengthens such learning effect, prompting firms to further raise initial quality, yet the optimal launch price may rise or fall against traditional views, contingent on market conditions. Notably, this integrated strategy boosts corporate profits as well as consumer surplus for all buyers, realizing a win-win situation instead of benefiting firms alone. Extended models covering reporting bias, uninformed consumers and other factors verify the robustness of the above findings.
