讲座题目：Dynamic Pricing and Product Quality with Review–Driven Learning
主讲人：Perera Sandun Chamara 教授
讲座时间：2026年7月21日（周二）下午14：30
讲座地点：九里校区零号楼0218室
主讲人简介：Perera Sandun Chamara，美国内华达大学雷诺分校商学院商业分析、供应链与人工智能专业教授，他同时拥有德克萨斯大学达拉斯分校金达尔管理学院运营管理博士和佛罗里达大西洋大学金融数学博士学位。其研究领域涵盖供应链管理、运营管理颠覆性技术、医疗运营管理，以及运营板块与其他商业职能领域的交叉融合方向。曾与多家跨国科技企业、零售企业、医院、血库、电动汽车及电池制造厂商、制药企业开展产学研合作。他的多篇学术成果发表于FT-50、UTD-24 等顶级期刊。他担任Production and Operations Management高级主编、IEEE Transactions on Engineering Management栏目主编，同时兼任OMEGA, Decision Sciences, and Transportation Research Part E副主编。此外，他还担任人本人工智能学会项目与对外拓展执行主任，并在国际生产与运营管理学会（POMS）担任多项核心管理职务，担任2026年POMS年会程序主席、会员事务副主席。
讲座内容：Online product reviews reduce consumer uncertainty and are widely studied from demand-side perspectives, yet supply-side pricing-quality integration strategies leveraging reviews remain underexplored. This lecture discusses a firm’s two-period dynamic pricing and quality decisions for new experience goods, where early reviews update both the firm’s and consumers’ uncertain beliefs about perceived quality. Two key review metrics—volume and valence—jointly create a learning precision effect that improves quality inference and guides optimal decisions. Without post-launch quality adjustment, firms set higher initial quality and lower launch prices to generate more reviews. When quality refinement is allowed, the learning effect strengthens, and first-period prices may rise or fall based on market conditions to boost reviews. This strategy raises total firm profit and consumer surplus for all buyers, delivering a win-win outcome, contrary to the common belief that quality adjustments benefit firms alone. Extensions covering reporting bias, uninformed consumers, marginal quality costs and alternative quality distributions confirm the robustness of core findings.
