报告时间、地点：

报告时间：2025年6月30日，星期一，下午16:00

报告地点：华南理工大学 大学城校区，B10中座101

报告摘要：

       Today, using AI can effectively create quality materials such as digital designs and videos. These AI generated contents (AIGCs) can now be created via services (e.g., ChatGPT, Google Veo 2) offered by AIGC firms (e.g., OpenAI, Google). To commercialize their services, AIGC firms commonly charge clients through two pricing schemes, namely subscription pricing mode and on-demand pricing mode. However, there is growing consumer concern regarding the potential exposure of private information during the utilization of these AIGC services. Motivated by the real practices of AIGC service operations, we analytically examine the pricing strategies of the AIGC firm in the presence of consumers’ privacy concerns. We find that the optimal pricing strategy is driven by the AIGC service’s usage value and consumers’ privacy concern level. Specifically, the AIGC firm prefers the on-demand pricing mode when either (i) the usage value is significantly large, or (ii) the usage value is moderate and consumers’ privacy concern level is low. Otherwise, the AIGC firm prefers the subscription pricing mode. We also explore the government’s preference and find that there exist win-win situations for the government and the firm. However, a conflict zone may exist when the usage value and consumers’ privacy concern level are moderate. We examine the effect of consumers’ learning ability on the AIGC firm’s decisions and find that our key findings remain the same regardless the learning ability is endogenous or not. However, compared with the case when consumer learning ability is endogenous, the region of AIGC firm preferring the subscription pricing mode expands when this learning ability is exogenous. To verify the robustness of our findings, we further consider two cases with the exogenous technology level and consumer utility maximizing technology level.

主讲人简介：

       华南理工大学副教授，博士生导师，现担任Sustainable Operations and Computers，Frontiers in Sustainability和International Journal of Business Performance and Supply Chain Modelling的副主编、Decision Sciences的编审委员、中国运筹学会随机服务与运作管理分会青年理事。先后主持国家级（国自科青年+面上）和省部级等6项，作为核心成员参与国家级和教育部级8项。在国内外权威期刊Naval Research Logistics, Decision Sciences, IISE Transactions, European Journal of Operational Research, International Journal of Production Economics, Transportation Research Part E和International Journal of Production Research上发表了40余篇，其中SCI/SSCI收录论文30余篇。获2020年European Journal of Operational Research最佳论文奖，2022年天津市优秀博士学位论文，2020年南开大学优秀博士学位论文奖，2017年European Journal of Operational Research杰出审稿人奖，2024年度华南理工大学优秀本科毕业论文指导教师。