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信息管理与商业智能系讲座（7月21日）
- 来源：
- 学校官网
- 收录时间：
- 2026-07-17 03:01:08
- 时间：
- 2026-07-21 13:30:00
- 地点：
- 李达三楼105室
- 报告人：
- 顾彬
- 学校：
- 复旦大学
- 关键词：
- Generative AI, Personalization, Cognitive Load, Elaboration Likelihood Model, Promotional Effectiveness, Information Systems, Field Experiment
- 简介：
- The rapid integration of generative AI (GenAI) into commercial platforms offers unprecedented opportunities for scalable, personalized promotional communication. This study examines the effects of GenAI-based production promotion on customer purchase decisions in the context of online food ordering. Partnering with a chain restaurant, we conducted a randomized field experiment in which customers were assigned to receive one of four types of promotional messages: AI-generated personalized messages, AI-generated generic messages, human-generated generic messages, or coupons only. Results show that both AI-generated message types significantly enhance customer purchasing behavior compared with the coupon-only group, with AI-personalized messages increasing daily order amounts by 14.3% and AI-generic messages by 10.4%, while human-generated generic messages show no significant improvement over the control. Moderation analyses reveal that AI-generic messages are particularly effective in high cognitive load contexts (e.g., in the workplace or during working hours), while AI-personalized messages maintain effectiveness across all contexts. We employ the Elaboration Likelihood Model (ELM) to identify a two-tier cognitive mechanism underlying these findings. First, AI-generated messages are more readable and present higher-quality arguments than human-generated messages, reducing comprehension barriers and enriching the substance available for persuasion. Second, we find that AI-generated generic messages leverage strong peripheral persuasion cues. In contrast, AI-generated personalized messages incorporate both peripheral and central route elements, enabling dual-pathway persuasion. This research contributes to the Information Systems (IS) literature by providing causal field evidence on the promotional effectiveness of GenAI, advancing cognitive load and ELM theories in the context of AI-generated content, and offering practical insights for AI-driven customer engagement strategies.
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报告介绍：
信息管理与商业智能系讲座（7月21日） 题目：When Generative AI Persuades Better: Cognitive Constraints, Personalization, and Promotional Effectiveness
报告人介绍：
Professor Bin Gu is Everett W. Lord Distinguished Faculty Scholar, Professor and Department Chair of Information Systems at the Questrom School of Business, Boston University. Professor Gu’s research interests are in using information technologies and artificial intelligence to address information asymmetry and social inequity in business and society. He examines more specifically information asymmetry and social inequity in fintech, digital platforms, the future of work, online social media and social network, and online retailing. His work has appeared in leading business academic journals including Management Science, MIS Quarterly, Information Systems Research, Journal of Management Information Systems and others and has received over 16000 citations. Professor Gu was awarded the INFORMS Information Systems Society Distinguished Fellow Award in 2022 for his outstanding intellectual contributions to the information systems discipline. Professor Gu obtained his PhD and MA degrees from the Wharton School of Business at University of Pennsylvania and his BEng degrees in International Business and Computer Science from Shanghai Jiaotong University. Before joining Boston University, Professor Gu was the Gladys Davis Distinguished Professor and associate dean of China Programs at the W P Carey School of Business at Arizona State University.
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