讲座题目：Too Much of Artificial Intelligence? AI adoption in procurement and product availability
主讲人：戚依南教授
讲座时间：2024年6月3日（周一）10:00
讲座地点：主教615会议室
主持人：刘晓红 中央财经大学商学院教授
主讲人介绍：
戚依南，对外经济贸易大学国际商学院供应链与运营管理学教授，发展规划处处长，国际供应链与运营管理学会前主席。研究领域包括供应链韧性、数字化供应链、供应链创新，供应链协同和可持续供应链。戚教授主持和参与国家级和省部级项目10余项，担任国家自然科学基金重点项目、国家社会科学基金重大专项基金项目子课题负责人，主持横向课题4项。在国内外知名学术期刊发表论文30余篇，包括Journal of Operations Management, Decision Sciences, International Journal of Production Economics, Journal of Supply Chain Management, International Journal of Operations and Production Management, Technovation等。戚教授发表2篇教学案例并被毅伟商学院案例库收录，与毅伟商学院合作出版案例集1部。
讲座摘要：
While extensive literature highlights the benefits of artificial intelligence (AI), recent research suggests its side effects. The overall influence of AI application may vary across empirical contexts and the AI adoption intensity (the extent to which firms use AI in their specific business processes). This paper analyzes the impact of suppliers’ AI adoption in retailers’ procurement processes on product availability. Theoretically, adopting AI in the procurement process generates two conflicting effects on product availability: the efficiency enhancement that increases product availability and the expertise exclusion that decreases product availability. The overall impact of AI adoption depends on the tradeoff between them. Moreover, the tradeoff between these opposing effects may change over the AI adoption intensity, thus potentially leading to a nonlinear impact on product availability. We collected proprietary data from a leading online retailer to investigate how the supplier’s AI adoption intensity affects the retailer’s product availability. The empirical results suggest a significant and positive overall impact of AI adoption intensity. Our nonlinear analyses show that this impact follows an inverted U-shaped pattern when AI adoption intensity increases. This nonlinear impact suggests that more AI adoption does not necessarily result in better product availability. Too much AI adoption in procurement adversely hurts product availability. Furthermore, to disentangle these two conflicting effects, we investigate how product variety moderates the impact of AI adoption intensity since increased product variety is expected to magnify the expertise exclusion effect. Our results imply the importance of tailored AI adoption strategies for suppliers considering their product variety. Specifically, firms should develop their workforce with domain expertise to handle extensive product varieties. Our study thus provides theoretical contributions and practical implications for AI adoption in managerial decision-making tasks.
