Reversing Algorithm Aversion: From Predictive to Generative AI
Prof. Han ZHANG
Dean and Chair Professor
School of Business, Hong Kong Baptist University
Date: 4 September 2025 (Thursday)
Time: 10:30-12:00
Venue: E22-G008
Host: Prof. Jacky Yan LIN, Assistant Professor in Business Intelligence and Analytics
Abstract
Artificial intelligence (AI) that focuses on forecasting- and prediction-oriented tasks (i.e., predictive AI) has been increasingly used across various domains in the past decade. With the release of ChatGPT in 2022, generative AI—a type of AI that can produce new content like text—has gained tremendous interest from the public and businesses. In this paper, we explore whether and why the dominant finding of algorithm aversion in prior studies of predictive AI may not be applicable to generative AI. Drawing on the algorithm aversion vs. appreciation literature, reactance theory, construal-level theory, and trust literature, we propose that algorithm aversion may be reduced for generative AI (vs. predictive AI), and this reduced aversion may be mediated by lower reactance or higher trust. Through a carefully designed experiment, we find that algorithm aversion is reversed for generative AI (vs. predictive AI) and that trust mediates algorithm appreciation. Our findings contribute to the algorithm aversion vs. appreciation literature by distinguishing between predictive and generative AI and identifying this essential AI type as a boundary condition for algorithm aversion. In addition, while algorithm aversion in predictive AI can be reduced through automatic processes, our results suggest that trust plays a more critical role in attenuating aversion (i.e., enhancing appreciation) in the context of generative (vs. predictive) AI. Methodologically, we extend the widely used weight-on-advice paradigm from predictive AI research to the generative AI context. Our paper also provides important implications for practitioners seeking to benefit from AI advancements. * Co-authors: Zhanfei Lei, University of Massachusetts Amherst; Dezhi Yin, University of South Florida
Speaker
Dr. Han Zhang is a Chair Professor of Information Systems and the Dean of the School of Business at Hong Kong Baptist University. He was a Full Professor of Information Technology Management (ITM) and Steven A. Denning Professor of Technology & Management at the Scheller College of Business, Georgia Institute of Technology (Georgia Tech). He received his Ph.D. in Information Systems from the University of Texas at Austin. Dr. Zhang’s research focuses on online trust and reputation, user-generated content, online healthcare, and human-AI interaction. His research on the institutional setup to help small businesses grow in the digital economy has been used as the basis for testimony before the Congressional House Committee on Small Business. His research on AI chatbots has been featured in the Wall Street Journal (March 20, 2023).
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