Professor Dai Hongyan of CUFE Business School, in collaboration with Dr. Ta La, Postdoctoral Researcher at Tsinghua University; Assistant Professor Wang Cong of Peking University; Tenured Associate Professor Cao Zike of Zhejiang University; and Professor Zhou Weihua of Zhejiang University, won the First Prize for Best Paper at the 29th International Conference on Management Science and Engineering for their paper titled “Social Comparison and Anthropomorphism in GenAI Performance Feedback: A Field Experiment.”
The International Conference on Management Science and Engineering serves as an interdisciplinary platform that brings together scholars and industry experts from around the world. It focuses on the integration of management science theories, engineering methodologies, and real-world application scenarios. The conference addresses core topics such as intelligent decision-making optimization, supply chain and logistics management, data-driven operations management, project and risk management, and information systems and digital governance. Through keynote speeches, thematic discussions, and paper presentations, participants share cutting-edge research findings and industry practice cases. This year’s conference was jointly organized by the School of Economics and Management of Harbin Institute of Technology and the Organizing Committee of the International Conference on Management Science and Engineering. Participants included researchers from universities and research institutes both in China and abroad. In addition to exploring key issues in the field of management science and engineering, the conference also aims to promote the practical application of research outcomes to address complex challenges in organizational operations and system optimization.
The award-winning paper examines the semantic analysis and feedback generation of unstructured data—such as sales calls—using generative artificial intelligence. The study conducted a field experiment involving telephone sales personnel from a publicly listed company to evaluate the practical effects of two interaction design strategies: the social comparison mechanism (providing relative performance feedback) and the anthropomorphic presentation of generative AI agents (delivering feedback in a human-like manner). Based on a 100-day experiment and the analysis of nearly 500,000 sales calls, the results show that although feedback generated by a generative AI-based performance evaluation system significantly improves employee performance compared with traditional evaluation benchmarks, its effectiveness depends heavily on the design of feedback delivery. Incorporating social comparison mechanisms can further enhance performance by stimulating employees’ competitive motivation. However, presenting relative performance feedback through a friendly anthropomorphic AI assistant produces a significant negative effect—it not only offsets the performance gains generated by social comparison mechanisms but also leads to a decline in employee performance.
Professor Dai Hongyan has conducted long-term and in-depth research in areas such as AI-driven optimization and decision-making and human–AI interaction based on large language models. She has achieved substantial research outcomes, publishing more than 40 academic papers in leading journals including Management Science, European Journal of Operational Research, and Journal of Management Sciences in China. She has led multiple national and provincial research projects, including a Cultivation Project of the Major Research Plan of the National Natural Science Foundation of China and General Program of the National Natural Science Foundation of China. She was awarded the First Prize in Scientific and Technological Progress by the China Federation of Logistics & Purchasing, the Second Prize of the "Management Practice Award" by the Chinese Scholars Association for Management Science and Engineering, the Third Prize in Scientific and Technological Progress by Zhejiang Province, and multiple Best Paper Awards (First Prize) at various international conferences. The AI-driven forecasting and scheduling systems developed by her research team have also been implemented in multiple enterprises.
