题　目：How Forced Intervention Facilitates Long-term Algorithm Adoption

嘉　宾：孙建坤 助理教授 帝国理工学院

主持人：许欢 教授 上海交通大学安泰经济与管理学院

时　间：2025年5月19日（周一）14:00-15:30

地　点：安泰楼A511室

内容简介：

Problem Definition: While artificial intelligence (AI) technologies are becoming increasingly powerful and useful in operations, human workers often resist adopting algorithmic recommendations—a phenomenon known as algorithm aversion. This aversion can undermine the effectiveness of algorithms in practice. Although many studies have explored short-term strategies to mitigate this aversion, this paper investigates whether and why forced interventions can promote algorithm adoption and reduce algorithm aversion in the long term. Methodology/Results: Data from a leading online education company show that sales workers underutilize a new matching algorithm and often only use it on low-quality leads. The company conducted a field experiment in which sales workers were forced to use, or not use, the algorithm for three weeks. The experimental results indicate that forcing workers to use the algorithm during the experiment causally increased their algorithm usage one month after the experiment by 15.8 percentage points. We developed a theoretical model to create empirical strategies for exploring the mechanisms behind this improvement. Contrary to traditional literature that focuses on habit formation, our findings suggest that learning is a key driver for long-term algorithm adoption among workers. Specifically, forced algorithm usage allows workers to experience the algorithm's unbiased performance firsthand and positively adjust their beliefs about it. As a result, after the experiment, the workers use the algorithm not only more frequently, but also more often on high-quality leads. Managerial Implications: The study provides empirical evidence that forced intervention can effectively improve long-term algorithm adoption among workers, which is crucial for the continuous development of these technologies. More importantly, we demonstrate that forced intervention works by enabling workers to experience the algorithm's unbiased performance and adjust their previously misinformed assumptions about its effectiveness. This suggests that firms can implement extrinsic interventions or educational programs to help workers recognize the benefits of algorithms and develop unbiased beliefs about their capabilities, thereby facilitating sustained algorithm usage.

演讲人简介：

Jiankun Sun is an Assistant Professor of Operations Management at Imperial College Business School, Imperial College London. Her research focuses on data-driven operations management in digital platforms, retail, and supply chain management. She applies both data analytics and theoretical modeling techniques to address practice-driven operational problems in her research. Jiankun Sun obtained her Ph.D. in Operations Management from Kellogg School of Management, Northwestern University, and her B.E. in Industrial Engineering from Tsinghua University.

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