How Forced Intervention Facilitates AI Adoption

• 成果类型: Article
• 署名作者: Cao, Xinyu; Hu, Chenshan; Sun, Jiankun; Zhang, Dennis J.
• 署名单位: Chinese University of Hong Kong; University of Colorado System; University of Colorado Boulder; Imperial College London; Washington University (WUSTL)
• 刊物名称: M&SOM-MANUFACTURING & SERVICE OPERATIONS MANAGEMENT
• ISSN/ISSBN: 1523-4614
• DOI: 10.1287/msom.2024.1137
• 发表日期: 2026
• 关键词: field experiment; habit formation; resistance; incentives; algorithms; workers; IMPACT; WILL; BIAS; GYM

摘要

Problem definition: Whereas artificial intelligence (AI) technologies are increasingly becoming powerful and useful in operations, human workers often resist adopting algorithms, known as algorithm aversion. This aversion can undermine the algorithm performance in practice. Whereas numerous studies explore short-term mitigation strategies for such aversion, this paper investigates whether and why forced interventions can promote AI adoption and reduce algorithm aversion in practice. Methodology/results: Data from a leading online education company reveal that sales workers underutilize a new matching algorithm and often selectively 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. Experimental results show that forcing workers to use the algorithm during the experiment causally increases their algorithm usage over the month after the experiment by 15.8 percentage points. We develop a theoretical model to derive empirical strategies for exploring the mechanisms behind this improvement. Contrary to the traditional literature focusing on habit formation, our findings suggest learning is a key driver for algorithm adoption among workers over the month after the experiment. Specifically, forced algorithm use allows workers to experience the unbiased algorithm performance and positively adjust their beliefs about it. Consequently, after the experiment, workers use the algorithm not only more frequently but also more on high-quality leads. Managerial implications: The study empirically shows that forced intervention can effectively improve persistent algorithm use after the intervention, which is crucial for continuous development of the algorithm. More importantly, forced intervention breaks the vicious cycle of biased beliefs and selective usage by enabling workers to form unbiased evaluation of the algorithm efficacy and mitigate selective adoption on low-quality cases. 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, thus facilitating sustained algorithm usage.