How Forced Intervention Facilitates Long-term Algorithm Adoption

Abstract

While artificial intelligence (AI) technologies increasingly become powerful and useful in operations, human workers often resist adopting algorithmic recommendations, known as algorithm aversion. This aversion can undermine the algorithms' performance in practice. While numerous studies explored short-term mitigation strategies for such 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 reveal that sales workers underutilize a new matching algorithm and often use it on low-quality leads. The company conducted a field experiment where 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 one 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 that learning is a key driver for long-term algorithm adoption among the workers. Specifically, forced algorithm usage allows workers to experience the algorithm's unbiased performance firsthand and positively adjust their beliefs about it. Consequently, after the experiment, the workers use the algorithm not only more frequently but also more 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 continuous development of these technologies. More importantly, we demonstrate that forced intervention works by enabling workers to experience an algorithm's unbiased performance and adjust their prior 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, thus facilitating sustained algorithm usage.

Keywords: AI adoption, algorithm aversion, learning, habit formation, field experiment