Toward Artificial Intelligence Compliance: Impacts and Mechanisms of Performance Feedback

As organizations increasingly adopt artificial intelligence (AI) to enhance performance, ensuring that employees use AI in compliance with organizational policies becomes crucial for realizing its full value. However, employees’ AI compliance is not guaranteed and can vary based on how their AI use is managed. This study offers timely and actionable insights into how performance feedback—both positive and negative—influences employees’ AI compliance, and how these effects vary with AI identity. Drawing on feedback intervention theory, we conduct a longitudinal field study and a randomized experiment and find that positive performance feedback promotes AI compliance, whereas negative performance feedback reduces it. Importantly, employees with high AI identity respond more strongly to both types of performance feedback. Our findings further uncover distinct underlying mechanisms—task-motivation, task-learning, and meta-cognitive processes—that channel the effects of positive and negative performance feedback on AI compliance. Taken together, organizations should tailor performance feedback as part of AI governance by considering employees’ AI identity. Positive reinforcement of AI compliance is especially effective for employees with high AI identity, whereas cautions are needed when delivering negative performance feedback to avoid undermining AI compliance. Policy guidelines should support identity-sensitive performance feedback in practice.

• 저널Information Systems Research
• 토픽Behavioral Health and Interventions · Applied Psychology
• DOI10.1287/isre.2023.0580
• 저자Shaobo Wei, Yuanyuan Zhang, John Qi Dong