Thriving or drained? The dual effects of ai-autonomy on workers’ performance outcomes

Interest in the role of artificial intelligence (AI) within organizations has surged in recent years. However, we know little about the underlying mechanisms that connect AI usage in organizations to employee job outcomes. Drawing on self-determination theory and self-categorization theory, this study explores when and how perceived AI-supported autonomy influences employee performance outcomes, providing a new perspective in the service context. To empirically test our proposed research model, we collected data from 256 full-time employees working in star hotels in China. The results revealed that perceived AI-supported autonomy is positively related to employee service job performance and work innovation. This study further uncovers that AI techno-exhaustion and thriving at work serve as alternative underlying mechanisms that connect perceived AI-supported autonomy to employee outcomes. Additionally, interdependent self-construal has been identified as an important boundary condition in our model, indicating that the associations between AI-supported autonomy and employee performance outcomes are moderated by interdependent self-construal. Our research enhances understanding of the intersection between AI and workers’ performance outcomes in organizations.