Workplace AI Usage, Intrinsic Motivation, and Job Burnout: Evidence from a Longitudinal Study

Abstract

Background: With the widespread application of artificial intelligence technology in organizational contexts, understanding its implications for employees’ motivation and well-being has become increasingly important. Yet, how employees’ AI usage is prospectively associated with intrinsic motivation and job burnout remains insufficiently understood. Based on conservation of resources theory, this study adopted a two-wave longitudinal design to examine the relationships among employees’ artificial intelligence usage, intrinsic motivation, and job burnout. Methods: Data were collected through a two-wave questionnaire survey. The final sample consisted of 313 employees. A cross-lagged panel model was adopted for analysis. Results: AI usage positively predicted subsequent job burnout and negatively predicted subsequent intrinsic motivation after accounting for prior levels and other model variables. Intrinsic motivation negatively predicted subsequent job burnout. In addition, higher job burnout positively predicted subsequent AI usage, indicating a potential bidirectional prospective relationship between AI usage and job burnout. The sensitivity analysis showed a generally similar directional pattern, although the job burnout to AI usage pathway was attenuated and became nonsignificant. Conclusions: Artificial intelligence usage should not be viewed solely as a job resource. Although it may enhance work efficiency and convenience, it may also be associated with higher subsequent job burnout and lower intrinsic motivation. Intrinsic motivation may represent an important psychological pathway linking AI usage to later job burnout.