The combination of artificial intelligence (AI) technology with the real economy has dramatically improved the efficiency of enterprises. However, the replacement of AI for employment also significantly impacts employees’ cognition and psychological state. Based on the Conservation of Resources Theory, the relationship between AI awareness and employee depression is explored in this article while examining the mediating role of emotional exhaustion, as well as the moderating role of perceived organizational support. Based on a sample of 321 respondents, the empirical results show that (1) AI awareness is significantly positively correlated with depression; (2) emotional exhaustion plays a mediating role between AI awareness and depression; (3) perceived organizational support negatively moderates the relationship between emotional exhaustion and depression; (4) perceived organizational support negatively moderates the mediating role of emotional exhaustion between AI awareness and depression. The research conclusions provide a theoretical basis for organizations to take measures to intervene in the negative impact of changes in AI technology on employees’ mental health.

Several previous studies have revealed a positive relationship between artificial intelligence (AI) technology development and employees’ employment, income, and job performance. If individuals can seize the opportunity to master the knowledge and skills relevant to the implementation of AI, they could make career progress and improve their workplace well-being (WWB). Based on the transactional theory of stress and resource conservation theory, we constructed a moderated mediation model to explore the relationship between AI opportunity perception and employees’ WWB and examine the mediating factor of informal learning in the workplace (ILW), as well as the moderating factor of unemployment risk perception (URP). Through a survey of 268 employees, our results showed the following: (1) AI opportunity perception was significantly positively correlated with employees’ WWB; (2) ILW played a mediating role in the positive relationship between AI opportunity perception and employees’ WWB; and (3) URP negatively moderated the mediating relationship of ILW between AI opportunity perception and employees’ WWB. Our research results have a guiding significance for enterprises seeking to promote WWB during AI application.

Purpose: Artificial Intelligence (AI) application imposes a complicated and uncertain situation for employees to comprehend and react. Existing research has examined employees’ positive and negative perceptions of AI and their reactions separately. Little is known about how the two opposing perceptions of AI could coexist and interact to shape employees’ behavioral reactions. Drawing on the categorization theory, our research aims to examine how employees’ opportunity and threat perceptions of AI interact to influence job crafting and boundary conditions for this interaction. Design/methodology/approach: We conducted a two-wave study of 250 employees in China who had experienced AI transformation in their organizations. SPSS and Hayes PROCESS were used to test the hypotheses. Findings: The positive effect of employees’ opportunity perceptions of AI on job crafting was attenuated by their threat perceptions. This attenuating effect was stronger for younger employees. Originality/value: Our research extends categorization theory and the literature on AI perceptions by demonstrating the interaction effect of employees’ opposing perceptions of AI on job crafting and identifying age as a boundary condition for this interaction.