Does AI-Driven Technostress Promote or Hinder Employees’ Artificial Intelligence Adoption Intention? A Moderated Mediation Model of Affective Reactions and Technical Self-Efficacy

Purpose: The increasing integration of Artificial Intelligence (AI) within enterprises is generates significant technostress among employees, potentially influencing their intention to adopt AI. However, existing research on the psychological effects of this phenomenon remains inconclusive. Drawing on the Affective Events Theory (AET) and the Challenge–Hindrance Stressor Framework (CHSF), the current study aims to explore the “black box” between challenge and hindrance technology stressors and employees’ intention to adopt AI, as well as the boundary conditions of this mediation relationship. Methods: The study employs a quantitative approach and utilizes three-wave data. Data were collected through the snowball sampling technique and a structured questionnaire survey. The sample comprises employees from 11 distinct organizations located in Guangdong Province, China. We received 301 valid questionnaires, representing an overall response rate of 75%. The theoretical model was tested through confirmatory factor analysis and regression analyses using Mplus and the Process macro for SPSS. Results: The results indicate that positive affect mediates the positive relationship between challenge technology stressors and AI adoption intention, whereas AI anxiety mediates the negative relationship between hindrance technology stressors and AI adoption intention. Furthermore, the results reveal that technical self-efficacy moderates the effects of challenge and hindrance technology stressors on affective reactions and the indirect effects of challenge and hindrance technology stressors on AI adoption intention through positive affect and AI anxiety, respectively. Conclusion: Overall, our study suggests that AI-driven challenge technology stressors positively impact AI adoption intention through the cultivation of positive affect, while hindrance technology stressors impede AI adoption intention by triggering AI anxiety. Additionally, technical self-efficacy emerges as a crucial moderator in shaping these relationships. This research has the potential to make a meaningful contribution to the literature on AI adoption intention, deepening our holistic understanding of the influential mechanisms involved. Furthermore, the study affirms the applicability and relevance of Affective Events Theory (AET) and the Challenge-Hindrance Stressor Framework (CHSF). In practical terms, the research provides actionable insights for organizations to effectively manage employees’ AI adoption intention. Keywords: challenge and hindrance technology stressors, AI adoption intention, positive affect, AI anxiety, technical self-efficacy