<p>The use of artificial intelligence (AI) in the workplace has become increasingly prevalent. However, the relationship between AI usage and employee innovative performance remains a subject of debate. This study examines the dual influences of AI usage on employee innovative performance in the workplace. Drawing on the job demands-resources (JD-R) theory and regulatory focus theory, a two-stage design was employed to collect 343 valid samples, analyzed using partial least squares structural equation modeling (PLS-SEM). The findings indicate that AI usage is associated with a dual-edged relationship with innovative performance: it can enhance autonomy and reduce job stress to foster innovation, but may also increase job demands and stress, thereby inhibiting innovation. Furthermore, the results show that a promotion focus strengthens the positive correlations between AI usage and innovation, while a prevention focus intensifies the negative correlations. By integrating the mediating roles of perceived job autonomy and job stress, as well as the moderating effects of regulatory focus, this research offers a deeper understanding of the relationships between AI usage and employee innovation. Theoretical contributions and practical guidance are provided for organizations to optimize the benefits of AI while addressing its potential drawbacks. </p>

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Booster or stumbling block? Unpacking the ‘double-edged’ influence of artificial intelligence usage on employee innovative performance

  • Shuxin Zheng,
  • Zhixin Guo,
  • Caisheng Liao,
  • Shuhua Li,
  • Xinze Zhan,
  • Xinshu Feng

摘要

The use of artificial intelligence (AI) in the workplace has become increasingly prevalent. However, the relationship between AI usage and employee innovative performance remains a subject of debate. This study examines the dual influences of AI usage on employee innovative performance in the workplace. Drawing on the job demands-resources (JD-R) theory and regulatory focus theory, a two-stage design was employed to collect 343 valid samples, analyzed using partial least squares structural equation modeling (PLS-SEM). The findings indicate that AI usage is associated with a dual-edged relationship with innovative performance: it can enhance autonomy and reduce job stress to foster innovation, but may also increase job demands and stress, thereby inhibiting innovation. Furthermore, the results show that a promotion focus strengthens the positive correlations between AI usage and innovation, while a prevention focus intensifies the negative correlations. By integrating the mediating roles of perceived job autonomy and job stress, as well as the moderating effects of regulatory focus, this research offers a deeper understanding of the relationships between AI usage and employee innovation. Theoretical contributions and practical guidance are provided for organizations to optimize the benefits of AI while addressing its potential drawbacks.