Can AI Be a Good Leader? Exploring the Effects of Artificial Intelligence Directive Leadership

Artificial Intelligence (AI) is swiftly reconfiguring workplaces and redefining established employee roles, positioning AI as an important force in management. Prior research has largely examined human-AI collaboration, highlighting AI’s supportive role in areas such as problem resolution, decision-making, and fostering new ideas. Yet as AI capabilities accelerate, its role is shifting beyond mere tool, raising the possibility that AI might also serve as an effective leader. Given the profound practical and theoretical importance of this topic, understanding the implications of AI-led management is increasingly important. Building on the job demands-resources (JD-R) model and the AI augmentation perspective, I investigate how AI-driven directive leadership influences employees’ perceptions and behaviors. Utilizing an experimental study and a field study, I examine how AI directive leadership (AIDL, i.e., an AI system that emulates leadership by instructing staff on goals, methods, and performance expectations) shapes employees’ work-related competence and cognitive depletion, and how these mechanisms ultimately relate to work performance and physical health. Results from the two studies show that AIDL is positively related to individual work-related competence and negatively related to individual cognitive depletion. In turn, competence is positively related to performance, and cognitive depletion is negatively related to physical health. Moreover, AI aversion emerges as a key boundary condition that attenuates the positive effect of AIDL. By theorizing and testing the influence of AIDL on employees, this research expands existing leadership frameworks to incorporate non-human agents into leadership roles and provides practical insights for companies considering AI for supervisory functions.

Author dc:creator

• Ying Wu (19057)

• Business Administration
• Management

Statement dc:rights

• In Copyright
• Open Access after 2028-05-01

DOI dc:identifier

https://doi.org/10.25417/uic.32995196.v1

OAI identifier oai:identifier

oai:figshare.com:article/32995196