# How does human-AI collaboration task complexity affect employee work engagement? The roles of humble leadership and AI self-efficacy

## TL;DR

This study explores how complex human-AI collaboration tasks affect employee engagement, and how leadership and confidence in AI can help reduce negative effects.

## Contribution

The study introduces a new framework showing how task complexity, anxiety, leadership, and self-efficacy interact in human-AI collaboration settings.

## Key findings

- High task complexity in human-AI collaboration reduces work engagement by increasing tech-learning anxiety.
- AI self-efficacy and humble leadership can buffer the negative impact of task complexity on engagement.
- The study identifies job and personal resources as key factors in mitigating the effects of complex AI collaboration tasks.

## Abstract

With the rapid advancement of artificial intelligence (AI) technology, human-AI collaboration has become increasingly prevalent in workplaces, profoundly impacting employees’ psychology and behavior. Based on the Job Demands-Resources (JD-R) theory, this study examines the effects of human-AI collaboration task complexity (HAI-C task complexity) on employees’ work engagement, with human-AI collaboration tech-learning anxiety (HAI-C tech-learning anxiety) as a mediator, and explores the moderating roles of humble leadership and AI self-efficacy.

This study employed a three-wave longitudinal survey design to collect matched data from 497 employees. Hierarchical regression analysis, along with bootstrapping methods, was employed for empirical testing.

The findings indicate that HAI-C task complexity negatively affects employees’ work engagement by amplifying their HAI-C tech-learning anxiety. AI self-efficacy can mitigate this negative indirect impact of HAI-C task complexity on work engagement. Humble leadership indirectly alleviates this negative indirect effect by enhancing employees’ AI self-efficacy.

The findings reveal the inhibitory effect of HAI-C task complexity on employees’ work engagement. From the two dimensions of job resources and personal resources, it explores corresponding mitigation mechanisms, as well as the contextual and psychological intervention mechanisms involved in how individuals evaluate job demands. This provides novel theoretical perspectives and practical implications for understanding the practical value of human-AI collaboration in organizational contexts and for enhancing employees’ work engagement within human-AI collaboration frameworks.