The Dark Side of Employee Collaboration with Robots: Exploring Its Impact on Self-Esteem Threat and Burnout

Abstract

The increasing application of robots in the workplace has made employee collaboration with robots a prevalent phenomenon. Existing literature on human-robot collaboration has both implicitly and explicitly highlighted the benefits of such collaboration, such as promoting efficiency and productivity. Drawing upon self-regulation theory, this study challenges this prevailing assumption by revealing a potential dark side of employee collaboration with robots, specifically its potential to lead to burnout. Our findings, derived from an experiment and a multi-wave field survey, demonstrate that employee collaboration with robots can lead to a self-esteem threat, which in turn results in burnout. Moreover, the perceived intelligence of robots moderated the indirect effect of employee collaboration with robots on burnout through self-esteem threat. This effect was more pronounced when the perceived intelligence of robots was high, as opposed to low. This study offers fresh insights into the consequences of employees collaborating with robots. It also highlights the need for future research to focus on the psychological well-being of employees engaged in such collaborations.

Keywords:

• Robot
• human-robot collaboration
• burnout
• self-esteem threat
• perceived intelligence of robots

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

The data in this article can be obtained from the first author upon request.

Notes

1 The participant information for each condition is as follows: (1) for the first condition (n = 42), 50.0% were female and 83.3% were Caucasian. Their average age was 39.3 years old (SD = 10.8), average education was 15.1 years (SD = 3.9), and average organizational tenure was 6.7 years (SD = 6.4); (2) for the second condition (n = 44), 45.5% were female and 90.9% were Caucasian. Their average age was 38.4 years old (SD = 10.1), average education was 16.0 years (SD = 3.9), and average organizational tenure was 6.3 years (SD = 6.9); (3) for the third condition (n = 44), 50.0% were female and 93.2% were Caucasian. Their average age was 42.9 years old (SD = 11.3), average education was 15.0 years (SD = 3.3), and average organizational tenure was 9.7 years (SD = 8.9); (4) for the fourth condition (n = 43), 48.8% were female and 88.4% were Caucasian. Their average age was 41.0 years old (SD = 10.2), average education was 16.0 years (SD = 3.4), and average organizational tenure was 7.6 years (SD = 7.5).

Additional information

Funding

This work was supported by the China Postdoctoral Science Foundation (grant number: 2022M713555) and Shenzhen University (grant number: 000001032226), awarded to Guohua He.

Notes on contributors

Guohua He

Guohua He is an assistant professor in the College of Management at Shenzhen University. He received his PhD from South China University of Technology and studied as a joint-training PhD candidate at the Desautels Faculty of Management, McGill University. His research interests are artificial intelligence, leadership, and ethics.

Xinnian Zheng

Xinnian Zheng is a lecturer in the College of Foreign Studies at Jinan University. She received her PhD from the University of Montreal in Canada. Her research focuses on the dynamic field of AI-assisted translation and intersection of AI and translation studies.

Wenpu Li

Wenpu Li is a PhD student in the Antai College of Economics & Management at Shanghai Jiao Tong University. Her research interests mainly focus on family motivation, leadership, creativity, and interdisciplinary research between AI and OB.

Lixun Zheng

Lixun Zheng is a PhD candidate in the School of Business Administration at South China University of Technology. Her research focuses on human-robot collaboration and leadership.

Yifan He

Yifan He is an undergraduate student in the School of Management at Hebei GEO University. Her research focuses on human resource management.