Leaders Inflate Performance Ratings for Employees Who Use Robots to Augment Their Performance

ABSTRACT With robot usage becoming increasingly prevalent in contemporary workplaces, a key task for supervisors is conducting performance ratings in the context of employee‐robot value co‐creation. In this research, we explore whether, how, and when employees' robot usage affects supervisors' performance ratings. Drawing upon attribution theory, we suggest that supervisors might overestimate the performance of employees who use robots at work. Across a pilot study, two experiments, and one field study, we find that employees' robot usage is positively associated with supervisors' illusory performance transference (i.e., supervisors' belief that robot–associated performance should be attributed to employees who use robots at work). In turn, this transference is positively associated with high performance ratings for the respective employees. Furthermore, the supervisor–perceived experience of robots (i.e., supervisors' perceptions that robots are able to feel emotions and sensations) weakens this indirect effect. We conclude by discussing the theoretical and practical implications of our findings and future directions.

Authors

Institutions

Publication Details

Journal
Human Resource Management
Published
2024-12-11
DOI
https://doi.org/10.1002/hrm.22267
Citations
9
Primary Topic
AI in Service Interactions
Type
article
Field-Weighted Citation Impact
3.13

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Leaders Inflate Performance Ratings for Employees Who Use Robots to Augment Their Performance

Kai Chi Yam, Puchu Zhao, Guohua He, Lixun Zheng et al.
9 citations
Human Resource Management
AI in Service Interactions
3.13
article

Leaders Inflate Performance Ratings for Employees Who Use Robots to Augment Their Performance

Kai Chi Yam, Puchu Zhao, Guohua He, Lixun Zheng, Xiaowei Dong, Xin Qin
article en
9 citations

Abstract

ABSTRACT With robot usage becoming increasingly prevalent in contemporary workplaces, a key task for supervisors is conducting performance ratings in the context of employee‐robot value co‐creation. In this research, we explore whether, how, and when employees' robot usage affects supervisors' performance ratings. Drawing upon attribution theory, we suggest that supervisors might overestimate the performance of employees who use robots at work. Across a pilot study, two experiments, and one field study, we find that employees' robot usage is positively associated with supervisors' illusory performance transference (i.e., supervisors' belief that robot–associated performance should be attributed to employees who use robots at work). In turn, this transference is positively associated with high performance ratings for the respective employees. Furthermore, the supervisor–perceived experience of robots (i.e., supervisors' perceptions that robots are able to feel emotions and sensations) weakens this indirect effect. We conclude by discussing the theoretical and practical implications of our findings and future directions.

Human Resource ManagementVol. 64(2)
Sun Yat-sen University (CN), National University of Singapore (SG), Shenzhen University (CN), Tsinghua University (CN)
Natural Science Foundation of Shenzhen City, National Natural Science Foundation of China, Sun Yat-sen University, Natural Science Foundation of Guangdong Province, Shenzhen University, Shenzhen Peacock Plan
Decent work and economic growth
Openalex Percentile: Top 7%
AI in Service Interactions
3.13
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

Leaders Inflate Performance Ratings for Employees Who Use Robots to Augment Their Performance — Kai Chi Yam, Puchu Zhao, et al. · Human Resource Management (2024) | TGRS Research Map | TGRS