Effects of Employee–Artificial Intelligence (AI) Collaboration on Counterproductive Work Behaviors (CWBs): Leader Emotional Support as a Moderator
The accelerated advancement of artificial intelligence (AI) has positioned it as a novel colleague. However, as employees collaborate with AI colleagues in daily work, their communication and interaction with human colleagues may decrease. This may result in feelings of loneliness and a potential re...
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| Format: | Article |
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MDPI AG
2025-05-01
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| Series: | Behavioral Sciences |
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| Online Access: | https://www.mdpi.com/2076-328X/15/5/696 |
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| author | Qingqi Meng Tung-Ju Wu Wenyan Duan Shijia Li |
| author_facet | Qingqi Meng Tung-Ju Wu Wenyan Duan Shijia Li |
| author_sort | Qingqi Meng |
| collection | DOAJ |
| description | The accelerated advancement of artificial intelligence (AI) has positioned it as a novel colleague. However, as employees collaborate with AI colleagues in daily work, their communication and interaction with human colleagues may decrease. This may result in feelings of loneliness and a potential reduction in emotional resources, potentially leading to counterproductive work behavior (CWB). Drawing from the conservation of resources (COR) theory, we hypothesize that employee–AI collaboration may amplify employees’ CWB due to loneliness and emotional fatigue. The potential mitigating effects of leader emotional support on these outcomes are also considered. To test these hypotheses, a 2 × 2 vignette experiment (N = 167) was conducted. The results demonstrate that employee–AI collaboration exerts a substantial positive influence on loneliness. Loneliness further increases employees’ emotional fatigue, which in turn increases CWB. Leader emotional support—the care and motivation demonstrated by leaders has been identified as a key factor in reducing loneliness. This research contributes to the extant literature on employee–AI collaboration and CWB, and expands the application scope of COR. Practical implications arise for managers, who are encouraged to consider the impact of employee–AI collaboration on interpersonal interaction and to address employees’ emotional needs in a timely manner. |
| format | Article |
| id | doaj-art-12fb1993ddc44b3f97e45ef3e483fc22 |
| institution | OA Journals |
| issn | 2076-328X |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Behavioral Sciences |
| spelling | doaj-art-12fb1993ddc44b3f97e45ef3e483fc222025-08-20T01:56:14ZengMDPI AGBehavioral Sciences2076-328X2025-05-0115569610.3390/bs15050696Effects of Employee–Artificial Intelligence (AI) Collaboration on Counterproductive Work Behaviors (CWBs): Leader Emotional Support as a ModeratorQingqi Meng0Tung-Ju Wu1Wenyan Duan2Shijia Li3Business School, Harbin Institute of Technology, Harbin 150001, ChinaSchool of Management, Harbin Institute of Technology, Harbin 150001, ChinaBusiness School, Harbin Institute of Technology, Harbin 150001, ChinaSchool of Management, Harbin Institute of Technology, Harbin 150001, ChinaThe accelerated advancement of artificial intelligence (AI) has positioned it as a novel colleague. However, as employees collaborate with AI colleagues in daily work, their communication and interaction with human colleagues may decrease. This may result in feelings of loneliness and a potential reduction in emotional resources, potentially leading to counterproductive work behavior (CWB). Drawing from the conservation of resources (COR) theory, we hypothesize that employee–AI collaboration may amplify employees’ CWB due to loneliness and emotional fatigue. The potential mitigating effects of leader emotional support on these outcomes are also considered. To test these hypotheses, a 2 × 2 vignette experiment (N = 167) was conducted. The results demonstrate that employee–AI collaboration exerts a substantial positive influence on loneliness. Loneliness further increases employees’ emotional fatigue, which in turn increases CWB. Leader emotional support—the care and motivation demonstrated by leaders has been identified as a key factor in reducing loneliness. This research contributes to the extant literature on employee–AI collaboration and CWB, and expands the application scope of COR. Practical implications arise for managers, who are encouraged to consider the impact of employee–AI collaboration on interpersonal interaction and to address employees’ emotional needs in a timely manner.https://www.mdpi.com/2076-328X/15/5/696employee–AI collaborationcounterproductive work behaviorlonelinessemotional fatigueleader emotional supportconservation of resources theory |
| spellingShingle | Qingqi Meng Tung-Ju Wu Wenyan Duan Shijia Li Effects of Employee–Artificial Intelligence (AI) Collaboration on Counterproductive Work Behaviors (CWBs): Leader Emotional Support as a Moderator Behavioral Sciences employee–AI collaboration counterproductive work behavior loneliness emotional fatigue leader emotional support conservation of resources theory |
| title | Effects of Employee–Artificial Intelligence (AI) Collaboration on Counterproductive Work Behaviors (CWBs): Leader Emotional Support as a Moderator |
| title_full | Effects of Employee–Artificial Intelligence (AI) Collaboration on Counterproductive Work Behaviors (CWBs): Leader Emotional Support as a Moderator |
| title_fullStr | Effects of Employee–Artificial Intelligence (AI) Collaboration on Counterproductive Work Behaviors (CWBs): Leader Emotional Support as a Moderator |
| title_full_unstemmed | Effects of Employee–Artificial Intelligence (AI) Collaboration on Counterproductive Work Behaviors (CWBs): Leader Emotional Support as a Moderator |
| title_short | Effects of Employee–Artificial Intelligence (AI) Collaboration on Counterproductive Work Behaviors (CWBs): Leader Emotional Support as a Moderator |
| title_sort | effects of employee artificial intelligence ai collaboration on counterproductive work behaviors cwbs leader emotional support as a moderator |
| topic | employee–AI collaboration counterproductive work behavior loneliness emotional fatigue leader emotional support conservation of resources theory |
| url | https://www.mdpi.com/2076-328X/15/5/696 |
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