The dark side of fairness: how perceived fairness in service robot implementation leads to employee dysfunctional behavior

Purpose The purpose of this study is to explore the unexpected effects of perceived fairness in the implementation of service robots on employee dysfunctional behavior within the hospitality industry. Contrary to the conventional view that perceived fairness always leads to positive outcomes, this study examines how fairness perceptions can increase negative behaviors through unmet expectations, overconfidence in job security and complacency. The moderating role of transformational leadership is also investigated to understand how it can mitigate these negative effects. Design/methodology/approach This study collected data from 400 employees in the hospitality sector who have experienced the integration of service robots in their work environment. Using quantitative analysis techniques, the relationships between perceived fairness, employee dysfunctional behavior, unmet expectations, overconfidence, complacency and transformational leadership were examined. Findings The findings reveal that perceived fairness in service robot implementation can unexpectedly lead to increased employee dysfunctional behavior, particularly when it results in unmet expectations, overconfidence and complacency. However, transformational leadership was found to significantly moderate these effects, reducing the likelihood of dysfunctional behaviors by realigning employee perceptions and expectations with organizational objectives. Originality/value This study contributes to the service marketing literature by challenging the assumption that perceived fairness always yields positive outcomes, highlighting the potential for fairness to produce unintended negative consequences in service robot implementation. It also identifies transformational leadership as a key factor in mitigating these effects, offering practical insights for hospitality managers on how to ensure successful integration of service robots by actively managing employee expectations and behaviors.

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Publication Details

Journal
Journal of Services Marketing
Published
2025-03-07
DOI
https://doi.org/10.1108/jsm-09-2024-0508
Citations
9
Primary Topic
AI in Service Interactions
Type
article
Field-Weighted Citation Impact
22.33
Controls
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article

The dark side of fairness: how perceived fairness in service robot implementation leads to employee dysfunctional behavior

Taeshik Gong
9 citations
Journal of Services Marketing
AI in Service Interactions
22.33
article

The dark side of fairness: how perceived fairness in service robot implementation leads to employee dysfunctional behavior

Taeshik Gong
article en
9 citations

Abstract

Purpose The purpose of this study is to explore the unexpected effects of perceived fairness in the implementation of service robots on employee dysfunctional behavior within the hospitality industry. Contrary to the conventional view that perceived fairness always leads to positive outcomes, this study examines how fairness perceptions can increase negative behaviors through unmet expectations, overconfidence in job security and complacency. The moderating role of transformational leadership is also investigated to understand how it can mitigate these negative effects. Design/methodology/approach This study collected data from 400 employees in the hospitality sector who have experienced the integration of service robots in their work environment. Using quantitative analysis techniques, the relationships between perceived fairness, employee dysfunctional behavior, unmet expectations, overconfidence, complacency and transformational leadership were examined. Findings The findings reveal that perceived fairness in service robot implementation can unexpectedly lead to increased employee dysfunctional behavior, particularly when it results in unmet expectations, overconfidence and complacency. However, transformational leadership was found to significantly moderate these effects, reducing the likelihood of dysfunctional behaviors by realigning employee perceptions and expectations with organizational objectives. Originality/value This study contributes to the service marketing literature by challenging the assumption that perceived fairness always yields positive outcomes, highlighting the potential for fairness to produce unintended negative consequences in service robot implementation. It also identifies transformational leadership as a key factor in mitigating these effects, offering practical insights for hospitality managers on how to ensure successful integration of service robots by actively managing employee expectations and behaviors.

Journal of Services MarketingVol. 39(4)
Hanyang University (KR)
Openalex Percentile: Top 1%
AI in Service Interactions
22.33
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The dark side of fairness: how perceived fairness in service robot implementation leads to employee dysfunctional behavior — Taeshik Gong · Journal of Services Marketing (2025) | TGRS Research Map | TGRS