Human or AI robot? Who is fairer on the service organizational frontline

Xiaolong Wu, Shuhua Li, Yonglin Guo and Shujie Fang

Journal of Business Research, 2024, vol. 181, issue C

Abstract: Research has focused on exploring the distinction between human employees and AI robots. However, little is known about customer perceptions of service fairness towards AI robots (vs. human employees). A mixed-methods approach was adopted including a qualitative study which aimed to generate an understanding of customer fairness perception towards AI robots (vs. human employees). The quantitative study examined this difference, the boundary conditions, and the downstream effect on customer responses. The results indicated that customers perceive robotic services as fairer than human employees, especially in relation to distributive and procedural fairness. This effect was stronger for customers with low power distance belief. Differences in fairness perceptions can also impact on customer revisit intention, recommendation intention, satisfaction, and subjective well-being. The study extends an understanding of customer attitudes towards AI robots by considering the machine heuristic model and fairness theory, and provides insights for managers to properly utilize AI robots to enhance service fairness on the service industry frontline.

Keywords: AI robot; Fairness theory; Machine heuristic model; Power distance belief; Mixed-methods approach (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:eee:jbrese:v:181:y:2024:i:c:s0148296324002340

DOI: 10.1016/j.jbusres.2024.114730

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