{"title":"Human or AI robot? Who is fairer on the service organizational frontline","authors":"Xiaolong Wu , Shuhua Li , Yonglin Guo , Shujie Fang","doi":"10.1016/j.jbusres.2024.114730","DOIUrl":null,"url":null,"abstract":"<div><p>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.</p></div>","PeriodicalId":15123,"journal":{"name":"Journal of Business Research","volume":null,"pages":null},"PeriodicalIF":10.5000,"publicationDate":"2024-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Business Research","FirstCategoryId":"91","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0148296324002340","RegionNum":1,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"BUSINESS","Score":null,"Total":0}
引用次数: 0
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.
期刊介绍:
The Journal of Business Research aims to publish research that is rigorous, relevant, and potentially impactful. It examines a wide variety of business decision contexts, processes, and activities, developing insights that are meaningful for theory, practice, and/or society at large. The research is intended to generate meaningful debates in academia and practice, that are thought provoking and have the potential to make a difference to conceptual thinking and/or practice. The Journal is published for a broad range of stakeholders, including scholars, researchers, executives, and policy makers. It aids the application of its research to practical situations and theoretical findings to the reality of the business world as well as to society. The Journal is abstracted and indexed in several databases, including Social Sciences Citation Index, ANBAR, Current Contents, Management Contents, Management Literature in Brief, PsycINFO, Information Service, RePEc, Academic Journal Guide, ABI/Inform, INSPEC, etc.