Karen Pei-Sze Tan , Yi Vanessa Liu , Stephen Wayne Litvin
{"title":"ChatGPT 和在线服务恢复：潜在客户如何应对管理层对负面评论的回应","authors":"Karen Pei-Sze Tan , Yi Vanessa Liu , Stephen Wayne Litvin","doi":"10.1016/j.tourman.2024.105057","DOIUrl":null,"url":null,"abstract":"<div><p>This study investigates the efficacy of generative artificial intelligence in online service recovery; specifically, the use of ChatGPT (vs. human employees) in preparing managerial response(s) (MR or MRs) to online hotel reviews is considered. ChatGPT could be used to generate human-like MRs for online service recovery but this could backfire due to algorithm aversion when an individual discounts algorithm decisions relative to human-made decisions. Data collected via interviews, a modified Turing test and an online experiment provide empirical support for this. Findings reveal that potential customers could not clearly differentiate between the two types of MR and could not clearly identify the ‘better’ of the two. Yet, when informed of the MR source, ChatGPT MRs led to lower affective, cognitive and conative outcomes. Findings also unveiled perceived authenticity and uncanniness as significant parallel mediating pathways in this algorithm aversion. Theoretical and managerial implications are discussed.</p></div>","PeriodicalId":48469,"journal":{"name":"Tourism Management","volume":"107 ","pages":"Article 105057"},"PeriodicalIF":10.9000,"publicationDate":"2024-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"ChatGPT and online service recovery: How potential customers react to managerial responses of negative reviews\",\"authors\":\"Karen Pei-Sze Tan , Yi Vanessa Liu , Stephen Wayne Litvin\",\"doi\":\"10.1016/j.tourman.2024.105057\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>This study investigates the efficacy of generative artificial intelligence in online service recovery; specifically, the use of ChatGPT (vs. human employees) in preparing managerial response(s) (MR or MRs) to online hotel reviews is considered. ChatGPT could be used to generate human-like MRs for online service recovery but this could backfire due to algorithm aversion when an individual discounts algorithm decisions relative to human-made decisions. Data collected via interviews, a modified Turing test and an online experiment provide empirical support for this. Findings reveal that potential customers could not clearly differentiate between the two types of MR and could not clearly identify the ‘better’ of the two. Yet, when informed of the MR source, ChatGPT MRs led to lower affective, cognitive and conative outcomes. Findings also unveiled perceived authenticity and uncanniness as significant parallel mediating pathways in this algorithm aversion. Theoretical and managerial implications are discussed.</p></div>\",\"PeriodicalId\":48469,\"journal\":{\"name\":\"Tourism Management\",\"volume\":\"107 \",\"pages\":\"Article 105057\"},\"PeriodicalIF\":10.9000,\"publicationDate\":\"2024-09-20\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Tourism Management\",\"FirstCategoryId\":\"91\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0261517724001766\",\"RegionNum\":1,\"RegionCategory\":\"管理学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ENVIRONMENTAL STUDIES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Tourism Management","FirstCategoryId":"91","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0261517724001766","RegionNum":1,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENVIRONMENTAL STUDIES","Score":null,"Total":0}
ChatGPT and online service recovery: How potential customers react to managerial responses of negative reviews
This study investigates the efficacy of generative artificial intelligence in online service recovery; specifically, the use of ChatGPT (vs. human employees) in preparing managerial response(s) (MR or MRs) to online hotel reviews is considered. ChatGPT could be used to generate human-like MRs for online service recovery but this could backfire due to algorithm aversion when an individual discounts algorithm decisions relative to human-made decisions. Data collected via interviews, a modified Turing test and an online experiment provide empirical support for this. Findings reveal that potential customers could not clearly differentiate between the two types of MR and could not clearly identify the ‘better’ of the two. Yet, when informed of the MR source, ChatGPT MRs led to lower affective, cognitive and conative outcomes. Findings also unveiled perceived authenticity and uncanniness as significant parallel mediating pathways in this algorithm aversion. Theoretical and managerial implications are discussed.
期刊介绍：
Tourism Management, the preeminent scholarly journal, concentrates on the comprehensive management aspects, encompassing planning and policy, within the realm of travel and tourism. Adopting an interdisciplinary perspective, the journal delves into international, national, and regional tourism, addressing various management challenges. Its content mirrors this integrative approach, featuring primary research articles, progress in tourism research, case studies, research notes, discussions on current issues, and book reviews. Emphasizing scholarly rigor, all published papers are expected to contribute to theoretical and/or methodological advancements while offering specific insights relevant to tourism management and policy.
求助内容：
应助结果提醒方式：
京公网安备 11010802042870号
