{"title":"Rethinking information disclosure to GenAI in hotels: An extended parallel process model","authors":"Cristian Morosan","doi":"10.1016/j.ijhm.2024.103965","DOIUrl":null,"url":null,"abstract":"<div><div>A critical, yet understudied aspect of generative AI functionality in hotels is consumers’ disclosure of personal information, which carries significant risks. By expanding the Extended Parallel Process Model, this research investigates the role of trust in hotels in impacting consumers’ perceptions of threat and coping efficacy. These perceptions, in turn, influence consumers’ fear perceptions and drive both adaptive behaviors (e.g., protective action and seeking help) and maladaptive behaviors (e.g., avoidance). Ultimately, the disclosure of personal information is strongly influenced by avoidance and, to some extent, by seeking help behaviors, but not by protective action behaviors. As the first study to examine consumers’ disclosure of personal information to generative AI, it extends the literature on processing arguments related to opaque systems like generative AI. Additionally, it provides insightful managerial implications, especially at a time when the hotel industry lacks clear guidance regarding the use of consumers’ personal information within generative AI.</div></div>","PeriodicalId":48444,"journal":{"name":"International Journal of Hospitality Management","volume":"124 ","pages":"Article 103965"},"PeriodicalIF":9.9000,"publicationDate":"2024-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Hospitality Management","FirstCategoryId":"91","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0278431924002779","RegionNum":1,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"HOSPITALITY, LEISURE, SPORT & TOURISM","Score":null,"Total":0}
引用次数: 0
Abstract
A critical, yet understudied aspect of generative AI functionality in hotels is consumers’ disclosure of personal information, which carries significant risks. By expanding the Extended Parallel Process Model, this research investigates the role of trust in hotels in impacting consumers’ perceptions of threat and coping efficacy. These perceptions, in turn, influence consumers’ fear perceptions and drive both adaptive behaviors (e.g., protective action and seeking help) and maladaptive behaviors (e.g., avoidance). Ultimately, the disclosure of personal information is strongly influenced by avoidance and, to some extent, by seeking help behaviors, but not by protective action behaviors. As the first study to examine consumers’ disclosure of personal information to generative AI, it extends the literature on processing arguments related to opaque systems like generative AI. Additionally, it provides insightful managerial implications, especially at a time when the hotel industry lacks clear guidance regarding the use of consumers’ personal information within generative AI.
期刊介绍:
The International Journal of Hospitality Management serves as a platform for discussing significant trends and advancements in various disciplines related to the hospitality industry. The publication covers a wide range of topics, including human resources management, consumer behavior and marketing, business forecasting and applied economics, operational management, strategic management, financial management, planning and design, information technology and e-commerce, training and development, technological developments, and national and international legislation.
In addition to covering these topics, the journal features research papers, state-of-the-art reviews, and analyses of business practices within the hospitality industry. It aims to provide readers with valuable insights and knowledge in order to advance research and improve practices in the field.
The journal is also indexed and abstracted in various databases, including the Journal of Travel Research, PIRA, Academic Journal Guide, Documentation Touristique, Leisure, Recreation and Tourism Abstracts, Lodging and Restaurant Index, Scopus, CIRET, and the Social Sciences Citation Index. This ensures that the journal's content is widely accessible and discoverable by researchers and practitioners in the hospitality field.