{"title":"Beyond the code: The impact of AI algorithm transparency signaling on user trust and relational satisfaction","authors":"","doi":"10.1016/j.pubrev.2024.102507","DOIUrl":null,"url":null,"abstract":"<div><div>This study investigates the effectiveness of AI-algorithm transparency signaling as a strategy to enhance organization-public relationships (OPRs) in AI-assisted communications. Building upon signaling theory and trust transfer theory, the study examines whether the AI algorithm transparency influences trust in AI systems by users and this trust can be transferred into trust in AI systems’ parent company, which in turn, influences the relational satisfaction with the company. An online experiment with 537 participants demonstrated that transparency signaling significantly improves users’ relational satisfaction with the AI parent company. However, this effect is mediated by trust in both the AI system and the parent company, rather than a direct relationship. Our findings offer practical guidelines for AI domain experts and public relations practitioners to deliberately convey the true essence of transparency in AI-mediated communication and ensure accountability in AI adoption, thereby improving public relations outcomes.</div></div>","PeriodicalId":48263,"journal":{"name":"Public Relations Review","volume":null,"pages":null},"PeriodicalIF":4.1000,"publicationDate":"2024-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Public Relations Review","FirstCategoryId":"91","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0363811124000869","RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"BUSINESS","Score":null,"Total":0}
引用次数: 0
Abstract
This study investigates the effectiveness of AI-algorithm transparency signaling as a strategy to enhance organization-public relationships (OPRs) in AI-assisted communications. Building upon signaling theory and trust transfer theory, the study examines whether the AI algorithm transparency influences trust in AI systems by users and this trust can be transferred into trust in AI systems’ parent company, which in turn, influences the relational satisfaction with the company. An online experiment with 537 participants demonstrated that transparency signaling significantly improves users’ relational satisfaction with the AI parent company. However, this effect is mediated by trust in both the AI system and the parent company, rather than a direct relationship. Our findings offer practical guidelines for AI domain experts and public relations practitioners to deliberately convey the true essence of transparency in AI-mediated communication and ensure accountability in AI adoption, thereby improving public relations outcomes.
期刊介绍:
The Public Relations Review is the oldest journal devoted to articles that examine public relations in depth, and commentaries by specialists in the field. Most of the articles are based on empirical research undertaken by professionals and academics in the field. In addition to research articles and commentaries, The Review publishes invited research in brief, and book reviews in the fields of public relations, mass communications, organizational communications, public opinion formations, social science research and evaluation, marketing, management and public policy formation.