信任对瑞士人力资源管理中人工智能人力资源工具感知有用性的影响:感知公平和隐私问题的中介作用

IF 4.7 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Guillaume Revillod
{"title":"信任对瑞士人力资源管理中人工智能人力资源工具感知有用性的影响:感知公平和隐私问题的中介作用","authors":"Guillaume Revillod","doi":"10.1007/s00146-025-02216-x","DOIUrl":null,"url":null,"abstract":"<div><p>This study looks at factors influencing, first, trust in artificial intelligence (AI) systems in human resources management, second, perceived usefulness of these tools. Based on a survey experiment provided to 324 private and public Swiss HR professionals’, it first explores how some trust in automation framework’s predictors are related to trust in HR AI tools and, then, how this trust is in return related to UTAUT’s perceived usefulness of these AI-enhanced tools. To do this, the following article is based on a PLS-SEM structural equation model. Its main findings are that reliability, familiarity, intention of developers and propensity to trust are directly positively related to trust in the HR AI tools studied here. Nevertheless, public employees declare more negative feelings toward AI in HRM. Indeed, the latter systematically have less trust in HR AI than private employees. However, public sector employees do not find them any less useful or efficient than private sector employees, except when it comes to the HR AI tools used to assess employee performance and behavior. In addition to this, trust in these tools is systematically positively linked to their perceived usefulness. This influence is partly mediated by the perceived decision fairness of our tools, but not by the absence of privacy concerns associated with them. This said, this article makes a significant contribution to the literature about private and public actors’ perceptions of nascent HR AI-enhanced tools.</p></div>","PeriodicalId":47165,"journal":{"name":"AI & Society","volume":"40 6","pages":"4789 - 4822"},"PeriodicalIF":4.7000,"publicationDate":"2025-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s00146-025-02216-x.pdf","citationCount":"0","resultStr":"{\"title\":\"Trust influence on AI HR tools perceived usefulness in Swiss HRM: the mediating roles of perceived fairness and privacy concerns\",\"authors\":\"Guillaume Revillod\",\"doi\":\"10.1007/s00146-025-02216-x\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>This study looks at factors influencing, first, trust in artificial intelligence (AI) systems in human resources management, second, perceived usefulness of these tools. Based on a survey experiment provided to 324 private and public Swiss HR professionals’, it first explores how some trust in automation framework’s predictors are related to trust in HR AI tools and, then, how this trust is in return related to UTAUT’s perceived usefulness of these AI-enhanced tools. To do this, the following article is based on a PLS-SEM structural equation model. Its main findings are that reliability, familiarity, intention of developers and propensity to trust are directly positively related to trust in the HR AI tools studied here. Nevertheless, public employees declare more negative feelings toward AI in HRM. Indeed, the latter systematically have less trust in HR AI than private employees. However, public sector employees do not find them any less useful or efficient than private sector employees, except when it comes to the HR AI tools used to assess employee performance and behavior. In addition to this, trust in these tools is systematically positively linked to their perceived usefulness. This influence is partly mediated by the perceived decision fairness of our tools, but not by the absence of privacy concerns associated with them. This said, this article makes a significant contribution to the literature about private and public actors’ perceptions of nascent HR AI-enhanced tools.</p></div>\",\"PeriodicalId\":47165,\"journal\":{\"name\":\"AI & Society\",\"volume\":\"40 6\",\"pages\":\"4789 - 4822\"},\"PeriodicalIF\":4.7000,\"publicationDate\":\"2025-03-27\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://link.springer.com/content/pdf/10.1007/s00146-025-02216-x.pdf\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"AI & Society\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://link.springer.com/article/10.1007/s00146-025-02216-x\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"AI & Society","FirstCategoryId":"1085","ListUrlMain":"https://link.springer.com/article/10.1007/s00146-025-02216-x","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
引用次数: 0

摘要

本研究着眼于影响因素,首先,对人力资源管理中人工智能(AI)系统的信任,其次,这些工具的感知有用性。基于对324名瑞士私人和公共人力资源专业人士的调查实验,它首先探讨了对自动化框架预测因子的一些信任如何与对人力资源人工智能工具的信任相关,然后,这种信任如何与UTAUT对这些人工智能增强工具的感知有用性相关。为了做到这一点,下面的文章是基于PLS-SEM结构方程模型。它的主要发现是可靠性、熟悉度、开发人员的意图和信任倾向与这里研究的人力资源人工智能工具的信任直接成正相关。然而,公共雇员对人工智能在人力资源管理中的负面情绪更多。事实上,与私营企业员工相比,后者对人力资源人工智能的信任度总体较低。然而,除了用于评估员工绩效和行为的人力资源人工智能工具之外,公共部门的员工并不认为它们比私营部门的员工更有用或更有效率。除此之外,对这些工具的信任与它们感知到的有用性有系统的正相关。这种影响部分是由我们的工具的感知决策公平性来调节的,而不是由与之相关的隐私问题的缺失来调节的。也就是说,本文对私人和公共行为者对新兴人力资源人工智能增强工具的看法的文献做出了重大贡献。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Trust influence on AI HR tools perceived usefulness in Swiss HRM: the mediating roles of perceived fairness and privacy concerns

This study looks at factors influencing, first, trust in artificial intelligence (AI) systems in human resources management, second, perceived usefulness of these tools. Based on a survey experiment provided to 324 private and public Swiss HR professionals’, it first explores how some trust in automation framework’s predictors are related to trust in HR AI tools and, then, how this trust is in return related to UTAUT’s perceived usefulness of these AI-enhanced tools. To do this, the following article is based on a PLS-SEM structural equation model. Its main findings are that reliability, familiarity, intention of developers and propensity to trust are directly positively related to trust in the HR AI tools studied here. Nevertheless, public employees declare more negative feelings toward AI in HRM. Indeed, the latter systematically have less trust in HR AI than private employees. However, public sector employees do not find them any less useful or efficient than private sector employees, except when it comes to the HR AI tools used to assess employee performance and behavior. In addition to this, trust in these tools is systematically positively linked to their perceived usefulness. This influence is partly mediated by the perceived decision fairness of our tools, but not by the absence of privacy concerns associated with them. This said, this article makes a significant contribution to the literature about private and public actors’ perceptions of nascent HR AI-enhanced tools.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
AI & Society
AI & Society COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-
CiteScore
8.00
自引率
20.00%
发文量
257
期刊介绍: AI & Society: Knowledge, Culture and Communication, is an International Journal publishing refereed scholarly articles, position papers, debates, short communications, and reviews of books and other publications. Established in 1987, the Journal focuses on societal issues including the design, use, management, and policy of information, communications and new media technologies, with a particular emphasis on cultural, social, cognitive, economic, ethical, and philosophical implications. AI & Society has a broad scope and is strongly interdisciplinary. We welcome contributions and participation from researchers and practitioners in a variety of fields including information technologies, humanities, social sciences, arts and sciences. This includes broader societal and cultural impacts, for example on governance, security, sustainability, identity, inclusion, working life, corporate and community welfare, and well-being of people. Co-authored articles from diverse disciplines are encouraged. AI & Society seeks to promote an understanding of the potential, transformative impacts and critical consequences of pervasive technology for societies. Technological innovations, including new sciences such as biotech, nanotech and neuroscience, offer a great potential for societies, but also pose existential risk. Rooted in the human-centred tradition of science and technology, the Journal acts as a catalyst, promoter and facilitator of engagement with diversity of voices and over-the-horizon issues of arts, science, technology and society. AI & Society expects that, in keeping with the ethos of the journal, submissions should provide a substantial and explicit argument on the societal dimension of research, particularly the benefits, impacts and implications for society. This may include factors such as trust, biases, privacy, reliability, responsibility, and competence of AI systems. Such arguments should be validated by critical comment on current research in this area. Curmudgeon Corner will retain its opinionated ethos. The journal is in three parts: a) full length scholarly articles; b) strategic ideas, critical reviews and reflections; c) Student Forum is for emerging researchers and new voices to communicate their ongoing research to the wider academic community, mentored by the Journal Advisory Board; Book Reviews and News; Curmudgeon Corner for the opinionated. Papers in the Original Section may include original papers, which are underpinned by theoretical, methodological, conceptual or philosophical foundations. The Open Forum Section may include strategic ideas, critical reviews and potential implications for society of current research. Network Research Section papers make substantial contributions to theoretical and methodological foundations within societal domains. These will be multi-authored papers that include a summary of the contribution of each author to the paper. Original, Open Forum and Network papers are peer reviewed. The Student Forum Section may include theoretical, methodological, and application orientations of ongoing research including case studies, as well as, contextual action research experiences. Papers in this section are normally single-authored and are also formally reviewed. Curmudgeon Corner is a short opinionated column on trends in technology, arts, science and society, commenting emphatically on issues of concern to the research community and wider society. Normal word length: Original and Network Articles 10k, Open Forum 8k, Student Forum 6k, Curmudgeon 1k. The exception to the co-author limit of Original and Open Forum (4), Network (10), Student (3) and Curmudgeon (2) articles will be considered for their special contributions. Please do not send your submissions by email but use the "Submit manuscript" button. NOTE TO AUTHORS: The Journal expects its authors to include, in their submissions: a) An acknowledgement of the pre-accept/pre-publication versions of their manuscripts on non-commercial and academic sites. b) Images: obtain permissions from the copyright holder/original sources. c) Formal permission from their ethics committees when conducting studies with people.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术官方微信