{"title":"探索个人采用人力资源分析的情况:行为信念和机器学习特征的作用","authors":"","doi":"10.1016/j.techfore.2024.123709","DOIUrl":null,"url":null,"abstract":"<div><p>The technological capabilities of Human Resource Analytics (HRA), enhanced by recent innovations in Machine Learning (ML), offer exciting opportunities. However, organisations often fail to realise these potentials because of a limited understanding of why individuals choose to adopt or disregard respective tools. Prior research on innovation adoption offers preliminary insights but fails to aggregate the determinants of individual adoption into actionable suggestions for decisions in the ML adoption process. Our study applies focused interviews to examine non-ML experts' reasoning for using a specific tool tailored to a public sector organisation, which corresponds to the usual end-user perspective of ML-based HRA adoption. By drawing from the HRA adoption framework, provided by <span><span>Vargas et al. (2018)</span></span>, we contribute to the literature by identifying relevant beliefs and experiences influencing one's intention to adopt ML-based HRA and by qualitatively linking these beliefs to ML characteristics such as transparency, automation and fairness. For practitioners, we provide actionable guidance emphasising the need to ensure fairness proactively, as interviewees do not consider this aspect when deciding to adopt ML-based HRA.</p></div>","PeriodicalId":48454,"journal":{"name":"Technological Forecasting and Social Change","volume":null,"pages":null},"PeriodicalIF":12.9000,"publicationDate":"2024-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0040162524005079/pdfft?md5=1489deb97ebced41ab481ad6dd55efac&pid=1-s2.0-S0040162524005079-main.pdf","citationCount":"0","resultStr":"{\"title\":\"Exploring the individual adoption of human resource analytics: Behavioural beliefs and the role of machine learning characteristics\",\"authors\":\"\",\"doi\":\"10.1016/j.techfore.2024.123709\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>The technological capabilities of Human Resource Analytics (HRA), enhanced by recent innovations in Machine Learning (ML), offer exciting opportunities. However, organisations often fail to realise these potentials because of a limited understanding of why individuals choose to adopt or disregard respective tools. Prior research on innovation adoption offers preliminary insights but fails to aggregate the determinants of individual adoption into actionable suggestions for decisions in the ML adoption process. Our study applies focused interviews to examine non-ML experts' reasoning for using a specific tool tailored to a public sector organisation, which corresponds to the usual end-user perspective of ML-based HRA adoption. By drawing from the HRA adoption framework, provided by <span><span>Vargas et al. (2018)</span></span>, we contribute to the literature by identifying relevant beliefs and experiences influencing one's intention to adopt ML-based HRA and by qualitatively linking these beliefs to ML characteristics such as transparency, automation and fairness. For practitioners, we provide actionable guidance emphasising the need to ensure fairness proactively, as interviewees do not consider this aspect when deciding to adopt ML-based HRA.</p></div>\",\"PeriodicalId\":48454,\"journal\":{\"name\":\"Technological Forecasting and Social Change\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":12.9000,\"publicationDate\":\"2024-08-29\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.sciencedirect.com/science/article/pii/S0040162524005079/pdfft?md5=1489deb97ebced41ab481ad6dd55efac&pid=1-s2.0-S0040162524005079-main.pdf\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Technological Forecasting and Social Change\",\"FirstCategoryId\":\"91\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0040162524005079\",\"RegionNum\":1,\"RegionCategory\":\"管理学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"BUSINESS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Technological Forecasting and Social Change","FirstCategoryId":"91","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0040162524005079","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
摘要
人力资源分析(HRA)的技术能力在最近的机器学习(ML)创新中得到了加强,提供了令人兴奋的机遇。然而,由于对个人选择采用或不采用相关工具的原因了解有限,组织往往无法实现这些潜力。先前关于创新采用的研究提供了初步的见解,但未能将个人采用的决定因素汇总成可操作的建议,以便在采用 ML 的过程中做出决策。我们的研究采用重点访谈的方式,考察了非人工智能专家使用为公共部门组织量身定制的特定工具的理由,这与采用基于人工智能的人力资源管理的通常最终用户视角相对应。通过借鉴 Vargas 等人(2018 年)提供的人力资源管理采用框架,我们确定了影响人们采用基于 ML 的人力资源管理意向的相关信念和经验,并将这些信念与 ML 的透明度、自动化和公平性等特征定性联系起来,从而为相关文献做出了贡献。对于从业人员,我们提供了可操作的指导,强调需要主动确保公平性,因为受访者在决定采用基于 ML 的人力资源管理时并未考虑到这一方面。
Exploring the individual adoption of human resource analytics: Behavioural beliefs and the role of machine learning characteristics
The technological capabilities of Human Resource Analytics (HRA), enhanced by recent innovations in Machine Learning (ML), offer exciting opportunities. However, organisations often fail to realise these potentials because of a limited understanding of why individuals choose to adopt or disregard respective tools. Prior research on innovation adoption offers preliminary insights but fails to aggregate the determinants of individual adoption into actionable suggestions for decisions in the ML adoption process. Our study applies focused interviews to examine non-ML experts' reasoning for using a specific tool tailored to a public sector organisation, which corresponds to the usual end-user perspective of ML-based HRA adoption. By drawing from the HRA adoption framework, provided by Vargas et al. (2018), we contribute to the literature by identifying relevant beliefs and experiences influencing one's intention to adopt ML-based HRA and by qualitatively linking these beliefs to ML characteristics such as transparency, automation and fairness. For practitioners, we provide actionable guidance emphasising the need to ensure fairness proactively, as interviewees do not consider this aspect when deciding to adopt ML-based HRA.
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