Systematic Review and Bibliometric Analysis of Artificial Intelligence Adoption in Human Resource Management

Systematic Review and Bibliometric Analysis of Artificial Intelligence Adoption in Human Resource Management | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Systematic Review and Bibliometric Analysis of Artificial Intelligence Adoption in Human Resource Management Nahid ABADI, Said OUTMANE This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8570465/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Objective: This study conducts a systematic and bibliometric review of scientific research on the adoption of artificial intelligence (AI) in human resource management (HRM). It examines how the literature conceptualizes determinants, obstacles, and paradoxical tensions linked to AI adoption, while proposing an integrative interpretation that goes beyond descriptive approaches. Design/Methodology/ approach: A systematic review was carried out following the PRISMA protocol. The corpus was extracted from Scopus using precise search equations. Bibliometric techniques (Bibliometrix, VOSviewer) enabled co-occurrence analysis, author-network mapping, and the identification of thematic clusters and emerging trends. Results: Findings show rapid growth of AI-related studies in HRM, mainly from the United States, Europe, and Asia. Four major research areas emerge: technological optimization, strategic transformation of the HR function, employee experience, and paradoxical tensions associated with algorithmic systems. A recurrent gap appears between AI’s technical promises and organizational realities, particularly in emerging contexts. Practical implications: The study highlights limits of linear adoption models and emphasizes the importance of aligning strategy, culture, data governance, and change management. Socially, it underscores ethical concerns such as algorithmic bias, transparency, and employee trust. Originality / Value: By combining PRISMA and bibliometric analysis, this review proposes an innovative interpretive model and identifies future research directions centered on ethics, emerging contexts, and the evolving role of HR professionals in the AI era. Artificial Intelligence Human Resource Management Technology Adoption Organi-zational Paradox Bibliometrics Systematic Literature Review Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8570465","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":586298530,"identity":"e967307c-e15a-4ac0-9edf-4fa9f2872a60","order_by":0,"name":"Nahid ABADI","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABB0lEQVRIiWNgGAWjYFACHoYDPAwMCQwMiY0PSNbSbACiiNLCANGSwCZBlBZz9rMHD7xhsMvjb09uq/j44zCDwfHDBxh+1DBE8zdg12LZk5dwcA5DcrHEmYdtN2ckALWcSUtg7DnGkDvjAHYtBgdyDA7zMDAnNtxIbLvNA9ICFGHgbWDIbcCl5fwbkJb6xPlALcV/QFrOv//A+BeoZT4uLTfAthxO3ADUwswA0nIjh4EZZMsGnFreGBycY3C82PDMw2bJnrR0HskbzwwOyxyTyN2I02E5xh/eVFTnyR1Pf/jhh421HN/55IcP39TY5M7DoQWqEcHkARFAxRL41I+CUTAKRsEoIAAASY9mTvgXrBMAAAAASUVORK5CYII=","orcid":"","institution":"","correspondingAuthor":true,"prefix":"","firstName":"Nahid","middleName":"","lastName":"ABADI","suffix":""},{"id":586298532,"identity":"f9ce5dc4-7986-4c3b-ab69-68d959e21c84","order_by":1,"name":"Said OUTMANE","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Said","middleName":"","lastName":"OUTMANE","suffix":""}],"badges":[],"createdAt":"2026-01-10 21:08:12","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8570465/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8570465/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":107705729,"identity":"bf19fdf9-b8f0-47ab-bbe5-e5782f957b7f","added_by":"auto","created_at":"2026-04-24 09:14:52","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":702366,"visible":true,"origin":"","legend":"","description":"","filename":"Manuscritrevised.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8570465/v1_covered_fe944131-b9f7-4111-8c7a-7540c946203d.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Systematic Review and Bibliometric Analysis of Artificial Intelligence Adoption in Human Resource Management","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Artificial Intelligence, Human Resource Management, Technology Adoption, Organi-zational Paradox, Bibliometrics, Systematic Literature Review","lastPublishedDoi":"10.21203/rs.3.rs-8570465/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8570465/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eObjective: This study conducts a systematic and bibliometric review of scientific research on the adoption of artificial intelligence (AI) in human resource management (HRM). It examines how the literature conceptualizes determinants, obstacles, and paradoxical tensions linked to AI adoption, while proposing an integrative interpretation that goes beyond descriptive approaches.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eDesign/Methodology/ approach: A systematic review was carried out following the PRISMA protocol. The corpus was extracted from Scopus using precise search equations. Bibliometric techniques (Bibliometrix, VOSviewer) enabled co-occurrence analysis, author-network mapping, and the identification of thematic clusters and emerging trends.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eResults: Findings show rapid growth of AI-related studies in HRM, mainly from the United States, Europe, and Asia. Four major research areas emerge: technological optimization, strategic transformation of the HR function, employee experience, and paradoxical tensions associated with algorithmic systems. 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