Artificial Intelligence in Human Resource Management: Opportunities for Digital Transformation and Administrative Performance Improvement among Employees in the Palestinian Health Sector

Artificial Intelligence in Human Resource Management: Opportunities for Digital Transformation and Administrative Performance Improvement among Employees in the Palestinian Health Sector | 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 Artificial Intelligence in Human Resource Management: Opportunities for Digital Transformation and Administrative Performance Improvement among Employees in the Palestinian Health Sector Sa'ed Basheer This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9666254/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 This study examines the impact of Artificial Intelligence (AI) on Human Resource Management (HRM) within the Palestinian healthcare sector, exploring opportunities for digital transformation and administrative performance enhancement. It aims to address the knowledge gap regarding the strategic utilization of AI to improve the efficiency of administrative operations by enhancing institutional communication, motivating employees, and increasing productivity. A mixed-methods approach (concurrent triangulation) was employed, integrating quantitative data (an electronic questionnaire administered to 75 participants from HR departments and hospitals) and qualitative data (43 Delphi-method expert and manager interviews from within and outside Palestine). A systematic literature review was conducted following the (Preferred Reporting Items for Systematic Reviews and Meta-Analyses-PRISMA) model, with data analyzed using (Statistical Package for the Social Sciences-SPSS version 16) and (Qualitative Data Analysis-MAXQDA version 2020). The findings indicate that successful AI adoption hinges on developing digital skills, enhancing institutional awareness, and providing suitable technical infrastructure. Limited variation in participant responses based on gender or age was observed, reflecting the greater influence of institutional over demographic factors. Key challenges identified include insufficient digital competencies, high costs, and absence of regulatory legislation. The study recommends formulating a comprehensive national strategy focused on capacity building, policy development, and partnership enhancement. Practically, a three-phase digital transformation roadmap is proposed: infrastructure development, capacity building, and periodic performance evaluation. Suggestions include revising job description cards, developing tools to measure (Return On Investment–ROI) in AI, and fostering administrative innovation. The study concludes with future research recommendations encompassing comparative studies and sustainable analyses focusing on psychological and behavioral dimensions of HR automation. Artificial Intelligence and Machine Learning Health Economics & Outcomes Research Public Administration Information Retrieval and Management Management Analysis Artificial Intelligence Human Resource Management Digital Transformation Administrative Performance Predictive Analytics Data-Driven Decision Making Full Text Additional Declarations The authors declare no competing interests. Consent to participate: Informed consent was obtained from all participants prior to their participation in the study. Participants were informed about the purpose of the research, the voluntary nature of their participation, their right to withdraw, and the confidentiality and anonymity of their responses. 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-9666254","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":637490389,"identity":"f63ef4f5-59a0-4764-bba4-3a3edad01e77","order_by":0,"name":"Sa'ed Basheer","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA6ElEQVRIiWNgGAWjYJACaSBOAGLGB0CCh48ULcwGIC1spGhhkwDxCGqRbz988HZBTV0eP3vvs8qvOXYybAzMDx/dwKPF4ExasvWMY4eLJXuOm92W3ZYMdBibsXEOPi0MOWbSPGwHEjfcSGO7LbmNGaiFh00anxb5/vffpHn+1SXuv/+MrVhyWz1hLQw3ctikeduYEzdIsLExftx2mLAWgxvPjK1n9h1OnHEmjVmacdtxHjZmAn6R709+eLvgW11if/sxxo8/t1Xb87M3P3yM12HIgJkHTBKrHAQYf5CiehSMglEwCkYMAAB9q0Sb7ejfkQAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0009-0009-3215-143X","institution":"Palestinian Ministry of Health","correspondingAuthor":true,"prefix":"","firstName":"Sa'ed","middleName":"","lastName":"Basheer","suffix":""}],"badges":[],"createdAt":"2026-05-09 20:33:10","currentVersionCode":1,"declarations":{"humanSubjects":true,"vertebrateSubjects":true,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":true,"humanSubjectConsent":true,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":true},"doi":"10.21203/rs.3.rs-9666254/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9666254/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":109222145,"identity":"fea7323d-cc92-4074-a10d-deb9fe47122b","added_by":"auto","created_at":"2026-05-13 21:02:46","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1443947,"visible":true,"origin":"","legend":"","description":"","filename":"ArtificialIntelligenceinHumanResourceManagement1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9666254/v1_covered_f17b5df7-eb04-4023-8ea2-aa33d7eaef3d.pdf"}],"financialInterests":"\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003eConsent to participate: Informed consent was obtained from all participants prior to their participation in the study. Participants were informed about the purpose of the research, the voluntary nature of their participation, their right to withdraw, and the confidentiality and anonymity of their responses.\u003c/p\u003e","formattedTitle":"\u003cp\u003eArtificial Intelligence in Human Resource Management: Opportunities for Digital Transformation and Administrative Performance Improvement among Employees in the Palestinian Health Sector\u003c/p\u003e","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Palestinian Health Sector","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, Digital Transformation, Administrative Performance, Predictive Analytics, Data-Driven Decision Making","lastPublishedDoi":"10.21203/rs.3.rs-9666254/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9666254/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study examines the impact of Artificial Intelligence (AI) on Human Resource Management (HRM) within the Palestinian healthcare sector, exploring opportunities for digital transformation and administrative performance enhancement. It aims to address the knowledge gap regarding the strategic utilization of AI to improve the efficiency of administrative operations by enhancing institutional communication, motivating employees, and increasing productivity. A mixed-methods approach (concurrent triangulation) was employed, integrating quantitative data (an electronic questionnaire administered to 75 participants from HR departments and hospitals) and qualitative data (43 Delphi-method expert and manager interviews from within and outside Palestine). A systematic literature review was conducted following the (Preferred Reporting Items for Systematic Reviews and Meta-Analyses-PRISMA) model, with data analyzed using (Statistical Package for the Social Sciences-SPSS version 16) and (Qualitative Data Analysis-MAXQDA version 2020).\u003c/p\u003e\n\u003cp\u003eThe findings indicate that successful AI adoption hinges on developing digital skills, enhancing institutional awareness, and providing suitable technical infrastructure. Limited variation in participant responses based on gender or age was observed, reflecting the greater influence of institutional over demographic factors. Key challenges identified include insufficient digital competencies, high costs, and absence of regulatory legislation. The study recommends formulating a comprehensive national strategy focused on capacity building, policy development, and partnership enhancement.\u003c/p\u003e\n\u003cp\u003ePractically, a three-phase digital transformation roadmap is proposed: infrastructure development, capacity building, and periodic performance evaluation. Suggestions include revising job description cards, developing tools to measure (Return On Investment–ROI) in AI, and fostering administrative innovation. The study concludes with future research recommendations encompassing comparative studies and sustainable analyses focusing on psychological and behavioral dimensions of HR automation.\u003c/p\u003e","manuscriptTitle":"Artificial Intelligence in Human Resource Management: Opportunities for Digital Transformation and Administrative Performance Improvement among Employees in the Palestinian Health Sector","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-05-12 16:07:53","doi":"10.21203/rs.3.rs-9666254/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","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}}],"origin":"","ownerIdentity":"f0db4ae9-69f3-46c4-a543-eed6f020f4e9","owner":[],"postedDate":"May 12th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":67853931,"name":"Artificial Intelligence and Machine Learning"},{"id":67853932,"name":"Health Economics \u0026 Outcomes Research"},{"id":67853933,"name":"Public Administration"},{"id":67853934,"name":"Information Retrieval and Management"},{"id":67853935,"name":"Management"},{"id":67853936,"name":"Analysis"}],"tags":[],"updatedAt":"2026-05-12T16:07:53+00:00","versionOfRecord":[],"versionCreatedAt":"2026-05-12 16:07:53","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9666254","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9666254","identity":"rs-9666254","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}