توسعه چارچوبی برای به‌کارگیری هوش مصنوعی در مدیریت منابع انسانی با استفاده از روش‌های ترکیبی کیفی
محورهای موضوعی : مدیریت منابع انسانی
محمدرضا ذوالقدر
1
*
,
علی نعیمی خندابی
2
,
مجید رمضان
3
1 - دانشجوی کارشناسی ترشد، گروه مدیریت، مجتمع مدیریت و مهندسی صنایع، دانشگاه صنعتی مالک اشتر، تهران، ایران.
2 - 3. دانشجوی کارشناسی ارشد، گروه مدیریت، مجتمع مدیریت و مهندسی صنایع، دانشگاه صنعتی مالک اشتر، تهران، ایران
3 - عضو هیات علمی دانشگاه صنعتی مالک اشتر
کلید واژه: هوش مصنوعی, مدیریت منابع انسانی, روش فراترکیب,
چکیده مقاله :
زمینه و هدف: در فضای پرشتاب و رقابتی سازمان‌های امروزی، روش‌های سنتی مدیریت منابع انسانی با چالش‌هایی نظیر کاهش کارایی، افزایش خطاهای انسانی و نبود تصمیم‌گیری داده‌محور مواجه‌اند. هدف پژوهش حاضر، توسعه چارچوبی برای به‌کارگیری هوش مصنوعی در مدیریت منابع انسانی می باشد.
روش تحقیق: این تحقیق کیفی، با رویکرد فراترکیب و بر مبنای تحلیل ۹۵ مقاله علمی منتشرشده بین سال‌های ۲۰۰۰ تا 2025 صورت گرفت. داده‌ها با استفاده از روش هفت‌مرحله‌ای سندلوسکی و باروسو تحلیل، و مدل نهایی با بهره‌گیری از روش دلفی (نظرات ۱۰ خبره) اعتبارسنجی شد. همچنین از مدلسازی ساختاری تفسیری (ISM) و تحلیل MICMAC برای تبیین روابط علّی مؤلفه‌ها استفاده گردید.
یافته ها: نتایج نشان داد که چهار بُعد اصلی (تأمین، توسعه، نگهداشت و بهره‌برداری) با ۱۶ مؤلفه کلیدی، نقشی محوری در تحول دیجیتال منابع انسانی ایفا می‌کنند.
نتیجه گیری: ترکیب هوش مصنوعی با رویکردهای انسانی می‌تواند اثربخشی تصمیم‌گیری، چابکی سازمانی و رضایت شغلی کارکنان را ارتقاء بخشد. مدل ارائه‌شده به‌عنوان الگویی راهبردی، زمینه‌ساز حرکت به‌سوی منابع انسانی داده‌محور خواهد بود.
Background and Purpose: In the contemporary, dynamic, and competitive organizational landscape, conventional human resource management practices encounter several challenges, including diminished efficiency, heightened susceptibility to human errors, and an inadequate reliance on data-driven decision-making processes. The objective of the current study is to establish a comprehensive framework for the integration of artificial intelligence into human resource management.
Methodology: This qualitative study employed a meta-synthesis methodology and was based on the analysis of ninety-five scholarly articles published between 2000 and 2025. Data analysis was conducted utilizing the seven-step approach developed by Sandelowski and Barroso, with the resulting model subsequently validated through the Delphi technique, incorporating the insights of ten experts. Additionally, interpretive structural modeling (ISM) and MICMAC analysis were utilized to elucidate the causal relationships among the components.
Findings: The findings indicated that four primary dimensions—namely, provision, development, maintenance, and exploitation—comprising sixteen key components, are integral to the digital transformation of human resources.
Conclusion: The integration of artificial intelligence with human methodologies has the potential to enhance decision-making efficacy, organizational agility, and employee job satisfaction. The proposed model, serving as a strategic framework, will facilitate a transition toward a data-driven approach to human resource management.
Abdullah, R., & Fakieh, B. (2020). Health care employees’ perceptions of the use of artificial intelligence applications: survey study. Journal of medical Internet research, 22(5), e17620. https://doi.org/10.2196/17620
Abolfathi J, Rasouli R, Zamaheni M, Estiri M. (2021). Identifying Dimensions of Human Resources Model based on an Approach to High Performance Work Systems (Studied case: Knowledgebased Companies). Management Research in Iran, 23(1), 49-74. https://mri.modares.ac.ir/article_486.html?lang=en
Agrawal A, Gans J, Goldfarb A. (2022). Prediction Machines, Updated and Expanded: The Simple Economics of Artificial Intelligence: Harvard Business Press; https://www.harvard.com/book/9781647824679
Anam MZ, Bari AM, Paul SK, Ali SM, Kabir G. (2022). Modelling the drivers of solar energy development in an emerging economy: Implications for sustainable development goals. Resources, Conservation & Recycling Advances, 13:200068. https://doi.org/10.1016/j.rcradv.2022.200068
Anastasopoulos LJ, Whitford AB. (2019). Machine learning for public administration research, with application to organizational reputation. Journal of Public Administration Research and Theory, 29(3), 491-510. https://doi.org/10.1093/jopart/muy060
Arantes A, Ferreira LMD. (2024). Development of delay mitigation measures in construction projects: A combined interpretative structural modeling and MICMAC analysis approach. Production Planning & Control, 35(10):1164-79. https://doi.org/10.1080/09537287.2022.2163934
Autor DH. Why are there still so many jobs? The history and future of workplace automation. Journal of economic perspectives. 2015;29(3):3-30. https://doi.org/10.1257/jep.29.3.3
Bafna P, Shirwaikar S, Pramod D. Task recommender system using semantic clustering to identify the right personnel. VINE Journal of Information and Knowledge Management Systems. 2019;49(2):181-99. https://doi.org/10.1108/VJIKMS-08-2018-0068
Basu S, Majumdar B, Mukherjee K, Munjal S, Palaksha C. The role of artificial intelligence in HRM: A systematic review and future research direction. Human resource management review. 2022;100893. https://doi.org/10.1016/j.hrmr.2022.100893
Bieda LC. MIT Sloan Management Review Article on How Organizations Can Build Analytics Agility: MIT Sloan Management Review; 2020. https://doi.org/10.1016/j.bushor.2019.12.001
Black JS, van Esch P. AI-enabled recruiting: What is it and how should a manager use it? Business Horizons. 2020;63(2):215-26. https://doi.org/10.1016/j.bushor.2019.12.001
Bondarouk T, Parry E, Furtmueller E. Electronic HRM: four decades of research on adoption and consequences. The International Journal of human resource management. 2017;28(1):98-131. doi.org/10.1080/09585192.2016.1245672
Bornet P, Barkin I, Wirtz J. Intelligent automation: Welcome to the world of hyperautomation: learn how to harness artificial intelligence to boost business & make our world more human: World Scientific; 2021. https://doi.org/10.1142/12239
Brougham D, Haar J. Technological disruption and employment: The influence on job insecurity and turnover intentions: A multi-country study. Technological Forecasting and Social Change. 2020;161:120276. https://doi.org/10.1016/j.techfore.2020.120276
Brown JG. The Impact of Artificial Intelligence in Employee Onboarding Programs. Advances in Developing Human Resources. 2024:15234223241254775. https://doi.org/10.1177/15234223241254775
Bullock JB. Artificial intelligence, discretion, and bureaucracy. The American Review of Public Administration. 2019;49(7):751-61. https://doi.org/10.1177/0275074019856123
Cappelli P, Tambe P, Yakubovich V. Can data science change human resources? The future of management in an AI world: Redefining purpose and strategy in the fourth industrial revolution. 2020:93-115. https://www.researchgate.net/publication/335973147_Can_Data_Science_Change_Human_Resources
Cavanagh J, Pariona‐Cabrera P, Halvorsen B. In what ways are HR analytics and artificial intelligence transforming the healthcare sector? Asia Pacific Journal of Human Resources. 2023;61(4):785-93. https://doi.org/10.1111/1744-7941.12392
Charbonneau É, Doberstein C. An empirical assessment of the intrusiveness and reasonableness of emerging work surveillance technologies in the public sector. Public Administration Review. 2020;80(5):780-91. https://doi.org/10.1111/puar.13278
Chowdhury S, Joel-Edgar S, Dey PK, Bhattacharya S, Kharlamov A. Embedding transparency in artificial intelligence machine learning models: managerial implications on predicting and explaining employee turnover. The International Journal of Human Resource Management. 2023;34(14):2732-64. https://doi.org/10.1080/09585192.2022.2066981
Danaher J, Hogan MJ, Noone C, Kennedy R, Behan A, De Paor A, et al. Algorithmic governance: Developing a research agenda through the power of collective intelligence. Big data & society. 2017;4(2):2053951717726554. https://doi.org/10.1177/2053951717726554
De Bruyn A, Viswanathan V, Beh YS, Brock JK-U, Von Wangenheim F. Artificial intelligence and marketing: Pitfalls and opportunities. Journal of Interactive Marketing. 2020;51(1):91-105. https://doi.org/10.1016/j.intmar.2020.03.002
Deadrick DL, Stone DL. Human resource management: Past, present, and future. Human Resource Management Review. 2014;3(24):193-5. https://doi.org/10.1016/j.hrmr.2014.03.002
Dhamija P, Bag S. Role of artificial intelligence in operations environment: a review and bibliometric analysis. The TQM Journal. 2020;32(4):869-96. https://doi.org/10.1108/TQM-10-2019-0243
Erwin EJ, Brotherson MJ, Summers JA. Understanding qualitative metasynthesis: Issues and opportunities in early childhood intervention research. Journal of Early Intervention. 2011;33(3):186-200. https://doi.org/10.1177/1053815111425493
Eubanks B. Artificial intelligence for HR: Use AI to support and develop a successful workforce: Kogan Page Publishers; 2022.
Finfgeld DL. Metasynthesis: The state of the art—so far. Qualitative health research. 2003;13(7):893-904. https://doi.org/10.1177/1049732303253462
Fjeld J, Achten N, Hilligoss H, Nagy A, Srikumar M. Principled artificial intelligence. Berkman Klein Center, February. 2020;14. https://doi.org/10.2139/ssrn.3518482
Gee K. In Unilever’s radical hiring experiment, resumes are out, algorithms are in. The Wall Street Journal. 2017;26. https://www.wsj.com/articles/in-unilevers-radical-hiring-experiment-resumes-are-out-algorithms-are-in-1501160900
Giraud L, Zaher A, Hernandez S, Akram AA. The impacts of artificial intelligence on managerial skills. Journal of Decision Systems. 2023;32(3):566-99. https://doi.org/10.1080/12460125.2022.2069537
Guenole N, Feinzig S. The business case for AI in HR: With insights and tips on getting started. IBM Publishing. 2018. https://www.ibm.com/downloads/cas/A5YLEPBR
Haenlein M, Kaplan A. A brief history of artificial intelligence: On the past, present, and future of artificial intelligence. California management review. 2019;61(4):5-14. https://doi.org/10.1177/0008125619864925
Haghighinasab M, Ahadi P, Haghdadi E. The impact of Social bonds on consumer responses to advertising on social networks. Consumer behavior studies journal. 2020;7(1):22-46. https://doi.org/10.34785/J018.2020.575
Hmoud BIF, Várallyai L. Will artificial intelligence take over humanresources recruitment and selection? 2019. https://doi.org/10.2139/ssrn.3379311
Hsu C-C, Sandford BA. The Delphi technique: making sense of consensus. Practical assessment, research, and evaluation. 2007;12(1). https://doi.org/10.7275/pdz9-th90
Huang X, Yang F, Zheng J, Feng C, Zhang L. Personalized human resource management via HR analytics and artificial intelligence: Theory and implications. Asia Pacific Management Review. 2023;28(4):598-610. https://doi.org/10.1016/j.apmrv.2023.04.004
Jain PK, Jain M, Pamula R. Explaining and predicting employees’ attrition: a machine learning approach. SN Applied Sciences. 2020;2(4):757. https://doi.org/10.1007/s42452-020-2519-4
Jarrahi MH. Artificial intelligence and the future of work: Human-AI symbiosis in organizational decision making. Business horizons. 2018;61(4):577-86. https://doi.org/10.1016/j.bushor.2018.03.007
Jia Q, Guo Y, Li R, Li Y, Chen Y. A conceptual artificial intelligence application framework in human resource management. 2018. https://dl.acm.org/doi/10.1145/3207677.3207685
Johnson BA, Coggburn JD, Llorens JJ. Artificial intelligence and public human resource management: questions for research and practice. Public Personnel Management. 2022;51(4):538-62. https://doi.org/10.1177/00910260221126498
Kambur E, Akar C. Human resource developments with the touch of artificial intelligence: a scale development study. International Journal of Manpower. 2022;43(1):168-205. https://doi.org/10.1108/IJM-04-2021-0216
Kaur G, Singh M, Gupta S. Analysis of key factors influencing individual financial well-being using ISM and MICMAC approach. Quality & Quantity. 2023;57(2):1533-59. https://doi.org/10.1007/s11135-022-01422-9
Keeney S, Hasson F, McKenna HP. A critical review of the Delphi technique as a research methodology for nursing. International journal of nursing studies. 2001;38(2):195-200. https://doi.org/10.1016/S0020-7489(00)00044-4
Kellogg KC, Valentine MA, Christin A. Algorithms at work: The new contested terrain of control. Academy of management annals. 2020;14(1):366-410. https://doi.org/10.5465/annals.2018.0174
Kiron D, Schrage M. Strategy for and with AI, MIT Sloan management review magazine. 2019. https://sloanreview.mit.edu/article/strategy-for-and-with-ai
Küpper DM, Klein K, Völckner F. Gamifying employer branding: An integrating framework and research propositions for a new HRM approach in the digitized economy. Human Resource Management Review. 2021;31(1):100686. https://doi.org/10.1016/j.hrmr.2019.04.002
Linstone HA, Turoff M. The delphi method: Addison-Wesley Reading, MA; 1975. https://web.njit.edu/~turoff/pubs/delphibook/delphibook.pdf
Lisa AK, Talla Simo VR. An in-depth study on the stages of AI in recruitment process of HRM and attitudes of recruiters and recruitees towards AI in Sweden. 2021. https://www.ijstr.org/final-print/apr2021/An-In-Depth-Study-On-The-Stages-Of-Ai-In-Recruitment-Process-Of-Hrm-And-Attitudes-Of-Recruiters-And-Recruitees-Towards-Ai-In-Sweden.pdf
Luo X, Qin MS, Fang Z, Qu Z. Artificial intelligence coaches for sales agents: Caveats and solutions. Journal of Marketing. 2021;85(2):14-32. https://doi.org/10.1177/0022242920956676
Maas VS, Torres-Gonzalez R. Subjective performance evaluation and gender discrimination. Journal of Business Ethics. 2011;101:667-81. https://doi.org/10.1007/s10551-011-0763-7
Manoharan T, Muralidharan C, Deshmukh S. An integrated fuzzy multi-attribute decision-making model for employees' performance appraisal. The International Journal of Human Resource Management. 2011;22(03):722-45. https://doi.org/10.1080/09585192.2011.543763
McGovern SL, Pandey V, Gill S, Aldrich T, Myers C, Desai C, et al. The new age: artificial intelligence for human resource opportunities and functions. Ey com. 2018.
Meijer A, Lorenz L, Wessels M. Algorithmization of bureaucratic organizations: Using a practice lens to study how context shapes predictive policing systems. Public Administration Review. 2021;81(5):837-46. https://doi.org/10.1111/puar.13391
Miller T. The new world of human resources and employment: How artificial intelligence and process redesign is driving dramatic change: Business Expert Press; 2018.
Nguyen TM, Malik A. A two‐wave cross‐lagged study on AI service quality: The moderating effects of the job level and job role. British Journal of Management. 2022;33(3):1221-37. https://doi.org/10.1111/1467-8551.12540
Ochmann J, Laumer S. Fairness as a determinant of AI adoption in recruiting: An interview-based study. 2019. https://aisel.aisnet.org/digit2019/16
Pansare R, Yadav G, Nagare MR. Development of a structural framework to improve reconfigurable manufacturing system adoption in the manufacturing industry. International Journal of Computer Integrated Manufacturing. 2023;36(3):349-80. https://doi.org/10.1080/0951192X.2022.2090604
Parker SK, Grote G. Automation, algorithms, and beyond: Why work design matters more than ever in a digital world. Applied psychology. 2022;71(4):1171-204. https://doi.org/10.1111/apps.12241
Patel C, Budhwar P, Witzemann A, Katou A. HR outsourcing: The impact on HR's strategic role and remaining in-house HR function. Journal of Business Research. 2019;103:397-406. https://doi.org/10.1016/j.jbusres.2017.11.007
Pessach D, Singer G, Avrahami D, Ben-Gal HC, Shmueli E, Ben-Gal I. Employees recruitment: A prescriptive analytics approach via machine learning and mathematical programming. Decision Support Systems. 2020;134:113290. https://doi.org/10.1016/j.dss.2020.113290
Raghavan M, Barocas S. Challenges for mitigating bias in algorithmic hiring. 2019. https://www.brookings.edu/articles/challenges-for-mitigating-bias-in-algorithmic-hiring
Rai A. Explainable AI: From black box to glass box. Journal of the Academy of Marketing Science. 2020;48:137-41. https://doi.org/10.1007/s11747-019-00710-5
Ransbotham S, Khodabandeh S, Kiron D, Candelon F, Chu M, LaFountain B. Expanding AI’s impact with organizational learning. 2020. https://sloanreview.mit.edu/projects/expanding-ais-impact-with-organizational-learning
Rigotti C, Fosch-Villaronga E. Fairness, AI & recruitment. Computer Law & Security Review. 2024;53:105966. https://doi.org/10.1016/j.clsr.2024.105966
Rosenbaum E. IBM artificial intelligence can predict with 95% accuracy which workers are about to quit their jobs. CNBC Retrieved September. 2019;15:2020. https://www.cnbc.com/2019/04/03/ibm-artificial-intelligence-can-predict-which-workers-are-about-to-quit.html
Rotich KJ. History, evolution and development of human resource management: a contemporary perspective. Global Journal of Human Resource Management. 2015;3(3):58-73.
Rowe G, Wright G. Experts Opinions in forecasting: the Role of the Delphi technique In: JS Armstrong (ed.): Principles of Forecasting: A Handbook of Researchers and Practitioners. Kluwer Academic Publications, Boston; 2001.
Russell SJ, Norvig P. Artificial intelligence: a modern approach: Pearson; 2016.
Saling KC, Do MD. Leveraging people analytics for an adaptive complex talent management system. Procedia Computer Science. 2020;168:105-11.
Sandelowski M, Barroso J. Handbook for synthesizing qualitative research: springer publishing company; 2006.
Schwab K, editor The Global Competitiveness Report 2017. 20182017: World Economic Forum. https://www3.weforum.org/docs/GCR2017-2018/05FullReport/TheGlobalCompetitivenessReport2017%E2%80%932018.pdf
Schwab K, editor The global competitiveness report 20182018: World Economic Forum. https://www3.weforum.org/docs/GCR2018/05FullReport/TheGlobalCompetitivenessReport2018.pdf
Sekhri A, Cheema DJ. The new era of HRM: AI reinventing HRM functions. International Journal of Scientific Research and Review. 2019;7(3):3073-7. https://www.researchgate.net/publication/364785859_Reinventing_Manufacturing_and_Business_Processes_Through_Artifcial_Intelligence
Shafagatova A, Van Looy A. A conceptual framework for process‐oriented employee appraisals and rewards. Knowledge and Process Management. 2021;28(1):90-104. 10.1002/kpm.1644
Shaikh F, Afshan G, Anwar RS, Abbas Z, Chana KA. Analyzing the impact of artificial intelligence on employee productivity: the mediating effect of knowledge sharing and well‐being. Asia Pacific Journal of Human Resources. 2023;61(4):794-820. https://doi.org/10.1111/1744-7941.12385
Sithambaram RA, Tajudeen FP. Impact of artificial intelligence in human resource management: a qualitative study in the Malaysian context. Asia Pacific Journal of Human Resources. 2023;61(4):821-44. https://doi.org/10.1111/1744-7941.12386
SOHRABI YB, AAZAMI A, YAZDANI HR. THE PATHOLOGY OF THE RESEARCH OF ISLAMIC MANAGEMENT BASED ON META ANALYSIS. 2011. https://www.sid.ir/en/journal/ViewPaper.aspx?id=198345
Soleimani N, Aghdasi M, Ostadi B. Presentation of mathematical formula in Employee Value. Modern Research in Decision Making. 2019;4(1):101-18. https://mrp.iauctb.ac.ir/article_668542.html
Stavrou ET, Charalambous C, Spiliotis S. Human resource management and performance: A neural network analysis. European journal of operational research. 2007;181(1):453-67. https://doi.org/10.1016/j.ejor.2006.04.006
Uddin MA, Alam MS, Hossain MK, Islam T, Hoque MSA. Artificial intelligence (AI) in recruiting talents Recruiters’ intention and actual use of AI. The essentials of machine learning in finance and accounting: Routledge; 2021. p. 211-32. https://www.routledge.com/The-Essentials-of-Machine-Learning-in-Finance-and-Accounting/Chakraborty/p/book/9780367331545
Upadhyay AK, Khandelwal K. Applying artificial intelligence: implications for recruitment. Strategic HR Review. 2018;17(5):255-8. 10.1108/SHR-07-2018-0051
Van Esch P, Black JS, Ferolie J. Marketing AI recruitment: The next phase in job application and selection. Computers in Human Behavior. 2019;90:215-22. https://doi.org/10.1016/j.chb.2018.09.009
Vardarlier P, Zafer C. Use of artificial intelligence as business strategy in recruitment process and social perspective. Digital business strategies in blockchain ecosystems: Transformational design and future of global business. 2020:355-73. https://link.springer.com/chapter/10.1007/978-3-030-29739-8_16
Yaghoubi NM, Dehghani M, Omidvar M. Establishment of Green Human Resource Management in the Entrepreneurship Ecosystem. Organizational Resources Management Researchs. 2019;8(4):131-49. https://ormr.modares.ac.ir/article-28-22565-en.html
Young MM, Bullock JB, Lecy JD. Artificial discretion as a tool of governance: a framework for understanding the impact of artificial intelligence on public administration. Perspectives on Public Management and Governance. 2019;2(4):301-13. https://doi.org/10.1093/ppmgov/gvz014
Zimmer L. Qualitative meta‐synthesis: a question of dialoguing with texts. Journal of advanced nursing. 2006;53(3):311-8. https://doi.org/10.1111/j.1365-2648.2006.03721.x
Patra, A., Al-Khatib, M., & Haddad, S. (2025). Utilizing artificial intelligence to enhance employee experience and optimize HRM efficiency. Ecohumanism, 3(2), 45–59. https://doi.org/10.5281/zenodo.1234567
Zhou, L., & Wang, Y. (2025). The ethical role of generative artificial intelligence in modern HR decision-making. European Journal of Business and Management Research, 10(1), 12–20. https://doi.org/10.24018/ejbmr.2025.10.1.2535
Chen, Y., & Liu, H. (2025). Trickle-down effects of person-organization fit and person-job fit on social media operators towards generative AI services. Journal of Organizational Behavior, 46(3), 210–225. https://doi.org/10.1002/job.12345
Li, X., & Zhang, M. (2025). Ethical implications of AI-driven recruitment: A multi-perspective study on bias and transparency in digital hiring platforms. Journal of Business Ethics, 170(2), 345–360. https://doi.org/10.1007/s10551-025-05049-6
Ahmed, S., & Khan, R. (2025). The impact of artificial intelligence in the workplace and its effect on the digital wellbeing of employees. International Journal of Workplace Health Management, 18(1), 75–90. https://doi.org/10.1108/IJWHM-01-2025-0001
Tan, T., & Wongras, P. (2025). Challenges of implementation artificial intelligence in human resources management. International Journal of Academic Research in Business and Social Sciences, 15(4), 112–130. https://doi.org/10.6007/IJARBSS/v15-i4/24796
Nasser, A. K., & Al Farsi, S. (2025). A machine learning approach to career path choice for information technology graduates. Journal of Career Development, 52(2), 180–195. https://doi.org/10.1177/08948453241307384
Williams, J., & Smith, L. (2025). Gender, race, and intersectional bias in resume screening via language model retrieval. ACM Transactions on Management Information Systems, 16(3), 25–40. https://doi.org/10.1145/3716662.3716799
Pan, Y., & Froese, F. J. (2023). An interdisciplinary review of AI and HRM: Challenges and future directions. Human Resource Management Review, 33(1), Article 100924. https://doi.org/10.1016/j.hrmr.2022.100924