طراحی الگوی مدیریت منابع انسانی الگوریتمی با روش tismفازی

نوع مقاله : مقاله پژوهشی

نویسندگان

1 استادیار گروه مدیریت بازرگانی، دانشگاه لرستان، خرم آباد، ایران

2 دانشجوی دکترا، گروه مدیریت بازرگانی، دانشکده مدیریت، دانشگاه لرستان، خرم آباد، ایران

10.22080/shrm.2025.6004

چکیده

سازمان‌های پیشگام سازمان‌هایی هستند که نه‌تنها می‌توانند با روندهای فن‌آوری همراه شوند، بلکه از آن‌ها جلوتر می‌زنند. ازجمله راه‌هایی که سازمان‌ها می‌توانند این کار را انجام دهند استفاده از منابع انسانی الگوریتمی است. لذا پژوهش حاضر باهدف طراحی الگوی مدیریت منابع انسانی الگوریتمی انجام پذیرفت. پژوهش حاضر از نظر هدف، کاربردی و در زمره پژوهش‌های اکتشافی قرار می‌گیرد. جامعه آماری پژوهش حاضر خبرگان بوده که با استفاده از روش نمونه‌گیری هدفمند و براساس اصل اشباع نظری ۲۰ نفر از آنان به عنوان اعضای نمونه انتخاب شده‌است. ابزار گردآوری اطلاعات در بخش کیفی مصاحبه و در بخش کمی پرسشنامه است. در این پژوهش برای تحلیل داده‌ها در بخش کیفی از روش تحلیل محتوا و کدگذاری با نرم افزار مکس‌کیودا استفاده شد. برای بررسی روایی و پایایی ابزار گردآوری اطلاعات در بخش کیفی از روش محتوایی و روایی نظری و پایایی درون کدگذار میان کدگذار استفاده شد همچنین روایی و پایایی ابزار گردآوری داده‌ها در بخش کمی، روایی اعتبار محتوا و پایایی بازآزمون بود. همچنین برای تحلیل کمی از روش مدلسازی ساختاری تفسیری کل فازی استفاده گردید. نتایج پژوهش مشتمل بر یافته‌های کیفی و کمی است، به‌طوری‌که در بخش کیفی مؤلفه‌های مدیریت منابع انسانی الگوریتمی شناسایی شدند. همچنین نتایج بخش کمی نشانگر الگوی مدیریت منابع انسانی الگوریتمی در چهار سطح است. الگوی پژوهش بر اساس چهار سطح ازجمله: فلسفه مدیریت منابع انسانی الگوریتمی، مکانیسم‌های پیاده‌سازی مدیریت منابع انسانی الگوریتمی، ابعاد مدیریت منابع انسانی الگوریتمی و پیامدهای آن، تدوین‌شده است.

کلیدواژه‌ها


عنوان مقاله [English]

Designing an algorithmic human resource management model using the fuzzy TISM method

نویسندگان [English]

  • ali shariatnejad 1
  • Milad Amraei 2
1 Lorestan university assistant professor, Management faculty, Korramabad, Iran
2 Phd student, Department of Business Administration, Faculty of Management, Lorestan University, Khorramabad, Iran
چکیده [English]

Pioneering organizations are organizations that can not only keep up with technological trends, but also stay ahead of them. One of the ways that organizations can do this is by using algorithmic human resources. Therefore, the present study was conducted with the aim of designing an algorithmic human resources management model. The present study is applied in terms of purpose and is classified as exploratory research. The statistical population of the present study was experts, of whom 20 were selected as sample members using purposive sampling and based on the principle of theoretical saturation. The data collection tool in the qualitative part is an interview and in the quantitative part is a questionnaire. In this study, the content analysis and coding method with MaxQda software was used to analyze the data in the qualitative part. To examine the validity and reliability of the data collection tool in the qualitative part, the content method and theoretical validity and intra-coder and inter-coder reliability were used. Also, the fuzzy interpretive structural modeling method was used for quantitative analysis. The research results include qualitative and quantitative findings, so that in the qualitative part, the components of algorithmic human resource management were identified. Also, the results of the quantitative part indicate the algorithmic human resource management model at four levels. The research model is based on four levels, including: the philosophy of algorithmic human resource management, the mechanisms of implementing algorithmic human resource management, the dimensions of algorithmic human resource management and its consequences.

کلیدواژه‌ها [English]

  • Intelligentization
  • Human Resources
  • Algorithmic Human Resources Management
  • Total Structural Modeling Fuzzy Interpretation
Akbari emami, S., Jamipour, M., & Fathi, S. (2023). Designing a framework for using artificial intelligence in human resource management: An exploratory approach. Journal of Sustainable Human Resource Management, 5(9), 284-263. (In Persian)
Alabdali, M. A., Khan, S. A., Yaqub, M. Z., & Alshahrani, M. A. (2024). Harnessing the Power of Algorithmic Human Resource Management and Human Resource Strategic Decision-Making for Achieving Organizational Success: An Empirical Analysis. Sustainability, 16(11), 4854.
Angelova, M. (2024, June). Pros and Cons of using Algorithmic Management in Human Resource. In ENVIRONMENT. TECHNOLOGIES. RESOURCES. Proceedings of the International Scientific and Practical Conference (Vol. 2, pp. 22-28).
Baird, L., D. Griffin, and J. Henderson. 2003. “Time and Space: Reframing the Training and Development Agenda.” Human Resource Management, 42(1), 39–52.
Bamel, N., & Bamel, U. (2021). Big data analytics based enablers of supply chain capabilities and firm competitiveness: a fuzzy-TISM approach. Journal of Enterprise Information Management, 34(1), 559-577.
Bassett, G. A. 1973. “Elements of Manpower Forecasting and Scheduling.” Human Resource Management 12(3), 35–40.
Cameron, L. D., and H. Rahman. 2021. “Expanding the Locus of Resistance: Understanding the Co-Constitution of Control and Resistance in the Gig Economy.” Organization Science 33, no. 1: 38–58.
Capasso, M., Arora, P., Sharma, D., & Tacconi, C. (2025). On the Right to Work in the Age of Artificial Intelligence: Ethical Safeguards in Algorithmic Human Resource Management. Business and Human Rights Journal, 1-15.
Duggan, J., Sherman, U., Carbery, R., & McDonnell, A. (2020). Algorithmic management and app‐work in the gig economy: A research agenda for employment relations and HRM. Human Resource Management Journal, 30(1), 114-132.
Gong, Q., Fan, D., & Bartram, T. (2024). Algorithmic human resource management: toward a functional affordance perspective. Personnel Review.
Jiang, Y., Fan, S., Zhu, Y., Wang, L., Ye, K., Zhou, J., & Rau, P. L. P. (2024, June). A Human-Centered Algorithmic Management Framework: A Literature Review. In International Conference on Human-Computer Interaction (pp. 54-71). Cham: Springer Nature Switzerland.
Keegan, A., & Meijerink, J. (2025). Algorithmic Management in Organizations? From Edge Case to Center Stage. Annual Review of Organizational Psychology and Organizational Behavior, 12(1), 395-422.
Khatwani, G., Singh, S. P., Trivedi, A., & Chauhan, A. (2015). Fuzzy-TISM: A fuzzy extension of TISM for group decision making. Global Journal of Flexible Systems Management, 16, 97-112. 
Kim, S., Khoreva, V., & Vaiman, V. (2024). Strategic Human Resource Management in the Era of Algorithmic Technologies: Key Insights and Future Research Agenda. Human Resource Management.
Kryscynski, D., Reeves, C., Stice‐Lusvardi, R., Ulrich, M., & Russell, G. (2018). Analytical abilities and the performance of HR professionals. Human Resource Management, 57(3), 715-738.
Lu, Y., Yang, M. M., Zhu, J., & Wang, Y. (2024). Dark side of algorithmic management on platform worker behaviors: A mixed‐method study. Human Resource Management, 63(3), 477-498.
Malik, A., P. Budhwar, and B. A. Kazmi. (2023). “Artificial Intelligence (AI)-Assisted HRM: Towards an Extended Strategic Framework.” Human Resource Management Review, 33(1), 100940.
Malik, A., P. Budhwar, C. Patel, and N. R. Srikanth. (2022). “May the Bots Be with You! Delivering HR Cost-Effectiveness and Individualised Employee Experiences in an MNE.” International Journal of Human Resource Management, 33(6) 1148–1178.
Meijerink, J., & Bondarouk, T. (2023). The duality of algorithmic management: Toward a research agenda on HRM algorithms, autonomy and value creation. Human Resource Management Review, 33(1), 100876.
Ramazanian, M., Moradi, M. & Soltani, F. (2015). Analysis of cultural barriers to interoperability in the automotive supply chain using interpretative structural modeling approach. Organizational Culture Management, 13(2), 369-391. (In Persian)
Rodgers, W., Murray, J. M., Stefanidis, A., Degbey, W. Y., & Tarba, S. Y. (2023). An artificial intelligence algorithmic approach to ethical decision-making in human resource management processes. Human resource management review, 33(1), 100925.
Saedi, A., Shariatnejad, A., & Hoseini, M. (2024). Cognitive Mapping of Algorithmic Human Resources Implications in the Oil Industry. Strategic studies in the oil and energy industry, 16 (63), 1-22. (In Persian) 
Scheibmayr, I., & Reichel, A. (2024). The Future of HRM Incentivizing Strathern’s Paradox? Workers’ Responses to Algorithmic Human Resource Management. Academy of Management Discoveries, 10(3), 393-415.
Shaikh, F., Afshan, G., Anwar, R. S., Abbas, Z., & Chana, K. A. (2023). Analyzing the impact of artificial intelligence on employee productivity: the mediating effect of knowledge sharing and well‐being. Asia Pacific Journal of Human Resources, 61(4), 794-820.
Sienkiewicz, Ł. (2021). Algorithmic Human Resources Management–Perspectives and Challenges. Annales Universitatis Mariae Curie-Skłodowska, Sectio H Oeconomia, 55(2), 95-105.
Srivastava, A., & Sushil. (2014). Modelling drivers of adapt for effective strategy execution. The Learning Organization, 21(6), 369-391.
Streng, S. (2024). Algorithmic Management and its impact on workers in HR.
Strohmeier, S. (2020). Algorithmic decision making in HRM. Encyclopedia of electronic HRM, 1, 54-59.
Talukder, M. B., Chowdhury, S. A., & Khan, M. R. (2025). The Gig Economy: Economic Innovations and Technological Advancements in Human Resource Management. In Economic Innovations and Technological Developments in HRM (pp. 381-404). IGI Global.
Tarafdar, M., Page, X., & Marabelli, M. (2023). Algorithms as co‐workers: Human algorithm role interactions in algorithmic work. Information Systems Journal, 33(2), 232-267.
Tomprou, M., & Lee, M. K. (2022). Employment relationships in algorithmic management: A psychological contract perspective. Computers in Human Behavior, 126, 106997.
Tong, S., N. Jia, X. Luo, and Z. Fang. 2021. “The Janus Face of Artificial Intelligence Feedback: Deployment Versus Disclosure Effects on Employee Performance.” Strategic Management Journal, 42(9), 1600–1631.
Yazdani, H., & Hakiminia, M. (2024). Identifying the challenges and opportunities of using artificial intelligence in Human resource management with a meta-synthesis approach. Journal of Sustainable Human Resource Management, 6(10), 139-113. (In Persian)
Yuan, S., B. Kroon, and A. Kramer. 2024. “Building Prediction Models with Grouped Data: A Case Study on the Prediction of Turnover Intention.” Human Resource Management Journal 34, no. 1: 20–38.
Zimmer, M. P., A. Baiyere, and H. Salmela. 2023. “Digital Workplace Transformation: Subtraction Logic as Deinstitutionalising the TakenFor-Granted.” Journal of Strategic Information Systems 32.