اعتبار سنجی مدل تحلیل منابع انسانی (مورد مطالعه : گمرک جمهوری اسلامی ایران)
محورهای موضوعی : مدیریت منابع انسانیبابک آقاویردی 1 , داریوش غلام زاده 2 * , احمد ودادی 3
1 - دانشجوی دکتری ،گروه مدیریت دولتی ، واحد تهران مرکزی، دانشگاه آزاد اسلامی، تهران، ایران .
2 - گروه مدیریت دولتی، واحد تهران مرکزی، دانشگاه آزاد اسلامی، تهران، ایران(نویسنده مسئول)
3 - گروه مدیریت دولتی، دانشکده مدیریت، دانشگاه آزاد اسلامی واحد تهران مرکزی، تهران، ایران
کلید واژه: تحلیل منابع انسانی, معیارهای منابع انسانی, پذیرش تحلیل, قابلیت های تحلیلی,
چکیده مقاله :
هدف تحقیق حاضرشناسایی ابعاد مدل تحلیل منابع انسانی و اعتبار سنجی الگوی مفهومی تحلیل منابع انسانی به دست آمده از مرور ادبیات تحلیل منابع انسانی می باشد. تحقیق حاضر از نظر هدف توسعه ای - کاربردی و به لحاظ شیوه اجرا توصیفی-تحلیلی و روش گردآوری داده ها ترکیبی از مطالعات کتابخانه ای، خبرگان و میدانی است. در این پژوهش ابتدا با استفاده از مطالعات قبلی در فاصله زمانی ۲۰۰۱ تا ۲۰۲۴ از پایگاه‌های اطلاعاتی موجود و معتبر الگوی مفهومی استخراج و سپس با کسب نظر از خبرگان مدل تحلیل منابع انسانی شامل ۷ بعد ( همسویی استراتژیک، زمینه تحلیلی، معیارها، قابلیت ها ، پذیرش ، چالش ها و نگاه به آینده) شناسایی گردید. در مرحله دوم اعتبار سنجی الگوی ارائه شده در بین کارمندان ستاد مرکزی گمرک جمهوری اسلامی ایران انجام شد. نمونه آماری شامل ۲۶۱ نفر بود. در مرحله سوم آزمون مدل اندازه گیری ، ضرایب بارهای عاملی تاییدی و ضرایب مسیر و همچنین برازش مدل مورد بررسی قرار گرفت. نرم افزار مورد استفاده برای تحلیل Smart PLS نسخه ۳ بود. پرسشنامه شامل ۷۶ گویه مربوط به ابعاد هر یک از عوامل شناسایی شده بود. یافته های پژوهش نشان داد معيار شـاخص نيكويي برازش مدل ارائه شده برابر ۷۴۳/٠ و بسيار مناسب می باشد.بنابراین توجه به ابعاد شناسایی شده و شاخص های مرتبط به آنها قبل از اتخاذ رویکرد داده محوری و انجام تحلیل منابع انسانی در سازمان ها ضروری است
The purpose of the current research is to identify the dimensions of the HRA model and validate the conceptual model of HRA obtained from the literature review of HRA .The current research is developmental-applied in terms of purpose. In terms of the execution method, it is descriptive-analytical, And the data collection method is a combination of library, expert and field studies. In this research, first by using previous studies between 2001 and 2024, the conceptual model was extracted from existing and valid databases, and then by obtaining the opinion of experts, the model of HRA includes 7 dimensions (strategic alignment, analytical context, metrics, capabilities, acceptance, challenges and looking to the future) were identified. In the second stage, validation of the presented model was done among the employees of the Central IRICA. The statistical sample included 261 people. In the third stage of the measurement model test, confirmatory factor loading coefficients and path coefficients as well as model fit were examined. The software used for analysis was Smart PLS version 3. The questionnaire included 76 items related to the dimensions of each identified factor. The findings of the research showed that the goodness of fit index of the presented model is equal to 0.743 and is very suitable. Therefore, it is necessary to pay attention to the identified dimensions and their related indicators before adopting a data-oriented approach and performing HRA in organizations.
1. Angrave, D., Charlwood, A., Kirkpatrick, I., Lawrence, M., & Stuart, M. (2016). HR and analytics: why HR is set to fail the big data challenge. Human Resource Management Journal, 26(1). https://doi.org/10.1111/1748-8583.12090
2. Aral, S., Brynjolfsson, E., & Wu, L. (2012). Three-way complementarities: Performance pay, human resource analytics, and information technology. Management Science, 58(5). https://doi.org/10.1287/mnsc.1110.1460
3. Bassi, L. (2011). No TitleRaging debates in HR Analytics. People & Strategy, 34.
4. Bhat, Mr. S. A., & Sheikh, Dr. B. A. (2024). Impact of Digitization on Human Resources: Challenges and Opportunities. Journal of Production, Operations Management and Economics, 42. https://doi.org/10.55529/jpome.42.1.7
5. Boudreau, J. W., & Ramstad, P. M. (2002). From “Professional Business Partner” To “Strategic Talent Leader”: “What’s Next” for Human Resource Management. www.ilr.cornell.edu/CAHRS/
6. Boudreau, J. W., & Ramstad, P. M. (2003). Strategic Industrial and Organizational Psychology and the Role of Utility Analysis Models. In Handbook of Psychology. https://doi.org/10.1002/0471264385.wei1209
7. Chinenye Gbemisola Okatta, Funmilayo Aribidesi Ajayi, & Olufunke Olawale. (2024). LEVERAGING HR ANALYTICS FOR STRATEGIC DECISION MAKING: OPPORTUNITIES AND CHALLENGES. International Journal of Management & Entrepreneurship Research, 6(4), 1304–1325. https://doi.org/10.51594/ijmer.v6i4.1060
8. Chowdhury, S., Dey, P., Joel-Edgar, S., Bhattacharya, S., Rodriguez-Espindola, O., Abadie, A., & Truong, L. (2023). Unlocking the value of artificial intelligence in human resource management through AI capability framework. Human Resource Management Review, 33(1). https://doi.org/10.1016/j.hrmr.2022.100899
9. Coco, C. T., Jamison, F., & Black, H. (2011). Connecting People Investments and Business Outcomes at Lowe’s: People & Strategy, 2.
10. Etukudo, R. (2019). Strategies for Using Analytics to Improve Human Resource Management. In Technology Business School.
11. Falletta, S. v., & Combs, W. L. (2021). The HR analytics cycle: a seven-step process for building evidence-based and ethical HR analytics capabilities. Journal of Work-Applied Management, 13(1). https://doi.org/10.1108/JWAM-03-2020-0020
12. Fitz-enz, J. (2010). Advance Praise for The New HR Analytics.
13. Fitz-enz, J., & Mattox, J. R. (2014). Predictive Analytics for Human Resources. In Predictive Analytics for Human Resources. https://doi.org/10.1002/9781118915042
14. Hamilton, R. H., & Sodeman, W. A. (2020). The questions we ask: Opportunities and challenges for using big data analytics to strategically manage human capital resources. Business Horizons, 63(1). https://doi.org/10.1016/j.bushor.2019.10.001
15. Handa, D., & Garima. (2014). Human Resource (HR) Analytics: Emerging Trend in HRM. International Journal of Research in Commerce & Management, 5(6).
16. Huang, X., Yang, F., Zheng, J., Feng, C., & Zhang, L. (2023). Personalized human resource management via HR analytics and artificial intelligence: Theory and implications. Asia Pacific Management Review, 28(4). https://doi.org/10.1016/j.apmrv.2023.04.004
17. Idowu Sulaimon Adeniyi, Nancy Mohd Al Hamad, Ololade Elizabeth Adewusi, Chika Chioma Unachukwu, Blessing Osawaru, Chisom Nneamaka Onyebuchi, Samuel Ayodeji Omolawal, Aderonke Omotayo Aliu, & Isiah Oden David. (2024). Educational reforms and their impact on student performance: A review in African Countries. World Journal of Advanced Research and Reviews, 21(2). https://doi.org/10.30574/wjarr.2024.21.2.0490
18. Lawler III, E. E., Levenson, A., & Boudreau, J. W. (2004). HR Metrics and Analytics: Uses and Impact. Human Resource Planning, 27(4).
19. Levenson, A. (2011). Using Targeted Analytics to Improve Talent Decisions. People & Strategy, 34(2).
20. Levenson, A., & Fink, A. (2017). Human capital analytics: too much data and analysis, not enough models and business insights. In Journal of Organizational Effectiveness (Vol. 4, Issue 2). https://doi.org/10.1108/JOEPP-03-2017-0029
21. Lydgate, X. K. M. (2018). Human Resource Analytics: Implications for Strategy Human Resource Analytics: Implications for Strategy Realization and Organizational Performance Realization and Organizational Performance. University Honors Theses. Paper 508.
22. Madsen, D. Ø., & Slåtten, K. (2017). The Rise of HR Analytics: A Preliminary Exploration. Global Conference on Business and Finance Proceedings, 1.
23. Margherita, A. (2022). Human resources analytics: A systematization of research topics and directions for future research. Human Resource Management Review, 32(2). https://doi.org/10.1016/j.hrmr.2020.100795
24. Muscalu, E., & Serban, A. (2014). HR Analytics For Strategic Human Resource Management. The 8th International Management Conference “Management Challenges For Sustainable Development”, November 6th-7th, 2014, Bucharest, Romania.
25. Narula, S. (2015). HR Analytics: Its use, techniques and impact. International Journal of Research in Commerce & Management, 6(8).
26. Pan, Y., Froese, F., Liu, N., Hu, Y., & Ye, M. (2022). The adoption of artificial intelligence in employee recruitment: The influence of contextual factors. International Journal of Human Resource Management, 33(6). https://doi.org/10.1080/09585192.2021.1879206
27. Pape, T. (2016). Prioritising data items for business analytics: Framework and application to human resources. European Journal of Operational Research, 252(2). https://doi.org/10.1016/j.ejor.2016.01.052
28. Rasmussen, T., & Ulrich, D. (2015). Learning from practice: How HR analytics avoids being a management fad. Organizational Dynamics, 44(3). https://doi.org/10.1016/j.orgdyn.2015.05.008
29. Rigamonti, E., Gastaldi, L., & Corso, M. (2024). Measuring HR analytics maturity: supporting the development of a roadmap for data-driven human resources management. Management.
30. Spahic, J. (2015). Exploring HR intelligence practices in Fortune 1000 and select global firms Drexel University].
31. Song, G. R., & Kim, K. S. (2020). Review and Suggestions of HR Analytics. Korean Academy of Organization and Management, 44(4). https://doi.org/10.36459/jom.2020.44.4.129
32. Turner, P., & Zytkowiak, W. (2016). HR Analytics — Turbo Charged or Stuck in Neutral? International Journal of HRD Practice, Policy and Research, 1(2).
33. Tursunbayeva, A., Pagliari, C., di Lauro, S., & Antonelli, G. (2022). The ethics of people analytics: risks, opportunities and recommendations. In Personnel Review (Vol. 51, Issue 3). https://doi.org/10.1108/PR-12-2019-0680
34. van den Heuvel, S., & Bondarouk, T. (2017). The rise (and fall?) of HR analytics: A study into the future application, value, structure, and system support. Journal of Organizational Effectiveness, 4(2). https://doi.org/10.1108/JOEPP-03-2017-0022
35. van der Togt, J., & Rasmussen, T. H. (2017). Toward evidence-based HR. Journal of Organizational Effectiveness, 4(2). https://doi.org/10.1108/JOEPP-02-2017-0013
36. Vargas, R., Yurova, Y. v., Ruppel, C. P., Tworoger, L. C., & Greenwood, R. (2018). Individual adoption of HR analytics: a fine grained view of the early stages leading to adoption.InternationalJournal of Human Resource Management, 29(22). https://doi.org/10.1080/09585192.2018.1446181