شناسایی پیشایندها و پسایندهای تحلیل منابع انسانی در گمرک ایران با روش فراترکیب و دلفی فازی
الموضوعات :
بابک 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
الکلمات المفتاحية: پیشایندها , پسایندها , تحلیل منابع انسانی, فراترکیب, دلفی فازی.,
ملخص المقالة :
در چشم انداز در حال تحول منابع انسانی ، سازمان ها به طور فزاینده ای از تحلیل منابع انسانی جهت تصمیم گیری استراتژیک استفاده می کنند. برای استفاده از این قابلیت و ادغام آن در فرایند مدیریت منابع انسانی ، شناسایی عوامل و الزامات پیشایندی و همچنین نتایج حاصل از پیاده سازی تحلیل منابع انسانی اهمیت ویژه ای دارد.بنابراین ، هدف این پژوهش شناسایی پیشایندها و پسایندهای تحلیل منابع انسانی در گمرک جمهوری اسلامی با روش آمیخته اکتشافی(روش فراترکیب و دلفی فازی) بود.نمونه آماری بخش کیفی را ۱۰ نفر از خبرگان دانشگاهی و سازمانی تشکیل داده بودند و در بخش کمی نیز شامل تعداد ۲۱ نفر از خبرگان سازمان گمرک و اساتید مدیریت بودند. نتایج حاصل از فراترکیب مطالعات پیشین و دلفی فازی نشان داد پیشایندهای تحلیل منابع انسانی شامل ۳ مضمون فراگیر عوامل سازمانی ، عوامل فردی و عوامل محیطی با ۴۰ مضمون سازمان دهنده و پسایندها شامل ۳ مضمون فراگیر سازمانی ، واحد مدیریت منابع انسانی و فردی با ۲۹ مضمون سازمان دهنده می باشند.در نهایت ، نتایج پژوهش حاکی از آن است که مهمترین عوامل پیشایندی جهت انجام تحلیل منابع انسانی در سازمان مورد مطالعه، شامل استفاده از تحلیل داده در تصمیم گیری، اصلاح و ادغام فرآیندها ، توانایی تفسیر داده ها و انتظارات خدمت گیرندگان ،تعامل سازمان های مرتبط و اتصال فناوری های بین بخشی و مهمترین شاخص های پسایندی شامل افزایش سطح عدالت ادراک شده، بهبود تصمیم گیری راهبردی، افزایش سطح ابتکارات کارکنان ، ارتقا سطح بهره وری ، تحلیل سناریوهای منابع انسانی و شیوه های منابع انسانی و جبران خدمت و خدمات رفاهی شخصی شده می باشد.
منابع و مآخذ: 1. سهرابی، آرزو؛یزدانی ، حمید رضا؛حکیم،امین و زارعی متین، امین.(۱۴۰۲). ارائه نقشه‌راه طراحی و پیاده‌سازی تجزیه‌وتحلیل منابع انسانی در سازمان‌های ایرانی با رویکرد فراترکیب،۱(۱۳):۱-۲۵.
2. کاردانی ملک نژاد،مونا؛رحیم نیا ، فریبرز؛اسلامی،قاسم و فراحی ، محمد مهدی.(۱۴۰۳). نوآوری داده محور و مدیریت منابع انسانی: ارائه چارچوب بکارگیری تحلیل منابع انسانی.دوفصلنامه مدیریت منابع انسانی پایدار،۶(۱۱):۲۱۵-۲۴۱.
3. مر،برنارد.(۱۴۰۰). منابع انسانی داده محور. مترجم پیران نژاد ، علی و فرجی جبه دار، وحید ،تهران،دانشگاه تهران.
4. Adeniyi, I. S., Al Hamad, N. M., Adewusi, O. E., Unachukwu, C. C., Osawaru, B., Onyebuchi, C. N., & David, I. O. (2024). Organizational culture and leadership development: A human resources review of trends and best practices. Magna Scientia Advanced Research and Reviews, 10(1), 243-255. https://doi.org/10.30574/msarr.2024.10.1.0025.
5. Arora, M., Prakash, A., Mittal, A., & Singh, S. (2021). HR analytics and artificial intelligence-transforming human resource management. In 2021 International Conference on Decision Aid Sciences and Application (DASA) (pp. 288-293). IEEE. https://doi.org/10.1109/DASA53625.2021.9682325
6. Al Hamad, N. M., Adewusi, O. E., Unachukwu, C. C., Osawaru, B., & Chisom, O. N. (2024). Integrating human resources principles in STEM education: A review. World Journal of Advanced Research and Reviews, 21(1), 1174-1183. https://doi.org/10.30574/wjarr.2024.21.1.0116
7. Andersen, M. K. (2017). Human capital analytics: the winding road. Journal of Organizational Effectiveness: People and Performance, 4(2), 133-136. https://doi.org/10.1108/JOEPP-03-2017-0024
8. 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
9. Bodie, M. T., Cherry, M. A., McCormick, M. L., & Tang, J. (2017). The law and policy of people analytics. U. Colo. L. Rev., 88, 961. https://scholar.law.colorado.edu/lawreview/vol88/iss4/4
10. Boudreau, J., & Cascio, W. (2017). Human capital analytics: why are we not there? Journal of Organizational Effectiveness: People and Performance, 4(2), 119-126. https://doi.org/10.1108/JOEPP-03-2017-0021
11. 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
12. Cho, W., Choi, S., & Choi, H. (2023). Human Resources Analytics for Public Personnel Management: Concepts, Cases, and Caveats. Administrative Sciences, 13(2), 41. https://doi.org/10.3390/admsci13020041
13. 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
14. Coulthart, S., & Riccucci, R. (2022). Putting big data to work in government: The case of the United States border patrol. Public Administration Review, 82(2), 280-289. https://doi.org/10.1111/puar.13431
15. Elugbaju, W. K., Okeke, N. I., & Alabi, O. A. (2024). Human Resource Analytics as a Strategic Tool for Workforce Planning and Succession Management. International Journal Of Engineering Research And Development, 20(11), 744-756. https://www.ijerd.com
16. Etukudo, R. (2019). Strategies for Using Analytics to Improve Human Resource Management. In Technology Business School. Doctoral Study Submitted in Partial Fulfillment of the Requirements for the Degree of Doctor of Business Administration.https://www.proquest.com/openview/f64cb476f623ee6dbd37db9d5038e1bf
17. Fitz-enz, J., & Mattox, J. R. (2014). Predictive Analytics for Human Resources. In Predictive Analytics for Human Resources. https://doi.org/10.1002/9781118915042
18. 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
19. Giermindl, L.M., Strich, F., Christ, O., Leicht-Deobald, U. and Redzepi, A. (2022), “The dark sides of people analytics: reviewing the perils for organisations and employees”, European Journal of Information Systems, Vol. 31 No. 3, pp. 410-435. https://doi.org/10.1080/0960085X.2021.1927213
20. Green, D. (2017). The best practices to excel at people analytics. Journal of Organizational Effectiveness: People and Performance, 4(2), 137-144. https://doi.org/10.1108/JOEPP-03-2017-0027
21. 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
22. 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
23. Hughes, C., Robert, L., Frady, K., & Arroyos, A. (2019a). Artificial intelligence, employee engagement, fairness, and job outcomes. Managing Technology and Middle- and LowSkilled Employees, July, 61–68. https://doi.org/10.1108/978-1-78973-077-720191005
24. Jiang, Y., & Akdere, M. (2022). An operational conceptualization of human resource analytics: implications for in human resource development. Industrial and Commercial Training, 54(1), 183-200. https://doi.org/10.1108/ICT-04-2021-0028
25. John, J. E., & Pramila, S. (2024). Leveraging AI in HR Analytics to Foster Green Human Resource Management. In Harnessing AI, Machine Learning, and IoT for Intelligent Business: Volume 1 (pp. 1067-1074). Cham: Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-67890-5_109
26. Johnson, B. A., Coggburn, J. D., & Llorens, J. J. (2022). Artificial Intelligence and Public Human Resource Management: Questions for Research and Practice. Public Personnel Management, 51(4), 538-562. https://doi.org/10.1177/00910260221126498
27. Kravariti, F., & Johnston, K. (2020). Talent management: a critical literature review and research agenda for public sector human resource management. Public management review, 22(1), 75-95. https://doi.org/10.1080/14719037.2019.1638439
28. Kremer, K. (2018). HR analytics and its moderating factors. Vezetéstudomány-Budapest Management Review, 49(11), 62-68. https://doi.org/10.14267/VEZTUD.2018.11.07
29. Lam, S., & Hawkes, B. (2017). From analytics to action: how Shell digitized recruitment. Strategic HR Review, 16(2), 76-80. https://doi.org/10.1108/SHR-01-2017-0005
30. Lawler III, E. E., Levenson, A., & Boudreau, J. W. (2004). HR Metrics and Analytics: Uses and Impact. Human Resource Planning, 27(4). https://ceo.usc.edu
31. 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
32. Lipkin, J. (2015). Sieving through the data to find the person: HR’s imperative for balancing big data with people centricity. https://ecommons.cornell.edu
33. Llorens, J. J. (2021). Rapid advances in HRM technologies and public employment systems: A research agenda for acquiring and managing talent. Handbook of public administration, 272-281. https://files.znu.edu.ua/files/Bibliobooks/Inshi72/0053215.
34. Maity, S. (2019). Identifying opportunities for artificial intelligence in the evolution oftraining and development practices. Journal of Management Development, 38(8),651–663. https://doi.org/10.1108/JMD-03-2019-0069
35. 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.2020.10079.
36. Marler, J. H., & Boudreau, J. W. (2017). An evidence-based review of HR Analytics. The International Journal of Human Resource Management, 28(1), 3-26. https://doi.org/10.1080/09585192.2016.1244699
37. Minbaeva, D. B. (2017). Building credible human capital analytics for organizational competitive advantage. Human Resource Management, 57(3), 701-713. https://doi.org/10.1002/hrm.21848
38. Muiruri, C. M. B., Waiganjo, S. N., & Munyao, R. M. (2015). Business analytics for decision making. International Journal of Social Sciences and Information Technology, 1(12), 1-14. http://ir-library.kabianga.ac.ke/handle/123456789/678
39. 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. https://conference.management.ase.ro/archives/2014/pdf/92
40. Nawaz, N., Arunachalam, H., Pathi, B. K., & Gajenderan, V. (2024). The adoption of artificial intelligence in human resources management practices. International Journal of Information Management Data Insights, 4(1), 100208. https://doi.org/10.1016/j.jjimei.2023.100208
41. Nocker, M., & Sena, V. (2019). Big data and human resources management: The rise of talent analytics. Social Sciences, 8(10), 273.https://doi.org/10.3390/socsci8100273
42. Nowicka, J., Pauliuchuk, Y., Ciekanowski, Z., Fałda, B., & Sikora, K. (2024). The use of data analytics in human resource management. https://www.um.edu.mt/library/oar/handle/123456789/122517
43. Okatta, C. G., Ajayi, F. A., & Olawale, O. (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
44. 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
45. 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
46. Pencheva, I., Esteve, M., & Mikhaylov, S. J. (2020). Big Data and AI–A transformational shift for government: So, what next for research? Public Policy and Administration, 35(1), 24-44. https://doi.org/10.1177/09520767187805
47. Qamar, Y., & Samad, T. A. (2022). Human resource analytics: a review and bibliometric analysis. Personnel Review, 51(1), 251-283. https://doi.org/10.1108/PR-04-2020-0247
48. 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
49. Reddy, P. R., & Lakshmikeerthi, P. (2017). HR analytics–an effective evidence based HRM tool. International Journal of Business and Management Invention, 6(7), 23-34. https://www.researchgate.net/profile/Lakshmi-Keerthi/publication/313465128
50. Rigamonti, E., Gastaldi, L., & Corso, M. (2024). Measuring HR analytics maturity: supporting the development of a roadmap for data-driven human resources management. Management. https://doi.org/10.1108/MD-11-2023-2087
51. Sandelowski, M., Barroso, J. & Voils, C.I. (2006). Using qualitative metasummary to synthesize qualitative and quantitative descriptive findings. Res Nurs Health. 30(1): 99-111. https://doi:10.1002/nur.20176
52. Sharma, P., & Khan, W. A. (2022). Revolutionizing Human Resources Management with Big Data: From Talent Acquisition to Workforce Optimization. International Journal of Business Intelligence and Big Data Analytics, 5(1), 35-45. https://research.tensorgate.org/index.php/IJBIBDA/article/view/64
53. Shet, S. V., Poddar, T., Samuel, F. W., & Dwivedi, Y. K. (2021). Examining the determinants of successful adoption of data analytics in human resource management–A framework for implications. Journal of Business Research, 131, 311-326. https://doi.org/10.1016/j.jbusres.2021.03.054
54. 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
55. Sousa, M. J., Ferraz, D., Sacavém, A., & Gomes, J. S. (2022). Human Governance Analytics for Public Organizations. HR Analytics and Digital HR Practices: Digitalization post COVID-19, 217-241. https://link.springer.com/chapter/10.1007/978-981-16-7099-2_9
56. Sovova, O., & Fiala, Z. (2017). Privacy protection and e-document management in public administration. Juridical Tribune Journal Tribuna Juridica, 7(2), 17-26. https://www.ceeol.com/search/article-detail?id=597829
57. Stone, D. L., Deadrick, D. L., Lukaszewski, K. M., & Johnson, R. (2015). The influence of technology on the future of human resource management. Human Resource Management Review, 25(2), 216-231. https://doi.org/10.1016/j.hrmr.2015.01.002
58. Thakral, P., Srivastava, P. R., Dash, S. S., Jasimuddin, S. M., & Zhang, Z. (2023). Trends in the thematic landscape of HR analytics research: a structural topic modeling approach. Management Decision, 61(12), 3665-3690. https://doi.org/10.1108/MD-01-2023
59. 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
60. 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.International Journal of Human Resource Management, 29(22). https://doi.org/10.1080/09585192.2018.1446181
61. Turner, P., & Zytkowiak, W. (2016). HR Analytics — Turbo Charged or Stuck in Neutral? International Journal of HRD Practice, Policy and Research, 1(2). https://doi.org/10.22324/ijhrdppr.1.112
62. 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
63. 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-03-2017-0022Vargas
64. Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS quarterly, 425-478. https://doi.org/10.2307/30036540
65. Wang, L., Zhou, Y., Sanders, K., Marler, J.H. and Zou, Y. (2024), “Determinants of effective HR analytics implementation: an in-depth review and a dynamic framework for future research”,Journal of Business Research, Vol. 170 October 2023, 114312. https://doi.org/10.1016/j.jbusres.2023.114312.
66. Wirges, F. and Neyer, A.K. (2022), “Towards a process-oriented understanding of HR analytics:implementation and application”, Review of Managerial Science, Vol. 17 No. 6, pp. 2077-2108,https://doi.org/10.1007/s11846-022-00574-0.
