مدیریت استراتژیک هوشمند

مدیریت استراتژیک هوشمند

ارائه الگوی مفهومی تحلیل منابع انسانی در گمرک جمهوری اسلامی ایران با رویکرد فراترکیب

نوع مقاله : مقاله پژوهشی

نویسندگان
گروه مدیریت دولتی، واحد تهران مرکزی، دانشگاه آزاد اسلامی، تهران، ایران
چکیده
هدف این پژوهش ارائه الگوی مفهومی تحلیل منابع انسانی در گمرک جمهوری اسلامی ایران با بهره گیری از رویکرد فراترکیب است. بر این اساس ۵۷ پژوهش پیشین مرتبط با اهداف مطالعه حاضر در بازه زمانی ۲۰۰۱ تا ۲۰۲۴ با استفاده از الگوی هفت مرحله ای سندلوسکی و باروسو بررسی گردید و به تجمیع، ترکیب و تفسیر یافته های این پژوهش ها پرداخته شد تا مدلی جامع و بدیع از تحلیل منابع انسانی در سازمان دولتی ارائه شود.شیوه تحلیل داده ها تحلیل مضمون بود که پس از شناسایی کد ها بر اساس میزان تشابه مفهومی دسته بندی و ترکیب شدند و در نهایت از روش دلفی شامل تعداد ۱۰ خبره دانشگاهی و سازمانی استفاده شد.یافته ها نشان داد مدل تحلیل منابع انسانی در گمرک ایران شامل ۷ مقوله اصلی همسویی استراتژیک ، زمینه تحلیلی ، قابلیت ، معیارها ، چالش ها ، پذیرش و نگاه به آینده در قالب ۲۰ مقوله فرعی و ۷۶ کد می باشد که الگوی تحقیق آن ترسیم شد. بنابراین می توان نتیجه گرفت تحلیل منابع انسانی رویکردی فعالانه است که با قابلیت هایی مانند بهبود مدیریت استعدادها ، منجر به ارتقاءعملکرد سازمانی و رضایت کارکنان می‌شود.
کلیدواژه‌ها

موضوعات


عنوان مقاله English

Providing a conceptual model for human resources Analytics in the Islamic Republic of Iran Customs with a meta-synthesis approach

نویسندگان English

Babak Aghavirdi
Daruosh Gholamzadeh
Ahmad Vedadi
Department of Public Administration, Central Tehran Branch, Islamic Azad University, Tehran, Iran.
چکیده English

The aim of this research is to present a conceptual model for human resource analytics in the Customs Administration of the Islamic Republic of Iran using a meta-synthesis approach. Accordingly, 57 prior studies related to the objectives of the current research, published between 2001 and 2024, were examined using Sandelowski and Barroso’s seven-stage model. The findings of these studies were aggregated, synthesized, and interpreted to propose acomprehensive and novel model for human resource analytics in a governmental organization. The data analysis method was thematic analysis, where codes were identified, categorized, and combined based on conceptual similarities. Finally, the Delphi method involving 10 academic and organizational experts was employed. The findings revealed that the human resource analysis model in Iranian Customs comprises 7 main categories: strategic alignment, analytical context, capability, criteria, challenges, acceptance, and future perspective, represented through 20 sub-categories and 76 codes, for which the research model was designed. Thus, it can be concluded that human resource analysis is a proactive approach that, through capabilities such as improving talent management, enhances organizational performance and employee satisfaction.

کلیدواژه‌ها English

Analysis
Data-driven
Human Resource Analytics
Meta-synthesis
 خنیفر،حسین و مسلمی، ناهید.(۱۳۹۵). اصول و مبانی روش‌های پژوهش کیفی.تهران،نگاه دانش.
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.
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.
مر،برنارد.(۱۴۰۰). منابع انسانی داده محور. مترجم پیران نژاد ، علی  و فرجی جبه دار، وحید ،تهران،دانشگاه تهران.
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.
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
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
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
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
Barrière, J. M. (2016). The influence of trust on attitude of employees towards HR Analytics in organisations .Master''s thesis. University of Twente. http://essay.utwente.nl/70805/1/Barriere_BA_BMS.pdf
Bechter, B., Brandl, B. and Lehr, A. (2022), “The role of the capability, opportunity, and motivation of firms for using human resource analytics to monitor employee performance: a multi-level analysis of the organisational, market, and country context”, New Technology, Work and Employment, Vol. 37 No. 3, pp. 398-424
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
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
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
Cardy, R. L., & Miller, J. S. (2005). eHR and performance management: A consideration of positive potential and the dark side. In H. G. Gueutal &D.L. Stone (Eds.), The brave new world of eHR: Human resources management in the digital age (pp. 138−165). San Francisco: Jossey Bass. http://5.202.73.55:8026/opac/temp/9982.
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
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
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
Dulebohn, J. H., and Johnson, R. D. 2013. Human resource metrics and decision support: a classification framework. Human Resource Management Review, 23, pp.71-83. https://doi.org/10.1016/j.hrmr.2012.06.005
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
Erwin, E. J., Brotherson, M. J. & Summers, J. A.,(2011). Understanding Qualitative Metasynthesis: Issues and Opportunities in Early Childhood Intervention Research.Journal of Early Intervention, 33(3), pp. 186-200. https://doi.org/10.1177/1053815111425493
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.
Fitz-enz, J., & Mattox, J. R. (2014). Predictive Analytics for Human Resources. In Predictive Analytics for Human Resources. https://doi.org/10.1002/9781118915042
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
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
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
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
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
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
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
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
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
King, K. G. (2016). Data analytics in human resources: A case study and critical review. Human Resource Development Review, 15(4), 487-495. https://doi.org/10.1177/1534484316675818
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
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
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
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
Leicht-Deobald, U., Busch, T., Schank, C., Weibel, A., Schafheitle, S., Wildhaber, I., & Kasper, G. (20۱۹). The challenges of algorithm-based HR decision-making for personal integrity. In Business and the Ethical Implications of Technology (pp. 71-86. https://doi.org/10.1007/s10551-019-04204
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
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
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.
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. Bachelor of Arts (B.A.) in Business Administration: University Honors Theses. http://archives.pdx.edu/ds/psu/24241
Magau, M. D., & Roodt, G. (2010). An evaluation of the human capital BRidgeTM framework. SA Journal of Human Resource Management, 8(1), 1-10. https://hdl.handle.net/10520/EJC95920
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
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.
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
Smith Jr, T. D. (2018). Perceptions of human resource professionals on using data analytics for talent management St. Thomas University. Submitted in Partial Fulfillment of the Requirements for the Degree of Doctor of Education in Leadership and Innovation.https://www.proquest.com/openview/180bb2ce11b741fa01136854ee577d74
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
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
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
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
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
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
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
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
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
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
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
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.
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.