پیوندهای مفید
اخبار و اعلانات
آمار
| تعداد نشریات | 49 |
| تعداد شمارهها | 1,311 |
| تعداد مقالات | 11,345 |
| تعداد مشاهده مقاله | 24,246,298 |
| تعداد دریافت فایل اصل مقاله | 16,189,573 |
واکاوی الگوی پیشایندها و پسایندهای تحلیلگری منابع انسانی در سازمانهای دولتی با رویکرد فراترکیب | ||
| مدیریت سازمانهای دولتی | ||
| مقالات آماده انتشار، پذیرفته شده، انتشار آنلاین از تاریخ 19 شهریور 1405 اصل مقاله (2.26 M) | ||
| نوع مقاله: اکتشافی | ||
| شناسه دیجیتال (DOI): 10.30473/ipom.2026.78362.5361 | ||
| نویسندگان | ||
| علی رحیم پور1؛ مهدی محمدی* 2؛ سید علی موسوی3؛ مهدی صفیان4 | ||
| 1دانشجوی دکتری، گروه مدیریت سرمایه انسانی، دانشکده مدیریت و برنامهریزی دانشگاه جامع امام حسین(ع)، تهران، ایران. | ||
| 2استادیار، مرکز علم و فناوری مدیریت سرمایه انسانی، دانشکده مدیریت و برنامهریزی، دانشگاه جامع امام حسین(ع) تهران، ایران. | ||
| 3استادیار، مرکز علم و فناوری مدیریت سرمایه انسانی، دانشکده مدیریت و برنامهریزی، دانشگاه جامع امام حسین(ع) تهران ، ایران. | ||
| 4استادیار، مرکز علم و فناوری مدیریت سرمایه انسانی، دانشکده مدیریت و برنامهریزی دانشگاه جامع امام حسین(ع)، تهران، ایران. | ||
| چکیده | ||
| هدف: هدف پژوهش حاضر، شناسایی و تبیین پیشایندها و پسایندهای تحلیلگری منابع انسانی و ارائه الگویی مفهومی بر پایه رویکرد فراترکیب، همراه با ارائه دلالتهای سیاستی و اجرایی برای سازمانهای دولتی است. روش: این پژوهش، با رویکردی کیفی به بررسی مقالات مرتبط با این موضوع پرداخته و ابعاد مختلف آن موردبررسی قرارگرفته است. بر این مبنا پیشایندها و پسایندهای این پدیده شناسایی و در مدلی جامع ارائهشدهاند. جامعه آماری پژوهش حاضر مقالات معتبر در خصوص تحلیل گری منابع انسانی است که از این میان مقالات متناسب با پروتکل رویکرد فراترکیب غربالگری و با استفاده از چارچوب پریسما (77 مقاله منتخب) مورد مطالعه قرار گرفتند. شاخص کاپا برابر با مقدار 0.714 به دست آمد که نشاندهنده اعتبار کدهای استخراج شده بوده است. یافتهها: از طریق دستهبندی مؤلفهها (مضامین فرعی) شناساییشده مرتبط با هم در قالب ابعاد (مضامین اصلی)، نسبت به مدلسازی نهایی اقدام شد. مضامین اصلی به 3 دسته پیشایندها، پدیده محوری و پیامدهای تحلیلگری منابع انسانی تقسیمبندی شدند. بر اساس تحلیلهای صورت گرفته، از مجموع تمهای اولیه 452 کد اولیه، این تمهای اولیه به 29 کد پایه و 7 کد سازمان دهنده تحت محوریت 3 تم فراگیر دستهبندی شدند. در زمینه پیشایندها، به سه دسته کلی زیرساختهای منابع انسانی، زیرساختهای سازمانی و زیرساختهای تکنولوژیکی و پیامدها، در سه بخش پیامدهای فردی، منابع انسانی و سازمانی دستهبندی شدند. نتیجهگیری: ترکیب زیرساختهای سهگانه، موفقیت تحلیلگری منابع انسانی را تضمین میکند. سازمانهای دولتی باید بر فرهنگ داده محور، آموزش تحلیلگران و همسویی استراتژیک تمرکز کنند. | ||
| کلیدواژهها | ||
| تحلیلگری منابع انسانی؛ تحلیلگری سیستم منابع انسانی؛ تحلیلگری توصیفی؛ تحلیلگری تشخیصی؛ تحلیلگری پیشگویانه | ||
| عنوان مقاله [English] | ||
| Exploring the Antecedent and Consequent Pattern of Human Resource Analytics in Public Organizations: A Meta-Synthesis Approach | ||
| نویسندگان [English] | ||
| Ali Rahimpour1؛ Mahdi Mohammadi2؛ Sayed Ali Mousavi3؛ Mehdi Safian4 | ||
| 1Ph.D. Student, Department of Human Capital Management, Faculty of Management, Imam Hossein University, Tehran, Iran. | ||
| 2Assistant Professor, Department of Human Resource Management. Faculty of Management, Imam Hossein University, Tehran, Iran. | ||
| 3Assistant Professor, Department of Human Resource Management. Faculty of Management, Imam Hossein University, Tehran, Iran. | ||
| 4Assistant Professor, Department of Human Resource Management. Faculty of Management, Imam Hossein University, Tehran, Iran. | ||
| چکیده [English] | ||
| Introduction Public organizations operate in an environment characterized by increasing pressure for transparency, accountability, efficiency, and service quality. At the same time, they often face severe resource constraints, rigid administrative structures, and fragmented information systems. In such conditions, human resource (HR) decisions that rely primarily on intuition, routine practices, or incomplete information may lead to inefficiency, reduced workforce effectiveness, and weakened organizational responsiveness. As public organizations seek to improve performance and build stronger capabilities for evidence-based management, human resource analytics has emerged as a strategic approach for transforming HR data into actionable insight. Human resource analytics goes beyond simple reporting or descriptive statistics. It enables organizations to collect, integrate, analyze, and interpret HR-related data in ways that support better decisions about recruitment, development, retention, performance management, and workforce planning. In the public sector, where decisions must also reflect legal requirements, fairness, public accountability, and administrative transparency, the adoption of HR analytics may provide significant value. However, despite growing attention to analytics in management and human resources, the literature remains fragmented, and relatively little research has systematically examined the antecedents and consequences of HR analytics in public organizations. This study addresses that gap by synthesizing previous research and developing an integrated conceptual model of human resource analytics in public organizations. Specifically, the study seeks to identify the key organizational, human, and technological conditions that support HR analytics, as well as the individual, HR-related, and organizational outcomes associated with its implementation. By doing so, the study aims to provide both theoretical clarity and practical guidance for public sector managers and policy makers. Mothodology This research employed a qualitative meta-synthesis approach. Meta-synthesis was selected because it allows the integration of findings from multiple qualitative and conceptual studies in order to produce a more comprehensive and higher-level understanding of a phenomenon than any single study can provide. The study followed the seven-stage framework proposed by Sandelowski and Barroso. These stages included: formulating the research question, reviewing the literature, searching and selecting studies, extracting relevant information, analyzing and synthesizing findings, ensuring quality control, and presenting the final model. The search covered Persian and English scientific sources published up to 2025. In the initial search process, 133 sources were identified. After screening according to relevance, quality, and methodological criteria, 77 studies were selected for final analysis. The extracted data were analyzed using thematic analysis. Through the coding process, 452 initial codes were identified, which were then condensed into 29 basic codes, 7 organizing codes, and 3 overarching themes. To enhance the reliability of the analysis, four researchers participated in the coding and interpretation process. The level of agreement among coders was assessed using Cohen’s Kappa, which resulted in a coefficient of 0.714, indicating acceptable inter-coder reliability. The final outcome of the synthesis was a conceptual model that links the antecedents, core dimensions, and consequences of human resource analytics in public organizations. Findings show that the antecedents of human resource analytics in public organizations can be grouped into three main categories: human resource infrastructure, organizational infrastructure, and technological infrastructure. Human Resource Infrastructure The first group of antecedents concerns the human capabilities required for successful implementation of HR analytics. These include the presence of competent and professionally skilled managers, strong support from top leadership, and employees who possess the knowledge and analytical ability to work with HR data. The study also highlights the importance of specialized HR analytics experts who can interpret data, design analytical models, and translate findings into practical recommendations. Participation of managers from different units and alignment of HR analytics with organizational strategy were also identified as essential factors. Organizational Infrastructure The second group of antecedents relates to the broader organizational environment. A data-driven culture was found to be one of the most important enabling conditions. In organizations where evidence-based decision-making is valued, HR analytics is more likely to be accepted and used effectively. Other key factors include coordination among organizational units, alignment between structure and processes, reduced resistance to change, clear rules and procedures, and support from senior leadership. In public organizations, legal compliance, transparency, accountability, and organizational legitimacy are particularly important, and HR analytics must operate within these constraints. Technological Infrastructure The third group of antecedents refers to the technological capacity needed to support analytics. This includes human resource information systems, information technology infrastructure, big data capabilities, tools for data collection and integration, and analytical software. Access to high-quality data and the ability to store, process, and connect multiple data sources were repeatedly emphasized in the reviewed studies. The findings also show that organizational training on technology use and analytics applications is necessary to ensure that digital systems are used effectively and not merely installed as technical artifacts. At the center of the model is human resource analytics itself, which was described in three main levels: descriptive, predictive, and prescriptive analytics. Descriptive analytics answers the question “What happened?” by summarizing historical HR data. Predictive analytics addresses “What is likely to happen?” by identifying patterns and forecasting future developments such as turnover, staffing needs, or performance trends. Prescriptive analytics asks “What should be done?” by recommending interventions and managerial actions based on data analysis. Although diagnostic analytics was discussed in the theoretical section of the study, the final model emphasized the three levels above as the most prominent and practical dimensions of HR analytics in public organizations. The consequences of human resource analytics were identified at three levels: individual, human resource, and organizational. Individual Consequences At the individual level, HR analytics contributes to better understanding and insight, increased motivation and job satisfaction, greater transparency, stronger trust, and improved participation and cooperation. When employees perceive that decisions are based on data rather than arbitrary judgment, perceptions of fairness and credibility are strengthened. HR-Related Consequences At the HR level, the study found that analytics improves evidence-based decision-making, workforce planning, identification of skill gaps, recruitment and training processes, prediction of turnover among key employees, and the strategic role of the HR function. HR analytics helps move the HR department from a primarily administrative role toward a more strategic and value-adding position. Organizational Consequences At the organizational level, the consequences include improved strategic decision-making, higher productivity and effectiveness, better alignment between employee performance and organizational outcomes, stronger educational and developmental policies, and increased long-term value creation. In public organizations, these outcomes are especially significant because they contribute to better public service quality, greater transparency, and more effective use of public resources. Discussion and Conclusion The results of this meta-synthesis suggest that human resource analytics is not merely a technical tool, but a strategic organizational capability. Its successful implementation requires the simultaneous development of human, organizational, and technological infrastructures. Technology alone is not sufficient. Even advanced systems will fail to create value if managers lack analytical literacy, if the organizational culture resists data use, or if leadership does not support evidence-based management. The model developed in this study shows that public organizations can benefit from HR analytics only when several conditions are in place: capable leaders, trained HR professionals, integrated data systems, supportive structures, and a culture that values data-driven decision-making. In the absence of these conditions, HR analytics may remain limited to reporting and documentation, without influencing real managerial decisions. From a theoretical perspective, this study contributes to the literature by integrating fragmented findings and presenting a coherent framework that explains both the antecedents and consequences of HR analytics in public organizations. From a practical perspective, the model can guide senior managers, HR professionals, and public policy makers in designing interventions for analytics adoption. These may include training programs in data analysis, development of real-time dashboards, establishment of interdisciplinary analytics teams, regular data review meetings, and the institutionalization of evidence-based HR practices. In public organizations, special attention must also be paid to legal requirements, privacy, transparency, and fairness. HR analytics should therefore be implemented not only as a performance-improvement tool but also as a means of strengthening accountability and public trust. Overall, the study demonstrates that human resource analytics can help public organizations move from intuition-based and reactive management toward a more proactive, evidence-based, and strategic approach to workforce management. | ||
| کلیدواژهها [English] | ||
| Human Resource Analytics, Human Resource System Analytics, Descriptive Analytics, Diagnostic Analytics, Predictive Analytics | ||
| مراجع | ||
|
Ahola, E. L. (2023). People Analytics-Use of Advanced Analytics in Human Resources. (Master’s Thesis). Benbya, H., Pachidi, S., & Jarvenpaa, S. (2021). Special issue editorial: Artificial intelligence in organizations: Implications for information systems research. Journal of the Association for Information Systems, 22(2), 10. DOI:10.17705/1jais.00662 Bersin, J. (2017). Transformative Tech: A disruptive year ahead. HR Magazine, 62(1), 28-36. Betgeri, S. N., & Chekuri, N. P. (2025). Leveraging data analytics in human resource management. International Journal of Science and Research Archive, 15(01), 373-380. DOI:10.30574/ijsra.2025.15.1.1009 Boon, C., Eckardt, R., Lepak, D. P., & Boselie, P. (2018). Integrating strategic human capital and strategic human resource management. The International Journal of Human Resource Management, 29(1), 34-67. https://doi.org/10.1080/09585192.2017.1380063 Cappelli, P. (2019). Data science can’t fix hiring (yet). Harvard Business Review, 97(3), 56-57. Cho, W., Choi, S., & Choi, H. (2023). Human resources analytics for public personnel management: Concepts, cases, and caveats. Administrative Sciences, 13(2), 1–22. https://doi.org/10.3390/admsci13020041 Chong, D., & Shi, H. (2015). Big data analytics: a literature review. Journal of Management Analytics, 2(3), 175-201. https://doi.org/10.1007/978-3-319-08976-8_16 Cui, Z., Damiani, E., & Leida, M. (2007, February). Benefits of ontologies in real time data access. In 2007 Inaugural IEEE-IES Digital EcoSystems and Technologies Conference (pp. 392-397). IEEE. DOI:10.1109/DEST.2007.372004 Davenport, T., & Harris, J. (2017). Competing on analytics: Updated, with a new introduction: The new science of winning. Harvard Business Press. Dreichuk, M., & Sytnyk, Y. (2025). HR Analytics as a Risk Monitoring Tool in Personnel Management Systems. Economics & Education, 10(1), 22-28. DOI:10.30525/2500-946X/2025-1-3 Ebrahimi, E. and Esmaeili Mahyari, M. (2023). Evolution and Intellectual-conceptual Foundations of Research in the Field of Human Resources Analytics. Journal of Human Resource Management, 13(3), 1-25. (In Persian). Doi: 10.22034/jhrs.2023.184114 Espegren, Y. (2025). Reasons for HR analytics adoption in public sector organisations: Evidence from Swedish public administrations. Personnel Review, 54(7), 1621–1642. https://doi.org/10.1108/PR-03-2024-0219 Etukudo, R. (2019). Strategies for using analytics to improve Human Resource Management (Doctoral dissertation, Walden University). Faraj, S., Pachidi, S., & Sayegh, K. (2018). Working and organizing in the age of the learning algorithm. Information and Organization, 28(1), 62-70. https://doi.org/10.1016/j.infoandorg.2018.02.005 Fazel, M., Khaefelahi, A. A., & Danaeefard, H. (2021). Identifying the Key Factors of the Successful Implementation of Human Capital Analysis System. Journal of Research in Human Resources Management, 13(3), 247-277 (In Persian). https://dor.isc.ac/dor/20.1001.1.82548002.1400.13.3.7.5 Fitz-Enz, J. (2010). The new HR analytics: American Management Association. Fitz-Enz, J., & John Mattox, I. I. (2014). Predictive analytics for human resources. John Wiley & Sons. Ghezelbash, A. (2022). Designing and testing a data‑driven decision‑making model in human resource management with a human resource analytics approach [Doctoral dissertation, Ferdowsi University of Mashhad, Faculty of Administrative and Economic Sciences (In Persian). Ghosh, A., & Basu, B. (2020). Leveraging analytics for talent acquisition: Case of IT sector in India. Asia Pacific Journal of Information Systems, 30(4), 879-918. https://dx.doi.org/10.14329/apjis.2020.30.4.879 Gohain, A., & Saikia, A. (2021). A study on “Human resource analytics for decision making in international business machine in India and United States.”. Journal of Contemporary Issues in Business and Government, 27(5), 1081-1094. https://www.cibgp.com/index.php/1323-6903/article/view/2042 Gowda, M., & Suvarna, A. (2025). Data-Driven Decision-Making in Human Resources: Leveraging Analytics to Enhance Workforce Performance and Boost Productivity. International Journal of Research in Commerce and Management Studies, 7, 33-55. DOI: https://doi.org/10.38193/IJRCMS.2025.7204 Holsapple, C., Lee-Post, A., & Pakath, R. (2014). A unified foundation for business analytics. Decision support systems, 64, 130-141. https://doi.org/10.1016/j.dss.2014.05.013 Hota, J., & Ghosh, D. (2013). Workforce analytics approach: An emerging trend of workforce management. AIMS International Journal, 7(3), 167-179. Isson, J. P., & Harriott, J. S. (2016). People analytics in the era of big data: Changing the way you attract, acquire, develop, and retain talent. John Wiley & Sons. Kangasmiaki, M. (2020). People Analytics Adoption: Institutional Logics Perspective (Master's thesis). Kiran, V. S., Shanmugam, V., Raju, R. K., & Kanagasabapathy, J. R. (2022). Impact of Human Capital Management on Organizational Performance with the Mediation Effect of Human Resource Analytics. International Journal of Professional Business Review, 7(3), e0667. https://doi.org/10.26668/businessreview/2022.v7i3.0667 Król, K., & Zdonek, D. (2020). Analytics maturity models: An overview. Information, 11(3), 142. https://doi.org/10.3390/info11030142 Lunsford, D. L., & Phillips, P. P. (2018). Tools used by organizations to support human capital analytics. Performance Improvement, 57(3), 6-15. https://doi.org/10.1002/pfi.21767 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. DOI: 10.1080/09585192.2016.1244699 McCartney, S., & Fu, N. (2022). Bridging the gap: Why, how and when HR analytics can impact organizational performance. Management Decision, 60(13), 25-47. https://doi.org/10.1108/MD-12-2020-1581 Milligan, S. (2017). HR-Then and Now-6 Trends that have shaped the workplace-and HR-over the past decade. HR Magazine, 62(6), 38. Mishra, D., Luo, Z., & Hazen, B. T. (2018). The role of informational and human resource capabilities for enabling diffusion of big data and predictive analytics and ensuing performance. Innovation and Supply Chain Management: Relationship, Collaboration and Strategies, 283-302. https://doi.org/10.1007/978-3-319-74304-2_13 Newman, D. T., Fast, N. J., & Harmon, D. J. (2020). When eliminating bias isn’t fair: Algorithmic reductionism and procedural justice in human resource decisions. Organizational Behavior and Human Decision Processes, 160, 149-167. Doi/10.1016/j.obhdp.2020.03.008 Nijjer, S., & Raj, S. (2020). Predictive analytics in human resource management: a hands-on approach. Taylor & Francis. https://doi.org/10.4324/9781003026822 OECD. (2023). Government at a glance 2023. OECD Publishing. https://doi.org/10.1787/3d5c5d31-en Organisation for Economic Co-operation and Development. (2025). Workforce insights from central governments: Findings of the 2024 OECD/EU Survey of Public Servants. OECD Publishing. Rousseau, D. M., & Barends, E. G. (2011). Becoming an evidence‐based HR practitioner. Human Resource Management Journal, 21(3), 221-235. DOI: 10.1111/j.1748-8583.2011.00173.x Salehi, S. H. (2022). Identifying the competencies required for human resource managers responsible for implementing data-driven human resource management (Doctoral dissertation). University of Tehran (In Persian). Sandelowski, M., & Barroso, J. (2007). Handbook for Synthesizing Qualitative research. New York: Springer. Schafheitle, S., Weibel, A., Ebert, I., Kasper, G., Schank, C., & Leicht-Deobald, U. (2020). No stone left unturned? Toward a framework for the impact of datafication technologies on organizational control. Academy of Management Discoveries, 6(3), 455-487. DOI: 10.5465/amd.2019.0002 Sharif, Y. M. (2015). Research development in human resources analytics in Malaysia. Full Paper Proceeding, 1, 109-113. Sheng, J., Amankwah-Amoah, J., & Wang, X. (2017). A multidisciplinary perspective of big data in management research. International Journal of Production Economics, 191, 97-112. https://doi.org/10.1016/j.ijpe.2017.06.006 Sohrabi, A., Yazdani, H. R., Hakim, A. and Zarei matin,H. (2023). Presenting a Roadmap for Designing and Implementing Human Resources Analysis in Iranian Companies Using a Meta-Synthesis Approach. Journal of Human Resource Management, 13(1), 1-25. (In Persian). Doi: 10.22034/jhrs.2023.362999.1989 Sousa, M. J. (2018, October). HR analytics models for effective decision-making. In ECMLG 2018 14th European Conference on Management, Leadership and Governance (p. 256). Academic Conferences and publishing limited. Strich, F., Mayer, A. S., & Fiedler, M. (2021). What do I do in a world of artificial intelligence? Investigating the impact of substitutive decision-making AI systems on employees’ professional role identity. Journal of the Association for Information Systems, 22(2), 9. https://doi.org/10.17705/1jais.00663 Tursunbayeva, A., Di Lauro, S., & Pagliari, C. (2018). People analytics—A scoping review of conceptual boundaries and value propositions. International Journal of Information Management, 43, 224-247. https://doi.org/10.1016/j.ijinfomgt.2018.08.002 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: People and Performance, 4(2), 157-178. https://doi.org/10.1108/JOEPP-03-2017-0022 Vidgen, R., Shaw, S., & Grant, D. B. (2017). Management challenges in creating value from business analytics. European Journal of Operational Research, 261(2), 626-639. DOI: 10.1016/j.ejor.2017.02.023 Von Krogh, G. (2018). Artificial intelligence in organizations: New opportunities for phenomenon-based theorizing. Academy of Management Discoveries. Von Krogh, G. (2018). Artificial intelligence in organizations: New opportunities for phenomenon-based theorizing. Academy of Management discoveries, 4(4), 404-409. DOI:10.5465/AMD.2018.0084 Waters, S. D., Streets, V., McFarlane, L., & Johnson-Murray, R. (2018). The practical guide to HR analytics: Using data to inform, transform, and empower HR decisions. Kogan Page Publishers. Zeng, L., Xu, L., Shi, Z., Wang, M., & Wu, W. (2006, October). Techniques, process, and enterprise solutions of business intelligence. In 2006 IEEE international conference on systems, man and cybernetics (Vol. 6, pp. 4722-4726). IEEE.DOI:10.1109/ICSMC.2006.385050 | ||
|
آمار تعداد مشاهده مقاله: 5 |
||