Interpretive Structural Modeling of the Drivers of Non-Elite Selection in Iranian Governmental Organizations
Keywords:
Interpretive Structural Modeling, Elite Selection, Governmental OrganizationAbstract
The objective of the present study is to develop an interpretive structural model of the drivers of non-elite selection in Iranian governmental organizations. This study falls within the category of mixed-methods research designs. In terms of purpose, the research is applied, and with respect to data collection, it is descriptive, employing a qualitative–quantitative approach. Initially, a list of drivers was identified through content analysis and semi-structured interviews with experts, then screened using the Delphi method, and subsequently stratified using the interpretive structural modeling (ISM) approach. The participants in the thematic analysis phase consisted of 15 academic experts and managers of governmental organizations. In the Delphi analysis and interpretive structural modeling phase, 12 academic experts and human resource managers from governmental organizations participated, who were selected using purposive sampling. The instrument used in the qualitative phase was semi-structured interviews; in the Delphi phase, an expert-based questionnaire was employed; and in the interpretive structural modeling phase, an ISM questionnaire was utilized. In this study, 15 key factors were identified and classified into six hierarchical levels: Level 1 (outcome factors: elite migration and intra-organizational distrust), Level 2 (elite-averse managerial fear and resistance to change), Level 3 (weak evaluation system, preference for personal relationships, and anti-elite managerial attitudes), Level 4 (inefficient employment structure and traditional recruitment processes), Level 5 (influence of powerful interest groups, lack of meritocracy, and centralized decision-making), and Level 6 (root factors: weak governance support, anti-elite public culture, and external political pressures). This model illustrates the causal relationships among these factors, and its innovation compared to previous studies lies in integrating internal and external factors to support comprehensive policymaking.
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[1] M. Dabic, J. Maley, M. Moeller, and B. Vlačic, "Embodiment of talent management within entrepreneurship: A Bibliometric approach," in Contemporary talent management: A research companion, I. Tarique Ed., 1st ed. New York: Routledge, 2021.
[2] B. Lanvin and F. Monteiro, "The Global Talent Competitiveness Index 2023: What a Difference a Decade Makes," INSEAD, 2023.
[3] L. Marinelli, A. Cioli, and G. L. Gregori, "Training, Reskilling, Recruiting: The Future of Work in the Age of Generative AI," Emerald Publishing Limited, 2025, ch. The Generative AI Impact: Reframing Innovation in Society 5.0, pp. 237-256.
[4] R. Sharifi, M. Zolghadr, and F. Jafari, "Pathological analysis of the Islamic Republic of Iran's policies regarding the recruitment and retention of elites and top talents," (in Persian), International Relations Research, vol. 11, no. 1, pp. 345-369, 2021.
[5] A. Skuza, H. Scullion, and A. McDonnell, "An analysis of the talent management challenges in a post-communist country: the case of Poland," The International Journal of Human Resource Management, vol. 24, no. 3, pp. 453-470, 2012, doi: 10.1002/hrm.3930240406.
[6] C. Foster, R. Oster, S. Shrestha, and B. Hidalgo, "Evaluation of Recruitment Methodologies for Under-Represented Adolescent Populations in Genetic and Epigenetic Studies of Type 2 Diabetes," Journal of Clinical and Translational Science, vol. 9, no. s1, pp. 95-95, 2025, doi: 10.1017/cts.2024.944.
[7] H. Okati, "The Role of Artificial Intelligence in Improving Recruitment and Selection Processes in Public Sector Organizations," Management Strategies and Engineering Sciences, vol. 7, no. 1, pp. 15-23, 2025, doi: 10.61838/msesj.7.1.3.
[8] M. F. N. Adillah, S. Suakanto, and N. I. Utama, "Implementation of Machine Learning-Based Classification Model in Employee Recruitment Decision Prediction," Journal La Multiapp, vol. 6, no. 2, pp. 328-339, 2025, doi: 10.37899/journallamultiapp.v6i2.2050.
[9] H. Alvandi and M. Mohammad-Mazaheri, "Analysis of managers' elite-avoidance strategies in Iranian government organizations," (in Persian), Management Research in Iran, vol. 24, no. 3, pp. 117-142, 2020.
[10] M. R. Dalvi, "Designing a Model for Managing the Organizational Behavior of Difficult Employees," Digital Transformation and Administration Innovation, vol. 2, no. 3, pp. 8-16, 2024, doi: 10.61838/dtai.2.3.2.
[11] H. Lea and J. C. Becker, "Exposure to Neoliberalism Increases Resentment of the Elite via Feelings of Anomie and Negative Psychological Reactions," The Social Psychology of Neoliberalism, vol. 75, no. 1, pp. 113-133, 2019, doi: 10.1111/josi.12311.
[12] W. Davies, "Elite power under advanced neoliberalism," Theory, Culture & Society, vol. 34, no. 5, pp. 227-250, 2017, doi: 10.1177/0263276417715072.
[13] B. Panahi, "Futures studies of talent management in Iranian government organizations," (in Persian), Management of Government Organizations, vol. 10, no. 1, pp. 139-154, 2021.
[14] F. Pazhoohan, M. Sultan-Hosseini, S. Naderian-Jahromi, and M. Jahanian, "Presenting a dynamic talent management model for forward-looking policymaking (Case Study: Ministry of Sports and Youth)," (in Persian), Sport Management, vol. 16, no. 1, pp. 57-74, 2024.
[15] H. Sharifpour and A. Safaei Ghadikolaei, "A Roadmap for Deploying Industry 4.0 Technologies in Selected Food Industries Using the Interpretive Structural Modeling (ISM) Technique," Transactions on Data Analysis in Social Science, vol. 2, no. 2, pp. 112-122, 2020, doi: 10.47176/TDASS.2020.112.
[16] M. S. Sajjadi, "Causes and consequences of elite migration," (in Persian), Economic Security Monthly, vol. 12, no. 2, pp. 51-66, 2024.
[17] T. N. Mmatabane, L. P. Dachapalli, and C. M. Schultz, "The future of talent management in the City of Tshwane Metropolitan Municipality," SA Journal of Human Resource Management, vol. 21, no. 0, p. a2386, 2023, doi: 10.4102/sajhrm.v21i0.2386.
[18] J. Voros, "Big History and Anticipation," in Handbook of Anticipation, R. Poli Ed. Cham: Springer, 2019, pp. 425-464.
[19] Y. Strengers, S. Pink, and L. Nicholls, "Smart energy futures and social practice imaginaries: Forecasting scenarios for pet care in Australian homes," Energy Research & Social Science, vol. 48, pp. 108-115, 2019, doi: 10.1016/j.erss.2018.09.015.
[20] G. Ghadirinejad, M. Rajab Beigi, and A. Golami, "Scenario Development of Talent Management System in Iran's National Oil Products Distribution Company," Journal of System Management, vol. 9, no. 4, 2023. [Online]. Available: https://www.magiran.com/p2642488.
[21] S. Shahi, E. Khajeh-Koulaki, Y. Mehr-Alizadeh, and M. Marashi, "Scenarios for Iranian teacher recruitment and training in the horizon of 2036," (in Persian), Journal of Iran Futures Studies, vol. 6, no. 2, pp. 137-166, 2021.
[22] M. Olkiewicz, "Quality improvement through foresight methodology as a direction to increase the effectiveness of an organization," Contemporary Economics, vol. 12, no. 1, pp. 69-81, 2018.
[23] R. Bendor, E. Eriksson, and D. Pargman, "Looking backward to the future: On past-facing approaches to futuring," Futures, vol. 125, pp. 102-666, 2021.
[24] R. Challa, M. D. Parne, M. Srinivas, and N. Shravya, "Crafting code keepers: An in-depth exploration of talent management strategies for sustainable employee retention in the software industry," MATEC Web of Conferences, vol. 392, p. 01053, 2024, doi: 10.1051/matecconf/202439201053.
[25] G. R. Ghadirinezhad, M. Rajab-Beigi, and A. Gholami, "Identifying effective components in talent management using thematic analysis in the National Iranian Oil Products Distribution Company," (in Persian), Strategic Studies in the Oil and Energy Industry, vol. 16, no. 62, 2024.
[26] F. Sohrabi, N. Babaeiyan Jelodar, and N. Bagheri, "Evaluation of Fertility Restorer Genotypes Using Multivariate Statistical Methods," Transactions on Data Analysis in Social Science, vol. 5, no. 4, pp. 213-218, 2023, doi: 10.47176/TDASS.2023.213.
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Copyright (c) 2026 Enayat Mohammadpour, Shams Sadat Zahedi, Mohammad Montazeri (Author)
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