عوامل مؤثر بر بلوغ مدیریت دانش هوشمند مبتنی بر تحلیل‌های پیش‌بین در یک سازمان بیمه‌گر اجتماعی

مهدی  سماواتی; محمدرضا ثنائی; دانیال  صابر سمیعی

Authors

Mehdi Samavati Department of Information Technology Management, Qa.C., Islamic Azad University, Qazvin, Iran Author https://orcid.org/0000-0003-1917-365X
Mohammad Reza Sanae Department of Information Technology Management, Qa.C., Islamic Azad University, Qazvin, Iran https://orcid.org/0000-0001-5356-0894
Danial Saber Samie Department of Public Administration, Qa.C., Islamic Azad University, Qazvin, Iran Author https://orcid.org/0009-0006-2261-569X

Keywords:

smart knowledge management, predictive analytics, maturity model, social insurance, artificial neural network, data envelopment analysis

Abstract

This study aimed to identify the factors affecting intelligent knowledge management maturity based on predictive analytics and to develop and validate a model for measuring and predicting maturity in the Social Insurance Fund for Farmers, Villagers, and Nomads. This applied and developmental study employed a sequential exploratory mixed-methods design. In the qualitative phase, a meta-synthesis of previous studies was conducted using the seven-stage Sandelowski and Barroso framework, followed by semi-structured interviews with experts. Qualitative data were analyzed through open, axial, and selective coding. In the quantitative phase, the extracted components were screened using fuzzy Delphi with 15 experts and weighted through the Best-Worst Method. The final questionnaire was administered to 120 managers and senior experts, and a weighted aggregate maturity score was calculated. XGBoost and Random Forest algorithms were used to predict maturity, and Monte Carlo simulation with 10,000 iterations was applied for future scenario analysis. Five dimensions were identified: strategic context and intelligent leadership, technology maturity and data integration, analytical maturity and predictability, human capital and knowledge-oriented culture, and performance evaluation and risk management. Analytical maturity and predictability received the highest weight (0.310). The overall maturity score was 2.636, indicating an intermediate maturity level, whereas analytical maturity had a mean score of 1.90 and remained at the aware level. XGBoost outperformed Random Forest, with RMSE=0.15, MAE=0.12, and R²=0.86. Monte Carlo simulation indicated that a 30% increase in analytical investment would raise the probability of reaching the advanced maturity level by 1407 to 82%. Intelligent knowledge management maturity in a social insurance organization depends on the integrated development of strategic leadership, data infrastructure, analytical capabilities, human capital, and performance evaluation, with predictive analytics and data integration representing the most critical levers for maturity advancement.

Downloads

Download data is not yet available.

References

Areed, S., Salloum, S. A., & Shaalan, K. (2021). The Role of Knowledge Management Processes for Enhancing and Supporting Innovative Organizations: A Systematic Review. In M. Al-Emran, K. Shaalan, & A. E. Hassanien (Eds.), Recent Advances in Intelligent Systems and Smart Applications (pp. 143-161). Springer International Publishing. https://doi.org/10.1007/978-3-030-47411-9_8

Carneiro, A. (2010). The Role of Intelligent Resources in Knowledge Management. Journal of Knowledge Management. https://journals.atu.ac.ir/article_12318.html?lang=en

Chen, G., Zhang, J., Zhang, S., & Yan, B. (2021). Customer knowledge management competence evaluation of tourism enterprises based on ahp-fuzzy comprehensive evaluation method. Journal of Intelligent & Fuzzy Systems, Preprint. https://doi.org/10.3233/JIFS-189902

Cheng, X., & Tian, J. (2026). Integrating Big Data and Knowledge Management for Intelligent Risk Prediction in Engineering Projects. International Journal of Knowledge Management, 22(1), 1-18. https://doi.org/10.4018/ijkm.402017

Hosseinzadeh, A., & Cheraghi, Z. (2025). The role of artificial intelligence in organizational learning and cognitive knowledge management: A systematic review. Interdisciplinary Information Science Studies, 2(1), 51-62.

Husayn, E. (2025). The Impact of Artificial Intelligence on Knowledge Management: Faculty Perspectives From the University of Zawia’s Faculties of Economics, Management, and Law. Jetti, 3(1), 52-66. https://doi.org/10.61227/jetti.v3i1.163

Kharashadizadeh, M. H., Mehrmanesh, H., & Haghighat Monfared, J. (2018). Designing a Knowledge-Sharing Model in Projects using an Analytical Approach. Intelligent Business Management Studies, 7(26), 161-191.

Lyapina, S. Y., Degtyareva, V. V., & Tarasova, V. N. (2021). Intelligent technologies for knowledge management at a modern company. In Digital Economy and the New Labor Market: Jobs, Competences and Innovative HR Technologies (pp. 459-469). https://doi.org/10.1007/978-3-030-60926-9_59

Nikmanesh, S., & Ehsanbakhsh, S. (2024). The Effect of Effective and Dynamic Capabilities on the Digital Innovation Development Process of Knowledge-Based Companies with the Moderating Role of Social Media Use. Journal of System Management and Intelligent Innovation, 6(3).

Rezaei, M. (2025). Artificial Intelligence in Knowledge Management: Identifying and Addressing the Key Implementation Challenges. Technological Forecasting and Social Change, 217, 124183. https://doi.org/10.1016/j.techfore.2025.124183

Rezazadeh Mehri Zadeh, M. H., & et al. (2010). Tools for Knowledge Management. Intelligent Signal Processing Research Institute.

Sehat Barmacheh, A., Shamakhy, H., & Yalfani, A. (2025). The Effect of Factors Based on Artificial Intelligence Components (Intelligence, Design, and Choice) on Auditors' Judgment. Management Accounting and Auditing Knowledge, 16(61), 265-278.

Sehat Barmacheh, A., Shamakhy, H., & Yalfani, A. (2026). Presenting an Artificial Intelligence-Based Audit Judgment Model. Management Accounting and Auditing Knowledge, 15(60), 191-210.

Shabani, M., Rafati Asl, S. M., & Sohrabi, S. (2022). Possibility of measuring the establishment of knowledge management in the intelligent technology organization. journal of Information and communication Technology in policing, 2(8), 67-84. https://www.magiran.com/paper/2423361

Shafiee, Z., & Fazlollahi Ghomshi, S. (2019). Relationship of knowledge management and intelligent leadership with the organizational learning. New Approach in Educational Sciences, 1(1), 19-31. https://journal.iocv.ir/article_100203.html?lang=en

Shafiei, Z., & Fazlollahi Qomshaei, S. (2019). The Relationship Between Knowledge Management and Intelligent Leadership with Organizational Learning. A New Approach in Educational Sciences, 1(1), 19-31. https://journal.iocv.ir/article_100203.html

Taheri Lari, M., Rafati, M., & Ajami Khales, A. (2025). The Effect of Artificial Intelligence on Organizational Transformation with the Mediating Role of Individual Creativity, Organizational Innovation, and Knowledge Management. First National Conference on Human Capital Management Challenges in Large-Scale Organizations, Mashhad.

Vedapradha, R., Ravi, H., Elango, S., Divyashree, V. R., Jaganathan, A. T., & Ponnusamy, M. (2024). Future of Knowledge Management in Investment Banking: Role of Personal Intelligent Assistants. Methodological Innovations, 17(4), 229-247. https://doi.org/10.1177/20597991241287118

Zeinali, S. (2016). The role of knowledge management in business process management. In First International Conference on New Paradigms of Intelligent Business and Organizational Management,

Zhang, L., & Huang, S. (2020). New Technology Foresight Method Based on Intelligent Knowledge Management. Frontiers of Engineering Management, 7(2), 238-247. https://doi.org/10.1007/s42524-019-0062-z