نوع مقاله : مقاله پژوهشی

نویسندگان

1 پژوهشکده توسعه و برنامه‌ریزی جهاد دانشگاهی، تبریز، ایران.

2 گروه مهندسی صنایع، دانشگاه آزاد اسلامی، واحد تهران مرکزی، تهران،ایران.

3 گروه مدیریت صنعتی، دانشکده‌ی مدیریت، دانشگاه تهران، تهران، ایران.

چکیده

هدف: شرکت‌های بیمه علاوه بر ایفای نقش مهمی که در ایجاد امنیت اقتصادی و توسعه سرمایه‌گذاری دارند، خود نیز سرمایه‌گذاری می‌کنند. صنعت بیمه کشور به‌عنوان یکی از مؤسسات مالی کشور از جایگاه ویژه‌ای در فرایند سرمایه‌گذاری برخوردار است و توجه ویژه به سیاست‌های سرمایه‌گذاری مناسب درزمینهٔ صنعت بیمه ضروری است. به‌طوری‌که کارایی این صنعت در تخصیص بودجه موجود، سایر بخش‌های اقتصادی را تحریک می‌کند. این مطالعه به دنبال مدل‌سازی سرمایه‌گذاری در عملکرد شبکه‌های پویای شرکت‌های بیمه است.
روش‌شناسی پژوهش: در این مقاله یک مدل نوین برای سرمایه‌گذاری برای بررسی عملکرد شبکه‌ای پویای شرکت‌های بیمه در ایران طراحی شده است. مدل طراحی شده با استفاده از نرم‌افزار GAMS اجرا و خروجی‌های حاصل از مدل بر اساس روش‌ رگرسیونی تحلیل شده است. اطلاعات موردنیاز بر اساس آمار شرکت‌های بیمه در ایران بین سال‌های 1391 تا 1398 جمع‌آوری شده است.
یافته‌ها: پس از ارزیابی این واحدها، از 15 شرکت مورد ارزیابی، 6 شرکت دارای عملکرد واحد بوده و به‌عنوان شرکت‌های کارآمد معرفی شدند. میانگین کارایی شرکت‌های بیمه 0.78 و انحراف معیار 0.2 است. نتایج نشان می‌دهد که افزایش ارزش سرمایه‌گذاری‌ها به دلیل کاهش زیاد هزینه است و از نظر سرمایه و سود خالص شرکت‌ها عدد زیادی است که پتانسیل مشخص و قوی برای شرکت‌های بیمه است.
اصالت/ارزش افزوده علمی: در این مقاله مدل‌سازی سرمایه‌گذاری برای بررسی عملکرد شبکه‌ای پویای شرکت‌های بیمه در ایران انجام پذیرفته است.

کلیدواژه‌ها

عنوان مقاله [English]

Investment Modeling to Study the Performance of Dynamic Networks of Insurance Companies in Iran

نویسندگان [English]

  • Javid Ghahremani-Nahr 1
  • Hamed Nozari 2
  • Mohammad Ebrahim Sadeghi 3

1 Faculty member of ACECR, Tabriz, Iran.

2 Department of Industrial Engineering, Islamic Azad University, Central Tehran Branch, Tehran, Iran.

3 Department of Industrial Management, Faculty of Management, University of Tehran, Tehran, Iran.

چکیده [English]

Purpose: In addition to playing an important role in creating economic security and investment development, insurance companies also invest. The country's insurance industry as one of the country's financial institutions has a special place in the investment process and special attention to appropriate investment policies in the field of insurance industry is essential. So that the efficiency of this industry in allocating the existing budget stimulates other economic sectors. This study seeks to model investment in the performance of dynamic networks of insurance companies.
Methodology: In this paper, a new investment model is designed to examine the dynamic network performance of insurance companies in Iran. The designed model is implemented using GAMS software and the outputs of the model are analyzed based on regression method. The required information has been collected based on the statistics of insurance companies in Iran between 2012 and 2019.
Findings: After evaluating these units, out of 15 companies evaluated, 6 companies had unit performance and were introduced as efficient companies. The average efficiency of insurance companies is 0.78 and the standard deviation is 0.2. The results show that the increase in the value of investments is due to the large reduction in costs and in terms of capital and net profit of companies is a large number that has a clear and strong potential for insurance companies.
Originality/Value: In this paper, investment modeling is performed to examine the performance of dynamic networks of insurance companies in Iran.

کلیدواژه‌ها [English]

  • Performance appraisal
  • Dynamic modeling
  • Insurance companies
Barros, C. P., Nektarios, M., & Assaf, A. (2010). Efficiency in the Greek insurance industry. European journal of operational research205(2), 431-436. https://doi.org/10.1016/j.ejor.2010.01.011
Ecer, F., & Pamucar, D. (2021). MARCOS technique under intuitionistic fuzzy environment for determining the COVID-19 pandemic performance of insurance companies in terms of healthcare services. Applied soft computing, 104, 107199. https://doi.org/10.1016/j.asoc.2021.107199
Gaganis, C., Hasan, I., & Pasiouras, F. (2013). Efficiency and stock returns: evidence from the insurance industry. Journal of productivity analysis40(3), 429-442. https://doi.org/10.1007/s11123-013-0347-x
Ghahremani-Nahr, J. (2020). Improvement the efficiency and efficiency of the closed loop supply chain: whale optimization algorithm and novel priority-based encoding approach. Journal of decisions and operations research4(4), 299-315. DOI: 10.22105/DMOR.2020.206930.1132
Ghahremani-Nahr, J., Nozari, H., & Bathaee, M. (2021). Robust box approach for blood supply chain network design under uncertainty: hybrid moth-flame optimization and genetic algorithm. International journal of innovation in engineering1(2), 40-62. https://doi.org/10.52547/ijie.1.2.40
Hung, S. W., He, D. S., & Lu, W. M. (2014). Evaluating the dynamic performances of business groups from the carry-over perspective: A case study of Taiwan׳ s semiconductor industry. Omega, 46, 1-10. https://doi.org/10.1016/j.omega.2014.01.003
Kaffash, S., Azizi, R., Huang, Y., & Zhu, J. (2020). A survey of data envelopment analysis applications in the insurance industry 1993–2018. European journal of operational research, 284(3), 801-813. https://doi.org/10.1016/j.ejor.2019.07.034
Khodabakhsh, M. (2021). Dynamic DEA based on DMAIC model to evaluate passengers’ transportation in road transportation organization. International journal of innovation in engineering1(1), 64-76. https://doi.org/10.52547/ijie.1.1.58
Kulustayeva, A., Jondelbayeva, A., Nurmagambetova, A., Dossayeva, A., & Bikteubayeva, A. (2020). Financial data reporting analysis of the factors influencing on profitability for insurance companies. Entrepreneurship and sustainability issues7(3), 2394.
Li, Z., Li, Y., & Long, D. (2020). Research on the improvement of technical efficiency of China's property insurance industry: a fuzzy-set qualitative comparative analysis. International journal of emerging markets16(6), 1077-1104. https://doi.org/10.1108/IJOEM-01-2020-0091
Lotfi, F. H. Z., Najafi, S. E., & Nozari, H. (Eds.). (2016). Data envelopment analysis and effective performance assessment. IGI Global. http://doi:10.4018/978-1-5225-0596-9
Nahaei, V. S., & Bahrami, M. (2021). Uncertainty analysis of business components in Iran with fuzzy systems: by comparing hypermarkets and net markets. International journal of innovation in management, economics and social sciences1(1), 45-55. https://doi.org/10.52547/ijimes.1.1.45
Nourani, M., Kweh, Q. L., Devadason, E. S., & Chandran, V. G. R. (2020). A decomposition analysis of managerial efficiency for the insurance companies: a data envelopment analysis approach. Managerial and decision economics41(6), 885-901. https://doi.org/10.1002/mde.3145
Ochola, P. (2017). A two stage performance improvement evaluation of the insurance industry in Kenya: an application of data envelopment analysis and Tobit regression model. International journal of economics, commerce and management5(5), 152-170. https://ijecm.co.uk/wp-content/uploads/2017/05/5511.pdf
Peykani, P., Mohammadi, E., & Emrouznejad, A. (2021). An adjustable fuzzy chance-constrained network DEA approach with application to ranking investment firms. Expert systems with applications166, 113938. https://doi.org/10.1016/j.eswa.2020.113938
Pooser, D. M., Wang, P., & Barrese, J. (2017). A governance study of corporate ownership in the insurance industry. Journal of insurance issues, 23-60. https://www.jstor.org/stable/44160925
Salami, S., Bagherzadeh, M. R., Mehrara, A., & Matani, M. (2021). An appropriate corporate governance model at Iran insurance company. Journal of system management7(1), 265-292. https://sjsm.shiraz.iau.ir/article_681458_d77835c69928c3fca1e4eee4e90eaf19.pdf
Tone, K., Kweh, Q. L., Lu, W. M., & Ting, I. W. K. (2019). Modeling investments in the dynamic network performance of insurance companies. Omega88, 237-247. https://doi.org/10.1016/j.omega.2018.09.005
Tuan, N. (2017). Efficiency of Vietnam’s insurance market: A DEA approach. Proceedings of NIDA international business conference 2017 innovative management: bridging theory and practice (pp. 83-101). https://faq.agu.edu.vn/handle/agu_library/12576
Wang, K., Huang, W., Wu, J., & Liu, Y. N. (2014). Efficiency measures of the Chinese commercial banking system using an additive two-stage DEA. Omega44, 5-20. https://doi.org/10.1016/j.omega.2013.09.005
Wanke, P., & Barros, C. P. (2016). Efficiency drivers in Brazilian insurance: a two-stage DEA Meta frontier-data mining approach. Economic modelling53, 8-22. https://doi.org/10.1016/j.econmod.2015.11.005