Measuring returns to scale based on the triangular fuzzy DEA approach with different views of experts: Case study of Iranian insurance companies

Mehdi Amiri; Mohsen Rostamy-Malkhalifeh; Farhad Hosseinzadeh Lotfi; Mohammadreza Mozaffari

doi:10.31181/dmame622023740

Authors

Mehdi Amiri Department of Mathematics, Science and Research Branch, Islamic Azad University, Tehran, Iran https://orcid.org/0000-0003-2305-0265
Mohsen Rostamy-Malkhalifeh Department of Mathematics, Science and Research Branch, Islamic Azad University, Tehran, Iran https://orcid.org/0000-0001-6105-7674
Farhad Hosseinzadeh Lotfi Department of Mathematics, Science and Research Branch, Islamic Azad University, Tehran, Iran https://orcid.org/0000-0001-5022-553X
Mohammadreza Mozaffari Department of Mathematics, Shiraz Branch, Islamic Azad University, Shiraz, Iran https://orcid.org/0000-0003-3160-271X

DOI:

https://doi.org/10.31181/dmame622023740

Keywords:

Fuzzy data envelopment analysis, uncertainty rate, insurance companies, returns to scale

Abstract

The importance of insurance companies in the economic growth of countries has led to them, so in this article, the efficiency of insurance companies is measured based on inputs, favorable and unfavorable outputs. The developed model, unlike the previous models, considers the unfavorable outputs of insurance companies in conditions of uncertainty with fuzzy data based on different views of experts. The required data for each of the inputs and outputs have been provided by experts in the form of triangular fuzzy numbers. The existence of different views of experts, including optimistic, likely, and pessimistic, has led to its impact on the returns to the scale of insurance companies. The results of the survey of 24 insurance companies in Iran, based on the different views of experts, show that the more optimistic the experts' view is, the higher the average return on the scale of insurance companies compared to other views. As the expert view has shifted from optimistic to pessimistic, returns to full scale for insurance companies have declined. In this way, the average return to the scale of all insurance companies is equal to 0.8972 in the optimistic view, in the probable view it is equal to 0.8863 and in the pessimistic view it is equal to 0.8336. The uncertainty rate also affects the inputs, desirable and undesirable outputs of the model, and with the increase of this rate, the desirable inputs and outputs decrease and the undesirable outputs increase. The result of this is the reduction of the average return to the scale of insurance companies with the increase of the uncertainty rate.

Downloads

Download data is not yet available.

References

Abd Aziz, N. A., Mohd Hashim, B. B., Khairil Anwar, D. A., & Fairul Rafiq, F. H. (2022). An integrated data envelopment analysis (DEA)/ fuzzy AHP/ assurance region (AR) method in measuring efficiency of life insurance companies in Malaysia / Nur Azlina, Mathematics in Applied Research, 2, 37–41.

Abdin, Z., Prabantarikso, R. M., Fahmy, E., & Farhan, A. (2022). Analysis of the efficiency of insurance companies in Indonesia. Decision Science Letters, 11(2), 105–112. https://doi.org/10.5267/j.dsl.2022.1.002

Al Omari, R., Alkhawaldeh, R. S., & Jaber, J. J. (2023). Artificial neural network for classifying financial performance in jordanian insurance sector. Economies, 11(4), 106. https://doi.org/10.3390/economies11040106

Ashiagbor, A. A., Dziwornu, R., Gbade, A. V., Offei-Kwafo, kwasi, & Liticia, G. (2023). Measuring efficiency and productivity changes: A non-parametric analysis of Ghanaian life insurance industry. Cogent Economics and Finance, 11(1). https://doi.org/10.1080/23322039.2023.2210855

Bao, N. J., Ramlan, R., Mohamad, F., & Yassin, A. M. (2018). Performance of malaysian insurance companies using data envelopment analysis. Indonesian Journal of Electrical Engineering and Computer Science, 11(3), 1147–1151. https://doi.org/10.11591/ijeecs.v11.i3.pp1147-1151

Barros, C. P., & Wanke, P. (2014). Insurance companies in mozambique: a two-stage DEA and neural networks on efficiency and capacity slacks. Applied Economics, 46(29), 3591–3600. https://doi.org/10.1080/00036846.2014.934436

Charnes, A., Cooper, W. W., & Rhodes, E. (1978). Measuring the efficiency of decision making units. European Journal of Operational Research, 2(6), 429–444. https://doi.org/10.1016/0377-2217(78)90138-8

Das, S. K. (2022). A fuzzy multi objective inventory model of demand dependent deterioration including lead time. Journal of Fuzzy Extension and Applications, 3(1), 1–18. https://doi.org/10.22105/JFEA.2021.306498.1163

Farnam, M., & Darehmiraki, M. (2021). Solution procedure for multi-objective fractional programming problem under hesitant fuzzy decision environment. Journal of Fuzzy Extension and Applications, 2(4), 364–376. https://doi.org/10.22105/JFEA.2021.288198.1152

Farnam, M., & Darehmiraki, M. (2022). Supply chain management problem modelling in hesitant fuzzy environment. Journal of Fuzzy Extension and Applications, 3(4), 317–336. https://doi.org/10.22105/JFEA.2022.337573.1216

Gharakhani, D., Toloie Eshlaghy, A., Fathi Hafshejani, K., Kiani Mavi, R., & Hosseinzadeh Lotfi, F. (2018). Common weights in dynamic network DEA with goal programming approach for performance assessment of insurance companies in Iran. Management Research Review, 41(8), 920–938. https://doi.org/10.1108/MRR-03-2017-0067

Ghosh, A., Sarkar, A., Dey, M., Guha, B., Jana, S., & Ghorui, N. (2021). Analyzing efficiency of Indian life insurance companies using DEA and SEM. Turkish Journal of Computer and Mathematics Education, 12(12), 3897–3919.

Grmanová, E., & Pukala, R. (2018). Efficiency of insurance companies in the czech republic and Poland. Oeconomia Copernicana, 9(1), 71–85. https://doi.org/10.24136/oc.2018.004

Jaloudi, M. M. (2019). The efficiency of Jordan insurance companies and its determinants using DEA, slacks, and logit models. Journal of Asian Business and Economic Studies, 26(1), 153–166. https://doi.org/10.1108/JABES-10-2018-0072

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

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 Markets, 16(6), 1077–1104. https://doi.org/10.1108/IJOEM-01-2020-0091

Mekawy, I. M. (2022). A novel method for solving multi- objective linear fractional programming problem under uncertainty. Journal of Fuzzy Extension and Applications, 3(2), 169–176. https://doi.org/10.22105/JFEA.2022.331180.1206

Micajkova, V. (2015). Efficiency of macedonian insurance companies: a DEA Approach. Journal of Investment and Management, 4(2), 61. https://doi.org/10.11648/j.jim.20150402.11

Naushad, M., Faridi, M. R., & Faisal, S. (2020). Measuring the managerial efficiency of insurance companies in Saudi Arabia: a data envelopment analysis approach. Journal of Asian Finance, Economics and Business, 7(6), 297–304. https://doi.org/10.13106/JAFEB.2020.VOL7.NO6.297

Nourani, M., Devadason, E. S., & Chandran, V. G. R. (2018). Measuring technical efficiency of insurance companies using dynamic network dea: an intermediation approach. Technological and Economic Development of Economy, 24(5), 1909–1940. https://doi.org/10.3846/20294913.2017.1303649

Omrani, H., Emrouznejad, A., Shamsi, M., & Fahimi, P. (2022). Evaluation of insurance companies considering uncertainty: a multi-objective network data envelopment analysis model with negative data and undesirable outputs. Socio-Economic Planning Sciences, 82(Part B), 101306. https://doi.org/10.1016/j.seps.2022.101306

Peykani, P., Mohammadi, E., & Emrouznejad, A. (2021). An adjustable fuzzy chance-constrained network DEA approach with application to ranking investment firms. Expert Systems with Applications, 166, 113938. https://doi.org/10.1016/j.eswa.2020.113938

Puspitasari, N., & Fauziyah, L. (2022). The efficiency of islamic general insurance using data envelopment analysis (DEA). International Journal of Islamic Business and Management Review, 2(1), 1–13. https://doi.org/10.54099/ijibmr.v2i1.134

Raj, K. K., Srinivasan, S., & Nandakumar, C. D. (2023). Efficiency analysis of reinsurers in India: a three stage fuzzy closed system DEA approach. Opsearch, 1–23. https://doi.org/10.1007/s12597-023-00651-2

Sadeghi, E., Miri Lavasani, M. R., Rostai Malkhalife, M., & Khanmohammadi, M. (2023). Evaluating the performance of iranian insurance companies using efficiency measurement method based on modified slack-based measure in the network data envelopment analysis approach. International Journal of Finance & Managerial Accounting, 8(29), 25-41. https://doi.org/10.30495/IJFMA.2022.65330.1788

Shahroudi, K., Taleghani, M., & Mohammadi, G. (2012). Application of two-stage DEA technique for efficiencies measuring of private insurance companies in Iran. International Journal of Applied Operational Research Journal, 1(3), 91–104. (In Persian).

Shobeiri, S. N., Rostamy- Malkhalifeh, M., Nikoomaram, H., & Miri Lavasani, M. (2022). Evaluating and classifying the insurers risk in the insurance industry using data envelopment analysis. Journal of New Researches in Mathematics, 7(34), 5–32. (In Persian).

Sinha, R. P. (2019). Network DEA efficiency of Indian non-life insurance companies. International Journal on Recent Trends in Business and Tourism, 3(3), 83–90. https://ejournal.lucp.net/index.php/ijrtbt/article/view/758/669

Suvvari, A., Raja Sethu Durai, S., & Goyari, P. (2019). Financial performance assessment using Grey relational analysis (GRA): an application to life insurance companies in India. Grey Systems, 9(4), 502–516. https://doi.org/10.1108/GS-05-2019-0010

Ucal Sari, I., & Ak, U. (2022). Machine efficiency measurement in industry 4.0 using fuzzy data envelopment analysis. Journal of Fuzzy Extension and Applications, 3(2), 177–191. https://doi.org/10.22105/JFEA.2022.326644.1199

Uckar, D., & Petrovic, D. (2022). Efficiency of insurance companies in croatia. Ekonomska Misao i Praksa, 31(1), 49–79. https://doi.org/10.17818/emip/2022/1.3

Zhao, T., Pei, R., & Pan, J. (2021). The evolution and determinants of Chinese property insurance companies’ profitability: A DEA-based perspective. Journal of Management Science and Engineering, 6(4), 449–466. https://doi.org/10.1016/j.jmse.2021.09.005