A New Approach to Investigate the Performance of Insurance Branches in Iran Using Best-Worst Method and Fuzzy Inference System

Ali Reza Torbati, Mohammad Kazem Sayadi


The purpose of this study is to present a fuzzy expert model to examine the performance of insurance branches in Iran. The aim is to weight the criteria for measuring the performance of insurance branches and according to the experts’ perspectives. Our method is developed by the use of Best-Worst Method (BWM) and Fuzzy Inference System (FIS). BWM is used to    determine the importance of each criteria and FIS to evaluate and rank the insurance branches. The data for this study was collected from the managers of 52 Dana insurance companies in Iran. By analyzing the data obtained by the questionnaire, it was determined that the criteria such as insurance costs, administrative, general and personnel costs, premium income, deferred claims, marketing and advertising costs, market share of number in issued insurance policies, degree of customer satisfaction, level of employee education, amount of investment, facility to employees, cost of education, research and development costs and manpower skills are the most important criteria for senior executives in measuring the performance of Dana insurance company. In addition, the results of BWM showed that the insurance costs criteria is the most important criteria among others. The results concluded that the proposed model is superior to other methods in the literature in terms of convenience and accuracy.


Fuzzy Inference System, Best-Worst method, Performance, Insurance branches

Full Text:

Abstract PDF


Ahmadi, H., Nilashi, M., & Ibrahim, O. (2015). Organizational decision to adopt hospital information system: An empirical investigation in the case of Malaysian public hospitals. International journal of medical informatics, 84(3), 166-188.

Alshennawy, A. A., & Aly, A. A. (2009). Edge detection in digital images using fuzzy logic technique. World Academy of science, engineering and technology, 51, 178-186.

Anderson, D., & Hall, L. (1999). MR. FIS: Mamdani rule style fuzzy inference system. Paper presented at the Systems, Man, and Cybernetics, 1999. IEEE SMC'99 Conference Proceedings. 1999 IEEE International Conference on.

Angle, H. L., & Perry, J. L. (1981). An empirical assessment of organizational commitment and organizational effectiveness. Administrative science quarterly, 1-14.

Born, P., Gentry, W. M., Viscusi, W. K., & Zeckhauser, R. J. (1995). Organizational form and insurance company performance: stocks versus mutuals. Retrieved from

Burca, A.-M., & Batrinca, G. (2014). The determinants of financial performance in the Romanian insurance market. International Journal of Academic Research in Accounting, Finance and Management Sciences, 4(1), 299-308.

Carter, T. (2006). Sales management coaching: A model for improved insurance company performance. Journal of hospital marketing & public relations, 16(1-2), 113-125.

Delaney, J. T., & Huselid, M. A. (1996). The impact of human resource management practices on perceptions of organizational performance. Academy of Management journal, 39(4), 949-969.

Efendigil, T., Önüt, S., & Kahraman, C. (2009). A decision support system for demand forecasting with artificial neural networks and neuro-fuzzy models: A comparative analysis. Expert Systems with Applications, 36(3), 6697-6707.

Ellinger, A. D., Ellinger, A. E., Yang, B., & Howton, S. W. (2002). The relationship between the learning organization concept and firms' financial performance: An empirical assessment. Human resource development quarterly, 13(1), 5-22.

Ertuğrul, İ., & Karakaşoğlu, N. (2009). Performance evaluation of Turkish cement firms with fuzzy analytic hierarchy process and TOPSIS methods. Expert Systems with Applications, 36(1), 702-715.

Gao, Y., & Er, M. J. (2003). Online adaptive fuzzy neural identification and control of a class of MIMO nonlinear systems. IEEE Transactions on Fuzzy Systems, 11(4), 462-477.

Georgy, M. E., Chang, L.-M., & Zhang, L. (2005). Utility-function model for engineering performance assessment. Journal of Construction Engineering and Management, 131(5), 558-568.

Gipps, C. (2002). Beyond testing: Towards a theory of educational assessment: Routledge.

Harris, S. E., & Katz, J. L. (1991). Organizational performance and information technology investment intensity in the insurance industry. Organization science, 2(3), 263-295.

Hwang, H.-S., Moon, C., Chuang, C.-L., & Goan, M.-J. (2005). Supplier selection and planning model using AHP. International Journal of the Information Systems for Logistics and Management, 1(1), 47-53.

Isa, K., & Pope, J. (2011). Corporate tax audits: Evidence from Malaysia. Global Review of Accounting and finance, 2(1), 42-56.

Katsikeas, C. S., Leonidou, L. C., & Morgan, N. A. (2000). Firm-level export performance assessment: review, evaluation, and development. Journal of the Academy of Marketing Science, 28(4), 493-511.

Kirkbesoglu, E., & Ozder, E. H. (2015). The Effects of Organizational Performance on The Relationship Between Perceived Organizational Support and Career Satisfaction: An Application on Insurance Industry. Journal of Management Research, 7(3), 35-50.

Kuo, M.-S., & Liang, G.-S. (2012). A soft computing method of performance evaluation with MCDM based on interval-valued fuzzy numbers. Applied Soft Computing, 12(1), 476-485.

Lotte, F. (2006). The use of fuzzy inference systems for classification in EEG-based brain-computer interfaces. Paper presented at the 3rd International Brain-Computer Interfaces Workshop and Training Course.

Mwangi, M., & Murigu, J. W. (2015). The determinants of financial performance in general insurance companies in Kenya. European Scientific Journal, ESJ, 11(1).

Nasab, S. (2012). Strategic orientation in evaluation of supply chain activities. Management Science Letters, 2(5), 1785-1794.

Nilashi, M., bin Ibrahim, O., & Ithnin, N. (2014a). Hybrid recommendation approaches for multi-criteria collaborative filtering. Expert Systems with Applications, 41(8), 3879-3900.

Nilashi, M., bin Ibrahim, O., & Ithnin, N. (2014b). Multi-criteria collaborative filtering with high accuracy using higher order singular value decomposition and Neuro-Fuzzy system. Knowledge-Based Systems, 60, 82-101.

Nilashi, M., Ibrahim, O., & Ahani, A. (2016). Accuracy improvement for predicting Parkinson’s disease progression. Scientific reports, 6, 34181.

Nilashi, M., Ibrahim, O., Ahmadi, H., & Shahmoradi, L. (2017). A knowledge-based system for breast cancer classification using fuzzy logic method. Telematics and Informatics, 34(4), 133-144.

Nilashi, M., & Ibrahim, O. B. (2014). A model for detecting customer level intentions to purchase in B2C websites using TOPSIS and fuzzy logic rule-based system. Arabian Journal for Science and Engineering, 39(3), 1907-1922.

Nilashi, M., Zakaria, R., Ibrahim, O., Majid, M. Z. A., Zin, R. M., Chugtai, M. W., . . . Yakubu, D. A. (2015). A knowledge-based expert system for assessing the performance level of green buildings. Knowledge-Based Systems, 86, 194-209.

Nilashi, M., Zakaria, R., Ibrahim, O., Majid, M. Z. A., Zin, R. M., & Farahmand, M. (2015). MCPCM: a DEMATEL-ANP-based multi-criteria decision-making approach to evaluate the critical success factors in construction projects. Arabian Journal for Science and Engineering, 40(2), 343-361.

Oscar Akotey, J., Sackey, F. G., Amoah, L., & Frimpong Manso, R. (2013). The financial performance of life insurance companies in Ghana. The Journal of Risk Finance, 14(3), 286-302.

Rezaei, J. (2015). Best-worst multi-criteria decision-making method. Omega, 53, 49-57.

Rezaei, J. (2016). Best-worst multi-criteria decision-making method: Some properties and a linear model. Omega, 64, 126-130.

Rezaei, J., Nispeling, T., Sarkis, J., & Tavasszy, L. (2016). A supplier selection life cycle approach integrating traditional and environmental criteria using the best worst method. Journal of Cleaner Production, 135, 577-588.

Rowlands, H., & Wang, L. R. (2000). An approach of fuzzy logic evaluation and control in SPC. Quality and Reliability Engineering International, 16(2), 91-98.

Safaei Ghadikolaei, A., Khalili Esbouei, S., & Antucheviciene, J. (2014). Applying fuzzy MCDM for financial performance evaluation of Iranian companies. Technological and Economic Development of Economy, 20(2), 274-291.

Shiu, Y. (2004). Determinants of United Kingdom general insurance company performance. British Actuarial Journal, 10(5), 1079-1110.

Soekarno, S., & Azhari, D. A. (2009). Analysis of Financial Ratio to Distinguish Indonesia Joint Venture General Insurance Company Performance using Discriminant Analysis. The Asian journal of technology management, 2(2), 110-122.

Yadegaridehkordi, E., Nilashi, M., Nasir, M. H. N. B. M., & Ibrahim, O. (2018). Predicting determinants of hotel success and development using Structural Equation Modelling (SEM)-ANFIS method. Tourism Management, 66, 364-386.

Yang, Y.-P. O., Shieh, H.-M., Leu, J.-D., & Tzeng, G.-H. (2008). A novel hybrid MCDM model combined with DEMATEL and ANP with applications. International journal of operations research, 5(3), 160-168.

Zare, M., Pahl, C., Rahnama, H., Nilashi, M., Mardani, A., Ibrahim, O., & Ahmadi, H. (2016). Multi-criteria decision making approach in E-learning: A systematic review and classification. Applied Soft Computing, 45, 108-128.

Zolfani, S. H., Chen, I.-S., Rezaeiniya, N., & Tamošaitienė, J. (2012). A hybrid MCDM model encompassing AHP and COPRAS-G methods for selecting company supplier in Iran. Technological and Economic Development of Economy, 18(3), 529-543.


  • There are currently no refbacks.

Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.