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PERFORMANCE EVALUATION OF NON-LIFE INSURANCE COMPANIES WITH BEST-WORST METHOD AND TOPSIS

Yıl 2020, , 108 - 125, 10.03.2020
https://doi.org/10.17130/ijmeb.700907

Öz

Like in any other field, performance of a company in the insurance industry is the key
factor of gaining competitive advantage and market share. In this study, insurance companies
are ranked according to their 2016 performance indicators using a two-step hybrid MCDM
method including BWM and TOPSIS. Only the non-life insurance companies are used in the
study. BWM is one of the relatively new MCDM methods, which has an advantage of using less
comparison, measuring the consistency between the pair-wise comparisons and giving interval
analysis for multi-optimal situations. In the proposed methodology, BWM is used to calculate
the criterion weights and TOPSIS to rank the alternatives. The solutions found are consistent
with the real life market shares of the insurance companies.

Kaynakça

  • Ahmad, W. N., Wan, K., Rezaei, J., Sadaghiani, S., & Tavasszy, L. A. (2017). Evaluation of the external forces affecting the sustainability of oil and gas supply chain using best worst method. Journal of Cleaner Production, 153, 242-252.
  • Akhisar, I., & Tunay, N. (2015). Performance ranking of Turkish life insurance companies using AHP and TOPSIS. Management International Conference. Portoroz, Slovenia.
  • Aktan, H. E., & Tosun, Ö. (2013). An integrated fuzzy AHP–fuzzy TOPSIS approach for AS/RS selection. International Journal of Productivity and Quality Management, 11(2), 228-245.
  • Altan, M. S. (2010). Türk sigortacılık sektöründe etkinlik: Veri zarflama analizi yöntemi ile bir uygulama. Gazi Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 12(1), 185-204.
  • Altman, E. I. (1968). Financial ratios, discriminant analysis and the prediction of corporate bankruptcy. The Journal of Finance, 23(4), 589-609.
  • Bawa, S. K., & Chattha, S. (2013). Financial performance of life insurers in Indian insurance industry. Pacific Business Review International, 6(5), 44-52.
  • Chang, S. H., Wu, T. C., Tseng, H. E., Su, Y. J., & Ko, C. C. (2012). Media mix decision support for schools based on analytic network process. International Journal of Industrial Engineering, 19(7), 297-304.
  • Cummins, J. D., Weiss, M. A., Xie, X., & Zi, H. (2010). Economies of scope in financial services: A DEA efficiency analysis of the US insurance industry. Journal of Banking & Finance, 34(7), 1525-1539.
  • Cummins, J. D., & Weiss, M. A. (2014). Systemic risk and the US insurance sector. Journal of Risk and Insurance, 81(3), 489-528.
  • Eling, M., & Luhnen, M. (2010). Efficiency in the international insurance industry: A cross-country comparison. Journal of Banking & Finance, 34(7), 1497-1509.
  • Ghaffari, S., Arab, A., Nafari, J., & Mangehti, M. (2017). Investigation and evaluation of key success factors in technological innovation development based on BWM. Decision Science Letters, 6, 295-306.
  • Gupta, H., & Barua, M. K. (2016). Identifying enablers of technological innovation for Indian MSMEs using best–worst multi criteria decision making method. Technological Forecasting and Social Change, 107, 69-79.
  • Gupta, P., Anand, S., & Gupta, H. (2017). Developing a roadmap to overcome barriers to energy efficiency in buildings using best worst method. Sustainable Cities and Society, 31, 244-259.
  • Klumpes, P. J. M. (2004). Performance benchmarking in financial services: Evidence from the UK life insurance industry. The Journal of Business, 77(2), 257-273.
  • Mohaghar, A., Sahebi, I. G., & Arab, A. (2017). Appraisal of humanitarian supply chain risks using bestworst method. International Journal of Social, Behavioral, Educational, Economic, Business and Industrial Engineering, 11(2), 259-264.
  • Özcan, A. İ. (2011). Türkiye’de hayat dışı sigorta sektörünün 2002-2009 dönemi itibariyle etkinlik analizi. Celal Bayar Üniversitesi Sosyal Bilimler Dergisi, 9(1), 61-78.
  • Özdemir, M. (2015). TOPSIS işletmeciler, mühendisler ve yöneticiler için operasyonel, yönetsel ve stratejik problemlerin çözümünde çok kriterli karar verme. B.F. Yıldırım, E. Önder (Eds.), Bursa:Dora Basım-Yayın.
  • Ren, J., Liang, H., & Chan, F. T. S. (2017). Urban sewage sludge, sustainability, and transition for eco-city: Multi-criteria sustainability assessment of technologies based on best-worst method. Technological Forecasting and Social Change, 116, 29-39.
  • Rezaei, J. (2015). Best-worst multi-criteria decision-making method. Omega, 53, 49-57.
  • Rezaei, J., Wang, J., & Tavasszy, L. (2015). Linking supplier development to supplier segmentation using best-worst method. Expert Systems with Applications, 42, 9152-9164.
  • 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.
  • Rezaei, J., Hemmes, A., & Tavasszy, L. (2017). Multi-criteria decision-making for complex bundling configurations in surface transportation of air freight. Journal of Air Transport Management, 61, 91-105.
  • Rezaei, J. (2016). Best-worst multi-criteria decision-making method: Some properties and a linear model. Omega, 64, 126-130.
  • Saeedpoor, M., Vafadarnikjoo, A., Mobin, M., & Rastegari, A. (2015). A servqual model approach integrated with fuzzy AHP and fuzzy TOPSIS methodologies to rank life insurance firms. Proceedings of the International Annual Conference of the American Society for Engineering Management, American Society for Engineering Management (ASEM).
  • Salimi, N., & Rezaei, J. (2016). Measuring efficiency of university-industry Ph.D. projects using best worst method. Scientometrics, 109(3), 911-1938.
  • Shen, K. Y., Hu, S. K., & Tzeng, G. H. (2017). Financial modeling and improvement planning for the life insurance industry by using a rough knowledge-based hybrid MCDM model. Information Sciences, 375, 296-313.
  • Taha, Z., Salaam, H. A., Phoon, S. Y., Tuan, Ya T. M. Y. S., & Mohamad, M. R. (2015). Application of integrated sustainability assessment: Case study of a screw design. International Journal of Industrial Engineering, 22(1), 1-10.
  • Tsai, T. N., & Tsai, C. W. (2014). Development of a closed-loop diagnosis system for reflow soldering using neural networks and support vector regression. International Journal of Industrial Engineering, 21(1), 18-32.
  • Turgutlu, E., Kök, R., & Kasman, A. (2007). Türk sigortacılık şirketlerinde etkinlik: Deterministik ve şans kısıtlı veri zarflama analizi. İktisat İşletme ve Finans, 22(251), 85-102.
  • Tuş Işık, A. (2016). QUALIFLEX and ORESTE methods for the insurance company selection problem. Alphanumeric Journal, 4(2), 55-68.
  • Vadlamannati, K. C. (2008). Do insurance sector growth and reforms affect economic development? Empirical evidence from India. Margin: The Journal of Applied Economic Research, 2(1), 43-86.
  • Venkateswarlu, R., & Rao, B. S. S. (2016). Profitability evaluation and ranking of Indian non-life insurance firms using GRA and TOPSIS. Journal of Insurance and Financial Management, 2(2), 59-89.
  • Wu, D, Yang, Z., Vela, S., & Liang, L. (2007). Simultaneous analysis of production and investment performance of Canadian life and health insurance companies using data envelopment analysis. Computers & Operations Research, 34(1), 180-198.
Toplam 33 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular İşletme
Bölüm Araştırma Makaleleri
Yazarlar

Gökhan Akyüz Bu kişi benim 0000-0003-1191-0766

Ömür Tosun Bu kişi benim 0000-0003-1571-7373

Salih Aka Bu kişi benim 0000-0002-6386-8582

Yayımlanma Tarihi 10 Mart 2020
Gönderilme Tarihi 11 Mart 2019
Kabul Tarihi 27 Ocak 2020
Yayımlandığı Sayı Yıl 2020

Kaynak Göster

APA Akyüz, G., Tosun, Ö., & Aka, S. (2020). PERFORMANCE EVALUATION OF NON-LIFE INSURANCE COMPANIES WITH BEST-WORST METHOD AND TOPSIS. Uluslararası Yönetim İktisat Ve İşletme Dergisi, 16(1), 108-125. https://doi.org/10.17130/ijmeb.700907

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