Evaluation of banks in terms of customer preferences with fuzzy SWARA and fuzzy MOORA integrated approach

Authors

  • Aşkın Özdağoğlu Department of Business Administration, Dokuz Eylul University, Buca, İzmir, Turkey
  • Murat Kemal Keleş Department of Transportation Services, Keçiborlu Vocational School, Isparta University of Applied Sciences, Isparta, Turkey
  • Mehrzad Şenefe English Business Administration Department PhD Student, Dokuz Eylul University, Buca, İzmir, Turkey

DOI:

https://doi.org/10.31181/jdaic10007122024o

Keywords:

fuzzy SWARA, fuzzy MOORA, banking sector, Multi Criteria Decision Making

Abstract

This study examines the factors influencing customer preferences in retail and private banking in Turkey, utilizing the fuzzy SWARA methodology. In response to global challenges and advancements in technology, the banking sector has undergone significant transformations over the past two decades, leveraging technology to enhance services and efficiency. However, customer preferences may vary across countries and cultures, with Turkish banking customers showing a mix of traditional and digital banking preferences. Against the backdrop of a dramatic financial crisis in 2001, trust emerges as a paramount consideration for Turkish banking customers, alongside factors such as mobile services, customer service quality, and pricing compatibility. Through an analysis of nine banks with fuzzy MOORA method, both private and state-owned, this study aims to provide valuable insights into the factors driving customer preferences in the Turkish banking sector. The findings contribute to the existing literature and offer guidance for banks seeking to meet the evolving needs and expectations of their customers in Turkey's dynamic banking environment.

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References

Altunöz, U. (2017). The Analysing of the Financial Performance of Banks by Using Fuzzy AHP and Fuzzy Moora Approaches: Case of Turkish Banks. Route Educational and Social Science Journal, 4(4), 116-132.

Beybur, M. (2022). A Research on the Factors Affecting the Negative Attitudes of Turkish Banking Sector Customers towards Branchless Digital Banking. Journal of Economics and Administrative Sciences, 23(4), 819-830.

Coşkuner, A., & Rençber, Ö. F. (2024). Determination of Performance Ranking of Participation Banks with CIRITIC-Based TOPSIS Method. Sakarya Üniversitesi İşletme Enstitüsü Dergisi, 6(1), 57-70.

Demir, G. (2022). Analysis of the financial performance of the deposit banking sector in the Covid-19 period with LMAW-DNMA methods. International Journal of Insurance and Finance, 2(2), 17-36.

Demir, G. (2021a). Özel Sermayeli Mevduat Bankalarında Performans Analizi: SWARA-RAFSI Bütünleşik Model Uygulaması. Atatürk Üniversitesi İktisadi ve İdari Bilimler Dergisi, 35(4), 1359-1382.

Demir, G. (2021b). Comparison of the Financial Performance of Turkish Cement Firms with Fuzzy SWARA-COPRAS-MAUT Methods. Gaziantep University Journal of Social Sciences, 20(4), 1875-1892.

Ecer, F., & Pamucar, D. (2022). A novel LOPCOW‐DOBI multi‐criteria sustainability performance assessment methodology: An application in developing country banking sector. Omega, 112, 102690.

Emovon, I., Okpako, O. S., & Edjokpa, E. (2021). Application of fuzzy MOORA method in the design and fabrication of an automated hammering machine. World Journal of Engineering, 18(1), 37-49.

Ersöz, F., Kıncı, C. H., & Ersöz, T. (2018). A model proposal for course selection with the fuzzy MOORA approach. Avrupa Bilim ve Teknoloji Dergisi, (14), 369-377.

Eshlaghy, A. T., Kazemi, M. A., Radfar, R., & Abdolmohammadi, N. (2011). Causal analysis of customer needs in the banking system by applying fuzzy group decision making. African Journal of Business Management, 5(21), 8417-8431.

Ghoushchi, S. J., Yousefi, S., & Khazaeili, M. (2019). An extended FMEA approach based on the Z-MOORA and fuzzy BWM for prioritization of failures. Applied Soft Computing Journal, 81, 1-13.

Jayamaha, R. (2002). Global Banking in Transition Opportunities and Challenges. Association of Professional Bankers, 1(27).

Karmakar, S., Bandyopadhyay, G., Pamucar, D., Mukhopadhyaya, J. N., & Biswas, S. (2023). Impact of COVID-19 on systemic risk for Indian financial institutions. International Journal of Applied Decision Sciences, 16(6), 686-716.

Khan, A., Maity, K., & Jhodkar, D. (2020). An integrated fuzzy-MOORA method for the selection of optimal parametric combination in turing of commercially pure titanium. In G. Kapil & G. Munish Kumar (Eds.), Optimization of manufacturing processes, Springer Series in Advanced Manufacturing (pp. 163-184). Cham: Springer.

Khorshidi, M., Erkayman, B., Albayrak, Ö. Kılıç, R., & Demir, H. İ. (2022). Solar power plant location selection using integrated fuzzy DEMATEL and fuzzy MOORA method. International Journal of Ambient Energy, 43(1), 7400–7409.

Kundakcı, N. (2023). Integration of Fuzzy PIPRECIA and Fuzzy MOORA Methods for Maintenance Strategy Selection. Pamukkale Üniversitesi İşletme Araştırmaları Dergisi, 10(2), 401-423.

Mavi, R. K., Goh, M., & Zarbakhshnia, N. (2017). Sustainable third-party reverse logistic provider selection with fuzzy SWARA and fuzzy MOORA in plastic industry. The International Journal of Advanced Manufacturing Technology, 91, 2401–2418.

Mermod, A. Y. (2020). Customer's Perspectives and Risk Issues on E-Banking in Turkey; Should We Still be Online? Journal of Internet Banking & Commerce, 16(1), 1-15.

Mishra, A. R., Rani, P., Pandey, K., Mardani, A., Streimikis, J., Streimikiene, D., & Alrasheedi, M. (2020). Novel multi-criteria ıntuitionistic fuzzy SWARA-COPRAS approach for sustainability evaluation of the bioenergy production process. Sustainability, 12(10), 4155.

Perçin, S. (2019). An integrated fuzzy SWARA and fuzzy AD approach for outsourcing provider selection. Journal of Manufacturing Technology Management, 30(2), 531-552.

Polat, T. K., & Yaşlı, G. S. (2024). Risk Prioritization in A Manufacturing Project with Fuzzy SWARA and Fuzzy MOORA Methods. Erzincan University Journal of Science and Technology, 17(1), 16-36.

Puška, A., Štilić, A., & Stević, Ž. (2023). A comprehensive decision framework for selecting distribution center locations: a hybrid improved fuzzy SWARA and fuzzy CRADIS approach. Computation, 11(4), 73.

Quynh, V. T. N. (2024). An extension of fuzzy TOPSIS approach using integral values for banking performance evaluation. Multidisciplinary Science Journal, 6(8), 2024155.

Rao, S. H., Kalvakolanu, S., & Chakraborty, C. (2021). Integration of ARAS and MOORA MCDM techniques for measuring the performance of private sector banks in India. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 29(2), 279-295.

Roy, P., & Shaw, K. (2023). A fuzzy MCDM decision-making model for m-banking evaluations: comparing several m-banking applications. Journal of Ambient Intelligence and Humanized Computing, 14(9), 11873-11895.

Sahebi, I. G., Arab, A., & Toufighi, S. P. (2020). Analyzing the barriers of organizational transformation by using fuzzy SWARA. Journal of Fuzzy Extension and Applications, 1(2), 84-97.

Sarğın, B., Alaboz, P., Karaca, S., & Dengiz, O. (2024). Pythagorean fuzzy SWARA weighting technique for soil quality modeling of cultivated land in semi-arid terrestrial ecosystems. Computers and Electronics in Agriculture, 227, 109466.

Ta, H. P., & Har, K. Y. (2000). A study of bank selection decisions in Singapore using the Analytical Hierarchy Process. International Journal of Bank Marketing, 18(4), 170-180.

Ulutaş, A., Karakuş, C. B., & Topal, A. (2020). Location Selection for Logistics Center with Fuzzy SWARA and CoCoSo Methods. Journal of Intelligent & Fuzzy Systems, 38(4), 4693–4709.

Villavarajah, R. (2008). Changing face of banking moving towards branchless banking. Bankers Journal, 26(2), 7-10.

Vivekanandana, L., & Jayasena, S. (2012). Facilities offered by the Banks and Expectations of IT Savvy Banking Customers. Procedia - Social and Behavioral Sciences, 40, 576 – 583.

Vrtagić, S., Softić, E., Subotić, M., Stević, Ž., Dordevic, M., & Ponjavic, M. (2021). Ranking road sections based on MCDM model: New improved fuzzy SWARA (IMF SWARA). Axioms, 10(2), 92.

Zulfiquar N. A., Ravi, K., & Ravi, S. (2020) Evaluation and ranking of solutions to mitigate sustainable remanufacturing supply chain risks: a hybrid fuzzy SWARA fuzzy COPRAS framework approach. International Journal of Sustainable Engineering, 13(6), 473-494.

Published

07.12.2024

How to Cite

Özdağoğlu, A., Keleş, M. K., & Şenefe, M. (2024). Evaluation of banks in terms of customer preferences with fuzzy SWARA and fuzzy MOORA integrated approach. Journal of Decision Analytics and Intelligent Computing, 4(1), 216–232. https://doi.org/10.31181/jdaic10007122024o