A novel multi-criteria analysis model for the performance evaluation of bank regions: an application to Turkish agricultural banking

Abstract

The banks serve in a highly dynamic and competitive environment and need to systematically evaluate their performance to improve their competitiveness. Performance evaluation is an important and complex process that requires flexible and analytic methods while handling the multidimensionality of the problem. This study presents a hybrid multi-criteria performance evaluation model for banking sector which combines two multi-criteria decision making methods that are simulation-integrated hesitant fuzzy linguistic term sets-based analytic hierarchy process method to determine the importance level of each criterion according to the decision makers’ subjective judgements and grey relational analysis method to rank bank regions according to their performance values. The presented model is based on both probability theory and fuzzy sets theory and thus better represents all the dimensions of the uncertainty inherent in decision making process. A real-life application of the proposed performance evaluation model for a private bank operating in agricultural banking sector in Turkey is also given to illustrate the effectiveness and the applicability of the model.

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Appendix A

Appendix A

See Tables 17, 18 and 19.

Table 17 Pairwise evaluations of the criteria with respect to goal
Table 18 Obtained envelopes for the HFLTS given in Table 17
Table 19 Obtained numerical intervals for the HFLTS given in Table 17

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Tüysüz, F., Yıldız, N. A novel multi-criteria analysis model for the performance evaluation of bank regions: an application to Turkish agricultural banking. Soft Comput 24, 5289–5311 (2020). https://doi.org/10.1007/s00500-019-04279-7

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Keywords

  • Banking
  • Performance evaluation
  • Simulation
  • Hesitant fuzzy sets
  • AHP
  • GRA