A fuzzy MCDM approach for evaluating banking performance based on Balanced Scorecard
Introduction
Financial liberalization and internationalization have been heavily advocated in Taiwan over the past decade, in response to increased global competition. Due to the government’s loosening control over the applications of establishing the medium-and-small business banks, the number of domestic headquarters and branches of financial institutions has increased from 6,127 to 6,365 between the years 2000 and 2005 (Central Bank of the Republic of China, 2005). Financial institutions are densely distributed in Taiwan. Moreover, the financial environment of Taiwan has undergone a drastic change since Taiwan entered the World Trade Organization (WTO). It is very important for Taiwan’s bank institutions to have a competitive advantage, because they are all quite homogeneous. Therefore, a fiercely competing financial market with relatively little profit, plus the new withdrawal mechanism regulations for low performance banks has resulted in a limited growth of banks in Taiwan. To outperform competing bank institutions, more emphasis on internal operational performance is required. This means it is imperative to develop an effective way to conduct performance evaluations that can measure the overall organizational performance and link it to the corporate goals. That is, a holistic evaluation model of banking performance is key to a bank’s survival.
Many different theories and methods of performance for conducting an evaluation have been applied in various organizations for many years. These approaches include ratio analysis, total production analysis, regression analysis, Delphi analysis, Balanced Scorecard, Analytic Hierarchical Process (AHP), Data Envelopment Analysis (DEA) and others. Each method has its own basic concept, aim, advantages and disadvantages (Dessler, 2000). Which one is chosen by management or decision makers for assessing performance depends on the status and type of the organization. However, all the successful enterprises have some common features, including a specific vision, positive actions, and an effective performance evaluation. The Balanced Scorecard (BSC) is an extensive and thorough performance evaluation tool to adequately plan and control an organization so it can attain its goals (Davis and Albright, 2004, Lawrie and Cobbold, 2004, Pinero, 2002). The BSC breaks through the traditional limitations of finance, examining an organization’s performance from the four main perspectives of finance, customer, internal business process, and learning and growth (Kaplan & Norton, 1992). It emphasizes both the aspects of the financial and non-financial, long-term and short-term strategies, and emphasizes internal and external business measures. Several studies have been conducted incorporating the four perspectives of the BSC in performance appraisal. To achieve the best possible result from a more effective performance, it is crucial to improve the banking relationship by matching the needs of the clients to the delivery process of client services (Nist, 1996). Therefore, the BSC is also utilized as a framework to develop evaluation indicators for banking performance (Davis and Albright, 2004, Kim and Davidson, 2004, Kuo and Chen, 2010).
Since Bellman and Zadeh (1970) developed the theory of decision behavior in a fuzzy environment, various relevant models were developed, and have been applied to different fields such as control engineering, artificial intelligence, management science, and Multiple Criteria Decision Making (MCDM) among others. The concept of combining the fuzzy theory and MCDM is referred to as fuzzy MCDM (FMCDM). Several practicable applications of utilizing FMCDM in criteria evaluation and alternatives selection are demonstrated in previous studies (Bayazita and Karpak, 2007, Chen et al., 2006, Chiou and Tzeng, 2002, Chiou et al., 2005, Chiu et al., 2006, Hsieh et al., 2004, Lee et al., 2008, Pepiot et al., 2008, Wang and Chang, 2007, Wu and Lee, 2007). Primarily, the MCDM problems are first classified into distinct aspects and different alternatives/strategies and the criteria are defined based on various points of view from stakeholders. Then, a finite set of alternatives/strategies can be evaluated in terms of multi-criteria. Choosing a suitable method to measure the criteria can help the evaluators and analysts to process the cases to be evaluated and determine the best alternative. Like most cases of evaluation, a number of criteria have to be considered for performance appraisal. Consequently, banking performance evaluation can be regarded as a MCDM problem. In addition, the multiple criteria used in the BSC are more objective and comprehensive than a single one. In this research, a FMCDM approach based on the four perspectives of the BSC was proposed to establish a performance evaluation model for bank institutions. The aims of this research are as follows: (1) screen performance indexes to fit the banks for constructing a hierarchical framework of performance evaluation; (2) use FAHP (Fuzzy Analytic Hierarchy Process) to find the fuzzy weights of the indexes by subjective perception; (3) apply SAW (Simple Average Weight), TOPSIS (Technique for Order Preference by Similarity to Ideal Solution method), and VIKOR (VlseKriterijumska Optimizacija I Kompromisno Resenje) to rank the performance and improve the gaps of three banks in the example; and (4) provide suggestions based on the research results for performance evaluation and serve as a reference for future research in this field.
The remainder of this paper is organized as follows. The concepts of performance evaluation and BSC are introduced and reviewed in Section 2. In Section 3, the performance evaluation framework and the analytical methods used in FMCDM for evaluating the banking performance are proposed. Section 4 provides an empirical example for banking performance, including the hierarchical framework of BSC performance evaluation indexes and the result analyses and discussion to illustrate the proposed performance evaluation model. Section 5 concludes the paper.
Section snippets
Performance evaluation and Balanced Scorecard
This section briefly reviews the underlying concepts adopted by this research, such as the definitions of performance evaluation, performance evaluation index, and Balanced Scorecard (BSC).
Performance evaluation framework and analytical methods
The analytical structure of this research is illustrated in Fig. 1. A performance analysis is conducted based on the selected evaluation criteria. First the FAHP approach is employed to calculate the relative weights of the performance evaluation indexes. Then, according to these weights the three MCDM analytical tools of SAW, TOPSIS, and VIKOR are used to rank and improve the banking performance and determine the best practice. The concepts of the fuzzy set theory and details of the analytical
An empirical example for banking performance
The four perspectives of BSC were taken as the framework for establishing performance evaluation indexes in this research. Based on this framework, the FAHP was used to obtain the fuzzy weights of the indexes. The three MCDM analytical tools, SAW, TOPSIS, and VIKOR were respectively applied to evaluate the banking performance based on the weight of each index, and to improve the gaps with three banks as an empirical example. The hierarchical framework of the BSC performance evaluation criteria,
Conclusions and remarks
In response to the rapid growth of service industries and the increased global competition, particularly for the banking institutions, the need for alternative controls and performance measures has attracted much attention. However, researchers are finding it difficult to measure banking performance because of the intangible nature of the products and services of the banking industry. According to the relevant literature, most studies only used financial factors to evaluate banking performance (
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