Examining Service Innovation Competencies of Turkish Deposit Banks With Fuzzy ANP, Fuzzy TOPSIS and Fuzzy VIKOR Methods

It is aimed to rank the service innovation issues of deposit banks in Turkey. In this context, 26 deposit banks of Turkey are analyzed by considering 16 different balanced scorecard-based criteria. Regarding the methodology, first of all, fuzzy ANP method is used to measure the significance of dimensions and criteria. It is defined that the most significant dimension is performance. It is also seen that organizational compliance (D3) is on the last rank. Additionally, return on investment (C1), profitability (C2) and customer expectations (C5) have highest significance. These alternatives are ranked by considering fuzzy TOPSIS and fuzzy VIKOR methods. It is understood that the results of both these two methods are very similar. It is concluded that there is not a comparative advantage among the banks regarding the ownership type. For example, the best bank (F15) is a foreign bank whereas the worst bank (F13) is also another foreign bank. Similar to this situation, some private banks (P1, P7, P8) have successful performance while some others (P4, P5) have weak performance.


INTRODUCTION
Service innovation is a very signi icant issue for almost all companies. The main reason is that globalization led to high competition between the companies in all industries (Ayuningrat, Noermijati., & Hadiwidjojo, 2016;Lamberth-Cocca & Meiren, 2017). Because of this situation, it can be said that in order to increase competitive power, companies should take some actions, such as service innovation. Otherwise, these companies cannot be preferred by the customers. Hence, it will not possible for these companies to survive in the market (Bernik, Azis, Kartini, & Harsanto, 2015;. While considering this situation emphasized below, it is understood that evaluating the performance of the companies regarding service innovation is crucial. Most of the methods to achieve the performance consider only inancial aspects. However, it can be said that these kinds of methods are not suf icient to achieve this objective. Within this framework, the popularity of balanced scorecard method increases since non-inancial issues are also considered in this process (Dinçer, Hacıoglu, & Yuksel, 2017;Jingnan, Yunus, & Kamal, 2018). This study aims to analyze Turkish banking sector for service innovation. For this purpose, 16 different criteria considering balanced scorecard dimensions are determined. Additionally, fuzzy ANP, TOPSIS and VIKOR methods are used. The criteria are assessed by using fuzzy ANP. Moreover, fuzzy TOPSIS and VIKOR approaches are considered for ranking different banking groups (state, foreign and private) regarding service innovation. This study has six different parts. After the introduction part, balanced scorecard approach will be detailed. In this part, four different balanced scorecard dimensions are explained. In the third part, literature is reviewed. The fourth part fuzzy ANP, TOPSIS and VIKOR methods are identi ied. Moreover, in the ifth part, the details of analysis results are explained. In the conclusion part, suggestions are shared based on these results.

Balanced Scorecard Approach
Especially in the last decades, competition among the companies increased very much due to the globalization. Therefore, measuring the performance of the companies play a more signi icant role to recognize any de iciency before it causes bigger problems. Within this framework, it is understood that classical performance measurement methods are not suf icient to satisfy this condition. The main is that these classical methods mainly focus on the inancial situation of the companies while measuring the performance. The signi icant difference between the balanced scorecard method from the others is that it also includes non-inancial issues in addition to the inancial factors. The term "balanced" refers to the condition that this method gives balanced importance to inancial and non-inancial aspects. This method mainly includes following four different dimensions (Kaplan & Norton, 2007;Tangpornpaiboon & Puttanapong, 2016). • Finance: The inancial performance is analyzed. It helps to learn whether there is a problem in the company regarding inancial aspect . • Customer: It analyzes the quality of the image of the companies. Within this framework, it concentrates on the expectations of the customers to increase customer satisfaction (Dincer, Yuksel, & Martinez, 2019;Kuo & Chen, 2015). • Internal Process: In this dimension, employee participation plays an important role. This situation contributes both the performance of the employees and the companies. When employee participate any aspects in the company, it increases the motivation of them. On the other side, by considering different ideas of the companies, company can reach its objectives more easily (Dinçer & Yuksel, 2018;Kozina, 2017). • Learning and Growth: This dimension de ines quali ication of the employees and adaption of the companies to any changes in the market (Alahoul, Azizan, & Alwi, 2016;Dincer, Yuksel, & Cetiner, 2019).

LITERATURE REVIEW
Service innovation concept is considered for many different aspects. Grif in and Page (1996) identi ied new services as investments. Therefore, they analyzed the returns of these investments. Y. Liu and Yang (2009) emphasized the aspect that successful service innovation contributes the pro itability of the companies. Moreover, Tajeddini (2011) underlined that service innovation provided cost ef iciencies. Furthermore, Grif in (1997) also determined the same conclusions. Furthermore, some other studies emphasized the effect of new service development on customer relationship. De Brentani (1995) identi ied that if new service development process could performed successfully, it helps to meet customer demands. Furthermore, Wu, Tzeng, and Chen (2009) determined that effective service innovation process increases customer satisfaction. Similarly, Y. Liu andYang (2009), Wu et al. (2009) showed that service innovation provides long term effective relationship with the customers. Additionally, the in luence of service innovation on organizational compliance is also emphasized in many different studies. For example, Homburg and Kuehnl (2014), Y. Liu and Yang (2009) and Perks and Riihela (2004) underlined that it has a positive effect on personnel motivation. Schilling and Hill (1998) de ined that with service innovation, employees can understand the goals of the organization. Edvardsson, Meiren, Schäfer, and Witell (2013) stated that employees should be considered in this aspect. Similar to these studies, Melton and Hartline (2010) and Stevens and Dimitriadis (2004) also underlined that employee participation in this process improves the quality of this process. Also, some researchers also considered the importance of technological improvement in new service development. Brown and Eisenhardt (1995) determined that in service innovation projects, market-based databases should be considered and that underlined that for this purpose, technological improvement is crucial. Similar to these studies, S. Liu (2012), Van Riel and Lievens (2004), Van Den Ende (2003) and Ittner and Larcker (1997) also identi ied that with the help of technological improvement, the data low in new service development process can be provided effectively. On the other side, Kuczmarski (1992) focused on the importance of professional training activities for the employees. Consequently, it is identi ied that many different studies focus on new service development concept in different aspects. For instance, some of them underline the importance of inancial situation while some others concentrate on the customer relationship in this process. In addition to these subjects, organizational compliance and the importance of training are also taken into the consideration. Therefore, a new study which analyzes all these concepts together in service innovation process. Within this context, it is obvi-ous that balanced scorecard approach helps to achieve this objective since it has all these concepts. Table 1 gives information about key performance indicators of service innovation competencies.  Page (1996), Oktar and Yuksel (2015), Yuksel (2017)

METHODOLOGY Fuzzy ANP Method
People face many dif iculties in order to make decision. The main reason is that they have to consider lots of dif icult situation at the same time in this process. ANP aims to make decisions in complex conditions. In addition to this situation, clusters can also affect each other in ANP method (Saaty, 1990). The details of ANP system are given below ISSN: 2414-309X DOI: 10.20474/jabs-5.1.3 (Chang, Kuo, Wu, & Tzeng, 2015). First of all, the problem should be explained in detail. Within this scope, the purpose, criteria and sub criteria related to this problem should be identi ied. Moreover, interaction between the elements is taken into the consideration. As a result, a supermatrix can be created. After that, a weighted matrix is developed by using the importance of the elements. These matrixes are illustrated below. In these matrixes, "a" represent criteria whereas "w" shows the weights (Dincer, Hacioglu, & Yuksel, 2016).
In this process, Chang's fuzzy extent analysis is considered which is also detailed below (Chang et al., 2015). a-"Fuzzy Synthetic Extent" (Si) is identi ied. The calculation is shown below.

Fuzzy TOPSIS Method
This approach is used to make decisions in complex environment. Hwang and Yoon developed this method in 1981. It refers to the irst letters of "technique for order of preference by similarity to ideal solution". The main advantage of this method is to use and make comments to the about results easily. In the analysis process, both positive and negative ideal solutions are generated (Dincer et al., 2016). In the equation below, X ij shows the value for criterion "j" and alternative "i". Additionally, it can be said that there are K decision makers.
Where i = 1, 2, 3, ..m and j = 1, 2, 3..n W j = 1 K + W 1 j + W 2 j + W 3 j + · · · + W K j While using these fuzzy weights, fuzzy decision matrix can be generated. The details are shown below. In this equation, "C" represents criteria while "A" gives information about the alternatives.
In the following step, fuzzy decision matrix is normalized by using the following equations.
Just then, positive and negative solutions are calculated with the formulas below.
In this context, "A+" demonstrates the fuzzy positive ideal solution whereas "A-" shows the fuzzy negative ideal solution. Furthermore, "Di-" refers to the distance from negative ideal solution. Moreover, "Di+" shows the distance from positive ideal solution. Hence, the closeness coef icient can be calculated by using following equations. people face many dif iculties when they try to select the best alternative. In VIKOR method, C refers to the criteria whereas A shows the alternatives. The different steps of this method are detailed below (Dincer et al., 2016). In the irst step, the criteria are identi ied as following. f * i = max f ij and f − i = min f ij On the other side, for the cost issue, the following equations are considered. f * i = min f ij and f − i = max f ij In the second step, Sj and Rj values are calculated as below. In these equations, w represents weights of the criteria.
In the third step, the index value (Qj) is calculated as below.
In the fourth step, the values are ranked.

ANALYSIS RESULTS
The irst phase of the analysis includes the fuzzy ANP methodology to measure the relative importance of dimensions and criteria. The project team has been also been deined as a decision-maker group to appoint the linguistic terms. The summary results can be seen in Table 2.  Table 2 shows that performance dimension (D1) has the highest importance in the balanced scorecard perspectives while the organizational compliance (D3) has the weakest weight of new service development competencies. Accordingly, the return on investment (C5) and customer expectation (C8) are the most importance criteria as the commitment (C8) has the weakest criterion with 0.02.
Following phase continues with the ranking the alternatives by using the fuzzy TOPSIS and fuzzy VIKOR methods comparatively. Both methods used the linguistic terms provided from the group decision. The following steps of the fuzzy TOPSIS have been applied to rank alternatives by calculating the negative D − 1 and positive D * 1 ideal solution as well as the closeness coef icient CC 1 .  In Table 3, "A" shows the alternatives. With respect to the ownership, "S" demonstrates state banks, "P" refers to the private banks and "F" focuses on the foreign banks. A26 is the best bank in service innovation competencies, and A24 has the worst degree of the service innovation performance. The fuzzy VIKOR is used to provide the comparative results of the banks' new service development competencies as well. The results are demonnstrated in Table 4.    S3  11  11  A4  P1  3  3  A5  P2  20  19  A6  P3  15  14  A7  P4  22  20  A8  P5  24  24  A9  P6  7  5  A10  P7  2  2  A11  P8  5  4  A12  F1  16  16  A13  F2  25  25  A14  F3  17  17  A15  F4  18  21  A16  F5  13  13  A17  F6  9  9  A18  F7  12  12  A19  F8  10  10  A20  F9  14  15  A21  F10  21  22  A22  F11  8  8  A23  F12  19  18  A24  F13  26  26  A25  F14  23  23  A26  F15  1  1 In Table 4, A26 is the best in the new service development competencies while A24 has the weakest performance in the banking sector. Table 5 presents the comparative results of two integrated decision making approaches under the fuzzy environment. FANP-FTOPSIS and FANP-FVIKOR models give the same re-sults for selecting the best and the worst bank in the performance measurement. Thus, both approaches could provide the coherent results for ranking the banks in the performance of service innovation competencies.

CONCLUSION
Nowadays, banks all around the world have to give very much importance to service innovation concept to increase the competitive advantage. Otherwise, they cannot ind a chance to survive in such a severe competition. This study aimed to assess the service innovation performance. In the analysis process, 26 Turkish deposit banks are examined. Also, 16 different criteria are identi ied by considering 4 different dimensions of balanced scorecard approach. Moreover, fuzzy ANP, fuzzy TOPSIS and fuzzy VIKOR are considered to achieve this purpose. It is concluded that performance (D1) has the highest importance (0.34). Furthermore, it is also determined that internal factors have the lowest weight (0.18). Furthermore, it is also identi ied that return on investment (C1), proitability (C2) and customer expectations (C5) are the most important criteria. However, commitment (C8), contribution (C11) and encouragement (C12) are accepted as the least important criteria. It is also de ined that the results of fuzzy TOPSIS and fuzzy VIKOR are very similar. In other words, both of these two methods give similar ranking results for the banks with respect to new service development competencies. In spite of this situation, by analyzing the performance of the banks according to the ownership type, it is identi ied there is not a comparative advantage of one type to another. For instance, the best bank in both methods is a foreign bank (F15). However, another foreign bank (F13) is also determined as the worst bank. Similarly, a private bank (P7) has the second highest performance while another private bank (P5) has a very bad performance. The concept of service innovation has a high popularity in banking sector. Banks have to consider this situation to increase power. Additionally, it can also be said that new service development concept will probably become more important in the future. Hence, this study has signi icant results by focusing on a very important topic for banking sector.