Performance Evaluation and Priority of Banking Services Using FAHP “Case Study; Led by Five Banks Operating in the City of Sari in 2016”

Special Issue INTERNATIONAL JOURNAL OF HUMANITIES AND February 2016 CULTURAL STUDIES ISSN 2356-5926

Performance evaluation and priority of banking services using FAHP “Case study; led by five banks operating in the city of Sari in 2016”

Mahboubeh Sadeghpour Haji1 , Hadi Pazoki Toroudi2 , Sarineh Naderi Alamdardehi3

1. Department of Industrial Engineering, Qaemshahr branch Islamic Azad University, Qaemshahr, Iran; [email protected] 2. Department of Industrial Engineering, Tajan of university, Qaemshahr, Iran; [email protected] 3. Bachelor of Industrial Engineering , Tajan of university, Qaemshahr, Iran; [email protected]

Abstract

Background and purpose of study: Banks and financial institutions play a crucial role in economic progress and development of the country. Considering increasing number of banks and financial and credit institutions in the country, as well as privatization process of state –owned banks and conversion of credit unions and financial institutions to the banks, performance evaluation of these institutions has been particularly important. The purpose of this article is to evaluate and prioritize the performance of banking services based on FAHP decision making method. Methods: The criteria for evaluating the performance of banks in three levels (interest, dividends facilities (loans) and e-banking services) were designed for five banks (Parsian Bank, EN Bank, Melli Bank, Mellat Bank, Saderat Bank). Then, bank’s clients’ opinions and SPSS software were used to analyze the data descriptively. After that, Fuzzy AHP Solver software was used to analyze banks’ rankings. Findings: Data were collected by distributing 100 questionnaires among customers. Criteria for deposits, loan interest (mortgage) and e-banking services were also analyzed by using FAHP method. The weight values of 0/548 and 0/311 and 0/139 were obtained respectively for the three criteria. Finally, the five banks of Parsian Bank, EN Bank, Melli Bank, Mellat Bank, Saderat Bank, with weight values of 0/12, 0/075, 0/055, 0/411 and 0/339, were prioritized respectively. Results and conclusions: The results of the study showed that performance evaluation of Parsian Bank considering criteria of interest, loan interest (mortgage) and electronic banking services compared to other banks were high. For the customers of above mentioned banks, the interest has the priority over loan interests and e banking services. Eventually, understanding of customers’ needs as well as their satisfaction can result in development of mentioned banks among their customers and other banks and financial institutions.

Keywords: Performance Evaluation, multi-criteria decision making MCDM, MADM, Fuzzy AHP, rankings.

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1. Introduction:

In recent years, banks have actually been affected by numerous enhancements of the market: Deregulation, liberalization, globalization, and various innovations, each of these developments will affect the banks competitive conditions. As the role of market power on the profitability of banks, the competitive behavior also affects the accuracy and stability of the financial sector. Competition and secure market structures play an important role in social welfare. In other words, low prices, low interest rates and favorable loans to consumers of small and medium-sized investment companies are highly effective [1]. To develop electronic banking, a true understanding of the barriers and motives within and outside the organization is needed. Without achieving this level of understanding, all attempts for the change of traditional systems to electronic banking, will be doomed to failure. A true understanding of organizational and environmental factors effecting e banking would help banking systems to make a better decisions in order to change their systems stay alive in the new economy [2]. Electronic banking emerges as a new style of banking industry to provide banking services using electronic environment. E-commerce is considered as a prerequisite for e-banking. E-commerce will also grow further as the spread of electronic banking [3]. Electronic services can be defined as a web-based service or services that can be delivered interactively on the Internet. The concept of electronics refers to interactive information services. Zeithaml et al (2002) defined electronic banking services that have to be delivered via the Internet [4] and [5]. Bank are looking for better systems in order to meet the changing needs of their customers through variety of benefits and services. Due to these measures, the nature of banking services and communication with customers has changed. Highly competitive and rapidly changing environment forced banks to revise their attitude toward customer satisfaction and optimization of service quality [6]. Factors such as competition, retention, offering new services and constant changing needs of customer’s demands have prompted banks to develop their strategies in a way to maintain current customers and attract new customers by offering better services. Therefore, it is necessary to measure organizational performance through a measurement system that offers administrators a comprehensive overview of the business [7].

1.1. Performance Evaluation: Companies need an effective way to develop performance assessment that can measure organizational performance of the company and relate it to the goals of the company. This means that a holistic model to evaluate the performance of IT companies is critical to their survival. A comprehensive system of evaluation and performance measurement includes a diverse set of indicators that relates to the strategy of the organization [8] and [9].

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1.2. Fuzzy logic: The concept of fuzziness means to be multi-valued. This can be compared with two-valued logic, in which only two answers to every question or concept or mode (right or wrong, black or white) can be placed [10]. Fuzzy set is used to simulate the system that there is not enough data or explicit information [11]. By using fuzzy logic, users will be able to define appropriate rules, find the relationship between parameters and understand the decision-making process in the system. Considering a range of possibilities and the benefits of statistical methods due to the potential of human experience in formulating knowledge of mathematics, fuzzy logic can be considered a useful tool in the modeling of natural phenomena [12].

1.3.FAHP MCDM refers to the method designed for selecting or rankings of current options among the available items in conflict environments [12]. This caused user and analyst confusion in selecting and applying appropriate model facing to real-world problems. MADM different methods provide different answers for a specific question. Users got confused since they don’t know how to select the best methods. In other words, variety of proposed answers by these methods is one of the most serious criticisms for the colection methods. Different methods of applying different weights, algorithms and quantifying qualitative indicators for the parameters are caused by some methods. [14]. AHP is one of the most famous multi-criteria decisions in which competing models, with different levels of hierarchical elements (to help decision tree) is shown so that each level includes the criteria to be affected by variables in previous level. Through pair comparison method of decision tree elements and calculations, the most appropriate criteria will be determined. This requires four steps: Modeling, preferred judgment, the relative weight calculation and integration are used in order to rank the relative weight of decision options [15]. Real phenomena are not only black or white, but somewhat gray. They are always fuzzy, ambiguous and imprecise. Fuzzy set theory is a mathematical theory designed to model the uncertainty of human knowledge is human-dependent processes [16]. The decision maker can freely select the desired values. Uncertain judgment of a person can be represented by fuzzy number. The various options in the decision-making process and the possibility of a sensitivity analysis on its criteria and sub criteria are also presented. In addition, this method is based on pair-wise comparison that facilitates the judgments and calculations and also shows compatibility and incompatibility of the decisions. This is one of the benefits of this technique in multi-criteria decision [16]. FAHP applies a range of values for the expression of uncertainty decision-makers [18]. Chang presented a new method for using fuzzy AHP as analytic developmental methods. The numbers used in this method were triangular fuzzy numbers [19]. Finally FAHP based on the concept of fuzzy set theory was built in 1965 by Professor Lotfi Zadeh [20].

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Evaluation of fuzzy approach can be positive or negative outlook on users' decisions about the value of language functions that are normally obtained through fuzzy triangular. Therefore, this method can be a good alternative to priorities assessment and ranking instead of numerical rules. In many studies, FAHP method is used to solve various management problems [20]. When decision makers face with a complex and uncertain problem, they state their judgments such non- definitive ratios as "about twice as more important than" and "between two to four times less important". Standard AHP steps, in particular, prioritized approach cannot be considered as the right procedures. AHP or Analytic Hierarchy Process is one of the most widely used techniques of MADM. This method was coined for the first time by Thomas. El. Saaty , an Iraqi-born American scientist in 1970 AD [18]. Fuzzy AHP expands AHP ((Saaty)) through its combination with fuzzy set theory. In FAHP, after creating a hierarchy of issues, the relative importance of the factors corresponding to the relative standards of comparison must be shown. In this way, a fuzzy judgment matrix is constructed, and final scores options are offered by fuzzy numbers. The optimal choice through ranking fuzzy numbers using specific arithmetic operations can also be achieved [22]. Chang Young (1996), a Chinese scholar, presented an analytical developmental method. In this methodology, Chang calculates triangular fuzzy numbers judgment matrix and vector weight elements [23].

2. Review of Literature Yalcin et al (2012) offered a new method for evaluating financial performance using fuzzy multi- criteria method for manufacturing industries in Turkey [24]. Also Shaverdi et al (2011) conducted a research titled (private banking sector's performance evaluation using the Balanced Scorecard with fuzzy multi-criteria decision approach) .Based on literature related to banking, balanced scorecard and its implications, 21 indicators were selected for further analysis by experts and managers. In addition, the weight of each index was calculated by the Analytic Hierarchy Process fuzzy (FAHP). In order to rank private banks, three methods of TOPSIS, Electre, VIKOR were used by researchers in this paper. The results showed that using the hybrid approach fuzzy multi-criteria decision along with BSC presents new and effective tools and useful model for performance evaluation [21]. Shaverdi and et al (2014) in other article titled (using fuzzy AHP approach to evaluate the financial performance of Iran's petrochemical industry) examined seven active companies by using FAHP. In this paper, evaluation performance of seven active companies in the field of petrochemical industry of analysis was discussed using fuzzy analytic hierarchy process (FAHP). At first Iran’s petrochemical industry was studied then a basic framework for a good decision model was introduced. After that, main criteria for the financial evaluation and ratios have been defined. These criteria are as follows: Current ratio, quick ratio, debt ratio, long-term debt, deductions, total assets, inventory turnover ratio, the proportion of total turnover, assets, fixed assets turnover ratio, received turnover

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accounting, net profit, ROI, ROE, asset growth and growth stocks among the shareholders of financial measures are being used. In the next step, fuzzy set and fuzzy AHP are described and analyses of results are presented. [25]. Pradina et al (2015) in the article (using Fuzzy AHP- TOPSIS to decide human resources process), studied human resources of Telecommunication company of Indonesia using Fuzzy AHP-TOPSIS [26]. Momeni and et al (2011) studied (evaluate the performance of private banks listed in the Tehran Stock Exchange using the hybrid approach fuzzy multi-criteria decision-making and Balanced Scorecard). In this paper, four aspects of BSC for evaluating the performance of the mission, goals, solutions and review of the literature were developed. Then some banking experts on the criteria and sub-criteria of methods TOPSIS, VIKOR, were used. In this study, Parsian Bank was ranked first, EN Bank and Karafarin Bank were also ranked second and third respectively [7]. Sarouj Koul and et al (2011), in an article titled the dynamic vendor selection based on fuzzy AHP, have noticed that many decision-makers or experts choose their sellers based on their experience or intuitions. To overcome this problem, a dynamic model was developed based on vendors’ support with the time axis. This model is not always definitive, but it involves a high degree of uncertainty and lack of ambiguity. Accordingly, the authors presented a model using analytic hierarchy process FAHP. This model can be the best method to choose the best seller under uncertainty in the time axis with a supply chain. It also can be used for other researchers and bureau members or whoever is responsible for vendor selection [27].

3. Design and Methodology: In order to collect data, two research methods have been used in this article. For this purpose, library research i.e. articles and journals, related books and theses were used to determine Introduction and research background. The field study is used to collect relevant information. The study was led by five banks operating in the city of Sari. Parsian Bank, Saderat Bank , EN Bank, Melli Bank and Mellat Bank were considered in this study. The population of this study was among customers of these five operating banks. (N=100) The designed model in this research measures interest, profit facilities (loans) and e banking services. The technique used in this study was FAHP. The data also collected through decision-making methods and a researcher made questionnaire titled the performance evaluation and priority banking service using FAHP (supervision of agent banks) in the city of Sari. In addition, SPSS software for the descriptive statistics analysis was used. The scoring technique of the mentioned questionnaire based on Table 1 (comparison test procedure linguistic scale FAHP) was also designed and then implemented. Finally, Fuzzy AHP Solver software was used to analyze the collected data and the compatibility of the results were also carried out and then obtained results were prioritized.

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3.1.Fuzzy Analytic Hierarchy Process (FAHP) To calculate the weight criteria of triangular fuzzy numbers, the hierarchical model should take its form and then respondents study indicators in the form of linguistic variables listed in Table 1. Accordingly, comparative matrix are shaped and combined with triangular fuzzy numbers. In order to control compliance judgments, the inconsistency percent of less than 0.1 can be checked and monitored. It is worth noting the weight of the indicators in this study, is obtained by using Chang method (1996). Chang (1996) on the use of fuzzy numbers assumed that X = {X1, X2, ..., Xn} set of factors and U = {U1, U2, ... Un} is a set of objective [17].

Table 1:Comparison of paired spectrum and linguistic scale of FAHP method

English courses for comparing Fuzzy Triangular fuzzy numbers numbers

Very high 9 (9,9,9) Relatively very high 8 (7,8,9)

high 7 (6,7,8) Relatively high 6 (5,6,7) average 5 (4,5,6) Relatively low 4 (3,4,5) low 3 (2,3,4) Relatively very low 2 (1,2,3) Equally important 1 (1,1,1)

Source: Gumus, 2009, [28].

To obtain the expansion phase composition according to the i-th factor, the equation (1) is used in this method. j Equation .1) Si= gi j -1 [ gj]

i i In this regardM gj is triangular fuzzy number in which the order of m for each factor expandsM gj

where i=1,2,…,n and j=1,2,…,m. can be achieved using equation (2).

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j Equation .2) gi = ( j , j ,

j To calculate , Operations of fuzzy values (j = 1,2,…,m)M g obtained from equation (3). j Equation .3) gi = ( i , i ,

In order to obtain weight criteria for triangular fuzzy numbers, the reverse form of equation (3) can be operated via the equation (4). j -1 Equation.4) [ gj] = ( ,

, )

Then, the degree of feasibility M2 ≥ M1 is calculated from the equation (5). Equation.5) V(M2 ≥ M1) = supy≥x[min(µM1(x) , µM2(y))] = hgt(M1 M2)

= µM2(d) =

In order to compare M1 and M2, V (M1 ≥ M2) and V (M2 ≥ M1) are calculated. The degree of

feasibility for each fuzzy number should be also larger than k, Mi, (i = 1,2, ..., k). This is considered in equation (6). Therefore, we can assume d '(A) = min V (Si ≥ Sk) and k = 1,2, ... n; k ≠ i, and weight vector can be defined by equation (7). After normalization, the gravimetric Dyfazy of W is obtained through the equation(8).

Equation.6) V(M ≥ M1, M2,…,Mk) = min V(M ≥ Mi), i=1,2,…

T Equation.7) W’ = (d’(A1),d’(A2),…,d’(An)) , i=1,2,…,n

T Equation.8) W = (d(A1),d(A2),…,d(An))

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3.2. Analytical model of research

Figure.1.Analytical model of research

Performance evaluation and priority of banking services using FAHP

Electronic Banking Interests On Deposits Loan Interests Services

Eghtesad Novin Parsian Bank Saderat Bank Melli Bank Mellat Bank Bank

4. Research findings

A: The results of descriptive statistics: According to a questionnaire completed by 100 customers of banks, the number of 61 men and 39 women were asked to complete the survey questionnaire.

Table no.2. men and women number and percentage men women

percentage number percentage number 61% 61 39% 39

According to table (2), 39 percent if respondents were among women and 61 percent of them were men. In this study, the number of men customers was more than women.

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Special Issue INTERNATIONAL JOURNAL OF HUMANITIES AND February 2016 CULTURAL STUDIES ISSN 2356-5926 Table.3.Descriptive statistics of respondents

total man woman index group row percentage number percentage number percentage number %30 30 %19 19 %11 11 single Marital 1 %70 70 %42 42 %28 28 married status 2 Under 30 %26 26 %17 17 %9 9 3 years old 30-39 years %37 37 %21 21 %16 16 4 old Age 40-50years %18 18 %8 8 %10 10 5 old 51 years %19 19 %15 15 %4 4 6 old High %19 19 %14 14 %5 5 school 7 diploma Associate %15 15 %7 7 %8 8 Degree of 8 degree education Bachelor %40 40 %24 24 %16 16 9 degree Above %26 26 %16 16 %10 10 10 masters According to Table 3, descriptive statistics of respondents based on their marital status, age and level of education is offered. 30% of the populations of this study were single and 70% were married, and the most age group was 37 % between ages of 30-39. Approximately 40 % of the survey are those who have a bachelor's degree.

B: determining the index weights based on AHP According to Table 1, and equations1 – 8, the results of comparative matrix and weighted deposit interest, dividend facilities (loans) and electronic banking services are displayed through Table 4. The comparative matrix results and weighing of Parsian Bank, Saderat Bank, EN Bank, Melli Bank and Mellat Bank is listed in Table 8.

Table 4: Matrix of pair wise comparison criteria

Main factors interests on deposits loan interests electronic banking services Interests on deposits (1,1,1) (2,3,4) (1,2,3) Loan intersts (0/25,0/333,0/5) (1,1,1) (1,2,3) Electronic banking services (0/333,0/5,1) (0/333,0/5,1) (1,1,1) Total rows Si Not normal weight Normal weight

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(4,6,8) (0/258,0/529,1/011) 1 0/548 (2/25,3/333,4/5) (0/145,0/294,0/568) 0/568782 0/311 (1/666,2,3) (0/107,0/176,0/379) 0/255166 0/139

As seen in table 4 and according to the analysis software based on Fuzzy AHP Solver, criteria for deposit, loan interest and electronic banking services, the weight values of 0/548, 0/311 and 0/139 have been achieved. They placed accordingly as first, second and third respectively. The values obtained are also consistent with our criteria considering that the rate of compatibility matrix in this table is 0/1.

Table 5: Matrix of pair wise comparison sub criteria of loan interest (mortgage)

Interests on Parsian Bank Saderat Bank EN Bank Melli Bank Mellat Bank deposits Parsian Bank (1,1,1) (5,6,7) (4,5,6) (5,6,7) (4,5,6) Saderat Bank (0/143,0/167,0/2) (1,1,1) (0.142,0.16,0.2) (4,5,6) (4,5,6) EN Bank (0/167,0/2,0/25) (5,6,7) (1,1,1) (4,5,6) (5,6,7) Melli Bank (0/143,0/167,0/2) (0/167,0/2,0/25) (0/167,0/2,0/25) (1,1,1) (4,5,6) Mellat Bank (0/167,0/2,0/25) (0/167,0/2,0/25) (0/143,0/167,0/2) (0/167,0/2,0/25) (1,1,1) Total rows Si Not normal Normal weight weight (19,23,27) (0/266,0/376,0/534) 1 0/596 (9/285,11/327,13/4) (0/13,0/185,0/265) 0 0 (15/167,18/45,21/292) (0/213,0/302,0/421) 0/675079 0/403 (5/477,6/567,7/7) (0/077,0/107,0/152) 0 0 (1/644,1/767,1/95) (0/023,0/029,0/039) 0 0

As seen in table 5 and according to analysis by software Fuzzy AHP Solver of Parsian Bank, Saderat Bank , EN Bank, Melli Bank and Mellat Bank, the benchmark interest weights of 0/596 , 0 , 0/403 , 0 and 0, are obtained respectively. The obtained values are compatible and the criterion of compatibility rate achieved in the matrix table is 0/03.

Table 6: Pair wise comparison matrix sub criteria of deposit interest

Loan interest Parsian Bank Sadeaat Bank EN Bank Melli Bank Mellat Bank Parsian Bank (1,1,1) (0/142,0/16,0/2) (4,5,6) (0/142,0/16,0/2) (7,8,9) Saderat Bank (5,6,7) (1,1,1) (0/142,0/16,0/2) (3,4,5) (0/142,0/16,0/2) EN Bank (0/167,0/2,0/25) (5,6,7) (1,1,1) (5,6,7) (3,4,5) Melli Bank (5,6,7) (0/2,0/25,0/333) (0/143,0/167,0/2) (1,1,1) (4,5,6)

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Mellat Bank (0/111,0/125,0/143) (5,6,7) (0/2,0/25,0/333) (0/167,0/2,0/25) (1,1,1) Not normal Total rows Si Normal weight wewight (12/284,14/32,16/4) (0/167,0/224,0/312) 0/708601 0/267 (9/284,11/57,13/442) (0/126,0/181,0/256) 0/40598 0/153 (14/167,17/45,20/292) (0/193,0/273,0/386) 1 0/378 (10/343,12 /667,14/575) (0/141,0/ 198,0/277) 0/530053 0/201 (6/478,7/825,8/768) (0/088,0/123,0/167) 0 0

In table 6 as can be seen, according to analysis by software Fuzzy AHP Solver of Parsian Bank, Saderat Bank, EN Bank, Melli Bank and Mellat Bank, the benchmark loan interest (loans) weights of 0/267, 0/153 ,0/378 , 0/210 and 0, are obtained respectively. The obtained values are compatible and the criterion of compatibility rate achieved in the matrix table is 0/04.

Table 7: Pair wise comparison matrix sub criteria of banking services Electronic Parsian Saderat Bank EN Bank Melli Bank Mellat Bank banking Bank services Parsian Bank (1,1,1) )0/166,0/2,0/25) (1,1,1) (1,1,1 (0/333,0/5,1) Saderat Bank (4,5,6) (1,1,1) (0/25,0/333,0/5) (0/166,0/2,0/25) (0/333,0/5,1) EN Bank (1,1,1) (2,3,4) (1,1,1) (0/142,0/16,0/2) (0/142,0/16,0/2) Melli Bank (1,1,1) (4,5,6) (5,6,7) (1,1,1) (0/166,0/2,0/25) Mellat Bank (1,2,3) (1,2,3) (5,6,7) (4,5,6) (1,1,1) Not normal Total rows Si Normal weight weight (3/499,3/7,4/25) (0/064,0/081,0/116) 0 0 (5/749,7/033,8/774) (0/105,0/154,0/239) 0/090961 0/049 (4/284,5/323,6/4) (0/078,0/116,0/174) 0 0 (11/166,13/45,15/316) (0/204,0/294,0/417) 0/76429 0/411 (12,16/25,20/072) (0/219,0/355,0/547) 1 0/539

In table 7 can be seen, according to analysis by software Fuzzy AHP Solver of Parsian Bank, Saderat Bank, EN Bank, Melli Bank and Mellat Bank, and based on electronic banking criteria, weights of 0, 0/049 ,0, 0/411 and 0/539, are obtained respectively. The obtained values are compatible and the criterion of compatibility rate achieved in the matrix table is 0/08.

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C: Final ranking index based on AHP

Table 8: Weight calculated and the final ranking

options Parsian Bank EN Bank Melli Bank Mellat Bank Saderat Bank

weight 0/411 0/339 0/12 0/075 0/055 rank 1 2 3 4 5

Finally, the options available were listed in Table 8. Based on FAHP, Parsian Bank, EN Bank, Melli Bank, Mellat Bank and Saderat Bank were ranked from 1 to 5 respectively Considering the analysis by the Software Fuzzy AHP Solver of Pasian Bank, Saderat Bank, EN Bank, Melli Bank and Mellat Bank, weights of 0/411, 0/339, 0/12, 0/075 and 0/055 are achieved respectively.

Figure 2: Rating Sort by 5 percent based on banking software output of Fuzzy AHP Solver

According to Figure 2 obtained from the application, the percentage of the final ranking of Pasian Bank 41/1%, EN Bank 33/9%, the Melli Bank 12%, Mellat Bank 7/5% and Saderat Bank 5/5 %. Among five mentioned banks, Parsian Bank, had the highest and Saderat Bank the lowest percentage based on three criteria defined in this study respectively.

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5. Discussion and conclusion

Banks and financial institutions play a crucial role in economic progress and development in their country. Now regarding to the considerable number of banks and financial and credit institutions in the country, as well as the process of privatization and conversion of credit unions and financial institutions to the bank, their performance evaluation is particularly important. As mentioned, the purpose of this study is the evaluation of performance and priority banking service, banking deposits based on profit, profit facilities (loans) and electronic banking services to help FAHP method, one of the country's decision-making procedures in the banking industry. This study attempted to rank the criteria by AHP. First interest’s deposit, loan interest (mortgage) and electronic banking services were analyzed using FAHP and weights 0/548, 0/113 and 0/139 were obtained respectively. The obtained weights values showed that the profit achieved was higher than loan interest and e-banking services. After that final weights of recorded matrix of findings before the ranking for Parsian Bank, EN Bank, Saderat Bank, Melli Bank and Mellat Bank due to the weight of benchmark interest were 0/596, 0, 0/403, 0 and 0, and according to the benchmark loan interest, the weight of 0/267, 0/153, 0/837, 0/210 and 0 and also for electronic banking services the criteria weights 0/049, 0, 0/411 and 0/395 were obtained. Finally, after ranking based on software Fuzzy AHP Solver, weights of 0/411, 0/339 and 0/12 and 0/075, 0/055 for Parsian Bank, EN Bank, Melli BAnk, Mellat Bank, Saderat Bank were obtained respectively. Finally, it was determined that evaluating the performance of Parsian bank in terms of deposit , loan interest services and e-banking services compared to the other banks were at the top place. The results also showed that for customers of the five mentioned banks, interest has higher priority over profit facilities (loans) and electronic banking services. According to the results, it is suggested that banking industry should try to boost deposits, loan interest (mortgage) e-banking services and also pay special attention to the customers’ comments about their experiences in all aspects of banking as well as their needs and expectations. To meet these goals, the capacity of general and specialized banks in providing banking services and the use of methods and practices and new technologies in the banking industry and the managers and employees attempt to understand, follow and respect the needs of customers and their contentment is very important.

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