Customer satisfaction factors (CSFs) with online banking services in

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Journal of Islamic Marketing Customer satisfaction factors (CSFs) with online banking services in an Islamic country: I.R. Iran Tooraj Sadeghi Kambiz Heidarzadeh Hanzaee

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To cite this document: Tooraj Sadeghi Kambiz Heidarzadeh Hanzaee, (2010),"Customer satisfaction factors (CSFs) with online banking services in an Islamic country", Journal of Islamic Marketing, Vol. 1 Iss 3 pp. 249 - 267 Permanent link to this document: http://dx.doi.org/10.1108/17590831011082428 Downloaded on: 28 April 2015, At: 05:17 (PT) References: this document contains references to 23 other documents. To copy this document: [email protected] The fulltext of this document has been downloaded 2108 times since 2010*

Users who downloaded this article also downloaded: Kamal Naser, Ahmad Jamal, Khalid Al-Khatib, (1999),"Islamic banking: a study of customer satisfaction and preferences in Jordan", International Journal of Bank Marketing, Vol. 17 Iss 3 pp. 135-151 http:// dx.doi.org/10.1108/02652329910269275 Saad A. Metawa, Mohammed Almossawi, (1998),"Banking behavior of Islamic bank customers: perspectives and implications", International Journal of Bank Marketing, Vol. 16 Iss 7 pp. 299-313 http:// dx.doi.org/10.1108/02652329810246028 Hayat Muhammad Awan, Khuram Shahzad Bukhari, Anam Iqbal, (2011),"Service quality and customer satisfaction in the banking sector: A comparative study of conventional and Islamic banks in Pakistan", Journal of Islamic Marketing, Vol. 2 Iss 3 pp. 203-224 http://dx.doi.org/10.1108/17590831111164750

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Customer satisfaction factors (CSFs) with online banking services in an Islamic country I.R. Iran

Customer satisfaction factors 249

Tooraj Sadeghi and Kambiz Heidarzadeh Hanzaee

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Department of Business Management, School of Management and Economics, Islamic Azad University (Tehran Science and Research Branch), Tehran, Iran Abstract Purpose – This paper seeks to investigate the key factors underlying customer satisfaction with electronic banking services in an Islamic country, Iran. Design/methodology/approach – The authors validate a measurement model for customer satisfaction evaluation in e-banking service quality based on different service quality models and theories such as technology acceptance model, theory of reasoned action and theory of planned behavior. Findings – The paper provides a model of seven factors on the following dimensions: convenience, accessibility, accuracy, security, usefulness, bank image, and web site design. Some of these factors illustrate a significant statistical difference between males and females. Originality/value – These dimensions are determinants of customer’s quality perception in e-banking services and this paper presents new directions in service quality research and offers new directions to researchers and managers in providing service quality improvement. Keywords Banking, Customer satisfaction, Customer services quality, Electronic commerce, Iran, Islam Paper type Research paper

Introduction The rapid spread of technology has made the internet the best channel to provide banking services and products to customers. Banks now consider the internet as part of their strategic plan. It will revolutionize the way banks operate, deliver, and compete, especially because the competitive advantages of traditional branch networks are eroding rapidly. A report from Booz Allen and Hamilton, for example, claims that the internet poses a very serious threat both to the customer base of the traditional banking oligopoly and to its profits (Alsajjan et al., 2006). Customers now demand new levels of convenience and flexibility in addition to powerful and easy to use financial management tools, products, and services that traditional retail banking cannot offer. Internet banking has allowed banks and financial institutions to provide these services by exploiting an extensive public network infrastructure (Yiu et al., 2007). As a result, the quality of electronic banking services (e-banking) has become a major area of attention among researchers and bank managers due to its strong impact on business performance, lower costs, customer satisfaction, customer loyalty, and profitability (Seth et al., 2004).

Journal of Islamic Marketing Vol. 1 No. 3, 2010 pp. 249-267 q Emerald Group Publishing Limited 1759-0833 DOI 10.1108/17590831011082428

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This paper presents a conceptual model that attempts to show the relationships that exist between salient variables. It is a simplified description of the actual situations. Conceptual models in service quality enable management to identify quality problems and help them plan the launch of a quality improvement program, thereby improving the efficiency, profitability, and overall performance (Ghobadian et al., 2004). Owing to cultural and environmental effects, consumers in different countries have different perception of what service quality is. Thus, managers who seek to develop service standards may not succeed unless they are aware of the value of environmental differences between countries in terms of economic development, political ideology, cultural value system, and other culture-specific factors. This study seeks to recognize the factors that can affect service quality perceptions in the e-banking sector by constructing a model to measure the quality of e-banking services in Iran. Online banking Virtual banks or “branchless banks” is a relatively new concept used to define banks that do not have a physical location such as a branch, but offer services only through the internet and ATMs to deposit or withdraw funds (Sayar and Wolfe, 2007). Online banking differs in many respects from traditional branch banking. One of the most notable differences concerns the connection to the bank’s information processing system. Previously, customers had a relationship with a bank’s front-desk employee, who had access to the bank’s information system. In online banking, customers have direct access to a bank’s information system from home, work, school, or any other place where a network connection is available. In this new situation, the customer is defined as an end-user of the bank’s data processing system. In end-user computing, the user’s personal computer (PC) plays a pivotal role (Pikkarainen et al., 2006). An online banking user performs at least one of the following transactions online: . check account balance and transaction history; . pay bills; . transferring funds between accounts; . request credit card advances; . order checks; and . manage investments and trade stocks. From a bank’s perspective, using the internet is more efficient than using other distribution mediums because banks are looking for an increased customer base (Alsajjan et al., 2006). People are becoming more comfortable with banking online and they believe that it will become necessary for all community banks to offer online banking services. Esser (1999) and Simpson (2002) noted that the benefits of e-banking include: . competitive advantage; . customer retention and attraction; . increased revenues; and . reduced costs (Simpson, 2002).

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Ebanking in Iran The trend of developing and expanding information technology (IT) throughout the world, especially in developed countries on the one hand, and commercial relationships between countries and nations on the other hand, have prompted Iranian banks to undertake widespread and extensive activities in line with applying computer systems in their banks in the 1980s and 1990s. Consequently, consumers’ knowledge and awareness have been enhanced regarding automated banking operations by gradually expanding access to the internet and PCs. As a result, Iranian commercial banks consider electronic banking in their future planning, along with improving their methods and moving toward modern banking (Sadeghi, 2004, moving toward electronic banking is an ambiguous and unstable step without creating its infrastructure. First electronic banking will only be able to move and secure a stable position with an integrated and comprehensive software and hardware system (Safarzadeh, 2009). Activities and measures banks are making to prepare a comprehensive integrated automation plan indicate that banks have also realized the need to provide infrastructure with a comprehensive and integrated automation system. At present, creating an integrated, comprehensive automation plan is top of the banks’ agenda in order to move toward developing modern banking. After implementing these plans, the banks will enjoy the readiness required for electronic banking. Electronic banking in other countries Through a review of the literature, this section describes the degree to which internet banking has been adopted in countries of the world. Electronic banking in Estonia The first internet bank in Estonia was introduced in 1996. Estonia has a relatively high penetration of PCs and internet access, with 45 percent of the Estonian population (ages 15-74) being internet users. In one of the most thorough comparisons of internet penetration and internet banking penetration conducted, Estonia and Scandinavian countries show similar patterns: 50 percent or more of internet users have adopted electronic banking. Estonia, however, clearly stands out as an extreme case among Central Eastern European countries. While one in four active internet users in Europe also uses an internet bank, 57 percent of the active Internet users in Estonia are als internet bank users. This indicates that, in the case of Estonia, background features other than internet penetration also play an important role in adopting internet banking (Eriksson et al., 2005). Electronic banking in Taiwan For several years, commercial banks in Taiwan have tried to introduce internet-based e-banking systems to improve their operations and to reduce costs. Despite their efforts aimed at developing better and easier internet banking systems, these systems have remained largely unnoticed by the customers, and certainly were underused in spite of their availability. In 2002, only about 33 percent of banking transactions in Taiwan were conducted via the Internet. A total of 1.25 million Taiwanese people reported having ever visited internet banking sites in May 2002.

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A need exists, therefore, to understand users’ acceptance of internet banking, and to identify the factors that can affect a consumer’s intention to use internet banking. This issue is important because the answer holds the clue that will help the banking industry formulate marketing strategies to promote new forms of internet banking systems in the future (Wang et al., 2003).

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Electronic banking in Turkey Based on research of Turkish internet banking users, the selection of an internet banking service provider is affected by security, reliability, and privacy. The researchers identify three segments underlying the selection of the bank: (1) “speed seekers” (who view download speed, transaction speed, user-friendliness of the site, and privacy); (2) “cautious users” (who value the reliability of the bank, security of the Internet branch, variety of services offered, and loyalty); and (3) “exposure users” (who are more open to the influence of external factors such as advertising and suggestions from others). Turkish customers have been found to be satisfied with the internet banking services they use, with those who have more experience with internet banking and use more of its services as being more satisfied and more likely to make recommendations to others (Sayar and Wolfe, 2007). Electronic banking in China In 1997, China Merchants Bank was first to launch the internet payment system in China. Thereafter, internet banking and telephone banking systems spread rapidly within mainland China. Chinese domestic banks are confident that electronic banking benefits will outweigh traditional banking services in the future. They are therefore eager to implement new technologies and services to penetrate the market and gain competitive advantage. Most retail banks in China now provide online banking as add-on services to the existing branch activities, while mobile banking is just starting to be implemented. One barrier that prevents active online trading in mainland of China is the lack of regulation. Chinese consumers might be more concerned, therefore, about the risks of new and unfamiliar technology-based financial services, such as online and mobile banking (Laforet and Li, 2005). Electronic banking in Malaysia The banking industry underwent a consolidation exercise in 1999 in which 54 domestic banks merged to form ten domestic anchor banks to meet the challenges of globalization and liberalization (Wei and Nair, 2006). Like most Muslim countries, Malaysia has a dual banking system; that is, it has a conventional banking system and an Islamic banking system. There are two Islamic banks in Malaysia: the Bank Islam Malaysia and Bank Muamalat. The early decade of the 1990s saw the emergence of automated voice response (AVR) technology. Using the AVR technology, banks offered telebanking facilities for financial services. With further advancements in technology, banks were able to offer services through personal computers owned and operated by customers at their

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convenience by using proprietary intranet software. The users of these services were, however, mainly corporate customers rather than retail customers. Since June 2000, with the Malaysian Central Bank giving the approval for commercial banks to offer e-banking services, all the anchor banks have created a web presence in various ways (Sohail et al., 2002).

Customer satisfaction factors

Behavioral adoption theories The following sections provide an overview of behavioral adoption models, note similarities and differences between them, and discuss each theory. The theories discussed are theory of reasoned action (TRA), theory of planned behavior (TPB), and technology acceptance model (TAM). These models follow the attitude-behavior paradigm that suggests that actual behavior is declared through intention toward the behavior. Intention is influenced by attitude and subsequently salient beliefs influence attitude. Ozdemir and Trott (2009) introduced TAM as an extension of the TRA, but with more focus on the context of computer use (Ozdemir and Trott, 2009). TPB is a further extension of the TRA that further explains computer use behavior (Gerrard et al., 2006).

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Theory of reasoned action Many technology adoption research studies have used theory. According to this theory, an individual’s intent to adopt an innovation is influenced by his attitude toward the behavior and subjective norm (SN). Subsequently, a person’s behavior is determined by his intention to perform the behavior. The attitude toward performing the behavior is an individual’s positive or negative belief about the performing of the specific behavior. In fact, attitudes are comprised of the beliefs a person accumulates over his lifetime. These beliefs are created from experiences, outside information, or from within the self. Only a few of these beliefs, however, actually influence attitude. SN is beliefs about what others will think about the behavior; in other words, the perceived influences of social pressure on an individual to perform or not perform the behavior. “The person’s belief that specific individual or groups think he should or should not perform the behavior and his motivation to comply with the specific referents” (Yahyapour, 2008). Fishbein (1980) proposed that variables not included in the model can affect intention and then behavior (Figure 1). The theory of planned behavior TPB is one of the most widely used models in explaining and predicting individual behavioral intention (BI) and acceptance of IT. TPB is an attitude – intention – behavior model, which posits that an individual’s behavior is determined by perceived behavioral control (PBC) and intention. A attitude, SN, and PBC, in turn, determine intention. The TPB proposed that an individual’s intention to perform an act is affected by his attitude toward the act, SN, and PBC (Johnson and Hall, 2005). According to TPB, an individual’s behavior is determined by BI and PBC, and BI is determined by attitude toward behavior (A), SN, and PBC. Attitudes toward behavior reflect one’s favorable or unfavorable feelings of performing a behavior. SN reflects

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Beliefs about the outcome of the behavior Evaluation of expected outcomes

Attitude towards action

254

Behavioral intention

Normative beliefs

Behavior

Subjective norms

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Figure 1. TRA model

Motivation to comply Source: Yahyapour (2008)

one’s perception of others’ relevant opinions on whether or not he or she should perform a particular behavior. PBC reflects one’s perceptions of the availability of resources or opportunities necessary to perform a behavior (Hsu et al., 2006) (Figure 2). The technology acceptance model Researchers and practitioners have widely used the TAM to help to predict and make sense of user acceptance of ITs (Yiu et al., 2007). TAM, introduced by Davis (DATE), adapts the TRA model, specifically to model user acceptance of IT. The goal of TAM is to explain the what determines computer acceptance capable of explaining user behavior across a broad range of end-user computing technologies and user populations, while being both cost-conscious and theoretically justified. TAM adapted the TRA model to the domain of user acceptance of IT, replacing the TRA model’s attitudinal determinants with two beliefs: perceived usefulness and perceived ease of use. TAM was found to be a simpler, easier to use, and more powerful model to uncover what determines user acceptance of IT, while both models where found to satisfactory predict an individual’s attitude (satisfaction) and BI. In addition, TAM’s attitudinal determinants outperformed the TRA model’s much larger set of measures (Chirani and Rahmati, 2009).

Figure 2. TPB model

Behavioral beliefs and outcome evaluations

Attitude toward behavior

Behavioral beliefs and outcome evaluations

Subjective norm

Behavioral beliefs and outcome evaluations

Perceived behavioral control

Source: Yahyapour (2008)

Behavioral intention

Actual behavior

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The two important variables in TAM are: (1) Perceived ease of use (PEOU) is defined as the degree to which a person believes that using a particular system would be free of effort. (2) Perceived usefulness (PU) is defined as the degree to which a person believes that using a particular system would enhance his or her performance (Al Sukkar and Hasan, 2005). PEOU and PU are influenced by external variables. External variables vary according to the context. Different variables have been used as external variables in TAM research, including computer anxiety, computer self-efficacy, playfulness, information richness, task characteristics, and experience (Haghighinasab, 2009). TAM helps senior managers responsible for offering and developing banking products on line and information systems developers predicate users’ BI. This can lead to actual changes and modifications in people’s behavior when thinking about and using internet banking technologies. This knowledge, or at least additional insight, allows information systems developers to devise ways to make as system appear easier to use, and allows banking and technology experts to develop new ways to support the needs and expectations of Internet banking customers (Karjaluto, 2002). Methodology Among the research studies in which the coordinate matrix or covariance is analyzed, authors can point to factor analysis or structural equation modelling (SEM), which has been used in this research. In factor analysis, the goal is to summarize data or reach the latent variables. In structural equation modeling, the goal is to test the structural relationships in compliance with existing research theories and findings. After pretesting and obtaining experts’ and authorities’ viewpoints, the completed questionnaire for this study was placed in the web site of some of the banks and the FABA Center[1] to reach users of electronic banking services (ATMs, telephone bank, internet banking). For a final evaluation of the questionnaire, we applied the Cronbach’s a method and the split-half method. In this research, the independent variables are electronic service quality factors including convenience, accessibility, accuracy, security (privacy), usefulness, bank management and image, and web site design (design/content/speed). The customers’ satisfaction with electronic banking services in Iran has been taken into consideration as a dependent variable. The main objective of this research is to identifying the factors effective in helping consumers feel satisfied with the electronic banking services in Iran. Structural equations have been compared using ANOVA statistical testing of the average of the mark given to each factor among males and females. Problem definition Internet is dramatically changing how financial services are designed and delivered to consumers. The Iranian banking industry still relies on physical branches and commercial retail banking, but forces from the cost side as well as the customers’ needs for better services, have pushed banks toward implementing internet-based systems to

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reduce the cost of services and improving response time to the customer. There is a need, therefore, to uncover the factors that can affect customer satisfaction that leads them to adopt internet banking. Demographic specifications Because the general specifications of the sample group are important in survey studies, we now describe the specifications of our survey as measured by age, gender, and education. Among the respondents, 67 percent are male and the rest, namely 33 percent, are female. The frequency of males is therefore higher than females. The frequency related to the respondents’ education shows 10 percent have a high school diploma or lower, 30 percent have a Bachelor’s degree, 46 percent have a Master’s degree, and 13 percent have a PhD. In terms of the frequency with which respondents used electronic services provided by banks, the highest number of respondents have used the electronic services of Melli Bank followed by Mellat Bank, then Saman Bank. Among private banks, Saman Bank held the first rank in terms of the highest number of users who accessed electronic services. Structural equations model To test this research study’s model, we have used data analysis with the help of SEM. Modeling of structural equations means creating a statistical model for the study of linear relations between latent (unviewed) variables and evident (viewed or observed) variables. In other words, structural equation modeling is a powerful statistical tool that combines a measurement model (affirmative factor analysis) and the structural model (regression of path analysis) into one statistical synchronic test. Fitness and appropriateness of the model Several criteria are used in the Smart-PLS for this work. One of the indices is reliability, a scale that measures the degree of confidence in the results. Reliability is measured by Cronbach’s a, which is an outstanding method for assessing the reliability of a coefficient. Cronbach’s a is a coefficient of reliability and adjustment and measures the internal adjustment of the model. In other words, Cronbach’s a measures how well a set of viewed variables describe a latent structure. As you see in Table I Cronbach’s a is high for all the factors (higher than 0.7). This indicates that the questions raised in each part of the questionnaire satisfactorily Factors

Table I. The coefficient of Cronbach’s a separated for each of the factors

F1: Convenience F2: Accessibility F3: Accuracy F4: Security F5: Usefulness F6: Image F7: Web site design Satisfaction

AVE

Composite reliability

Cronbach’s a

0.489825 0.504489 0.577323 0.431697 0.451801 0.412257 0.421609 0.348535

0.760396 0.800467 0.872179 0.866889 0.827976 0.826504 0.84942 0.7684

0.586442 0.671166 0.821306 0.827697 0.749741 0.75919 0.796964 0.646035

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meet the required reliability and are suitable for measuring the factors. This enhances the degree of confidence in the results. On the other hand, the composite reliability index, which is also higher than 0.7 for all factors, indicates that each factor has been appropriately described based on the evaluation and measurement questions. Composite reliability indicates how well each structure has been described by the viewed and observed variables. Quantities higher than 0.7 express how well the concerned structure has been described by the observed and viewed variables. In view of these results, the reliability of the data is confirmed.

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Validity of structure Validity of the structure is another important item in analyzing structural equations and correlations among factors. A higher the degree of correlation indicates the questions were answered consistently and viewpoints coordinated. It is evident that the more coordinated the results, the more the results can be trusted and and inference and decisions made in view of the data. At this stage, we used discriminant validity to study the structure validity. We used average variance extracted (AVE) between the factors. In this state, if the correlations between the factors are lower than the root f, this quantity the discriminant validity is confirmed. Structure validity is measured with the help of AVE, which must be higher than 0.5 or there about. The last row in Table II is very important because it compares the relationship of the satisfaction variable with the seven other factors. As you see in Table II the factors 3, 6, and 7 (accuracy, bank image and web site desiogn) have the highest correlation with the satisfaction factor. On the other hand, the fourth factor (security) has the lowest correlation with satisfaction. In Table II, the quantities that have been placed in the diameter are the root of AVE. In view of the fact that they are higher than their identical pillar correlations, the validity of the factors is confirmed. Quantities of R 2 In each statistical model with a general shape Y ¼ B0 þ B1X1 þ B2X2 þ . . . is a title given to Xi variables of descriptive (independent) variables and to Y of the answer variable or the dependent variable. In fact, R 2 indicates how and how much the correlation between dependent and independent variables is. In other words, R 2 indicates how much of the changes are explained by the dependent variables (Table III).

Satisfaction

1

F7

F6

F5

F4

1 1 0.450459 1 0.543574 0.437817 1 0.653871 0.579758 0.521874 0.720443 0.732133 0.640428 0.484801

F3

F2

F1

1 0.572576 0.570732 0.617246 0.611803 0.712344

1 0.701055 0.511105 0.414409 0.591374 0.556533 0.671489

1 0.536859 0.54919 0.436257 0.378325 0.439815 0.556057 0.564327

F1 F2 F3 F4 F5 F6 F7 Satisfaction

Table II. Correlation between the factors

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Measurement equations Measurement equations show how the factors are hypothesized through the questions. Furthermore, when we use the coefficient, the quantity of coefficient in the equation indicates the importance of the question. In other words, if the coefficient of the second question in the equation is higher than the other coefficients, this indicates the second question is a more important measurement of the factor. It also indicates the information load of this question is more than other questions. If there is a question whether the fact that all respondents have chosen a particular choice in the same manner, we can say that this question has no information load: Fac1 ¼ ð0:64* Q121 Þ þ ð0:67* Q122 Þ þ ð0:10* Q123 Þ þ ð0:67* Q125 Þ

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For instance, in the said example, the effect of the question Q1-3 (in proportion to other variables) on the first factor is very little. On the other hand, if a person chooses the choice, “I greatly agree” in the first two questions and chooses the choice, “I agree” in the two succeeding questions, this person’s opinion regarding the first factor is equal to: Fac1score ¼ ð0:64* 5Þ þ ð0:67* 5Þ þ ð0:10* 4Þ þ ð0:67* 4Þ Other measurement equations are as follows: Fac2 ¼ ð0:64* Q221 Þ þ ð0:67* Q222 Þ þ ð0:10* Q223 Þ þ ð0:67* Q225 Þ Fac3 ¼ ð0:63* Q321 Þ þ ð0:70* Q322 Þ þ ð0:76* Q323 Þ þ ð0:69* Q325 Þ þ ð0:71* Q326 Þ Fac4 ¼ ð0:59* Q421 Þ þ ð0:76* Q4210 Þ þ ð0:88* Q422 Þ þ ð0:71* Q423 Þ þ ð0:79* Q424 Þ ð0:72* Q425 Þ þ ð0:80* Q426 Þ þ ð0:84* Q427 Þ þ ð0:32* Q428 Þ Fac5 ¼ ð0:88* Q522 Þ þ ð0:80* Q523 Þ þ ð0:50* Q524 Þ þ ð0:67* Q525 Þ þ ð0:70* Q526 Þ ð0:93* Q527 Þ Fac6 ¼ ð0:69* Q621 Þ þ ð0:62* Q622 Þ þ ð0:62* Q623 Þ þ ð0:44* Q625 Þ þ ð0:50* Q626 Þ ð0:73* Q627 Þ þ ð0:69* Q628 Þ Fac7 ¼ ð0:68* Q721 Þ þ ð0:67* Q722 Þ þ ð0:53* Q723 Þ þ ð0:68* Q724 Þ þ ð0:55* Q725 Þ ð0:74* Q726 Þ þ ð0:64* Q727 Þ þ ð0:50* Q728 Þ

Factors

Table III. Quantities of R 2

Fac1 Fac2 Fac3 Fac4 Fac5 Fac6 Fac7

R2 0.32 0.45 0.51 0.24 0.41 0.54 0.52

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Conceptual model The conceptual model of this research shows the relationship between the factors defined in this study. The conceptual model shows the relationships between the variables. The authenticity of each variable is tested with experimental data. Figure 3 shows the conceptual model of the present research, which shows the relationships between the research variables. In fact, the coefficients are the same as the coefficients of the equations. Of course, two types of coefficients are calculated in the software: standard coefficients and non-standard coefficients.

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Diagram of the quantities of the statistical item T After approximating the mark for each factor by structural equations, we deal with studying the quantity of the mark in the factors in relation to gender. The authors compare the average of the mark given to each factor among the males and females through the ANOVA statistical test. Here, we deal with the statistical hypotheses and model: H0. The variable of gender is not effective in the quantity of the mark given to convenience. H1. The variable of gender is effective in the quantity of the mark given to convenience. Q2_1 Q3_1

Q1_1 Q2_2

Q3_2 Q4_1

Q1_2 Q2_3

Q3_3

Q1_3 Q2_4

Fac1

Q4_10 Q4_2 Q4_3

Q1_5

Q3_5 Q7_1 Q3_6

Fac2

Fac3

Q7_2

Q1_4 Q4_4

Q7_3

Q3_4 Q4_5 Fac4

Q7_4

Q4_9

Q4_6

Q7_5

Q5_1 Q4_7

Q7_6

Satisfy

Q6_4 Q4_8 Q5_2

Q6_9

Q5_3

Q7_9

Q5_6 Q5_7

Q7_7

Q6_2

Q7_8

Q6_3

Q5_4 Q5_5

Q6_1

Fac5

Fac7

Q6_5 Fac6 Q6_8

Q6_6 Q6_7

Figure 3. Conceptual model of the research

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In view of the quantity of P for the variable of gender, and in view of the fact that P is higher than 0.05, the supposition of 0 is not rejected. In other words, the mark given to the factor of convenience does not have a significant statistical difference. Essentially, the quantity of the convenience of working with electronic banking does not differ for males and females (Table IV, Figure 4). The average of the mark is equal to 3.17 for females and 3.09 for males: H0. The variable of gender is not effective in the quantity of the mark given to accessibility.

In view of the quantity of P, which is lower than 0.05, the supposition of 0 is not rejected. This is to say the quantity of the mark differs for the availability factor among males and females (Table V). This quantity is averaged at 3.09 for females and 2.75 for males. This difference is seen in Figure 5: H0. The variable of gender is not effective in the quantity of the mark given to accuracy.

Effect Table IV. ANOVA statistical test of the mark given among the males and females

SS

Intercept Sex Error

1,349.510 0.225 136.758

Univariate tests of significance for F1 Degree of freedom MS F 1 1 154

1,349.510 0.225 0.888

1,519.652 0.254

Sex; Unweighted means current effect: F (1, 154) = 0.25354, p = 0.61531 effective hypothesis decomposition vertical bars denote 0.95 confidence intervals 3.5 3.4 3.3 3.2 F1

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H1. The variable of gender is effective in the quantity of the mark given to accessibility.

3.1 3.0 2.9

Figure 4. Mark given among the males and females

2.8

Men

Women Sex

p 0.000000 0.615310

H1. The variable of gender is effective in the quantity of the mark given to accuracy. Again, here in view of the quantity of P, we see a difference between the marks of males and females (Table VI, Figure 6). The quantity of average shows that males are more suspicious of electronic banking: H0. The variable of gender is not effective in the quantity of the mark given to security.

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H1. The variable of gender is effective in the quantity of the mark given to security.

Effect

SS

Intercept Sex Error

1,172.994 3.881 104.931

Univariate tests of significance for F2 Degree of freedom MS F 1 1 154

1,172.994 3.881 0.681

1,721.521 5.695

p 0.000000 0.018225 Table V.

Sex; Unweighted means currenteffect: F (1, 154) = 5.6952, p = 0.01823 effective hypothesis decomposition vertical bars denote 0.95 confidence intervals 3.4 3.3 3.2 3.1 F2

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A significant statistical difference was not found among the males and females for the factor of security and privacy of information (Table VII, Figure 7).

3.0 2.9 2.8 2.7 2.6 2.5

Men

Women

Figure 5.

Sex

Effect Intercept Sex Error

SS 1,392.774 3.550 104.699

Univariate tests of significance for F3 Degree of freedom MS F 1 1 154

1,392.774 3.550 0.680

2,048.616 5.221

p 0.000000 0.023677 Table VI.

JIMA 1,3

Sex; Unweighted means current effect: F (1, 154) = 5.2213, p = 0.02368 effective hypothesis decomposition vertical bars denote 0.95 confidence intervals 3.7 3.6 3.5

262

3.4

F3

3.3 3.2 3.1

2.9 2.8 2.7 Men

Figure 6.

Effect

Table VII.

Women Sex

SS

Intercept Sex Error

1,560.433 0.001 57.952

Univariate tests of significance for F4 Degree of freedom MS F 1 1 154

1,560.433 0.001 0.376

4,146.649 0.003

p 0.000000 0.955408

Sex; Unweighted means current effect: F (1,154) = 0.00314, p = 0.95541 effective hypothesis decomposition vertical bars denote 0.95 confidence intervals 3.6

3.5

3.4 F4

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3.0

3.3

3.2

3.1

Figure 7.

Men

Women Sex

The average of the mark was very near to one another in the two groups: H0. The variable of gender is not effective in the quantity of the mark given to usefulness. H1. The variable of gender is effective in the quantity of the mark given to usefulness. A significant statistical difference was not found among the females and males for the factor of usefulness, either. This supports our correct prediction and expectation that the average of the mark would be very near to one another in the two groups (Table VIII, Figure 8):

Customer satisfaction factors 263

H1. The variable of gender is effective in the quantity of the mark given to image of bank. In terms of the factor regarding the bank’s image, the average mark also differs for males and females. The females’ image and impression of the banking is better than that of the males (Table IX, Figure 9):

Effect

SS

Intercept Sex Error

2,031.903 0.291 56.390

Univariate tests of significance for F5 Degree of freedom MS F 1 1 154

2,031.903 0.291 0.366

5,549.086 0.795

p 0.000000 0.374131 Table VIII.

Sex; Unweighted means current effect: F (1, 154) = 0.79451, p = 0.37413 effective hypothesis decomposition vertical bars denote 0.95 confidence intervals 4.1

4.0

3.9 F5

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H0. The variable of gender is not effective in the quantity of the mark given to image of bank.

3.8

3.7

3.6

Men

Women Sex

Figure 8.

JIMA 1,3

H0. The variable of gender is not effective in the quantity of the mark given to web site design. H1. The variable of gender is effective in the quantity of the mark given to web site design.

264

The factor regarding the bank’s web site also differs for males and females. The females’ impression of the web site design is better than that of the males (Table X, Figure 10).

Effect

Table IX.

SS

Intercept Sex Error

1,569.017 2.810 68.036

Univariate tests of significance for F6 Degree of freedom MS F 1 1 154

1,569.017 2.810 0.442

3,551.489 6.360

p 0.000000 0.012689

Sex; Unweighted means current effect: F (1,154) = 6.3599, p = 0.01269 effective hypothesis decomposition vertical bars denote 0.95 confidence intervals 3.8 3.7 3.6 3.5 F6

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Conclusion and suggestions According to this study, the authors see that those who use electronic banking services in Iran have a higher educational background. In other words, better educated people use banks electronic services more frequently than others.

3.4 3.3 3.2 3.1 3.0 Men

Figure 9.

Effect

Table X.

Women Sex

Intercept Sex Error

SS 1,732.156 0.887 70.139

Univariate tests of significance for F7 Degree of freedom MS F 1 1 154

1,732.156 0.887 0.455

3,803.202 1.947

p 0.000000 0.164920

Sex; Unweighted means current effect: F (1,154) = 1.9470, p = 0.16492 effective hypothesis decomposition vertical bars denote 0.95 confidence intervals 3.9

Customer satisfaction factors

3.8 3.7

265

F7

3.6 3.5

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3.4 3.3 3.2 Men

Women Sex

Moreover, according to the findings governmental banks (Melli Bank and Mellat Bank) have the largest number of electronic services users with saman bank, which is a private bank, being the third. This could be due to customers having more confidence in governmental banks in Iran. After calculating the variance average between factors (AVE), we found that the accuracy, reliability, image, impression of the bank and management, and web site design are the main grounds for satisfaction. The factors of security and privacy had the least correlation with satisfaction. This might also be due to the confidence customers have in electronic banking services, especially in governmental banks. Finally, no discernible difference was perceived between males and females regarding general satisfaction. In line with the abovementioned study we present the following suggestions: . Owing to finding a direct relationship between the degree to which bank’s electronic services are used by people and their level of education, enhancing people’s knowledge and awareness can a determining factor in increasing the degree to which consumers use these services and how frequently they do so. . Males entertain more suspicion concerning electronic banking services. Therefore, added urgency should be given to remove obstacles. Note 1. A center for enhancing teaching and culture for e-banking in Iran. References Alsajjan, A., Bander, B. and Dennis, C. (2006), “The impact of trust on acceptance of online banking”, paper presented at European Association of Education and Research in Commercial Distribution, Brunel University, West London ( June 27-30).

Figure 10.

JIMA 1,3

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Corresponding author Kambiz Heidarzadeh Hanzaee can be contacted at: [email protected]

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