Abstract
In any economy, the financial sector is interpreted as the driving force in the process of achieving growth through the appropriate and optimal allocation of resources to productive economic sectors. Financial institutions are defined as customer service providers. The generalization of the obtained results to the whole statistical population is the last step in this research. The research is a survey that can provide data on attitudes, feelings, beliefs, past behaviors, recorded behaviors, and the recognition of the acquisition of personal characteristics. Considering that the services available in commercial banks are relatively the same and it is difficult for most banks to differentiate these services from competitors, therefore, many banks around the world have tended to use the relationship marketing approach. The concept of relationship marketing was first mentioned in the American marketing literature in a 1983 article by Barry, who considered relationship marketing as a strategy for attracting, retaining, and strengthening customer relationships. This means that if the bank’s reciprocal relationships increase by 1 unit, it is 95% likely that the value of customer satisfaction with financial services will increase by 0.663 units. Factor loads also have a very good quality in explaining their construct because they have a value of more than 5%. On the other hand, the value of the t significance coefficient for the relationship between the variables of the bank’s reciprocal relationships and customer satisfaction with financial services is 8.471 at the 95% confidence level, which is more than 1.96, indicating the importance of the bank’s reciprocal relationships on customer satisfaction with financial services. To evaluate the sixth hypothesis that “the bank’s reciprocal relationships affect customer satisfaction with financial services,” factor load and significance coefficient obtained from structural equation modeling were used. Considering the path coefficient related to this hypothesis, it can be concluded that the bank’s reciprocal relationships with the value of 0.663 affect customer satisfaction with financial services.
1. Introduction
There may be different and various interpretations of the relationship marketing concept by different researchers, and its benefits and effect on improving the performance of organizations cannot be hidden. Financial institutions are defined as intermediaries of funds between depositors and investors [1]. Banks are defined as a business unit that aims to generate revenue from expenses incurred during the course of business. On the other hand, due to the economic conditions governing in the banking system of the country, where banking exchanges and the opening of LCs are difficult and do not allow remittances from our country to other countries and also foreign commercial companies are subject to fines for doing business with our country, Bank Melli and other banks have faced many problems in carrying out their activities [2]. Financial institutions as economic institutions always seek to meet the expectations and needs of their customers. Banks and financial institutions should be able to provide their services in a way that creates value (satisfaction) in customers and succeed in their main task, which is to satisfy customers. Those organizations are victorious in this quantitative and qualitative competition whose managers adapt their organization to environmental conditions and improve internal conditions. On the other hand, the profitability of a company that ensures its survival depends on designing the right market management program based on meeting the needs of customers [3]. Today, banks and financial institutions seek to attract customers and increase their satisfaction, constant communication with customers is of particular importance, and on the other hand, competition between banks and Qarz al-Hasna institutions in the diversity of banking services has increased. The closer and more intimate the relationship between the organization and the customers, the closer they can be made to the organization [4]. With the more and more in-depth and extensive application of information technology in management, the implementation of the management information system has gradually matured in the technology [36].
Philip Kotler defines satisfaction as the pleasant or unpleasant feelings or compares the performance of commodities compared with the expectations of the consumer. The role of customer satisfaction is especially important in highly competitive industries, that is, industries where there is a large difference between loyalty of simply satisfied and completely satisfied (happy) customers [5]. For example, the result of a study on customers of a bank branch showed that loyalty of a completely satisfied customer was approximately 42% higher than customers who were simply satisfied. Research results show that communication has a positive effect on service improvement and customer satisfaction has a positive effect on customer loyalty and service improvement has a positive effect on customer satisfaction. In other research, the results showed that the services provided to customers have a positive effect on customer satisfaction and loyalty, and also, easy technology has a positive effect on customer satisfaction [6]. Research shows that among the components of relationship marketing, trust, commitment, shared value, and reciprocal relationships have a positive effect on customer satisfaction, while communication and empathy (the power to understand customer behavior) do not have such an effect. Interestingly, the role, trust, and commitment in customer satisfaction are better than shared value and reciprocal relationships [7]. Identifying and characterizing different aspects of project complexity to understand more efficiently the stakes of project management complexity can be of great support in assisting the global project management community [8].
Relationship marketing is a strategy to attract, retain, and improve customer relationships so that the goals of both parties in this relationship are met. Relationship marketing employs the attraction, retainment, and promotion of customer relationships [9]. This is beneficial because attracting new customers is much costlier than retaining existing customers; customers have less price sensitivity and allow free and open word-of-mouth advertising of the company’s products. The concept of relationship marketing has attracted considerable research attention from marketing researchers over the past two decades [10]. The fundamental advantage of a relationship marketing approach is claiming to build stronger customer relationships that increase the results of operations that include growth of sales, market share, profits, return on investment, and customer retention. Given the issues raised and the importance of customer satisfaction and the role of relationship marketing components, this question arises in the mind of the researcher whether relationship marketing affects the customer satisfaction of financial institutions such as banks [11].
2. Theoretical Framework
2.1. Relationship Marketing
Today, project managers have gained recognition and employment opportunities beyond construction, aerospace, and defence in pharmaceuticals, information systems, and manufacturing [12]. Relationship marketing is an activity that aims to create long-term and beneficial links between an organization and its customers to provide mutual benefit to both parties. Relationship marketing focuses on long-term related exchanges in which each individual exchange is considered part of a long-term relationship in which both parties benefit from the long-term relationship [13]. Relationship marketing emphasizes the importance of creating and maintaining a relationship between customers and buyers. Relationship marketing focuses on building a close relationship between the customer and the service provider to increase commitment and trust between the parties. Relationship marketing focuses on creating, maintaining, and enhancing relationships with customers and other partners in an effort to retain and improve an organization’s customer base and profitability [14]. Relationship marketing refers to long-term relationship exchanges in which the parties benefit from forward communication. Relationship marketing focuses more on how to develop, maintain, and enhance customer relationships throughout the customer life cycle rather than attracting new customers. According to Grönroos, there are three key components to consider when creating relationship marketing: seeking direct contact with customers, creating a database, and creating customer-oriented services. In contrast, Barry recalls five different strategies named strategy of main services, customization of relationships, enhancement of service providing, pricing of relationships, and internal marketing [15].
The term marketing is usually associated with activities of the institutions that sell their products or services. The idea that business relationships can increase the volume of business activities that benefit the institution is not new [16]. This did not happen, however, until the late twentieth century when scientific communities such as Grönroos, Barry, and Christopher theorized about its importance [17]. Their philosophy in business over time has gone from a cycle of starting with monopoly economic production and then a sales orientation to a customer-market orientation and the end of a social orientation and now the beginning of the shift to the relationship marketing orientation. From the perspective of traditional marketing strategies, customers are often placed at the end of the value chain of goods and services through the process of searching, evaluating, buying, and using. A company conducts a value chain analysis by evaluating the detailed procedures involved in each step of its business. The purpose of a value chain analysis is to increase production efficiency so that a company can deliver maximum value for the least possible cost, but today the concern of marketers is to create a satisfied and even happy customer with the highest level of loyalty, which requires involvement in the value chain in all processes, activities, and decisions of the organization, and in the relationship marketing paradigm instead of hostile attitude to bargaining, the buyer and seller in each exchange agree with each other to achieve their goals and find commitments to each other in the planned form and form their relationships [18]. A value chain is a business model that describes the full range of activities needed to create a product or service. For companies that produce goods, a value chain comprises the steps that involve bringing a product from conception to distribution and everything such as procuring raw materials, manufacturing functions, and marketing activities. Proponents of relationship marketing believe that two-way affiliations reduce transaction costs and improve quality. In short, better quality at a lower cost is achieved through interdependence among value chain actors. Therefore, the goal of relationship marketing is to enhance marketing productivity by achieving effectiveness and competency. Relationship marketing is not just about providing services at the place, time, and price demanded by the target market, but it wants to build relationships with the target market that will buy from it again in the future and encourage others to do so. Relationship marketing seeks to retain more customers and lose fewer customers [19]. Researchers have linked three factors to the popularity of relationship marketing in the late 1990s.
2.1.1. Energy Crisis
The energy crisis in 1970 was followed by inflation with the recession, which led to excess capacity and high raw material costs [20]. As competition between all groups intensified, it became necessary to focus on retaining customers rather than focusing on making a profit from them, which led to a continuous relationship exchange versus a one-time transaction exchange.
2.1.2. The Emergence of Service Marketing
At the same time, service marketing emerged as a new field. Many articles have been published on the fundamental differences between services and products, including intangibility, inseparability, technology, concurrency, and interactivity [21].
2.1.3. Supplier Participation
In industrial marketing, many companies have established key customer management processes and programs to strengthen and increase each customer’s business share to preferably connect with fewer suppliers. Eventually, the partnership led to trade exchanges taking place in a relationship [22].
2.2. The Importance of Relationship Marketing in the Financial Services Sector
Current customers are very large and often global. They prefer to have suppliers that can deliver a range of goods and services in a coordinated manner in different parts of the world and solve problems in different parts of the country or the world quickly and cooperate with the customer (customer teams) to improve processes and products, and they will be successful [23]. From these customers’ point of view, sales are just the beginning. Today, the financial services sector is undergoing changes that have not been experienced before in history. These changes have had a profound effect on both the structure of the industry and the nature of competition. Not surprisingly, in this turbulent environment with accelerating changes, financial institutions have been forced to change the way they react to the market so that instead of focusing on products, they focus more on customers and relationships and also take long-term vision rather than short-term vision [24]. Marketing based on building and maintaining a relationship is based on the assumption that the company must constantly pay attention to important accounts (customers) [9]. Research shows that the best seller is the one who is highly motivated and can conclude the final contract, but more successful than them is the one who solves customer problems and builds a lasting relationship with the buyer. The relationship approach that seeks to build excellent long-term relationships with its customers is especially appropriate for the service industry [10]. According to the great managers of the world’s famous banks, only 5% of customers make up more than 85% of the bank's’ profitability. Also, research by economic researchers has shown that banks increase their profitability in an unprecedented way by increasing their valuable and first-rate customers and creating effective customer satisfaction [11]. The development of relationship marketing in bank is done by developing the relationship with customers. Maintaining a relationship with the customer is a long-term issue, and instead of the current results and consequences, we should pay attention to its future consequences [16].
2.3. Customer Satisfaction
Customer satisfaction is generally defined as the overall evaluation of customers based on the purchase and consumption experience of products and services. Years ago, Peter Drucker said that the only valid definition of a business goal is customer satisfaction. Satisfaction is defined as an emotional state that results from the interaction of customers with the service provider over time [20]. Oliver defines satisfaction as the function of a cognitive comparison of pre-consumption expectations with practical experience. The concept of customer satisfaction has attracted much research attention in recent years. Satisfied customers in today’s competitive market are a business challenge [25]. Today, companies have recognized the importance of understanding, addressing, and anticipating customer needs. Customer satisfaction is the response and reaction to the fulfillment of customer demands. Researchers have defined customer satisfaction as an individual inference from the performance of a product or service in relation to their experience. Furthermore, customer satisfaction is a change in attitude that results from the consumer experience [2].
Customer satisfaction is essential to increase the competitiveness of institutions and achieve customer goals. It is essential to identify customer needs and expectations and ensures that they will be met to improve customer satisfaction. Customer satisfaction is basically a reaction to evaluating a product or service and inferring its performance [1]. Customer satisfaction is generally defined as the complete fulfillment of customer expectations and feelings and attitudes about a product or service after using it. Customer satisfaction in general is one of the most important long-term goals of institutions. The concept of marketing states that a satisfied buyer is more likely to repurchase, or at least pay attention to repurchase than dissatisfied people [3]. Customer satisfaction is a fundamental structure in marketing research and has been extensively investigated in service marketing. Satisfaction is the result of purchase and uses that result from comparing rewards and purchase costs in relation to the buyer’s expected results [13]. Customer satisfaction has been extensively researched in the field of marketing. Due to its general nature and global measurability for all types of products and services, it is one of the most common and widely used customer-oriented criteria by managers [4].
Customer satisfaction plays an essential role in service. Simultaneously with the development of the service sector, researchers have taken greater steps to define and understand customer satisfaction [26]. McKenna suggests that to achieve satisfied customers, organizations should forget about market research, advertising, and promotions and focus on developing the right infrastructure to meet customer needs by providing the right products and services. Customer should be able to achieve true satisfaction through the inferred value and quality of the goods and services [5]. Oliver describes satisfaction as “complete customer submission.” In fact, satisfaction is the result of the customer’s judgment about the extent to which the feature of a product or service is able to meet customer expectations at the desired level. This definition highlights the evaluative nature of satisfaction through which the customer determines whether a product, brand, or store meets his or her expectations. Due to the complexity of the satisfaction structure, there are numerous ways to evaluate it. Organizations, with the understanding that they have gained the importance of customer satisfaction, are gradually moving away from traditional marketing towards relationship marketing [6].
Relationship marketing was introduced by Leonard Berry in 1983. He defines relationship marketing as attracting, retaining, strengthening, and enhancing customer relationships [7]. Retaining a customer and turning them into a loyal customer add value to the life of the organization. In relationship marketing, it should be noted that the needs, personality, and position of customers are different from each other. Therefore, to implement relationship marketing, the needs, personality, position, and personal interests of customers should be taken into consideration [11]. Relationship marketing is one of the key dimensions of modern marketing strategy, because it emphasizes building close and stable relationships with customers. What pervades relationship marketing is its application in all areas of marketing, such as goods, services, and inter-business trade. Customer satisfaction leads to greater customer loyalty [17]. By increasing loyalty, customer satisfaction guarantees future earnings, reduces the cost of future transactions, reduces price flexibility, and minimizes the likelihood that customers will slip in the face of disadvantages [27]. Customer satisfaction is widely considered as a key effect in shaping customers’ future purchasing intentions [19].
Customer satisfaction is a measure of how products and services produced by an organization meet customer expectations. It varies from person to person and from service to service. In a general attitude, every customer (generally) is satisfied or dissatisfied after receiving a service or buying and using a product. Satisfaction is the presence of a positive feeling that eventually develops in the consumer or recipient. Basically, this feeling is created by meeting customer expectations and supplier performance [22]. Depending on whether the customer’s expectations and the goods or services received are at the same level, or the goods are higher or lower than the customer’s expectations, it creates a feeling of satisfaction or excitement or dissatisfaction. Emotionally, satisfaction is what the customer expects, but enjoying the product is something that encourages the customer. From the customer’s point of view, enjoying the product can be considered as something that is a little more than the expected added value. Enjoying the product adds value to its long-standing relationship with the organization. Relationship that exists between supplier of products/services and the customers is shown in Figure 1 [25].
3. Methodology
Since this study aimed to evaluate the effect of relationship marketing on customer satisfaction in the financial services sector, it uses the survey research and field method and collects the required information from the statistical sample using data collection techniques such as questionnaires, face-to-face interviews, and document review and finally, using appropriate statistical software (such as SPSS and Smart PLS), tests the hypotheses that have already been presented based on its evidence and information. Survey is the systematic collection of information from respondents to understand or predict some behavioral aspects of the target population, which should be accompanied by sampling, questionnaire design, and data analysis.
3.1. Data Collection Tools
Collecting information required for research is one of its basic steps, and due to its importance, sometimes data collection methods are mistakenly called research methods. In this research, the researcher used the field method to collect information and the data collection tool is a questionnaire that Prabandari used for their research in 2017 and includes 29 questions as described in Table 1 [25].
3.2. Statistical Population
The statistical population is all the people, events, or things that the researcher wants to research so that they share at least one attribute. The statistical population of this study includes all people who have used the financial services of 20 branches of Bank Mellat in Tehran during the last 6 months. Therefore, the statistical population of the research is considered indefinitely. Sampling is one of the most important topics in statistics of humanities [28]. On the other hand, due to the size of the population or the subjects studied, the researcher has to take samples. Due to the lack of accurate information on the number of these customers over the past year, the statistical population is considered infinite and the formula for an unknown population was used. The population of this study is considered as an unknown population in terms of the number of people because in the formula, the ratio is established. To estimate the sample size, Cochran’s formula for infinite populations is used, which can be seen as follows:
In this equation, is the statistical value of Z at a significance level of 95% (equal to 1.96), is the standard deviation of the population, which is according to reliable statistical sources, its value is estimated according to the range of changes in the answers (five Likert spectrum) as follows: , and is the error level (acceptable error value) in behavioral sciences, and a value less than 8% is acceptable; in this study, to be more sure, an accuracy of 6.37% is considered. Therefore, the number of statistical samples in this study is 434 people. The sampling method in this study is non-probability and convenience, and to investigate the effect of relationship marketing dimensions on customer satisfaction, the required information was collected from customers of 20 branches of Bank Mellat in Tehran. Questionnaires were randomly distributed and collected among customers on different days, in different places (different bank branches), and at different hours. The demographic characteristics of the statistical sample are in accordance with the data in Table 2.
The results of analysis on descriptive statistics show that 26.7% of respondents are 30 to 40 years old, 48.4% of respondents are 40 to 50 years old, and 24.9% of respondents are over 50 years old. According to the results, it can be seen that most of the subjects are between 40 and 50 years old. 33.3% of respondents are female, and 66.7% are male. The results show that most of the respondents are men. 70.4% of respondents have a master’s degree, and 29.6% of respondents have a Ph.D. degree and higher. According to the results, it can be seen that most of the subjects have a master’s degree. 23.5% of respondents used financial services under 20 times, 41.3% of respondents between 20 and 40 times, and 35.2% of respondents over 40 times. The results show that the number of times the financial services are used by most of the subjects is between 20 and 40 times.
4. Data Analysis Method
In the present research, in the quantitative part, according to the research questions, statistical methods in the form of descriptive statistics and inferential statistics are used to analyze the data collected using research questionnaires. In the present research, in descriptive statistics, frequency, frequency percentage related to demographic variables (gender, etc.), and mean and standard deviation, related to research variables, are used. In the inferential statistics part of the present research, the Kolmogorov–Smirnov (KS) test will be used to test the hypotheses for testing the normality, the correlation test will be used to examine the relationship between the variables, and structural equation modeling will be used to analyze the data if it is normal (SEM), and in case of non-normal data, the structural equation modeling method based on partial least squares (PLS) will be used.
4.1. Kolmogorov–Smirnov Test
This test serves as a distribution matching test for quantitative data. If the researcher has a sample of quantitative sizes and wants to determine whether this sample is obtained from a population with a normal distribution, he/she will use this test (Khaki, 2007). In the present research, the Kolmogorov–Smirnov test was used to evaluate the normality of data for the dependent variable. Therefore, the null and alternative hypotheses are presented as follows: the null hypothesis—the data distribution is normal. In the alternative hypothesis, the data distribution is not normal. The output of the statistical software regarding the normal distribution of relationship marketing variables (trust, commitment, communication, shared value, empathy, and reciprocal relationships) and customer satisfaction shows that at a significance level of 5%, the null hypothesis is accepted and the data have a normal distribution as described in Table 3.
4.2. Testing the Conceptual Model
In this research, to test the conceptual model of the research using Smart PLS software, it is performed in two general stages including “model fit evaluation” and “hypothesis test.” The model fit evaluation has three stages: in the first stage, the measurement model is examined through validity and reliability analyses. In the second stage, the structural model is examined by estimating the path between the variables. In the third stage, the overall fit of the model is examined. Finally, if the model has a good overall fit in the above three stages, then the research hypotheses can be examined. The stages of hypothesis testing with structural equation modeling (SEM) using Smart PLS software are presented in Table 4.
4.3. Evaluation of the Measurement Model
Factor load coefficients: first, the research model is tested based on factor load coefficients. If the factor load is less than 0.3, the relationship is considered to be weak and is ignored. A factor load of between 0.3 and 0.6 is acceptable, and if that is greater than 0.6, it is highly desirable. The structural equation model of the research is plotted in the standard factor load estimation mode in Figure 2. The test results showed that all factor loads of the indicators are above 0.4, and the factor load of the indicators is desirable.
Cronbach’s alpha coefficient was invented by Cronbach and is one of the most common methods of measuring the reliability of questionnaires. Reliability of a questionnaire means that if the measured attributes are remeasured with the same tool and under the same conditions and at different times, the results will be almost the same. In this research, using Smart PLS software, Cronbach’s alpha for relationship marketing variables (trust, commitment, communication, shared value, empathy, and reciprocal relationships) and customer satisfaction was calculated and reported in Table 5. As mentioned, the closer this coefficient is to 1, the more appropriate it is. In this research, the reliability of the questionnaire about independent and dependent variables has been obtained at a very acceptable level. Composite reliability is a more modern criterion than Cronbach’s alpha that calculates the reliability of variables not absolutely but according to the correlation of their indicators with each other. If the value of the composite reliability for each variable is more than 0.7, it indicates the appropriate internal stability of the model. The composite reliability of each of the research variables is described in Table 5. As can be seen in the table, all variables have a composite reliability of 0.7 and above, and the model is also confirmed in terms of composite reliability.
The average variance extracted (AVE) was used to examine the convergent validity of the model. This criterion shows the degree of correlation of a structure with its indicators that the higher this correlation, the greater the fit of the model. This indicator is used in latent variables with a reflective model and is not applicable in hybrid models. Al-Msallam and Alhaddad [29] introduced the AVE criterion for measuring convergent validity and stated that the critical value of this criterion is 0.5, and this means that a value of AVE above 0.5 indicates acceptable convergent validity. The values of this criterion for the research model are described in Table 5. As can be seen, the AVE value for all variables is greater than 0.5, which means that the convergent validity of the model is confirmed.
To evaluate the divergent validity of the model, Al-Msallam and Alhaddad criterion was used. This criterion determines the degree to which a variable relates to its indicators in comparing the relationship of that variable with other variables sp that the acceptable divergent validity indicates that one variable has more interaction with its indicators than with other variables. Al-Msallam and Alhaddad state that divergent validity is acceptable when the AVE value for each variable is greater than the pooled variance between that variable and the other variables. In Smart PLS software, this is performed by a matrix whose cells contain the values of the correlation coefficients between the variables and the square root of the AVE values for each variable. The following table shows this matrix that corresponds to the variables. If the numbers in the original diameter of the matrix are greater than its lower values, it has acceptable divergent validity. As can be seen in Table 6, all main diagonal numbers are larger than their lower column numbers, which means acceptable divergent validity of the model.
4.4. Evaluation of the Structural Model
The structural model or the external model represents the relationships between the latent variables of the model. In fact, in this section, the questions (indicators) are not considered and only the latent variables along with the relationships between them are examined. In evaluating the structural model, several criteria are used, each of which is discussed. The most basic criterion for measuring the relationship between variables in the model is t significance values. If the value of these numbers is more than 1.96, it indicates the correctness of the relationship between the variables and thus confirms the relationship or relationships at the 95% confidence level. Figure 3 shows the test results of the conceptual model of the research in the significant state of t-coefficients. The values calculated on the arrows represent the t significance values. The T value results reported are all greater than 1.96, so it can be concluded that at the 95% significance level, all questions are considered for the structural equation model and none of the questions need to be eliminated from the model.
The criterion R2 represents the effect that an independent variable has on a dependent variable. The criterion R2 is calculated only for the dependent variable of the model, and in the case of the independent variable, the value of this criterion is zero. The higher the value of R2 related to the dependent variable of the model, the better the fit of the model. Hennig-Thurau and Hansen [30] introduce three values of 0.19, 0.33, and 0.67 as the criterion values for weak, medium, and strong values of R2. If the structures of a given internal path model describe an endogenous latent variable (dependent variable) with a small number (one or two) of the extrinsic latent variable, R2 is acceptable at the intermediate level, but if the endogenous latent variable depends on several exogenous latent variables, the variable R2 must be at least at a significant level. Table 6 shows the value of R2 for the dependent variables of the research, which is extracted from Figure 2. As can be seen, the R2 values for customer satisfaction are 0.700, which has a strong R2 value.
Another way to evaluate a structural model is to examine the model’s ability to predict. The dominant criterion for the predictive relationship is the Q2 index. This criterion, introduced by Stone (1975), determines the predictive power of the model. This criterion, which is usually measured using the blindfolding (BF) method, claims that the model should be able to provide a prediction of endogenous latent variable representations. It should be noted that the BF method is used only for the endogenous latent variable, which is implemented as a reflective measurement model. Accordingly, if the value of Q2 for a dependent variable is zero or less than zero, it indicates that the relationships between the other variables in the model and that dependent variable are not well determined. In other words, if this value is greater than zero for a given endogenous variable, their independent variables have a predictive relationship. Regarding the intensity of predictive power of the model, three values of 0.02, 0.15, and 0.35 have been determined, which indicate the weak, medium, and strong predictive power of the model in relation to it, respectively. Given the value of Q2 obtained for the dependent variables of the model shown in Table 7, it is clear that the predictive power of the model for the dependent variables is at a strong level.
4.5. Evaluation of the General Model
The general model includes both the measurement and structural models, and by confirming its fit, evaluating the fit of the model is completed. For the overall fit of the model, only one criterion is used as GoF (goodness-of-fit index). Due to the fact that this index depends to some extent on the mean commonality, then this index can be used conceptually when the measurement model is of reflective type [31]. This criterion was developed by Muhumuza et al. [32] and is calculated according to the following formula:
It is obtained from the mean of the commonality values of the independent and dependent variables of the model and shows how much of the variability of the indicators (questions) is explained by the corresponding variable. Also, is the mean of R2 values for the dependent variable of the model. Three values of 0.01, 0.25, and 0.36 have been introduced as weak, medium, and strong values for GOF, respectively. This means that if 0.01 and values close to it are calculated for GOF of a model, it can be concluded that the overall fit of that model is weak and the relationships between the structures of the model need to be modified. For values of 0.25 and 0.36, the overall fit of the model is at an acceptable level [33]. According to the given explanations, the communality and R2 values for the dependent variable of the model and the mean of these two criteria are listed in Table 8.
The GOF value for the model of this research is 0.632, which indicates a strong and very suitable overall fit of the model. Given the strong fit of the overall model, it is now possible to investigate the research hypotheses.
4.6. Investigating the Research Hypotheses
In this study, the regression method is used to evaluate the main hypothesis and the data extracted from structural equation modeling are used to evaluate the sub-hypotheses presented in Table 9.
The significance level of F statistic is less than 5%, and it shows that the hypothesis is accepted with 95% confidence and there is a significant relationship between relationship marketing and customer satisfaction. Also, according to the coefficient of determination (0.784), it can be said that the proposed model is fitted with high accuracy, and on the other hand, the value of the adjusted coefficient of determination (0.783) is evidence of the fact that the independent variable explains about 78% of the changes in the dependent variable. The value of the Durbin–Watson statistic (2.098) is between 1.5 and 2.5; therefore, it can be concluded that there is no problem of serial autocorrelation between the variables in the regression disturbance components. Significance levels for the coefficient of the independent variable “relationship marketing” indicate that this independent variable in the proposed regression model affects the dependent variable “customer satisfaction.” On the other hand, the significance level of the y-intercept indicates that the regression model has a y-intercept as well. On the other hand, the coefficient of relationship marketing (0.508) has been reported with a positive sign, which indicates a direct relationship between these coefficients and the dependent variable.
In the following, the sub-hypotheses are evaluated using structural equation modeling. According to the data analysis algorithm in the PLS method, after examining the fit of measurement, structural, and general models, the research hypotheses are tested by examining the t significance coefficients of each path and also the standardized coefficients of factor load related to the paths. If the value of the significance coefficient of each path is more than 1.96, the corresponding path is confirmed at the 95% confidence level and the corresponding hypothesis is confirmed. The results of factor load values (Figure 2) and significance coefficient (Figure 3) of the conceptual model are presented in Table 10.
To evaluate the first hypothesis that “trust building by the bank affects customer satisfaction with financial services,” factor load and significance coefficient obtained from structural equation modeling were used. Considering the path coefficient related to this hypothesis, it can be concluded that trust building by the bank with the value of 0.599 affects customer satisfaction with financial services. This means that if trust building by the bank increases by 1 unit, it is 95% likely that the value of customer satisfaction with financial services will increase by 0.599 units. Factor loads also have a very good quality in explaining their construct because they have a value of more than 5%. On the other hand, the value of the t significance coefficient for the relationship between the variables of trust building by the bank and customer satisfaction with financial services is 6.198 at the 95% confidence level, which is more than 1.96, indicating the importance of trust building by the bank on customer satisfaction with financial services.
To evaluate the second hypothesis that “the bank’s commitment to provide services affects customer satisfaction with financial services,” factor load and significance coefficient obtained from structural equation modeling were used. Considering the path coefficient related to this hypothesis, it can be concluded that the bank’s commitment to provide services with the value of 0.574 affects customer satisfaction with financial services. This means that if the bank’s commitment to provide services increases by 1 unit, it is 95% likely that the value of customer satisfaction with financial services will increase by 0.574 units. Factor loads also have a very good quality in explaining their construct because they have a value of more than 5%. On the other hand, the value of the t significance coefficient for the relationship between the variables of the bank’s commitment to provide services and customer satisfaction with financial services is 6.031 at the 95% confidence level, which is more than 1.96, indicating the importance of the bank’s commitment to provide services on customer satisfaction with financial services.
To evaluate the third hypothesis that “the bank’s communication affects customer satisfaction with financial services,” factor load and significance coefficient obtained from structural equation modeling were used. Considering the path coefficient related to this hypothesis, it can be concluded that the bank’s communication with the value of 0.406 affects customer satisfaction with financial services. This means that if the bank’s communication increases by 1 unit, it is 95% likely that the value of customer satisfaction with financial services will increase by 0.406 units. Factor loads also have a very good quality in explaining their construct because they have a value of more than 5%. On the other hand, the value of the t significance coefficient for the relationship between the variables of the bank’s communication and customer satisfaction with financial services is 8.441 at the 95% confidence level, which is more than 1.96, indicating the importance of the bank’s communication on customer satisfaction with financial services.
To evaluate the fourth hypothesis that “the bank’s shared value affects customer satisfaction with financial services,” factor load and significance coefficient obtained from structural equation modeling were used. Considering the path coefficient related to this hypothesis, it can be concluded that the bank’s shared value with the value of 0.815 affects customer satisfaction with financial services. This means that if the bank’s shared value increases by 1 unit, it is 95% likely that the value of customer satisfaction with financial services will increase by 0.815 units. Factor loads also have a very good quality in explaining their construct because they have a value of more than 5%. On the other hand, the value of the t significance coefficient for the relationship between the variables of the bank’s shared value and customer satisfaction with financial services is 6.079 at the 95% confidence level, which is more than 1.96, indicating the importance of the bank’s shared value on customer satisfaction with financial services.
To evaluate the fifth hypothesis that “the bank’s empathy affects customer satisfaction with financial services,” factor load and significance coefficient obtained from structural equation modeling were used. Considering the path coefficient related to this hypothesis, it can be concluded that the bank’s empathy with the value of 0.663 affects customer satisfaction with financial services. This means that if the bank’s empathy increases by 1 unit, it is 95% likely that the value of customer satisfaction with financial services will increase by 0.663 units. Factor loads also have a very good quality in explaining their construct because they have a value of more than 5%. On the other hand, the value of the t significance coefficient for the relationship between the variables of the bank’s empathy and customer satisfaction with financial services is 8.471 at the 95% confidence level, which is more than 1.96, indicating the importance of the bank’s empathy on customer satisfaction with financial services.
To evaluate the sixth hypothesis that “the bank’s reciprocal relationships affect customer satisfaction with financial services,” factor load and significance coefficient obtained from structural equation modeling were used. Considering the path coefficient related to this hypothesis, it can be concluded that the bank’s reciprocal relationships with the value of 0.663 affect customer satisfaction with financial services. This means that if the bank’s reciprocal relationships increase by 1 unit, it is 95% likely that the value of customer satisfaction with financial services will increase by 0.663 units. Factor loads also have a very good quality in explaining their construct because they have a value of more than 5%. On the other hand, the value of the t significance coefficient for the relationship between the variables of the bank’s reciprocal relationships and customer satisfaction with financial services is 8.471 at the 95% confidence level, which is more than 1.96, indicating the importance of the bank’s reciprocal relationships on customer satisfaction with financial services.
5. Conclusion
According to many studies in the field of marketing, in each of these researches, different key dimensions such as trust, equality, benevolence, commitment, empathy, competency, communication, internal relationship marketing, commitment to covenant, good experiences, social link, customer satisfaction, conflict management, and participation in the insistence for relationship marketing are considered. In this study, we focus on dimensions on a one-dimensional structure that includes six components: trust, commitment, communication, shared value, empathy, and reciprocal relationships. To evaluate the first hypothesis that “trust building by the bank affects customer satisfaction with financial services,” factor load and significance coefficient obtained from structural equation modeling were used. Considering the path coefficient related to this hypothesis, it can be concluded that trust building by the bank with the value of 0.599 affects customer satisfaction with financial services. This means that if trust building by the bank increases by 1 unit, it is 95% likely that the value of customer satisfaction with financial services will increase by 0.599 units. Factor loads also have a very good quality in explaining their construct because they have a value of more than 5%. On the other hand, the value of the t significance coefficient for the relationship between the variables of trust building by the bank and customer satisfaction with financial services is 6.198 at the 95% confidence level, which is more than 1.96, indicating the importance of trust building by the bank on customer satisfaction with financial services. Trust is the belief that the parties in business communication tell the truth freely and always [34]. Trust is a key factor that enables people to build relationships in situations of uncertainty [35]. It is also a vital factor in building strong customer relationships and gaining market share and should be achieved before achieving customer loyalty.
To evaluate the second hypothesis that “the bank’s commitment to provide services affects customer satisfaction with financial services,” factor load and significance coefficient obtained from structural equation modeling were used. Considering the path coefficient related to this hypothesis, it can be concluded that the bank’s commitment to provide services with the value of 0.574 affects customer satisfaction with financial services. This means that if the bank’s commitment to provide services increases by 1 unit, it is 95% likely that the value of customer satisfaction with financial services will increase by 0.574 units. Factor loads also have a very good quality in explaining their construct because they have a value of more than 5%. On the other hand, the value of the t significance coefficient for the relationship between the variables of the bank’s commitment to provide services and customer satisfaction with financial services is 6.031 at the 95% confidence level, which is more than 1.96, indicating the importance of the bank’s commitment to provide services on customer satisfaction with financial services. Commitment is the intention of one party in relationship to continue or maintain the relationship with the other. Rashid defines commitment as the desire to maintain a valuable relationship. Commitment is described as persistent desire and maintaining an important relationship that may require short-term self-sacrifice. Commitment is defined as the highest level of communication. Customers with strong communication with the institution, through such a commitment, are more satisfied than those who do not have such communication. To evaluate the third hypothesis that “the bank’s communication affects customer satisfaction with financial services,” factor load and significance coefficient obtained from structural equation modeling were used. Considering the path coefficient related to this hypothesis, it can be concluded that the bank’s communication with the value of 0.406 affects customer satisfaction with financial services. This means that if the bank’s communication increases by 1 unit, it is 95% likely that the value of customer satisfaction with financial services will increase by 0.406 units. Factor loads also have a very good quality in explaining their construct because they have a value of more than 5%. On the other hand, the value of the t significance coefficient for the relationship between the variables of the bank’s communication and customer satisfaction with financial services is 8.441 at the 95% confidence level, which is more than 1.96, indicating the importance of the bank’s communication on customer satisfaction with financial services. Communication, the desire to communicate, refers to the positive and open attitude of an institution and the connection of communication with their customers honestly and in a timely manner. Different institutions often have different attitudes towards exchanging information with their customers. Communication is an effective relationship that creates a strategy that helps resolve differences, achieves harmonious goals, and reveals new value that creates opportunities. Effective communication accelerates positive interactions and increases customer satisfaction.
To evaluate the fourth hypothesis that “the bank’s shared value affects customer satisfaction with financial services,” factor load and significance coefficient obtained from structural equation modeling were used. Considering the path coefficient related to this hypothesis, it can be concluded that the bank’s shared value with the value of 0.815 affects customer satisfaction with financial services. This means that if the banks’ shared value increases by 1 unit, it is 95% likely that the value of customer satisfaction with financial services will increase by 0.815 units. Factor loads also have a very good quality in explaining their construct because they have a value of more than 5%. On the other hand, the value of the t significance coefficient for the relationship between the variables of the bank’s shared value and customer satisfaction with financial services is 6.079 at the 95% confidence level, which is more than 1.96, indicating the importance of the bank’s shared value on customer satisfaction with financial services. Shared value: Porter and Karmer argue that when institutions want to focus on the shared value that engages economic value creation, it leads to the intrinsic goal of creating value for society by addressing its needs and challenges. The parties to the transaction with shared values are more committed to their participation. Zeineldin and Johnson also note that shared values are the most important factors affecting the commitment of the parties. The more the relationship increases through shared value, the more satisfied customers are likely to exist. To evaluate the fifth hypothesis that “the bank’s empathy affects customer satisfaction with financial services,” factor load and significance coefficient obtained from structural equation modeling were used. Considering the path coefficient related to this hypothesis, it can be concluded that the bank’s empathy with the value of 0.663 affects customer satisfaction with financial services. This means that if the bank’s empathy increases by 1 unit, it is 95% likely that the value of customer satisfaction with financial services will increase by 0.663 units. Factor loads also have a very good quality in explaining their construct because they have a value of more than 5%. On the other hand, the value of the t significance coefficient for the relationship between the variables of the bank’s empathy and customer satisfaction with financial services is 8.471 at the 95% confidence level, which is more than 1.96, indicating the importance of the bank’s empathy on customer satisfaction with financial services. Empathy may facilitate communication between buyer and seller, thereby enhancing buyer understanding of how the industry operates. Institutions that better understand customer wants and desires are better able to satisfy customers. Reciprocal relationships: without reciprocal relationships, an organization may have a dissatisfied customer because their customers are unable to communicate their needs and problems with the organization. [36].
Data Availability
Requests for access to these data should be made to the corresponding author’s email address: [email protected].
Conflicts of Interest
The author(s) declare that there are no conflicts of interest regarding the publication of this study.