The Mediating Effect of Customer Satisfaction on Fintech Literacy and Sustainable Intention of Using Mobile Financial Services ()
1. Introduction
Banking and financial institutions use modern technologies to develop a new channel which reduces the dependency on traditional banking services. Banks offer the customer to manage financial transactions by using digital assistance, such as mobile tablets and smartphones; this is known as mobile financial services (MFS), also termed mobile banking or m-banking ( Naruetharadhol et al., 2021 ). MFS makes banking activities more accessible and convenient to customers with its real-time online transaction settlement facilities from anywhere at any time. Bala et al. (2021) state that the concept of mobile banking is a superb breakthrough for the banking industry to add a new product line to the channel as well as for the customers to make financial transactions at home. After the introduction of MFS in Bangladesh, it reached almost every corner of the country within a short period of time targeting unbanked and low-income group people. Customers are getting digital banking services through MFS that contain more attractive and modern features than traditional banking systems ( Rahman, 2021 ).
MFS is a self-service technology that needs customers’ adequate Fintech knowledge to access and operate financial transactions. In MFS, customers have the opportunity to perform financial transactions by using application software or simply dialing a particular code as produced by the service providers. Service providers add some of the latest technical features in the MFS with the time for the customers to avail of relevant services. But, this is a new experience for most of the customers that cause difficulties in their financial operation due to the lack of advanced technological knowledge or Fintech knowledge ( Mohammadi, 2015 ). Customers are making mistakes in accomplishing financial transactions in MFS for their inadequate Fintech knowledge ( Kaur & Arora, 2023 ). So, Customers’ Fintech knowledge is very important for self-service technological services such as MFS and is related to their satisfaction and continued usage intention.
For customers who lack Fintech knowledge, there is a chance of facing challenges like risks of error in transactions, threats of hacking passwords, or chance of losing information, etc. at the time of using MFS. It may dissatisfy customers as well as lose their continuous use intention ( Yuan et al., 2016 ). Despite many benefits of MFS, customers’ usage rate is still at a basic level or low in Bangladesh due to their low Fintech knowledge ( Rahman, 2021 ). If customers have sufficient Fintech literacy, they feel safe and secure in financial transactions. It would satisfy and encourage them to make financial transactions in MFS more frequently. It also improves customers’ attachment to the services. Customers will intend to continue the use of MFS for a long time. Therefore, there is a positive relationship between Fintech knowledge, customer satisfaction, and continuance intention to use MFS in the post-adoption stage ( Lim et al., 2019 ).
In the prior studies, researchers had mostly focused on the relationship between Fintech literacy, customer satisfaction, and continuous intention or sustained usage intention to use MFS descriptively and/or theoretically ( Khan & Chaipoopirutana, 2020 ; Lim et al., 2019 ; Sharma & Sharma, 2019 ; Yuan et al., 2016 ) but hardly conducted an empirical study on the impact of Fintech literacy on satisfaction and continuance intention of MFS ( Khan et al., 2022 ). The extant pieces of literature are evident that still there is a dearth of empirical study on the effect of Fintech literacy on sustainable intention to use MFS with customer satisfaction as a mediator. Thus, the present study attempts to fill this gap and aims at investigating the intervening effect of customer satisfaction on the relationship between Fintech literacy and sustainable intention to use MFS. The next part of the study covers the following sections: Section 2 presents a relevant literature review on mobile financial services, Fintech literacy, customer satisfaction, and sustainable intention to use MFS. Section 3 shows the conceptual framework and hypothesis development. Section 4 explains the research methodology. Section 5 describes data analysis, results and discussions of the study, and Section 6 provides conclusion, limitations and further direction of the study.
2. Literature Review
2.1. Mobile Financial Services (MFS)
Mobile financial service (MFS) is globally known as mobile banking services or m-banking services. MFS refers to the services that a service provider provides to its customer through personal digital assistance or mobile devices ( Naruetharadhol et al., 2021 ; Uddin, 2022 ). It has created a massive opportunity for customers to get financial services from anywhere at any time ( Lubna et al., 2018 ). In MFS, customers get an opportunity to operate financial transactions such as cash-in, cash-out, transfers, utility payments, and merchant payments ( Afroze & Rista, 2022 ) and also to perform non-financial transactions, like balance inquiries and statements ( Lan & Giang, 2021 ).
MFS providers can easily reach people who are either living in rural or urban areas for the facilities of mobile network coverage and may facilitate effective financial services ( Mujeri & Azam, 2018 ). MFS does not require a physical visit to the service provider’s premises, so it becomes more time-effective, cost-saving, and useful for rural people ( Gupta et al., 2017 ).
2.2. Fintech Literacy
Fintech literacy refers to the extent to which customers have adequate knowledge to process mobile financial transactions, and to enable handling and protecting any threats/difficulties raising at the time of operating transactions. This is a primary concern for the customers to adopt and use MFS ( Tun, 2020 ). Susanto et al. (2016) stated that Fintech knowledge is associated with self-efficacy and habit to the post-use of m-banking services. If a customer has sufficient knowledge to operate mobile financial services, there is a chance of using it continuously ( Singh & Srivastava, 2018 ).
2.3. Customer Satisfaction
Satisfaction refers to the customers’ positive feelings achieved through the actual usage of mobile financial services ( Lim et al., 2019 ). It is a psychological outcome of users’ post-usage reactions related to their expected feelings on the usage experience of mobile banking services ( Gupta et al., 2020 ). It gradually progresses through the confirmation of customers’ expectations towards the services. Satisfied customers would become loyal to the MFS providers which leads to positive intent to continue or reuse the services ( Khasbulloh & Suparna, 2022 ). If customers’ pre-adoption expectation met, indicating that they are pleased with the experience of using services, and likely to continue the usage of services ( Bhattacherjee, 2001a ).
2.4. Sustainable Intention to Use MFS
Customers’ perceptions, emotions, and feelings often change over time. Consequently, this is obviously challenging for the service providers to retain customers on their services for a long. But the success and subsistence of the service providers depend more on the customer’s continuous usage intention or reuse intention of services. Organizational interaction with customers builds and confirms attachment to the services ( Morgan-Thomas & Veloutsou, 2013 ). Customers’ emotional relationships, services diversification and functionality, and expected performance of services are the key drivers of making customer relationships with the services ( Pradana & Ha, 2021 ). Additionally, the usage frequency and duration of customer interaction with services reflect the stability of the relationship between customers and service providers ( Morgan-Thomas & Veloutsou, 2013 ). It justifies the customers’ sustainable intention of using services. Sustainable usage intention refers to the extent to which a customer perceives his willingness to use m-banking for a long time ( Kang et al., 2012 ).
3. Theoretical Framework and Hypotheses of the Study
3.1. Fintech Literacy and Customer Satisfaction
Lim et al. (2019) explained that each customer should have Fintech knowledge for operating mobile financial services at safe and securely. It has a positive relationship with perceived security, customer satisfaction, and continuance intention in the post-adoption stage of MFS. Phuah et al. (2018) stated in their study on Fintech knowledge that it would enhance customers’ recognition and satisfaction. Khan et al. (2022) empirically examined the influence of Fintech knowledge on customer satisfaction. The result revealed that Fintech knowledge significantly affects customer satisfaction with Fintech services. Hence, the hypothesis is proposed as follows:
H1: Fintech literacy has a positive and significant influence on customer satisfaction toward MFS.
3.2. Fintech Literacy and Sustainable Intention to Use MFS
Uddin and Nasrin (2023) found Fintech literacy as a promising antecedent of customers’ continuance intention. Rahman (2021) explored that Fintech knowledge significantly impacts on customers’ actual usage behavior of MFS. Bhuvana and Vasantha (2019) highlighted that customers’ higher Fintech knowledge increased their efficiency in the safe use of MFS and stimulates them to the retention and sustained usage intention of services. So, the hypothesis is postulated as follows:
H2: Fintech literacy has a positive and significant impact on sustainable intention to use MFS.
3.3. Customer Satisfaction and Sustainable Intention to Use MFS
Customer satisfaction is the key dimension of MFS continuance intention ( Franque et al., 2021 ). Satisfied customers relished the continuous use of mobile banking services which forces service providers to ensure better services ( Avornyo et al., 2019 ). If the performance of MFS exceeds customers’ expectations, they will be satisfied and intend to use the services continuously for a long period of time ( Wu et al., 2022 ). So, customer satisfaction and sustainable intention are significantly related with each other. Therefore, the following hypothesis is proposed:
H3: Customer satisfaction has a significant influence on sustainable intention to use MFS.
3.4. Customer Satisfaction as Mediator
Singh and Srivastava (2018) state that if a customer has adequate knowledge to operate mobile financial services, there is a greater chance of continuous usage. Khan et al. (2022) discourse that customers who are late adopters require more knowledge about Fintech services to reuse or continue their usage. The analytical study showed that Fintech knowledge significantly influences customers’ continuance usage intention in the presence of satisfaction as a mediator. Thus, the hypothesis is proposed for the present study as follows:
H4: Customer satisfaction has a mediating effect on Fintech literacy and sustainable intention to use MFS.
Based on the aim of the research and literature reviews, the following theoretical framework has been developed for the current study as presented in Figure 1.
4. Research Methodology
4.1. Research Approach
The study is quantitative in approach and empirical in nature. This method is extensively used in social science studies. Under this method, data is collected through survey procedure and applied statistical techniques in data analysis ( Yazid, et al., 2020 ).
4.2. Sampling Design and Data Collection
The purposive sampling technique is used in the current study. Data has been collected from both primary and secondary sources but the study is basically based on primary data. The secondary data were collected from previous literatures, articles, working papers, and journals. The primary data was collected from the respondents through questionnaire survey from the study area from May 2023 to June 2023. A well-structured questionnaire was administered and distributed to the users through Google Docs explaining the objective of data collection. The questionnaire comprises two sections: section 1 includes respondents’ demographic profile like gender, age, educational qualification, occupation, and residential area. Section 2 contains respondents’ opinion on Fintech literacy, customer satisfaction, and sustainable intention to use MFS. Users who have experience of using MFS at least six months, for adapting new technology and growing their clear perception on MFS, were selected for the study. Respondents have been chosen conveniently from the account holders of bKash (a subsidiary of BRAC Bank Ltd.) because of holding the top market share with transactions (around 75%) among the MFS providers in Bangladesh ( Shah, 2022 ). In the current study, 260 questionnaires were distributed to the customers who are located at different area of Kuhlna Region in Bangladesh. Out of 260 questionnaires, researchers received 200 responses from the users which mean 77 percent of response rate. Hair et al. (2010) recommended that the sample size for a research should be minimum five times of measurement items in the questionnaire. As the present questionnaire has 13 measurement items, the number of respondents should be 65. But, In PLS-SEM, the least sample size is 100 ( Reinartz et al., 2009 ). The present study collected data from 200 respondents that surpassed the minimum recommended criteria. The ethical issues were addressed properly by giving assurance to the respondents about the confidentiality of their responses.
4.3. Measurement Scales
There are three constructs, such as Fintech literacy (FL), customer satisfaction (CS), and sustainable intention to use MFS (SIU) used in developing the theoretical framework of the study. Each construct has individual measurement items. All the items have been adapted from prior studies. For example, Fintech Literacy has four measurement items adapted from ( Lim et al., 2019 ); Customer Satisfaction has also four items that adapted from ( Bhattacherjee, 2001b ); and Sustainable Intention to Use MFS has five measurement items adapted from Kang et al. (2012) . All the items were assessed by using five-point Likert scale, within the range from 1 “Strongly Disagree” to 5 “Strongly Agree”.
4.4. Statistical Tool/Technique
The study followed the SmartPLS algorithm to evaluate the casual-effect relationship between exogenous and endogenous constructs by using PLS-SEM. Thus, the constructs of study are Fintech literacy (FL), customer satisfaction (CS), and sustainable intention to use MFS (SIU). A two-step model in PLS-SEM such as measurement model and structural model were used in study. The first step model was the measurement model that assessed the reliability of data and validity of constructs. The second step was the structural model that tested the hypotheses of the study.
5. Results and Discussion
5.1. Respondents Demographic Statistics
Table 1 shows the respondents’ demographic profile such as gender, age, educational qualification, occupation, and residential areas. The results demonstrate that there were a higher number of male respondents than females in the study. There were five age groups of respondents where a major portion from the range of 20 - 30 years followed by 31 - 40 years, and 41 - 50 years of age group. A limited number of respondents were below 20 years, and above 50 years of age group. In the educational qualification aspects, the higher portion held a Graduate degree followed by a Post-graduate degree and a high secondary level. In terms of occupation, more than half of the respondents were students and the rest represented service holders, business professionals and others. For residential area status, a greater portion of respondents were located in urban areas followed by rural and semi-urban areas respectively.
5.2. Measurement Model
The measurement model assesses the data reliability and validity of constructs in the conceptual research model. This is the first stage of PLS-SEM. The measurement model is shown in Figure 2 which mainly presents the indicator loadings. A loading value less than 0.50 should be deleted and leave any items with a loading value higher than 0.90 ( Chen et al., 2022 ). Hair et al. (2019) also suggest that the value of indicator loading should be greater than 0.708 since it represents that the construct explains more than 50 percent of the indicator’s variance. The indicator loading value was found to be acceptable in this study.
5.2.1. Reliability Analysis
Internal consistency reliability of a given construct is measured by using Cronbach’s alpha and Composite reliability parameters ( Henseler et al, 2009 ). The
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Table 1. Respondents demographic profile.
value of Cronbach’s Alpha is 0.7 and above is acceptable but above 0.50 is permissible for further analysis of data ( Chen et al., 2022 ; Hair et al., 2019 ). Composite reliability is a more logical choice to determine internal consistency or accuracy instead of individual dependability ( Hair Jr et al, 2017 ). Hair et al. (2019) stated that the composite reliability value between 0.60 and 0.70 is considered acceptable and ranging from 0.70 to 0.90 flows ‘satisfactory to good’, but the value 0.95 and higher are problematic and it declined the construct validity. Table 2 represents the output of internal consistency reliability where Cronbach’s alpha and composite reliability values were found within the range, meaning it was above the 0.7 thresholds recognized in Table 2.
5.2.2. Validity Test
After the assessment of the reliability of indicators, it requires to measure the validity of constructs. The validity test includes convergent as well as discriminant validity. Hence, convergent validity is the degree to which constructs converge with a view to explaining the variance of the measurement items. The metric used to measure the convergent validity of constructs is AVE (average variance extracted) for the items on every construct. The value of AVE should be greater than 0.50 to achieve convergent validity ( Hair et al., 2019 ). Discriminant validity entails other factors with cross loading. Therefore, each indication loading must be greater than the sum of all cross loading for it to be valid ( Chin, 1998 ). Table 3 explains the Fornell-Larcker criterion to present convergent and discriminant validity. The corresponding value of AVE for each construct is above 0.5, meaning convergent validity is achieved. On the other hand, the discriminant validity as per the Fornell-Larcker criterion demonstrates that the value is diagonal and
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Table 2. Output of reliability test.
Source: Output of SmartPLS 4. Note: CS—Customer Satisfaction; FL—Fintech Literacy; SIU—Sustainable Intention to Use.
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Table 3. Validity test results—Fornell-Larcker criterion.
Source: Output of SmartPLS 4. Note: CS—Customer Satisfaction; FL—Fintech Literacy; SIU—Sustainable Intention to Use.
greater than the underneath value. Thus, it justifies that the present study has no discriminant value among the constructs and established the discriminant validity.
Similarly, Table 4 presents the output of the Heterotrait-Monotrait Ratio Matrix (HTMT) to measure the discriminant validity of constructs. The HTMT value is smaller than 0.90 implies the discriminant validity is achieved at the upper bound of the 95% confidence interval ( Hair et al., 2019 ). As per the HTMT value stated in Table 4, the current study doesn’t have any discriminant value.
The factor loading is another statistical measure of discriminant validity. Its value should be greater than the corresponding cross loading ( Chin, 1998 ). Table 5 describes the discriminant validity Output-cross loading value in which it indicates that the factor loadings are greater than the available cross loading. It denotes the establishment of discriminant validity for the study.
5.3. Structural Model
Since the measurement model is satisfactory, the structural model would be
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Table 4. Discriminant validity—HTMT output.
Source: Output of SmartPLS 4.
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Table 5. Discriminant validity output-cross loading.
Source: Output of SmartPLS 4.
assessed in evaluating the partial least-squares structural equation modeling (PLS-SEM). The study has also no multicollinearity issues as the VIF (Variance Inflation Factor) value of the items ranged from 1.312 to 2.615. The latent structures and the structural model are linked in using PLS-SEM ( Sohaib et al, 2019 ) which discerns the relationship between the models ( Hair Jr et al., 2017 ). The sample size was estimated by using an observed bootstrapping process and determined the statistical significance with t-value and p-value. The present study used normal bootstrapping with a range of 5000 bootstraps and 200 cases to ascertain the value of the direction coefficients. As per the conceptual research model presented in Figure 3, there is a latent extrinsic process (FL), a mediating component (customer satisfaction), and a latent endogenous element in the study (SIU).
The study measures the casual-effect relationship between Fintech literacy and sustainable intention to use MFS with customer satisfaction as an intervening role. Figure 3 represents the structural model of the study as well as describes the path model with the mediation effect.
Table 6 represents the path coefficient value of constructs and its relationship. Based on Table 6, the output showed that Fintech literacy has a positive relationship (beta value 0.268) and a significant impact on customer satisfaction (t-value > 1.96 and p-value < 0.05). Therefore, Hypothesis 1 (H1) is supported. Hypothesis 2 indicates that Fintech literacy has a positive impact on sustainable intention to use MFS. Here, the result disclosed that Fintech literacy has a positive relationship and significant effect on sustainable intention to use MFS (Beta value 0.228, t-value >1.96, and p-value < 0.05). Thus, Hypothesis 2 (H2) is supported. Hypothesis 3 illustrated that customer satisfaction has a positive impact on sustainable intention to use MFS. The result displayed that there is a positive
relationship between customer satisfaction and sustainable intention to use MFS as the beta value is 0.433, and customer satisfaction significantly influences sustainable intention to use MFS because the t-value is greater than 1.96 and p-value is lower than 0.05. So, the hypothesis 3 (H3) is supported.
In the case of the predictive accuracy of the model, the coefficient of determination i.e., R-square value, was determined to describe the variance in the endogenous construct. In Table 6, the value of R-square denotes 0.293 in Sustainable Intention to Use MFS explaining 29.30% of the total variance. This indicates the moderating predicting power of the model. On the other hand, the R-square value of 0.072 in customer satisfaction denotes 7.2% of the total variance and it indicates the weak predicting power.
5.4. Mediation Effect Analysis
To test the mediating effect of customer satisfaction in the relationship between Fintech literacy and sustainable intention to use MFS (H4), this study used path analysis relying on PLS-SEM to test the indirect effect. Based on the result of mediating effect (indirect effect) in Table 7, customer satisfaction mediates the relationship between Fintech literacy and sustainable intention to use MFS (β = 0.116, t = 3.536, and p < 0.05). Therefore, the hypothesis (H4) is supported.
When the direct effect is not significant but the indirect effect is significant, then it is a full mediation. On the other hand, while both the direct and indirect effects are statistically significant, then it represents partial mediation ( Carrión et al., 2017 ). It is shown in Table 8 that the direct effect of Fintech literacy on sustainable intention to use MFS is significant (β = 0.228, t-Value = 3.777, and p-Value < 0.05). Likewise, the indirect effect of Fintech literacy on sustainable intention to use MFS in the presence of customer satisfaction is significant (β = 0.116, t-Value = 3.536, and p-Value < 0.05). Thus, it means that customer satisfaction has a partial mediating effect on the relationship between Fintech literacy and sustainable intention to use MFS.
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Table 6. Path coefficients and hypothesis testing.
Source: Output of SmartPLS 4.
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Table 7. Mediating or indirect effect of customer satisfaction and hypothesis testing.
Source: Output of SmartPLS 4.
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Table 8. Direct effect and indirect effect.
Source: Output of SmartPLS 4.
6. Conclusion
6.1. Summary
This study aimed at examining the mediating effect of customer satisfaction on Fintech literacy and sustainable intention to use mobile financial services in Bangladesh. The study showed that Fintech literacy has a positive and significant influence on customer satisfaction. The study found that customer satisfaction positively and significantly affects sustainable intention to use MFS. The study revealed that Fintech literacy has both direct and indirect impact on sustainable intention to use MFS. The empirical result in mediation analysis demonstrated that customer satisfaction partially mediates the relationship between Fintech literacy and sustainable intention to use MFS. The result indicates that customers’ Fintech literacy positively interrelated with customer satisfaction and sustainable intention to use MFS. MFS providers should take initiative to educate their customers on Fintech services for attaining higher customer satisfaction and sustainability in usages of MFS. This research offers significant insights and inference in MFS sectors in Bangladesh. The verdicts of this study play vital role to the policy implication for the stakeholders in MFS market.
6.2. Theoretical Implications
The present study theoretically contributes to the existing literature. The main contribution of the study is providing the empirical evidence of the mediating effect of customer satisfaction on the relationship between Fintech literacy and sustainable intention to use MFS. This postulates in respect of why and how Fintech literacy has impact on customer satisfaction and sustainable intention to use MFS. The suggested model of this study provides a significant insight in mobile financial services literatures in customers’ behavioral management and service marketing research. This validation will act as an evidence for future studies in the area of banking services in a different regional/cultural context.
6.3. Practical Implications
This study practically contributes to the stakeholders, specifically to the management of MFS providers. It is important for the service providers to pay distinct attention to understand the customers’ Fintech knowledge on MFS as it affects satisfaction and sustainable usage intention. The higher level of Fintech literacy stimulates customer to use mobile financial services continuously. Thus, the findings of the study can also be useful to the MFS providers for taking initiatives in improving the Fintech knowledge of customers, their satisfaction and the sustainable usage intention of MFS.
6.4. Limitations and Further Research Guide
This study has shortcomings that may affect the generalizability. The major limitation of the study is related to the sample selection as the data were collected from a particular region of Bangladesh on a cross-sectional basis. Future studies may take place with a longitudinal approach in a different culture. Future studies should confirm these findings across industries like banking. For the higher predicting power of the suggested model, few other variables like, privacy, security, and risks might be added to the further investigation in MFS context.
Funding
This study did not receive a grant from any institutions.