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Article

Internet Banking Service Perception in Mexico

by
Elena Moreno-García
Campus Torrente, Universidad Cristóbal Colón, Veracruz 91930, Mexico
J. Risk Financial Manag. 2023, 16(8), 364; https://doi.org/10.3390/jrfm16080364
Submission received: 9 May 2023 / Revised: 21 July 2023 / Accepted: 31 July 2023 / Published: 7 August 2023
(This article belongs to the Section Banking and Finance)
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Abstract

:
The perception, adoption, use and satisfaction regarding Internet banking in Mexico have been scarcely explored. This research contributes to the limited literature on Internet banking in Mexico. Its objective is to analyze the perception that a population of workers has about the online service provided by banks in Mexico. The information was collected from a sample of 197 workers who make use of Internet banking. A very acceptable Cronbach’s alpha index was obtained (α = 0.919), which gives evidence of good internal consistency and reliability. The results of an exploratory and confirmatory analysis with a structural equation model (SEM) show that ten out of the eleven attributes explain workers’ perception of Internet banking services. From the eleven attributes analyzed, only four of them are significant in the Mexican context. These attributes are: security, monthly account statement, speed in decision-making and accessibility. In terms of implications for banking practice, the results of this research provide deeper insights for bank managers and policy makers to understand Mexicans’ motivation and develop appropriate strategies to increase Internet banking use.

1. Introduction

The services offered by banks in Mexico and around the world have changed rapidly in recent years, moving from a traditional form of branch service to telephone banking, then to electronic banking and now to financial services on mobile devices and biometric identification, among other advances (Avendaño 2018).
Billions of people around the world need to use financial services to reach even modest levels of financial well-being. Financial inclusion means access to a range of appropriate financial services for all in a fair, transparent, and equitable manner at an affordable cost (Pradhan and Dahal 2021; Dircio-Palacios-Macedo et al. 2023). On the subject of digital financial services, Durai and Stella (2019) pointed out the significant growth that financial inclusion is experiencing, and they partially attributed this to the enabling of new banking technology. Therefore, Internet banking is a financial inclusion driver that contributes to economic development. This is expected of a sustainable financial system that addresses and takes into account social and environmental factors in order to promote sustainable development. Therefore, a sustainable financial system is defined as a resilient system that contributes to the needs of society by supporting sustainable and equitable economies while protecting the natural environment (Ziolo et al. 2019). Currently, Internet banking has a very important place in the banking sector, and it has become one of the most revolutionary systems in today’s economic development (Singh et al. 2019).
Although, by 2019, the use of Internet banking had not reached the desired levels (Yeşildağ 2019), in the wake of the COVID-19 pandemic, many human activities were rethought in order to avoid contagion. In this sense, the adoption of electronic banking was an alternative to prevent physical attendance at a bank branch (Quintero Peña and Mejía Baños 2022). Therefore, Internet banking services were decisive during lockdown, since the population had to use these services to be able to carry out their daily financial transactions at a time of crisis that affected the world’s population.
Financial systems have innovated in the provision of financial services, and have given way to the digital finance market. This has allowed a new way of operating different services through mobile phones and personal computers, turning this banking industry into one with higher benefits and insurance in their financial services.
According to Cassimon et al. (2022), Mexico has ample room for increasing financial depth and people’s access to finance. With slightly less than 70% of adults using at least one financial product (checking account, credit, insurance or retirement savings) in 2018, financial inclusion indicators remain low when compared to other countries. Moreover, access to financial services is significantly unequal across income levels and genders, between rural and urban areas, and across states. Therefore, it is essential to understand customers’ perception of Internet banking because it can help us to understand why its adoption has not reached a greater portion of the Mexican population. It can also help the bank to formulate appropriate strategies aimed at increasing its use. Based on the above, the question arises: what is the perception that users in Mexico have of the attributes of Internet banking?
This study will focus on the activity carried out by the financial sector and specifically on Internet banking. The perception, adoption, use and satisfaction regarding Internet banking in Mexico has scarcely been explored. Despite the fact that in the first year of the COVID-19 pandemic, the use of Internet banking services increased 24.9% (Statista 2023), only a few recent studies have been carried out on this subject, including those by Quintero Peña and Mejía Baños (2022); Moreno-García et al. (2021); Martínez-Domínguez (2021); Ramírez Barón et al. (2019) and Avendaño (2018). Therefore, this research contributes to the limited literature on Internet banking in Mexico, specifically on the perception of this service. The objective of the study is to assess the perception that the population has about the online service provided by banks in Mexico, considering the model proposed by Durai and Stella (2019). The following section presents the literature review. Section 3 describes the methodology used in the research. Section 4 shows the results of the research and finally the conclusions and implications are presented.

2. Literature Review

Customers are the sole judges of service quality. If they perceive it to be good service, then it is (Roy and Saha 2015). According to Gronroos (1982) and Parasuraman et al. (1988), customers assess the quality of service by comparing their expectation with perception. Perception is man’s primary form of cognitive contact with the world around him (Efron 1969). The behavior of human beings and societies are shaped under the influence of perception, and therefore people’s perceptions have a direct influence upon their decision-making and consequently the results of their decisions (Özleblebici and Çetin 2015).
Digitalization in the form of technological innovation has had a profound and significant effect on the financial sector. Digital transformation strategies focus on transforming products, processes and organizational aspects due to new technologies (Guerra et al. 2023). The benefits for consumers of such innovations are significant: responsible digital finance enables greater access to financial services and may positively impact financial inclusion. It provides consumers with a wider choice of financial products and services, at lower costs, increased speed, convenience and security, and can contribute to increasing operations efficiency of financial services providers by spurring competition (OECD 2020). Although Internet banking provides many positive effects, such as faster transactions and lower handling fees, customers’ perceptions of this system are still the main factor affecting adoption rates (Shanmugam et al. 2015) and could be a determinant for continuing using Internet banking. This is of great importance considering that Internet banking’s success is based on continuous use rather than initial adoption (Rahi et al. 2021; Yuan et al. 2019).
Many models have been proposed to explain and predict the use of a system, but the technology acceptance model (Davis 1985) has captured the most attention (Chuttur 2009; Vuković et al. 2019). This model proposed that system use can be explained by user motivation, which is directly influenced by an external stimulus consisting of the actual system features and capabilities. According to Davis (1985), a user’s motivation can be explained by three factors: perceived ease of use, perceived usefulness and attitude towards using the system.
Subsequent to this model, a number of studies were carried out to study the perception, adoption and use of Internet banking services and proposed several factors that contribute to increase their use (Picoto and Pinto 2021). According to George (2018), the finding that service quality, as an antecedent to perceiving ease of use and perceiving usefulness, has indirect effects on Internet banking use should attract the attention of banks in order for them to take the necessary steps to improve the service quality dimensions, such as fulfillment, efficiency, reliability, website attributes, responsiveness and privacy.
During the COVID-19 pandemic, Castillo-Villar and Castillo-Villar (2022) explored the perception of a group of older adults regarding the advantages and restrictions of Internet banking. Based on their experiences, older adults identified functional aspects such as saving time, avoiding physical risks, and having control over their finances as advantages. They also found social advantages such as support and bonding with family and friends. The restrictions identified those of a non-technological type (lack of useful resources and bank employees’ limited patience) and technological restrictions (concerns about cybersecurity, data privacy and passwords). Vig et al. (2022) sought to investigate the acceptance of customers regarding online banking by examining the services which were most preferred by the customers during the coronavirus pandemic. They found that customers mostly prefer online banking services over branch banking due to cost-savings, reliability, convenience, and the user- and environmentally friendly system.
In a study carried out on a population of Internet banking users in Turkey, Yeşildağ (2019) identified the following seven factors that affect customer preferences: social effects, benefits of Internet banking, usefulness, speed and time savings, user friendly and cost, the ability to use the Internet, and suitability to lifestyle and work. It was also identified that women, single people and those in the 18 to 25 age group use this service more.
In order to gain a comprehensive understanding of Internet banking in the United Kingdom (UK) Shanmugam et al. (2015) researched customers’ perceptions. The results indicate that customers are highly satisfied with Internet banking. They also identified that money transfers and bill payments are the most popular operations among users and that security is the most important factor affecting the adoption rate of Internet banking in the UK. Also in the UK, Mbama and Ezepue (2018) found that the main factors which determine customer experience with Internet banking are service quality, functional quality, perceived value, employee–customer engagement, perceived usability and perceived risk.
Anouze and Alamro (2020) focused on Internet banking in Jordan to explain its slow adoption. The results of their analysis reveal that ease of use, perceived usefulness, perceived security, self-efficacy, awareness and perceived price have significant and positive impacts on intention to use Internet banking. Nguyen and Tran’s results (2020) coincide with those of Anouze and Alamro (2020). Their study found that except for service costs, other factors such as convenience, speed, security, ease of use, reliability, quality of service, and procedure are positively related to customer satisfaction related to electronic banking in Vietnam. In the same country, Vietnam, Duc (2022) explored the possible relationship between customer satisfaction and digital banking services. His results show that focusing on minimizing the response time to customers’ inquiries and individualizing services to each one of them are what digital banks in Vietnam need to do.
In India, Shankar and Jebarajakirthy (2019) researched customer loyalty towards Internet banking platforms via service quality practices. Their findings show that reliability along with privacy and security enhanced customer loyalty to Internet banking. Also in India, Arshad Khan and Alhumoudi (2022) found that Internet banking efficiency, reliability and service quality have a significant direct effect on customer satisfaction and customer retention. Raza et al. (2020) also found that site organization, responsiveness, usability and personal need significantly impact Internet banking customer satisfaction.
A study carried out by Ling et al. (2016) found that in a group of workers in Malaysia, the five factors that can influence customer satisfaction regarding Internet banking include service quality, website design and content, security and privacy, convenience and speed.
There are several studies around the world that have contributed to the empirical evidence on the perception of Internet banking users and their motives for adoption and use. However, the present research contributes to the existing literature on this topic in a Latin American context, specifically in Mexico.

Conceptual Model

Considering the theoretical arguments and taking the model presented in Figure 1 as a reference, Internet banking was taken as a latent variable of study.
The eleven satisfaction attributes that the population has about Internet banking services are described in Table 1.

3. Materials and Methods

This study was non-experimental and confirmatory, with the purpose of verifying if the attributes of the model proposed by Durai and Stella (2019) to identify the satisfaction of financial services users via the Internet are the same in the Mexican context. The Internet banking variable was evaluated through eleven attributes: convenience, adaptability, security, user friendly, low service charge, accurate and timely service, monthly account statement, speed in decision-making, easy transfer, Internet connectivity and accessibility. There were five response options for each question: not at all satisfied (NS), not very satisfied (PS), satisfied (S), very satisfied (MS) and extremely satisfied (SS) (Appendix A). If this was not applicable or unanswered by the participants because they had not used said service, then a value of zero was assigned.
This study was carried out in Mexico, in a population of workers from a corporation that provides security services. The application of the instrument provided national coverage, supported by the Human Resources department of the Company in different states of the country. The participants included 357 workers who were active at the time of applying the instrument and who agreed to participate as long as their anonymity and confidentiality were respected.
The data analysis was based on the validity of the instrument and the normality tests to justify the use of multivariate statistical procedures. First, the exploratory factor analysis determined the factorial structure that explained the user’s perception towards the attributes of Internet banking. Subsequently, the resulting measurement model was applied to the confirmation plane, using structural equation modeling (SEM). For the statistical analysis, the IBM software, SPSS AMOS v23, was used.
Of the total sample of 357 participants, two cases were excluded because they did not fully respond to the survey. Of the 355 valid cases, 197 do use Internet banking and the rest do not (158). In addition, atypical cases were eliminated from the database, so the calculation was performed with 146 cases who responded that they use Internet banking. The age of the participants was as follows: two workers between 18 and 20 years old (1%); 39 workers between 21 and 25 years old (19.8%); 49 workers between 26 and 30 years old (24.9%); 49 workers between 30 and 40 years old (24.9%); and 58 workers who were aged over 40, representing the highest percentage (29.4%). In relation to the instrument, a very acceptable Cronbach’s alpha index was obtained (α = 0.919 in the 13 items that include gender, age and the eleven attributes), which gives evidence of good internal consistency and reliability. In addition, the Cronbach’s alpha values for each indicator were greater than >0.9 in all cases if the element was deleted (Table 2).
The Kaiser–Meyer–Olkin measure presented a value of 0.934, while a chi-square value of 1835.052 with n gl(55) and a p-value < 0.001 were obtained, which is acceptable. The MSA values (>0.5) and the correlations (Table 3) provide proof of the reliability of the database for the exploratory analysis, which was confirmed by the SEM methodology.

4. Results

Table 4 shows the total explained variance matrix and the factorial matrix resulting from the exploratory factorial analysis, in which a single factor with an auto value of 7.428 is observed and represents 64.336% of the variance of the phenomenon being analyzed.
It is observed that the underlying structure described in Table 4, which is the result of the exploratory factor analysis, is a one-factor structure with the eleven original attributes that are submitted to the analysis, all of them with factor loads > 0.5 and communalities (h2) > 0.5 that explain the greatest variance observed in the group with 64.336%.
The underlying structure, resulting from the exploratory factor analysis, presents an accommodation in the ordering of the attributes (Figure 2). However, it is necessary to confirm this structure in order to be able to determine the role played by each indicator within the variance explained by the factor and the saturation of the indicators.

SEM Methodology for Confirmatory Analysis

To confirm the exploratory factorial structure described in Table 4, the structural equation model is used to measure the unobserved latent variable (Internet banking) that is incorporated (Cea 2020). It seeks to first verify the dependency relationship to verify the exploratory model of one factor (Batista and Coenders 2000).
The exploratory model in Figure 2 presents values of structural adjustment and parsimony that do not align with the suggested theoretical criteria, so adjustments are made in the index modification, resulting in a final adjusted model.
The values of the measurement model are: a chi-square (CMIN/DF) of 4.521, a CFI of 0.915, a TLI of 0.894, and a, RMSEA of 0.134, which are not in the suggested theoretical range, since it is desirable that the RMSAE is less than 0.05 for a better fit of the model. On the other hand, the parsimony fit measure exceeds the suggested threshold (>0.5). However, the BI1 indicator (0.68) presents a value below 0.7 and should be excluded from the model. In the same way, it is necessary to correlate the measurement errors e13, e14, e15, e16, e17, e19 and e20, and an adjusted model is obtained, which is shown in Figure 3.
The values of the measurement model are a chi-square (CMIN/DF) of 2.081, a CFI of 0.980, a TLI of 0.971 and an RMSEA of 0.07, which are not in the suggested theoretical range, since it is desirable that the RMSAE is less than 0.05 for the model to fit better, although the likelihood ratio and the CFI and TLI fit the suggested theoretical criteria. Therefore, a new adjustment is made to errors to obtain the following model (Figure 3b).
The values of the model are: a chi-square (CMIN/DF) of 1.682, a CFI of 0.989, a TLI of 0.981), an RMSEA of 0.05, a parsimony PRATIO of 0.600, a PNFI of 0.584, and a PCFI of 0.593, showing the best adjustment that could be obtained without modifying the indices. However, negative correlations are still observed among the errors e14, e18 and e19.
The model evaluation according to Weston and Gore (2006) seeks to determine whether the relationships between the variables in the measurement model reflect the relationships that were observed in the data. The order in which they contribute with the greatest weight to the attributes according to the users is as follows: accurate and timely service (BI6, 0.89), easy transfer (BI9, 0.86), connectivity (BI10, 0.86), accessibility (BI11, 0.85), speed in decision-making (BI8, 0.84), monthly account statement (BI7, 0.80), adaptability (BI2, 0.76), user friendly (BI4, 0.76), security (BI3, 0.74), and low service charge (BI5, 0.74). However, they still do not present a structural fit and parsimony that allow for establishing the best fit of the model.
In the diagrams shown below (Figure 3c), with the standardized and non-standardized estimators, it can be seen that several errors of the estimators were correlated, and some show a negative correlation, which affects the fit of the model.
Therefore, the model is adjusted with the elimination of the indicators that present correlations in the errors of the estimators, resulting in model 4 as a definitive model that explains the satisfaction of the workers who participated in the study regarding the Internet banking service.
In the final adjusted model in Figure 4, the attributes of financial services regarding which users felt satisfied are shown. It is observed that of the eleven attributes proposed by Durai and Stella (2019), only four of them are significant in the Mexican context. These attributes are security (BI3, 0.84), monthly account statement (BI7, 0.89), speed in decision-making (BI8, 0.81) and accessibility (BI11, 0.79).
Table 5 shows the adjustment of the model through its indicators and values. It is observed that each of them shows a good fit. Once the model has been verified, the construct integrated by the four attributes was evaluated through reliability and the extraction of the extracted variance, in order to evaluate if the specified indicators are sufficient in their representation of the construct. Regarding reliability, the value exceeds the recommended value of 0.70. Regarding the extracted variance (69.5%), the value is greater than the recommended percentage of 50 percent. The difference indicates that 30% of the specified indicators were not taken into account for the construct.

5. Discussion

The objective of this research is to analyze the perception that a population of workers of a national corporate have about the service offered by Internet banking in Mexico. The results of the confirmatory analysis show that only four out of the eleven attributes are significant in the Mexican context: security (BI3, 0.84), monthly account statement (BI7, 0.89), speed in decision-making (BI8, 0.81) and accessibility (BI11, 0.79). These results contrast with those found by Durai and Stella (2019). Their results show that usability, convenience, accurate timing, and an easy interbank account facility have positive impacts on Internet banking.
Security is an attribute valued by Internet banking users in Mexico and in many populations and countries, as observed in Castillo-Villar and Castillo-Villar (2022), Anouze and Alamro (2020), Akhter et al. (2020), Yeşildağ (2019), Nguyen and Tran (2020), Shankar and Jebarajakirthy (2019), Jung and Shin (2019), Ling et al. (2016) and Shanmugam et al. (2015). Regarding security, Arshad Khan and Alhumoudi (2022), Raza et al. (2020) and Nagar and Ghai (2019) referred to reliability as an attribute with significant influence on Internet banking customer satisfaction.
The speed in decision-making as something perceived positively among Internet banking users coincides with the results of Castillo-Villar and Castillo-Villar (2022), Ling et al. (2016), Nguyen and Tran (2020) and Duc (2022).
Yeşildağ (2019) further identified that the suitability of Internet banking to one’s lifestyle and work was considered as a valuable motive. This is directly related to having a monthly account statement, speed in decision-making and accessibility, attributes valued by the participants in this research and that undoubtedly influence their lifestyle and work.
The convenience attribute was excluded from the fitted model of this research. These results contrast with those found by Ahn and Lee (2019), who showed that the convenience value is the most important factor in the acceptance of Internet banking services. The results about the convenience attribute found in this research could be explained by the multidimensional approach of this attribute. A possible explanation for the exclusion of this attribute in the fitted model could lie in the fact that efficiency and the affective aspect are also inherent to other attributes analyzed.
Regarding user friendliness, the results coincide with Akhter et al. (2020). Nguyen and Tran (2020) also found that ease of use was not a significant attribute in using mobile banking.

6. Conclusions

In this era of digital technology, the banking sector has revolutionized its operations through the use of Internet banking services. Internet banking has transformed the banking industry into an anytime, anywhere, fast, and customized service offering, and at the same time has changed the way banks service their customers (Nagar and Ghai 2019). However, the success of these services depends on the continuity of intention of the Internet banking user and not only on their initial adoption (Rahi et al. 2021).
Mexico is ranked 12th in terms of gross domestic product (GDP), but its financial growth does not correspond to its level of economic activity, since the country has the lowest credit to GDP ratio among the OECD countries. Furthermore, its income inequality is one of the highest in the Western hemisphere, and low financial inclusion may be one of the reasons for this deficiency (Dircio-Palacios-Macedo et al. 2023).
There are some important implications for theory and practice. Regarding theory, the findings of this research contribute to the existent literature on Internet banking perception and adoption in a little-explored population. This adds evidence of what internet banking users in Mexico value about the digital service. In terms of implications for banking practice, the results of this research provide deeper insights for bank managers and policy makers to understand the motivation of Mexicans and develop appropriate strategies to increase the use of Internet banking. The results confirm that attention should be given to users’ security as a very important attribute that determines user’s perceptions.
The limitations of this study are related to the characteristics of the sample. It is a group of workers that does not represent the Mexican population in its entirety. Future research should consider a larger sample of the population in Mexico, as well as studying specific sectors of the population. Young people from different regions of Mexico, for example, are a very interesting group to analyze because they will represent the economically active population in the coming years. It should also be considered how other sociodemographic variables affect the perception, adoption and use of Internet banking. For example, a gender-based approach in this type of study or analysis by locality, comparing rural and urban areas, could yield very important results for decision making.

Funding

This research received no external funding.

Data Availability Statement

The data presented in this study are available on request from the corresponding author.

Conflicts of Interest

The author declare no conflict of interest.

Appendix A

Table A1. Questionnaire based on Durai and Stella (2019).
Table A1. Questionnaire based on Durai and Stella (2019).
Not at All SatisfiedNot Very SatisfiedSatisfiedVery SatisfiedExtremely
Satisfied		Not
Applicable
Convenience
Adaptability
Security
User friendly
Low service charge
Accurate and timely service
Monthly account statement
Speed in decision-making
Easy transfer
Internet connectivity
Accessibility

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Figure 1. Conceptual model of satisfaction of Internet banking attributes. Source: Durai and Stella (2019).
Figure 1. Conceptual model of satisfaction of Internet banking attributes. Source: Durai and Stella (2019).
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Figure 2. Exploratory model of satisfaction towards internet banking.
Figure 2. Exploratory model of satisfaction towards internet banking.
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Figure 3. (a) Fitted model. (b) Fitted model. (c) Standardized and non-standardized estimates of the model.
Figure 3. (a) Fitted model. (b) Fitted model. (c) Standardized and non-standardized estimates of the model.
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Figure 4. Satisfaction model towards internet banking.
Figure 4. Satisfaction model towards internet banking.
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Table 1. Page layout description.
Table 1. Page layout description.
Latent VariableCodeIndicator
Internet bankingBI1Convenience
BI2Adaptability
BI3Security
BI4User friendly
BI5Low service charge
BI6Accurate and timely service
BI7Monthly account statement
BI8Speed in decision-making
BI9Easy transfer
BI10Internet connectivity
BI11Accessibility
Table 2. Reliability of the instrument.
Table 2. Reliability of the instrument.
Latent
Variable		IndicatorsAverage Scale If the Element Has Been SuppressedScale Variance If the Element Has Been SuppressedTotal Item
Correlation Corrected		Squared
Multiple
Correlation		Cronbach’s Alpha If the Item Has Been Deleted
Internet bankingBI138.654889.0740.6680.5930.912
BI238.624487.7970.7870.7550.908
BI338.502588.8020.7590.6820.909
BI438.203088.6220.7440.6170.910
BI538.517888.1390.6960.5990.911
BI638.492485.5880.8580.7660.905
BI738.451886.7290.7610.6650.909
BI838.588886.5390.8040.7030.907
BI938.213286.5260.8320.7410.906
BI1038.436586.0230.8080.7200.907
BI1138.304686.4580.7890.7310.907
Table 3. Matrix of correlations and MSA.
Table 3. Matrix of correlations and MSA.
ItemBI1BI2BI3BI4BI5BI6BI7BI8BI9BI10BI11MSA
BI110.750.630.480.420.590.510.560.580.550.520.928 a
BI2 10.740.60.50.690.580.680.660.640.640.883 a
BI3 10.640.610.640.590.590.660.620.560.914 a
BI4 10.610.670.70.60.660.620.610.947 a
BI5 10.660.560.630.60.630.650.914 a
BI6 10.740.770.760.710.740.958 a
BI7 10.70.680.650.660.934 a
BI8 10.70.710.710.948 a
BI9 10.780.760.951 a
BI10 10.780.953 a
BI11 10.939 a
a Measure of sampling adequacy.
Table 4. Factor matrix a.
Table 4. Factor matrix a.
IndicatorsFactor Attributes of Internet Bankingh2FactorInitial Eigenvalues
Total% of
Variance		%
Accumulated
BI60.8790.77317.42867.53167.531
BI90.8670.75220.8037.29874.829
BI100.8450.71430.5384.89279.721
BI110.8400.70640.4824.38384.103
BI80.8360.69950.3893.53787.641
BI70.7950.63260.2942.66990.31
BI20.7940.63070.2682.43792.747
BI40.7640.58480.2392.17594.922
BI30.7630.58390.2312.09797.018
BI50.7320.536100.1871.69998.717
BI10.6830.467110.1411.283100
Extraction method: maximum likelihood. a.1 extracted factors. Four iterations needed.Extraction Sums of Squared Charges
% of variance% of variance% of variance
7.07764.33664.336
Table 5. Adjustment indicators.
Table 5. Adjustment indicators.
Summary of Model Fit
IndexValue
Chi-square (2 g·L)2.353
Probability level0.308
  GFI0.992
  CFI0.999
  RMSEA0.035
Reliability and variance extracted
Construct reliability0.732
Variance extracted0.695
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Moreno-García, E. Internet Banking Service Perception in Mexico. J. Risk Financial Manag. 2023, 16, 364. https://doi.org/10.3390/jrfm16080364

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Moreno-García E. Internet Banking Service Perception in Mexico. Journal of Risk and Financial Management. 2023; 16(8):364. https://doi.org/10.3390/jrfm16080364

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Moreno-García, Elena. 2023. "Internet Banking Service Perception in Mexico" Journal of Risk and Financial Management 16, no. 8: 364. https://doi.org/10.3390/jrfm16080364

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