This research will help us to know factors affecting e-banking usersare service quality, which consists of reliability, privacy and security, design on application or website, and custom
INTRODUCTION
Introduction
In the first half of 2021, we have witnessed rapid progress in the earnings before tax of Vietnam's banking and finance industry Impressive numbers continue to appear and shock a series of scores from the same year last year With new changes, applications, and integration of many advanced technology platforms, eBanking Vietnam is going further and further
According to Statista 2020, there are more than 120 million mobile subscribers in Vietnam, of which more than 75% can register to use financial services, banking, and online payments This is a keyboard for eBanking development, and originally, the mobile platform came first Especially in the context of COVID19 spreading globally as well as in Vietnam, non-cash payment can be prioritized in terms of safety, limiting the spread and spread, this is also a meaningful explanation for the strong trend of converting cash payments to non-cash payments
In Vietnam, internet penetration is high with 68.17 million users as of January 2020 Mobile banking has also experienced rapid growth, reaching 30 million daily users However, despite its popularity, E-Banking's additional services remain underutilized, with low awareness (30-60%) and usage (20%) These services include savings deposit, international money transfer, credit card payment, insurance purchase, and ATM card management.
To explain the above phenomenon, the authors of this article think that many banks have not yet implemented expanded services as well as the digital conversion motivation of E-Banking users as the main reason for the level of awareness and low additional service usage At the present time, customers use E-Banking mainly in the ability to be proactive, save transaction time and be flexible in transaction locations (>80% of customers) Therefore, the form of using E-Banking by consumers has only stopped at normal daily transactions (transfer, phone top-up, electricity and water payment) but has not been extended to other services Services that need assurance of confidentiality and need direct advice such as savings or insurance.
When asked about the barriers preventing them from using E-Banking, the majority of users said that the weakness of this platform is that it still has limited features (for example, low transaction limits) Moreover, some users still do not trust E-Banking because the system is not stable or there is an error causing the transaction to be interrupted In addition, the desire to directly listen to advice at the table is also one of the important barriers in the eyes of users during the transition to a digital platform However, in general, most of the current E-Banking services of the above banks meet the basic needs of customers, even reaching their expectations with only 16% of customers expressing dissatisfaction with digital banking applications But the problem is we’re currently living in a middle of a pandemic, so the desire of being able to directly listen to advice at the table instead of using e-banking apps is hard to please So that the main reason for us to choose this topic is to analyze and make recommendations for banks to develope their e-banking apps to reach their customers needs and from that forming a behaviour in using e-banking more often for every citizens in Viet Nam.
The Covid 19 pandemic that suddenly hit Vietnam at the beginning of 2020 forced us to isolate and limit social activities such as shopping and playing All trading and trading activities are restricted and stagnant, forcing us to gradually shift to buying and selling and mainly using e-banking services With the growth rate of Mobile banking up to 200% and about 30 million people using banking payment services every day in Vietnam Banks in Vietnam need to know how to retain and find their customers through changing their interface and services to suit the needs of their customers over time.
Problem statement
1.2.1 Reason for choosing the topic
The purpose of this research paper is to provide recommendations for banks based on real research to maintain customer satisfaction and loyalty during the covid pandemic.
1/ Investigate the factor influence the changes in the economic structure as well as the way people consume and live through the covid pandemic in 2022.
2/ As citizens of Generation Z, we usually used ebanking as an alternative to cash early on because of its convenience And the covid pandemic has made the use of e banking more necessary than ever It's not limited to young people, but everyone should use e banking to do some sort of social distance.
3/ Deep consumer insights will give banks a competitive edge when planning marketing campaigns, social media campaigns and upgrading their app and e banking system.
Purpose of research
By authentic and scientific evidence, statistics based on reliable sources This research will be the necessary source of a database on customer behavior when using e-services (online banking services) during the pandemic Thereby, it can help to limit difficulties and improve service quality to satisfy the needs of customers in the banking industry.
With the development level of science and technology as today, the application of electronic services in the business model is very necessary That creates high efficiency and fast Therefore, with the practical purpose of this study, it will help banks to consider developed directions in the future At the same time, building customer satisfaction and loyalty through putting them at the center
The study identifies challenges and risks faced by businesses, particularly during the COVID-19 pandemic, to facilitate proactive measures Additionally, it explores the benefits of enhancing e-services to empower businesses in effectively capturing customers.
Research question
To fulfill the research objectives, this study proposes the following research questions:
1/ What factors influence the changes in the economic structure as well as the way people consume and live through the covid pandemic in 2022?
2/ What is the extent of the impact of these factors?
3/ What is the implication/ recommendation/ suggestion to the e banking industry?
LITERATURE REVIEW
Research concepts & definitions
To fulfill the research objectives, this study proposes three main variables, and two definitions:
Internet Banking, also known as net-banking or online banking, is an electronic payment system that enables the customer of a bank or a financial institution to make financial or non-financial transactions online via the internet This service gives online access to almost every banking service, traditionally available through a local branch including fund transfers, deposits, and online bill payments to the customers
Internet banking can be accessed by any individual who has registered for online banking at the bank, having an active bank account or any financial institution After registering for online banking facilities, a customer need not visit the bank every time he/she wants to avail a banking service It is not just convenient but also a secure method of banking Net banking portals are secured by unique User/Customer IDs and passwords
E-banking service quality and e-banking loyalty (EBSQ)
It is extensively discussed in literature that loyalty depends on EBSQ There are four dimensions of EBSQ, namely, reliability, privacy and security, website design and customer service and support of e- banking
The first dimension, reliability in e-banking holds great importance as response of customers is crucial Reliability is one of the factors which can influence responses
The second dimension, privacy and security, reflects the degree to which an e-banking user submits personal information on an e- banking platform with confidence.
The third dimension of EBSQ, website design is defined as various interactive features of the e-banking service that helps provide consumers with structure of transaction during and summary afterwards the structure of processing a transaction and more.
The fourth dimension, customer service and support can be explained as the rapidity of retort toward any delinquent reported by user during or after service experience The users here can be referred as both individuals and organizations.
E-banking service quality and e-banking satisfaction
The relationship of EBSQ and satisfaction remains a great academic debate Contradictory findings and views of researchers can be noted, as some argued EBSQ as an antecedent of e- banking customer satisfaction Older studies termed satisfaction as antecedent of service quality and the findings of these classical studies cannot be neglected even if the service quality is now read as electronic service quality and digital aspect is also attached to satisfaction and loyalty An empirical analysis of customer perception of US banks on service quality reports higher quality of EBSQ leads to satisfaction and for greater customer loyalty, privacy and navigation interface must be focused The service providers especially in e-banking are strictly advised to ensure focus on high level of secure information, confidentiality and transaction privacy
Mediating role of e-banking satisfaction
Customer satisfaction remains a primary goal for businesses, as it directly impacts customer loyalty Providing high levels of satisfaction through e-satisfaction initiatives fosters positive attitudes towards the brand, resulting in e-loyalty In the banking sector, e-satisfaction translates into increased usage of banking services and strengthens the likelihood of e-loyalty.
Previous research
“Impact of e-banking service quality on e-loyalty in pandemic times through interplay of e-satisfaction”
The purpose of this study is to investigate the impact of e-banking service quality on e-banking loyalty via a mediating effect of e- banking satisfaction During COVID-19, banking customers of three domestic systemically important banks in Pakistan were polled to assess the electronic services offered by these institutions.
The information was gathered utilizing a customized questionnaire and emails and messaging apps The database of a local marketing firm in Pakistan was utilized, and the analysis contained
976 replies To examine the study's hypotheses, structural equation modeling was employed.
According to the data, e-banking loyalty is boosted by reliability and website design, especially during COVID-19 The relationship between e-banking privacy and security and e-banking loyalty was found to be totally mediated by e-banking satisfaction; nevertheless, the indirect influence of website dependability and security was found to be significant.
The relationship between design and e-banking loyalty was partially mediated.
Customers value secure e-banking platforms, as evidenced by the mediating effect of e-banking satisfaction between privacy and security and e banking loyalty Because of privacy considerations, there may be some difference in their loyalty.
“Internet banking service quality, e-customer satisfaction and loyalty: the modified e-SERVQUAL model”
The impact of service quality parameters in Internet banking on e- customer satisfaction and loyalty is investigated in this study Based on distinct conceptions, this study attempts to investigate the structural relationship between Internet banking service quality, electronic customer happiness, and electronic customer loyalty.
Customer satisfaction is shown to have a substantial and positive impact on customer loyalty, whereas customer satisfaction is found to have a significant and positive impact on customer loyalty.
Service quality plays a pivotal role in shaping customer experiences in online banking and broader societal interactions It establishes the foundation for how individuals perceive and engage with digital services, ultimately influencing their satisfaction, loyalty, and overall functionality within the online banking ecosystem.
RESEARCH METHODOLOGY
Research design
The group's research process is divided into two stages: experimental and formal research Because of the limited time and resources available at the experimental stage, the team does not use any of the methods, instead relying on previous studies' models and scales to adjust the survey subjects to be university students in Vietnam Later, when the research entered the formal phase, the group decided to take a quantitative approach by conducting a survey directly through questionnaires Concerning the survey, after reviewing the questionnaire, I translated it into Vietnamese in order to better suit Vietnamese students, and I submitted it to their staff for review and approval Following the acceptance of the questionnaire, the group began requesting the correct study group that the students were studying at universities in Ho Chi Minh City, Vietnam The Google Form tool was used to create the questionnaire, which featured a number of simple questions on the personal information part as well as the important questions.
Questionnaires design
A structured questionnaire is used to collect the sample for this study The framework was created based on the preliminary study's findings The questionnaire was divided into two sections:Part 1: Personal information
Gender, age, industry, and personal income were assembled to characterize the study's participants and understand the major individuals who completed the questionnaires based on demographic standards.
Leading Factors, Risk Perception, Social Effect, Usefulness, Engagement, Perceived Ease of Use and Customer Satisfaction.
Measurement scale
Measurement scales for variables in the model
Items/Variables Codin g Measurement/Construct Source
Are you worried about your safety when using e-banking?
RE2 E-banking always ensures the fastest service time
Using E-banking can help extract transaction details quickly
The information and policies announced through E-banking are always accurate
The bank's technical team always fixes errors in a timely manner
I feel the security of customer data is guaranteed
My financial information is protected on an e-banking platform
Website information is updated regularly
Its products, services and features are properly presented and easily found in the website
Easy to use interface Hair et al., 2010
The interface is pleasing to the eye, not messed up
Hot tone website will be more attractive?
WD4 Cold tone website will be more attractive?
Would a website with a simple, neutral look be more attractive?
The website has a sophisticated and colorful interface, will be more attractive?
Customer support staff is knowledgeable about services and promotions of E-banking application during COVID-19
Customer support staff are always willing and enthusiastic to help customers
My questions will be answered quickly by the support staff
CSS4 E-banking customer service staff always put the interests of customers first
Online support staff is always available 24/7
Are you satisfied with the bank's online services during the COVID-19 pandemic?
Are the bank's websites easy to use during access?
Are you satisfied with the way the bank handles access or payment problems?
ES4 Do bank promotions or incentives make you happy?
Are you satisfied with all of the bank's online products and services?
I will recommend E-banking to everyone
My preference for this E- banking service won't change easily
I prefer using E-banking over other types of e-wallets
I intend to continue using E- baking
Sampling
Our target population are college students in Ho Chi Minh city Because of the limit of time so this survey our team chose a non- probability method to collect data More detail our team uses
Convenience sampling and Snow sampling in a Non-Probability method for this survey
According to research by Tabachnick and Fidell (1996), the minimum sample size required is:
N = 50 +8 *m (m is number of independent variables)
According to research by Hair, Anderson, and Black (1998), they found the minimum sample size to be five times the total observed variable The formula below is used by researchers to analyze elements (Comrey, 1973 and Roger, 2006):
N = 5*m (is number of question) Based on these two formulations, we have result:
Collection method
The study was conducted in a quantitative approach using practical sampling techniques and question-based sampling techniques Data assessing consumer response to electronic banking apps were analyzed using a two-step method proposed byAnderson and Gerbing (1988).[2]
To collect data for research purposes, the author conducted a survey of people of all ages on the behavior of using electronic banking in Vietnam over the past 2 years from the beginning of the pandemic to the present.
First, the author sends a survey using Google Form to the survey subjects by posting on social networks with pages with a large following of young people, students, or banking-related groups in general After the survey is completed, thanks to the help of people from the Student Forum or MB Bank Community (Military Bank), Vietcombank Community A total of 271 survey results were collected, and 271 surveys provided complete and valid information for distribution data analysis that will help the author in this essay to analyze the influence of e-service quality on customer satisfaction and loyalty in the banking industry in Covid
DATA ANALYSIS
Descriptive Statistics
The demographic characteristics are shown in Table below During the survey, our group received 280 answer sheets, although the incorrect rate amounted to 1.8 percent (equivalent to 5 forms) So, after summarizing, my group only contains 274 samples, accounting for 98.2 percent, and they are coded with Smart PLS software, yielding the following results:
Table 3 Demographic characteristics based on 274 samples
In this research paper, our group has a total of 274 valid questionnaires, of which 161 men accounted for 58.76 percent and
11 women accounted for 40.36 percent We examined five age groups: the first, under 18, has 11 people, accounting for 4.00 percent; the second 18-23 years old, has 221 people, accounting for 80.66 percent; the third, 24-35 years old, has 35 individuals, accounting for 12.73 percent; and the fourth, 36-45 years old, has
The survey revealed that respondents were primarily from TPHCM (90.88%), with four distinct groups based on employment status: students (77.37%), graduated but unemployed (0.73%), graduated and employed (10.55%), and working individuals (10.91%) Regarding age distribution, the majority of respondents (86.96%) were between 16 and 44 years old, with a small percentage (2.18%) over 44 years old In terms of income, the majority (51.46%) earned below 3 million VND, while 25.82% earned between 3 and 7 million VND, 12.73% earned between 7 and 10 million VND, 5.45% earned between 10 and 15 million VND, and only 3.27% earned over 15 million VND.
Scale measurement/model
The research data needs to be checked for the applicability of the observed variables and the research model in order to develop a particular and accurate analysis The research model's latent variables are transformed into observable variables in the form of reflecting indicators As a result, the observed variables are related to and affect one another; to assess the applicability of these parameters, the team devised the following outer loadings indexes:
Table 4 Conformity results of factors
According to (Joe and his Partner, 2011) [3] have shown that the relevance of the factors is expressed through the value of the outer loadings index and this number must be greater than 0.7 That means, through table 4.2 above, this model has an observation variable RE1 that gives an index of 0.543 which is less than 0.7 There are also variables RE5, PS4,PS5,and ES4 that also have indexes of 0.675,0.660,0.690,and 0.630 respectively, all less than 0.7 Therefore, this means at the above variables are not qualified and to ensure the appropriateness of the factors and the later analysis process gets better results, those variables are excluded from the model On the other hand,variables
RE2,RE3,RE4 have outer loadings values of 0.794,0.770,0.736 which are all greater than 0.7, so they are considered satisfactory Similarly, the remaining variables from CSS1 to CSS5, from EL1 to EL4 ,PS1,PS3,WD2,ES1,ES2,ES3 and finally ES5 all have values greater than 0.7 are satisfactory as well as.
Table 5 Conformity results of factors
Outer loading VIF AVE Composite
According to (Joe and his Partner, 2011) [3] Internal Consistency Reliability is measured by Composite Reliability Internal
Consistency Reliability is an element to examine the consistency of items in the same variable, i.e to determine whether different measurements in the same hidden variable are compatible with each other (Hair and his Partner, 2013) [4] Also according to (Joe and his Partner, 2011) [3], the degree of consistency of items in the same variable are shown when the value of the Composite Reliability index is greater than 0.7 (which means that value from 0.6 to 0.7 is acceptable to continue the research) Therefore, the author should conduct an analysis of the Composite Reliability index to consider the internal homogeneity of reliability Based on the result of table: Construct Reliability and Validity (PLS Algorithm calculate), Composite Reliability index of all variables is greater than 0.7, within it, WD variable has the highest index of 1.000 and the variable that has the lowest index of 0.816 is PS.
4.2.2 Assessing the accuracy of the model
According to (Hair and his tner,Par 2013) [4] the assessment of convergent validity aims to test the extent to which a scale positively correlates with alternative measures in the same variable In other words, convergent validity ensures that the variables belong to the latent structure to be measured (Wang, French & Clay, 2015) [5] In 1997, a research group consisting of (Irgbaia, Zinatelli, Cragg, and Cavaye, 1997) [6] demonstrated that a variable is said to be good if the latent variable shows factor loading greater than or equal to 0.50 Also, according to (Hair and Partner, 2019) [7] proposed average variance extracted (AVE) as a measure of convergence validity because AVE can explain the extent to which items are shared between structures in the structural equation model (SEM), where the general rule is that anAVE value greater than or equal to 0.50 is acceptable (Hair and hisPartner, 2013) [4]; (Barclays and partner, 1995) [8] Therefore, if the AVE of the variable is less than 0.5, then the variable will be excluded from the research model.
All Average Variance Extracted (AVE) indices in Table 4.3 exceed 0.5, indicating convergent validity This ensures that the variables are suitable for further analysis in subsequent stages.
To check the validity of the discriminant value, the group used the criteria (Fornell - Larcker, 1981) [9] and cross-loading (Wong,
2016) [10] to ensure the accuracy of the discriminant value of each variable Fornell and Larcker used the square root of the extracted mean-variance (AVE) with the correlation of latent structures Another measure to determine discriminant validity is the correlation ratio, which is the correlation index between latent variables (LVC) The discriminant value of the factor is evaluated by comparing the square root of AVE and LVC, in which the square root of AVE for each factor must be greater than the correlation coefficient, then the model has a suitable discriminant value with the research model criteria (Fornell - Larcker, 1981) [9]; (Joe and Partner, 2011) [3].
Table 6 Discriminant value of factors according to Fornell & Larcker
CSS EL ES PS RE WD
1.00 0 Value in bold is AVE square root
Table 4.4 reveals that the AVE of square base is greater than LVC This finding, supported by Fornell and Larcker's criterion, confirms the model's discriminant validity and demonstrates the differentiation of its factors.
Structure model
4.3.1 The overall coefficient defines R Square
Coefficient of Determination R Square is a metric for estimating a linear model's fit If R Square 0.50, the model fits the data set 50% of the time, the rest is attributable to measurement error, and other independent variables may explain the remaining 50% the dependant variable that was left out of the research model, R
Square values range from 0 to 1; the closer R Square is to 1, the more appropriate the model has formed, and vice versa However, to make it easier to distinguish, analyze, and evaluate (Joe and Partner, 2011) (3) devised three levels of measurement: 0.75 is considered high, 0.50 is considered average, and 0.25 is considered low, which according to scale Adjusted R Square, like R Square, reflects model fit, but Adjusted R Square created from R Square is more widely used since it more closely matches the fit of the multivariable linear regression model Despite the fact that it allows values from 0 to 1, an Adjusted R Square cannot get a value of 1, regardless of how precise the model is Take a look at the information below:
The R Square index for the dependent variables EL and ES is 0.541 and 0.674, respectively, indicating that the independent variables and intermediary factors influencing the dependent variables EL and ES are in the low to medium range In other words, the independent factor accounts for 54.1 percent of the variation in the EL variable and 67.4 percent of the variation in the ES variable The values in the R Square Adjusted column of table are all within the safe range, indicating that the model used in this investigation is completely acceptable
A refundable sampling method is called "the bootstrap" method Bootstrapping requires a minimum of 280 samples, with the number of cases equal to the number of original sample observations After bootstrapping, a line of T-statistics will be generated to check the model's significance The importance of thestructural path is calculated by T-statistics to estimate the relevance of the structural path using 274 data from the initial data of over 280 samples in this study When associations exist in the model with absolute values and a level of significance of 0.05, the relationship has 98.2 percent confidence; however, the T- statistics value must be larger than or equal to 1.96 for the relationship to be satisfactory.
According to (Joe and Partner, 2011) [3], the T-statistics value grows as the level of significance drops; for example, the specific value is 1.65 with a level of significance of 0.1, the T-value value is 1.96, and the level of significance is accordingly For a level of significance of 0.01 is 0.05 and 2.58, respectively The author uses the P-value index to examine the reliability of the assumptions from H1 through H14 above The lower the P-value, the lower the reliability, and values less than 0.05 are regarded as appropriate for evaluating the study model (Hair and his colleagues, 2013) [4]
In this study, the author also uses the F-square index to calculate the coefficient of determination (R2) and determine the level of impact of the independent variable on the dependent variable.The greater the f-square index, the greater the impact, which is measured on the following levels: Low impact (0.02), moderate impact (0.15), and high effect (0.17) (0.35) (Cohen, 2010) If the f- square is less than 0.02, the impact between the variables is minimal or non-existent
Customer service and Support (CSS) -> E- banking customer Loyalty (EL) 3.964 0.000 0.058
Customer service and Support (CSS) -> E- banking Satisfaction (ES) 9.657 0.000 0.442
Private and Security (PS) -> E-banking customer Loyalty (EL) 0.646 0.519 0.001
Private and Security (PS) -> E-banking customer Satisfaction (ES) 1.761 0.079 0.011
Satisfaction (ES) 7.346 0.000 0.290 Web design (WD) -> E-banking customer
Web design (WD) -> E-banking customer
E-banking customer Satisfaction (ES) -> E- banking customer Loyalty (EL) 3.225 0.001 0.058
Table 5 Examines the model's relationship both directly and indirectly using path coefficients, standard deviation, and T- statistics values.
Table 9 Path Coefficients include T Statistics (Bootstrapping)
Private and Security (PS) -> E-banking customer Satisfaction (ES) 1.761 0.079 0.011
Web design (WD) -> E-banking customer
Customer service and Support (CSS) -> E- banking Satisfaction (ES) 9.657 0.000 0.442
Private and Security (PS) -> E-banking 0.646 0.519 0.001 customer Loyalty (EL)
Web design (WD) -> E-banking customer
Customer service and Support (CSS) -> E- banking customer Loyalty (EL) 3.964 0.000 0.058
E-banking customer Satisfaction (ES) -> E- banking customer Loyalty (EL) 3.225 0.001 0.058
Table shows that the direct and indirect associations have T- statistics values larger than or equal to 1.96 and P-values less than 0.05, respectively
As a result, the hypotheses from H1 through H4; from H5 to H8, and H9 have been proven to be quite dependable, and the following data has been used to test them:
Hypothesis H1 is Reliability (RE) affected on E-banking Customer Satisfaction (ES) with T-statistics of 7.492 and path coefficient of 0.000; similar in hypothesis H2 is The remarkable relationship between Privacy and Security (PS) and E-banking Satisfaction (ES) with T-statistics of 1.797 and path coefficient of 0.073 (reject); hypothesis H3 is An important relationship between Web design (WD) and E-banking customer Satisfaction (ES) with T-statistics of 3,692 and path coefficient of 0.000; hypothesis H4 is A meaningful connection between Customer service and Support (CSS) and E- banking customer Satisfashion (ES) has a T-statistic of 9.922 and a path coefficient of 0.000 ;
Hypotheses H5 and H7 propose significant relationships between Reliability and Web Design, respectively, and E-banking customer Loyalty (T-statistics: 2.466, 4.316; path coefficients: 0.014, 0.000) However, hypotheses H6 and H8 fail to establish substantial connections between Privacy and Security, Customer Service and Support, and E-banking customer Loyalty (T-statistics: 0.613, 0.613; path coefficients: 0.540, 0.000).
Hypothesis H9 is E-banking customer Satisfaction (ES) impact on E-banking customer Loyalty (EL) with T-statistics of 3.441 and path coefficient of 0.001.
In conclusion, all hypotheses from H1-H4 and H5-H8; H9 are reliable and accepted while hypothesis H2 and H6 are unreliable and rejected.
CONCLUSION, AND RECOMMENDATIONS/ LIMITATIONS
Research summary
This study will provide the foundation for a database of customer behavior when using e-services (online banking services) during the pandemic As a result, it can assist in limiting issues and improving service quality to meet the expectations of customers in the banking industry In general, the data in chapter four has been thoroughly examined, and below is a summary of the study conclusions reached by my group, specifically as follows:
E-banking Loyalty (EBL) is significantly influenced by factors such as Reliability (RE), Privacy and Security (PS), Web Design (WD), Customer Service and Support (CSS), and Ease of Bank System (EBS) Among these factors, E-banking satisfaction is the most impactful, accounting for 28.7% of EBL Customer service and Support contributes 23.8%, while Web Design has an impact of 21.7% Reliability and Privacy/Security also play important roles, with contributions of 14.3% and 2.7%, respectively.
Moreover, the engagement factor is influenced by the components that our group classifies as independent variables: Reliability (RE), Privacy and Security (PS), Web Design (WD) and Customer service and Support (CSS) The CSS factor has the most impact on EBS (46.0 percent), followed by the second influencing factor RE (39.9 percent), WD (15.8 percent), and the last variable has the lowest impact levels of PS (-6.6 percent) (rejected) In summary, the research results all show that investigating the factors that impact changes in the economic structure as well as the way people consume and live in 2022 will give banks a competitive advantage when planning marketing efforts, social media campaigns, and modernizing their app and e banking system; however, for their learning results to improve, we must find ways to increase Privacy and Security as well as enhance the security of consumers' financial information on the e-banking platform, and be satisfied When we do this, we will be able to demonstrate and determine student satisfaction with using e-Banking and to do so, we will need a lot of effort from five different factors, which are Reliability, Privacy and Security, Web Design, Customer service and Support.
It is similar to the previous research study about RE, PS and WD all have an impact on E-banking Satisfaction (EBS) but CSS; RE, WD and EBS all have an impact on E-banking Loyalty (EBL) but PS and CSS We fixed a Customer Service and Support bug that the previous authors were unable to solve, however it remains a Privacy and Security (PS) bug.
The reason these two hypotheses are rejected is that when users send personal information to the bank, some are concerned that their information will be stolen and used for malicious purposes, whereas others are unconcerned that their information will be used to improve the personal experience of their banking application.
Many study articles have recently been published on the impact ofE-banking Satisfaction and Loyalty on young people in general and students in particular The idea is based on a well-developed model and hypothesis, which they finalized and presented in Saudi Arabia (Z Ghali, 2021) and Pakistan (IU Haq and TM Awan, 2020) Furthermore, while the research results based on the research model proposed by IU Haq and TM Awan in 2020 are generally quite good to apply in our country, it is still not exactly the same and exact, namely in the second and sixth hypothesis (H2, H6), when applied and studied within Ho Chi Minh City, Vietnam, were rejected because each country has different cultures as well as lifestyle and thinking of each individual.
The analysis of research findings informs recommendations to businesses, enhancing e-banking services and facilitating customer payments To foster trust, data security must be prioritized, coupled with the resolution of any e-banking system issues These measures aim to enhance the overall user experience and drive customer satisfaction.
Recommendations
Based on the findings of this study, we can summarize there are many advantages in the topic of the group that can be recommended to businesses First, e-banking services can make it easier for people to pay Consumers will save time and effort by using e-banking to make transactions Second, it supports people to practice social distancing better during the present Covid-19 epidemic Instead of people having to buy and pay directly, e- banking can help them pay on the app and they do not need to move to crowded places Finally, e-banking offers consumers an exciting experience of modern technology and digital ways This is a step that marks the development of technology application in the banking sector This will give consumers better access to future innovations.
Despite the benefits of e-banking, concerns remain One concern is the security of customer data, as surveys indicate that individuals are apprehensive about their information being stolen Additionally, system errors persist, causing discomfort among users These errors can result in failed payments or delays in transactions, despite the system indicating completion.
To sum up, banks should fix these problems as soon as possible This will help them to reinforce the trust of customers At the same time, they still must continue to promote their strengths and advantages to help customers get the best experience.
RE -> ES (Reliability -> E-banking customer Satisfaction)
According to the conclusions of the study, the RE factor has an impact on ES System reliability, defined as system dependability, is investigated by the probability of demand satisfaction, as a key performance indicator for evaluating the service level of the financial organization transmission system Reliability of internet banking has a significant positive effect on customer satisfaction
The person who plays an important role in this case is none other than the Banking’s consumer, the financial firm needs to be trustful of how they provide service and protect their customer policy that will directly affect their interest outcomes.
In sum, The financial cooperation nowadays has excellently completed almost all conditions to gain the trust of customers
PS -> ES (Privacy and Security -> E-banking customer
Since there is a lot of bad news about how untrustworthy banks these days are, such as people complaining why they lost their money after using e-banking for a while for no reason or why they keep getting unnecessary calls from some telesale staff
To bolster trust amid customer skepticism, banks must prioritize cybersecurity investments This entails allocating resources to enhance their IT infrastructure, particularly their firewalls, to safeguard sensitive customer data on their platforms By investing in robust security measures, financial institutions can mitigate risk and demonstrate their commitment to customer protection, fostering confidence and loyalty.
WD -> ES (Web design -> E-banking customer Satisfaction)
The goal of this study is to find empirical proof of the link between web design (WD) and e-satisfaction, as well as their impact on e- loyalty High quality of website design (WD) is essential If banks are to maintain and ensure their clients' electronic satisfaction (ES), high website design (WD) is essential According to our result, an Easy-to-use interface is the factor that users appreciate and care about the most Moreover, the elements of colors and items are still interesting, but customers do not have certain standards, so banks need to shape the consistent style of the brand to attract interaction from users.
Therefore our suggestion for this element is that banking firms should have a detailed and clear survey of the "easy to use" conditions of users and need an eye-catching design because nowadays the brand's image is a prerequisite for customer satisfaction.
Limitations and directions for future research
Our first limitation is geographically constricted, and despite efforts to survey other universities, the number of respondents remains insufficient, and it is still limited to Ho Chi Minh City As a result, the findings cannot be applied to the entire country So, it is advised that data be collected from individuals from various locations of the country or the world for future research, which will provide a more complete outcome In the future, researchers may use alternative ways to conduct in-depth analysis And we only looked at the mediated impact of e-banking customer satisfaction, and it was difficult for us to establish a link between EBSQ dimensions and e-banking satisfaction due to the paucity of literature in this area.
Second, while our study only discovered four factors influencing customer satisfaction and loyalty in the banking industry in the Covid 19 pandemic in Vietnam, we believe there are other factors that we have not had enough time to investigate, so we hope that future research will result in a more comprehensive research model to address issues that we are still on a quest to solve For example, potential researchers could look at various online service classifications, such as the volume of sales or the products purchased Future research should concentrate on other factors such as technological speed, interface quality, perceived usefulness, compatibility, and their relationship with customers, as well as the decision to use Internet banking and the importance of building trust in order to adopt online banking through the use of wireless networks.
The current circumstances have increased the demand for e- banking, had a significant impact on the use of traditional banking, and focused on the impact during the pandemic, but their preferences related to reliability, web design, privacy and security, customer service and support, which is an interesting departure from previous research The probable reason is the hard times of the ongoing pandemic (COVID-19).
Banks in Vietnam can use our research and recommendations to establish e-banking strategies, particularly in times of epidemics and natural calamities, that will help banks maintain existing account holders and attract new ones Customers' perceptions and elements that make them more satisfied and loyal to e-banking services may also be understood by service providers This study also provides insight into consumers' goals and focal requirements from e-banking services in order for them to become loyal and content with online banking platforms throughout the lockout It also aids banks in making strategic decisions for advances in Vietnam's e-banking future, as well as managing COVID-19 and digitalization.