MINISTRY OF EDUCATION AND TRAINING THE STATE BANK OF VIETNAM HO CHI MINH CITY UNIVERSITY OF BANKING PHAM THAI DAT FACTORS INFLUENCING THE DECISION TO PAY TUITION FEES ONLINE BY STUDENTS OF HO CHI M[.]
OF RESEARCH TOPICS
Objects, research questions
The research paper aims to clearly identify the factors that directly influence the decision to pay tuition fees online for HUB students, thereby giving relevant administrative implications, with specific objectives as follows:
- Identifying factors affecting the decision to pay tuition fees online of students of Banking University
- Determining the level of the influence of this factors affecting the decision of students to pay tuition fees online
- Proposing administrative implications with the goal of increasing the proportion of students paying tuition fees of students of Ho Chi Minh City Banking University
In order for the topic to achieve the research objectives, the research questions of the topic used by the author are as follows:
What factors influence the decision to pay online tuition fees of students at Banking University?
- The degree of influence of factors affecting the decision to pay tuition fees online of students of Banking University
- Based on the results of the study, it is necessary to identify and propose what relevant administrative implications towards the electronic payment system of the banking university.
Scope, research subjects
This study examines the factors that influence the decision of students at The Bank of Ho Chi Minh City to pay their tuition online, analyzing how these factors shape students' willingness to use online tuition payment options and offering insights for enhancing digital payment adoption, student satisfaction, and the efficiency of tuition collection for the institution.
The scope of research of the topic is mainly students who are studying at The Banking University of Ho Chi Minh City
Full-time students at Thanh Vuong Ho Chi Minh University, particularly those in years 2 through 4, are evaluating how to implement online payment forms and deciding whether to pay at the university or at the bank These students balance academics with work commitments, shaping their preference for quick, secure, and accessible payment options The analysis shows strong interest in online payment forms and streamlined university payments, with key considerations including processing speed, reliability, and user experience They compare paying at the university desk versus bank payments or online portals, weighing convenience, fees, and security Understanding these preferences helps the university optimize its payment workflow, improve enrollment processes, and deliver a smoother payment experience for students.
Research methods
This study was conducted through two methods: qualitative and quantitative
This study investigates the critical factors that influence banking university students’ decisions to pay tuition online by applying a literature-based data approach that draws on findings from previous studies By identifying key determinants—such as perceived ease of use, perceived usefulness, trust and security, payment convenience, time savings, and transaction costs—this research translates prior evidence into measurable variables for analysis Grounding the methodology in existing data strengthens validity and comparability while outlining a clear data collection plan, including sampling, survey design, and statistical testing The goal is to provide actionable insights for university administrations, fintech providers, and policymakers seeking to optimize online tuition payment processes for banking students In doing so, the study aligns with SEO-focused practices by using relevant terms like online tuition payment, banking university students, factors influencing payment decisions, and data from previous research to facilitate replication and evidence-based decision making.
Both qualitative preliminary research and quantitative formal research are conducted by reviewing relevant studies and deploying a Detailed Survey Questionnaire administered online via Google Forms After collection, data are processed, cleaned, and encrypted, then analyzed in SPSS 20 to determine factor influences using descriptive statistics Reliability and validity of the scales are evaluated with Cronbach's Alpha and Exploratory Factor Analysis (EFA) to assess scale quality and screen study concepts Independent samples t-test (and ANOVA) are used to identify meaningful differences among student groups, and accreditation is employed to rank the importance of the factors that influence bank-choice decisions.
After collecting survey data, descriptive statistics were used to summarize the study sample with frequency distributions and percentages Descriptive indicators such as the mean and standard deviation were employed to assess the distribution and the level of agreement among respondents for the observed variables This approach yields a concise profile of customer attitudes and supports comparisons across factors.
Calculation of Cronbach's Alpha coefficients: to eliminate inappropriate variables in the model, assess the reliability of the scale
Exploratory Factor Analysis (EFA) is used to identify the variables required for the study and to explore the relationships among them It also helps shrink the initial set of variables, reducing dimensionality to create an optimal, parsimonious research model By uncovering the underlying factor structure, EFA informs variable selection and improves the overall model fit.
Develop a linear regression model to describe the functional form of the link between the toxic variable and the dependent variable, and perform a set of diagnostic tests to validate the model The analysis includes checks for multicollinearity, assessments of any nonlinear relationships in the link, and evaluations of differences across qualitative variables using suitable coding schemes.
The theoretical contribution
Survey results give managers and banking universities clearer insights into the factors that influence banking students’ choices, especially their selection of banking as a field of study, quantify how much each factor matters, and summarize the governance implications for policy, curriculum design, admissions, and institutional strategy.
This survey also serves as a reference to the relevant survey on the factors that influence students' decisions to pay tuition fees.
The structure of the research paper
Research on the factors influencing the decision to pay tuition fees of students of
Ho Chi Minh City Banking University Hcm will be divided into 5 specific chapters:
Chapter 1: Overview of research topics
Chapter 2: Theoretical basis and related studies
Chapter 5: Conclusions and implications of governance
Chapter 1 establishes the need for the topic and defines the research paper’s purpose, framing the study’s rationale and significance It sets out the study’s goals, research questions, scope, and subjects, offering a clear blueprint for what will be investigated The chapter also outlines the overall structure of the paper, providing the background and context that supports the analysis in the following chapters.
BASIS
Basic concepts
Chapter 2 clarifies the core concepts of online payments, surveys the current state of online payments in Vietnam, and identifies the main factors that influence payment decisions It introduces foundational ideas such as Consumer Behavior Theory and the key characteristics of online payments, providing a framework for understanding how Vietnamese consumers evaluate options like payment methods, security, fees, and speed The chapter defines essential online payment concepts, including transaction flow, digital wallets, mobile wallets, and gateway technology, and explains how these elements affect trust, perceived risk, and convenience By linking consumer behavior with payment mechanisms in Vietnam, this section explains why drivers like ease of use, perceived security, incentives, and merchant availability shape payment decisions The resulting analysis supports marketers and fintech designers in optimizing online payment experiences for the Vietnamese market.
Kotler's Consumer Behavior Theory (1967) remains a foundational framework in marketing research, widely used to explain the causes of consumer product selection by analyzing the patterns that organize consumer buying behavior The theory highlights how information processing, attitudes, preferences, and situational factors shape purchase decisions, enabling marketers to predict buying patterns and design more effective strategies.
Figure 2.1: The model of organizing consumer buying behavior (Source: Philip
Kotler argues that consumer decision-making when choosing products or services is often unconscious and shaped by a range of social factors The accompanying diagram shows that decisions about which product to buy, which brand to trust, where to shop, and the size of the consumer base influence marketing mix elements—product, price, place, and promotion—as well as environmental factors such as technology, culture, and legal norms By applying consumer behavior theory, we can explain why online payment systems are used to pay student tuition at the University of Banking.
2.1.2 The concept of online payments
According to the Ministry of Commerce's National Report on E-Commerce, electronic payments are transactions conducted over the Internet and via electronic devices as an alternative to cash payments Fatonah et al (2018) argue that the ICT era and digital innovation have driven dynamic changes in the business environment, with transactions increasingly moving from cash-based to electronically based methods Electronic payment systems are not introduced to replace cash; they offer a better alternative for trading exchanges These systems are payment mechanisms that use electronic means and are not tied to cash, making e-payment a key aspect of e-commerce Dennis (2004) defines electronic payment systems as an electronic-based form of financial commitment between buyers and sellers.
Electronic payments are viewed as a key form of connectivity between organizations and individuals, supported by banks to enable currency exchanges through electronic systems (Briggs & Brooks, 2011) Peter and Babatunde (2012) define electronic payments as any form of funds transfer conducted over the Internet Other definitions describe electronic payment systems as money exchanges conducted through electronic means in the context of electronic commerce (Kaur & Pathak, 2015).
Popular forms of electronic payment today such as credit cards, debit cards, purchase cards, e-wallets, cryptocurrencies
2.1.3 The role of online payments
Online payment brings a lot of benefits to consumers in particular and the economy system in general such as:
Online payments offer fast, convenient, and accurate transactions that streamline everyday activities such as shopping, paying bills, entertainment subscriptions, and travel As digital payments grow, consumers can complete purchases quickly with security and reliability, enhancing the overall user experience This rapid adoption also enables businesses to operate more efficiently and stay aligned with market flow, while expanding capabilities for international transactions The proliferation of online payments enhances economic flexibility, supporting growth for businesses and the wider economy by easing cross-border payments and expanding access to digital commerce.
Electronic payments are easy to monitor and track, as nearly all transactions are saved and can be searched for older records when needed This capability benefits consumers by simplifying budgeting and record-keeping, while also supporting tax authorities and regulatory bodies with reliable audit trails and oversight to improve compliance and governance.
Modernizing business processes is key to success in online commerce, where diverse payment options—credit cards, internet banking, e-wallets, and QR codes—fuel the popularity of online purchases by offering convenience to shoppers To stay competitive, businesses must scale operations and upgrade technology, which improves the performance, security, and reach of electronic payments In Vietnam, this ongoing upgrade of facilities and processes strengthens the electronic payments market, enabling smoother transactions for both merchants and consumers and aligning with evolving market demands.
2.1.4 The concept of tuition fees
Under Decree No 81/2021/ND-CP, tuition fees for higher education are the amounts learners are obliged to pay to cover part or all of the costs of education and training services These fees are set according to a basic roadmap to ensure the cost of education and training is covered.
2.1.5 Methods of paying tuition fees
Currently, there are many methods to pay tuition fees indirectly and directly:
In person: students can go to the accounting department of the University of Banking to solve tuition problems or go to bidv bank branches to make tuition payments
Indirectly: students make tuition payments via the e-pay app of the individual's bank and transfer money to stk 1111.000.0004541 BIDV bank to make closing transactions.
Organizationof online payment behavior
According to Kotler (2009), consumer behavior is a collection of specific human behaviors when making decisions such as procurement, transactions, payments, use or disposal of goods and services
Kabir and colleagues' 2015 study, which leveraged information systems and analyzed electricity payment data from 2010–2015, demonstrates that electronic payment systems are increasingly among the most useful payment methods for both individuals and businesses worldwide Electronic payments are expanding in both developed and developing countries as they simplify transactions and drive efficiency, convenience, and timeliness in business operations.
According to a study on "Trends and Innovation" by young consumers, Wood
(2013) identified four trends that are likely to characterize the Younger Generation as consumers: Focus on innovation, Insist on convenience, Implicit desire for security and the tendency to escape
Lin (2003) contends that online-service providers must deliver the greatest perceived value to be validated and accepted by customers, so they see it as a clear advantage and remain loyal This value can be assessed through customer satisfaction, with the primary drivers being customer needs, perceived value, and cost, all of which influence how customers perceive and engage with online services.
According to Mostaghel (2006) and Heskett et al (1994), consumer satisfaction is a key driver of improving the financial efficiency of a company's services and ranks just behind profits as a top driver of business value Companies view rapid technological development as an opportunity to boost customer satisfaction and loyalty at lower costs Numerous studies have shown that e-commerce has dramatically transformed how businesses operate.
According to Kim et al (2010), strong security boosts consumer trust, and greater awareness of secure and reliable systems gradually increases e-commerce usage Customer awareness of the security of electronic payment systems has emerged as a major driver of e-commerce development in the market The study, based on 219 participants in South Korea, proposes a conceptual model that identifies the factors shaping perceived safety and perceived trust among consumers, and investigates how these perceptions affect the adoption of electronic payment systems.
Yan and Dai (2009) show that online transactions offer consumer benefits such as convenience, the ability to evaluate the characteristics of goods and services, access to rich information, and Asia‑related factors that influence decision making Akbar and James (2014) argue that perceptions of online purchases are shaped by four core factors—demographics, knowledge, reputation, and ease of use—along with perceived risk.
Table 2.1: Summary of Related Studies
No Author Country Influencing factors
(2014) Thailand Demographics, reputation, ease of use, risk awareness
South Korea Security and reliability
(2015) China Demand, value, cost of caveguests
Convenience,commodity characteristics, rich information, price factors
2.2.2 Summary of research on online tuition fees
Ai-Hawari et al (2020) examine a web-based service for online tuition payment that allows students to transfer tuition fees to the university account through electronic banking, credit cards, and debit cards.
The study identifies several strategies to strengthen online payment security, including robust server security, enhanced network security, and comprehensive data protection Implementing these measures can boost customer trust and influence students' decisions to pay tuition online.
Ai-Emran and Salloum (2019) investigated the factors influencing the adoption of electronic payment systems in higher education institutions, conducting the study across nine UAE universities with a sample of 528 respondents The results show that perceived usefulness, awareness, and ease of use strongly influence students’ trust in and adoption of electronic payments, while the influence of these factors on Arab students’ decision-making is comparatively negligible.
This study examines the factors shaping Indonesian private school students’ adoption of the Rokhmah online payment system, using classification methods and evaluating accuracy on 236 data samples It identifies Information Convenience as the most influential factor, with a classification accuracy of 95% for predicting student interest, outperforming Naive Bayes at 85% and K-NN at 81%.
An e-payment system research project at The Polytechnic University of Palestine, led by Abeer and colleagues in 2008, aims to streamline tuition payments and complete electronic registration The system informs students of the registration fees, enables selection of eligible grants or scholarships, and calculates the remaining amount to be paid to finalize the registration It operates with prepaid cards carrying monetary value: students purchase cards, log into the Electronic Payment System, and, by entering the card number as instructed, add balance after the system validates the card The project also envisions prepaid card issuance managed by a card transmitter manager, who can supply the cards to a specific bank, while the system supports document management and other services Overall, electronic payments bring efficiency and convenience, saving time and effort for both students and university staff.
H1 hypothesis: Personal intention factor has an influence the positive effect on the decision of students to pay tuition fees online of banking university students
H2 hypothesis: Safety and security factors influence the positive effect on the decision to pay tuition fees online of students of Ho Chi Minh City Banking University
H3 hypothesis: Convinience has the positive effect on the decision to pay tuition fees online of students of Ho Chi Minh City Banking University
H4 hypothesis: Information has the positive effect on the decision to pay tuition fees online of students of Ho Chi Minh City Banking University
H5 hypothesis: The pandemic factor has an effect in the same direction on the decision to pay tuition fees online of students of Ho Chi Minh City Banking University
Figure 2.2: Research model ( Source: self-compiled author )
2.2.4 Factors that influence the decision to pay online tuition fees for students
Convenience is a primary driver of online payment decisions, as traditional payment methods requiring a physical visit are increasingly obsolete due to complexity, time loss, and costs With smart devices enabling quick and easy transactions, a growing array of low-cost payment options beyond traditional banks streamlines consumer activity and saves time for other tasks Salehi et al (2012) show that intensified competition among online retailers expands services and products sold online, which increases customer convenience and is echoed by many e-commerce studies that identify convenience as a key determinant of online payments and consumer behavior Similarly, Lai and Lin (2012) find that convenience is the main driver of customer satisfaction and retention in online purchases, while Srinivasan et al (2002) identify convenience as a factor shaping loyalty in online B2C activities.
Safety and security are key in online trading, as customers worry that their personal information could be stolen or misused In Vietnam, relatively lenient regulations around online transactions can enable fraudsters to exploit people with tactics such as deceptive messages, fake links, and impersonations of authorities While higher perceived security can boost the adoption of online trading services, research shows online transactions remain vulnerable to scams and protections are often insufficient, with risks of credit card payment disclosures across banks Trust emerges as a critical factor in high-risk trading relationships, making trust as important as safety for the success of electronic payment systems (EPS); therefore, identifying and understanding the factors that influence trust and security is essential for anyone engaging in EPS transactions, particularly when handling consumer payments and student tuition payments at the University of Banking.
Based on the Theory of Rational Action (TRA) proposed by Fishbein and Ajzen
The Theory of Reasoned Action (TRA) holds that a behavior is predicted by an individual's intention to perform that behavior, an intention shaped by attitude toward the behavior and subjective norms, with intention acting as the motivating force that determines the effort to enact the behavior Sun (2003) demonstrated that the intention to use a technology is a valid and reliable predictor of actual usage The Theory of Planned Behavior (TPB) extends TRA by incorporating perceived behavioral control, enabling a more complete prediction of behavior such as payment habits, where trust assessments can influence adoption Norman and Conner (2006) reported that TPB accounts for substantial variance in intention, with self-efficacy, attitudes, and cognitive control among the key determinants Venkatesh and Davis (2000) argued that intention affects usage behavior, noting that some users prefer more convenient and user-friendly systems, and that the decision to use is shaped by both individual differences and system characteristics.
Traditional payment methods often require two to seven days to update payment notices, whereas online payments refresh all information in real time In addition, the ability to display current bank balances online is far more convenient than relying on paper bills, and real-time visibility helps improve transaction accuracy Research shows that payment speed and update frequency influence the choice of payment method: Schuh and Stavins (2015), using data and models from the Payment Options Survey (SCPC), identify payment speed factors as significant determinants of method selection In contrast, Lee, Yu, and Ku (2001) argue that e-commerce offers clear advantages over traditional commerce—openness, speed, anonymity, and global accessibility—that simplify life and enhance individual quality of life.
Research has shown that since the outbreak of covid, consumers have changed much to consumers' payment decisions because of social distancing policies and anxiety, shielding electronic payments from booming around the world In the study of Aji et al (2020) in Malaysia and Indonesia, under the influence of the pandemic and the government's encouragement in activities to avoid contact between individuals, e-wallets are gradually thriving and becoming a general payment trend Not only that, due to the impact of the pandemic, government organizations around the world such as WHO propose to use digital currencies and electronic payment methods when possible (Brown, 2020) More specifically, in universities, not going to school leads to all activities such as studying, exchanging through online form and paying for school is no exception, nearly 100% of students pay for study online, which has greatly influenced habits and decisions with new students or have never used the form of students This in the past, it can be said that the pandemic factors have greatly influenced the decision to pay tuition fees online of students of The Banking University of Ho Chi Minh City HCM Although electronic payments existed before the COVID-19 crisis, studies are needed on how to change the social model of electronic payments after the COVID19 outbreak (Yi, 2020) This can be as useful as knowledge for the public, business actors, and governments to develop strategies to overcome current problems and crises
METHODS
Research process
The research process on the topic "Factors that influence the decision of bank students to pay tuition fees online" has 9 basic steps including:
Figure 3.1: Research process diagram (Source: Self-compiled author)
Research methods
In order to achieve the research objectives set out, the research paper will use two main research methods: qualitative research method (Synthesis of related tumor studies, expert opinion ) and quantitative research method (Correlation analysis, regression )
Qualitative research relies on secondary documents to identify the underlying factors that influence Banking University students’ decisions to pay tuition fees, clarifying how they fund their studies The study also incorporates expert consultation with lecturers and faculty holding master’s and doctoral degrees at Banking University to guide the construction of a survey questionnaire that captures these factors and yields a robust instrument for data collection.
Study objectives Theoretical basis Qualitative research
Cronbach's Alpha and EFA analysis
Model testing (correlation, regression, T-test, Anova)
Conclusion and managerial implications: The study tackles relevant and challenging questions by identifying questionnaire topics appropriate for HUB students and refining the instrument through expert consultation and evaluation The author has distilled the instrument into a concise questionnaire comprising five factors and eighteen variables, providing a practical, scalable tool for researchers and managers seeking reliable insights efficiently.
Formal quantitative research involves administering surveys, collecting responses, and preparing the data for analysis Using SPSS as the data processing tool, researchers undertake the main analytical steps: assessing reliability with Cronbach's alpha, conducting exploratory factor analysis (EFA) to identify underlying constructs, and analyzing correlations and relationships between variables to reveal patterns and implications.
Building a scale
From the research model, the author proposes a seven-factor scale with 18 variables to explain bank students’ decisions to pay tuition online in Ho Chi Minh City The model organizes determinants into five explicit groups—Personal Intentions (3 variables), Security and Safety (3 variables), Convenience (4 variables), Information (5 variables), and Pandemic (3 variables)— totaling 18 variables, with two additional factors not detailed in the excerpt This seven-factor framework aims to capture the key drivers influencing online tuition payments, offering insights for universities and banks to optimize the online payment experience for students in Ho Chi Minh City.
Specific observational variables in the topic use a 5-point Likert scale with 5 levels: Level 1: Totally disagree
I pay online because I see friends around me who used to use it
I like the feeling of paying for school online
I pay online because of my habit of not using cash
Har Lee, Cyril Eze and Oly Ndubisi (2011)
1.I decided to pay for online education because of the high security
I decided to pay for online school because of the close connection between the school and the bank
3 I see that banks have many forms of verification when paying money online
Har Lee, Cyril Eze and Oly Ndubisi (2011)
I decided to pay for online school because of the convenience
I've seen many forms of online school payments
I find paying for online education very simple to use
I decided to pay for online school because it took less time
5.I see that online school payment services are free of charge
Har Lee, Cyril Eze and Oly Ndubisi (2011)
1.I find that paying online school fees updates information faster than paying in person
I always get all the information when I pay online
3.I can check the amount when paying for online education through my bank account
Purwandari, Suriazdin, Hidayanto, Phusavat, Maulida (2022)
4.I received the information immediately after the payment
1.I switched to online payment during covid-19
2.I keep the habit of paying money online during covid
3.I will pass completely to pay money online even if it runs out of money
Purwandari, Suriazdin, Hidayanto, Phusavat, Maulida (2022)
Method of selecting samples, collecting and processing data
- The subjects of the study are students of The Banking University of Ho Chi Minh City, by chance
Determine sample size (sample size): the sample size of the study group is limited to 150 students
• Qualitative research: Using the method of interviewing 10 students of Banking University from years 2 to 4 who have paid tuition fees online
• Quantitative research: using the descriptive method (by survey slip) with 150 students of Banking University, using google form as a survey with variable-related questions prepared to conduct the survey
Preliminary data processing is performed to minimize errors and improve data quality for analysis After collection, questionnaire responses are encrypted to protect privacy and converted into numerical codes for entry into data analysis software such as Excel or SPSS The encoding method varies by question, and the data are entered in Excel where missing, mis-entered, or implausible answers are removed or excluded from analysis.
From late 2019, when the COVID-19 outbreak began, data for this quantitative study have been collected up to the present The team encrypts, cleans, and then aggregates the data, applying descriptive statistics with tools such as Microsoft Excel and Google Forms to process it All response data are processed using SPSS software Initially, the data are encrypted and cleaned, with unsatisfactory questionnaires removed, followed by the key analytical steps that guide the subsequent analysis.
Cronbach's Alpha is used to preliminarily evaluate a scale's reliability by assessing inter-item correlations and identifying items that undermine reliability so they can be removed By pruning items that fail to meet reliability criteria with Cronbach's Alpha, unsatisfactory variables are eliminated, strengthening the scale After this item-reduction step, a factor analysis is conducted to uncover the underlying factor structure of the instrument.
Correlation analysis uses the Pearson correlation coefficient to quantify the linear relationship between independent variables and a dependent variable The strength and direction of this relationship are indicated by the coefficient, with its absolute value approaching 1 signifying a strong linear correlation, and values near 0 indicating a weak or no linear relationship.
Regression analysis is a statistical technique used to estimate the equation that best fits a set of observational results, showing how the dependent variable responds to changes in independent, quantitative predictors This method helps quantify the strength and direction of relationships between variables and enables predictions based on observed data In particular, linear regression is the standard approach for measuring the impact of quantitative predictors on a continuous dependent variable, providing a clear framework to assess the effects of these variables.
Test the hypothesis and suitability of the model
R-squared (the coefficient of determination) measures the strength of the relationship between the independent variables and the dependent variable in a regression model It represents the proportion of variance in the dependent variable explained by the predictors included in the model The closer this value is to 1, the better the model fits the data; however, R-squared tends to increase as more predictors are added, even if they do not improve predictive power For this reason, adjusted R-squared provides a more reliable measure of model relevance by penalizing unnecessary predictors and balancing model complexity with explanatory power.
ANOVA variance testing and analysis
ANOVA variance analysis examines how qualitative independent variables influence quantitative dependent outcomes, helping to quantify the effect sizes in the model The standardized regression coefficient, or beta, indicates each predictor’s relative importance for forecasting the dependent variable—the larger the beta, the greater its impact Variance Inflation Factor (VIF) measures multicollinearity among predictors; a VIF above 2 signals potential multicollinearity, above 10 confirms it, and a VIF below 2 suggests minimal or no multicollinearity When multicollinearity is detected, researchers commonly address it by removing the problematic independent variable, after which the regression coefficients of the remaining predictors can change and may lose statistical significance.
Test the differences in qualitative variables
Accreditation has a difference between qualitative variables such as gender, subject, year to the decision to pay online tuition fees of students of Ho Chi Minh City Banking University HCM
This chapter introduces and develops robust research methods, outlines effective sample selection strategies, and explains data processing procedures, preparing the groundwork for the analytical steps that follow in the next chapter.
RESULTS
Study sample characteristics
The study of 150 samples obtained the following results:
According to the survey results, the gender distribution among respondents shows 104 female students (73%) and 38 male students (27%), for a total of 142 participants, with female students outnumbering males by about 2.7 times, reflecting a majority of female enrollment at the Banking School.
The study primarily included students in years 2 to 4 who had experience paying through various methods The chart shows that fourth-year students comprised 50% of participants and third-year students 45%, while second-year students accounted for about 5%.
An online survey of 150 respondents reveals how students are distributed across disciplines: 45 are studying Business Administration, 44 are pursuing Finance and Banking, 16 are studying Accounting, and about 45 are enrolled in other disciplines such as English Language and Management Information Systems.
Statistical analysis described with variables
Table 4.2: Information table of research variables
I The "PERSONAL INTENTION" factor when paying money online INT
1 I paid online because I saw that my friends around me used to use it INT1
2 I like the feeling of paying for school online INT2
3 I pay online because of my habit of not using cash INT3
II The "Security" factor when paying money online SER
1 I decided to pay for online school because of the high security SER1
2 I choose to pay online because banks have many forms of verification when paying money SER2
3 I feel that paying for online education is vulnerable to personal information being exposed SER3
III The "convenience" factor when paying money online CON
1 I choose to pay online because banks have many forms of verification when paying money CON1
2 I find paying for online education very simple to use CON2
3 I decided to pay for online education because it took less time CON3
4 I find that online school payment services are free of charge CON4
IV The "Information" factor INF
1 I found that paying for online school updates information faster than paying in person
2 I always get all the information when I pay online INF2
3 I can check the amount when paying for online education through my bank account
4 I received the information immediately after paying INF4
5 I find the content and design of the paid site to be easily designed INF5
1 I switched to online payments during the Covid-19 outbreak PA1
2 I keep the habit of paying money online during the Covid pandemic
3 I'll pass it completely online even if I run out of money PA3
4 The pandemic was the factor that made me change my decision to pay my tuition fees
VI Decision to pay student's tuition fees DC
1 I'm happy to pay my tuition online DC1
2 I will continue to pay my tuition online DC2
3 I would recommend friends to pay tuition fees online DC3
The 5-scale model of the element is independent of (consisting of 18 observed variables) and 1 dependent factor scale (including 3 observational variable)
A look at the statistics describing the factors: benefits of paying for online education, the influence of personal intentions, convenience, security, information, pandemics, shows:
The minimum value of the observed variables of all factors is 1
The maximum value of the observed variables of all factors is 5
Personal Intent Factor shows a mean value of INT around 4, suggesting respondents largely agree with the view The standard deviation for INT1, INT2, and INT3 remains close to 1, indicating limited variation in the chosen answers and high consistency across respondents.
Security factor analysis shows that the mean value of SER1–SER4 lies between 3 and 3.5, indicating that respondents generally agree with the view The standard deviation of NT1, NT2, and NT3 remains around 1, meaning there is little variation in how respondents answered these items.
Pandemic factor: The mean scores for pa1, pa2, pa3, and pa4 fall in the 3–4 range, indicating that respondents generally agree with these views The standard deviations for TH1, TH2, TH3, and TH4 hover around 1, signaling relatively low variability in responses and a clear consensus among participants on these items.
The Convenience Factor shows mean scores for CON1, CON2, CON3, and CON4 all in the 3–4 range, indicating that respondents generally agree with these views The standard deviations for CON1–CON4 hover around 1, suggesting minimal variation in responses and a relatively uniform set of ratings.
Information Factor indicators INF1–INF5 show mean scores that indicate respondents generally agree with the view under assessment The standard deviations hover around 1, implying limited variation in responses and a strong consensus among participants.
Cronbach's Alpha Analysis
4.3.1 The "intention" scale when paying online tuition fees
Table 4.4: Cronbach's Alpha Analysis (INT)
Looking at the table above, the team found that:
Three observation variables were included in the test, and the Cronbach's alpha for the scale is 0.749, which exceeds the 0.6 threshold and indicates strong internal consistency All corrected item-total correlation values are greater than zero, confirming each item contributes positively to the scale Together, these reliability metrics show that the measurement scale is reliable, meets standard quality criteria, and suitable for use in subsequent analyses.
Thus, when testing the reliability of the conformity scale with 3 observed factors, all 3 factors meet the inspection requirements of the scale So it's appropriate to take the next steps
4.3.2 'Safety and security' scale when paying money online
Table 4.5: Cronbach's Alpha Analysis(SER)
Looking at the table above, the team found that:
Three observation variables were included in the inspection The Cronbach's alpha value is 0.740, well above the 0.6 threshold, indicating good internal consistency The Corrected Item-Total Correlation values exceed 0.3 for all items, supporting reliable item performance Overall, these results demonstrate that the measurement scale is standard, reliable, and of high quality.
Reliability testing of the conformity scale, which comprises four observed factors, indicates that all four factors meet the scale’s inspection requirements, making it appropriate to proceed with the following steps.
4.3.3 The 'Convenience' scale when paying money online
Table 4.6: Cronbach's Alpha Analysis (CON)
Looking at the table above, the team found that:
Four observation variables were included in the test, with a Cronbach's alpha of 0.677, which is above 0.6 The Corrected Item–Total Correlation values are all greater than 0.3, indicating satisfactory item consistency These metrics show that the scale is reliable and of good quality.
Thus, when testing the reliability of the conformity scale with 4 observed factors, all 4 factors meet the inspection requirements of the scale So it's appropriate to take the next steps
4.3.4 "Information" scale when paying for online education
Table 4.7: Cronbach's Alpha Analysis(INF)
Looking at the table above, the team found that:
Five observation variables were included in the test, and Cronbach's alpha was 0.748, which is above 0.6 The Corrected Item–Total Correlation values were all greater than 0.3, indicating adequate item–scale reliability These results suggest that the scale is reliable and of good quality.
Thus, when testing the reliability of the conformity scale with 4 observed factors, all 4 factors meet the inspection requirements of the scale So it's appropriate to take the next steps
4.3.5 The 'pandemic' factor when paying money online
Table 4.8: Cronbach's Alpha Analysis(PA)
Five observed variables were included in the test, and Cronbach's alpha was 0.768, exceeding the 0.6 threshold The corrected item-total correlations for all items were greater than 0.3, indicating solid item discrimination Together, these results demonstrate good internal consistency and reliability of the scale, meeting standard quality criteria for reliable measurement.
Thus, when testing the reliability of the conformity scale with 4 observational factors, all 4 factors meet the inspection requirements of the scale So it's appropriate to take the next steps
4.3.6 Cronbach's Alpha analysis for dependent variables
Table 4.9: Cronbach's Alpha Analysis(DC) ( SPSS 20 software results)
There are 3 observed variables included in the test, the Cronbach's Alpha value of this scale is 0.655 > 0 These 6 coefficients show that the scale is highly meaningful
Corrected item-total correlations above 0.3 indicate that the conformity scale meets standard psychometric criteria, ensuring strong reliability Reliability analysis of the three-factor conformity scale shows that all three observed factors satisfy the scale’s criteria, indicating it is appropriate to proceed to the next steps.
Table 4.10: Synthesis of variables and scales following Cronbach's Alpha analysis
Turn to satisfy reliability Disqualified variable Number of variables
INF1 INF2 INF3 INF4 INF5
Deciding to pay for online
As such, satisfactory observational variables in the scale ensure reliability for the following analytical steps
The original model was unchanged, still consisting of 5 toxic elements lập (18 observed variables) and one dependent element (consisting of 3 observed variables).
EFA Analysis
4.4.1 Check the extracted variance variance of the elements (% Cumulative variance) for the independent variable
4.12:Total Variance Explained Analysis for Independent Variables
In the Total Variance Explained table, the standard for accepting the variance > 50% The results showed that 19 turned the observed group into 5 groups
In this Exploratory Factor Analysis (EFA), the total variance explained is 59.991%, which exceeds the 50% threshold, indicating that the model is appropriate and that these factors account for 59.991% of the data variability The eigenvalue for the retained factor is 1.676, greater than 1, satisfying the Kaiser criterion for factor retention.
4.4.2 Verify the KMO coefficient for independent variables
KMO coefficient = 0 637 satisfies the exhaustionn 0.5 ≤ KMO ≤ 1 should analyze the appropriate factor with the research data
Bartlett's test of sphericity yielded a chi-square value of 837.059 with a p-value of 0.000 (p < 0.001), well below the 0.05 significance level, indicating that the observed variables are correlated in the population Consequently, the null hypothesis that the correlation matrix is an identity matrix is rejected, meaning the factor model is suitable and the data are appropriate for factor analysis.
4.4.3 Factor loading test for independent variables
Table 4.13: Factor Loading Analysis for Independent Variables
The EFA results for the independent variables using the rotated factor matrix indicate that all observed variables have factor loadings exceeding 0.5, meeting the criterion for meaningful loadings Consequently, five factors were extracted in the analysis.
4.4.4 KMO testing for dependent variables
Table 4.14: KMO Analysis for Dependent Carpentry
KMO coefficient = 0 645 satisfies the exhaustionn 0 5 ≤ KMO ≤ 1 should analyze the appropriate factor with the research data
Bartlett's test produced a highly significant result (sig = 0.000 < 0.05), leading to the rejection of the null hypothesis that the observed variables are uncorrelated as a group This indicates intercorrelations among the variables and supports the suitability of factor analysis Consequently, the data are appropriate for factor modeling.
4.4.5 Check the variance oftubers a factor for dependent variables
Table 4.15: Total Variance Explained Analysis for Dependent Variables
In theTotal Variance Explained table, the standard for accepting variance extracts > 50%
In the above analysis results, the total variance explained in the component line
No 1 cumulative % has a cumulative variance value of 59,631% > 50% meet the standard
4.4.6 Factor-Loading test for dependent variables
Table 4.16: Factor Loading Analysis for Dependent Variables
Exploratory Factor Analysis (EFA) of the specified dependent variables shows that all observed variables have factor loadings of at least 0.5 Specifically, the loadings for Factor 1 meet this threshold, and no observed variables are eliminated during the factor extraction.
DC1, DC2, DC3 factors are the decision when paying for online education
Person Correlation Analysis
Looking at the Pearson correlation table above, the team found that the Pearson correlation sig value between the five independent variables and the DC dependent variable was all less than 0.05 As such, there is a linear connection between these independent variables and dependent variables
Between independent variables, there is no correlation that is too strong when absolutely treating the correlation coefficient between pairs of variables is less than 0.5, so the likelihood of a multilinear/multilinear phenomenon is also lower.
Regression analysis
Adjusted R-squared indicates how much of the variance in the dependent variable is explained by the independent variables In this study, the five predictors—INT, SER, CON, INF, and PA—collectively account for 65.9% of the change in the dependent variable, while the remaining 34.1% arises from out-of-model factors and random error An adjusted R-squared value of 0.659 suggests a good model fit, indicating that the five-factor regression model with INT, SER, CON, INF, and PA is suitable for explaining the observed outcomes.
The sig value of the F test is 0.000 < 0.05 As such, the built-in linear regression model is in line with the overall
The regression coefficient analysis table shows:
The Sig value checks t each independent variable Here, the Sig of variables all
< 0.05 (equal to 0), which means that all 5 independent variables are meant to explain the dependent variable in the model, none of which are removed - The VIF
< 2 coefficient shows that the INT, SER, CON, INF, PA variables are satisfied with the inspection and there is no multi-plus phenomenon of discharge
The Beta standardized regression coefficient column indicates which independent variable has the largest Beta coefficient, which has the most influence on the change of the dependent variable, specifically:
+ INT factor influence of 'Personal intention' factor on the decision of bank students to pay online tuition fees is 25.9%
+ SER factor influence of 'Safety and loss' factor on bank students' decision to pay online tuition fees is 34.6%
+ The influence of the 'Convenience' factor on the decision of bank students to pay online tuition fees is 43.8%
+ INF factor influence of the 'Information' factor on the decision of bank students to pay online tuition fees is 30.4%
+ PA factor influence of the "Pandemic" factor on the decision of bank students to pay online tuition fees is 31.2%
Thus, the standardized regression equation is:
QC= 0.259*INT + 0.346*SER + 0.438*CON + 0.304*INF + 0.312*PA
Figure 4.1: Histogram Plot ( SPSS 20 software results )
The chart shows a normal (bell-shaped) distribution curve superimposed on the frequency plot, aligning with the standard normal distribution The mean is approximately zero and the standard deviation is 0.983, essentially 1, indicating that the residuals are distributed nearly as a standard normal distribution Therefore, the normality assumption for the residuals is not violated.
Figure 4.2: Normal P-P Plot ( Spss20 software results)
Diagnostic inspection shows the residual points concentrated along a diagonal, indicating that the assumption of a standard distribution for the residuals is not violated This diagonal pattern supports the validity of the residuals’ distribution and reinforces the reliability of the model diagnostics.
Figure 4.3: Scatterplot ( Source: SPSS20 software results)
As we can see, the allocation standardization balance is concentrated around the zero-degree toss line, so the assumption of linear relations is not violated.
Check sample differences
4.7.1 The hypothetical test has a difference in gender with the decision to pay for online education of students at the Banking University of Ho Chi Minh
Results of T-test analysis for 2 gender groups for variables decided to pay themonline fees
3 Sex N Mean Std Deviation Std Error Mean
An analysis by gender (male and female) shows that the male sample includes 46 observations and the female sample 104 observations in Ho Chi Minh City The average score for the decision to choose a bank loan among individual customers is 3,978 for males and 3,941 for females.
Table 4.22: Independent Samples Test (Gender)
Levene test results show a Sig= value of 0.041 < 0.05 Therefore, using the results of the Independent-samples T-test in response to the variance case2 groups are not the same
An independent-samples t-test accounting for unequal variances between the two gender groups produced a p-value of 0.458 (p > 0.05), indicating no statistically significant difference between male and female students on the measured variable.
4.7.2 Check out the differences in the academic year for the decision of bank students to pay online tuition fees
Table 4.23: Levene Statistic Analysis (Academic Year)
Test of Homogeneity of Variances
Levene Statistic df1 df2 Itself
Levene's test indicates homogeneity of variances with Sig = 0.756, which is greater than 0.05 This supports the assumption of equal variances across groups, allowing the one-way ANOVA to be used to assess whether the group means are equal.
Table 4.24: Anova Analysis (Academic Year)
Sum of Squares Df Mean Square F Itself
A one-way ANOVA was conducted to determine whether the average decision to pay online tuition fees differed by year of study among Banking University students The analysis yielded a p-value of 0.607, which is greater than the 0.05 significance level, indicating no statistically significant difference in online tuition payment decisions across different years of study.
4.7.3 Check the differences in the field of study for the decision of bank students to pay online tuition fees
Table 4.25: Levene Statistic Analysis ( Disciplines)
Test of Homogeneity of Variances
Levene Statistic df1 df2 Itself
Levene's test shows Sig = 0.099 > 0.05, indicating homogeneous variances across groups Therefore, a one-way ANOVA is appropriate to determine whether the sample means are equal.
Sum of Squares Df Mean Square F Itself
One-way ANOVA analysis shows no statistically significant difference in the average willingness of students to pay online tuition fees across programs at the University of Banking and across different student disciplines The p-value is 0.775, exceeding the 0.05 significance level, indicating that program or discipline does not influence students’ decisions to pay tuition online.
Evaluate the results of thestudy
Using regression analysis on a theoretical model estimated from 150 surveys, this study identifies five factors that influence students’ decisions to pay for online education: Personal Intentions, Security and Safety, Convenience, Information, and Translation Factors The results show that all five factors align with the original hypotheses, underscoring their influence on the willingness of students to pay for online education.
For 3 factors: gender, the student's academic year is no different because of the rejection of the H6, H7, H8 hypothesis
The standardization coefficient will be analyzed specifically as follows:
The Convenience factor has the largest coefficient in the regression model, indicating its dominant role in shaping online tuition payment decisions among students For online learners, this factor exerts a substantial influence on whether they pay tuition fees digitally, given that many students juggle work and personal commitments Online payment delivers significant convenience, including time savings and reduced costs, and enables highly motivated students to complete transactions 24/7 without depending on school or bank operating hours.
The Safety and Security factor strongly influences students’ online payment decisions at BIDV Bank With high security standards and robust online payment policies, BIDV Bank directly partners with universities to streamline tuition-fee collection, giving students confidence when paying through the bank’s online apps.
The pandemic strongly influences bank students' decisions to pay for online education During the one-year transition period, state contact-restriction policies require students to complete payments online, a shift that has helped many prospective students understand and develop the habit of online payments.
Information is a strong factor influencing bank students’ decision to pay tuition online Online tuition payments deliver faster, real-time, and accurate information updates, with students receiving immediate confirmation once their fees are paid Processing payments online also makes it easier for schools to reconcile student accounts using electronic invoices, reducing losses and discrepancies compared with traditional paper bills.
The "Personal Intentions" factor had the weakest influence in the model, indicating that students are less affected by external factors such as friends and family when making online payment decisions Nevertheless, this factor remains relevant when choosing the full form of online payment, driven by students' own payment habits and the global trend toward cash restrictions.
Meaning
Scientific significance: Investigating the factors that influence students' decisions to pay tuition online at the Ho Chi Minh City University of Banking is highly relevant in today’s digital era, where modernization and technology are transforming every activity For students who strive to stay up-to-date and leverage digital tools, online tuition payment offers convenience and clear benefits, highlighting the impact of online payment in higher education This study assesses how various factors shape students’ willingness to pay tuition fees online and the reasons behind choosing online payment It contributes to clarifying the individual determinants that influence the decision to pay tuition online at the Ho Chi Minh City University of Banking, offering insights to enhance online payment adoption and the overall student experience.
Practical meaning: The results of the study will help to give relevant administrative implications in the issue of choosing the form of student payment at
Ho Chi Minh City Banking University
In chapter 4, the author presented and analyzed the results of the study, processing the data obtained using spss software
Initially, a statistical assessment of each variable's distribution was conducted, followed by an evaluation of Cronbach's Alpha to gauge internal consistency The Cronbach's Alpha coefficient exceeded 0.6, indicating acceptable reliability and supporting the data's suitability for exploratory factor analysis (EFA).
Then, as an efa discovery factor analysis, the results obtained by the load coefficient of the surveyed variables are greater than 0.5 to meet the inspection needs
Next, the author conducts correlation analysis, testing regression assumptions that produce dependent variable results and independently satisfy the requirements, assumptions that are met and not violated
Finally, an examination of the hypotheses related to hub students' decisions to pay online tuition fees was conducted The analysis found no significant differences in payment decisions by gender, discipline (field of study), or academic year, thereby ruling out the original hypotheses.
The specific standardized regression model is as follows:
QC= 0.438*CON+0.346*SER+0.312*PA+ 0.304*INF+0.259*INT
Additionally, in Chapter 4, the author presents and critically evaluates the study results, clarifies the meaning and limitations of the research, and uses these insights to guide the conclusions and outline the administrative implications to be explored in the subsequent chapter.
CONCLUSIONS AND IMPLICATIONS
Conclude
This study examines the factors shaping students’ decisions to pay for online education at Ho Chi Minh City Banking University, drawing on online payment theory and current tuition payment forms It identifies five determinants of online tuition payment: personal intention, perceived security, convenience, information availability, and pandemic-related factors Employing a mixed-methods design that combines qualitative insights with quantitative testing, the research develops and tests a model linking these five factors to students’ willingness and intent to pay tuition online Data were collected via a questionnaire distributed through Google Docs to banking university students aged 20–24, yielding a sample of 150 respondents Cronbach’s Alpha values for all variables exceeded 0.6, indicating acceptable internal consistency The results provide empirical support for the proposed five-factor model and offer actionable implications for increasing online tuition payment adoption among students.
Linear regression analysis identified several factors that influence Ho Chi Minh City Banking University students’ decision to pay tuition fees online: personal intention, perceived security, convenience, information availability, translation capability, and referential considerations Together they explain 65.9% of the variance in the online tuition payment decision (R² = 0.659) The T-test shows no significant difference in online payment decisions between male and female students One-way ANOVA indicates no significant differences in the decision across different academic disciplines or academic years.
Administrative implications
Drawing on the results in Chapter 4 and the conclusions in Section 5.1, Section 5.2 presents managerial implications to help readers understand online tuition payment and to promote students’ use of online payment options at Banking University HCMC These implications are ordered by the relative influence of key factors in descending order: Convenience, Safety and Security, Pandemic effects, Information, and Personal Intentions To capitalize on Convenience, the university should streamline the payment workflow, offer multiple channels, and ensure a seamless, device-friendly experience For Safety and Security, implement robust authentication, transparent privacy policies, and strong data protection to build trust In light of Pandemic effects, address health and contactless concerns and emphasize remote payment options Regarding Information, provide clear, accessible guidance on options, deadlines, and penalties across portals and communications Finally, align with Personal Intentions by tailoring messages to student motives, offering reminders, incentives, and personalized support to encourage ongoing online tuition payments.
According to the study results, Convenience is the factor that most strongly influences students' decisions to pay tuition fees online, outperforming the other factors It has a standardized beta of 0.438, meaning that for each one-unit increase in Convenience, a student's online tuition payment decision increases by 0.438.
Key drivers for online education payments include that the process is simple to use and that paying online saves time, which motivates students to choose digital options To boost this, banks and schools should promote student‑oriented affiliate programs that increase payment convenience by enabling multiple channels—bank transfers, e-wallets, and, in the future, school‑branded payment apps or student accounts that provide clear information The University of Banking’s e-student app currently handles small fees, but it still has operational gaps, especially the requirement for a dedicated bank account Expanding convenience will attract more students to pay online and reduce cash usage, aligning with global digital trends As students gain better access to technology and smart devices, their willingness to use apps grows, making payments not only for tuition but also for travel, goods, and bills, thereby simplifying life and modernizing the student experience.
Second, the study shows that the Security Safety factor is the second most influential variable, with a standardized beta of 0.346, meaning that a one-unit increase in perceived security raises the likelihood of online tuition payments by bank students by 0.346 units Because tuition fees are typically much higher than everyday purchases, banks must strengthen privacy and identity-verification policies—such as facial recognition, PIN codes, and date of birth—while keeping student payment information confidential This focus on security helps prevent criminals from exploiting data and causing fraud Universities should alert students to fake messages about tuition payments or bogus bank account numbers, and schools should regularly educate students on online fraud prevention and establish policies to support students in case of payment errors Together, these measures boost student confidence in online tuition payments and reduce fraud risk for both banks and educational institutions.
Results indicate that the "Pandemic" factor positively influences students’ decision to pay online for tuition, with a standardized beta of 0.312, meaning a one-unit increase in the pandemic factor is associated with a 0.312-unit rise in the likelihood of choosing online tuition payments While the pandemic’s course remains difficult to predict, it is driving favorable changes and calls for policy measures to support this shift Students should be proactive in online exchange and payment activities, keeping school contact information current to resolve issues quickly and minimize in-person contact For schools, policies such as extending payment deadlines, offering tuition relief where feasible, and encouraging digital payment methods help reduce cash handling and face-to-face interactions while maintaining access to online education.
The study finds that the “Information” factor positively influences students’ decision to pay online tuition, with a standardized beta of about 0.304 In practical terms, a one-unit increase in information is associated with roughly a 0.30-unit rise in the likelihood that Banking University students will choose online tuition payment While information can support learning and off-campus activities, the current information available for payments remains limited, with updates often routed through Facebook pages or groups As a result, many students miss important payment deadlines, risking canceled or unrecognized courses To address this, the university could partner with the bank to integrate payment-app notifications that display the amount due, due date, and other details, sending updates directly to students within the payment app rather than through Facebook groups This approach would improve information visibility, keep students informed, and reduce missed payments.
The study found that the factor of Personal intentions has a significant positive effect on students’ decision to pay for online education, with a standardized beta coefficient of 0.259 A one-unit increase in Personal intentions is associated with a 0.259-unit increase in the likelihood of paying online tuition fees among students Looking forward, alongside government development policies, students should formulate clear plans for their future development and continuously update information and global trends to stay aligned with national progress, a move that can boost the rate of payment for online education and the use of electronic services, foster self-development habits, and reduce cash usage.
Limitations of research
Limited in time and experience, the study was conducted only with a small sample size (n0), so the study results were not highly accurate
The article notes that only a handful of factors are discussed regarding why students choose to pay for online education, while other potentially influential variables remain underexplored A more comprehensive analysis would consider aspects such as perceived value, program quality, course accessibility, financial aid availability, tuition pricing, time flexibility, technology readiness, and marketing exposure Identifying these drivers can help institutions optimize pricing strategies, design engaging online programs, and communicate value effectively to prospective students, ultimately supporting better enrollment decisions and broader access to online learning.
There is no guarantee of accuracy and honesty in the survey answer, because there are respondents who are not honest or do not read the question when answering
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