Factors Affecting The Decision On Using Mobile Banking Of University Students An Empirical Study In Ho Chi Minh City.pdf

HO CHI MINH UNIVERSITY OF BANKING GRADUATION THESIS FACTORS AFFECTING THE DECISION ON USING MOBILE BANKING OF UNIVERSITY STUDENTS: AN EMPIRICAL STUDY IN HO CHI MINH CITY MAJOR: FINANCE –

INTRODUCTION

Rationale of the study

The worldwide impact of the Fourth Industrial Revolution on the everyday lives of people has increasingly become more evident Nowadays, individuals can carry out most of their necessary activities solely using a smartphone or a computer connected to the Internet In order to meet customer demands and avoid being left behind in this new technological era, commercial banks in Vietnam have been striving to introduce advanced banking services such as Internet Banking, Mobile Banking and Smart Banking In fact, developing and enhancing the quality of online service products can be considered as essential for banks if they want to thrive in the future and maintain their customer base

According to Alkrisheh (2022), the development of information technology is critically important in the evolution of e-commerce transactions in general and banking transactions specifically on online platforms Technology has become habitual, gradually altering customer behavior People seek secure and convenient financial services, enabling transactions anytime, anywhere, right from their handheld electronic devices, rather than spending time and effort commuting Alongside these advancements, Mobile Banking stands out as one of the innovations by banks to adapt to this change, allowing banks to gain a competitive edge by efficiently utilizing technological innovations to enhance operational efficiency and retain customer usage rates

In fact, the COVID-19 pandemic during the 2020 – 2021 years had posed significant challenges to the economy and people's lives The restriction of cash usage to avoid physical contact during transactions caused a temporary disruption in people's daily habits During that time, online payments in general and specifically Mobile Banking emerged as the "key" to avoid payment difficulties As a result, the number of users of these services surged significantly

Moreover, according to the General Statistics Office of Vietnam, as of April

2023, Vietnam's population is estimated to reach 100 million people The Ministry of Information and Communications also reported that by May 2023, Vietnam had approximately 123.26 million mobile subscribers Of these, smartphone users were estimated at 101 million subscribers, an 8.72% increase year-over-year, equivalent to a rise of 8.1 million subscribers Basic phone users (also known as 2G) were 22.26 million, a decrease of 4.14 million Hence, this is a vast and promising market for Mobile Banking development, especially considering the Vietnamese government's push towards cashless payments

Therefore, university students - a demographic known for being young, dynamic, and tech-savvy - are becoming one of the primary targets for banks and financial services to develop Mobile Banking Espicially in Ho Chi Minh City (HCMC), acting as a leading economic hub in the Southern key economic region, where a diverse young population, especially students from all over the country gather - with an extensive network of banks covering the city

Building upon this, the author conducted a study titled "Factors affecting the decision on using Mobile Banking of University students: An empirical study in Ho Chi Minh City" to identify and assess the impact of various factors on the intention to use Mobile Banking among this potential customer segment The insights derived from this research can serve as a foundation for banks to formulate marketing strategies and develop services tailored to the increasing needs of users This initiative could bolster satisfaction among existing customers and attract a considerable influx of new customers, contributing to the enhancement of their position and accelerating technological transformation in the banking sector.

Research objectives

The overall objective of the study is to identify the factors influencing the adoption of Mobile Banking among university students in HCMC Based on the results, the author also proposed several policy implications aimed at supporting banks in developing Mobile Banking product, with a particular focus on the group of university students in HCMC area

Based on the overall objective outlined above, the study aims to achieve specific objectives:

• Firstly: Identifying the factors influencing the decision on using Mobile

Banking among university students in HCMC

• Secondly: Assessing the level of impact each factor has on the decision on using Mobile Banking among university students in HCMC

• Lastly: Providing policy implications to contribute to the development of

Mobile Banking services in HCMC.

Research questions

To address the research questions of the topic, specific research objectives are proposed, each corresponding to a research question The research questions for the topic could be formulated as follows:

• Firstly: Which factors influence the decision-making process regarding Mobile Banking usage among university students in HCMC?

• Secondly: To what extent do these factors impact the decision on using Mobile Banking among university students in HCMC?

• Lastly: Which implications should be proposed for banks in order to attract customers to use Mobile Banking services?

Research subject and Research scope

The research subject of this study focuses on the factors influencing the decision on using Mobile Banking among university students in HCMC

• Scope of space : The author conducted in HCMC

Research method

In order to achieve the research objectives, the study employed both two research methods: qualitative and quantitative

In the initial stages of the study, the author employed diverse qualitative research techniques to identify the factors influencing the adoption of Mobile Banking among university students in HCMC These methods including:

• Conducting in-depth interviews with open-ended questions

The survey method using Google Forms platform was conducted on university students in HCMC, with a sample size of 300 Utilizing primary data collected from the survey, the study employed SPSS 20.0 software to analyze the impact of expected factors on the decision to use Mobile Banking This involved testing the reliability of the measurement scale through Cronbach’s Alpha coefficient, Exploratory Factor Analysis (EFA), Pearson correlation analysis, and regression analysis to identify factors influencing the decision to use Mobile Banking and validate the hypotheses set in the research Additionally, the author utilized Independent Sample T-Test and One-way ANOVA to examine differences in Mobile Banking usage decisions among students with different characteristics Based on these analyses, the author synthesized and drew conclusions about the factors affecting students' decisions to use Mobile Banking in HCMC.

Contribution of the study

In theoretical terms, this study combines theoretical frameworks relating to Mobile Banking services generally, by reviewing both domestic and international studies, it identifies research gaps then proposes a model, and formulates research hypotheses regarding influencing factors Later, it examines these on university students in HCMC The research findings could provide a foundation for future studies related to determinants of Mobile Banking usage in other areas or nationwide

In practical terms, the results of the study support banks in the electronic payment industry to grasp customer behavior, especially in the changing scenario where online payments are steadily becoming the norm after the Covid-19 outbreak

By surveying students in HCMC, a vibrant and potentially tech-savvy customer group, service providers can plan marketing strategies and technological improvements that cater to customer requirements This strategy is aimed at broadening their market presence and solidifying their brand standing.

Structure of the study

The structure of the research includes 5 chapter as follows:

LITERATURE REVIEW

Overview of Mobile Banking

In this modern days, consumers can install various applications on their smartphones to meet different needs According to Hanafizadeh et al (2014), the banking sector has introduced numerous electronic banking channels to supply diverse requirements from customers and a recent addition to these channels is Mobile Banking - which can offer a wide range of financial services to consumers through communication technologies

Wessels and Drennan (2010) argue that Mobile Banking represents a new dimension of electronic banking, distinct from traditional telephone banking services that offer limited functionalities, it serves as a versatile platform for automated banking and various financial services

Ngo Duc Chien (2022) defined “Mobile banking is a service offered by banks or other financial institutions that enables their customers to conduct financial transactions remotely using mobile devices such as smartphones and tablets Unlike internet banking, it involves the use of software, typically referred to as an app, provided by financial institutions for this purpose Mobile banking is usually available 24/7.”

In summary, in this study, Mobile Banking can be understood as a banking service performed on mobile devices (smartphones or tablets) with an internet connection, enabling customers to conduct a wide range of transactions, fulfilling many customers’ needs without the need to visit the bank in person, offering convenience and accessibility anytime, anywhere

2.1.2 Benefits of using Mobile Banking

According to Tran Huu Ai and Cao Hung Tan (2020), Mobile Banking has significantly changed the operations of banks, contributing to cost reduction and increased efficiency for customers The essence of Mobile Banking lies in conducting transactions through portable devices Mobile banking enables users to perform diverse financial transactions anywhere and anytime, allowing 24/7 financial services, therefore customers can enjoy a wide range of services, including checking balances, transferring funds, paying bills, making purchases through e-wallets, and engaging in online shopping (Tran Huu Ai and Cao Hung Tan, 2020)

Therefore, using Mobile Banking helps customers save time and transportation costs, especially when there is a considerable geographical distance between the bank and the customer Compared to the traditional approach of traveling to the bank branch, waiting in line for transaction sessions or searching for the nearest ATM for basic financial needs, customers can now fulfill these requirements anytime they want with just a few taps on their portable devices

On the other hand, for banks, Mobile Banking significantly reduces overhead costs and staff expenses - conducting transactions online shortens processing times, standardizes procedures, and enhances efficiency in document retrieval and processing Consequently, it boosts operational productivity and the bank's revenue (Ngo Duc Chien, 2022)

Furthermore, the data storage feature of Mobile Banking ensures that customers can securely access their transaction history and account information conveniently This not only deducts paperwork needs but also improves transparency and accountability in financial transactions Also, by allowing customers to track their savings and loan accounts in real-time, Mobile Banking allows them to make informed financial decisions and manage their finances more effectively

At the moment, most banks have already deployed and developed Mobile Banking services For example, Vietcombank offers the VCB Digibank, Standard Chartered with SC Mobile, VietinBank ưith VietinBank iPay, Sacombank provides Sacombank Pay,…

Theoretical literatures

2.2.1 Theory of Reasoned Action – TRA

The Theory of Reasoned Action (TRA), developed by Fishbein and Ajzen in

1975, suggests that behavioral intentions lead to consumer behavior, and intentions are determined by an individual's attitude along with subjective norms regarding the performance of those behaviors

Figure 2.1 Theory of Reasoned Action (TRA)

In the TRA model, the attitude factor is measured by customers' perceptions of the attributes of the service This factor is specifically reflected in the research model on the factors influencing the decision to use Mobile Banking, where customers often consider the benefits they receive to make their final decision As for the "subjective norms" factor, it is measured through individuals who have relationships with customers such as family members, friends, acquaintances, colleagues, etc., and the impact of these relationships on customers' decisions The closer the relationship, the higher the level of trust and decision-making regarding usage

2.2.2 Theory of Planned Behavior – TPB

The Theory of Planned Behavior (TPB), developed by Ajzen in 1991, suggests that actual behavior is influenced by the intention to perform the behavior, and this intention is affected by three specific factors: attitude, subjective norms, and perceived behavioral control To overcome the limitations of the TRA model, the

"perceived behavioral control" factor is added, reflecting customers' perceptions of the difficulty or ease of performing the behavior and whether the behavior is controlled or constrained

Figure 2.2 Theory of Planned Behaviour (TPB)

Compared to the TRA model, the TPB model is considered more optimal in predicting and explaining the behavior of a customer in the same situation, timeframe, and research content

Figure 2.3 Technology Acceptance Model (TAM)

Another theory built on the foundation of the TRA is the Technology Acceptance Model (TAM) by Davis (1989) This is one of the most influential theories, serving as the focus of studies examining beliefs, intentions to use, technology acceptance, and having high practical value The TAM predicts technology usage behavior through two factors: Perceived ease of use and Perceived usefulness

In TAM, Davis (1989) defines Perceived usefulness as "the degree to which a user believes that using the technology will enhance their job performance" Additionally, Perceived ease of use refers to the ease of using technology with existing skills without requiring specialized knowledge Figure 2.3 shows that both factors impact individuals' attitudes towards technology adoption, thereby indirectly influencing behavioral intentions Particularly, perceived usefulness can directly and indirectly influence the intention to use technology Therefore, the more positive the attitude towards technology usage, the higher the likelihood of actual usage by users.

RESEARCH METHOD

Research model

Based on theoretical foundations from theories TRA, TPB, and notably TAM, the author decides to utilize three factors: perceived usefulness, perceived ease of use, and social influence to examine their impact on the decision to use Mobile Banking among students in HCMC Additionally, drawing from the findings of literature reviews of experimental studies, the author takes in three additional factors - transaction costs, brand image, and perceived risk - into the research model to explore new dimensions of the psychological and behavioral intentions of present-day students Therefore, the research model is proposed as in Figure 3.1

Source: Proposed by the author

Research hypothesis

Perceived usefulness is understood as the extent to which an individual perceives that using a specific system will enhance their job performance (Davis, 1989) Mobile Banking is perceived to have advantages over traditional services due to its flexibility, unrestricted access to time and space Users, especially students, often have a tendency to follow trends, so they are interested in experiencing new technologies and integrating them into their daily lives when they perceive them as useful Not only foreign studies by Riquelme and Rios (2010), Mamun et al (2023), Jeong and Yoon (2013), Sakala and Phiri (2019), but also recent domestic studies by Ngo Duc Chien (2022) and Ha Nam Khanh Giao (2022) have all indicated that perceived usefulness has a positive impact on the decision to use Mobile Banking Building on these research findings, the author expects perceived usefulness and the decision to use Mobile Banking to have a positive relationship and proposes a hypothesis accordingly

H 1 : Perceived usefulness positively influences the decision to use Mobile Banking among students in HCMC

Perceived ease of use refers to the extent to which an individual believes that using a specific system requires minimal effort (Davis, 1989) It encompasses simple operations, understandable interfaces, and clear, specific features in Mobile Banking applications The level of ease of use varies depending on each individual's proficiency Alongside perceived usefulness, ease of use is considered a crucial factor in technology adoption, influencing long-term technology usage Ease of use has consistently shown a positive correlation with Mobile Banking adoption across various studies on the subject, such as those conducted by Riquelme and Rios (2010), Sitorus et al (2019), and Ngo Duc Chien (2022) Therefore, the author proposes the following hypothesis as the next step

H 2 : Perceived ease of use positively influences the decision to use Mobile

Banking among students in HCMC

Perceived risk is understood as the perception of uncertainty regarding the safety of using a product and its potential to cause harm to customers In other words, risk perception is the negative attitude of customers towards a product Concerns when using Mobile Banking may arise from transaction errors, login failures, the risk of account information leakage, or the risk of financial loss Risk perception is an important factor influencing users' intention to use a product because, according to general psychology, users tend to avoid and minimize losses Studies by Luo, Li, Zhang, and Shim (2010), Alalwan et al (2016), Thusi and Maduku (2020), Vo Thi Phuong Lan and Nguyen Thanh Giang (2021), Ngo Đuc Chien (2022), and Mamun et al (2023) have shown that risk perception negatively impacts the decision to use Mobile Banking Therefore, the author proposes the following hypothesis for further research

H 3 : Perceived risk negatively influences the decision to use Mobile Banking among students in HCMC

Social influence refers to the extent of influence of external factors on an individual's behavioral intention In the study, these external factors stem from the advice and opinions of people around, including family, friends, and colleagues The greater the trust of users in the entity providing advice, the stronger the social influence This factor has been shown to have a positive relationship with the decision to use Mobile Banking through experimental studies by Makanyeza (2017), Ngo Duc Chien (2022), Le Hoang Ba Huyen and Le Thi Huong Quynh (2018), Vo Thi Phuong Lan and Nguyen Thanh Giang (2021), and Sitorus et al (2019) Therefore, the author continues to expect that social influence positively impacts the decision to use Mobile Banking and proposes a hypothesis accordingly

H 4 : Social influence positively influences the decision to use Mobile Banking among students in HCMC

Transaction costs refer to the amount of money users need to pay to use a product or service Since Mobile Banking operates on Internet-based devices, when using Mobile Banking, customers need to pay for expenses such as smartphones or tablets, Internet access fees, service registration fees, etc When faced with the decision to use any product or service, individuals weigh the benefits received against the costs incurred, same situation applied to Mobile Banking Therefore, each customer's perception of costs varies According to Awad and Dessouki (2017), Ngo Duc Chien (2022), Jeong and Yoon (2013), and Mamun et al (2023), there exists a positive relationship between costs and the decision to use Mobile Banking Based on this, the author presents the fifth hypothesis of the study

H 5 : Reasonable transaction costs positively influence the decision to use Mobile Banking among students in HCMC

Brand image refers to the perceived value of a bank in terms of credibility and quality by customers Brand image can stem from a bank's market position, service quality, customer care policies, or the effectiveness of its marketing strategies A bank with a strong brand image is more accessible, instills trust in customers, and thus provides a basis for encouraging customers to use its services Studies by Ngo Duc Chien (2022) and Vo Thi Phuong Lan and Nguyen Thanh Giang (2021) have demonstrated that a bank with a strong brand image has a positive impact on customers' decisions to use Mobile Banking services Therefore, the author anticipates a positive relationship between brand image and the decision to use Mobile Banking and proposes the hypothesis accordingly

H 6 : Brand image positively influences the decision to use Mobile Banking among students in HCMC

Perceived usefulness positively influences the decision to use Mobile Banking among university students in HCMC +

Perceived ease of use positively influences the decision to use Mobile Banking among university students in HCMC +

Perceived risk negatively influences the decision to use Mobile Banking among university students in HCMC -

Social influence positively influences the decision to use Mobile Banking among university students in HCMC

Reasonable transaction costs positively influence the decision to use Mobile Banking among university students in HCMC +

Brand image positively influences the decision to use Mobile Banking among university students in HCMC +

Source: Proposed by the author

Research process

To ensure the progress and quality of the research, the author outlines a research process consisting of 7 steps, as depicted in Figure 3.2

Source: Proposed by the author

Step 1: The author conducted a search and synthesis of theories on Mobile Banking and behavioral intentions to enhance the theoretical foundation for the study Additionally, the author consulted literature from scientific journals and selected empirical studies on factors influencing the decision to use Mobile Banking domestically and worldwide

Step 2: Based on the similarities among the reviewed empirical studies and the market context, the author identified factors expected to affect the decision to use Mobile Banking among students in HCMC to propose a formal research model and appropriate hypotheses

Step 3: The author built and adjusted the survey questionnaires based on the research model and theoretical framework, in final comprises 32 survey questions

Step 4: The author using Google Forms and distributed the survey on social media platforms and student groups in HCMC

Step 5: After collecting and filtering data from the survey, the author utilized SPSS 20.0 to examine the impact of expected factors on the decision to use Mobile Banking In addition to descriptive statistics, analysis methods included testing the reliability of the measurement scale using Cronbach's Alpha coefficient, EFA, Pearson correlation, regression analysis, and testing differences

Step 6: After running the regression model, the author presented the data analysis results based on SPSS findings using each method, drawing conclusions about the research outcomes

Step 7: Based on the research findings, the author provided recommendations to help banks develop Mobile Banking services, contributing to increasing their alignment with customer usage needs.

Scale construction

The study utilized a Likert scale to investigate students' attitudes towards Mobile Banking, with responses displayed on a 5-point scale: (1) Strongly Disagree, (2) Disagree, (3) Neutral, (4) Agree and (5) Strongly Agree Through the research process, the author selected appropriate variables related to the topic from existing experimental studies and constructed a scale consisting of 27 explanatory variables for one dependent variable and six independent variables as presented in Table 3.2

No Features Variable code Source

Riquelme and Rios (2010), Mamun et al

(2023), Jeong and Yoon (2013), Sakala and Phiri (2019)

1 Mobile Banking helps me deal with financial needs flexibly PU1

2 Mobile Banking saves me time and transportation costs PU2

3 I have accessed more banking services thanks to Mobile Banking PU3

4 Mobile Banking helps me manage my finances efficiently PU4

2 Perceived ease of use PEU

Ngo Duc Chien (2022), Sitorus et al (2019), Riquelme and Rios (2010), Ha Nam Khanh Giao (2022)

5 I can quickly install Mobile Banking on my device PEU1

6 I find it easy to learn how to use Mobile

7 The use of Mobile Banking are simple to perform PEU3

8 I can proficiently use Mobile Banking PEU4

Luo, Li, Zhang, and Shim (2010), Alalwan et al (2016), Thusi and Maduku (2020),

Vo Thi Phuong Lan and Nguyen Thanh Giang (2021), and Mamun et al (2023)

I worry that my personal information may be leaked when using Mobile

I fear that if I lose my phone with

Mobile Banking, I might lose my money as well

I am concerned about losing money in case of errors during transactions through Mobile Banking

When using Mobile Banking, I fear the possibility of my account being stolen by hackers/thieves

Makanyeza (2017), Ngo Duc Chien (2022), and Sitorus et al (2019)

I am encouraged by my family/friends/teachers/… to use Mobile

I use Mobile Banking because of recommendations from my family/friends/teachers/…

15 I feel confident using Mobile Banking when I see everyone around me using it SI3

Using Mobile Banking helps me save more costs compared to transactions at the counter

Awad and Dessouki (2017), Ngo Duc Chien (2022), and Jeong and Yoon (2013)

I am satisfied with the fees associated with Mobile Banking because I receive corresponding benefits

18 I am well aware of the fees for using

19 The fees for Mobile Banking are reasonable TC4

Ngo Duc Chien (2022), Vo Thi Phuong Lan and Nguyen Thanh Giang (2021)

20 I use Mobile Banking because it is a product of a reputable bank BI1

21 I am satisfied with the quality of the bank's services BI2

22 I am attracted by the favorable policies of the bank BI3

23 I receive decent support when transaction issues arise BI4

Ngo Duc Chien (2022), Xiao et al (2017), Lambert et

24 I feel that Mobile Banking is an essential application SD1

25 I will continue to use Mobile Banking services SD2 al (2019), Makanyeza (2017)

26 I intend to explore more features in the future SD3

27 I will recommend people around me to use Mobile Banking SD4

Source: Proposed by the author

Sampling methods

The study utilized the EFA method, hence the sample size was determined based on the recommendation by Hair et al (2010), which suggests that the sample size should be at least 5 times the number of observed variables In this study, the author constructed a scale comprising 27 observed variables, thus the minimum sample size required is 27 * 5 = 135 Therefore, to ensure objectivity and reliability of the data, the author decided to select a sample size of 300 surveys for the study

The survey was conducted online via the Google Forms platform, targeting individuals currently studying at universities in HCMC area who use Mobile Banking services After data collection, the survey results will be tabulated and screened to eliminate invalid responses (incomplete surveys) Finally, the valid results will be used to facilitate the analysis process

RESEARCH RESULTS AND DISCUSSIONS

Descriptive statistics of the sample

Table 4.1 Demographic statistics of the survey Characteristic Frequency Percentage (%)

Come from Ho Chi Minh City 105 35.71

Regarding gender, the female respondents a bit outnumber male Specifically, out of the 294 students surveyed, 157 are female, showing a slight imbalance as the remaining 137 students are male

In terms of residence, similar to the gender distribution, the two residential groups in the survey are nearly balanced The group of students from HCMC consists of 105 individuals, equivalent to 35.7% of the total surveyed students, while the group of students from other provinces comprises 189 individuals, representing 64.3% of the surveyed population

Concerning academic years, fourth-year students outnumber the others, while the first-year student group constitutes a smaller portion The survey results indicate that out of the total 294 students, 32 are freshmen, 61 are sophomores, 105 are juniors, and 96 are seniors

In terms of academic majors, for every 10 surveyed students, approximately 3 belong to the Economics Specifically, the Economics major takes the lead with 37.1% among the 294 students, followed by the Language major with 12.6%, the Technology major with 15.6%, the Engineering major with 17.0%, the Cultures and Arts major with 10.5%, and others with 7.1%

Concerning the primary source of income, the majority of surveyed students rely on financial support from their families As shown in Table 4.1, a total of 191 students receive financial support from their families, equivalent to 65%, while only

103 students sustain their expenses through part-time jobs, representing 35.0%.

Cronbach's Alpha reliability coefficient analysis

The evaluation of scale reliability using Cronbach's Alpha coefficient involves analyzing six independent variables: PU (Perceived usefulness), PEU (Perceived ease of use), PR (Perceived risk), SI (Social influence), TC (Transaction costs) and BI (Brand image) Additionally, there is one dependent variable, namely Student’s decision (SD)

This analysis aims to assess the suitability of utilizing 27 observed variables to clarify the factors within the model As detailed in Chapter 3, the scale is considered acceptable only if the Cronbach's Alpha coefficient attains a minimum of 0.6, and the total correlation coefficient of observed variables is 0.3 or higher

The results of conducting reliability analysis for the scale on the independent variables in the model will be presented sequentially by the author in Table 4.2

Table 4.2 Reliability Statistics for Independent variables

Cronbach's Alpha if Item Deleted

As indicated in Table 4.2, it is noteworthy that:

+ The Cronbach's Alpha coefficient for each variable surpasses 0.6

+ Additionally, the Corrected Item-Total Correlation for each observed variable also exceeds 0.3

These findings collectively signify that the scale demonstrates strong reliability Consequently, the scale is deemed suitable for subsequent EFA procedures, providing a solid foundation for further in-depth examination and validation

For each observed variable within the scale, if removed by the author, the Cronbach's Alpha coefficient consistently decreases compared to the initially recorded figure This implies a reduction in the overall quality of the scale, except for variables PEU4 and BI4 The author could potentially enhance the Cronbach's Alpha coefficient for the PEU and BI scales by excluding variables PEU4 and BI4 However, the achieved values of 0.766 and 0.734, respectively, compared to the initial values of 0.720 and 0.715, show no significant deviation Furthermore, the total correlation values for PEU4 and BI4 are 0.314 and 0.370, respectively, exceeding the minimum threshold, indicating their continued relevance in explaining the respective factors Consequently, all observed variables within the scale for the independent variables are retained for further analysis

Similar to the scales of the independent variables, the scale for usage decision also exhibits a high Cronbach's Alpha coefficient, specifically 0.788 Detailed test results will be presented in Table 4.3

Table 4.3 Reliability Statistics for Dependent variables

Cronbach's Alpha if Item Deleted

Corresponding to the observed variables SD1, SD2, SD3, and SD4, their total correlation values are 0.602, 0.598, 0.633, and 0.553, respectively Clearly, the total correlation coefficients for all observed variables exceed 0.3 Coupled with a Cronbach's Alpha coefficient of 0.788, which is greater than 0.6, it indicates that the author has effectively constructed the usage decision scale

In summary, through the reliability testing method using Cronbach's Alpha, all 27 proposed observed variables within the scale are deemed appropriate and meaningfully contribute to effective measurement These observed variables will be retained for the subsequent analytical methods in the study.

Exploratory Factor Analysis

The EFA method assists the author in once again screening the observed variables for the model, ensuring meaningful explanatory power for each factor Retained factors must satisfy specific constraints, including: the Bartlett test being statistically significant, 0 < KMO coefficient < 1, total variance extracted exceeding 50%, Eigenvalue greater than 1, and factor loading greater than 0.5

The author will sequentially present the analysis results corresponding to each imposed constraint Table 4.4 illustrates the outcomes of the first constraint regarding the KMO coefficient and the Bartlett test for the 6 independent variables in the model

Table 4.4 Results of KMO and Bartlett test (I)

Kaiser-Meyer-Olkin Measure of Sampling Adequacy .755

The EFA analysis results reveal a KMO coefficient of 0.755, falling within the range (0.5; 1), signifying the compatibility of this method with the survey data Simultaneously, the Sig coefficient result is 0.000, which is lower than 0.05, indicating with a 5% significance level, the variables in the overall dataset are correlated The subsequent conditions for EFA analysis pertain to the Eigenvalue and total extracted variance The extracted variance of the independent variables will be presented in Table 4.5

Component Initial Eigenvalues Extraction Sums of Squared

Rotation Sums of Squared Loadings Total % of

Extraction Method: Principal Component Analysis

Based on Table 4.5, six factors are extracted from a total of 23 observed variables, with Eigenvalues exceeding 1 The first factor has the highest Eigenvalue of 6.744, while the sixth factor has an Eigenvalue of 1.443 Simultaneously, the cumulative total extracted variance is relatively high, reaching 65.632% Clearly, the total extracted variance exceeds 50%, meeting the predefined criterion, once again affirming the appropriateness of the author's choice of the EFA method for the study

Table 4.6 will present the final constraint, which is the factor loading, illustrated through the factor rotation matrix

Table 4.6 Results of Rotated Component Matrix (I)

Extraction Method: Principal Component Analysis

Rotation Method: Varimax with Kaiser Normalization a Rotation converged in 6 iterations

The author strives to pick out high-quality observed variables, favoring a factor loading threshold of 0.5 over one based on sample size After contrasting this threshold with the outcomes in the rotation matrix, PEU4 and BI4 are singled out as potential candidates for elimination

+ Variable BI4 loads on both Component 1 and 3, factor loadings -0.541 and 0.688 The difference between two factor loadings is: 0.688 - |-0.541| = 0.147 < 0.3

+ Variable PEU4 loads on both Component 2 and 5, factor loadings 0.591 and 0.664 The difference between two factor loadings is: 0.664 - 0.591 = 0.073 < 0.3

Various methods exist for removing poor variables, each depending on the researcher's rationale Some may remove all poor variables in one EFA analysis, while others may successively eliminate each poor variable and evaluate changes in the rotation matrix after each elimination After relook at the research questions and consulting academic advisor, the author chose to successively remove each poor variable, starting with PEU4 due to its smaller factor loading compared to BI4 (0.073

< 0.147) Afterward, the author conducted another EFA exploration analysis without PEU4, and the results are presented in the tables below:

Table 4.7 Results of KMO and Bartlett test (II)

Kaiser-Meyer-Olkin Measure of Sampling Adequacy .789

According to the EFA results, the KMO coefficient stands at 0.789, falling within the appropriate range of 0.5 to 1, signifying the compatibility of this method with the survey data Additionally, with the Sig value of the Bartlett’s test being

0.000 < 0.05, indicating with a 5% significance level, the variables in the overall dataset are correlated

Table 4.8 Total Variance Explained (II)

Component Initial Eigenvalues Extraction Sums of Squared

Rotation Sums of Squared Loadings Total % of

Extraction Method: Principal Component Analysis

Referring to Table 4.8, six factors are extracted from a total of 22 observed variables, with Eigenvalues > 1 The first factor has the highest Eigenvalue of 6.211, and the sixth factor has an Eigenvalue of 1.399 Simultaneously, the cumulative total extracted variance is relatively high, reaching 64.915% Clearly, the total extracted variance exceeds 50%, meeting the predefined criterion, once again affirming the appropriateness of the author's choice of the EFA method for the study

Table 4.9 Results of Rotated Component Matrix (II)

Extraction Method: Principal Component Analysis

Rotation Method: Varimax with Kaiser Normalization a Rotation converged in 6 iterations

Following the analysis of the rotation matrix, once the less effective variable PEU4 was removed, it became evident that the persistent underperforming variable BI4 suggested consideration for elimination as well BI4 exhibited loadings on both Component 1 and Component 2, registering factor loadings of -0.541 and 0.690, respectively The discrepancy in factor loadings amounted to 0.149, falling below the threshold of 0.3

Applying the same rationale as in the initial EFA analysis, the author proceeded to eliminate the poor variable BI4 and conducted the EFA for the third time The results are presented in the tables below::

Table 4.10 Results of KMO and Bartlett test (III)

Kaiser-Meyer-Olkin Measure of Sampling Adequacy .834

The outcome of EFA analysis show a KMO coefficient of 0.834, falling within the range (0.5; 1), signifying the compatibility of this method with the survey data Additionally, the Bartlett test yields a Sig coefficient of 0.000, which is below 0.05, indicating with a 5% significance level, the variables in the overall dataset are correlated

Table 4.11 Total Variance Explained (III)

Component Initial Eigenvalues Extraction Sums of Squared

Rotation Sums of Squared Loadings Total % of

Extraction Method: Principal Component Analysis

Referring to Table 4.11, six factors were extracted from a total of 21 observed variables, with Eigenvalues surpassing 1 The foremost factor achieved the highest Eigenvalue at 5.637, and the sixth factor reached an Eigenvalue of 1.343 Simultaneously, the cumulative total extracted variance was notably high, reaching 64.154% Clearly, the cumulative total variance exceeded 50%, meeting the established criterion, once again affirming the appropriateness of the author's EFA method choice for the study

Table 4.12 Results of Rotated Component Matrix (III)

Extraction Method: Principal Component Analysis

Rotation Method: Varimax with Kaiser Normalization a Rotation converged in 6 iterations

The outcomes from the third rotation matrix revealed the categorization of 21 observed variables into 6 factors Moreover, every observed variable exhibited factor loadings surpassing 0.5, and there were no poor observed variables

In summary, the EFA for the independent variables underwent three performance Initially, 23 observed variables were taken into the analysis, with the subsequent removal of two variables, PEU4 and BI4, due to non-compliance with the set criteria Eventually, in the third analysis, all criteria were fulfilled after eliminating the underperforming variables

Similarly to the independent variables, the conditions for the EFA method remain constant for the dependent variable The EFA results for the usage decision variable, including the KMO coefficient, Bartlett's test, variance extracted, and the factor rotation matrix, will be synthesized and presented in Table 4.7 by the author

Table 4.13 Results of KMO and Bartlett test for Dependent variable

Kaiser-Meyer-Olkin Measure of Sampling Adequacy .791

Pearson correlation coefficients

Table 4.16 illustrates the results of Pearson correlation analysis of variables within the model

SD PU PEU PR SI TC BI

** Correlation is significant at the 0.01 level (2-tailed)

Through Pearson correlation analysis, the author discerns the relationships among variables within the model, particularly the connection between the dependent variable and independent variables At a 95% confidence level, any two variables are considered to have a significant correlation when the Sig coefficient < 0.05

At a 1% significance level, all independent variables in the model exhibit linear relationships with the dependent variable, as indicated by the Sig coefficient

= 0.000 < 0.01 Notably, the correlation coefficients between Student’s decision and Perceived risks exhibit an inverse effect The Pearson correlation coefficients, ranked in descending order, are as follows: between usage decision and Perceived usefulness (0.605), with Social influence (0.538), with Perceived ease of use (0.522), with Transaction costs (0.509), with Brand image (0.477), and finally with Perceived risks (-0.472) The impact between the dependent and independent variables is moderate or stronger; therefore, all independent variables are utilized for regression analysis to ascertain the influence of each factor on student Student’s decisions On the other hand, also exist linear relationships among independent variables at a 99% confidence level.

Regression analysis

The main purpose of the study is to utilize regression analysis to assess the impact of each expected factor on the decision to use Mobile Banking among students in HCMC With the reliability testing, EFA and correlation analysis results, the author decides to conduct a regression analysis on one dependent variable and six independent variables Therefore, the author establishes the regression equation as follows

SD = β 0 + β 1 PU + β 2 PEU + β 3 PR + β 4 SI + β 5 TC + β 6 BI + ε

Where SD is the Student’s decision on using Mobile Banking, PU is Perceived usefulness, PEU is Perceived ease of use, RR is Perceived risks, SI is Social influence,

TC is Transaction costs, BI is Brand image, and 𝜀 is the random error

Firstly, the author will examine the explanatory power of the independent variables in the model through the adjusted R 2 coefficient Detailed results are presented in Table 4.17

Std Error of the Estimate

1 813 a 661 654 39715 1.956 a Predictors: (Constant), BI, PEU, PR, SI, TC, PU b Dependent Variable: SD

The regression results indicate an adjusted R 2 value of 0.654, meaning that 65.4% of the variance in usage decisions is explained by the six independent variables mentioned above, while the remaining 34.6% is influenced by external variables and random error Additionally, the Durbin-Watson coefficient for the model is 1.956, falling within the range of (1.5; 2.5) Therefore, following Qiao (2011), there is no evidence of residual autocorrelation in the model As a result, it can be tentatively concluded that the regression model is meaningful Furthermore, the ANOVA table shows a Sig value for the F-test of 0.000, which is less than 0.05 Hence, at a 5% significance level, the regression model is suitable For detailed results, refer to Table 4.18

Total 133.576 293 a Dependent Variable: SD b Predictors: (Constant), BI, PEU, PR, SI, TC, PU

Source: Extracted from SPSS 20.0 The regression coefficients are estimated to assess the impact of each factor on the dependent variable The strength or weakness of the impact depends on the regression coefficient value of the independent variable in the model However, the regression coefficient is only significant when the Sig value is < 0.05 Additionally, the estimation helps the author detect multicollinearity based on the VIF values The results of the linear regression are presented in Table 4.19

B Std Error Beta Tolerance VIF

Based on Table 4.19, the Sig coefficients of the variables in the model range from 0.000 to 0.005, all satisfying the condition of being less than 0.05 Thus, all six independent variables affect the dependent variable, Student's decision, with only the perceived risks variable having a negative impact In addition, the VIF coefficients for the variables PU, PEU, PR, SI, TC and BI are 1.306, 1.206, 1.258, 1.281, 1.302, 1.287, all less than 2 Therefore, it can be concluded that the model does not experience multicollinearity

Based on the results, the regression equation is rewritten in descending order of estimated coefficients

S𝐷 = 0.311PU + 0.253PEU + 0.225SI + 0.166TC – 0.152PR + 0.134BI + ε

Utilizing the regression results, the author has presented the hypothesis testing results outlined in the initial part of the study The summarized outcomes are presented in Table 4.20

Table 4.20 Results of testing the research hypothesis

H1 Perceived usefulness positively influences the decision to use Mobile Banking among university students in HCMC

H2 Perceived ease of use positively influences the decision to use Mobile Banking among university students in HCMC

H3 Perceived risks negatively influence the decision to use

Mobile Banking among university students in HCMC Accepted

Social influence positively influences the decision to use Mobile Banking among university students in HCMC

Reasonable transaction costs positively influence the decision to use Mobile Banking among university students in HCMC

H6 Brand image positively influences the decision to use

Mobile Banking among university students in HCMC Accepted

Source: Compiled by the author

Testing differences of the sample characteristics

An Independent Sample T-Test was conducted to examine whether there is a difference in means in the decision to use Mobile Banking concerning variations in gender, residence, and income The reason for employing this method is that the categorical variables mentioned above only have two values, making this test optimal for obtaining results As discussed in Chapter 3, a significant difference exists when the Sig value of the t-test < 0.05 Results of the first categorical variable, gender, are presented in Table 4.21

Table 4.21 Independent Samples Test (Gender)

Levene's Test for Equality of Variances t-test for Equality of Means

95% Confidence Interval of the Difference Lower Upper

Based on Table 4.21, the Sig value of the F-test is 0.306 > 0.05, indicating no significant difference in variances between the values of the gender variable (male and female) As a result, we utilized the t-test outcome based on the "Equal variances assumed" column Continuing with the t-test, the Sig value when variances are assumed to be equal is 0.530 > 0.05 Thus, there is no difference in the means of the decision to use Mobile Banking when gender changes

The next categorical variable examined using this method is place of birth Through Table 4.22, the author will assess the similarity of the decision to use Mobile Banking among students from HCMC and students from other provinces

Table 4.22 Independent Samples Test (Place of birth)

Levene's Test for Equality of Variances t-test for Equality of Means

95% Confidence Interval of the Difference Lower Upper

Based on Table 4.22, the Sig value of the F-test is 0.768 > 0.05, indicating no significant difference in variances between the values of the residence variable As a result, we utilized the t-test outcome based on the "Equal variances assumed" column Continuing with the t-test, the Sig value when variances are assumed to be equal is 0.496 > 0.05 Thus, there is no difference in means between students from HCMC and students from other provinces in deciding to use Mobile Banking

Table 4.23 will present the results of testing differences in the decision to use the categorical variable with only 2 values, the last variable of the study

Table 4.23 Independent Samples Test (Main income)

Levene's Test for Equality of Variances t-test for Equality of Means

95% Confidence Interval of the Difference Lower Upper

Based on Table 4.23, the Sig value of the F-test is 0.048 < 0.05, indicating that the variances between students who do part-time jobs and students who receive 100% financial support from their families are different As a result, we utilized the t-test outcome based on the "Equal variances not assumed" column However, upon further examination of the t-test, the Sig value when variances not assumed is 0.102 > 0.05 Therefore, there is no significant difference in the decision to use Mobile Banking between students with income from part-time jobs and students financially supported by their families

In this study, One-way ANOVA tests were conducted sequentially on the decision to use and two categorical variables with multiple values, namely academic year and major Through the Levene's test results, the differences in the decision to use among different categorical value groups were confirmed when the Sig value of the Welch's test or the F-test was less than 0.05 The detailed results of the ANOVA tests for the academic year and major variables will be presented in Table 4.24 and Table 4.25, respectively

Table 4.24 Test of Homogeneity of Variances (Year of study)

SD Levene Statistic df1 df2 Sig

The Sig value obtained from Levene's test is 0.645, which exceeds the significance level of 0.05, indicating no significant differences in variances among the year of study groups Consequently, we opted to utilize the F-test outcome from the ANOVA table

Table 4.25 ANOVA (Year of study)

Additionally, the Sig value of the F-test is 0.677 > 0.05 Therefore, with a confidence level of 95%, there is no significant difference in the decision to use Mobile Banking among the first-year, second-year, third-year, and fourth-year student groups

The results of the ANOVA test for the final categorical variable in the study, which is the field of study, will be presented in Tables 4.26 and 4.27

Table 4.26 Test of Homogeneity of Variances (Major)

SD Levene Statistic df1 df2 Sig

The Levene's test result shows a Sig coefficient 0.862, which is higher than 0.05, suggesting no notable difference in variances among the majors of the students Consequently, we opted to utilize the F-test outcome from the ANOVA table:

In this case, the author proceeded with the F-test, and the ANOVA table shows that the Sig value of the F-test is 0.945 > 0.05 Hence, it can be concluded that there is no significant difference in the decision to use Mobile Banking among the groups of students majoring in economics, languages, technology, or engineering,…

In summary, based on the results of the Independent Sample T-Test and One- way ANOVA for differences, the author observed that the decision to use Mobile Banking by universitiy students is not influenced when values related to gender, place of residence, year of study, field of study, and main income vary In other words, in this study, the categorical variables do not affect the decision of university students to use Mobile Banking.

Discussions on the results

The research results reveal that the decision to use Mobile Banking among students in HCMC is influenced by six factors ranked in descending order of impact, including Perceived usefulness, Perceived ease of use, Social influence, Transaction costs, Perceived risks, and Brand image

Perceived usefulness, with a standardized Beta coefficient of 0.311, emerges as the factor exerting the most significant impact on the decision to use Mobile Banking It is evident that the usefulness of Mobile Banking is a key aspect of university students in HCMC’s concern, explained by the ability of Mobile Banking to help students quickly and conveniently address their daily financial issues Students no longer need to visit the bank in person to conduct transactions; they can handle everything from their mobile phones The positive impact of perceived ease of use on the decision to use Mobile Banking has also been substantiated by various studies, including those conducted by Riquelme and Rios (2010), Mamun et al (2023), Jeong and Yoon (2013), and Sakala and Phiri (2019)

Perceived ease of use is the second most impactful factor among the total of six factors mentioned, with a standardized Beta coefficient of 0.253 Accordingly, students tend to use Mobile Banking more when the interface and operations are simpler The positive impact of perceived ease of use on the decision to use Mobile Banking has also been demonstrated in various studies by Ngo Duc Chien (2022), Sitorus et al (2019), Riquelme and Rios (2010), and Ha Nam Khanh Giao (2022)

Social influence is another factor that positively affects the decision of university students, ranking third with a standardized Beta coefficient of 0.225 This implies that advice from family, friends, and the usage behavior of individuals around them are reasons driving students in HCMC to use Mobile Banking The consistent positive impact of this factor has been confirmed in the studies by Makanyeza (2017), Ngo Duc Chien (2022), and Sitorus et al (2019)

Transaction costs also have a positive impact on the decision to use Mobile Banking by students, with a standardized Beta coefficient reaching 0.166 Most students only use simple services, so the costs mainly come from transfer fees, service maintenance fees, or fluctuating balance notification fees They tend to use Mobile Banking when they perceive the fees for the service as reasonable This result aligns with findings in studies by Awad and Dessouki (2017), Ngo Duc Chien (2022), and Jeong and Yoon (2013)

Perceived risks with a standardized Beta coefficient of -0.152, is the factor unlike other factors - have an inverse relationship with the decision to use this service Most surveyed students only use Mobile Banking when they perceive it as having a low likelihood of causing financial losses They are individuals with limited experience in expense management, making risk a noticeable concern The study's results are consistent with experimental studies by Luo, Li, Zhang, and Shim (2010), Alalwan et al (2016), Thusi and Maduku (2020), Vo Thi Phuong Lan and Nguyen Thanh Giang (2021), and Mamun et al (2023)

Brand image also positively influences students' decisions to use Mobile Banking, but with the smallest impact, as the standardized Beta coefficient only reaches 0.134 As new customers in the financial sector, students are often attracted by promotional policies and gifts from banks Additionally, a reputable bank is a priority criterion for students when choosing to use the service The positive impact of brand image has also been demonstrated in the research of Vo Thi Phuong Lan and Nguyen Thanh Giang (2021) and Ngo Duc Chien (2022)

Therefore, all the factors expected in the study have an impact on the decision to use Mobile Banking by students in HCMC Based on the results of data analysis, the hypotheses constructed by the author are all accepted Furthermore, the research results also indicate that the decision to use Mobile Banking is not influenced by differences in demographic characteristics

In this chapter, the author systematically presents the results of data processing using SPSS software Employing descriptive statistical methods, the research data is delineated according to various demographic characteristics, providing an overall view of the study's sample size Through the application of statistical methods such as the Cronbach's Alpha reliability test, EFA, and Pearson correlation analysis, the author identified representative variables for conducting regression analysis to uncover the impact of expected factors on Mobile Banking usage behavior The research results indicate that perceived usefulness, perceived ease of use, social influence, transaction costs, and brand image are positively associated with students' decisions to use Mobile Banking, while perceived risk has a negative impact Additionally, this decision is not influenced by demographic characteristics The findings and conclusions presented in Chapter 4 serve as a foundation for the author to propose recommendations in the subsequent chapter, aiming to assist service providers in developing Mobile Banking services tailored to the behavior and psychology of students in HCMC

CONCLUSIONS AND RECOMMENDATIONS

Conclusions

With the assistance of SPSS 20.0 software, the author successfully identified the factors influencing the decision to use Mobile Banking services among students in HCMC Utilizing the TAM model as the foundation, the author proposed six factors and constructed a scale with 27 observed variables to examine their impact The data, collected from 300 samples representing full-time students across various universities in the city, with 294 samples used for the official study

Through stages ranging from data processing, encoding, reliability testing of the scale, EFA to linear regression, the research results indicate that all six expected factors significantly affect the decision to use Mobile Banking These factors, ranked in decreasing order of impact, include Perceived usefulness, Perceived ease of use, Social influence, Transaction costs, Perceived risks and Brand image Notably, only perceived risk has a negative impact, while the remaining five factors positively impact the decision to use Mobile Banking Therefore, students in HCMC tend to use Mobile Banking due to its usefulness and convenience for transactions from any locations, simplicity in using Additionally, the impact of people surrounding, application of favorable policies, reasonable pricing, reputation of the bank are criteria that encourage students to use the service Furthermore, demographic characteristics such as gender, place of residence, educational background, field of study, or income source do not impact the decision to use Mobile Banking In other words, there is no difference in the decision to use Mobile Banking based on whether students are male or female, from HCMC or other provinces, enrolled in the first, second, third, or fourth year, studying in the fields of economics or technology, and having income from part-time work or family support

The research results also can contribute to enriching the literature on customer Mobile Banking behavior, particularly as the surveyed subjects and scope introduce innovation compared to existing experimental studies By focusing on the younger consumer segment, the study aids both economic organizations in general and banks in particular in understanding the current needs of a substantial potential customer base Consequently, the author has a foundation to propose policy implications that can assist banks in developing services to reach more new customers and move closer to a dominant market position in the Mobile Banking sector.

Recommendations

Perceived usefulness is the most impactful factor in the study and plays a significant role in retaining users On the other hand, the increasing competition with electronic wallets demands that Mobile Banking diversify its features to meet user needs To enhance the usefulness of the service, the author suggests the following policy implications

Firstly, supplementing the feature of paying tuition fees by strengthening collaboration with universities in HCMC For students, tuition fees are a fixed cost that needs to be paid before participating in each academic semester or year This expense is often paid regularly during each semester or academic year Updating the online tuition fee payment feature on Mobile Banking helps students save transaction time at counters and reduces the risk of theft when handling a large amount of cash

Secondly, collaborate with diversified businesses such as e-commerce platforms, supermarkets, food and beverage chains Currently, it is not difficult to find these places accept payments through Mobile Banking, typically through QR code scanning Additionally, payment through this service would be more robust if accompanied by incentives, for instance, discounts ranging from 5% to 10% Given the preference for discounts, the student group is likely to be attracted to payment methods that benefit them compared to others groups, even if the received benefits are not substantial

Thirdly, collaborate with stock brokerage firms to develop features for tracking accounts and buying/selling securities on Mobile Banking to tap into customers within this industry The research focuses on the behavior of students, with more than 35% majoring in economics Therefore, considering their foundational knowledge, interest and desire for profit, students can potentially become investors Hence, this feature can be utilized by various customer segments, not just benefical to students

Lastly, ensuring the quality of each utility In addition to expanding the quantity of Mobile Banking features, banks should also focus on the quality of each feature Transferring and balance checking are the primary features of interest to students Therefore, when performing maintenance or upgrading the system, banks should notify users to avoid disrupting the cash flow circulation

5.2.2 On perceived ease of use

Based on the survey, students perceive Mobile Banking positively in terms of ease of use, and this factor is the second significant influencer of the decision to use the service Therefore, in addition to maintaining the existing advantage of a user- friendly interface and simple operations, banks can implement the following policies to enhance the service

Firstly, ensure the compatibility of the application with users' devices Owning the application is a prerequisite for customers to use Mobile Banking Therefore, banks need to design the application in multiple versions concerning configuration, memory, and capacity to fit different operating systems Mobile Banking must be installed and operate well on users' devices, leveraging the advantage of the functionality of the new application

Secondly, design the interface scientifically Mobile Banking is a multi-utility service, providing customers with the ability to perform transactions from simple to complex When the number of features reaches dozens, customers may easily get confused in finding the required feature, especially in urgent situations Therefore, banks can categorize and organize features into groups such as payments, card services, telecommunications, etc., creating convenience in usage

Thirdly, publish articles, images, and tutorial videos guiding customers on using Mobile Banking whenever there are changes or additional features Mobile Banking is a service directly related to students' main source of income, so they may be cautious and hesitant when using it Creating vivid instructional materials helps students overcome technological barriers, encouraging them to explore and confidently learn about new transaction procedures to meet their usage needs

Fourthly and lastly, update features based on customer feedback when using Mobile Banking Users are the direct subjects interacting and conducting transactions on the application, so each individual will have various experiences and levels of satisfaction Therefore, through the feedback, banks can list out dissatisfaction points or difficulties that customers encounter during usage and try to fix them

The research results indicate that this is a negative factor influencing the decision to use Mobile Banking Students only choose to use the service when they perceive the risk at a low level Below are some recommendations to help service providers reduce barriers and promote students' use of Mobile Banking

Firstly, continuously invest in technology development, upgrading security measures for the application Mobile Banking is a technological product, so continuous updating and applying advanced technological achievements are crucial Banks should focus on researching modern techniques both domestically and internationally to select suitable technologies for developing product features

Secondly, increase customer awareness of the service's safety When using Mobile Banking, users often worry about issues such as personal information leakage or the risk of losing money for various reasons Therefore, banks need to use multimedia communication channels to disseminate and explain to customers about information security methods and how the application operates Official information from the bank will provide users with an accurate understanding of the service they are using

Thirdly, design an informative support page for users Most banks provide hotlines to assist customers when issues arise in using Mobile Banking; however, contacting these hotlines may incur telephone charges for customers An information page with frequently asked questions and corresponding handling instructions can help customers confidently resolve issues while saving on telecommunications costs

Fourthly, alert customers to fraudulent behaviors Banks need to provide early warnings to users about fraudulent methods through official information pages, messages and email to increase vigilance, avoiding cases where users inadvertently provide login passwords or click on unclear, unverified links Additionally, banks need to prepare response plans when detecting abnormalities to minimize potential losses for customers

Limitations of the study

Despite the author's efforts in investing time and focus on exploring the factors influencing students' decisions to use Mobile Banking in HCMC, the research still has some limitations due to time constraints and the author’s capability

Firstly, the representativeness of the research sample is not high Although the surveyed subjects were identified as normal students at universities in HCMC, the study was conducted based on data from only 294 samples Therefore, compared to the total number of students in this area, the sample size is not big enough to fully represent the behavior of the entire population

Secondly, the research data is not optimal The data collection process using Google Form due to limited time makes the accuracy of the data highly dependent on the respondents' seriousness and subjectivity in understanding the questions Thus, the author cannot completely control the authenticity of the respondents

Thirdly, the limited number of affecting factors The research results only indicate that the six proposed factors, including Perceived usefulness, Perceived ease of use, Perceived risk, Social influence, Transaction costs, and Brand image, explain only about 65.4% of the decision to use Mobile Banking This implies that the remaining 34.6% of the reasons come from factors that have not yet been identified.

Future research directions

In order to improve future studies with similar topics, the author recommends potential developments by expanding the sample size and diversifying the data collection subjects Subsequent research should consider extending the scope and increasing the survey samples, not limited to HCMC but also encompassing students from other regions, thereby enhancing the research's reliability Addionally, changing the data collection method from online to direct approaches could enhance objectivity and data accuracy by investing more in manpower and time

Lastly, future studies may explore alternative factors beyond the six factors in this study such as Habit, Technology impact, Effort Expectancy, Facilitating Condition, etc They should continue inheriting and refining results from various studies to incorporate new factors into the model and conduct validations with their respective subjects This approach could ensure the effectiveness and high significance of the new research

In this chapter, the author provides conclusions for the entire study based on the analysis results presented in Chapter 4 Additionally, suggestions for policy implications are proposed for each factor influencing the decision to use Mobile Banking, aiming to guide financial institutions in developing services that align with user preferences, thereby increasing satisfaction and attracting new customers The final chapter of the research also addresses the existing limitations in the project implementation, proposing improvement directions to enhance the quality of future studies

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Hello everyone, I am a senior student at Ho Chi Minh City University Of Banking Currently, I am working on my graduation thesis with the topic "Factors affecting the decision on using Mobile Banking of University students: An empirical study in Ho Chi Minh City"

In order to conduct my research, I need to gather opinions from individuals who are currently students at universities in Ho Chi Minh City and use Mobile Banking services I guarantee you that all of the information collected through this survey will be used privately for the making of my study and will be processed confidentiality Therefore, I am looking forward hoping that you can spare some of your time to help me completing this survey

I sincerely appreciate your time and effort to support me doing this thesis survey

5 Your income mainly comes from

Below are statements related to factors that may influence your Mobile Banking usage behavior You will conduct an assessment in the form of a questionnaire regarding your personal perspective on the given statements using a 5- point scale, specifically: (1) Strongly disagree; (2) Disagree; (3) Neutral; (4) Agree; (5) Strongly agree

1 Mobile Banking helps me deal with financial needs flexibly

2 Mobile Banking saves me time and transportation costs

3 I have accessed more banking services thanks to

4 Mobile Banking helps me manage my finances efficiently

5 I can quickly install Mobile Banking on my device

6 I find it easy to learn how to use Mobile Banking

7 The use of Mobile Banking are simple to perform

8 I can proficiently use Mobile Banking

9 I worry that my personal information may be leaked when using Mobile Banking

10 I fear that if I lose my phone with Mobile Banking,

I might lose my money as well

I am concerned about losing money in case of errors during transactions through Mobile

12 When using Mobile Banking, I fear the possibility of my account being stolen by hackers/thieves

13 I am encouraged by my family/friends/teachers/… to use Mobile Banking

I use Mobile Banking because of recommendations from my family/friends/teachers/…

15 I feel confident using Mobile Banking when I see everyone around me using it

16 Using Mobile Banking helps me save more costs compared to transactions at the counter

17 I am satisfied with the fees associated with Mobile

Banking because I receive corresponding benefits

18 I am well aware of the fees for using Mobile

19 The fees for Mobile Banking are reasonable

20 I use Mobile Banking because it is a product of a reputable bank

21 I am satisfied with the quality of the bank's services

22 I am attracted by the favorable policies of the bank

23 I receive decent support when transaction issues arise

24 I feel that Mobile Banking is an essential application

25 I will continue to use Mobile Banking services

26 I intend to explore more features in the future

27 I will recommend people around me to use Mobile

Thank you for your time and effort to respond on this survey

Frequency Percent Valid Percent Cumulative

Frequency Percent Valid Percent Cumulative

Frequency Percent Valid Percent Cumulative

Frequency Percent Valid Percent Cumulative

Frequency Percent Valid Percent Cumulative

APPENDIX 3: TESTING THE RELIABILITY USING CRONBACH'S ALPHA

Scale Variance if Item Deleted

Cronbach's Alpha if Item Deleted

* Perceived ease of use (PEU)

Scale Variance if Item Deleted

Cronbach's Alpha if Item Deleted

Scale Variance if Item Deleted

Cronbach's Alpha if Item Deleted

Scale Variance if Item Deleted

Cronbach's Alpha if Item Deleted

Scale Variance if Item Deleted

Cronbach's Alpha if Item Deleted

Scale Variance if Item Deleted

Cronbach's Alpha if Item Deleted

Scale Variance if Item Deleted

Cronbach's Alpha if Item Deleted

APPENDIX 4: EXPLORATORY FACTOR ANALYSIS RESULTS

Kaiser-Meyer-Olkin Measure of Sampling Adequacy .755

Component Initial Eigenvalues Extraction Sums of Squared

Extraction Method: Principal Component Analysis

Extraction Method: Principal Component Analysis

Rotation Method: Varimax with Kaiser Normalization a Rotation converged in 6 iterations

Kaiser-Meyer-Olkin Measure of Sampling Adequacy .789

Component Initial Eigenvalues Extraction Sums of Squared

Extraction Method: Principal Component Analysis

Extraction Method: Principal Component Analysis

Rotation Method: Varimax with Kaiser Normalization a Rotation converged in 6 iterations

Kaiser-Meyer-Olkin Measure of Sampling Adequacy .834

Component Initial Eigenvalues Extraction Sums of Squared

Extraction Method: Principal Component Analysis

Extraction Method: Principal Component Analysis

Rotation Method: Varimax with Kaiser Normalization a Rotation converged in 6 iterations

* EFA on dependent variable (SD)

Kaiser-Meyer-Olkin Measure of Sampling Adequacy .791

Component Initial Eigenvalues Extraction Sums of Squared Loadings

Extraction Method: Principal Component Analysis

Rotated Component Matrix a a Only one component was extracted The solution cannot be rotated

APPENDIX 5: PEARSON CORRELATION COEFFICIENTS RESULTS

SD PU PEU PR SI TC BI

** Correlation is significant at the 0.01 level (2-tailed)

Std Error of the Estimate

1 813 a 661 654 39715 1.956 a Predictors: (Constant), BI, PEU, PR, SI, TC, PU b Dependent Variable: SD

Model Sum of Squares df Mean Square F Sig

Total 133.576 293 a Dependent Variable: SD b Predictors: (Constant), BI, PEU, PR, SI, TC, PU

B Std Error Beta Tolerance VIF

Gender N Mean Std Deviation Std Error Mean

Levene's Test for Equality of Variances t-test for Equality of Means

95% Confidence Interval of the Difference Lower Upper

Come from N Mean Std Deviation Std Error Mean

SD Ho Chi Minh City 105 3.8024 67466 06584

Levene's Test for Equality of Variances t-test for Equality of Means

95% Confidence Interval of the Difference Lower Upper

Main income N Mean Std Deviation Std Error Mean

Levene's Test for Equality of Variances t-test for Equality of Means

95% Confidence Interval of the Difference Lower Upper

Upper Bound Freshman 32 3.7109 62253 11005 3.4865 3.9354 2.50 4.75 Sophomore 61 3.8730 64650 08278 3.7074 4.0385 2.75 5.00 Junior 105 3.8286 68493 06684 3.6960 3.9611 1.75 5.00

Test of Homogeneity of Variances

Levene Statistic df1 df2 Sig

Sum of Squares df Mean Square F Sig

Robust Tests of Equality of Means