Introduction
The rise of digital banking is an essential aspect of the fourth technological revolution, facilitating the growth of non-cash payment transactions both domestically and internationally The banking industry has undergone significant transformations due to technological advancements over the decades (BNP Group, 2013) The journey began with the introduction of ATMs, developed by John Shepherd-Brown in the 1960s, which allowed customers to withdraw cash without relying on bank tellers The creation of the personal identification number (PIN) in 1970 further enhanced security for ATM users By 1983, online banking emerged, paving the way for a broader range of banking services to be offered through internet platforms.
The influence of online banking in the United Kingdom is extending to other nations, including the US, France, and Asian countries like China and India Initially, online banking in the US faced skepticism as consumers were wary of its security Chemical Bank pioneered this service by allowing individuals and small businesses to manage electronic checkbook registers, view account balances, and facilitate transfers Following this, major banks such as Citibank, Chase Bank, and Manufacturer Hanover quickly adopted similar online banking services In France, online banking was introduced in 1988, marking the beginning of a digital banking revolution.
Minitel was an early videotex online service provided free of charge by the government, allowing users to access information via telephone lines In Japan, Sumitomo Bank introduced the first online banking service in 1997, marking a significant advancement in digital financial services just a year after Minitel's launch.
Utilizing the PEST model provides a comprehensive understanding of the current payment tool landscape in Vietnam This analytical framework examines external factors that significantly impact the research issue, offering clear insights into the political, economic, social, and technological dynamics within the country.
Political: Vietnamese government started to deploy non - cash payment in
In 2011, the State Bank of Vietnam issued Circular No 29/2011/TT-NHNN on January 29, which established guidelines for ensuring safety and confidentiality in internet banking services This was further reinforced by Decree No 101/2012/NĐ-CP, which outlined the SBV's responsibilities for managing non-cash payment activities, ensuring information security, and supervising the operational systems used in mobile banking.
In 2012, Vietnam initiated a series of decrees and resolutions aimed at promoting non-cash payment projects and advancing fintech technology A significant milestone was reached in 2020 with the issuance of Decision No 711/QĐ-NHNN by the State Bank of Vietnam, which focused on fostering the growth of digital technology enterprises to enhance the digital banking landscape The Vietnamese government is actively supporting the development of digital banking across both enterprises and the banking sector, as evidenced by the issuance of Decision No 1238/QĐ-NHNN.
The Bank of Vietnam plays a crucial role in fostering a supportive environment for the banking sector, ensuring its growth and alignment with the advancements of the Fourth Industrial Revolution.
Economic: In general, Vietnam has relatively stable economic growth The
Vietnam's GDP experienced a notable increase, rising from 6.81% to 7.017% over three years The global economy faced significant challenges due to the Covid-19 pandemic; however, the Vietnamese government effectively managed the crisis, allowing the economy to stabilize and transition to a new normal Although GDP growth fell by 2.91% in 2020, it remained relatively stable The implementation of the EU-Vietnam Free Trade Agreement on August 1, 2020, presents a promising opportunity for Vietnam to attract leading technology companies for investment in the region.
Figure 1.1: Grow rate of GDP in Vietnam from 2017 to Q1/2021
Source: Vietnam GSO, World Bank
In 2020, the General Statistics Office reported that Vietnam's population reached approximately 97.58 million, with around 53.59 million individuals in the workforce The country's youthful demographic facilitates the adoption of new technologies and trends in daily life, making Vietnam well-positioned to embrace innovation.
Vietnamese culture strongly favors cash transactions, with many individuals relying on this method to ensure successful payments Ms Le Thi Thuy Sen, Director of the Communications Department of the State Bank of Vietnam, highlighted that altering user habits poses significant challenges to the advancement of non-cash payments To facilitate this change, it is essential to shift users' perceptions and behaviors regarding payment methods Although nearly 40% of the population holds a bank account, approximately 80% of daily expenses are still settled in cash, with 98% of purchases under VND 100,000 made in cash and nearly 85% of ATM transactions being cash withdrawals However, thanks to the implementation of non-cash payment policies, the percentage of ATM cash withdrawal transactions has gradually declined, dropping from 62% in 2018 to 42% in 2019.
2019 It can be seen that the consumption habits of people have begun to change when gradually shifting from cash transactions to using non-cash payment methods (Quang
The Covid-19 pandemic significantly accelerated the shift towards non-cash payments, driven by government directives aimed at social distancing In response to the increased demand for seamless payment solutions, over 50% of banks reduced online transaction fees from 7,000 VND to 0 VND As a result, 65% of payment transactions benefited from fee exemptions or reductions, with 70% of payments under 2 million VND being conducted online (Quang Tuan, 2020).
People's consumption habits significantly shift from cash to non-cash payments, a trend that is expected to accelerate with the implementation of digital banking policies in the upcoming year.
Technology plays a crucial role in developing innovative products and services, offering numerous advantages to consumers It enhances the quality of life by enabling quicker task completion and ensuring high levels of authenticity Additionally, advancements in technology have opened doors for new and startup tech companies in Vietnam, fostering a vibrant entrepreneurial landscape.
Vietnam's non-cash payment system is experiencing significant growth, driven by substantial investments in infrastructure and technology to meet both domestic and international payment demands Most banks and payment service providers utilize electronic money transfers via SWIFT for foreign currency transactions Additionally, advancements in mobile and internet banking have led to the adoption of innovative features such as mPOS contactless payments and QR codes integrated into electronic applications The implementation of electronic Know Your Customer (eKYC) processes has further enhanced security and streamlined customer registration Overall, the integration of technology in banking operations is positively impacting both management efficiency and customer experience.
In conclusion, Vietnam's prudent government policies, robust GDP growth, and youthful population present significant opportunities for development in the context of the Fourth Industrial Revolution Notably, the banking sector stands at a pivotal moment, poised to expand modern digital services, particularly digital banking, which is set to become a key offering among banks in the near future Leveraging Vietnam's strengths identified through PEST analysis, this topic is particularly relevant.
"Factors affecting the adoption to use digital banking of Vietnam" to clarify the factors affecting the customers’ adoption of digital banking services in Vietnam.
Literature review
Literature review about digital banking
In the era of the 4.0 technology revolution, technology permeates all aspects of life, including everyday items like machines, home appliances, and smartphones The banking industry has also embraced this shift, with the term "digital banking" gaining significant traction in Vietnam since 2018.
Carmen Cuesta and partners (2015) highlighted that while they did not provide a precise definition of digital banking, they emphasized its focus on the supply, distribution, and sales of financial products through digital channels, particularly in retail banking Their research identified a transformative process in digital banking, which is influenced by various institutional factors and can be categorized into three key phases: responding to competition, adopting technology, and strategic positioning Darryl Proctor (2019) defined digital banking as the digitization of traditional banking activities that were previously accessible only in physical branches, outlining essential features such as money deposits, withdrawals, transfers, and account management Additionally, a report by Gartner (2019) further explored the parameters defining digital banking.
“ongoing financial management” and “digital aided by people” meaning that there is no need for human intervention in the use of translation digital banking.
In Vietnam, digital banking plays a crucial role in enhancing customer loyalty, making it essential for banks to adopt these services (Nguyen Thi Oanh, 2020) Unlike traditional banking, digital banking encompasses a wider range of services, including online banking, internet banking, virtual banking, and electronic banking It signifies the comprehensive integration of digital technology into banking services, business operations, and customer interactions Other definitions, such as wire transfers and payment services, only address specific aspects of digitalization without encompassing the full spectrum of banking operations (Pham Tien Dat and Luu Anh Nguyet).
Digital banking refers to the transformation of traditional banking services into a digital format, allowing users to perform all typical banking activities through a digital banking application However, merely defining digital banking as the digitalization of banking services does not fully capture its essence.
Many people confuse digital banking with electronic banking, although they share similarities in facilitating online transactions Electronic banking services, such as internet and mobile banking, focus on features like electronic money transfers, online savings deposits, and account inquiries Trust in banking systems drives the adoption of these services A survey of over 300 customers at major retail banks in South Africa revealed that customer confidence in e-banking is a crucial factor influencing the use of electronic banking services on the internet and mobile platforms.
Digital banking represents a comprehensive modernization of banking services, encompassing all core functions such as customer identification and account opening through complete digitalization While e-banking is a subset of digital banking, serving as a complementary service to traditional banking, the 2018 survey by the State Bank revealed that 94% of joint stock commercial banks are actively pursuing digital transformation through technologies like artificial intelligence and digital branches Research indicates that a new acceptance model for electronic banking services in Vietnam accounts for 57% of the variations in e-banking usage It is essential to clarify that digital banking is often misunderstood as merely a component of e-banking, with some believing that services like TP Bank's live banking fall under digital banking In line with Gartner's perspective, true digital banking products are those that are digitally enhanced by human interaction.
Literature review about factors affecting thecustomers’adoption
The objective of my graduate thesis is to analyze the factors influencing customer adoption of digital banking in Vietnam Research by Polatoglu indicates that early adopters and frequent users of internet banking report higher satisfaction levels compared to other customers Most studies utilize models related to customer adoption, particularly the Technology Acceptance Model (TAM), which is grounded in the Theory of Reasoned Action (TRA) as defined by Davis and colleagues The TAM model aims to trace the impact of external factors on internal beliefs, attitudes, and intentions, identifying key variables that determine perceived and affective acceptance of technology The model highlights two main external factors: perceived ease of use and perceived usefulness, where perceived ease of use significantly influences perceived usefulness These factors also shape users' attitudes towards digital banking, ultimately affecting their actual usage.
User motivation to adopt new technology, particularly in digital banking, is influenced by three key constructs: perceived ease of use (PE), perceived usefulness (PU), and intention to use The Technology Acceptance Model (TAM) also incorporates external factors such as perceived risk and trust, as highlighted in research by Nuno Fortes and Paulo Rita This study will examine the critical factors affecting customer acceptance of digital banking services, focusing on perceived ease of use, perceived usefulness, perceived risk, trust, attitude towards use, and intention to use.
Figure 1.3: Research model framework for the customers adoption to use digital banking of
Fred D Davis (1989) defines perceived usefulness as the extent to which an individual believes that utilizing a specific system can enhance their work performance This concept emphasizes the practical benefits of a system, which can significantly improve work efficiency I concur with this definition, as effective systems not only increase productivity but also save time and reduce costs compared to traditional banking methods For instance, digital banking services allow users to manage large volumes of transactions simultaneously, such as processing employee salaries or making bulk payments for goods.
In addition, with 6 assumptions made, surveying more than 200 customers and applying linear regression model combined with TAM model, the end result gives the
"Perceived usefulness" factor which is most influential Banks should formulate
11 this service can be accessed account query like any previous transactions These features are all integrated by banks on their e-banking products to help customers use them more conveniently.
The term "ease of use" refers to the simplicity and effortlessness with which a user can interact with a system, as defined by the dictionary Davis emphasizes that it reflects the belief that using a system requires minimal effort, suggesting that applications perceived as easier to use are more likely to gain user acceptance Fortes supports this notion by stating that the process must be easy to understand I concur with both perspectives, as they accurately capture the essence of "ease of use," which encompasses effortless interaction, straightforward manipulation, and clear understanding This concept is particularly evident in the design of banking applications, where each feature is represented by intuitive images, enhancing user experience.
The use of e-banking services is very convenient and comfortable for users, but sometimes it also makes them think about risk perception when using online services.
Fortes highlights that risk perception significantly hinders the growth of the e-consumer market, posing a challenge to traditional services While e-banking offers convenience and comfort, it often raises concerns about potential risks associated with online transactions.
Fortes highlights that risk perception significantly hinders the growth of the consumer electronics market, especially in competition with traditional services E-commerce encompasses four levels of risk perception: perceived technological risk, vendor risk, consumer risk, and product risk I concur with two of these levels: perceived technological risk, which includes potential losses from internet and technology infrastructure issues like security vulnerabilities, and consumer risk, which involves the unauthorized use of personal information Therefore, addressing perceived risks associated with digital banking services is crucial for safeguarding customers.
Customers' confidence in online products and services is crucial, yet challenges arise from the inability to physically interact with these offerings Research by McKnight et al highlights that a lack of trust contributes to consumer hesitancy in engaging in online behaviors, such as sharing personal information and making purchases Consequently, users are more likely to utilize online services when they have confidence in the applications they choose In the banking sector, trust in digital banking services is essential, as customers rely on the credibility of their banks, which is influenced by factors like brand reputation and service efficiency.
(5) Attitude towards use and Intention to use
Customer attitudes towards services significantly influence their usage decisions Adoption, defined as the acceptance and initiation of new services, plays a crucial role in this context Ana Sousa emphasizes that a consumer's perception of a product, particularly its alignment with their self-image, affects purchasing behavior For instance, digital banking offers convenience, allowing customers to perform transactions anytime and anywhere, leading to a positive attitude towards its use Research indicates that favorable service attitudes and perceived usefulness enhance customers' intentions to utilize digital banking In line with Fishbein and Ajzen's (1975) theory, intention is a key predictor of user behavior, serving as a precursor to action Jenifer et al (2014) support this notion, suggesting that intentions are critical in shaping individual behaviors Ultimately, intention is a decisive factor that influences whether users embrace new services.
In conclusion, the five key factors—perceived ease of use, perceived usefulness, perceived risk, perceived trust, and attitude towards use—play a crucial role in understanding user behavior and intentions in my research topic.
Gap, research objectives and research questions
Gap of the previous researches
In the world, it had the study about reinforces confidence in the adoption and use of existing e-banking services for customers The research of Daniel Kofi Madu
A 2014 study highlighted that customer confidence is a crucial factor in the adoption of electronic banking services in South Africa However, it focused solely on electronic banking, neglecting the broader scope of digital banking Additionally, the research conducted by Daniel was limited to South Africa and did not explore similar trends in other countries, such as Vietnam In contrast, another study investigated the factors influencing the adoption of digital banking specifically within Omani retail banks.
Research in Vietnam has primarily focused on the acceptance and intention to use e-banking services, with findings indicating that existing models, such as those proposed by Nguyen Duy Thanh and colleagues (2011), do not significantly outperform others, resulting in a limited ability to gauge customer acceptance Furthermore, studies have not generalized the application of digital banking usage Nguyen Thi Oanh's research highlights the importance of increasing customer awareness about the benefits of digital banking through effective advertising and counseling However, her study primarily addresses customer perceptions of digital banking's usefulness and its impact on usage intention, suggesting that enhancing awareness is crucial in the current context rather than merely accepting digital banking in everyday life (Nguyen Thi Oanh, 2020).
Research on digital banking services is more prevalent internationally than in Vietnam, where studies primarily focus on the acceptance of mobile banking rather than the broader context of digital banking Most local research emphasizes two key factors: ease of use and usefulness, while neglecting critical elements such as user trust and risk perception.
Researcher Oanh Nguyen Thi highlights the distinction between the intention to use digital banking and the actual acceptance of it Most studies in this field rely on the Technology Acceptance Model (TAM), which effectively assesses consumer behavior This model has been selected for my research to explore the factors influencing digital banking adoption.
Given the existing gaps in previous research on digital banking, I have chosen to explore the topic "Factors Affecting the Adoption of Digital Banking in Vietnam." This study aims to uncover new insights that earlier studies overlooked and to contribute positively to the understanding and utilization of digital banking in Vietnam.
Research objectives
This article aims to assist banks in identifying their customers' needs and providing tailored digital banking solutions In the context of the 4.0 technology revolution, it explores how digital banking impacts customer experiences and perceptions By understanding these factors, banks can enhance customer satisfaction and influence consumption behaviors Additionally, the research seeks to clarify common misconceptions about digital banking, offering customers a comprehensive understanding of its benefits and functionalities.
Research questions
This research paper addresses the critical questions of whether customers in Vietnam will embrace digital banking services and what enhancements are necessary to boost their confidence compared to traditional banking options By exploring these key inquiries, bank leaders can gain valuable insights into customer needs and emerging trends, enabling them to devise effective strategies that align with the digital banking landscape while satisfying customer demands.
In the opening chapter, the author sets the stage for Vietnam's position in the fourth technological era through a PEST analysis and reviews existing literature on digital banking, incorporating both Vietnamese and international studies The author identifies key theoretical factors influencing digital banking adoption, including perceived ease of use, perceived usefulness, perceived risk, trust, attitude towards use, and intention to use, which form the basis of the research framework Highlighting gaps in previous research, the study aims to enhance banks' understanding of Hanoi citizens' needs and acceptance of digital banking services The research poses two critical questions that will guide banks in developing customer-centric products Ultimately, the chosen topic, "Factors Affecting the Adoption of Digital Banking in Vietnam," is justified by the research objectives and identified gaps.
Research method
Research approach and design
This study employs quantitative research, distinguishing it from qualitative research, which impacts the nature of the data collected While qualitative research emphasizes in-depth information, quantitative research allows for a broader analysis of various perspectives Qualitative data is valuable for exploring, describing, and explaining relationships between existing or new data items.
In 2009, it was noted that qualitative data offers significant advantages in measurement The reliability of this data allows researchers to effectively generate insights when employing qualitative methods (Bryman, 2012) Consequently, utilizing the Technology Acceptance Model (TAM) to identify factors influencing the adoption of digital banking makes qualitative data particularly appropriate for this study.
Data source
Data can be categorized into two types: primary data and secondary data Secondary data, gathered by other researchers for similar topics, can significantly reduce the time and costs associated with data collection (Bryman, 2012) However, it is important to note that secondary data may become outdated and may not always reflect current conditions, potentially affecting its accuracy.
Most researchers rely on qualitative primary data gathered through methods such as surveys, questionnaires, face-to-face interviews, and observations (Bryman, 2012; Saunders et al., 2009) However, preparing for the study and collecting this data can be time-consuming, and there is a risk of low response rates, which can negatively impact the quality of the data and the conclusions drawn (Bryman, 2012; Saunders et al., 2009).
PE1 Conditions and terms when using digital banking services are clear.
This research primarily relies on primary data sources, supplemented by secondary sources, due to the limited availability of relevant articles on the topic in Vietnam Most existing literature is found in foreign journals, making the use of primary data a reasonable approach Incorporating secondary data will further clarify and strengthen the arguments presented in this study.
Data collection method
The article discusses the independent variance related to adoption and usage, concluding with an assessment section that summarizes the identified problems and customer insights It highlights the impact of questionnaire questions on the model's factors, referencing previous research The survey process lasted approximately two weeks, with an additional month dedicated to the preparation and completion of the questionnaire.
Form of questionnaire
PE2 Product description of digital banking service is easy to see and understand.
^PE3 Using guidelines are easy to read and understand.
PU1 Using digital banking services to save large expense.
PU2 Using digital banking services to save transaction time.
PU3 Using digital banking services to easily handle a large transaction volume at a time.
PU4 I use digital banking services anytime, anywhere.
Davis (1993) 5 - point Likert scale PU5 I easily re-query my finances as needed.
PR1 Personal information security is not high.
PR2 Updating my card number on the digital banking services application is easy to get information stolen.
^^PR3 Digital banking service often has system error when I am making transaction.
PR4 Digital banking service does not guarantee the successful transfer and receipt of money.
T1 I choose to use digital banking services in here because of the bank’s brand name.
~ T 2 Using digital banking services helps me control my finances better.
^T3 The digital banking services at this bank which I choose is very reliable.
Attitude towards use and Intention to use
ATT1 I accept to use digital banking services in the long term.
ATT2 I will introduce to everyone to use digital banking services.
INT I will use digital banking services instead of traditional banking services.
Actual use I accept to use digital banking services in Vietnam
To draw conclusions and results of the dependent variable
Actual use How long do you use digital banking services?
Chang (2004) Multiple choice questions Actual use How much digital banking services do you use? And for what?
To establish the link between the research and the respondent (doing the basis for the
Demographic Please state you gender To establish the link between the research
A closed - ended question,personal and the respondent (doing the basis for the situation) question about the respondents’ gender
Demographic Please state your age (divide the age in different generations)
To establish the link between the research and the respondent (doing the basis for the
A closed - ended question, personal question about the respondents’ age.
Demographic Please state your occupation
(classified by job title levels by age group above, from student to retired)
To establish the link between the research and the respondent (doing the basis for the
A closed - ended question, personal question about the respondents’ occupation.
Demographic Please state the major in which you are working (classified by field in society).
To establish the link between the research and the respondent (doing the basis for the
A closed - ended question, personal question about the respondents’ majors.
Demographic Please state your current income level (based on the average income of people in Hanoi)
To establish the link between the research and the respondent (doing the basis for the
A closed - ended question, personal question about the respondents’ income.
Source: Student summarized and researched
Sample data
Descriptive data
The research received 181 responses, which, while lower than anticipated, remained within an acceptable range After filtering out invalid votes and addressing the mutation phenomenon, 150 valid responses were retained These responses met the survey requirements and were evenly distributed The majority of participants were female, predominantly from the Banking and Finance sector, with experience in Digital Banking ranging from 1 to over 3 years.
Figure 2.1 illustrates the gender distribution among respondents, revealing that 74% are female, while males make up 26% Although there are a few respondents identifying as other genders, their numbers are negligible This disparity in gender representation can be attributed to the higher shopping frequency among women, resulting in their greater experience with checkout transaction features compared to men.
Figure 2.2: The respondents about age group
The pie chart illustrates the distribution of respondents by age group, revealing a fairly even representation among those aged 18 to under 22, 22 to under 30, and 30 to under 50, while individuals over 50 constitute a minor percentage Notably, the 30 to under 50 age group, representing Generation X, comprises 46% of the total respondents, indicating a significant presence of educated individuals with stable employment.
The age groups of 18 to under 22 and 22 to under 30 represent significant portions of digital banking users, accounting for 22% and 28% respectively The 22 to under 30 age group, known as millennials, includes individuals born in the late 1980s to early 1990s, who are increasingly influenced by technology despite facing limitations Meanwhile, the 18 to under 22 age group, representing Generation Z, has early access to technology and smart devices but also experiences pressure from material and prestige competition; this group comprises 22% of digital banking users and includes students, unemployed individuals, and job seekers In contrast, those over 50 years old, making up just 4% of respondents, tend to be retired, have less exposure to technology, and prioritize safety in their banking choices.
2.2.1.3 Respondents’ occupation and fields of operation
Figure 2.3: Respondents’ occupation of research
The research categorizes participants into four occupational groups: students/unemployed/job seekers, occupants, freelancers/self-employed/business owners, and retirees Among these, occupants represent the largest segment, comprising approximately 69% of the total with about 103 individuals, attributed to their higher proficiency in utilizing digital banking compared to the other groups.
Figure 2.4: Respondents’ fields of operation
In a survey of 100 respondents, 67% are engaged in the Banking and Finance sector, highlighting a strong presence in this field Following this, 12% work in various business sectors, while smaller percentages are represented in sociology (3%), information technology (7%), and other areas such as education, accounting, auditing, and construction (11%) The dominance of the Banking and Finance sector can be attributed to their early adoption of digital banking, making them well-versed in this area compared to other professions This expertise and experience in digital banking are key reasons for the preference towards individuals in the Banking and Finance sector over other fields.
Figure 2.5: Distribution of respondents’ income Unit: VND million
According to the Vietnamese General Statistics Office, the average salary for new employees is approximately 4.2 million VND To account for additional income sources, salary benchmarks of 5 million, 20 million, and 50 million VND are used, corresponding to entry-level positions, regular employees, and department heads, respectively The survey results indicate that most respondents earn between 5 million and under 20 million VND.
90 persons, relatively 60% of total This can understand that the people in this income immediately with 33 people, equivalent to 22% These can be students working part
The workforce is predominantly composed of individuals earning low salaries, with a significant portion making between 20 million to under 50 million VND, accounting for 13% of workers Additionally, only 5% of employees earn above 50 million VND, highlighting the challenges faced by those new to the job market or in lower-paying positions.
2.2.1.5 Actual usage of digital banking services time
Figure 2.6: Respondents’ time to use digital banking services Unit: Year
Time to use digital banking services
The figure 2.6 shows that the distribution of users using digital banking services.
A significant majority of respondents, 78% or 117 out of 150, have been utilizing digital banking services for over three years In contrast, 19% of users, which equals 29 individuals, have engaged with these services for one to three years Only 3% of the respondents, representing three users, have been using digital banking services for less than one year.
2.2.1.6 Number of digital banking services
Figure 2.7: Respondent's number of digital banking services
Respondent's number of digital banking services
In Vietnam, digital banking services are widely utilized, with options including Internet banking, mobile banking, ATMs, and cash deposit machines (CDMs) Among a surveyed group of 150 individuals, over 90% reported using Internet Banking, Mobile Banking, and ATMs, with ATMs being the most popular, accessed by 139 users, representing 93% of the total Additionally, e-wallets linked to banks, such as Viettel Pay and VinID, are used by 57% of respondents Meanwhile, innovative digital banking solutions like Livebank offer video banking services.
TPBank has a lower user rate compared to other banks, with approximately 30% and 13% respectively The current number of Cash Deposit Machines (CDMs) in Hanoi is limited, with only a few banks having deployed them; ACB has one unit, VPBank has 27 units, and Agribank is also involved.
(4 units), etc and deployed digital banking services via video so that it cannot have as many customers as other services.
Data analysis
This article analyzes research data using multivariate methods, adhering to the criteria of a satisfactory correlation coefficient and a Cronbach's Alpha value of 0.6 or higher, as established by Hoang Trong and Chu Nguyen Mong Ngoc (2008) However, during the pilot test, issues arose with variable pooling and insufficient observed variables To address these challenges, additional factors were utilized for validation.
Exploratory factor analysis (EFA) is employed to identify meaningful groupings of observed variables through the Pattern Matrix This Pattern Matrix is then utilized in confirmatory factor analysis (CFA) Structural equation modeling (SEM) is applied to examine the interactions among factors influencing service use attitudes and usage intent, with a significance level of 5% deemed acceptable Additionally, further tests are conducted on the pooled variable group during the SEM analysis to enhance the reliability of the findings.
Research Hypotheses
Before formulating hypotheses, it's essential to address the gaps in my research Fishbein and colleagues highlighted that "Behavioral intention" reflects an individual's capacity to perform a behavior, serving as a specific type of belief that influences attitudes toward that behavior Consequently, if customers intend to use a service, this intention is likely to manifest in their usage frequency and trust in the service My research incorporates "perceived trust," which, along with perceived usefulness, significantly affects long-term acceptance of internet services (Jian Mou et al., 2015) Additionally, trust can enhance the intention to use a product through perceived ease of use and usefulness (Yi-Hsuan La et al., 2013) Due to limitations in developing a questionnaire, I will merge the two intermediate variables, "attitude towards use" and "intention to use," into a single construct.
Basing on the theoretical concepts, the previous researches and the explanation of five variance which the study have outlined above, in this section I will build
With this variance, perceived ease of use effect on perceived usefulness The hypothesis is proposed as follow:
H1: Perceived ease of use has positive impact on the perceived usefulness when using digital banking service.
To evaluate the effectiveness and utility of a product or service for users, understanding perceived risk is crucial This insight leads to the formulation of a hypothesis regarding its impact on user satisfaction and decision-making.
H2: Perceived risk has a negative impact on perceived usefulness.
Because perceived usefulness and trust are both impact on accept use internet service for long time (Jian Mou et al, 2015) With this variance, the hypothesis is proposed as follow:
H3: Perceived usefulness has a positive impact on trust when using digital banking service.
With this variance, the hypothesis is proposed as follow:
H4: Trust has a positive impact on the attitude towards use and intention to use.
(5) Attitude towards use and intention to use
H5: Attitude towards use and intention to use have a positive impact on the
H1 (PE) Perceived ease of use has positive impact on the perceived usefulness when using digital banking service
H2 (PR) Perceived risk has a negative impact on perceived usefulness.
H3 (PU) Perceived usefulness has a positive impact on trust (+) Accepted
Figure 2.8: Research model and hypotheses
The table below outlines the anticipated effects of the hypotheses presented in this article This forecast is essential as it allows the study to verify the accuracy of the predictions after evaluating the results.
Table 2.2: Forecast effect of research hypotheses when using digital banking service
H4 (T) Trust has a positive impact on the attitude towards use and intention to use
(ATTINT) Attitude towards use and intention to use have a positive impact on the acceptance of use.
In the opening of chapter two, the author discusses various research methods and data collection sources, drawing on previous studies The chapter includes a detailed questionnaire format and outlines the data sample utilized in the research With a total of 150 collected samples, the author introduces five research hypotheses that correspond to the proposed model.
Research results
Reliability
To evaluate a scientific theory, it is essential to assess the reliability of the scale and analyze its significance This is achieved by employing Cronbach Alpha analysis to examine a collection of variables that encompass various factors representing the primary variable In this study, the pooled variable comprises the variable set of Attitude towards Use and Intention to Use.
This research has 5 independent variances, so that we renamed these variances: +) Perceived Ease of use - PE
+) Attitude towards use and Intention to use - ATTINT
Alpha Number of items Item remove
Kaiser-Meyer-Olkin Measure of Sampling
Table 3.1: Overview of Cronbach Alpha
Source: Results of IBM SPSS Statistics
According to Nunnally (1994), measurement variables should have a corrected item-total correlation of at least 0.3, and the Cronbach’s Alpha if an item is deleted should be less than or equal to the overall Cronbach’s Alpha Hoang Trong and colleagues suggest that a reliability score between 0.8 and 1 indicates excellent reliability, while a score of 0.6 and above is considered acceptable.
The analysis of five variables demonstrates that they all meet the established conditions, with Cronbach's Alpha reliability scores exceeding 0.8 Notably, the PR variable has the highest reliability at 0.897, while the T variable, with a score of 0.816, remains above the acceptable threshold Additionally, each observed variable exhibits a Corrected Item-Total Correlation of at least 0.3, and the Cronbach's Alpha if item deleted is less than or equal to the overall Cronbach's Alpha, indicating that no observed variables from the five groups need to be excluded Consequently, all variables are validated and proceed to the next phase of model analysis.
Results of exploratory factor analysis (EFA)
Table 3.2: KMO and Bartlett’s Test
Extraction Sums of Squared Loadings
Rotation Sums of Squared Loadings a Total
Source: Results of IBM SPSS Statistics
The KMO and Bartlett’s Test table is essential for assessing the suitability of a model for Exploratory Factor Analysis (EFA) The Kaiser-Meyer-Olkin (KMO) index indicates the appropriateness of factor analysis, with values ranging from 0.5 to 1 deemed suitable; a KMO value of 0.820 suggests that the factor analysis in this model is appropriate Additionally, Bartlett’s Test of Sphericity assesses the correlation between observed variables, with a significance level below 0.05 indicating a strong correlation; in this case, the significance index of 0.00 confirms that the conditions for conducting EFA are met.
In Exploratory Factor Analysis (EFA), the eigenvalue index is crucial for determining the number of factors to retain, with factors having an eigenvalue of 1 or greater being included in the model The accompanying table illustrates the total variance explained by the model, where factors are categorized into groups corresponding to the variance count A suitable model is characterized by a total variance explained of 50% or more, indicating the proportion of variance accounted for by the extracted factors and the percentage of variance from the observed variables that is not captured.
Source: Results of IBM SPSS Statistics
Extraction Method: Principal Axis Factoring 6
Rotation Method: Promax with Kaiser Normalization. a Rotation converged in 7 iterations. _
With 18 factors, it is possible to load only 5 group factors with Eigenvalues greater than 1 and which is condensed into 5 groups with a total extracted variance of 65.570% ≥ 50% (suitable condition) These 5 groups are equivalent to the 5 variables being watched.
In exploratory factor analysis (EFA), a model is deemed suitable when the total variance explained exceeds 50% The pattern matrix, generated using the promax rotation method, displays coefficients sorted by size, with small coefficients below 0.5 suppressed This matrix reveals five group factors, each with factor loadings greater than 0.5 If any factor loadings fall below 0.5, those variances are excluded, and the analysis is rerun to refine the results.
Source: Results of IBM SPSS Statistics
The pattern matrix effectively illustrates distinct groups of factors, with each observed variable clearly aligned with its corresponding variable group, ensuring no overlap Notably, the factor loadings are robust, exceeding 0.5, particularly within the PR group, which boasts the highest loadings ranging from 0.738 to 0.937 Following this, the PU, PE, T, and ATTINT groups display significant loadings as well Although the ATT1 factor has the lowest loading at 0.594, it remains above the 0.5 threshold, confirming its inclusion is justified Overall, the pattern matrix reveals five distinct groups of factors, corresponding to the variables PU, PR, PE, T, and ATTINT.
Result of Confirmatory factor analysis (CFA)
According to the Hair and partners (2010), the result of CFA can be acceptable which
+) CMIN/df ≤ 2: positive, CMIN/df ≤ 5: acceptable
+) CFI ≥ 0.9: positive, CFI ≥ 0.95: perfectly positive, CFI ≥ 0.8: acceptable +) GFI ≥ 0.9: positive, GFI ≥ 0.95: perfectly positive
Due to the limited sample size in the study, achieving a GFI index threshold of 0.9 may be challenging However, as noted by Baumgartner and Homburg (1995), a GFI index of at least 0.8 is considered acceptable.
The result of CFA showed that: CMIN/df = 1.920 < 5: acceptable, CFI = 0.925
In confirmatory factor analysis, ensuring convergent validity, discriminant validity, and reliability is crucial Without guaranteed validity and reliability, the analysis results may be biased, leading to numbers that do not accurately reflect the significance of the data and its real-world implications.
According to Hair and partners (2010), the measure of Reliability test and Validity test is defined that:
+) Standardized Loading Estimates ≥ 0.5: positive, perfectly positive is ≥ 0.7 +) Composite Reliability (CR) ≥ 0.7: positive
+) Average Variance Extracted (AVE) ≥ 0.5: positive
Discriminant test +) Maximum Shared Variance (MSV) < Average Variance Extracted (AVE) +) Square Root of AVE (SQRTAVE) > Inter - Construct Correlations
In Confirmatory Factor Analysis (CFA), the Standardized Loading Estimates serve as a crucial indicator for evaluating model fit The Standardized Regression Weights table presents these estimates, where values above 0.5 are considered significant and retained in the analysis Conversely, any observations with Standardized Loading Estimates below 0.5 should be excluded, necessitating a re-evaluation of the CFA model.
Source: Results of IBM AMOS Graphics
Table 3.5 indicates that all observed variances demonstrate significant reliability, as the Standardized Loading Estimates exceed 0.5 The reliability values range from 0.680 to 0.944, confirming that all observed variances are significant, with increases of 0.2 to 0.4 units Consequently, no observational variables were removed, and there is no need to rerun the model.
Source: Results of IBM AMOS Graphics
Table 3.6 displays the results of Composite Reliability, indicating that all variances exceed the acceptable threshold of 0.7, ranging from 0.792 to 0.897 This confirms that the conditions for Composite Reliability are met.
The validity is determined through the Convergent test and Discriminant test. About Convergent test, the Average Variance Extracted (AVE) represents the guaranteed convergence if it is greater than 0.5.
Source: Results of IBM AMOS Graphics
The research findings indicate that the Average Variance Extracted (AVE) for all factors, with factor loadings exceeding 0.5, surpasses the 50% threshold, meeting the standard criteria Notably, the AVE index for PR is the highest at approximately 0.692, or 69.2%, while the AVE for T is relatively lower at about 0.591 Overall, the AVE results align with the acceptable standards, confirming the model's convergence.
The discriminant test involves two key factors: Maximum Shared Variance (MSV) and the Square Root of Average Variance Extracted (SQRTAVE) For the test to confirm the distinctness of factors, the condition that MSV must be less than AVE, along with the SQRTAVE of inter-construct correlations, must be satisfied.
Table 3.8: Comparation of Maximum Shared Variance and Average Variance Extracted
Source: Results of IBM AMOS Graphics
Table 3.8 reveals that all independent variables exhibit a Maximum Shared Variance (MSV) lower than the Average Variance Extracted (AVE), with AVE values exceeding 50% Additionally, the Square Root of AVE (SQRTAVE) for each factor surpasses its Inter-Construct Correlations, confirming that the scales meet the discriminant validity criteria To accurately compute SQRTAVE, it is essential to adjust the last value in the Inter-Construct Correlations table to 1000, ensuring a clear comparison between SQRTAVE and Inter-Construct Correlations.
Table 3.9: Comparation of Square Root of AVE and Inter - Construct Correlations
Source: Results of IBM AMOS Graphics
Table 3.9 presents the findings of the Square Root of Average Variance Extracted (SQRTAVE) and Inter-Construct Correlations The results indicate that SQRTAVE significantly exceeds the Inter-Construct Correlations, confirming the scales' discriminant validity Specifically, the Average Variance Extracted exceeds 50%, the Maximum Shared Variance (MSV) is lower than the AVE, and the SQRTAVE for each factor surpasses its Inter-Construct Correlations, demonstrating that the measurement meets the criteria for the discriminant validity test.
Result of Structural Equation Modeling
Figure 3.1: Research of Structural Equation Modeling framework
Source: Student researched, IPM AMOS Graphics
The application of Structural Equation Modeling yields results akin to those derived from the CFA model, demonstrating a CMIN/df of 1.884, which is below the threshold of 5, a CFI of 0.925, exceeding the 0.9 benchmark, and a GFI of 0.845, surpassing the 0.8 standard (Appendix 3) These findings enable the identification of factors that positively influence attitudes towards and intentions to utilize digital banking services.
After assessing the model's reliability and validity at a 5% significance level, the p-value is compared to 0.05 If the p-value for any factor exceeds 0.05, the corresponding hypothesis will not be accepted or further tested.
Table 3.10: Regression Weights of SEM
Source: Results of IBM AMOS Graphics
The analysis reveals that the p-value for the relationship between PR and PU is 0.303, significantly exceeding the 0.05 threshold, which indicates insufficient statistical evidence to reject the hypothesis In contrast, all other variables demonstrate a significance level of 0.000, confirming their meaningful relationships Consequently, the findings suggest that PE positively impacts PU, PU positively influences T, and T has a significant relationship with ATTINT.
Table 3.11: Standardized Regression Weights of SEM
Source: Results of IBM AMOS Graphics
The research will analyze the impact of independent variables on dependent variables by utilizing the estimated regression coefficients from the Standardized Regression Weights table The independent variables, which include T, PU, PE, and PR, will be ranked in descending order of their influence on the dependent variables.
Table 3.12: Squared Multiple Correlations of SEM
Source: Results of IBM AMOS Graphics
The Squared Multiple Correlations table reveals the R-squared value, indicating the influence of independent variables on the dependent variable Specifically, the R-squared value for Perceived Usefulness (PU) is 0.485, signifying that the independent variables, including Perceived Relevance (PR) and Perceived Ease (PE), account for 48.5% of the variance in PU.
The R-squared value of T indicates that perceived usefulness (PU) accounts for 23.8% of its variance, with a value of 0.283 Additionally, the R-squared value for attitude towards intention (ATTINT) is 0.591, signifying that 59.1% of the changes in ATTINT can be influenced by T Overall, these R-squared values suggest a significant interaction between the variables PU, T, and ATTINT.
Result of Multivariate regression analysis
This study aims to test hypothesis H5 by selecting a representative variable from each factor group, calculated as the mean of the observed variables within those groups The dependent variable will be utilized in this analysis to ensure accurate results.
“Acceptance to use - ADT” and independent variables “Attitude towards use and Intention to use - ATTINT” as the following formula below:
10 1.873 a Predictors: (Constant), ATTINT b Dependent Variable: ADT _
1 a Dependent Variable: ADT 49 b Predictors: (Constant), ATTINT _
Table 3.13: Model summary and ANOVA of linear regression
Source: Results of IBM AMOS Graphics
Table 3.13 presents the results of R-squared, Durbin-Watson, and significance values The R-squared value of 0.606 indicates that ATTINT accounts for 60.6% of the variance in the dependent variable (ADT), leaving 39.4% attributed to external factors and random error Additionally, the Durbin-Watson statistic is 1.873, with dL at 1.473 and dU at 1.783.
< 1.873 < 4 - d U = 2.217, this model does not have autocorrelation phenomenon Sig. value = 0.00 < 0.05 which means that this variance is meaningful for independent variance - ADT.
Source: Results of IBMAMOS Graphics
H1 (PE) Perceived ease of use has positive impact on the perceived usefulness when using digital banking service
H2 (PR) Perceived risk has a negative impact on perceived usefulness.
H3 (PU) Perceived usefulness has a positive impact on trust when using digital banking service
H4 (T) Trust has a positive impact on the attitude towards use and intention to use
Attitude towards use and intention to use have a
The results indicate that the significance value from the t-test is 0.00, which is below the 0.05 threshold, confirming that the ATTINT variable is significant to the model Additionally, the beta coefficient of 0.778 reveals that attitude towards use and intention to use accounts for 77.8% of the acceptance to use.
The Variance Inflation Factor (VIF) of 1.000 indicates the absence of multicollinearity in the model Consequently, it is evident that both attitude and intention to use positively influence the acceptance of digital banking services, leading to the acceptance of the final hypothesis.
Testing hypotheses
As initially, the research paper gives 5 hypotheses After testing the hypotheses through surveyed data, we have the following results:
Table 3.15: Result of Testing hypotheses positive impact on the acceptance of use.
The analysis using SEM model and linear regression accepted four out of five hypotheses: H1, H3, H4, and H5 H1 confirmed that perceived ease of use positively influences perceived usefulness, while H3 revealed a significant relationship between perceived usefulness and trust in digital banking services H4 demonstrated that trust and attitudes towards use positively impact the intention to use digital banking Lastly, H5 established that attitudes towards use and intention to use significantly affect user acceptance The findings, with a p-value of 0.000, indicate strong correlations among perceived ease of use, perceived usefulness, trust, and attitudes Hypotheses with p-values greater than 0.05 were rejected.
Discussion
This study aims to elucidate the key factors influencing the adoption of digital banking services, distinguishing itself from previous research that primarily utilized the Technology Acceptance Model (TAM) to examine user intentions Unlike earlier studies, this survey specifically investigates customer acceptance of digital banking in Vietnam, yielding novel insights that may not align with established theories According to Davis et al (1989), perceived ease of use and perceived usefulness significantly impact user acceptance The findings of this research support this notion, demonstrating that a user-friendly interface, clear terms and conditions, and accessible user guides positively influence customers' attitudes towards digital banking services.
The perceived usefulness of digital banking services significantly enhances customer trust, aligning with findings from Jian Mou et al (2015) To foster trust, banks must prioritize the usefulness of their products, focusing on features that reduce transaction costs, save time, and handle high transaction volumes However, existing issues such as slow processing times can disrupt customer transactions, particularly in urgent situations Therefore, to sustain and enhance customer trust, banks should continually improve product quality and service efficiency.
Trust significantly influences users' attitudes and intentions towards service usage, independent of risk perception Research by Oanh Nguyen Thi (2020) indicates that trust indirectly affects attitudes through the positive perception of risk A strong sense of trust in a bank indicates high customer satisfaction and is often linked to the bank's reputable brand However, banks must remain vigilant in product development, as customers' high risk perception necessitates careful attention to avoid disappointment and maintain their confidence.
The study focuses on customers aged 30 to 50, primarily those born in the 1980s to early 1990s, all of whom are knowledgeable professionals in the banking and finance sector This demographic's understanding of digital banking will shed light on their attitudes and intentions toward using digital banking services The findings suggest a positive correlation between their attitudes and the likelihood of adopting digital banking products.
Digital banking is gaining popularity in Vietnam, although the country is still in the early stages of digital transformation Many banks have yet to develop their own digital banking products, often partnering with fintech companies to use their branding Joint stock commercial banks are beginning to conceptualize and test their own digital offerings, with some even establishing dedicated digital banking centers and hiring skilled staff This growing interest in digital banking services presents a positive opportunity for banks to enhance their offerings Overall, the survey results align well with the current landscape of digital banking in Vietnam.
In Chapter Three, the author presents the findings from a reliability analysis using Cronbach's Alpha on 150 samples, confirming that all variables are reliable Following this, Exploratory Factor Analysis (EFA) is employed to identify factor groups, which informs the development of a Confirmatory Factor Analysis (CFA) model The results, including Composite Reliability (CR), Average Variance Extracted (AVE), and the square root of AVE, indicate that the model's variables are sufficiently reliable for hypothesis testing Utilizing AMOS software for Structural Equation Modeling (SEM), the author tests the proposed hypotheses, finding that 4 out of 5 can be validated, with 3 being accepted These findings are particularly relevant in the context of Vietnam's ongoing digital transformation, reflecting a positive attitude and intention among consumers towards adopting digital banking services.
SITUATION AND RECOMMENDATION FOR VIETNAM