1. Introduction
Digital technology is gradually changing the form of providing financial and banking services. The banking industry is applying more advanced technologies in providing financial products and services such as the Internet of things (IoT), artificial intelligence (AI), machine learning (ML), and so on. Technology helps banks provide innovative products for customers and enhance the customer experience. However, the development and application of digital technologies in the banking industry also come with many challenges such as institutional change requirements, large investment capital, and human resources equipped with knowledge and capacity to master new technologies. New technology also affects the behavior of customers (Leung, 2009). Customers would be willing to choose another bank if their bank did not provide services via an online channel. Therefore, it is necessary for banks to utilize digital technologies to accelerate their competencies and activities in an increasingly competitive market (Nguyen & Dang, 2018).
Digital banking services will help the banks increase their competitiveness, maintain current, and attract new customers (Nguyen, 2020). The Vietnamese government is in the process of promoting the digital economy. Lately, the digital economy has had a high rate of growth; hence, it requires all sectors of the economy to undergo digital transformation, especially in the context of the outbreak of the Covid-19 pandemic (Ha, 2020). Especially in the banking and finance sector, the Vietnamese government has always shown support for digital transformation and cashless payments. Therefore, it is necessary to conduct research on the intention of customers to use digital banking services in Vietnam to help banks have proper strategies to promote digital banking services (Shaikh & Karjaluoto, 2016).
Although the factors affecting customers’ intention to use digital banking services in Vietnam have been researched, a limited number of studies focused on young people. Furthermore, research results on the impact of these factors on the intention to use digital banking services have not been consistent. For instance, while Nguyen (2020) reports that the convenience of digital banking services does not have a direct effect on the intention to use, Nguyen et al. (2020) confirmed the opposite. Hence, this study will estimate the impact of factors on the intention to use digital banking services of young retail customers in Vietnam.
2. Literature Review
2.1. Digital Banking Services Overview
Shaikh and Karjaluoto (2016) defined digital banking as an anytime and anywhere banking system removing paper procedures such as payment slips, checks, and so on. Digital banking gives customers the liberty to access and perform all traditional banking 24/7 without having to go to physical branches to get their work done. Digital banking can be completed through the customer’s personal computers, laptops, tablets, smartphones, and other technical devices. Sharma (2017) reports that when a bank undergoes a process of digital transformation, it digitalizes its operation and services supplied to customers. In short, the bank will be restructured both back-office, middle and end-office. Digital banking is the provision of banking products and services based on digital technology and it refers to the end-office part. Moreover, digital banking allows a user to set up automatic payments for regular utility bills such as electricity, gas, phone, and credit cards. The customer no longer has to make a conscious effort of remembering the due dates. The customer can opt for alerts on upcoming payments and outstanding dues.
Many people in Vietnam still misunderstand the concept of digital banking and think digital banking is the same as online banking, Internet banking, or E-banking. The concept of digital banking can be seen to have a broader and more comprehensive scope than the concepts of online banking, electronic banking, virtual banking, and so on (Shaikh & Karjaluoto, 2016). Digital banking refers to the digital integration of banks for all banking services, the application of digitalization in business operations, and interactions with customers.
The research on intention to use digital banking services mainly utilized the Unified theory of acceptance and use of technology (UTAUT) (Venkatesh et al., 2003) and the Technology acceptance model (TAM) (Davis, 1989). Venkatesh et al. (2003) reported that UTAUT models can explain up to 70% of the variation in intention to use technology and 50% in general technology use. In terms of TAM, King and He (2006) asserted that TAM is one of the most popular models to study the intention to use technology services/products and suitable for new service. Hence, TAM is suitable for studying new services like digital banking. Several studies on intention to use digital banking services have been conducted based on TAM such as studies by Kuruppu et al. (2019) and Suhaimi and Hassan (2018). In addition, TAM has been extended with many new determinants. In this article, the following factors will be examined: (1) Perceived ease of use (PEU), (2) Convenience (CNV), (3) Technology self-efficacy (TSE), (4) Social influence (SI), (5) Perceived security (PS) and (6) Customer support (CS).
2.2. Research Hypotheses
Perceived ease of use is defined as the degree to which a person believes that using a particular system would be free of effort (Davis, 1989). Digital banking helps the customers access and perform all traditional banking services 24/7 without going directly to a bank branch. The websites and mobile applications that have clear and simple interfaces will encourage young customers to use digital banking services. Several studies find that the ease of using digital banking services has a direct and positive impact on intention to use (Kuruppu et al., 2019; Martono et al., 2020; Phan & Bui, 2019; Suhaimi & Hassan, 2018). Abu-Assi et al. (2014) reported that perceived ease of use has a positive impact on the intention to use electronic banking in Jordan. Besides, Guriting and Ndubisi (2006) reported the same in Malaysia, Kassim and Abdullah (2006) in Qatar, and Rawashdeh (2015) in Jordan. Based on studies on the intention to use E-banking services and recent studies on digital banking services, the author proposes the following hypothesis:
H1: Perceived ease of use has a positive effect on the intention to use digital banking services.
Nguyen (2020) asserted that convenience improves access to systems easily with equal or better performance than others. Moreover, Seiders et al. (2007) claimed that convenience can reduce transaction time and technical issues. Chen et al. (2016) defined convenience in the context of Internet banking as an online service that is automatically accessible 24 hours a day and seven days a week, which increases users’ comfort while reducing the cost of time and effort. Nguyen et al. (2020) found that convenience has a positive impact on the intention to use digital banking services in Vietnam. Therefore, the following hypotheses are proposed:
H2a: Convenience has a positive effect on the intention to use digital banking services.
H2b: Convenience has a positive effect on perceived ease of use.
Self-efficacy refers to an individual’s belief in his or her capacity to execute behaviors necessary to produce specific performance attainments (Bandura, 1977). Self-efficacy reflects confidence in the ability to exert control over one’s own motivation, behavior, and social environment (Bandura, 1977). In simpler terms, self-efficacy is one’s belief in his or her ability to succeed in a particular situation. Banking services, when digitalized, require customers to use application services on the Internet, computers, and mobile phones. Hence, the user needs to have adequate technical knowledge to use the banking applications. Several studies have identified the influence of self-efficacy to have a positive effect on behavioral intentions (Mutahar et al., 2018; Singh & Srivastava, 2020). Guriting and Ndubisi (2006) found that computer self-efficacy has a positive effect on the intention to use electronic banking services. Additionally, Singh and Srivastava (2020) reported that self-efficacy in using a mobile phone has a positive impact on the perceived ease of use of mobile banking. The author proposes the following hypotheses:
H3a: Technology self-efficacy has a positive effect on the intention to use digital banking services.
H3b: Technology self-efficacy has a positive effect on perceived ease of use.
Social influence is the process by which an individual’s attitudes, beliefs, or behavior are modified by the presence or action of others. Singh and Srivastava (2020) defined that social influence refers to the way individuals change their behaviors based on others’ opinions. Digital banking presents an innovative technological service for the customers using traditional banking services; therefore, they tend to consult others’ opinions who have already used or experienced the digital banking services. The positive relationship between social influence and intention to use has been reported in many studies (Singh & Srivastava, 2020; Tarhini et al., 2016). In addition, Arora and Sandhu (2018) found that social influence has a positive impact on the intention to use E-banking services in India, Chaoualiet et al. (2016) found the same result in Tunis, and Yaseen and El Qirem (2018) also claimed that in their study in Jordan. Hence, the author proposes the following hypothesis:
H4: Social influence has a positive effect on the intention to use digital banking services.
Perceived security is defined as the degree to which the mobile payment user believes that transaction on mobile payment platforms is secure in both financial and personal information aspects (Cheng et al., 2006). Digital banking services bring customers many benefits, but also potential risks because all transactions are performed on digital technology platforms. Security is a concern not only for customers but also for the service providers themselves who must assure the customers that their services are safe and secure. The positive relationship between perceived security and intention to use was confirmed by Cheng et al. (2006), Zhang et al. (2018), and Singh and Srivastava (2020). In addition, Abu-Assi et al. (2014) proved that perceived security has a positive impact on the intention to use E-banking services in Jordan, Kassim and Abdullah (2006) found the same result in Qatar, and Rawashdeh (2015) found the same result in Jordan. Accordingly, the following hypothesis is proposed:
H5: Perceived security has a positive effect on the intention to use digital banking services.
Santos (2003) defined that customer support as support presented in the form of guidelines, help pages, frequently asked questions, or personal assistance. Digital banking services require customers to perform transactions mostly on the digital platform so many customers expect prompt and accurate assistance when technical issues happen. Moreover, if customers are assisted whenever they need it, they will find digital banking services easier to use. While Lin (2013) and Rahi and Ghani (2019) found that there is a positive relationship between customer support and intention to use, Singh and Srivastava (2020) proved that this relationship is insignificant. However, Singh and Srivastava (2020) asserted that customer support has a positive impact on perceived security. The author offers the following hypotheses (see Figure 1).
H6a: Customer support has a positive effect on the intention to use digital banking services.
H6b: Customer support has a positive effect on perceived security.
3. Research Methods
3.1. Research Design
The measures of the survey questionnaire were formulated after reviewing previous studies on the intention to use E-banking and the intention to use digital banking services. Besides, the author has applied the translation–back translation methodology to build the questionnaire meeting the language standards. After synthesizing the studies and giving the survey questions in English, the author translated them into Vietnamese. Then, these questions were translated back to English.
Figure 1: Research Model
The young customers demonstrated their agreement through a 5-point Likert scale consisting of “Totally disagree”, “Partially disagree or Disagree”, “Neutral”, “Partially agree or Agree” and “Totally agree”. The reason the author chose a scale of 1 to 5 over 1 to 7 is that previous studies have suggested that a 5-level Likert scale should be used to increase the rate and quality of responses with a reduction in frustration of the respondents and improvement in reliability (Jenkins & Taber, 1977). The questionnaire is illustrated in Table 1.
3.2. Data Collection and Sample
The subjects surveyed in this article were high school students, university students, graduates, and other young people under the age of 35 in Hanoi, Vietnam. The author created the questionnaire via free survey administration software Google Forms. Then, lecturers of other universities and teachers of high schools were contacted to share the link of the questionnaire with their students. In addition, the author also took time to visit a number of universities and randomly surveyed students walking on campuses by using hard copies of questionnaires. In order to obtain a more diverse dataset, the survey link was shared with young people who are friends and acquaintances of the author in cities and provinces of Vietnam. Therefore, there were respondents with intermediate and college qualifications. The number of responses collected was 534. The dataset was then checked with 9 submissions removed due to bias responses. Tabachnick et al. (2007) claimed that the sample size of 500 respondents or more is considered very good. According to Table 2, the percentage of the female respondents was higher than that of males, (59% and 41% respectively). Regarding education, the majority of customers taking part in the survey had Bachelor’s degree or higher (81.9%.) while high school students made up 8.8%. Next came young people with intermediate and college level, with 6.5% and 2.9% respectively. Moreover, students accounted for 70.3% of the sample. The officers and public servants made up 21.9%, while the housewives and people with other occupations constituted 4% and 3.8%. In terms of income, 60.8% of the respondents earned less than VND 5 million. Next came people with an income level of more than 10 million VND (17.3%). The people with an income level of VND 5 million – 7 million and VND 8 million – 10 million made up 11.6% and 10.3% respectively. Besides, 40.8% of the respondents were aged between 20 to 25. The people aged under 20 and between 26 to 35 accounted for 33.9% and 25.3% correspondingly. While 77.3% of the respondents were single, the others were married.
Table 1: Questionnaire
Table 1: Continued
Table 2: Descriptive Statistics
3.3. Data Analysis
The dataset was examined for validity, reliability and also utilized to estimate the correlations of variables. First, Cronbach’s Alpha, exploratory factor analysis (EFA), and confirmatory factor analysis (CFA) were employed to assess the validity and reliability of the instruments. Besides, a structural equation model (SEM) was used to estimate the correlation between variables. Finally, the Bootstrap test was performed to evaluate the reliability of the estimates.
4. Results
The results of scale reliability showed that perceived security (PS) met the reliability requirement, but this factor was removed during CFA analysis. Cronbach’s Alpha of all remaining scales was higher than 0.8 indicating a good level of reliability (Cortina, 1993). All variables correlation coefficients exceeded the cut-off point of 0.3. However, when conducting EFA analysis, all observed variables of the two scales perceived ease of use and convenience were in the same group. After considering the questions in the two scales, the similarity in questioning was found; thus, they were grouped into one scale which is the perceived ease of use. Observed variables CNV1, CNV2, CNV3, CNV4 were renamed to PEU5, PEU6, PEU7, PEU8 respectively. Cronbach’Alpha of all variables after eliminating unsuitable items and discovery factor analysis (EFA) are described in Table 3. EFA with Principal Axis Factoring extraction method and Promax rotation method illustrated that KMO = 0.873 > 0.5; Sig. of Bartlett’s Test of Sphericity = 0.000 < 0.05; Cumulative (%) = 63.141 > 50 and Eigenvalues = 1.057 > 1. Hence, the validity and reliability of all variables after removing the unsatisfactory variables were confirmed (Table 3).
Table 3: Cronbach’ Alpha and Exploratory Factor Analysis After Eliminating Unsuitable Items
Note: N = 525; CS: Customer support; TSE: Technology self-efficacy; PEU: Perceived ease of use; IU: Intention to use and SI: Social influence.
Furthermore, confirmatory factor analysis (CFA) was conducted to examine reliability, convergent and discriminant validity. The authors referred to many articles using structural equation model (SEM) to evaluate model fit through Chi- Square/df, CFI, GFI, RMSEA, PCLOSE and TLI. Specifically, the results of CFA showed that Chi-Square/df = 2.542 < 3; GFI = 0.928 > 0.9; CFI = 0.961 > 0.9; RMSEA = 0.054 < 0.06; TLI = 0.954 > 0.9 and PCLOSE = 0.137 > 0.05. All of these indices met the requirements. According to Table 4, composite reliability (CR) values of all constructs were higher than 0.7; average variance extracted (AVE) values of all constructs exceeded cut-off point of 0.5 (Anderson & Gerbing, 1988); maximum shared variance (MSV) < average variance extracted (AVE) and square root of AVE (SQRTAVE) > inter construct correlations. All of these confirmed the reliability, convergent and discriminant validity (Table 4).
Besides, the structural equation model (SEM) was used to estimate relationships between variables. The values of all indices confirmed that the model was fit: χ2/df = 2.626 < 3; GFI = 0.925 > 0.9; CFI = 0.958 > 0.9; RMSEA = 0.056 < 0.06; TLI = 0.951 > 0.9; PCLOSE = 0.071 > 0.05.
The regression weights depicted in Table 5 can be used to estimate the relationship between variables in SEM. The analysis revealed that intention of young people to use digital banking (IU) was directly correlated with social influence (SI) (β = 0.450; p-value < 0.001), perceived ease of use (PEU) (β = 0.194; p-value = 0.001 < 0.05) and customer support (CS) (β = 0.171; p-value = 0.003 < 0.05). However, intention of young people to use digital banking (IU) was not impacted by technology self-efficacy (TSE) (β = 0.004; p-value = 0.935 > 0.05). In addition, perceived ease of use (PEU) was not affected by technology self-efficacy (TSE) (β = –0.004; p-value = 0.910 > 0.05).
Table 4: The Reliability, Convergent, and Discriminant Validity of Variables
Note: N = 525; CS: Customer support; TSE: Technology self-efficacy; PEU: Perceived ease of use; IU: Intention to use and SI: Social influence
Table 5: Regression Weights and p-value
Note: *p-value < 0.1; **p-value < 0.05; ***p-value < 0.001. Significant at the 0.05 level.
Finally, 5000 bootstraps were conducted. The values of CR are calculated by the absolute value of Bias/SE-Bias. All CR values ≤ 2 supported the reliability of the estimates in the model.
5. Discussion and Managerial Implications
The results illustrate that perceived ease of use positively affected intention to use digital banking services, which is consistent with the TAM model (Davis, 1989). This result is also found in studies of Kuruppu et al. (2019), Phan and Bui (2019), and Suhaimi and Hassan (2018). This implies that banks should design their website and mobile application to be customer-friendly. The instructions should be easily available so the customers will find it easy and convenient to use digital banking services. Instructions should be concise, clear, easy to understand, and do not use complicated sentences. The bank can organize seminars, introductory events, and manuals for customers. Prioritized guests of these events must be those who have little opportunity to access banking services. In addition, services need to be fully developed on all means of technology such as applications for smartphones and tablets, websites to be accessed by laptops and desktop computers. Only then will customers be able to easily use it anywhere by different means.
In addition, social influence had a positive and the greatest effect on the intention to use digital banking services. Mufarih et al. (2020) and Venkatesh et al. (2003) have also found a positive relationship between social influence and intention to use. This implies that when banks provide innovative and convenient services at reasonable costs, customers will share their positive experiences with their relatives, friends, colleagues, and people around them. This positive spillover helps to increase customer confidence in digital banking services. Besides, commercial banks should invite KOLs (Key opinion leaders) who are familiar to young people. For example, a bank can invite many people to make videos on social media to review its service. The content of the videos needs to include a comparison and highlight the advantages of digital banking services compared to those of traditional banks. Moreover, commercial banks need to coordinate with the State Bank of Vietnam (SBV) to create and run campaigns to popularize basic financial education and financial services for Vietnamese people. This will help enhance people’s awareness of financial and banking services.
Furthermore, customer support had a positive impact on the intention to use digital banking services. Chung and Kwon (2009) also found the same results. However, this contradicts the results from the study of Singh and Srivastava (2020) in India. The results are not necessarily the same between the two nations as it depends on the ability and knowledge of customers to handle the issues themselves. Moreover, the regression analysis results showed that technology-self efficacy did not impact perceived ease of use and intention to use digital banking services. These results are consistent as when customers rely on the support service, their confidence to use technological devices does not affect their intention to use digital banking services.
6. Conclusion and Limitations
Digital transformation is an inevitable trend and banks need to adapt to survive in the increasingly competitive market. In terms of young customers, digital banking services help the customers access more services with flexibility and convenience. After reviewing the studies assessing the factors affecting intention to use mobile banking and digital banking services, the author identified the research gap and proposed a research model to examine the impacts of determinants affecting intention to use digital banking services of young customers in Vietnam. The results indicate that social influence has a positive and the greatest impact on the intention of young customers to use digital banking services. Besides, perceived ease of use and supporting services also affect intention to use digital banking services. It implies that commercial banks need to ensure that their services not only meet the needs of customers but are also easy to use. Furthermore, customer support service needs to support anytime, anywhere, and dedicated and professional customer support service is needed to understand customers’ needs. Banks should ensure good services, professionally resolve customer complaints so that customers can promote the bank to their relatives, acquaintances, and people around them. The use of KOLs also needs to be properly applied by banks. Banks also should coordinate with the State Bank of Vietnam (SBV) to have events or campaigns to educate people about finance, especially young people.
This article has some limitations in the survey procedure. It did not reflect all the determinants which could have an impact on the intention of young retail customers to use digital banking services in Vietnam. Therefore, this is a research gap for the author and other researchers who are interested in researching customer’s behavioral intention to use digital banking services. In addition, the respondents of the survey were mostly young people from Hanoi which is the capital of Vietnam, and other cities and provinces in the North of Vietnam. There were also respondents from other cities and provinces in the Middle and the South. However, the number of respondents from these areas was few. The research in the future could be conducted based on data collected from other areas like Da Nang and Ho Chi Minh City which are the biggest cities in the Middle and the South of Vietnam respectively.
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