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Factors Affecting the Intention to Use Digital Banking Services: A Case Study on Elderly Customers in Vietnam1
Nga Tran Thi Thanh
University of Finance - Marketing, 778 Nguyen Kiem str., Ward 4, Phu Nhuan District, Ho Chi Minh City, Vietnam.
E-mail: ngatcnh@ufm.edu.vn
Based on Technology Acceptance Theory (TAM) and Linear Structural Model (SEM), the author predicts factors affecting the intention to use digital banking of customers from 50 years old in Vietnam. For this study, 350 valid responses out of 398 survey participants have been collected and utilized for data analysis, digital banking are found easy to use, helpful, reliable, and less risky for elderly customers, which might increase the elderly's demands and intentions to use them. Regarding the behaviors of elderly customers, this study will provide an insight into elderly customers' expectations accessing digital banking services during the COVID-19 pandemic in emerging markets. Furthermore, the researcher proposes an integrated model to predict behaviors and examines main.
Key words: digital banking; elderly; SEM; Technology Acceptance Theory (TAM).
JEL Classification: G21, G41.
DOI: 10.17323/1813-8691-2023-27-2-270-289
For citation: Nga Tran Thi Thanh. Factors Affecting the Intention to Use Digital Banking Services:
A Case Study on Elderly Customers in Vietnam. HSE Economic Journal. 2023; 27(2): 270-289.
1 The research topic was supported by The Youth Incubator for Science and Technology Programme, managed by Youth Promotion Science and Technology Center - Ho Chi Minh Communist Youth Union and Department of Science and Technology of Ho Chi Minh City, 06/2022/HB-KHCNT-VU; signed on 30 th, December, 2022.
Nga Tran Thi Thanh - Faculty of Finance - Banking.
The article was received: 17.04.2023/The article is accepted for publication: 15.05.2023.
1. Introduction
Chen, Chen, Lin, Liu (2019) believes that information technology (IT) will be the trending field on which all other businesses' activities shall depend. Digital banking services are the current trend of customers [Singh, Srivastava, Sinha, 2017]. Customers' experience is always a great concern of organizations [Kumar, Ramakrishnan, Krishnamacharyulu, 2021]. The COVID-19 pandemic has shaped many changes in the business models [Seetharaman, 2020]. Changes in customers' behaviors towards digital banking have been recognized during the COVID-19 pandemic [Baldwin, Mauro, 2020; Wojcik, Loannou, 2020]. Technological innovations through Automated Teller Machine (ATM) platforms, online banking, mobile banking, Unified Payments Interface (UPI) enhance bank solvency [Glavee-Geo, Shaikh, Karjaluoto, Hinson, 2020] and boost bank-customer connections [Ozili, 2018]. It is noted that differences in culture, age, area from countries to countries result in different performances and growth of digital banking [Alnemer, 2022; Takieddine, Sun, 2015]. Consequently, understanding the factors affecting the intention to use digital services of every single customer is essential for the banking management [Liebana-Cabanillas, Marinkovic, Kalinic, 2017]. Numerous studies on technology used among older adults have been conducted to response to the trend of the world's aging population [Pan, Jordan-Marsh, 2010] including social networking for older adults [Braun, 2013]; telehealth for the elderly [Zhou et al., 2019]; anti-aging technology [Chen, Chan, 2014]; digital games for older adults [Wang, Sun, 2016]; technology taking care of older adults [Quaosar, Hoque, Bao, 2018]; the use of technology in learning and reading among the elderly [Lai, 2020].
It is obvious that documents on banking services focus on E-banking [Anouze, Alamro, 2019; Chi, 2021; Haq, Awan, 2020; Trang, 2022], M-Banking [Baabdullah, Alalwan, Rana, Kizgin, Patil, 2019; Hamidi, Safareeyeh, 2019; Merhi, Hone, Tarhini, Ameen, 2020; Picoto, Pinto, 2021; Singh, Srivastava, 2020], I-Banking [Alalwan, Dwivedi, Rana, Algharabat, 2018; Bharti, 2016; Hamidi, Safareeyeh, 2019; Oruf, Tatar, 2017]; digital banking [Alnemer, 2022; Egala, Boateng, Men-sah, 2021; Kaur, Kiran, Grima, Rupeika-Apoga, 2021; Montazemi, Qahri-Saremi, 2015]; relationship between digital banking adoption and demographic characteristics of gender, age, education level, occupation and income [Alnemer, 2022], which shows that males aged from 25-49 years with better education, income and still in service are likely to use digital banking services. These results reveal a gap in the research literature on the intention to use digital banking services among elderly customers, especially after the COVID-19 pandemic. Despite a few studies on factors affecting the intentions to use digital banking, none of in-depth research has been conducted on digital banking for a specific customer age. As a response to the world's aging population, worldwide banks need to manage the factors attracting the elderly to use digital banking. As the greatest aging population in the world, it is common in Vietnam to make financial transactions in a traditional way, specially among the elderly [Nam, Duc, 2021]. According to the 2021 statistical report, the elderly population increases by 4.35% per year while the total population growth rate is 1.14% per year in the period of 2009-2019. The group aged 60-69 experiences the highest growth of 3.1 million people while figures of 70-79 and from 80-years-olds are 200,000 and 570,000, respectively. In details, the group aged 60-64 increases approximately 7.5% per year while the 65-69-years-olds grows 5.62% per year. Recognizing the above, it is highly recommended that banks need to update and detect factors affecting the intention to use digital banking of the elderly [Nguyen, Nguyen, Mai, Tran, 2020].
From that, this study will provide an insight into elderly customers' expectations accessing digital banking services during the COVID-19 pandemic in emerging markets. Furthermore, the researcher proposes an integrated model to predict behaviors and examines main factors affecting the intention to use digital banking services among elderly customers in Vietnam.
2. Theoretical Basis 2.1. Digital banking
There are many different perspectives on approaching digital banking services, but digital banking in this study is considered as a method performing all banking transactions such as depositing, transferring, withdrawing, managing current and savings accounts, lending, bills payment, financial products, and account services on electronic platforms [Ling, Fern, Boon, Huat, 2016; Windasari, Kusumawati, Larasati, Amelia, 2022; Anggraeni, Hapsari, Muslim, 2021].
Digital banking services also requires innovations for customers across mobile digital or AI devices, payment strategies, data, blockchain, API, distribution channels and technology [Mba-ma, Ezepue, Alboul, Beer, 2018]. Through digital devices, the scope of digital banking includes electronic banking services (e.i. T-banking, E-banking, M-banking, debit cards, ATMs and Paypal machines at electronic point-of-sell (PoS). Telephone banking (T-banking) allows customers to transact via mobile phones [Alalwan, Dwivedi, Rana, Simintiras, 2016]. Online banking (E-ban-king) can perform internet banking services at home [Martins, Oliveira, Popovic, 2014]. Mobile banking (M-banking) allows financial services to be managed via mobile devices [Tam, Oliveira, 2017].
2.2. The role of technology to the elderly
There are many different views on the definition of "the elderly" among countries around the world. In the United Kingdom and the United States of America, only people from 65 years old are considered the elderly while in developing countries such as Malaysia, Laos, Cambodia, it is from 55 years of age and above [Wikipedia, 2012]. The United Nations defines "elderly people" as those aged 60 years and above [Hutton, 2008]. In Vietnam, a man or a woman must be over 50 years old to be considered "the elderly" [Giao; Dat, 2014; Suoranta, Mattila, 2004]. Paul (2009) notes that it is necessary to base on age, income level, working capacity, health status, time, and relationship with other generations to give an accurate definition of the elderly. With the increasing aging population in Vietnam and a lot more elderly people are living on their own [Nam, Duc, 2021], the author believe that this study's implications are significantly important for worldwide managers in banking industry in general, especially those in developing countries.
2.3. Theories/Models related to the use of technology of the elderly
Regarding technology acceptability, among popular models of Rational Action Theory [Fishbein, Ajzen, 1977], Acceptance Model [Davis, 1989], Theory of Planned Behavior planning [Ajzen, 1991], Extended Technology Acceptance Model (TAM2) [Venkatesh, Davis, 2000], the
most widely used to predict the adoption of technology of the Technology Acceptance Model (TAM) currently is valid with different samples in many different situations [Davis, 1989]. This study believes that the success of technology adoption largely depends on users' attitudes and perceptions about the products/applications, which in turn depends on the ease or difficulty that users have experienced. Technology Acceptance Model Theory (TAM) provides a useful tool for managers to assess factors affecting the acceptance or rejection of a new technology, especially elderly customers who are reluctant to change [Abu-Bader, Rogers, Barusch, 2003]. Chau, Lai (2003) argues that the technological environment of digital banking services is completely different from other conventional business activities; therefore, the current Technology Acceptance Model (TAM) is not sufficient to explain the adoption of digital banking. In addition to conventional structures of Technology Acceptance Model (TAM) (i.e. Perceived Ease of Use), the study proposes variables related to the intention to use digital banking services of elderly customers. Proposed by [Ajzen, 1991; Fishbein, Ajzen, 1977], the Theory of Planned Behavior (TPB) develops an additional element of Behavioral Control from the Theory of Reasoned Action (TRA). The Theory of Planned Behavior (TPB) believes that the behavioral intention of customers will be affected by Attitude, Subjective Norm, Behavioral Control. Taylor, Todd (1995) suggests combining TPB and TAM to overcome each model's limitations that the TAM model pays much attention to the perceived impact on user acceptance while Attitude variable in the TPB model favors an explanation of consumer perception.
2.4. The impact of research factors on the elderly's intention to use digital banking services
Perceived Usefulness (PU): refers to an extent to which customers believe that using a product or service helps them improve their job performance [Davis, 1989]. If customers find digital banking services are more useful, then they will posibly try to use the new technology instead of the traditional way and vice versa. Furthermore, technology improves the quality of life of older adults in various ways [Bobillier-Chaumon, Michel, Tarpin-Bernard, Croisile, 2014] so Perceived Usefulness will influence the intention to use technology of the elderly. Several studies find a positive relationship of Perceived Usefulness (PU) to the intention to use M-banking services [Baabdullah et al., 2019; Hamidi, Safareeyeh, 2019]; E-banking [Anouze, Alamro, 2019]; other services [Dutot, Bhatiasevi, Bellallahom, 2019]; the internet [Pan, Jordan-Marsh, 2010]; social networks [Braun, 2013]; Telehealth [Zhou et al., 2019]; anti-aging technology [Chen, Chan, 2014]. Therefore, the study proposes the following hypothesis:
HI: Perceived Usefulness has a positive effect (+) on the intention to use digital banking services.
Perceived Ease of Use (PEOU): Perceived Ease of Use refers to users' confidence that technology is easy to be carried out on the system and helps to save time [Singh, Sinha, Liebana-Cabanillas, 2020]. Many differences have been found in the way young and old customers apply technology in their daily lives [Mitzner et al., 2010] so supporting the elderly to accept and use a particular technology is significantly important [Tsai, Shillair, Cotten, Winstead, Yost, 2015]. Many studies have found a positive relationship between Perceived Ease of Use and the intention to use banking services [Alalwan et al., 2019; Baabdullah et al., 2019], consistent with [Braun, 2013; Pan, Jordan-Marsh, 2010; Zhou et al., 2019] in which PE has positive influences on the
elderly's intention to apply technology in different fields. Therefore, the study proposes the following hypothesis:
H2: Perceived Ease of Use has a positive (+) influence on intention to use digital banking services.
Risk Perception (RP): Haq, Awan (2020) believes that matters on the level of security and privacy of customers during and after the transaction process or any bank's service provision are all risk-related issues. Ananda, Devesh, Al Lawati (2020) argues that security can be seen as one of the most important reasons for customers' opposition to banking services, so it is crucial for banks and financial organizations to build trust in customers in new banking services. Risk Perception influences the customers' intention to use banking services [Ananda et al., 2020; Anggraeni et al., 2021; Chen, Chan, 2014; Haq, Awan, 2020; Lai, 2020]. When sensing possible risks, customers will tend to refuse to use banking services [Li, Ma, Chan, Man, 2019]. From that, this study proposes the following hypothesis:
H3: Risk negatively affects (-) the intention to use digital banking services.
Attitude (AT): Several research have been conducted on attitudes of customers to apply technology in a particulart service or field. The paper of Simonson, Maurer, Montag-Torardi, Whitaker (1987) refers to an individual's attitude towards a new technology. AT is considered as a negative emotional response related to customers' unsatisfying experience using technology [Meuter, Ostrom, Bitner, Roundtree, 2003]. Some studies suggest that AT negatively affects technology adoption [Chen, Chan, 2014; Deng, Mo, Liu, 2014; Ke, Lou, Tan, Wai, Chan, 2020]. Deng et al. (2014) shows that AT positively affects on behavioral intentions of 50-year-olds and above but does not significantly influence the group aged from 40-50. Many studies have been conducted on the impact of attitudes on the elderly's technology adoption in different fields such as mobile health services [Deng et al., 2014], assistive technology for moving stairs [Tural, Lu, Cole, 2020] and tablet use [Ke et al., 2020]. Most studies reveals that the elderly has positive attitudes towards technology and by showing interests in adopting hi-tech products/services. If these older customers are not fans of technology, they will not intend to apply any available technology. Therefore, this study hypothesized this relationship as follows:
H4: Attitude positively impacts on the intention to use digital banking services.
Behavioral Control (BC): Ajzen (1991) defines that Behavioral Control is the combination between confidence and the ability that an individual controls himself/herself in performing a behavior, reflecting how easily or hard the behavior is performed. This partially depends on the availability of resources and opportunities to perform the behaviour. It is found that the Behavioral Control has a positive influence on the intention to use banking services [Ahmad, Ra-sul, Yousaf, Zaman, 2020; Alalwan et al., 2019; Ananda et al., 2020; Anggraeni et al., 2021; Anouze, Alamro, 2019; Choudrie, Junior, McKenna, Richter, 2018; Kizgin, Jamal, Dey, Rana, 2018; Mbama et al., 2018]. Based on the evidence of the above studies, this relationship is hypothesized as follows:
H5: Behavioral Control positively influences (+) intention to use digital banking services.
Subjective norm (SN): reflects the degree of support or opposition of related influen-cers affecting the intention to use a technology service and the motivation of influencers [Ajzen, 1991; Taylor, Todd, 1995]. According to the theory of intended behavior, the support of family, friends, and colleagues is an important factor in promoting behavioral intentions. Previous studies [Ahmad et al., 2020; Ananda et al., 2020; Lai, 2020] have suggested that Subjective Norm
has a positive effect on behavioral intention, which in turn affects intention to use banking services. Based on the above, the study proposes the following hypotheses:
H6: Subjective norm positively affects (+) intention to use digital banking services.
Table 1.
Summary of variables in the research model
Variable
Sympbol
Expectation
Related studies
Intention to Use
Perceived Usefulness
Perceived Ease of Use
Risk Perception Attitude
Subjective Norm
Behavioural Control
IU PU PE
RP
AT SN BC
(+) Alalwan et al., 2019; Anouze, Alamro, 2019;
Baabdullah et al., 2019; Chen et al., 2019; Kim, Shin, 2015; Luarn, Lin, 2005; Taylor, Todd, 1995
(+) Ahmad et al., 2020; Anouze, Alamro, 2019;
Baabdullah et al., 2019; Dai, Palvi, 2009; Davis, 1989; Luarn, Lin, 2005; Taylor, Todd, 1995
(+) Alalwan et al., 2019; Anouze, Alamro, 2019;
Baabdullah et al., 2019; Chen et al., 2019; Kim, Shin, 2015; Luarn, Lin, 2005; Taylor, Todd, 1995
(-) Ananda et al., 2020; Anggraeni et al., 2021; Chen,
Chan, 2014; Haq, Awan, 2020; Lai, 2020
(+) Ananda et al., 2020; Kizgin et al., 2018
(+) Ahmad et al., 2020; Ananda et al., 2020; Lai, 2020
(+) Ahmad et al., 2020; Alalwan et al., 2019; Ananda
et al., 2020; Anggraeni et al., 2021; Anouze, Alamro, 2019; Choudrie et al., 2018; Kizgin et al., 2018; Mbama et al., 2018
Source: summarized by the author.
3. Research Methodologies
The survey is designed as closed-ended questions with a 5-point Likert scale, ranging from 1 "Strongly disagree" to 5 "Strongly agree". To ensure the questionaire's format and content, 5 banking experts and 5 staff are also involved. To ensure the objectivity of the questionnaire, word clarity, relevance and customer's completion time, the author has conducted a pilot study with 10 participants who had used selective digital banking services in different banks.
Table 2.
Characteristics of the research models
Criteria
Quantity
Rate, %
Sex
Male
Female
Total
216
134 350
61.71 38.29 100
276 HSE Economic Journal No 2
Continues
Criteria Quantity Rate, %
From 50-59 264 75.43
Age 60-69 86 24.57
Total 350 100
High school 298 85.1
College 21 6
Qualifications University 24 6.9
Post-Graduates 7 2
Total 350 100
< 10 milliom 235 67.14
From 10-15 million 83 23.72
Income From 15-20 million 21 6
Over 20 million 11 3.14
Total 350 100
Yes 350 100
Have experienced digital banking services before No 0 0
Total 350 100
Totally clear 0 0
Knowledge in banking industry Clear Unclear 316 34 90.29 9.71
Total 350 100
Source: summarized by author.
For this study, 350 valid responses out of 398 survey participants have been collected and utilized for data analysis, reaching 96.69% with 216 male respondents, accounting for 61.71% and 134 female attendees, attaining 38.29%. In details, 316 respondents have known digital banking services before, taking up 90.29%; while skillful participants reach 9.71% only, or 34 out of the valid 350 reponses. Most respondents have banking knowledge, and all have been exposed to digital banking services.
Thanks to the ability to calculate measurement errors of not only official variables but potential research terms in the same theoretical model, hypothesis testing and SEM have shown many advantages, which far outweights traditional methods [Hulland, Chow, Lam, 1996]. Technically, there are two approaches to perform SEM: Structural Equation Modeling based on Co-variance Based-Structural Equation Modeling (Covariance-Based SEM) and Structural Equation
Modeling based on Partial Least Squares (PLS-SEM). PLS-SEM does not establish a model fit model, which limits the author to test and confirm the theory [Hair, Babin, Krey, 2017]. Regarding the sample size, CB-SEM requires a larger database than PLS-SEM. However, similars study results are collected by either PLS-SEM or CB-SEM for sample size from 250 [Hair et al., 2017]. For this study, the author has used CB-SEM approach to analyze data via SPSS 23 and AMOS 23 software with the scale tested by Cronbach's Alph reliability coefficient, EFA exploratory factor analysis and CFA confirmatory factor analysis with tested parameters (Chi-square, degrees of freedom, p-value, CFI indexes, GFI, TLI, RMSEA) and some indicators in CB-SEM (AVE, CR, MSV) are based on standardized regression coefficients calculated from AMOS software.
4. Research Analysis 4.1. Scales tests
The research terms (PU, PE, RP, AT, SN, BC, IU) all satisfy the conditions in analyzing the reliability of the scale through Cronbach's Alpha coefficient [Nunnally, 1994]. Therefore, these observed variables are used in the EFA analysis in the next section to check the structure of the scale. Conducting EFA factor analysis for all research terms, the author indicates that KMO coefficient = 0.837 (> 0.5) and Bartlett's test has significance Sig. = 0.000 (< 0.05), which provides reliable proof of appropriate data and correlated variables to the analysis, satisfying the conditions to perform EFA. The extracted variance is 71.173% > 50% at Eigenvalue = 1.104, so the EFA model is suitable, the research terms attain both convergent and discriminant validity. Factor loading coefficients of all measured variables > 0.5 (ranging from 0.537 to 0.942) means the research acquires practical significance [Hair, Black, Babin, Anderson, 2009].
Table 3.
Test results of official scales
Definitions and summary of the research variables Standardized coefficients
Perceived Usefulness (PU) (Cronbach's Alpha = 0.915, CR = 0.916, AVE = 0.734)
PU1 Digital banking services can help me to reduce expenses 0.911
PU2 Digital banking services can help me complete my work more easily 0.906
PU3 Digital banking services can help to do transactions anytime or anywhere with internet connection 0.877
PU4 Digital banking services can help me to save time and efforts compared to conventional transactions at the banks 0.818
Perceived Ease Of Use (PEOU) (Cronbach's Alpha = 0.851, CR = 0.862, AVE = 0.619)
PE1 Digital banking services I think learning how to use digital banking is easy 0.627
PE2 I think the performing banking transactions via digital banking is simple 0.873
PE3 I think completing banking transactions via digital banking is easy 0.915
PE4 All in all, I find digital banking services are easy to use 0.880
Continues
Definitions and summary of the research variables Standardized coefficients
Tinh rüi ro (RP) (Cronbach's Alpha = 0.929, CR = 0.931, AVE = 0.770) Risk Perception (RP) (Cronbach's Alpha = 0.929, CR = 0.931, AVE = 0.770)
RP1 Digital bankingI think that doing banking transactions via digital banking is not safe 0.895
RP2 I think passwords for transactions via digital banking services are easily hacked 0.942
RP3 I think my identity information can be stolen/hacked via digital banking 0.895
RP4 I think that the risks of digital banking far outweight the advantages 0.897
Atitudes (AT) (Cronbach's Alpha = 0.7, CR = 0.813, AVE = 0.591)
AT1 I think using digital banking services is a good idea 0.667
AT2 I think using digital banking services for financial transactions will be a wise decision 0.827
AT3 I think using digital banking services is interesting 0.833
AT4 Personally, I would love to use digital banking services 0.801
Subjective norm (SN) Cronbach's Alpha = 0.755, CR = 0.819, AVE = 0,606)
SN1 My family and friends encourage me to use digital banking services 0.734
SN2 People who influence me possibly think that I should use digital banking services 0.815
SN3 People who give me valuable opinions will recommend digital banking services to me 0.537
SN4 Banks encourage me to use digital banking services 0.880
Behavioral Control (BC) (Cronbach's Alpha = 0.875, CR = 0.877, AVE = 0.590)
BC1 I can control my behaviors using digital banking services 0.786
BC2 I have enough necessary sources to use digital banking services 0.827
BC3 I have enough documents, insights, and ability to use digital banking services 0.784
BC4 I can perform digital banking services on my own without any assistance 0.886
BC5 I am going to use digital banking services in near future 0.761
Intention to Use (IU) (Cronbach's Alpha = 0.811, CR = 0.803, AVE = 0.577)
IU1 I would love to use digital banking services 0.790
IU2 I will seriously consider using digital banking services 0.775
IU3 I would love to use digital banking services if I have a chance 0.792
Notes: AVE: Average variance extracted.
Source: summarized by the author from the Appendix.
Next, Confirmatory Factor Analysis (CFA) of the scales reveals appropriate model indexes: Chi-square/df = 1.669 < 3, of which 278 of freedom and Chi-square XA2 (df = 464.054), (P = 0.000), CFI index = 0.965, GFI = 0.911, TLI = 0.959 all > 0.9; RMSEA = 0.044 < 0.08 [Hair et al., 2009]. The normalized loading coefficients of the observed variables are all greater than 0.5 (ranging from 0.593 to 0.936). The Composite Reliability (CR) ranges from 0.803 to 0.931, both higher than 0.70, so the scales are reliable [Hair et al., 2009]. The mean Extracted Variance (AVE) ranges from 0.577 to 0.770, both higher than 0.50 [Bagozzi, Yi, Phillips, 1991; Fornell, Larcker, 1981; Hair et al., 2009]. It is concluded that the observed variables of all scales have convergent values. All scales have discriminant values because the Maximum Specific Variance (MSV) < mean Extracted Variance and SQRTAVE > correlation coefficient between terms. The analysis results show that the correlation coefficient between each pair of concepts is significantly different from 1 with P-value < 0.05. Therefore, the discriminant validity of these terms is reached [Steenkamp, Van Trijp, 1991].
Table 4.
The correlation matrix evaluates the discriminant value of the scale
Factor
Correlation coefficients
IU BC RP PU PE SN AT
IU 0.760
BC 0.544 0.768
RP 0.024 -0.004 0.878
PU 0.508 0.559 0.049 0.857
PE 0.171 0.109 -0.009 0.083 0.787
SN 0.092 -0.014 -0.009 0.008 -0.004 0.778
AT 0.483 0.466 -0.187 0.530 0.190 0.307 0.796
Intention to use scale (IU); Scale of perceived Behavioral Control (BC); Risk Scale (RP); The Perceived Usefulness scale; Perceived Ease of Use (PE) Scale; Subjective norm scale (SN); Attitude Scale (AT).
Note: the value in bold on the diagonal is the square root of AVE.
4.2. Measurement model test results
81
Fig. 1. SEM results of the research model (standardized)
Table 5.
Model fit test results
Indicators Standard threshold CFA SEM Previous study
Accepted Good
Chi-Square/df < 5 < 3 1.669 1.526 Hair et al., 2009; Hu, Bentler, 1999
TLI > 0.8 > 0.9 0.959 0.97 Hair et al., 2009; Hu, Bentler, 1999
GFI > 0.8 > 0.9 0.911 0.923 Hair et al., 2009; Hu, Bentler, 1999
CFI > 0.8 > 0.9 0.965 0.974 Hair et al., 2009; Hu, Bentler, 1999
RMSEA < 0.08 < 0.05 0.044 0.039 Hair et al., 2009
4.3. Discussions
Table 6.
Test of causal relationship between research terms
Unstandar- dized Coefficients Standardized Error S.E. Critical Ratio C.R. Standardized Coefficients P-value Results
Intention to use —— Perceived Usefulness 0.233 0.045 5.205 0.349 0.000*** Accepted
Intention to use —— Perceived Ease of Use 0.072 0.036 2.014 0.102 0.044** Accepted
Intention to use — Risk Perception -0.127 0.031 -4.12 -0.204 0.000*** Accepted
Intention to use — Atitude 0.173 0.033 5.313 0.287 0.000*** Accepted
Intention to use — Subjective Norm 0.151 0.061 2.481 0.179 0.013** Accepted
Intention to use — Behavioral Control 0.101 0.045 2.241 0.159 0.025** Accepted
Note: *** at 1% significance level, ** at 5% significance level, ** at 10% significance level.
Perceived Usefulness and attitude have the most positive influence on the intention to use digital banking of the elderly with standardized regression coefficients p = 0.349, p = 0.287, respectively. The results are consistant with previous studies [Ahmad et al., 2020; Anouze, Alamro, 2019; Baabdullah et al., 2019; Dai, Palvi, 2009; Luarn, Lin, 2005; Taylor, Todd, 1995], but the degree of influence is different due to different subjects, sample sizes or survey scopes. Many studies around the world have shown that perceived use of technology improves the quality of life of older adults in various ways [Bobillier-Chaumon et al., 2014; Nef, Ganea, Mûri, Mo-simann, 2013], so the Perception of Usefulness has a large impact on the intention to use technology of older people is quite relevant. Use of technology services is associated with reduced feelings of depression [Cotten, Ford, Ford, Hale, 2014] and loneliness in older adults [Francis, Rikard, Cotten, Kadylak, 2019]. Furthermore, accessibility to digital services also helps to support older adults with a greater sense of independence and social inclusion [Mitzner et al., 2010].
Therefore, customers who have positive attitudes towards digital banking services will increase their intention to use banking services [Ananda et al., 2020; Kizgin et al., 2018]. Perceived Ease of Use has a positive impact on the intention to use digital banking among the elderly with standardized regression coefficients (p = 0.102, Table 6. This result is consitent with previous studies such as (A) [Alalwan et al., 2019; Anouze, Alamro, 2019; Baabdullah et al., 2019; Chen et al., 2019; Kim, Shin, 2015; Luarn, Lin, 2005; Taylor, Todd, 1995]).
Calculation of risks negatively affects the intention to use digital banking of the elderly (Digital banking) with standardized regression coefficient (p = -0.204, Table 6). The third largest
standardized regression coefficients in the observed variables, showing that risk is a factor that reduces the intention to use digital banking services of elderly customers in Vietnam, supporting other studies of [Ananda et al., 2020; Anggraeni et al., 2021; Chen, Chan, 2014; Haq, Awan, 2020; Liu, Tai, 2016] but opposes [Luarn, Lin, 2005]. The Behavioral Control has a positive impact on customers' intention to use digital banking with a standardized regression coefficient (P = 0.159, Table 6). This relationship also once again confirms the results of previous studies on the positive impact of Behavioral Control on the intention to use Digital banking, consitent with [Ahmad et al., 2020; Alalwan et al., 2019; Ananda et al., 2020; Anggraeni et al., 2021; Anouze, Alamro, 2019; Choudrie et al., 2018; Kizgin et al., 2018; Mbama et al., 2018]. The Subjective Norm factor has a positive effect on the intention to use Digital banking of elderly customers with standardized regression coefficient (P = 0.179, p-value = 0.013, reliability of 99%), and has the lowest explanatory level compared to that in previous studies due to the difference in research subjects and the survey size. This is consistent with behavioral psychology because elderly customers who intend to use digital banking services shall be impacted by family, friends and society [Ahmad et al., 2020; Dai, Palvi, 2009; Pavlou, Fygenson, 2006].
Carrying out bootstrap with repeated sample N = 750, the author shows that the estimates in the research model are reliable because when estimating 750 samples are averaged with the bias still within acceptable limits (as shown in Table 7).
Table 7.
Results estimated by bootstrap with N = 750
Relationship SE SE-SE Average Bias SE-Bias C/R
IU —— BC 0.076 0.002 0.345 -0.004 0.003 (1.33)
IU —— RP 0.06 0.002 0.103 0 0.002 0.00
IU — PU 0.052 0.001 -0.201 0.004 0.002 2.00
IU — PE 0.065 0.002 0.288 0 0.002 0.00
IU — SN 0.071 0.002 0.185 0.006 0.003 2.00
IU — AT 0.071 0.002 0.16 0.001 0.003 0.33
Note: SE - Standard Error; SE-SE - Standard Error-Standard Deviation; Bias - bias; SE-Bias - standard deviation of the bias.
Source: summarized by the author.
5. Conclusions and Implications
Regarding older customers who intend to use digital banking services in an emerging market like Vietnam, this study has made important empirical contributions as this is perhaps the first typical study involving digital technology for older adults in emerging markets.
First, the study applies the TAM model to examine factors affecting the intention to use digital technology among the elderly in Vietnam in the context that the accessibility of digital technology for elderly customers has received a lot of attention in recent times.
Second, this research has added two variables including Subjective Norms and Behavioral Control factors to the model to better understand how the intention to use digital technology services of the elderly in Vietnam is affected by social norms and other environmental barriers such as lack of intenet access, training, and technical supports.
Third, this study has contributed in-depth insights in this area related to Perceived Usefulness and Ease of Use that play a very important role in the decision to use digital banking services among the elderly. The risk negatively affects the intention to use digital banking of elderly customers, which implies that bank administrators should be more cautious about the security, privacy, and other confidential information of users, especially that of the elderly. To banking managers, this research results will have significant implications because it is critically important for bank administrators to develop better strategies with more efficient and effective financial services through online platforms to serve older users in emerging markets like Vietnam.
Finally, the findings of this study may also enable technology professionals in the banking sector to invent or upgrade current financial services to meet various needs of diverse range of customers.
Besides, the author focuses on surveying 6 factors that affect the intention to use digital banking, and data used for analysis are among the elderly customers in Vietnam only, which is the limitation of the study. Therefore, subsequent studies can either concentrate on different age range to enhance the study's reliability or supplement diferent factors that have not been studied in the research model.
Appendix
Table A1.
Results of EFA analysis
Pattern Matrix3
Component
1 2 3 4 5 6 7
BC4 0.886
BC2 0.827
BC1 0.786
BC3 0.784
BC5 0.761
RP2
RP4
RP1
RP3
PU1
PU3
0.942 0.897 0.895 0.895
0.911 0.906
Continues
Pattern Matrix3
Component
1 2 3 4 5 6 7
PU2 0.877
PU4 0.818
PE3 0.915
PE4 0.880
PE2 0.873
PE1 0.627
AT3 0.833
AT2 0.827
AT4 0.801
ATI 0.667
SN4 0.880
SN2 0.815
SN1 0.734
SN3 0.537
IU3 0.792
IU1 0.790
IU2 0.775
Direction wrong quote 24.290 36.700 46.674 56.250 62.266 67.231
Eigenvalue 6.801 3.475 2.792 2.681 1.685 1.390
KMO and Bartlett's Test Approx. Chi-Square df Sig 5713.933 378 0.000
71.173 1.104
Source: Data analysis results from the author.
Table A2.
Composite Reliability, Average Variance Extracted, Correlation Coefficients
Factor Composite Reliability (CR) Average Variance Extracted (AVE) Correlation Coefficients
CR AVE IU BC RP PU PE SN AT
IU 0.803 0.577 1.000
BC 0.877 0.590 0.544 1.000
RP 0.931 0.770 0.024 -0.004 1.000
PU 0.916 0.734 0.508 0.559 0.049 1.000
PE 0.862 0.619 0.171 0.109 -0.009 0.083 1.000
SN 0.819 0.606 0.092 -0.014 -0.009 0.008 -0.004 1.000
AT 0.813 0.591 0.483 0.466 -0.187 0.530 0.190 0.307 1.000
Note: Intention to use scale (IU); Scale of Perceived Behavioral Control (BC); Risk Perception (RP) scale; The Perceived Usefulness scale; Perceived Ease of Use (PE) scale; Subjective Norm (SN) scale; Attitude (AT) Scal.
Table A3.
Correlation matrix to evaluate the discriminant value of the research scale
Factor Correlation Coefficients
IU BC RP PU PE SN AT
IU 0.760
BC 0.544 0.768
RP 0.024 -0.004
PU 0.508 0.559
PE 0.171 0.109
SN 0.092 -0.014
AT 0.483 0.466
0.878
0.049 -0.009 -0.009 -0.187
0.857
0.083 0.008 0.530
0.787
-0.004 0.190
0.778
0.307
0.796
Intention to Use scale (IU); Scale of Perceived Behavioral Control (BC); Risk Scale (RP); Perceived Usefulness scale; Perceived Ease of Use (PE) Scale; Subjective Norm (SN) scale; Attitude (AT) scale. Note: The value is the square root of AVE (in bold on diagonal).
* * *
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