Yếu tố tác động đến ý định thay đổi công nghệ trong hoạt động kinh doanh ngân hàng thời đại cách mạng công nghiệp 4.0 tại Việt Nam
Kỷ yếu Hội thảo quốc tế “Thương mại và phân phối” lần 2 năm 2020
FACTORS AFFECTING INTENTION TO CHANGE TECHNOLOGY IN BANKING
OPERATION IN THE 4TH INDUSTRIAL REVOLUTION IN VIETNAM
YẾU TỐ TÁC ĐỘNG ĐẾN Ý ĐỊNH THAY ĐỔI CÔNG NGHỆ TRONG HOẠT ĐỘNG KINH
DOANH NGÂN HÀNG THỜI ĐẠI CÁCH MẠNG CÔNG NGHIỆP 4.0 TẠI VIỆT NAM
Nga Phan Thi Hang - Hanh Thai Tran Van
University of Finance – Marketing.
Abstract
This paper aims to investigate the the factors and determinants of intention to change technology for
banking operation in Vietnam in the era of industry 4.0, proposing six factors including (1) perceived
usefulness; (2) perceived ease of use; (3) trust; (4) social influence; (5) innovativeness; and (6) efficiency; of
which efficiency was a new factor in this research. The authors have used both qualitative and quantitative
research methods to solve the problems related to the research. Cronbach’s alpha was used to assess the scale
reliability, Exploratory Factor Analysis and Confirmatory Factor Analysis helped test scale development and
scale adaptation study, and Structural Equation Modeling was used for testing the model by using AMOS. The
results showed that the perceived usefulness and perceived ease of use affect the change of technology in banks.
In addition, the trust and innovativeness of bank managers also influence the intention to develop bank’s
automation. And the new one which the authors have explored in this research – the efficiency – have positive
effect on technological alternatives in commercial banks located in Vietnam. From this, implications were given
for helping bankers spur to replace technology to improve operating efficiency in industry 4.0.
Key words: factors; technological change intention; banking business; industrial revolution 4.0.
Tóm tắt
Mục tiêu của bài nghiên cứu là tìm hiểu yếu tố tác động đến ý định thay đổi công nghệ để phục vụ kinh
doanh trong ngành Ngân hàng của Việt nam trong bối cảnh cách mạng công nghiệp 4.0. Kết quả nghiên cứu cho
thấy sự hữu ích; tính dễ sử dụng của công nghệ có tác động đến sự thay đổi công nghệ của các ngân hàng. Ngoài
ra sự tin tưởng; yếu tố đổi mới của các nhà quản trị ngân hàng cũng có ảnh hưởng đến ý định thay đổi công nghệ
của các ngân hàng. Đặc biệt một phát hiện mới của nhóm nghiên cứu là yếu tố sự hiệu quả có tác động tích cực
đến sự thay đổi công nghệ của các Ngân hàng thương mại của Việt Nam. Từ kết quả nghiên cứu tác giả đề xuất
các khuyến nghị nhằm giúp các nhà quản lý ngân hàng đẩy mạnh sự thay đổi công nghệ và khai thác hiệu quả
công nghệ để nâng cao hiệu quả hoạt động kinh doanh ngân hàng trong bối cảnh cuộc CMCN 4.0.
Từ khóa: Yếu tố, ý định thay đổi công nghệ, kinh doanh ngân hàng, công nghiệp 4.0
1. Introduction
Recently, our world has been in the fourth industrial revolution (Industry 4.0), in which
technologies like virtual reality (VR), internet of things (IoT), 3D printing, and artificial intelligence
have been applied in almost all sectors of economy and society. The revolution is a big trend that
affects socio-economic development in every country and all over the world, including Vietnam. There
are some new technologies which have not been used or just been partly used in Vietnamese
commercial banks’ business operation. Very little Internet of Things (IoT) has been applied, the banks
have not collected data and managed their services by using sensor and smart equipment yet, and
communication between digital devices is still very rare nowadays. Implementation of biometric
technology ralated to customer security just appears somewhere in a few cases. No robots are placed to
contact with customers or to do some certain tasks. Machine learning for data analysis is not very
popular either, in this industry currently.
However, these techs have been developping very rapidly in other fields, because they are very
useful and convenient to people when purchasing goods or services. So, if banks have not been
investing in that kinds of technologies, their marketshare may lost to competitors coming from other
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industries. In this research, authors decided to investigate the reasons why commercial banks in
Vietnam have not adopted much these technologies in business operation, then “Factors affecting
intention to change technology in banking operation in the 4th industrial revolution in Vietnam” had
been done to see if which factors and how they would make banks improve their techonologies. From
the project, the authors would like to offer implications for the commercial banks’ managers to
enhance technology adoption for a more modern banking, and they can meet customers’ demand. This
will lead to boost their operating effectiveness.
2. Background theory and literature review
2.1. Background Theory
Attitude toward a behavior: According to (Ajzen, 1991), attitude toward a behavior is a
personal assessement about the performance of behaviour. Attitude toward a behavior is the degree to
which result of the behavior is evaluated positively or negatively. Prior to that, (Fishbein and Ajzen,
1975) affirmed that consumers’ behavior influences their own intention. Based on this concept,
research of (Chaniotakis, Lymperopoulos, and Soureli, 2010) showed that attitude toward a behavior is
an evaluation that the behavior worths amount of money given out. (Giner-Sorolla, 1999) built scales
for model of attitude toward behavior which include the fact that a person who intended to behave
thought of those goods or services previously and believed that his or her behaviour was very good for
himself/herself. All of researches above showed that there are always relationships between attitude
and behaviour. The more positive customer’s attitude is, the more easily their intention arises.
Subjective norms: (Fishbein & Ajzen, 1975) defined subjective norms as the belief that
important people would approve and support a specific behavior, then subjective norms are determined
by the perceived social pressure from other persons for a man to perform or not to perform the
behavior. A research of (Taylor & Todd, 1995) told that this pressure comes from supportive or
unsupportive attitudes of one’s family, friends and other important people. Besides, (Ajzen, 1991)
developed his own definition of subjective norms that an individual who intended to behave in a
certain manner with supportive attitudes from influential society recognised that a lot people also did
the same way. These researches all represent the positive correlation between subjective norms and
behavioral intentions. Then, the more supportive attitudes consumers get from community, the more
easily their behavioral intentions occur.
Theory of planned behavior: The theory of planned behavior appeared in 1985 and was
developed in 1991 by Ajzen (Ajzen, 1985) to deal with some limitations of the theory of reasoned
action (TRA) (Fishbein & Ajzen, 1975). Based on TRA, this theory inherits its two factors and adds
one more that is perceived behavioral control. Perceived behavioral control refers to persons’
perception of their ability to perform the behavior of interest and if the fact of performing the given
behavior is controled or limited or not (Fishbein & Ajzen, 1975). In the theory, behavioral intentions
usually happen before actual behavior occurs, intentions are considered the precursor of behavior
(Ajzen, 1991).
Theory of transaction cost: The concept of transaction cost was first discussed by Ronald
Coase (1937) in his developing a theoretical framework related to predicting when certain economic
tasks would be conducted by firms. Transaction costs includes costs of negotiating and concluding
contracts. This theory then was developed by Foss (1996) in a research which suggested that
technological replacement could reduce transaction costs as well as production costs, then customers
could buy goods or services with lower price and the same quality. Chen and Zhu (2004) also
discussed relationship between technology and productivity and they confirmed that using technology
would improve performance and reduce transaction costs. In banking system, this kind of costs would
decrease if the firms apply supportive technology to deal with requests from customers, because the
customers could conduct the transactions anywhere instead of going to the bank. Besides, theory of
transaction costs indicated that bank’s use of technology would enhance quality of goods or services
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and improve productivity of serving their clients. Technology developing also could measure how their
transaction costs vary.
2.2. Liturature Review
Engel et al. (1978) use EKB model for researching factors related to consumer’s behavior. It
showed that basic relationships among factors which affect their behavior include four stages: (1) input
data, (2) data process, (3) stage of making buying decision, and (4) impact of external elements on the
process of making buying decision. This model emphasized the value of social norms which affect the
buyer’s behaviors.
Davis et al. (1989, 1993) built technology acceptance model (TAM) based on theory of
reasoned action (TRA), in which relationships between variables were established to explain behaviors
of human being related to process of accepting information system use. The research described relation
between usefulness, the ease of use and user’s behavior to that technology. From that result, user’s
behaviors are affected by two factors those are (1) perceived usefulness and (2) perceived ease of use.
In addition, TAM model suggested that perception of technology users is influenced by environmental
variables like experience, knowledge, training level and technology process.
Venkatesh et al. (2003) built the unified theory of acceptance and use of technology which was
developed by a systematic review and consolidation of the constructs of the models of TRA, TPB, TAM,
MM, TAM2, DOI, SCT and model of personal computer use. The comprehensive model explains
behavior and usage behavior of consumers to technology. The theory shows four key constructs
including performance expectancy, effort expectancy, social influence and facilitating conditions.
Navaretti et al. (2017) stated that technological innovation and technology-based business
models help banks create value, thanks to deliver more products and services. However, banks have to
face rising competitive presure, adopt new strategies and they have to alter their business models,
change their operation strategies and policy.
Alavudeen and Rosa (2015) analyzed the impact of technological development on business in
banking industry and challenges they must cope with in that situation. The authors concluded that
technology has significant impact on bank’s operating effectiveness and their products and services,
like ATM, large value payment systems, retail payment systems and so on which are provided to their
customers.
In this study, authors also base on behavioral theories and use previous research findings to
cary out the research as mentioned above applied for Vietnam commercial banks. Named variables are
used include perceived usefulness, perceived ease of use, trust, social influence, and innovativeness of
the products. In addition, in their studies, Venkatesh (et al. 2003) noticed the facility condition, and
Alavudeen and Rosa (2015) reported technological effectiveness as one of the components. Besides,
the theory of transaction costs brought up the effectiveness of using technology in the model. Then the
authors add product efficiency as one more variable to consider its impact on the technology changes
in banking operations in Vietnam.
3. Research model
As detailed earlier, the research model with variables related to the study of what creates
intention to replace technology in banking operations of commercial banks in Vietnam in the fourth
industrial revolution is shown as below.
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Perceived usefulness
H1(+)
H2(+)
H3 (+)
H4 (+)
H5 (+)
Perceived ease of use
Trust
Intention to change
technology
Social influence
Innovativeness
H6 (+)
Effectiveness
Fifure 1: Proposal model
3.1. Perceived Usefulness, Perceived Ease of Use
Perceived usefulness and perceived ease of use when using new products, services or
technologies have been confirmed as the most important factors which determine attitude and behavior
of customers, who are managers of banks in this study, to accept technology changing. From preceding
researches of Taylor and Todd (1995), Pikkarainen et al. (2004), Suh and Han (2002), Laforet and Li
(2005), Luarn and Lin (2005), Yiu et al. (2007) and Nguyen and Cao (2014), they all proved this with
empirical evidence. Then in this research, the authors consider them elements in the model, and state
hypothesis as follows:
Hypothesis H1: Perceived usefulness is positively related to intention to change technology in
banking operation.
Hypothesis H1: Perceived ease of use is positively related to intention to change technology in
banking operation.
3.2. Trust
Chaudhuri and Holbrook (2001), Rauyeuen et al. (2007) found that “trust leads to commitment
in business – to – business relational exchange”. Trust gives customers safety feeling when the
business fulfills their expectation. To make the customers trust the entity, form and maintain their
relationship in between for a long term, they need to perceive the safety of the deals. Then the research
model considers “trust” a variable which stems from studies of Wang et al. (2003), Suh and Han
(2002), Luarn and Lin (2005), Sudeep (2008), Safeena et al. (2011), Taylor and Todd (1995), Amin et
al. (2008), Cheng et al. (2008), Zhou et al. (2010;2012), Foon and Fah (2011), Saibaba et al. (2013),
Ali O.AI-Jaafreh (2014). Since the prominent feature of banking operation is that bank’s products and
services relate to finance and credit, they directly influence customer’s money or wealth. Then trust is
a vital element which determine buyer’s decision. For that reason, the hypothesis is proposed as below.
Hypothesis H3: Trust is positively related to intention to change technology in banking
operation.
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3.3. Social Influence
Venkatesh et al. (2003) stated that social influence is the degree of one’s perception that most
people who are important to them think that they should use new products. Researches of Venkatesh et
al. (2003); Kotier (2004); Venkatesh and Zang (2010); Foon and Fah (2011); Sripalawat et al. (2011)
all prove that social influence has impact on intention of changing technology and behavior of
customers. Amin et al. (2008) believed that personal intention to use cellphone services is affected
significantly by people around them. Singh et al. (2010) discovered that individual decision to accept
mobile services is impacted by their friends and family members. Puschel et al. (2010), Riquelme and
Rios (2010), Sripalawat et al. (2011), Chitungo and Munongo (2013), and Yu (2012) confirmed that
social influence is an outstanding factor which determines intention to use a product, service or
technology. That is why the author form the hypothesis as below.
Hypothesis H3: Social influence is positively related to intention to change technology in
banking operation.
3.4. Innovativeness
As reported by Steenkamp et al. (1999), consumer’s innovativeness is defined a trend of
purchasing new products or services, love the new and distinctative feature of that ones. Manning et al.
(1995), Agarwal and Prasad (1998), Venkatraman (1990, 1991), Steenkamp et al. (2003), and Foxall et
al. (1988) asserted that innovativeness is defined as a desire to find new and different things and
challenges in life. Research of Agarwal and Prasad (1998) shows that longing for innovativeness is a
pivotal element having impact on intention of using a new technology. Im et al. (2003), Joshua (2009),
and Sentosa et al. (2012) all have the same opinion that innovativeness impact heavily upon intention
of buyer’s acceptance of new products and services and then use them. For those reasons, the authors
propose hypothesis as follows.
Hypothesis H4: Innovativeness is positively related to intention to change technology in
banking operation.
3.5. Effectiveness
Technology has strong impact on socio-economic development, and it spurs sustained economic
growth. An economy needs at least three components those are capital, labour and technological progress
(Solow, 1987; Boskin and Lau, 1992) for its development. Reform of technology to exist in the
increasing competitive environment is essential in banking industry. Research of Schilling (2009) shows
that technology innovation allows people to have more options related to products and services, and it
also improves service quality. Innovating technology in banking operation will enhance bank’s
competitive capabilites and strengthen their performance. When the right technology policy is applied, it
will help technology develop, and technological development will spur production of more wealth, or
spur bank’s operation with greater diversity of products and services, and then the banks may work more
effectively. Technology creates added value for business. It means that technology have impact directly
on efficency and effectiveness of production and trade, since new and advanced technology used in a
right way will increase productivity, produce higher product quality, and lower production costs. This
process will create more valuation for the business and give more satisfactory to customers. Based on
above literature, the authors suggest hypothesis as below.
Hypothesis H5: Effectiveness is positively related to intention to change technology in banking
operation.
4. Research data and methods
4.1. Research Data
Hair et al. (2006) proved that the minimum of sample size is to have “at least five times as
many observations as the number of variables to be analyzed”. So, in this research, with six
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independent variables, the minimum sample size is 30. However, Hair’s study also says that
researchers would not analyze a sample of lower than 50 observations, and the sample size should be
100 or higher. Then the authors conducted a survey of 400 managers and information technology staff
working at commercial banks in Vietnam and collected 350 answer responses. After survey data
cleaning, 26 responses were removed, and 324 responses were put into analysis process. Time of
survey had been carried from June 2018 to April 2019.
4.2. Research Methods
This research had been conducted in three stages: First, the authors carried out qualititive
research by interviewing 15 experts of the field of finacnce and banking, performed a group discussion
of 50 managers who were charged with managing information technology system at commercial banks
in Vietnam in order to find out factors affecting their intention to replace technology, then built the
research model and developed scale, design survey questionnair for trial testing at the first time, before
putting them into the real test. Then, Cronbach’s Alpha was analyzed to measure scale reliability. After
that, exploratory factor analysis (EFA) and confirmatory factor analysis (CFA) were conducted.
Finally, the author used Structural Equation Model (SEM) to test the model.
5. Research results
5.1. Scale Reliability
Cronbach’s Alpha (α) was used to test scale reliability, the result showed that α of all factors
scored relatively are greater than 0.7, from which the lowest Cronbach’s Anpha of TT – Trust – is
0.830 (see Table 1 – Scale Reliability Analysis). Besides, all the corrected item-total correlations are
higher than 0.3. So, all of scale items are accepted to be reliable for the model, and the data can be
used for exploratory factor analysis.
Table 1: Scale Reliability Analysis
Factors
Measurement items
Cronbach’s Alpha
0. 868
HI
SD
TT
XH
DM
HQ
YD
Usefulness
Ease of use
6/4
4
0. 875
Trust
5/4
4
5
5
6
0. 830
0. 867
0. 870
0. 833
Social influence
Innovativeness
Effectiveness
Intention/Technology Change
0. 867
5.2. Exploratory Factor Analysis (EFA)
In order to check if the data are suitable to run exploratory factor analysis, we used Kaiser-
Meyer-Olkin (KMO) test to measure sampling adequacy for each variable in the model. Bartlett’s test
was used to check the null hypothesis H0 which states that there is no correlation among variables in
correlation matrix. According to Hoang and Chu (2008), in Bartlett’s test if p-value is lower than
Significant level of 0.05, it allows the researchers to reject hypothesis H0, and value of KMO as
0.5<KMO<1 means that the data are suitable for EFA.
Table 2: KMO Test
KMO and Bartlett’s Test
KMO (Kaiser-Meyer-Olkin of Sampling Adequacy)
Approx. Chi-Square
0.835
5014.840
496
Bartlett’s Test of
Df
Sphericity
Sig.
0.000
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KMO and Bartlett’s test result that KMO of 0.835 which is higher than 0.5 and Sig. value of
Bartlett’s test is 0.000 lower than 0.05 shows that the all the observations have correlations and are
suitable for EFA.
Variance Explained Criteria: EFA can be used if variance explained criteria is not smaller that
50%. Then with result after running EFA, total variance explained is 58.56% and Eigenvalues of all
factors are greater than 1, EFA is suitable for this research model.
Table 3: Result of EFA for factor scale
Factor loadings
Items
1
0.793
0.773
0.768
0.687
0.640
0.626
2
3
4
5
6
7
YD4
YD6
YD1
YD2
YD5
YD3
DM1
DM4
DM2
DM3
DM5
HQ2
HQ3
HQ5
HQ1
HQ4
0.855
0.825
0.804
0.710
0.608
0.772
0.721
0.698
0.697
0.645
SD3
SD4
SD1
SD2
0.805
0.801
0.797
0.783
HI4
HI1
HI6
HI3
0.826
0.809
0.808
0.733
XH3
XH2
XH4
XH1
0.861
0.783
0.766
0.754
STT4
STT1
STT3
STT2
Eigenvalues
Average Variance
Extracted (%)
0.782
0.779
0.746
0.682
1.607
3.644
6.237
18.153
3.166
8.716
2.78
7.486
2.739
7.327
2.583
6.906
2.45
6.328
Total Variance Explained: 58.556%
Result shown above indicates total 32 observations with seven factors then they can be used
for next steps of the analysis process.
5.3. Confirmatory Factor Analysis (CFA)
Before conducting the analysis, the authors test if the research model fit the data collected.
Result of the test is shown in Table 4 – Indices of model fit analysis
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Table 4: Indices of Model Fit Analysis
Indices
CMIN/DF
Value
1.289
0.907
GFI
TLI
0.970
CFI
0.973
0.030
RMSEA
Information from table 4 displays all needed indices meet the related condition: CMIN/DF is
1.289 which is smaller than two, TLI, CFI and GFI all are greater than 0.9, and RMSEA is 0.030 that
is lower than 0.08. Therefore, the model fit the research data. However, it still needs to consider about
scale reliability, convergent validity, unidimensionality, and discriminant validity of research data.
Reliability assessment of measurement model
The measurement model is reliable if construct reliability (CR) and average variance extracted
(AVE) both are greater than 0.5 (Hair et al. 1995, and Nunnally, 1978). From the table below, the CR
and AVE of all factors are above 0.5. Hence, the measurement model is considered reliable.
Table 5: Construct Reliability and Average Variance Extracted
Factors
Construct Reliability (CR)
Average Variance Extracted (AVE)
YD
DM
HQ
0.868
0.874
0.834
0.522
0.585
0.502
SD
0.874
0.635
HI
XH
TT
0.873
0.869
0.833
0.633
0.624
0.555
Convergent Validity Test
Convergent validity of a factor is accepted if all standardized loadings of items are significant
and higher than 0.5 (Gerbing and Anderson, 1988; Hair et al., 1992). Besides, one more criterion is
used to check convergent validity is average variance extracted (AVE) of the items. Fornell and
Larcker (1981) confirmed that a factor would reach convergent validity if AVE is 0.5 or higher. As
result of this test, all standardized and unstandardized loadings of items above are higher than 0.5, and
all AVE are greater than 05 as well, then all factors are considered reaching convergent validity.
Test of Unidimensionality
As Steenkamp and Van Trijp (1991) stated, if the set of items is unidimensional, the research
model would fit the research data, unless there is correlation between measurement errors. From the
result of the test, the model is considered fit research data, and there is no correlation between
measurement errors, and the items are unidimensional.
Discriminant Validity Test
According to Fornell and Larcker (1981) and Henseler et al. (2015), criteria for discriminant
validity assessement include (1) Evaluate if Heterotrait-monotrait (HTMT) ratios of correlation
between factors are different from one or not, and (2) Compare square root of construct’s AVE with
the correlation of latent constructs, and the square root of each construct’s AVE should have a higher
value than the correlations with other latent constructs.
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Table 6: Matrix of construct’s correlation
YD
1
DM
HQ
SD
1
HI
XH
TT
YD
DM
HQ
0.395 1
0.432
0.312
0.322
0.137
0.296
0.132 1
SD
HI
0.153
0.084
0.106
0.161
0.108
0.120
0.033
0.123
1
0.042
0.053
0.069
1
XH
TT
-0.034
0.047
1
0.083
Result of the test shows that correlation ratios between factors are distinct from one with 95
percent confidence interval. When comparing square root of construct’s AVE with the inter-construct
correlation of latent constructs, the table shows that each factor’s AVE is higher than square of the
correlation between that factors and others. With information as shown, it is conclusion that all
constructs or measurement scales reach discriminant validity.
The confirmatory factor analysis CFA is conducted as displayed below.
Figure 1. The confirmatory factor analysis model
5.4. Structural Equation Model (SEM)
5.4.1. Assessment of Model and Hypothesis Testing
Result of model evaluation is shown at Figure 2 – Result of structural Equation Model SEM.
The result displays that the model fit well the research data with Chi square/df= 1.289 (<2); TLI=
0.970 (> 0. 9); CFI = 0.973 (>0. 9); RMSEA= 0.03 (<0.08).
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Figure 2. Result of Structural Equation Model (SEM)
5.4.2. Research model based on SEM result analysis
Outcome of running SEM indicates that factor XH – Social influence with p>0.05 is not
statistically significant, and it means that this factor has no impact on intention to change technology,
meanwhile, factor HQ – Effectiveness has the strongest influence on intention of technological alteration
of bank managers with standardized Beta is 0.320. The standardized Beta of factor DM – Innovativeness,
HI – Usefulness, SD – Ease of Use, and TT – Trust are 0.262, 0.247, 0.262, and 0.182 respectively.
Table 7: Analysis Result of Structural Equation Model SEM
Correlation between factors Estimate
S.E.
0.054
C.R.
P
Standardized Beta coefficient
0.247
YD
YD
YD
YD
YD
YD
<---
<---
<---
<---
<---
<---
HI
0.246
0.178
0.242
0.153
0.063
0.234
4.537 0.000
3.872 0.000
5.433 0.000
3.270 0.001
1.549 0.121
4.674 0.000
SD
HQ
TT
XH
DM
0.046
0.045
0.047
0.041
0.050
0.211
0.320
0.182
0.081
0.262
Therefore, conclusion form using SEM for analysis research data, factors affecting intention to
change technology in banking operation in the fourth industrial revolution in Vietnam include
Perceived usefulness, Perceived ease of use, Effectiveness, Trust, and Innovativenes, which are
accepted with 95 percent confidence interval.
6. Discuss and implications
6.1. Discuss of Result
Change of technology is one of essential elements which spur operating performance in
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Kỷ yếu Hội thảo quốc tế “Thương mại và phân phối” lần 2 năm 2020
banking system. Result of the empirical research conclude that the technoloy replacement at
commercial banks in Vietnam recently is affected by factors of perceived usefulness, perceived ease of
use, trust, innovativeness and effectiveness. Of these factors, “Effectiveness” (HQ) is the new variable
which is put into the research model built and applied for commercial banks in Vietnam under the
fourth industry revolution. “Effectiveness” of using technology impacts on the change of technology in
banking operation, and it is the most influence factor in the reseach with coefficient of 0.320). This
result is similar to previous stuties of Navaretti et al. (2017) which stated that technology innovation
would help banks get better performance. The analysis conducted by Mala (2017), Alavudeen and
Rosa (2015), Campanella, Peruta and Giudice (2015), Ho and Mallick (2006), Pham (2017), and Bui
(2017) also have the same conclusions, and confirm that technology bring a lot of important value,
increasing competitive capability, enxpanding sales and market share to the banks. This research
outcome also reveals that theory of transaction costs totally fits at commercial banks in Vietnam.
Other factors of the research including perceived usefulness, perceived ease of use, trust and
innovativeness all are positively related to intention to replace technology in banks. The conclusions all
agree with those of Taylor and Todd (1995), Pikkarainen et al. (2004), Suh and Han (2002), Laforet and
Li (2005), Luarn and Lin (2005), Yiu et al. (2007), Venkatesh and Zang (2010), Safeena et al. (2011),
Sripalawat et al. (2011), Dasgupta et al. (2011), Muneer et al. (2012), Chitungo and Munongo (2013),
Nguyen and Cao (2014). Of which, Innovativeness (DM) is the second strongest impact factor with beta
coefficient of 0.262, while Trust (TT) is the lowest influence one with the coefficient of 0,182.
6.2. Implications for Commercial Banks in Vietnam
Based on research output, the authors have implications for commercial banks in Vietnam as
below.
First, Innovativeness is positively related to changing technology at banks, hence, the banks
need to boost business model applying technology, continue to advance existed operation technology,
and replace the old and obsolete technology by modern solutions.
Second, the banks should improve the factor of perceived usefulness (HI) by diversifying their
useful technology-based products and expanding types of hi-tech services. The banks should also
widen uniting with hi tech service suppliers, which would build up products’ usefulness for customers.
Third, banks should try to use current technology effectively to save costs and consequently
enhance operating performance. Besides, having high-skilled employees is needed to run a hi-tech
system, then the banks should build a strategy for training and developing labour forces for a long
term, focus on cooperation with international bodies in this field and try to take advantages related to
finance and training support from partners.
And finally, commercial banks should reinforce customer’s trust in current technology, it
means that it is vital to ensure the absolute safety of customer’s data.
Limitation and recommendation for future research: In this research, the authors have not
investigated the relationship between intention to replace technology and decision of changing it. Then
this issue will be considered in next study.
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