THE IMPACT OF INFORMATION TECHNOLOGY ON IMPROVING BANKING PERFORMANCE EVIDENCE FROM VIETNAM

CONFERENCE PROCEEDINGS
4th International Conference on Contemporary Issues in

ECONOMICS, MANAGEMENT AND BUSINESS
November 11th – 12th, 2021, Hanoi - Vietnam

NATIONAL ECONOMICS UNIVERSITY PUBLISHING HOUSE


CONFERENCE PROCEEDINGS
4th International Conference on Contemporary Issues in

ECONOMICS, MANAGEMENT AND BUSINESS
November 11th – 12th, 2021, Hanoi - Vietnam

NATIONAL ECONOMICS UNIVERSITY PUBLISHING HOUSE


HUMAN RESOURCE MANAGEMENT AND INTRAPRENEURIAL
BEHAVIORS IN VIETNAMESE SUBSIDIARIES OF JAPANESE
MULTINATIONAL COMPANIES: DO WE NEED INNOVATIVE HUMAN
RESOURCE MANAGEMENT? ......................................................................... 1189
Tran Huy Phuong
National Economics University
THE INFLUENCE OF TALENT DEVELOPMENT PRACTICES ON TEACHER
PERFORMANCE IN GENERAL SCHOOLS IN HANOI ................................. 1209
Nguyen Thuy Van Anh
Faculty of Human Resource Management and Economics,
National Economics University
Pham Tung Anh
International School of Management and Economics,

National Economics University
THE FACTORS AFFECTING DEVELOPMENT OF HIGH QUALITY HUMAN
RESOURCE IN HIGH-TECH AGRICULTURAL ENTERPRISES IN VIETNAM ... 1233
Le Thi Hien
Thuongmai University
THE DIRECT AND INDIRECT EFFECTS OF GREEN HUMAN RESOURCE
MANAGEMENT ON EMPLOYEES’ ORGANISATIONAL COMMITMENT .... 1252
Nguyen Ngoc Phu, Nguyen Ngoc Thang
Hanoi School of Business and Management, Vietnam National University
Tran Thi Van Hoa
National Economics University
Nguyen Thi Thu Huong
Ghent University
SESSION 16: TECHNOLOGY & INNOVATION
OPEN INNOVATION AND INTERNAL R&D EXPENDITURES: THE
MEDIATING ROLE OF ABSORPTIVE CAPACITY ....................................... 1267
Tran Lan Huong, Le Tri Nhan
Nguyen Thi Ngoc Anh, Nguyen Thuy Linh
Faculty of Management Science, National Economics University
THE IMPACT OF INFORMATION TECHNOLOGY ON IMPROVING
BANKING PERFORMANCE: EVIDENCE FROM VIETNAM ....................... 1287
Vu Thi Huyen Trang
Thuy Loi University


THE IMPACT OF INFORMATION TECHNOLOGY ON IMPROVING
BANKING PERFORMANCE: EVIDENCE FROM VIETNAM
Vu Thi Huyen Trang
Thuy Loi University
Abstract:

The paper analyses the impact of investment in information technology (IT) on the
performance of Vietnamese commercial banks. The study applies the random-effects
model (REM) to the data of 30 Vietnam’s commercial banks in the period from 2016
to 2020. The results show that an increase (decrease) in IT investment (Technical
infrastructure, IT human resource infrastructure, Online banking service) leads to an
increase (decreased) ROA, ROE of Vietnamese commercial banks. Based on the
findings, the authors give some recommendations to Vietnamese commercial banks
in case of investments in IT to improve performance.
Keywords: Commercial banks, information technology, performance, ROA, ROE.
1. Introduction
Gunasekaran et al. (2001) argue that because of globalization and development
in information technology, thereby stimulating and strengthening the establishment
of global competition. As a result, businesses are forced to spend billions of dollars
on investments in new IT infrastructure to remain sustainable and competitive in the
market (Nustini, 2003). However, the economic recession of 2008, has compelled
companies to reassess IT investments, the benefits or returns they are likely to derive
in the future investments in IT infrastructures (Alves, 2010; Creswell, 2004;
Czerwinski, 2008; Gunasekaran et al., 2001; Tynan, 2005). Many companies have
responded to the changing business environment by transforming their IT strategies
and investing significant sums in new IT infrastructure to improve their performance
and stay competitive. However, the returns from these IT expenditures are difficult
to measure (Dehning and Richardson, 2002; Gunasekaran et al., 2001; Nustini, 2003).
Lloyd-Walker and Cheung (1998) have shown that in the banking industry, IT
can help deliver superior customer services by providing a fast, accurate, and reliable
service. Kim and Davidson (2004) stated that the banking industry environment has
become IT-intensive. Porter and Millar (1985) emphasizes that the banking industry
has a high IT content in both products and processes, just like journalism and aviation.
Thus, the banking industry is one of the industries that use accounting information
systems (AIS) with a very high IT content, which has contributed to banking
operations, reducing costs, time and improving service quality offered to customers.

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However, investing in information technology is an expensive process that requires
considerable effort, time, and money at every stage (planning, analysis, design,
development, implementation and upgrade).
Many studies have tried to show the direct impact of accounting information
systems, specifically investment in information technology on business performance,
but the results of these studies are different. The researchers conducted in the first
half of the 1990s by Strassmann (1990), Weill (1992), Brynjolfsson (1993), and
Landauer (1995) showed that there was no link between investments in IT and
business performance. However, the researchers conducted in the second half of the
1990s by Brynjolfsson (1995), Dewan (1997), Hitt (1996) concluded that there is a
positive relationship between investment in IT and enterprise performance. Because
the research results on the relationship between IT investment and business
performance in the world show many different results, so empirical studies in this
area are still very necessary.
By interpreting the previous findings on "the productivity paradox", our
research attempts to empirically validate the relationship between IT investment and
performance in the context of the emerging country of Vietnam. Our study is
therefore devoted to examining the following key question: What is the impact of
information technology on the performance of Vietnam commercial banks?
To empirically validate the relationship between IT investment and the
performance of Vietnam commercial banks, we use the most commonly used
traditional measures such as ROA and ROE. Thus, the objective of this work is to
evaluate the performance of banks during the period 2016–2020 while identifying,
the impact of different information technologies components introduced by banks on
their performance. The paper is organized as follows. Section 2 provides the
theoretical Foundation and literature review for our study. Section 3 outlines the
methodological approach and illustrates the sample and data. Finally, Section 4

describes the empirical results, and Section 5 is the conclusion.
2. Literature review
Through the review, the author found that there are many studies on the
relationship between IT investment and performance, and these studies give different
results. Several studies examine the correlation between IT investment and ROA (Shin,
2001; Rai et al., 1997; Hitt and Brynjolfsson, 1996; Weill, 1992; Strassmann, 1997),
between IT spending and ROE (Shin, 2001; Rai et al., 1997; Hitt and Brynjolfsson,
1996), and between investment in IT and intermediate variables of performance, which
is turn drive profits (Markus and Soh, 1993; Barua et al., 1995). However, these studies
only focus on other industries, but there are few studies in the banking industry.
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Brynjolfsson and Hitt (1994) separate the benefits of investing in information
technology into three distinct areas: increased productivity, improved business
performance, and increased value for consumers. The sample includes 367 Fortune
500 manufacturing and service companies in surveys conducted by the International
Data Group and from Standard and Poor's Compustat. The author's analysis is based
on production theory, competitive advantage theory, and consumer theory. The
dependent variables measuring the business performance include value-added, total
shareholder return, ROA, ROE. The independent variables that measure investment
in information technology are used by the same author in his research in 1993,
including information system labor, non-information labor, computer capital, noncomputer capital, firm size, year, industry. The research methods used by the author
for each different data set are OLS regression analysis, estimation methods unrelated
regressions (ISUR), 2-stage regression method (2SLS). The author finds that there
are many contradictions around the Productivity Paradox, where IT spending has a
positive impact on productivity and creates significant value for consumers.
However, IT expenditures have little, if any, positive impact on business performance
and may have a negative impact.
Brynjolfsson and Hitt (1996) used 1000 observations between 1987 and 1991

that appeared simultaneously in the International Data Group (IDG) and Standard and
Poor's Compustat II data sources. The independent variable measuring IT spending
in the study is IT stock - the market value of a company's IT systems plus three times
the company's spending on IT personnel. Dependent variables measure the
performance of the business: ROA, ROE. Research results show that there is a
positive relationship between IT stocks and ROA for three consecutive years, but
there is no relationship between IT stocks and ROE.
Shin (1997) studied the relationship between IT investment and coordination
costs – selling and administrative costs minus non-administrative costs (e.g.,
advertising costs, research and development costs). Shin's research results are in
contrast with the research results of Mitra and Chaya (1996). Shin (1997) shows that
IT spending has a negative relationship with coordination costs. Further, Shin (1997)
found that there was a positive relationship between non-administrative expenses and
administrative costs. Therefore, Mitra and Chaya's (1996) finding was a result of the
exclusion of non-administrative expenses from general and administrative.
Apparently, Mitra and Chaya's (1996) research design was based on scaling by sales,
while Shin's (1997) research design was based on scaling by employees’ census. To
harmonize these conflicts in findings and obtain understandable relationships
between IT expenditure and selling, general and administrative expenses, additional
studies are inevitable (Dehning and Richardson, 2002). Further, Shin (1997)
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examined the combination of IT expenditure and coordination costs plus the cost of
capital, cost of labor, and research and development costs in order to elucidate a
company’s aggregate number, which is sales plus change in inventory number. Shin
found a company’s productivity is positively related to IT expenditure, costs of
coordination, cost of capital, cost of labor, and research and development expenses.
Rai et al. (1997) was an attempt to validate Shin’s findings and it, by and large,
confirmed Shin’s findings. Rai et al. (1997) used a sample of 497 companies from

1994 Information Week and 1994 Compustat. Selected companies are among the top
500 with the highest cost data for IT. The independent variables used by the author to
measure IT investment include IT capital, budget, server, staff training, hardware,
software, telecommunication equipment. Dependent variables to measure performance
include company output (direct labor division revenue, total revenue), operating
efficiency (ROA, ROE), intermediate operating efficiency (direct labor productivity,
management productivity). Control variables: firm size and company sector. Rai et al.
(1997) found a positive relationship between a company’s productivity and all
expenditure measures. Additionally, they found a positive relationship between IT
capital, server expenses, and ROA. Rai et al. (1997) conducted a productivity test, and
the results indicated that labor productivity is positively associated with IT capital, IT
budget, server expenses, IT employee expenses, software expenses, and telecom
expenses. IT investment positively affects labor productivity directly but negatively
affects the productivity of managers. The limitation of this study is that it used crosssectional data at one point in time, not multiple time points.
According to the author's knowledge, there are some studies on the
relationship between IT investment and bank performance. The author focuses on
studies that use the same research method in this research.
Beccalli (2007) expanded on previous studies on IT investment and
performance of 737 banks in Europe (specifically in France, Germany, Italy, Spain,
UK) for the period from 1995 to 2000. Independent variables: IT investment in
hardware, software, and other IT services. Dependent variables: ROA, ROE, costeffectiveness, profit efficiency. The author uses methods: OLS regression, two-stage
regression (2SLS), and SFA. Despite banks being major investors in IT the research
finds little relationship between total IT investment and improved bank profitability
or efficiency indicating the existence of a profitability paradox. The impact of
different types of IT investments is heterogeneous: while investments in hardware
and software reduce the efficiency of banks, IT services from external providers have
a positive effect on ROA, ROE, and profit efficiency. This study of the author has
overcome some limitations of previous studies by using both a traditional accounting
profit measure (ROA, ROE) and a more advanced measure of operational efficiency
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called X-efficiency. Moreover, the author does not study investment in IT as a single
variable like previous studies but has specifically divided it into three components of
IT investment namely hardware, software and IT services to consider the IT
investment in different areas have different effects on bank performance.
Karim and Hamdan (2010) studies the impact of IT investment on the
performance of 15 Jordanian banks in the period from 2003 to 2007. Independent
variables on IT investment in the article include investment in hardware, software,
online banking, telephone banking, number of ATMs, use of online branches, and SMS
banking. Dependent variables include: financial performance such as market value added
(MVA), return on investment (ROI), and return per share (EPS), and operating efficiency
includes net profit margin (NPM), return on assets (ROA), and employee profitability
(PE). Using the regression method, the research results show that IT affects market
value added (MVA), return per share (EPS), and return on total assets (ROA). and
net profit margin (NMP), but IT has no effect on return on equity (ROE).
Kabiru (2012) studies the impact of IT investment on the performance of banks
in Nigeria in the period from 2000 to 2010. Independent variables on IT investment
include investment in hardware, software, and the number of ATMs. The
performance-dependent variable is a return on assets (ROA). The study used
multivariate regression analysis. Research results show that investment in software,
investment in hardware, and the number of ATMs have a significant impact on return
on assets (ROA) because the t-statistics are all significant at this point 1 percent level.
Bilkisu and Kabiru (2015) studies the impact of IT investment on the
performance of 10 banks in Nigeria in the period from 2006 to 2010. The
independent variable of IT investment includes hardware investment, software
investment, and ATMs. The dependent variables include return on total assets
(ROA), return on equity (ROE), net profit margin (NPM), and earnings per share
(EPS). The control variables: total revenue (TR) and total cost (TC). Research
using regression method, the results show that IT investment has a negative effect

on ROA, ROE, and EPS at 5% significance level, but not statistically significant
with NPM at 5% and 10% level significance. This means that an increase in IT
investment leads to a decrease in the performance of Nigerian banks, hence the IT
productivity paradox in the Nigerian banking industry.
Tam (2015) researched the impact of technology investment on the
performance of the commercial banking system in Vietnam, thereby assessing the
impact of technology investment on banks. At the same time, give recommendations
to commercial banks on the level of investment in technology to improve the
operational efficiency of Vietnamese commercial banks. Using the GMM method for

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one-year lagged dynamic panel data of 15 commercial banks in Vietnam with data
for six years (2009-2014), the study analyzed the impact of IT on ROE and ROA.
The resulting research showed that when other factors held constant, increasing IT
(ratio of technology investment on fixed assets) by 1% will increase ROA (rate of
return on total assets) by 10%. In addition to IT, the operational efficiency of the
commercial banking system in Vietnam was also affected by factors such as the ratio
of liquid assets to total assets (liquidity) and macro factors such as economic growth
rate (GDP), consumer price index (CPI) and exchange rate change (tygia), but the
level of impact of these factors was quite low in the model.
Many researchers have researched the relationship between IT investment and
operational performance. However, the research results still lack consistent in research
results and the author found that previous studies have the following limitations:
- The cross-sectional research designs adopted and the nature of the problem
also do not make it possible to indicate the causality of any relationship. It is
conceivable that high performance causes high investment in IS. A more historical
approach is required that gathers time investment in IT. A more historical approach
is required that gathers time-series data and presents more cautious conclusions about

the relationships involved.
- The variables chosen to represent IT investment and performance were different.
- The following three issues remain unresolved: (1) All companies were
assumed to convert their IT investments to produce outputs with the same level of
success, (2) All IT investments were treated equally, (3) The time lag between
investment and performance was ignored.
The limitations of these prior studies are also the prior research gaps that this
paper attempts to fill. Our paper, therefore, aims to investigate the existence of the IT
profitability paradox for the Vietnam banking industry, and to extend and integrate
the above IT literature by focusing on the traditional profitability measures derived
from the banking literature.
3. Methodology
3.1. Methodology
The study uses a regression method to examine the relationship between IT
investment and performance of Vietnamese commercial banks whether it follows the
productivity paradox. The author uses a model to analyze the relationship between IT
investments and performance according to Strassmann (1990), Beccalli (2007) as follows:
Pt = β0 + βtITt + Ɛt

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where: Pt: annual accounting performance ratios; ITt: IT capital investment or
IT ratios (IT to various size measures); Ɛt: error term. Each variable refers to the
banking industry at time t.
It should be noted that the performance measure used in these models refers
to financial profitability. Two measures of bank performance have been employed
here: 1- ROA, which measures how effectively a bank utilizes its assets to earn
income. 2- ROE, which provides a measure – increasingly examined by managers –
of how well the bank is managing resources invested by shareholders.

In this research, similar to Beccalli's (2007) study, the author does not use only
one overall IT variable, but the author will divide the IT variable into four component
variables: technical infrastructure (TI), human resource infrastructure (HR), banking
internal IT application (IA) and online banking services (OS).
where:
- Technical infrastructure (TI) is an average variable from 5 indicators: Server
and workstation infrastructure; Communication infrastructure, ATM/POS
infrastructure; Deploying information security and data safety solutions; Datacenter
and disaster recovery center.
- Human resource infrastructure (HR) is an average variable from 3 indicators:
Percentage of staff specialized in IT, Percentage of staff in charge of information
security, Percentage of IT professionals with international information technology
certificates/Total number of specialized IT staff.
- Bank's internal IT application (IA) is an average variable from 3 indicators:
deploy Core banking, deploy basic applications, deploy electronic payments.
- Online banking service (OS) is an average variable from 5 indicators: website
of the bank, internet banking for individual customers, internet banking for corporate
customers, other e-banking services, other e-banking services.
The author chooses such IT investment variables to overcome two limitations
in previous studies. First, the previous studies assumed that all firms are converting
their IT investments into outputs with the same degree of success (Huang, 2002).
Previous studies were based on data on IT investment costs, but the results of the IT
investment process could not be clarified. Therefore, the use of IT investment
performance indicators will overcome this limitation. These are the general indicators
developed by the Ministry of Information and Communications of Vietnam for the
general assessment of commercial banks, so the indicators are comprehensive in
terms of IT aspects and are quite reliable. Second, many previous studies assume that
all investments in IT are treated equally by using only one aggregate IT variable
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(Huang, 2002). In the study, the author uses four IT variables namely technical
infrastructure, IT human resource infrastructure, banking internal IT application, and
banking online services, so the specific impact of each type of IT investment will be
measured on bank performance.
To consider the impact on the performance of the various categories of IT
investments, the estimated equation is:
Pt = β0 + βtTIt + βtHRt + βtIAt + βtOSt +Ɛt
Where: TIt= Technical infrastructure; HRt= Human resource infrastructure;
IAt= Bank's internal IT application; OSt= Online banking service.
Through the overview literature, the author adds some control variables
including: a loan to total assets ratio (LOTA) (Isik and Hassan, 2003), equity to total
assets (ETA) (Berger and DeYoung, 1997), the bank’s total asset to total assets of the
bank industry over the same period (MARK) (Isik and Hassan, 2003), Deposit to loan
(DLR) (Hung, 2008).
3.2. Data sources
Research using the information on IT investment in banks in terms of technical
infrastructure, human infrastructure, internal banking IT application, and banking
online services from Vietnam IT index report as well as data from financial
statements of 30 commercial banks for the period from 2016 to 2020. After excluding
some banks that do not participate in the Vietnam ICT index report and some banks
that do not disclose financial statement information, we have data include 138
observations presented in Table 1.
Table 1: The banks list during 2016 -2020
No

Bank

Code

Observations

1

Tien Phong Commercial Joint Stock Bank

TBP

4

2

Nam A Comercial Join Stock Bank

NAB

5

3

JSC Bank for Investment and Development of Vietnam

BID

5

4

VietNam Technological and Commercial Joint Stock Bank

TCB

5

5

Military Commercial Joint Stock Bank

MBB

5

6

JSC Bank for Foreign Trade of Vietnam

VCB

5

7

Vietnam Thuong Tin Commercial Joint Stock Bank

VBB

3

8

Orient Commercial Joint Stock Bank

OCB

5

9

Sai Gon Joint Stock Commercial Bank

SCB

5

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No

Bank

Code

Observations

10

Sai Gon Thuong Tin Commercial Joint Stock Bank

STB

5

11

Ho Chi Minh City Housing Development Bank

HDB

5

12

Bac A Commercial Joint Stock Bank

BAB

5

13

Southeast Asia Commercial Joint Stock Bank

SSB

5

14

An Binh Commercial Joint Stock Bank

ABB

5

15

Vietnam Prosperity Joint Stock Commercial Bank

VPB

5

16

Kien Long Commercial Joint Stock Bank

KLB

4

17

Vietnam International and Commercial Joint Stock Bank

VIB

5

18

Vietnam Maritime Joint – Stock Commercial Bank

MSB

3

19

Vietcapital Commercial Joint Stock Bank

BVB

5

20

Joint Stock Commercia Petrolimex Bank

PGB

4

21

Vietnam Bank for Agriculture and Rural Development.

AGB

5

22

Saigon – Hanoi Commercial Joint Stock Bank

SHB

5

23

Asia Commercial Joint Stock Bank

ACB

5

24

Vietnam Asia Commercial Joint Stock Bank

VAB

4

25

Vietnam Public Joint Stock Commercial Bank

PVB

5

26

Saigon Bank for Industry and Trade

SGB

4

27

Vietnam Export Import Bank

EIB

5

28

Vietnam Joint Stock Commercial Bank for Industry and Trade

CTG

5

29

Bao Viet Joint Stock Commercial Bank

BAO

4

30

National Citizen Commercial Joint Stock Bank

NCB

3

The study used STATA software to conduct correlation analysis between
variables, build regression models, and test models. The research study explains the
level of impact of the independent variable on the dependent variable. Finally, a
predictive model from the research sample is given.
4. Empirical results
The descriptive statistics of the independent and dependent variables
shown in Table 2 show the mean, standard deviation, maximum and minimum
values of the variables. The results show that the outliers were removed from
the study sample.

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Table 2: Descriptive statistics of variables
Variables

Code

Mean Std.Dev

Min

Max

Technical infrastructure

TI

0.4583

0.1198

0.1535 0.7586

Human resource infrastructure

HR

0.3876

0.2376

0.0000 1.0000

Bank's internal IT application

IA

0.4913

0.2174

0.0000 1.0000

Online banking service

OS

0.5809

0.1949

0.0150 1.0000

Loan to total assets ratio

LOTA

0.6205

0.0923

0.3539 0.8006

Equity to total assets

ETA

0.0770

0.0292

0.0262 0.1845

The bank’s total asset to total
MARK 0.0362
assets of the bank industry over the
same period

0.0425

0.0021 0.1479

Deposit to Loan

DLR

1.1362 0.17098 0.7311 1.7231

Return on Asset

ROA

0.0078

0.0068

0.0001 0.0286

Return on Equity

ROE

0.0991

0.0715

0.0008 0.2583

Next, the study will test to assess whether the fixed effects model (FEM) or
the random effects model (REM) is a suitable model for measuring the impact of IT
investment on bank performance. If the residuals and the independent variables do
not correlate with each other, choose the random-effects model (REM) and vice versa,
choose the fixed effects model (FEM).
The Hausman test is performed with the following hypothesis:
H0: The REM model is the suitable model
H1: The FEM model is the suitable model
With the results of running the Hausman test according to Table 3, prob. = 0.3272 <
0.05, so the hypothesis H0 is accepted, so the random effects model (REM) is suitable.
Table 3: The results of Hausman test
Chi2(8) = 9.18
Prob > chi2 = 0,3272

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Sqrt (Diag (V_bV_B))

(b)

(B)

(b-B)

Fem

Rem

Difference

TI

.0079514

.0081922

-.0002408

.0002149

HR

.0031575

.0022171

.0009405

.0006271

IA

-.0025848

-.0024459

-.0001389

.000206

S.E.


Sqrt (Diag (V_bV_B))

(b)

(B)

(b-B)

Fem

Rem

Difference

OS

.0036122

.0042793

-.0006671

.00032

LOTA

.0003739

-.0077944

.0081683

.0056112

ETA

.0907751

.0935383

-.0027631

.0082505

MARK

.0837439

.0417718

.0419721

.0825756

DLR

-.0105884

-.013843

.0032546

.0019444

S.E.

The results of the regression according to the random effects model (REM) are
shown in Table 4.
Table 4: The regressions according to the random effects model (REM)
Variables

ROA

ROE

Coef.

(P>|t|)

(Coef.)

(P>|t|)

TI

0.0082*

0.000

0.0778*

0.011

HR

0.0022***

0.060

0.0197

0.231

IA

-0.0024***

0.091

-0.0355***

0.083

OS

0.0043*

0.000

0.0507**

0.002

LOTA

-0.0078

0.419

-0.0073

0.951

ETA

0.0935

0.000

-0.1475

0.577

MARK

0.0418

0.048

0.4403

0.111

DLR

-0.0138

0.000

-0.1489

0.001

_cons

0.0136

0.161

0.2114

0.091

Observations

138

138

R-Squared

48.76%

36.82%

Wald chi2(8)

145.56

73.32

p-value

0.0000

0.0000

Note: *, **, *** means statistically significant at the 1%, 5% and 10%.
The regression results according to REM are shown in Table 4. First, there is
a positive and statistically significant correlation between technical infrastructure
(TI), online banking services (OS), and ROA at 1% statistically significant; human
resource infrastructure (HR) and ROA at 10% statistically significant. Meanwhile,
there is a positive and statistically significant correlation between Bank's internal IT
application (IA) and ROA at 10% statistically significant.
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Similar results, there are three independent variables about IT, namely
technical infrastructure (TI) and online banking services (OS), with p_value < 0.01
and p_value < 0.05, showing that these variables are statistically significant at the
level of significance at 1% and 5% having a positive impact on the dependent variable
ROE. There is another independent variable, which is the online banking services,
with p_value < 0.1, showing that this variable has statistical significance at the 10%
significance level, the sign of the regression coefficients has a positive sign. The
human resource infrastructure (HR) variable had a negative impact on ROE but there
is no statistical significance.
From the table of regression results, the study identifies a regression model
that reflects the level of factors to the performance of Vietnamese commercial banks.
ROA = 0.0136 + 0.0082 TI + 0.0022HR – 0.0024IA + 0.0043OS –
0.0078LOTA + 0.0935 ETA + 0.0418 MARK - 0.0138 DLR (1)
ROE = 0.2114 + 0.0778 TI + 0.0197HR – 0.0355IA + 0.0507OS –
0.0073LOTA - 0.1475 ETA + 0.4403MARK - 0.13489 DLR (2)
From the regression equation (1), it shows that if other factors are
constant, IT infrastructure increases by 1%, ROA will increase by 0.79%;
internal IT applications increased by 1%, ROA decreased by 0.24%; Online

banking services increased by 1%, ROA increased by 0.37% and IT personnel
increased by 1%, ROA increased by 0.31%. This result shows that IT variables
such as IT technical infrastructure, Online banking service, and IT human
resources have a positive influence on ROA and these variables are statistically
significant. That is, the investment of the bank's resources in these variables will
increase the bank's performance. Meanwhile, the internal IT application variable
has a negative effect on ROA, the investment in IT internal application will reduce
ROA due to the effect of the productivity paradox.
From the regression equation (2), it shows that if other factors are constant, IT
infrastructure will increase by 1%, ROE will increase by 7.69%; internal IT
applications increased by 1%, ROE decreased by 3.83%; Online banking services
increased by 1%, ROE increased by 4.27% and IT personnel increased by 1%, ROE
increased by 3.68%. This result shows that IT variables such as IT technical
infrastructure, Online banking service, and IT human resources have a positive
influence on ROE and these variables are statistically significant. That is, the
investment of the bank's resources in these variables will increase the bank's
operational efficiency. Meanwhile, the variable IT internal application has a negative
effect on ROE, the investment in IT internal application will reduce ROE due to the
effect of the productivity paradox.
1298


5. Conclusions
This study aims to analyze the influence of IT investment on the performance
(ROA, ROE) of Vietnamese commercial banks. The research results show that three
IT factors, namely IT technical infrastructure, online banking services, and IT human
resource infrastructure, have an influence on ROA and ROE with statistical
significance of 1%, 5%, and 10%, respectively. The sign of the regression coefficients
has a positive sign, showing a positive relationship between IT investment and ROA
and ROE in Vietnamese commercial banks. Only one IT factor, which is an internal

application of banking IT, does not affect ROE and affects ROA with a statistical
significance of 10%. The sign of the regression coefficient has a negative sign,
showing that there is a negative relationship between bank IT internal application and
ROA, ROE. There is exists a productivity paradox between investment in banking IT
internal application and performance.
Based on these results, the author makes recommendations for banks to
promote investment in IT in the aspects of IT technical infrastructure, banking online
services, and IT human resource infrastructure because it increases performance.
However, it is necessary to study and consider carefully the investment in the internal
application of banking IT because there is a productivity paradox, the investment in
the internal application of banking IT can reduce ROA and ROE.
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