Research on the Effects of Information Technology on Operational Efficiency in Vietnam Commercial Banks Using the Data Envelopment Analysis Method

2021 International Conference on Finance, Accounting and Auditing CONFERENCE PROCEEDINGS NATIONAL ECONOMICS UNIVERSITY PUBLISHING HOUSE Hanoi, 2021 Hanoi, 2021 BOOK NOT FOR SALE 4th INTERNATIONAL CONFERENCE ON FINANCE, ACCOUNTING AND AUDITING International Conference on Finance, Accounting and Auditing ICFAA 2021 2021 NATIONAL ECONOMICS UNIVERSITY PUBLISHING HOUSE 786043 301441 CONFERENCE PROCEEDINGS CONFERENCE PROCEEDINGS th INTERNATIONAL CONFERENCE ON FINANCE, ACCOUNTING AND AUDITING ICFAA 2021 NATIONAL ECONOMICS UNIVERSITY PUBLISHING HOUSE Hanoi, 2021 72 Financing for Wind Energy in Vietnam: Evaluations and Policy Implications 938 Tran Phi Long, Cao Truong Giang, Bach Quoc Trung, Le Thu Tra Pham Lam Anh, Pham Quynh Giang 73 The Capital Structure of Firms: A Bayesian Approach 953 Hoang Thi Hong Le, Phan Thuy Duong, Dang Thu Hang 74 Research on Relationship between Cash Flows and Earnings per Share among Non-financial Listed Companies in Vietnam 964 Nguyen Thanh Hieu 75 Ceo’s 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Thi Thu Hai, Nguyen Thi Thu Ha Hoang Thi Phuong Le, Trieu Thuy Linh 82 Research on the Influence Factors of Financial Risk for Telecommunications Companies Listed on Vietnam's stock market 1083 Nguyen Thi Mai Chi 83 Financial Policies in Renewal Energy Development Investment in Vietnam 1100 Nguyen Duc Duong, Tran Tuan Anh 84 Formal and Informal Credit Access of Farm Households in Rural Areas of Vietnam: A Case Study of Four Provinces in The Red River Delta 1112 Nguyen Thi Bich Hang, Do Hong Nhung xiii The 4th International Conference on Finance, Accounting and Auditing (ICFAA 2021) December 18th, 2021 Hanoi City, Vietnam Research on the Effects of Information Technology on Operational Efficiency in Vietnam Commercial Banks: Using the Data Envelopment Analysis Method Vu Thi Huyen Tranga, Tran Trung Tuanb a Thuyloi University, bNational Economics University Submission date: 16/11/2021 Revision date: 26/11/2021 Acceptance date: 2/12/2021 Abstract The paper analyses the impact of information technology (IT) on the performance of Vietnamese commercial banks The study applies the random effects model (REM), the fixed effects model (FEM) and the regression analysis to the data of 30 Vietnam’s commercial banks in the period from 2016 to 2020 We employ the Data Envelopment Analysis on panel data to generate estimates of cost efficiencies and revenue efficiencies Measuring the impact of various categories of information technology (Technical infrastructure, IT human resource infrastructure, bank’s internal IT application, Online banking service) on banks efficiencies suggests that “the Productivity Paradox” does not affect all ormation technology investments Based on the findings, the authors give some recommendations to Vietnamese commercial banks in case of investments in IT to improve performance Keywords: Bank’s performance, Commercial banks, Information technology JEL code: M40 Introduction This study examines the impact of information technology (IT) investment on performance in Vietnam commercial banks There are two reasons for this research topic to become an urgent study Firstly, the banking and financial sectors are considered the lifeblood of the economy Banking is an intermediary activity linking the movement of the entire economic sector and the influence of the banking industry on all socio-economic activities Therefore, the improvement of the bank performance brings widespread effects not only to the banking industry but also to other industries Secondly, the banking industry is one of the leading industries in 1013 applying IT in management and operation Information gathering and processing is central to the banking industry, so the impacts of IT innovation can be far-reaching Many companies in general and banks in particular have invested a lot of money in IT but have very little understanding of the impact of IT on operational efficiency Without scientific impact assessment measures, investment decisions will only be based on emotions Therefore, the development of measures to assess the impact between IT investment and performance will be of real value both academically and practically Research on the link between IT investment and performance began with Robert Solow (1987) in his statement as follows: “You can see the computer age everywhere except in in efficiency statistics" Then, there are a number of studies investigating the impact of IT on operational performance at different levels: entire economy, industry, company, division, and an individual application However, in this research, the author only focuses on company-level studies Previous studies had shown the mixed results on the relationship between IT and performance The researchs 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 performance However, the researchs conducted in the second half of the 1990s by Dewan (1997), Hitt (1996) concluded that there was a positive positive relationship between investment in IT and operational performance Because the research results on the relationship between IT investment and performance in the world show many different results, moreover, this research topic in Vietnam is very small, so the author has chosen the topic "Research on the effects of Information technology on operational efficiency in Vietnam commercial banks: Using data envelopment analysis" as the research topic for this research 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 using the data envelopment analysis method 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 provides the introduction Section provides the literature review and methodological approach for our study Finally, Section describes the empirical results, and Section is the conclusion Literature Review/ Theoretical Framework and Methods 2.1 Literature Review Besides the traditional approach when evaluating performance, the world now uses the data envelopment analysis method This method calculates a relative efficiency index based on 1014 comparing the distance of units (banks) with a marginal best performing unit (this margin is calculated from the dataset because in practice) This tool allows us to calculate the overall performance index of each bank based on their performance and allows to rank the performance of the banks the data envelopment analysis method will calculate TFP composite factor productivity, cost effectiveness and profit efficiency of each company Therefore, recent studies have evaluated the effect of IT investment on operating performance according to performance measures from the data envelopment analysis method for two reasons First, studies that evaluate the impact of IT on traditional performance measures may underestimate the impact of IT because computer use is often associated with large changes in output quality It is difficult to measure accurately Second, the use of technology can take time to adjust to the organization and the skills of employees Therefore, the recent studies trying to explore and evaluate the impact of IT on changes in corporate organizations and in corporate performance, traditional performance measures such as ROA, ROE not reflect these changes Therefore, in this study, the author will focus on studies that analyze the relationship between IT and performance based on the data envelopment analysis method Gopal, Wang and Zionts (1992) conducted a study on the firm-level performance of 36 banks The author used the data envelope analysis (DEA) method to measure performance They used the adjusted DEA model because the pure DEA model did not consider intermediate manufacturing processes and did not provide the detailed information about the effects of specific variables Data sources were obtained from Computerworld Premier 100, Standard and Poor's Industry Surveys and Standard and Poor's Compustat The first stage output variable was the total amount of deposits The stage one input variables were IT assets, staff and budget The second stage output variables were Profit and loan percentage recovered The authors conclude that there was a positive relationship between operating results and the intensity of capital use for IT The downside of that study was that it didn’t factor in the possible time lag between IT investment and operational performance Furthermore, in the authors' model it was assumed that the only intensive use of IT is in the deposit sector; however, IT was used intensively in at least two other areas - loan approval/collection and loan management Therefore, the author had not studied the impact of IT on performance in these areas Courtney (1993) studied performance at the firm level in several industries based on the DEA method suggested by Gopal (1992), using least squares regression and discriminant analysis The author studied a sample of 325 companies from the same data source as in Gopal (1992) using Computerworld Premier 100 for intermediate variables, while higher-level variables were used from Standard and Poor's sources, Industry Surveys and Standard and Poor's Compustat The information system investment variables in the research were: Budget for information system, budget for information system staff, training budget for information system staff, terminal equipment/staff, processor value, year, sector The performance variables in the study were ROA and stock price The DEA method was used to determine the effective classifier for each company The numerical discriminant analysis uses the output of the DEA to determine if a relationship exists between the performance classification and IT investment Finally, the least squares regression method compares the DEA and the discriminant analysis 1015 results The authors did not find a direct relationship between IT and operational performance for all industries, although a positive relationship had been noted in the paper, chemical, and oil refining industries Beccalli's study (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 The independent variables were IT investment in hardware, software, and other IT services The dependent variables were ROA, ROE, cost effectiveness and profit efficiency The author uses the following methods: OLS regression, two-stage regression (2SLS) and SFA The research results showed that although banks invest large in IT, there is a small relationship between total IT investment and operational efficiency at the bank, confirming the existence of a productivity paradox The impact of different types of IT investments was different: while investments in hardware and software reduced the efficiency of banks, IT services from outside providers had a positive effect to ROA, ROE and profit efficiency This study of the author had overcome some limitations of previous studies by using both a traditional accounting profit measure (ROA, ROE) and a more advanced measure of operational efficiency, which called Xefficiency Moreover, the author did not study investment in IT as a single variable like previous studies but had specifically divided into three components of IT investment namely hardware, software and IT services to consider the different IT areas 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 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 Huong and Nhu (2018) researched the influence of information and communication technology on Vietnam commercial banks through the Vietnam ICT Index The author researched the data of 24 commercial banks from 2006 to 2017 according to the linear regression model Research results show that there is a positive relationship between ICT index and operational performance From there, the author makes recommendations that banks should strengthen policies to improve ICT indicators and combine with strategies to expand bank scale, loans and deposits However, this study only focuses on the composite index of IT without further research on the specific component indexes of ICT index Currently, there are very few studies on the relationship between IT investment and operational efficiency at banks using the marginal efficiency analysis in the world so the imperial studies are also essential Moreover, according to the author's knowledge, the studies between the 1016 relationship between IT investment and the performance of banks in Vietnam have only mostly researched in the direction of evaluating the direct relationship between IT and operational efficiency according to the traditional assessment method is (ROA and ROE) (Tam, 2015; Huong and Nhu, 2018) but very few studies have evaluated the relationship between IT and the performance of Vietnam commercial banks according to the method of marginal efficiency analysis (DEA method), the author finds that this is a research gap that needs to be filled 2.2 Methodology In this research, the author uses a regression method to evaluate the impact of IT on the performance of X-efficiency Performance (X-efficiency) = f(IT) In which, X-efficiency will be approached according to the method of marginal efficiency analysis This method calculates a relative efficiency index based on comparing the distance of units (banks) with a best performing unit on the edge (this compile from the data files because on the reality of compile results to the theory is not know) The marginal efficiency analysis method has two approaches: parametric approach and non-parametric approach The requirement parameter approach needs to have a specific form of function for the efficient frontier and has a specified random error or inefficient distribution Therefore, the outcome of the parametric approach is greatly influenced by the choice of the functional form The nonparametric approach does not need to specify a particular form of function and does not constrain the distribution of inefficiencies like the parametric approach, except that the efficiency indices must be between and 1, and assume there is no random error or measurement error in the data Therefore, the main limitation of the non-parametric method is that it is very sensitive, so if there is a random error in the data, it will affect the results In this research, the author uses a non-parametric approach, namely the data envelopment method DEA is a linear programming technique to evaluate a decision-making unit (DMU or bank) how does it perform relative to other banks in the sample? This technique generates a marginal set of efficient banks and compares it with inefficient banks to measure efficiency The author chooses the DEA method because the banking industry is a complex service industry and there are many relationships between inputs and outputs Therefore, when evaluating the performance of a bank, it is necessary to consider simultaneously many inputs and many outputs Whereas the parametric approach has to specify a specific form of function between the input and the output, so it is very likely that it will give wrong results if the choice of the function form is not correct The key point of this approach is to specify the bank's inputs and outputs appropriately According to research results on bank performance, there are five approaches in determining outputs and inputs (Hung, 2008)  Production approach: considers banks as service providers, so deposits are considered outputs and interest payments on deposits are not included in banking costs (Ferrier and Lovell, 1990) 1017  Intermediary approach: considers banking as financial institutions that mobilize and allocate loans and other assets so that deposits are treated as inputs and interest payments are part of total expenditures banking fee  Asset approach: consider liabilities as inputs and assets as outputs  Value added approach: treat all balance sheet items as outputs if it attracts the respective contributions of capital and labor hence deposits are considered outputs  Usage-cost approach: considers the net contribution to a bank's revenue as an input and output, hence deposits as an output According to Berger and Humphrey (1997), there is no perfect approach that reflects all the activities and roles of a bank, but the intermediary approach may be the most appropriate when assessing the bank's performance because it is concerned with interest payments, which often account for ½ to 1/3 of a bank's total operating costs Moreover, the intermediary approach is also concerned with the profitability of the bank because minimizing costs is a necessary condition for profit maximization Therefore, in this research, the author also uses an intermediary approach that considers deposits as an input to create outputs such as lending, investment, interest income and non-interest income According to Hung (2008), the author chooses the input and output variables in the DEA model as follows:  Input variables include: Total net fixed assets (K.TSCD), total expenses for employees (L.ChiNV), total mobilized capital (W.TGKH)  Outputs: Interest and future income (Thulai) and Non-interest income (Thungoailai) After estimating the efficiency measures by the DEA method, the author will obtain the technical efficiency of banks according to the revenue maximization function under CRS (CRSTEmax) and VRS (VRSTEmax) conditions and the author will obtain the technical efficiency of banks according to the cost minimization function under CRS (CRSTEmin) and VRS (VRSTEmin) conditions These results are used by the author as a dependent variable to evaluate the performance of banks Next, a regression model is used to analyze the impact of IT on these performance measures In which, the independent variable IT is obtained by the author from the report on readiness for development and application of IT and communication in Vietnam (ICT index) dedicated to the banking industry The Vietnam ICT Index report was developed by Vietnam Association for Information Processing according to the standards of E-Government Development Index (EGDI) of the United Nations The Vietnam ICT Index is calculated on the basis of statistical reports of central and local state management agencies, that is, an internal assessment and hardly depends on the subjective feelings of digital providers (Report of 10 years of implementation of Viet Nam ICT index) The ICT index has been correlated with other sets of socio-economic indicators of Vietnam (PCI provincial competitiveness index; PAR administrative reform index, governance efficiency index and Provincial public administration (PAPI, e-commerce index EBI) all show a high degree of correlation This proves both 1018 academically and practically that the ICT index data is reliable From 2016, the ICT index adjusted according to the standards of the E-Government Development Index – EGDI will include sub-indexes: infrastructure technology (HT), human infrastructure (NL), internal banking IT applications (UD) and online banking services (DV) Variable Definition HT Technical infrastructu re Indicator Measure Server and - The ratio of Virtual Servers/Total Servers workstation - The ratio of workstations (PC/Laptop) in the last infrastructure years/Total Workstations - The ratio of workstations running proprietary Communication and manufacturer-supported operating infrastructure systems/Total workstations - The ratio of Internet bandwidth providing Internet Banking services/Total number of Internet Banking customers - Ratio of Internet bandwidth provided to internal users/Total number of computers connected to the Internet - Ratio of wide area network bandwidth/Total number of terminals ATM and - The ratio of ATM /Total number of payment POS cards infrastructure - The ratio of ATMs accepting chip cards/Total number of ATMs - The ratio of ATMs with recharge function/Total number of ATMs - The ratio of POS machine/Total payment cards - The ratio of (mPOS and wireless POS)/Total POS Deployment of information security and data safety solutions - The ratio of workstations with anti-virus software installed/Total workstations - The ratio of servers installed anti-virus software/Total servers - Rate of database installed on SAN + Rate of database installed at TTDPTH + Rate of database backed up to hard disk + Rate of database backed up to magnetic tape - ATTT (TTDL, TTDPTH) = Total of main solutions + 0.2 x Other solutions 1019 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 (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 performance of the various categories of IT investments, the estimated equation is: Pt = β0 + βtHTt + βtNLt + βtUDt + βtDVt +Ɛt Where: HTt= Technical infrastructure; NLt= Human resource infrastructure; UDt= Bank's internal IT application; DVt= Online banking service 2.3 Data Research using 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 not participate in the Vietnam ICT index report and some banks that not disclose financial statement information, we have data include 138 observations presented at table Table The banks list during 2016 -2020 No Bank Code Observations Tien Phong Commercial Joint Stock Bank TBP Nam A Comercial Join Stock Bank NAB JSC Bank for Investment and Development of Vietnam BID VietNam Technological and Commercial Joint Stock Bank TCB 5 Military Commercial Joint Stock Bank MBB JSC Bank for Foreign Trade of Vietnam VCB Vietnam Thuong Tin Commercial Joint Stock Bank VBB Orient Commercial Joint Stock Bank OCB Sai Gon Joint Stock Commercial Bank SCB 1022 No Bank Code Observations 10 Sai Gon Thuong Tin Commercial Joint Stock Bank STB 11 Ho Chi Minh City Housing Development Bank HDB 12 Bac A Commercial Joint Stock Bank BAB 13 Southeast Asia Commercial Joint Stock Bank SSB 14 An Binh Commercial Joint Stock Bank ABB 15 Vietnam Prosperity Joint Stock Commercial Bank VPB 16 Kien Long Commercial Joint Stock Bank KLB 17 Vietnam International and Commercial Joint Stock Bank VIB 18 Vietnam Maritime Joint – Stock Commercial Bank MSB 19 Vietcapital Commercial Joint Stock Bank BVB 20 Joint Stock Commercia Petrolimex Bank PGB 21 Vietnam Bank for Agriculture and Rural Development AGB 22 Saigon – Hanoi Commercial Joint Stock Bank SHB 23 Asia Commercial Joint Stock Bank ACB 24 Vietnam Asia Commercial Joint Stock Bank VAB 25 Vietnam Public Joint Stock Commercial Bank PVB 26 Saigon Bank for Industry and Trade SGB 27 Vietnam Export Import Bank EIB 28 Vietnam Joint Stock Commercial Bank for Industry and Trade CTG 29 Bao Viet Joint Stock Commercial Bank BAO 30 National Citizen Commercial Joint Stock Bank NCB Source: Authors synthesized 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 Results and Discussion Technical efficiency estimation results by the DEA method After selecting the input and output variables for the research sample of 30 Vietnamese commercial banks in the period 2016 to 2020, the author uses the DEA method to estimate the global efficiency (CRSTE) and technical efficiency (VRSTE) in terms of cost minimization and revenue maximization The results of technical efficiency estimation are presented in Tables and below 1023 Table Total efficiency, technical efficiency and scale efficiency in the period 2016 – 2020 as function of cost minimization Source: Authors synthesized Table Total efficiency, technical efficiency and scale efficiency in the period 2016 – 2020 as function of revenue maximization Source: Authors synthesized The descriptive statistics of the independent and dependent variables shown in Table show the mean, standard deviation, maximum and minimum values of the variables The results show that the outliers have been removed from the study sample 1024 Table Descriptive statistics of variables Variables Code Mean Technical efficiency under CRS as cost minimization function CRSTEmin 0,8100 Technical efficiency under VRS as cost minimization function VRSTEmin Technical efficiency under CRS as revenue maximization function Technical efficiency under VRS as revenue maximization function Std.Dev Min Max 0,1368 0,5563 1,0000 0,8835 0,1292 0,5897 1,0000 CRSTEmax 0,8100 0,1368 0,5563 1,0000 VRSTEmax 0,8683 0,1326 0,5618 1,0000 Technical infrastructure HTC 0,4583 0,1198 0,1535 0,7586 Human resource infrastructure NLC 0,3876 0,2376 0,0000 1,0000 Bank's internal IT application UDC 0,4913 0,2174 0,0000 1,0000 Online banking service TTC 0,5809 0,1949 0,0150 1,0000 Source: Authors synthesized Table shows the correlation coefficient between the independent variables in the model The research results show that the independent variables have a low correlation, in which the highest correlation coefficient is between IT technical infrastructure and online services with a correlation coefficient of 0.4680 Table Correlation coefficients between independent variables Variables HTC NLC UDC TTC HTC 1,0000 0,1208 0,0936 0,4680 NLC UDC TTC 1,0000 -0,1521 0,0750 1,0000 0,2051 1,0000 To evaluate whether fixed effects (FEM) or random effects (REM) models are suitable for measuring the influence of IT investment on bank performance, the author uses the test Hausman with dependent variables CRSTEmin, VRSTEmin, CRSTEmax, VRSTEmax, respectively If the residuals and the independent variables have no correlation with each other, choose the random effects model (REM) and otherwise, choose the fixed effects model (FEM) The Hausman test is performed with the following hypothesis: H0: The REM model is the right model H1: The FEM model is the right model With the results of running Hausman test for dependent variables CRSTEmin, VRSTEmin, CRSTEmax, VRSTEmax respectively according to table 6, table 7, table 8, table 9, then prob > 0.05, the null hypothesis H0 should be rejected, so the REM random effects model is appropriate 1025 Table Hausman test results for the dependent variable CRSTEmin Chi2(4) = 1,18 Prob > chi2 = 0,8813 (b) (B) (b-B) Sqrt (Diag (V_b-V_B)) Fem Rem Difference S.E HTC 0292216 0382755 -.009054 0149551 UDC -.0227867 -.0174968 -.0052898 0075026 TTC 0955566 0906164 0049402 0113914 NLC 0605962 0725063 -.0119101 0187795 Source: Authors synthesized Table Hausman test results for the dependent variable VRSTEmin Chi2(4) = 9,75 Prob > chi2 = 0,0448 (b) (B) (b-B) Sqrt (Diag (V_b-V_B)) Fem Rem Difference S.E HTC -.0074332 0068134 -.0142466 0116707 UDC -.0141668 -.0038019 -.0103649 0058491 TTC 0828982 0687887 0141095 0099187 NLC 0410899 0454398 -.0043499 0193833 Source: Authors synthesized Table Hausman test results for the dependent variable CRSTEmax Chi2(4) = 1,18 Prob > chi2 = 0,8813 (b) (B) (b-B) Sqrt (Diag (V_b-V_B)) Fem Rem Difference S.E HTC 0292216 0382755 -.009054 0149551 UDC -.0227867 -.0174968 -.0052898 0075026 TTC 0955566 0906164 0049402 0113914 NLC 0605962 0725063 -.0119101 0187795 Source: Authors synthesized 1026 Table Hausman test results for the dependent variable VRSTEmax Chi2(4) = 13,24 Prob > chi2 = 0,0102 (b) (B) (b-B) Sqrt (Diag (V_b-V_B)) Fem Rem Difference S.E HTC -.2009944 -.1568752 -.0441193 0173974 UDC -.037957 -.0166035 -.0213535 0087003 TTC 0398031 0287377 0110654 0150694 NLC 1456903 1298349 0158554 0291415 Source: Authors synthesized The results of running Hausman test for dependent variables are CRSTEmin, then prob =0.8813 > 0.05, so the null hypothesis H0 is rejected, so the REM random effects model is appropriate; VRSTEmin prob = 0.0448 < 0.05, so the hypothesis H0 is accepted, so the FEM fixed-effects model is suitable; CRSTEmax then prob =0.8813 > 0.05, so the null hypothesis H0 is rejected, so the REM random effects model is appropriate; VRSTEmax then prob = 0.0102 > 0.05, so the hypothesis H0 is accepted, so the FEM fixed-effects model is suitable The author selects a suitable model for each dependent variable and then runs the regression model The results show that the regression model between the independent variables IT and the dependent variable CRSTEmin, CRSTEmax, VRSTEmax is suitable while the regression model between the independent variables IT and the dependent variable VRSTEmin is not suitable (due to Pro > F = 0.2785) The results of running the regression model are presented in Table 10 and Table 11 below Table 10 Regression results according to random effects model (REM) for dependent variables CRSTEmin and CRSTEmax CRSTEmin CRSTEmax (Coef.) (P>|t|) (Coef.) (P>|t|) HTC ,0382755 0,555 ,0382755 0,555 UDC -,0174968 0,590 -,0174968 0,590 ** TTC ,0906164 0,037 ,0906164 0,037** NLC ,0725063 0,096*** ,0725063 0,096*** _cons ,7192952 0,000 ,7192952 0,000 Observations 138 138 R-Squared 0,0656 0,0656 Wald chi2(4) 11,37 11,37 Prob > chi2 0,0227 0,0227 *, **, *** means statistically significant at the 1%, 5% and 10% Variables Source: Authors synthesized 1027 Table 11 Regression results according to fixed effects model (FEM) for dependent variable VRSTEmax VRSTEmax Variables (Coef.) (P>|t|) HTC -.2009944 0.016** UDC -.037957 0.360 TTC 0398031 0.476 NLC 1456903 0.015 ** _cons 8994574 0.000 Observations 138 R-Squared 0.0129 F(4,104) 3,02 Prob > F 0,0213 *, **, *** means statistically significant at the 1%, 5% and 10% Source: Authors synthesized Next, the author performs a test of variance across entities in the FEM and REM models with dependent variables CRSTEmin, CRSTEmax and VRSTEmax, respectively with the following hypothesis H0: There is no variance in the model H1: There is a variable variance in the model Fig 1: Test results of variance of variance across entities in REM for dependent variables CRSTEmin and CRSTEmax 1028 Source: Authors synthesized Fig Results of testing variance of variance across entities in FEM for dependent variable VRSTEmax Source: Authors synthesized The test results on Fig and Fig show that p-value < 0.05, therefore, rejecting H0 means that there is a variable variance in the FEM and REM models Then, the author performs a series of correlation test with the following hypothesis: H0: There is no serial correlation H1: There is a phenomenon of series correlation The test results in Fig show that p-value < 0.05, so rejecting H0 means that there is a phenomenon of series correlation 1029 Fig Results of the series correlation test Source: Authors synthesized Finally, the author performs the multicollinearity test The results of the multicollinearity test of the research variables are shown in Table 12 below Table 12 Checking for multicollinearity of research variables Variables VIF 1/VIF TTC 1,33 0.752622 HTC 1,29 0.773438 UDC 1,08 0.929526 NLC 1,05 0.955761 Mean VIF 1,19 Source: Authors synthesized The VIF indexes are all < 2, showing that in the independent variables there is no multicollinearity phenomenon Thus, in all three models, corresponding to three dependent variables, CRSTEmin, CRSTEmax and VRSTEmax, respectively, there is no multicollinearity phenomenon, but there is a phenomenon of variable variance and a phenomenon of series correlation Run the error repair model to fix these errors The regression results according to robust FEM and robust REM are shown in Tables 13 and 14 1030 Table 13 Error correction regression results according to random effects model (REM) for dependent variables CRSTEmin and CRSTEmax Variables CRSTEmin CRSTEmax (Coef.) (P>|t|) (Coef.) (P>|t|) HTC ,0382755 0,498 ,0382755 0,498 UDC -,0174968 0,516 -,0174968 0,516 TTC ,0906164 0,022** ,0906164 0,022** NLC ,0725063 0,066*** ,0725063 0,066*** _cons ,7192952 0,000 ,7192952 0,000 Observations R-Squared Wald chi2(4) Prob > chi2 138 138 0,0656 0,0656 8,57 8,57 0,0729 0,0729 *, **, *** means statistically significant at the 1%, 5% and 10% Source: Authors synthesized The results of the regression according to REM are shown in Table 13 for the dependent variables CRSTEmin and CRSTEmax It shows that in the four independent variables about IT, two variables are online banking services and IT human resource infrastructure with p_value < 0.05 shows that these variables have statistical significance at the 5% level of significance, that is, they have an impact on the dependent variable CRSTEmin, the sign of the regression coefficients has a positive sign The remaining two independent variables, which are IT technical infrastructure and internal banking applications have p_value > 0.1, so there is no statistical significance Table 14 Error correction regression results according to random effects model (REM) for dependent variable VRSTEmax Variables VRSTEmax (Coef.) (P>|t|) HTC -.2009944 0.015** UDC -.037957 0.260 TTC 0398031 0.392 NLC 1456903 0.014** _cons 8994574 0.000 Observations 138 R-Squared 0.0129 F(4,104) 2,84 Prob > F 0,0420 *, **, *** means statistically significant at the 1%, 5% and 10% Source: Authors synthesized 1031 The regression results according to FEM are shown in Table 14 for the dependent variable VRSTEmax, showing that in the four independent variables about IT, there are two variables that are IT technical infrastructure and IT human resource infrastructure with p_value < 0.05 for each variable These variables are statistically significant at the 5% level of significance, that is, they have an impact on the dependent variable VRSTEmax, the sign of the regression coefficients of the IT technical infrastructure has a negative sign while the sign of the nuclear infrastructure is negative IT force has a positive sign The remaining two independent variables, which are internal banking IT applications and banking online services, have p_value > 0.1, so there is no statistical significance From the table of regression results, the author identifies a regression model that reflects the influence of IT factors on the performance of Vietnamese commercial banks as follows: CRSTEmin = 0.7193 + 0.0383HTC – 0.0175UDC + 0.0906TTC + 0.0725NLC (1) CRSTEmax = 0.7193 + 0.0383HTC – 0.0175UDC + 0.0906TTC + 0.0725NLC (2) VRSTEmax = -0.8995 - 0.201HTC - 0.0380UDC + 0.0398TTC + 0.14571NLC (3) From the regression equation (1), it shows that, other things being equal, online services increase by 1%, then technical efficiency as a function of minimizing the constant conditional cost of the bank's size increases by 9, 06%; IT human resources increased by 1%, technical efficiency as a function of minimizing cost condition constant to scale increased by 7.25% and these variables were all statistically significant at 5% level While the remaining two IT variables, IT infrastructure, increased by 1%, technical efficiency as a function of minimizing the cost condition constant to scale increased by 3.83% and internal application of the bank increased by 1% Technical results according to the cost minimization function condition constant to scale decreased by 1.75% and these two variables were not statistically significant This result shows that IT variables such as online services and IT human resources have a positive effect on technical efficiency as a function of cost minimization, conditionally constant to scale and these variables are statistically significant That is, investing the bank's resources in these variables will increase the bank's performance From regression equation (2) shows that, other things being equal, online services increase by 1%, then the technical efficiency as a conditional revenue maximization function constant with the size of the bank increases by 9, 06%; IT human resources increased by 1%, technical efficiency as a revenue maximization function, conditionally constant to scale, increased by 7.25% and these variables were all statistically significant at 5% While the remaining two IT variables, IT infrastructure, increased by 1%, technical efficiency as a revenue maximization function constant to scale increased by 3.83%, and bank internal application increased by 1%, efficiency and profitability increased by 1% Technical results according to the revenue-maximizing function, which is constant to scale, decreased by 1.75% and these two variables were not statistically significant This result shows that IT variables such as online services and IT human resources have a positive effect on technical efficiency as a conditional revenue maximization function that is constant to scale and these variables are statistically significant That is, investing the bank's resources in these variables will increase the bank's performance 1032 From the regression equation (3), it shows that, other things being equal, the IT infrastructure increases by 1%, then the technical efficiency as a revenue maximization function changes with the size of the bank decreases by 2.00 %; IT human resources increased by 1%, technical efficiency according to the revenue maximization function in terms of scale increased by 14.57% and these variables were all statistically significant at 5% While the remaining two IT variables, internal applications, increased by 1%, technical efficiency as a function of revenue maximization of conditions of scale decreased by 3.80% and online banking services increased by 1%, Technical efficiency according to the revenue maximization function of the condition of scale increased by 3.98% and these two variables were not statistically significant This result shows that the IT infrastructure variable has a negative effect on technical efficiency according to the revenue maximization function, the condition varies with scale due to the inverse effect of productivity and IT human resources has a positive effect to technical efficiency as a function of revenue maximization, the condition varies with scale and these variables are statistically significant, that is, the investment of the bank's resources in IT human resources will increase operational efficiency of the bank Conclusions and Policy Implications This study aims to analyze the influence of IT investment on the performance of Vietnamese commercial banks using marginal efficiency analysis method (CRSTEmin, CRSTEmax, VRSTEmax) The research results show that two IT factors, namely banking online services and IT human resources, have an influence on technical efficiency as a function of cost minimization and revenue maximization statistical significance level of 5% The sign of the regression coefficients has a positive sign, indicating a positive relationship between IT investment and technical efficiency as a function of cost minimization and revenue maximization under constant conditions of size in banks Vietnamese trade Meanwhile, the IT infrastructure factor has a negative effect on technical efficiency according to the revenue maximization function with the condition of changing the size and the IT human resource factor positively affects the technical efficiency according to the maximum function Maximize revenue with the variable of scale at the 5% level of significance Thus, there exists a productivity paradox between IT infrastructure and technical efficiency as a revenuemaximizing function with varying terms of scale Based on these results, the author makes recommendations for banks to step up investment in IT in the aspects of banking online services and IT human resources because it makes technical efficiency as a function of minimizing costs Fees and revenue maximization conditions remain constant to the size of the bank However, it is necessary to study and consider carefully the investment in IT infrastructure because there is a productivity paradox, investment in banking IT infrastructure can reduce technical efficiency according to the revenue maximization with varying terms of scale References Beccalli, E (2007) "Does IT investment improve bank performance? 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Information Systems Research 3(4): 307-333 1034 CONFERENCE PROCEEDINGS 4th INTERNATIONAL CONFERENCE ON FINANCE, ACCOUNTING AND AUDITING ICFAA 2021 NATIONAL ECONOMICS UNIVERSITY PUBLISHING HOUSE Address: 207 Giai Phong, Hai Ba Trung, Hanoi Website: http//nxb.neu.edu.vn - Email: nxb@neu.edu.vn Tel/ Fax: (024) 36280280/ Exit: 5722 In charge of publication: NGUYEN ANH TU, Ph.D Director In charge of publication: NGUYEN THANH DO, Prof.Ph.D Editor-in chief Editing: TRINH THI QUYEN Electronic Editing: HOA HONG Cover design: HOA HONG Proofreading: TRINH THI QUYEN Printing 100 copies, size 20.5x29.5cm at Phu Ha PM Company Limited Address: No 193 Bach Mai Street, Cau Den Ward, Hai Ba Trung District, Ha Noi Publishing Registration Number: 4652-2021/CXBIPH/3-424/ĐHKTQD and ISBN: 978-604-330-144-1 Publishing Decision Number: 445/QĐ-NXBĐHKTQD, December, 16th, 2021 Printed and Deposited for Archives in Quarter IV, 2021 2021 International Conference on Finance, Accounting and Auditing CONFERENCE PROCEEDINGS NATIONAL ECONOMICS UNIVERSITY PUBLISHING HOUSE Hanoi, 2021 Hanoi, 2021 BOOK NOT FOR SALE 4th INTERNATIONAL CONFERENCE ON FINANCE, ACCOUNTING AND AUDITING International Conference on Finance, Accounting and Auditing ICFAA 2021 2021 NATIONAL ECONOMICS UNIVERSITY PUBLISHING HOUSE 786043 301441 CONFERENCE PROCEEDINGS ... 18th, 2021 Hanoi City, Vietnam Research on the Effects of Information Technology on Operational Efficiency in Vietnam Commercial Banks: Using the Data Envelopment Analysis Method Vu Thi Huyen Tranga,... this research topic in Vietnam is very small, so the author has chosen the topic "Research on the effects of Information technology on operational efficiency in Vietnam commercial banks: Using data. .. only to the banking industry but also to other industries Secondly, the banking industry is one of the leading industries in 1013 applying IT in management and operation Information gathering and