The impact of technology investment on financial performance of commercial banks in vietnam

INTRODUCTION

Reasons for choosing the study

The fourth industrial revolution has significantly impacted the global economy, particularly the banking industry, by integrating advanced technology into financial operations (Chaarani & Abiad, 2018) This transformation can be divided into three developmental stages: the first stage, from 1866 to 1967, saw globalization enhance financial connections and cross-border transactions, culminating in the introduction of the first Automatic Teller Machine (ATM) in 1967, marking a pivotal link between finance and technology The second stage, spanning 1967 to 2008, was characterized by the emergence of credit cards and the establishment of electronic interbank payment systems, such as SWIFT.

Since 2008, the rapid advancement of digital technology has led to the emergence of numerous fintech startups that offer e-payment services, effectively replacing traditional banking methods This shift aims to mitigate the risks associated with banking operations that were highlighted during the 2008 global financial crisis.

In today's fast-evolving digital landscape, banks are compelled to increase their technology investments, despite the uncertain outcomes of these expenditures (Uddin et al., 2020) DeYoung (2001) forecasted that advancements in technology would render existing service models in commercial banks obsolete, paving the way for new frameworks centered around digital essentials like Artificial Intelligence (AI), the Internet of Things (IoT), and Big Data Over the last twenty years, the banking sector has experienced profound changes driven by rapid technological innovation (Ratten, 2008; Rishi &).

Saxena, 2004) In particular, banks provide different products and services to their own customers based on modern technology software (Lee et al., 2021)

Investing in technology allows commercial banks to enhance information processing and transmission speed, broaden their product and service networks, and strengthen both regional and global connectivity This investment also improves customer access and awareness, ultimately leading to significant enhancements in the bank's financial performance.

In Vietnam, the commercial banking system has increasingly invested in information technology, recognizing its crucial role in achieving success (Goh, 2005; Kamath, 2007) A significant 96% of banks are developing strategies centered around 4.0 technologies, underscoring the importance of technological investment in business operations The fourth industrial revolution has notably influenced Vietnamese commercial banks, leading them to adopt innovative technical solutions that enhance their operations and service offerings This strategic focus on digital technology has not only improved financial performance but also significantly enriched the customer experience (Trinh & Tri, 2022).

Recent research has focused on the impact of digital technology on banking operations globally, including Barroso & Laborda's (2022) exploration of digital transformation and the rise of fintech, as well as Arjun & Kuanr's (2021) study on bank intelligence in emerging markets Despite the growing interest in technology investment within Vietnam's banking sector, there remains a lack of empirical studies examining its effects on the financial performance of Vietnamese commercial banks Notable domestic studies by Sang (2017), Thuong (2017), Thanh (2019), and Nhi have contributed to this field, yet a comprehensive analysis is still needed.

(2021) focus on factors affecting bank performance However, all these studies have not taken into account the impact of technology investment on the financial performance of commercial banks in Vietnam

This thesis aims to offer new insights into measuring the technology investment index by presenting empirical evidence on how technology investment affects the performance of Vietnamese commercial banks Consequently, the author has titled the thesis "The Impact of Technology Investment on Financial Performance of Commercial Banks in Vietnam."

Research Objectives

This article examines the influence of technology investment on the financial performance of commercial banks in Vietnam from 2012 to 2022, using relevant theoretical frameworks The findings will inform strategic recommendations for bank administrators to optimize technology investments and improve overall financial outcomes.

In order to achieve the above general objective, the author carries out three specific objectives as follows:

Firstly, the study determines the impact of technology investment on the financial performance of commercial banks in Vietnam

Secondly, the thesis measures the level and direction of micro and macro factors to commercial banks in Vietnam based on quantitative analysis models

Finally, the author proposes some appropriate recommendations to increase the financial performance of Vietnamese commercial banks in the near future through investment in technology.

Research questions

After identifying the problem and research objective, the research questions are posed to shape the scientific idea Specifically, the research will mainly focus on the following research questions:

How does technology investment affect the financial performance of commercial banks in Vietnam?

How does micro and macro factors impact the financial performance of Vietnamese commercial banks?

Which recommendations are suitable for Vietnamese commercial banks to improve financial performance in the future through investment in technology?

The research subject and scope of study

The main object of the study is the impact of technology investment on the financial performance of commercial banks in Vietnam

This study analyzes the annual financial statements of Vietnamese commercial banks from 2012 to 2022, a period marked by significant changes in the banking sector The selection of this timeframe is crucial, as it begins with the government's 2012 decision to restructure banks to stabilize the interest rate market Additionally, the impact of the Covid-19 pandemic from 2019 onwards has accelerated the rise of digital banking, leading to a shift in customer behavior and preferences Consequently, this period is vital for examining how technology investments have influenced the financial performance of banks amidst these transformative changes.

The author analyzes the impact of technology investment on the financial performance of 26 out of 31 Vietnamese commercial banks These selected banks are listed on the Ho Chi Minh Stock Exchange (HOSE), Hanoi Stock Exchange (HNX), and UPCOM, ensuring access to comprehensive and censored financial data over the years for thorough analysis.

Research methodology

To solve the research objectives, this study combines both research methods, which are qualitative research methodology and quantitative research methodology

The author employs qualitative research methodologies, utilizing aggregation, statistics, description, comparison, and analysis of relevant data to organize theoretical frameworks on technology investment and its impact on the financial performance of commercial banks in Vietnam Additionally, a review of previous studies will serve as a foundation for proposing research models and hypotheses.

The author will conduct quantitative research using secondary data from 26 Vietnamese commercial banks spanning the period from 2012 to 2022 Data analysis will be performed using STATA 14.0, employing various estimation methods such as Pooled Ordinary Least Squares (Pooled OLS), Fixed Effects Method (FEM), and Random Effects Method (REM) to analyze the influencing factors The research will utilize an F-test to determine whether to use OLS or FEM, followed by the Hausman test for selecting between FEM and REM, and the Breusch & Pagan test for choosing OLS or REM After identifying the appropriate model, the author will assess autocorrelation and variable variance, applying the Feasible Generalized Least Squares (FGLS) method if issues are detected Additionally, the study will implement the System Generalized Method of Moments (S-GMM) to address endogeneity, heteroscedasticity, and autocorrelation, ensuring comprehensive results.

Contributions of the research

This study offers empirical evidence regarding the influence of technology investment on the financial performance of Vietnamese commercial banks, utilizing secondary data from 2012 to 2022 The findings will enable the author to present targeted recommendations for bank administrators aimed at enhancing financial performance through strategic technology investments in the future.

Disposition of the dissertation

The structure of the study consists of 5 chapters as follows:

This chapter outlines the rationale for selecting the topic, establishing clear objectives and corresponding research questions It also defines the research object and scope, while detailing the chosen research methodology Ultimately, this chapter highlights the contributions and organization of the thesis.

THEORETICAL FRAMEWORK AND REVIEW OF PREVIOUS

An overview of technology investment

2.1.1 Definition of technology investment in banks

Information technology in banks, as defined by Ige (1995), enhances information processing and telecommunications Langdon (2006) further describes technology as a set of interrelated components that facilitate the collection, processing, storage, and distribution of information to aid decision-making and cooperation The technological framework in banking comprises four key components: technique (T), which includes machinery and equipment; human (H), encompassing knowledge, skills, attitudes, and experiences; information (I), which consists of technical, individual, and organizational data; and organization (O), representing the institutional framework that establishes regulations and responsibilities to optimize the use of technology and effectively address challenges.

Farouk and DanDago (1970) highlighted that technological advancements in commercial banks significantly enhance financial performance by developing infrastructure that integrates employees, hardware, software, automated teller machines, and data storage operations This integration facilitates the creation of stored information, improving overall organizational functions According to Khalil and Ezzat (2005), technology in banking encompasses the knowledge and application of products, processes, methods, tools, and systems that generate goods and services Ultimately, leveraging technology in business operations is essential for maximizing resources and achieving optimal benefits.

2.1.2 The benefits of technology for commercial banks in Vietnam

Digitization in the banking sector is essential for survival and growth amid the fourth industrial revolution and the rise of Fintech Vietnam stands out as a promising market for digital banking due to its developing economy, youthful population, and high internet penetration Consequently, investing in technology is crucial for the advancement of Vietnamese commercial banks.

Thanks to technological advancements, banks can now efficiently manage data sets for administrative activities The core banking system enables a synchronous online connection between the head office and small branches nationwide, allowing for effective management, supervision, and risk inspection across banking operations Additionally, headquarters can oversee remittance data, automate inspections, and streamline traditional checking processes, while also managing banking personnel information effectively.

The application of technology in credit risk activities enables commercial banks to efficiently manage and secure customer credit ratings while automating individual credit processes By digitizing the entire retail process—from customer inquiries to application approval and post-disbursement record management—banks can effectively monitor online processing and reduce information gaps Innovations like the SmartLender Collateral and Limits Management System (CLIMS) have further enhanced the convenience of credit risk management.

Thirdly, for payment activities, the application of technology has helped e- payment activities in Vietnam develop rapidly and catch up with the world trend

The popularity of online payments is on the rise, enhancing safety and security while offering multifunctional features for bill payments Commercial banks have successfully adopted modern payment services for utilities such as electricity, water, telecommunications, and tuition fees, accessible across various devices including iPads, iPhones, and multiple web browsers.

Previous studies indicate various methods for measuring technology investment in commercial banks Gideo, Kofi, and Simon (2011) identified investments in computer software as a key component of technology expenditures in financial reports Casolaro and Gobbi (2007) highlighted that the IT-CAP variable, representing technology investment, is calculated from the total hardware and software investments in the bank's computer systems, with software amortized at 44% per year and hardware at 32% per year Additionally, the ICT variable encompasses the value of hardware and software beyond computer systems, including automatic teller machines, e-banking, and mobile banking (Do et al., 2022).

Previous studies highlight varying methods for measuring technology investment variables In this research, the author exclusively utilizes data from the financial statements of commercial banks to assess technology investment levels in Vietnam This approach draws on the findings of Ho & Mallick to provide a comprehensive analysis.

Research by Casolaro & Gobbi (2007) and Lin (2007) suggests that technology investment positively influences the performance of commercial banks in Vietnam The author utilizes a measurement method for technology investment based on Duong's (2017) formula, which examines the impact of technology on the competitiveness of these banks.

An overview of financial performance

2.2.1 Definition of financial performance in banks

Financial performance is a crucial indicator of how effectively an enterprise utilizes its assets to generate revenue and reflects its overall financial health over time In the banking sector, it encompasses a bank's business and investment performance while considering internal factors within a specific socio-economic context This consolidated economic indicator showcases the level of financial resources employed by enterprises to achieve optimal performance Financial performance is assessed through various metrics, including capital adequacy ratio, liquidity, leverage, solvency, and profitability, which collectively represent a company's financial status over a defined period.

Hoang & Hang (2019) assert that the financial performance of commercial banks can be evaluated through two key aspects: the efficiency in converting inputs into outputs, which includes profit generation and cost reduction for enhanced competitiveness, and the maintenance of a sufficient capital adequacy ratio Consequently, the financial performance serves as an indicator of the quality of business operations within commercial banks, grounded in effective strategic management.

In today's financial landscape, various criteria are utilized to assess the performance of commercial banks, with profitability indicators being the most prevalent, as highlighted by Hoang (2011) Profitability serves as a key metric that encapsulates a bank's overall performance while also factoring in the risk elements that influence its financial outcomes.

The net profit on the average total assets is called ROA which stands for

Return on Assets (ROA) is a key profitability indicator for banks, reflecting their ability to generate revenue from total assets (Ongore, 2013; Khrawish, 2011) It is calculated as the percentage of net profit after tax divided by average total assets, demonstrating how effectively a bank utilizes its resources to produce net income A higher ROA signifies greater efficiency in asset use, indicating that banks are generating more income with less investment in assets (Nguyen et al., 2020) However, a high ROA may not always indicate optimal asset utilization; it can sometimes result from insufficient investment in assets, potentially leading to a decline in asset value and affecting the bank's long-term growth.

The net profit on the average total equity is called ROE which stands for

"Return on Equity" According to Ongore (2013), ROE is a financial indicator related to the profit earned by an enterprise compared with the total amount of equity invested

Return on Equity (ROE) is a key financial metric that assesses a bank's effectiveness in using shareholder investments to generate earnings, defined as the ratio of net profit after tax to average total equity According to Khrawish (2011), this index reflects the proportion of profits earned relative to the capital invested by shareholders Nguyen et al (2020) emphasized that ROE is crucial for shareholders, as it indicates their return on investment and the bank's ability to generate intrinsic cash flow A higher ROE signals greater attractiveness for investors, suggesting efficient use of capital However, a high ROE may not always indicate better financial health, as banks can artificially inflate this ratio by increasing debt and reducing equity Therefore, it is essential to evaluate ROE alongside other financial indicators for a comprehensive assessment of a bank's financial performance.

Net Interest Margin (NIM), also known as the marginal interest income ratio, is a crucial financial metric for banks that evaluates profitability and efficiency It measures the difference between the interest income earned from lending and the interest paid to depositors, relative to the interest generated from bank assets NIM reflects how effectively banks manage interest rate differentials on loans and deposits, indicating their lending and investment efficiency According to Ongore (2013), NIM represents the gap in interest income from loans and other lending activities, highlighting the cost of banking services and overall bank efficiency A higher NIM signifies greater profitability and stability, making it a key indicator of a bank's financial health.

2.2.3 The impact of technology investment on financial performance of commercial banks

Investment management theory involves managing capital in monetary terms, necessitating both risky and risk-free assets The return on risk-free assets highlights the time value of currency, although no asset is entirely devoid of risk (Wilson & Fabozzi, 1995) Essentially, investing lays the groundwork for future performance through strategic capital allocation (Halim, 2005) Moreover, investment in technology encompasses the use of electronic systems for business information at all levels, including computer-based systems and telecommunications for efficient information storage, processing, and dissemination (Cole et al., 1994).

Relying solely on technology is insufficient for sustainable development unless companies also invest in other strategic resources (An & Rau, 2021) While technology investments can influence performance, the returns vary significantly based on the level and type of investment (Beccalli, 2007; Kửster & Pelster, 2017) Specifically, technology services from external providers positively impact banks' profitability, whereas investments in software and hardware may hinder performance (An & Rau, 2021) Moreover, some studies suggest that technology investments do not necessarily enhance profitability for enterprises (Farouk & DanDago, 1970).

Investment in technology is crucial for enhancing the financial performance of banks, particularly in the banking sector, as highlighted by Chowdhury (2003) It facilitates improved communication and restructures business processes, while also enabling the development of sophisticated products and reliable risk control techniques These advancements support geographical diversification and create a foundation for business growth, ultimately increasing revenue and operational efficiency Additionally, Apulu et al (2011) stress the importance of technology investment for sustainable development, while Beccalli (2007) notes its significant impact on financial performance Furthermore, Romdhane (2013) indicates that investments in Internet and mobile banking can enhance bank performance, reinforcing the hypothesis that technology investment positively affects the financial performance of commercial banks in Vietnam.

Factors affecting the financial performance of commercial banks

2.3.1 The internal (bank-specific) factors

2.3.1.1 The effect of Bank Size on Financial Performance

Assessing assets is crucial for commercial banks, as the size and quality of these assets significantly influence a bank's growth The size is quantified by the book value of total assets, often measured using the logarithm of total assets (Hoang & Hang, 2019) Research by Angraini & Prastiwi (2020) indicates that Return on Assets (ROA) and Return on Equity (ROE) positively correlate with bank size, as larger capital enables banks to better meet customer borrowing needs, invest in technology, expand their networks, and ultimately enhance profitability and financial performance Conversely, Vincent & Gemechu (2013) argue that an increase in bank size can negatively affect ROA and ROE, as rapidly growing banks may face rising operational costs that outpace profit growth, leading to deteriorating financial performance.

2.3.1.2 The effect of Equity-to-Total Assets ratio on Financial Performance

Equity serves as a stable capital source that consistently grows throughout a bank's operations, forming the foundation for its growth (Hoang, 2011) The equity ratio reflects a bank's resilience to financial risks and its ability to recover during crises Deger & Adem (2011) identified this factor as a key independent variable in assessing a bank's financial performance, noting that higher equity correlates with lower risks for commercial banks, thereby enhancing customer and shareholder confidence Supporting this perspective, Ongore (2013) also found that the equity ratio positively influences the financial performance of commercial banks.

2.3.1.3 The effect of Technology Investment on Financial Performance

Most commercial banks expect that investment in technology for the bank's business activities will help maximize resources and bring considerable profits (Ngoc,

In 2021, it was observed that commercial banks are focusing on enhancing their business operations to boost income and improve financial performance (Ngoc & Giang, 2022) The effectiveness of technology investments aimed at enhancing financial performance is influenced by the level of investment and the size of the banks According to An & Rau (2021), larger banks tend to experience a more significant positive impact on their financial performance compared to smaller banks.

2.3.1.4 The effect of The Ratio of Total Deposits on Financial Performance

Deposits serve as a crucial and cost-effective funding source for commercial banks, enabling them to extend credit to customers while paying interest on their total deposits when due A higher volume of deposits allows banks to seize more business opportunities and enhance profitability through low-cost loans However, an excessive amount of deposits can create a financial burden due to the obligation to pay interest to customers Consequently, the ratio of total deposits to total assets can influence a bank's financial performance in both positive and negative ways Research indicates that deposits generally positively impact the profitability of commercial banks (Gul et al., 2011), while other studies suggest a negative effect on profitability, particularly in the Palestinian banking sector (Gaber, 2018).

2.3.1.5 The effect of Loans-to-Assets ratio on Financial Performance

Loans play a crucial role in generating income for commercial banks, with their size often assessed by the loans-to-total-assets ratio While some studies suggest that a higher ratio may indicate lower profitability due to increased credit risk, particularly for banks lacking effective credit risk management (Sohail et al., 2013), others argue that this ratio can positively correlate with profitability According to Tan (2016), a higher loans-to-total-assets ratio can enhance bank profits, as increased interest from loans contributes to greater overall earnings.

2.3.1.6 The effect of Non-performing Loans ratio on Financial Performance

Non-performing loans ratio is measured by the total value of bad debts in Group

The non-performing loans (NPL) ratio is a crucial indicator of the quality and risk associated with a bank's loan portfolio, as highlighted by Angraini & Prastiwi (2020) A high NPL ratio suggests that the bank is exposed to significant risks and may struggle with loan quality management Additionally, when non-performing loans arise, banks must establish contingency funds, which can adversely affect their profitability and overall financial performance Conversely, a declining NPL ratio compared to previous years signals an improvement in credit quality San & Heng (2013) further emphasize that there is a negative correlation between the NPL ratio and key financial metrics such as return on assets (ROA) and return on equity (ROE).

2.3.1.7 The effect of Liquidity Ratio on Financial Performance

Liquidity in a commercial bank refers to its capacity to promptly fulfill customer withdrawal requests and disburse approved credits A bank's liquidity ratio is inversely related to financial risk; insufficient market capital can jeopardize the bank's reputation and lead to insolvency Thus, effective liquidity management is crucial for risk management and ensuring stable financial operations This ratio reflects the proportion of loans funded by deposits, with a higher ratio indicating lower liquidity According to Muhammad (2014), there is a positive correlation between liquidity ratios and the financial performance of banks.

(2014) argued that the liquidity ratio was negatively related to the bank's financial performance

2.3.2.1 The effect of Gross Domestic Product Growth Rate on Financial Performance

The economic growth rate serves as a fundamental indicator of a nation's development, with a high GDP growth rate leading to improved loan quality due to stable employment and enhanced living standards This favorable environment benefits enterprises, creating more profit opportunities Conversely, a decline in GDP growth signals economic instability, negatively impacting production A stable economy is crucial for the efficient operation of banks, and numerous studies have explored the link between GDP growth and the financial performance of commercial banks Ece & Sayılgan (2020) found that increased economic activity boosts business opportunities, resulting in a higher demand for loans and positively influencing bank profitability.

2.3.2.2 The effect of Inflation Rate on Financial Performance

Inflation refers to the rise in the overall prices of goods and services within an economy over time, leading to a decrease in currency value This increase in commodity prices results in a reduction of deposits in commercial banks, forcing them to raise mobilization capital rates in line with capital market movements However, this adjustment is challenging due to the typically long-term nature of loans, which prevents banks from swiftly aligning lending interest rates with deposit rates The unpredictable nature of inflation often causes banks to lag in interest rate adjustments, potentially leading to expenses outpacing income and decreased operational efficiency While Krawish (2011) noted a negative impact of inflation on Return on Assets (ROA) and Return on Equity (ROE), Wahdan & Leithy (2017) contended that there is no significant relationship between inflation and financial performance.

RESEARCH METHODOLOGY

Implementation process

In order to solve the specific objectives, the author carried out the research according to the following steps:

Step 1 Identify the research problem

Based on the identification of the problem to be studied, the author determines the research problem as the impact of technology investment on the financial performance of

Step 2 An overview of the theoretical basis and related studies

In this phase, the author performs an extensive analysis of both domestic and international research relevant to the study Utilizing theoretical foundations and empirical evidence, the author has developed a suitable research model.

Step 3 Determine research model, collect and process data

The research model will draw from relevant studies published in esteemed scientific journals, while the author will formulate hypotheses and utilize secondary data from the financial statements of 26 commercial banks to analyze associated expenditures.

Step 4 Test research model and hypotheses

In this phase, the author employs STATA 14 software to apply quantitative methods, including descriptive statistics, to assess the influence of independent variables on the financial performance of commercial banks Following this analysis, the author utilizes OLS, FEM, and REM models to determine the most suitable model for the data To enhance the reliability of the research findings, the author conducts essential tests such as multicollinearity assessments and applies the FGLS method to address issues of autocorrelation and heteroscedasticity Ultimately, the GMM method is implemented to resolve endogenous variables, ensuring the consistency of the research model's results.

Step 5 Analyze the empirical results

The author examines the effects of technology investment on the financial performance of commercial banks in Vietnam, highlighting key research findings Additionally, the study compares these results with findings from other related experiments, providing a comprehensive analysis of the topic.

Based on the empirical results, the author provides conclusions and recommendations to improve the efficiency of technology investment and financial performance of commercial banks in Vietnam

The process of the study is summarized in Figure 3.1 as follows:

Research model

To meet the thesis objectives, the author selects variables for the regression model based on findings from prior empirical studies to ensure real-world relevance The chosen research model is that of Petria, Capraru, and Ihnatov (2015), which focuses on "Determining Banks' Profitability: Evidence from European Union 27 Banking Systems," published in the journal "Procedia Economics."

An overview of the theoretical basis and related studies

Determine research model, collect and process data

Test research model and hypotheses

Finance" and the authors Chhaidar, Abdelhedi & Abdelkafi (2022) with the title "The Effect of Financial Technology Investment Level on European Banks Profitability" are published in "Journal of the Knowledge Economy"

The author selected Return on Equity (ROE) as the dependent variable to assess financial performance, a choice backed by numerous empirical studies referenced in Chapter 2 This ratio effectively demonstrates a bank's efficiency in utilizing investments to drive earnings growth, with a higher ROE signifying more effective use of equity to enhance profitability.

This thesis incorporates independent variables identified in previous research, including technology investment (TE), bank size (SIZE), equity-to-total assets ratio (CAP), liquidity ratio (LIQ), non-performing loans (NPL), loans-to-assets ratio (LOANS), total deposits ratio (DEP), gross domestic product growth rate (GDP), and inflation rate (INF), all of which have been found statistically significant Consequently, the author proposes a regression model utilizing these variables for the study.

𝑅𝑂𝐸it: Return on equity is used to measure the financial performance of commercial banks (i) at a time (t)

Size of the commercial bank (i) at a time (t)

Equity-to-total assets ratio of the commercial bank (i) at a time (t)

: Technology investment of the commercial bank (i) at a time (t)

The ratio of total deposits of the commercial bank (i) at a time (t)

Loans-to-assets ratio of the commercial bank (i) at a time (t)

Non-performing loans ratio of the commercial bank (i) at a time (t)

Liquidity ratio of the commercial bank (i) at a time (t)

: Gross domestic product growth rate at a time (t)

The coefficients are the slopes of the independent variables and are the random error.

Research hypotheses

3.3.1.1 The hypothesis between Bank Size and Financial Performance

SIZE = Natural Logarithm of Total Assets

Bank size, measured by total assets, is often viewed as a key intrinsic factor influencing financial performance However, research by Ngoc & Giang (2022) and Petria et al (2015) indicates that this variable may not significantly impact the financial performance measurement models of commercial banks In contrast, Safari & Yu's findings suggest alternative perspectives on this relationship.

Research from 2014 suggested that larger bank sizes negatively impact financial performance due to the potential for excessive costs and management challenges Conversely, studies by Chhaidar et al (2022), Roy (2021), Kriebel & Debener (2019), Nga & Dat (2021), and Ngoc (2021) found a positive correlation between bank size and financial performance, asserting that as banks grow, they can better meet customer demands for financial services, enhancing competitiveness and profitability Therefore, the author hypothesizes that bank size significantly influences financial performance.

: Bank size has a positive effect on financial performance of Vietnamese commercial banks

3.3.1.2 The hypothesis between Equity-to-Total Assets ratio and Financial Performance

Owner's equity is crucial in banking operations, serving as a key indicator of a bank's capital status, safety, and financial soundness, with a higher ratio indicating better capital adequacy However, some researchers, including Nga & Thanh (2019), argue that this ratio negatively affects financial performance, while Ngoc & Giang (2022) and Kriebel & Debener (2019) find it statistically insignificant in measuring financial performance through the ROE variable Conversely, studies by Duong (2017), Nga & Dat (2021), Ngoc (2021), Petria et al (2015), and Roy (2021) highlight that customer psychology influences deposit behavior, with banks boasting higher equity levels instilling greater confidence and attracting deposits with stable interest rates Hence, this study posits the following hypothesis.

: Equity-to-total assets ratio has a positive impact on the financial performance of Vietnamese commercial banks

3.3.1.3 The hypothesis between Technology Investment and Financial Performance

Investing in technology is essential for enhancing bank operations and maximizing resources, leading to significant financial returns (Ngoc, 2021) This investment not only improves customer experience with banking products and services but also boosts overall financial performance (Roy, 2021) Consequently, banks can develop their business operations, resulting in increased earnings and improved financial outcomes (Ngoc & Giang, 2022) Previous studies by Duong (2017) and Nga & Dat (2021) support these findings.

Safari & Yu (2015); Kriebel & Debener (2019); Chhaidar et al (2022) argued that technology investment had a positive effect on financial performance Therefore, the author hypothesizes as follows:

: Technology investment has a positive effect on financial performance of Vietnamese commercial banks

3.3.1.4 The hypothesis between The Ratio of Total Deposits and Financial Performance

The deposit-to-asset ratio indicates the proportion of a bank's deposits relative to its total assets, serving as a key metric for assessing the impact of financing structure on commercial bank performance Deposits are typically the primary source of low-cost financing, enhancing bank profitability as increased deposits provide more opportunities for credit operations (Safari & Yu, 2015) However, if a bank accumulates excessive deposits while facing low loan interest rates, it may incur higher interest payment costs, negatively affecting financial performance Effective capital mobilization and reinvestment strategies are essential to mitigate this risk, as highlighted by Roy (2021) and Ngoc (2021), who noted that a high total deposit ratio can adversely impact financial outcomes.

The ratio of total deposits has a negative impact on the financial performance of Vietnamese commercial banks

3.3.1.5 The hypothesis between Loans-to-Assets ratio and Financial Performance

The credit activities ratio indicates the proportion of a bank's total assets that are allocated to loans, serving as a crucial metric for its operational efficiency Excessive lending without stringent oversight can adversely impact the bank's financial performance (Kriebel & Debener, 2019) Conversely, a higher credit activities ratio typically correlates with increased interest income, enhancing the bank's profitability Empirical studies by Ngoc & Giang (2022) and Safari & Yu (2015) support this connection, demonstrating that banks can boost earnings through effective lending strategies Thus, the author posits a hypothesis regarding this ratio's significance.

Loans-to-assets ratio has a positive impact on the financial performance of

3.3.1.6 The hypothesis between Non-performing Loans ratio and Financial Performance

The non-performing loans (NPL) ratio serves as a key indicator of credit risk in banking activities, with a higher NPL ratio signaling increased credit risk and consequently diminishing financial performance for banks This decline occurs because banks must allocate funds for contingencies or face capital losses due to uncollectible loans While Duong (2017) argued that the NPL ratio positively influences return on equity (ROE), the majority of research, including studies by Kriebel & Debener (2019), Ngoc (2021), and Chhaidar et al (2022), indicates a negative relationship between the NPL ratio and financial performance Thus, the author hypothesizes about the implications of this variable.

: Non-performing loans ratio has a negative impact on the financial performance of Vietnamese commercial banks

3.3.1.7 The hypothesis between Liquidity Ratio and Financial Performance

The liquidity ratio, which reflects the total value of liquid assets such as cash and deposits, is crucial for banks as it enables them to manage financial risks and minimize bankruptcy risks while lowering borrowing costs (Ngoc & Giang, 2022) However, maintaining high liquidity often leads to lower interest earnings, which can negatively impact a bank's profitability Thus, the optimal liquidity ratio is contingent upon the bank's business strategy Research by Petria et al (2015), Kriebel & Debener (2019), and Chhaidar et al (2022) indicates a negative correlation between liquidity ratios and financial performance, leading to the hypothesis for this study.

: Liquidity ratio has a negative impact on the financial performance of Vietnamese commercial banks

3.3.2.1 The hypothesis between Gross Domestic Product Growth Rate and Financial Performance

: Gross domestic product at a time (t)

Gross domestic product at a previous time (t - 1)

Economic growth is intricately linked to the financial performance of banks However, a study by Ngoc & Giang (2022) found that GDP does not have a statistically significant impact on the financial performance measurement model using Return on Equity (ROE) This finding aligns with earlier research by Ngoc (2021) and Nga & Dat.

Research by Petria et al (2015) and others in 2021 suggests that robust economic development positively influences banks' financial performance This correlation arises because a growing economy boosts both the supply and demand for loans, investments, and customer savings Additionally, a well-developed economy fosters stable business operations, ensuring job security and thereby reducing credit risks Consequently, the authors propose the following hypothesis:

Gross domestic product growth rate has a positive impact on the financial performance of Vietnamese commercial banks

3.3.2.2 The hypothesis between Inflation rate and Financial Performance

Inflation rate at a previous time (t - 1)

Inflation represents the decline in currency purchasing power and impacts various economic factors, particularly interest rates Research by Nga & Thanh (2019) and Ngoc & Giang (2022) indicates that a predictable inflation rate can enhance banks' financial performance by enabling them to adjust deposit and lending rates effectively, thus boosting profits Conversely, unpredictable inflation may lead to rising financial costs that outpace revenue growth, negatively affecting profitability Studies by Ngoc (2021), Petria et al (2015), Roy (2021), and Chhaidar et al (2022) further highlight the adverse effects of inflation on financial performance, prompting the author to hypothesize about this variable's impact.

Inflation rate has a negative impact on the financial performance of

Table 3.2 Statistics of expected signs of variables in the research model

Symbol Variable name Measurement method Source Expected sign

Petria et al (2015); Duong (2017); Kriebel & Debener (2019); Nga & Thanh (2019);

Roy (2021); Nga & Dat (2021); Ngoc (2021); Ngoc &

SIZE Bank size Log (Total assets)

Safari & Yu (2014); Petria et al (2015); Kriebel & Debener (2019); Nga & Dat (2021), Ngoc (2021); Roy (2021);

Equity-to- total assets ratio

Petria et al (2015); Duong (2017); Kriebel & Debener (2019); Nga & Thanh (2019); Nga & Dat (2021);

Safari & Yu (2015); Duong (2017); Kriebel & Debener (2019); Nga & Dat (2021), Roy (2021); Ngoc (2021);

The ratio of total deposits

LOANS Loans-to- assets ratio

Debener (2019); Ngoc (2021); Chhaidar et al

& Debener (2019); Chhaidar et al (2022); Ngoc & Giang

Gross domestic product growth rate

Ngoc (2021); Ngoc & Giang (2022); Chhaidar et al

Source: Statistics from the author

Research data

The author utilized secondary data from the audited financial statements of 26 commercial banks in Vietnam, covering the period from 2012 to 2022, resulting in 276 observations Out of a total of 31 commercial banks in Vietnam, only 26 were included in the study due to insufficient financial information from the remaining 5 banks during the specified timeframe.

In 2022, several key banks in Vietnam made significant strides, including the Vietnam Bank for Agriculture and Rural Development (AGR), Sai Gon Joint Stock Commercial Bank (SCB), Baoviet Joint Stock Commercial Bank (BaoVietBank), Vietnam Public Joint Stock Commercial Bank (PVcomBank), and Vietnam Thuong Tin Commercial Joint Stock Bank (VBB).

As of December 31, 2022, the State Bank of Vietnam reported that commercial banks hold total assets amounting to VND 12.7 million billion Notably, the 26 commercial banks included in the research sample represent over 70% of the total assets within the banking system, making this sample highly representative of Vietnam's commercial banking landscape.

Macroeconomic variables, including the gross domestic product (GDP) growth rate and inflation rate (INF), are sourced from the annual statistics provided by the General Statistics Office of Vietnam, accessible at https://www.gso.gov.vn/.

THE ANALYSIS OF EMPIRICAL RESULTS

Descriptive statistical analysis

The author utilizes the SUM command in STATA 14.0 software to conduct a descriptive statistical analysis of research variables, providing insights into total observations, mean, standard deviation, and minimum and maximum values This analysis is based on secondary data gathered from 26 Vietnamese commercial banks and the General Statistics Office of Vietnam, covering the period from 2012 to 2022, and is presented in a detailed statistical table.

Table 4.1 Statistical results of variables used in the research model

Variable Obs Mean Std.Dev Min Max

Source: Analysis results from STATA software

Between 2012 and 2022, the average Return on Equity (ROE) for 26 Vietnamese commercial banks was 10.29%, with a standard deviation of 7.77% Among these banks, the National Citizen Commercial Joint Stock Bank (NVB) recorded the lowest profitability.

The Covid-19 pandemic significantly affected enterprises, leading to losses and increased bankruptcies, which in turn raised the non-performing loans ratio and negatively impacted business results In 2021, the economic impact was reflected in a minimal growth rate of just 0.03%.

In 2022, Lien Viet Post Joint Stock Commercial Bank (LPB) achieved a remarkable Return on Equity (ROE) of 39.61%, reflecting significant improvements in its business operations following the challenges posed by the Covid-19 pandemic.

The average asset size of Vietnamese banks is 18.7349, with a standard deviation of 1.1461 In 2012, Saigon Bank For Industry And Trade (SGB) recorded a minimum asset value of 16.5137, while the highest asset value of 21.4749 was achieved by the Joint Stock Commercial Bank for Investment and Development of Vietnam (BID) in 2022 Notably, BID has maintained its position as the largest bank in terms of asset size for six consecutive years, highlighting a significant disparity in total assets among commercial banks in Vietnam.

The Capital Adequacy Ratio (CAP) has an average of 9.32% with a standard deviation of 6.07% In 2021, the Joint Stock Commercial Bank for Foreign Trade of Vietnam (VCB) achieved the highest CAP ratio at 90.77%, while BIDV recorded the lowest at 4.06% in 2017 This decline in BIDV's ratio highlights the banking system's focus on increasing equity to meet capital adequacy requirements By 2021, banks began to enhance their equity levels, driven by the need to mitigate risks and maintain a higher capital adequacy ratio in the face of market fluctuations, particularly before the Covid-19 pandemic.

In Vietnam, the technology efficiency (TE) of commercial banks has seen significant fluctuations, with Southeast Asia Commercial Joint Stock Bank (SSB) recording a low of 0% in 2012 and Vietnam International Commercial Joint Stock Bank (VIB) achieving a high of 99.99% in 2021 The average TE stands at 22.93%, with a standard deviation of 25.14% Recent years have witnessed a surge in technology investment driven by state policies, particularly accelerated by the Covid-19 pandemic, which shifted customer preferences from traditional banking methods to online services.

In 2012, Vietnam's Asia Commercial Joint Stock Bank (VAB) recorded the lowest deposit-to-equity ratio (DEP) at 7.67%, while Saigon Thuong Tin Commercial Joint Stock Bank (STB) achieved the highest at 89.37% in 2015 The average DEP across banks stands at 66.76%, with a standard deviation of 11.42%, indicating a significant reliance on deposit activities This substantial market share of deposits underscores their critical role in enabling banks to mobilize currency and capital for various operations A high DEP ratio reflects effective bank operations and fosters customer trust.

In 2018, the loans-to-assets ratio for LPB was the lowest at 10.11%, while BID recorded the highest at 78.80% The average ratio across banks was 60.84%, with a standard deviation of 11.6%, indicating that loan activities remain the primary focus for banks, following capital mobilization Furthermore, this ratio demonstrates a stable trend in the loans-to-assets ratio among commercial banks, showing minimal fluctuations over the years.

The Non-Performing Loan (NPL) ratio in the banking sector averages 2.33% with a standard deviation of 2.76% Following economic fluctuations from 2009 to 2011, the State Bank implemented regulations to tighten credit activities, mandating that commercial banks maintain an NPL ratio below 3% In 2020, the lowest recorded NPL ratio was 0.46% at Vietnam Technological and Commercial Joint Stock Bank (TCB), while Viet Capital Commercial Joint Stock Bank (BVB) reported a maximum NPL of 0.4029 in 2022 BVB's high NPL ratio, which surged to 21% by the end of 2022, reflects significant business performance challenges faced during the year.

The liquidity (LIQ) in the sample averages 71.24%, with a standard deviation of 14.80% Following the economic events of 2008, banks recognized the necessity of maintaining liquidity levels between 80% and 85%, in line with State Bank regulations, as this range optimizes profitability and ensures stable operations Notably, Bac recorded the lowest LIQ value at 1.38%, as indicated in Table 4.1.

In 2014, the Commercial Joint Stock Bank (BAB) recorded a compliance value of 99.71%, with Vietnam Prosperity Joint Stock Commercial Bank (VPB) achieving the highest compliance rate in 2021 This highlights the varying levels of compliance among commercial banks, indicating that exceeding this ratio may expose banks to significant liquidity risks.

In 2021, Vietnam's GDP experienced a mean value of 5.83% with a standard deviation of 1.58%, marking its lowest level in a decade at 2.58% due to the severe impact of the fourth wave of the Covid-19 pandemic, which imposed social distancing measures that hindered production and consumption Consequently, GDP plummeted to a negative growth of 6.02% in the third quarter However, in 2022, GDP rebounded to a peak of 8.02%, driven by government support through Decree No 31/2022/NĐ-CP, which provided loans and assistance to businesses and employees affected by the pandemic, facilitating a gradual economic recovery and growth compared to the previous year.

Between 2012 and 2022, Vietnam experienced an average inflation rate of 3.77%, with a standard deviation of 2.22% The peak inflation rate during this period occurred in 2012, reaching 9.09%, largely due to the National Assembly's resolution aimed at socio-economic development, which set a target to maintain inflation below 10% This decision was influenced by a previous high inflation rate of 18.58%.

In 2012, inflation rates peaked at their highest value in a decade, while 2015 recorded a significant low of just 0.63% This decline can be attributed to Decision No 254/QD-TTg, marking the final year of a five-year plan where authorities actively employed strategies to ensure macroeconomic stability and effectively control inflation.

Correlation matrix analysis

Table 4.2 Correlation coefficients between research variables

ROE SIZE CAP TE DEP LOANS NPL LIQ GDP INF

Correlation analysis reveals that five variables—bank size, equity-to-total assets ratio, technology investment, loans-to-assets ratio, and liquidity ratio—are positively correlated with Return on Equity (ROE), indicating that increases in these factors lead to improvements in financial performance by 0.592, 0.006, 0.305, 0.301, and 0.203 times, respectively However, the strength of these positive relationships remains relatively weak Conversely, four variables—total deposits ratio, non-performing loans ratio, gross domestic product growth rate, and inflation rate—are negatively correlated with financial performance, suggesting that decreases in these metrics result in increases in ROE by 0.139, 0.213, 0.039, and 0.139 times, respectively The correlation coefficients among the independent variables range from -0.224 to 0.592, indicating no multicollinearity, as all values are below 0.8.

The analysis of regression results (OLS/FEM/REM Model)

Table 4.3 Results of Pooled OLS, FEM and REM

Model Pooled OLS FEM REM

TE 0.0686*** 0.0000 -0.0072 0.6870 0.0344** 0.0280 DEP -0.1683*** 0.0000 -0.1393*** 0.0010 -0.1878*** 0.0000 LOANS 0.1649*** 0.0000 0.1943*** 0.0010 0.2015*** 0.0000 NPL -0.3646*** 0.0030 -0.2662** 0.0130 -0.2676** 0.0150 LIQ 0.0275** 0.0430 0.0603 0.1410 0.0432** 0.0340

Note:*, **, *** represent statistical significance level at 10%, 5%, 1%, respectively

In the Pooled OLS model, three variables—CAP, GDP, and INF—are not statistically significant, while the remaining variables show significant relationships with ROE At a 1% significance level, SIZE and LOANS positively influence ROE, whereas DEP and NPL negatively affect it Additionally, the LIQ variable demonstrates a positive correlation with ROE at a 5% significance level The model's R-squared value of 0.5210 indicates that the independent variables account for 52.1% of the variation in the dependent variable.

The FEM model analysis reveals that four variables—CAP, TE, LIQ, and GDP—lack statistical significance, while SIZE, DEP, LOANS, and INF demonstrate significant relationships with the independent variable at a 1% significance level Notably, SIZE and LOANS exhibit a positive correlation with ROE, while the NPL variable negatively impacts the independent variable at a 5% significance level Additionally, the model's R-squared value of 0.5297 indicates that the independent variables account for 52.97% of the variance in the ROE variable.

In the REM model, CAP, GDP, and INF are not statistically significant, similar to the Pooled OLS model, indicating they do not explain the correlation with ROE Conversely, SIZE, DEP, and LOANS are statistically significant at the 1% level, with only DEP showing a negative correlation with ROE Additionally, at the 5% significance level, the NPL variable negatively affects ROE, consistent with the FEM model The REM model's R-squared value of 0.5019 indicates that 50.19% of the variation in ROE can be explained by the independent variables.

Selection of appropriate models

Pooled OLS and FEM FEM and REM Pooled OLS and REM F-test F(25, 241) = 6.08

Conclusion Choosing FEM Choosing FEM Choosing REM

From the regression results of Pooled OLS model, FEM model and REM model, the author compares and selects the model as follows:

Firstly, the author uses the F-test to choose between two models Pooled OLS and

FEM The test results in Table 4.4 show that the p-value is less than 5% (Prob > F 0.0000 < 0.05), so with this result, the author concludes to choose the FEM model

Secondly, to choose between two models FEM and REM, the author uses the

Hausman test with the hypothesis:

: No correlation between the independent variables and the residual which means that the REM model is suitable (p-value > 5%)

: There is a correlation between the independent variables and the residual which means that the FEM model is suitable (p-value < 5%)

The analysis presented in Table 4.4 indicates a p-value of 0.0000, which is below the statistical significance threshold of 0.05 Consequently, the hypothesis is rejected, confirming that the Fixed Effects Model (FEM) is the most suitable model for this research.

Finally, the author uses the Breusch and Pagan test to select the last pair of models which are the Pooled OLS model and REM model with the hypothesis:

: The Pooled OLS model is suitable (p-value > 5%)

: The REM model is suitable (p-value < 5%)

The findings in Table 4.5 indicate a p-value of 0.0000, which is below the 5% significance threshold (Prob > Chibar2 < 0.05) Consequently, the author rejects the null hypothesis and selects the Random Effects Model (REM) for analysis.

In summary, the Fixed Effects Model (FEM) is determined to be the most suitable among the three models—Pooled OLS, FEM, and Random Effects Model (REM) The results of the Hausman test further validate the choice of the FEM for analyzing the thesis's subsequent findings.

Test of defects prediction model

To enhance the reliability of estimation results and prevent multicollinearity among variables, the author employs the variance inflation factor method for testing The findings from the multicollinearity assessment are presented below.

Table 4.5 Results of multicollinearity test

As a rule, if VIF is greater than 10, there is a sign of multicollinearity The results in Table 4.5 show that the variance inflation factor has an average value of 1.22 less than

2 and the independent variables all range from 1.04 to 1.55 Thus, there is no multicollinearity between independent variables, because the values of these variables are all lower than 10

The comparison of OLS, FEM, and REM models indicates that the Fixed Effects Model (FEM) is the most appropriate for analyzing financial performance Consequently, it is essential to evaluate the research model to identify its limitations and enhance the accuracy of the findings To test for autocorrelation, the author employs the Wooldridge test, establishing a hypothesis for this analysis.

: There is no autocorrelation in the FEM model

: There is autocorrelation in the FEM model

F(1,25) = 41.614 Prob > F = 0.0000 The research model has autocorrelation

At a 5% statistical significance level, the autocorrelation test results indicate that Prob > F = 0.0000, which is less than 5% Consequently, the null hypothesis is rejected while the alternative hypothesis is accepted, confirming the presence of autocorrelation in the Fixed Effects Model (FEM).

After checking the autocorrelation phenomenon, the author continues to test the heteroscedasticity phenomenon with the hypothesis:

: The model does not have the phenomenon of Heteroscedasticity

: The model has the phenomenon of Heteroscedasticity

Table 4.7 Modified Wald test results

Chi2(26) = 1634.74 Prob > Chi2 = 0.0000 The research model has heteroscedasticity

The test results in Table 4.7 indicate that the probability (Prob > Chi2) is 0.0000, which is less than the 5% significance level Consequently, the author rejects the null hypothesis and accepts the alternative hypothesis, concluding that the FEM model exhibits heteroscedasticity.

In statistical analysis, a P-value of 5% or lower leads the author to reject the hypothesis, indicating that the variable is endogenous Conversely, a P-value exceeding 5% results in the acceptance of the hypothesis, suggesting that the variable is exogenous.

Table 4.8 Endogenous and Exogenous variables in the research model

The analysis conducted using Stata14.0 reveals a research model comprising four endogenous variables: bank size (SIZE), non-performing loans ratio (NPL), gross domestic product growth rate (GDP), and inflation rate (INF) Additionally, the model includes five exogenous variables: equity-to-total assets ratio (CAP), technology investment (TE), total deposits ratio (DEP), loans-to-assets ratio (LOANS), and liquidity ratio (LIQ).

The results of overcoming the research model by FGLS method

The analysis and model testing indicate that the Finite Element Method (FEM) model is the most appropriate; however, the research model reveals issues of autocorrelation and heteroscedasticity, as identified by the Wooldridge and Modified Wald tests To address these challenges, the author implements the Feasible Generalized Least Squares (FGLS) method, yielding the following results.

Table 4.9 The results of model by FGLS method

The FGLS method effectively addressed autocorrelation and heteroscedasticity, resulting in a regression model with a statistical significance level of 1% (Prob = 0.000), confirming its appropriateness Analysis from Table 4.8 reveals that the variables SIZE, TE, DEP, LOANS, NPL, and LIQ significantly influence ROE, while CAP, GDP, and INF are not statistically significant Notably, SIZE, TE, DEP, LOANS, and NPL demonstrate a 1% significance level, although DEP and NPL exert a negative impact on ROE.

The analysis reveals that the LIQ variable positively influences ROE at a significance level of 5% However, after addressing the model's shortcomings, it is evident that the CAP, GDP, and INF variables do not demonstrate statistical significance.

The research model evaluates the influence of technology investment on the financial performance of commercial banks in Vietnam over a decade, from 2012 to 2022.

GMM regression model method

Following the successful application of the FGLS method to address the research model, the author advances to tackle the endogenous variables using the GMM regression method The findings derived from the GMM regression model are summarized in the table below.

Table 4.10 GMM endogenous test results

Arellano-Bond test for AR(2) in first differences Prob > z = 0.122

Sargan test of overid restrictions Prob > chi2 = 0.515 Hansen test of overid restrictions Prob > chi2 = 0.872

To assess the effectiveness of treating endogenous variables, the author examines four key factors First, the number of tools is less than the number of groups, as shown in Table 4.9 (24 tools vs 27 groups), satisfying the initial condition Second, the Arellano-Bond test yields a p-value of 0.122, indicating no autocorrelation in the model Third, Sargan's test shows a p-value of 0.515, confirming the suitability of the instrumental variable and the presence of an endogenous phenomenon Finally, Hansen's test reports a p-value of 0.872, demonstrating the appropriateness of the tools used Consequently, it can be concluded that the GMM model effectively addresses the endogenous issue by utilizing the lagged variable of the endogenous variable in the regression analysis The empirical findings provide a comprehensive overview of the impact of technology investment on the financial performance of commercial banks in Vietnam from 2012 to 2022, as presented in the following model.

CONCLUSIONS AND RECOMMENDATIONS

Conclusions

This thesis employs panel data regression to analyze the influence of technology investment on the financial performance of Vietnamese commercial banks, specifically measured by return on equity (ROE) Key independent variables include bank-specific factors such as bank size (SIZE), technology investment (TE), equity-to-total assets ratio (CAP), liquidity ratio (LIQ), non-performing loans ratio (NPL), and total deposits ratio (DEP), alongside macroeconomic indicators like gross domestic product growth rate (GDP) and inflation rate (INF) The study utilizes financial data from 26 Vietnamese commercial banks spanning from 2012 to 2022, with macroeconomic data sourced from the General Statistics Office of Vietnam.

The author utilized STATA 14.0 software to analyze the collected data, presenting the experimental research results through descriptive statistics and correlation analysis Various estimation methods were employed, including Pooled-OLS, FEM, and REM regression models, alongside F-tests, Breusch-Pagan tests, and Hausman tests to identify the most suitable model Additionally, the FGLS model was estimated to address issues of heteroscedasticity and autocorrelation, while the GMM method was applied to manage endogenous variables, ensuring more reliable and accurate outcomes.

The empirical analysis reveals that several variables significantly influence financial performance At a 1% significance level, SIZE, TE, and GDP positively impact financial outcomes Additionally, CAP and LIQ also show positive effects at significance levels of 10% and 5%, respectively Conversely, NPL and DEP negatively affect financial performance at the 1% significance level Furthermore, the study finds that LOANS and INF do not significantly influence the financial performance of Vietnamese commercial banks, indicating their lack of statistical significance in the research model.

In summary, this thesis successfully met the general and specific objectives outlined in Chapter 1, addressing all research questions The empirical research conducted has clarified the impact of technology investment on the financial performance of commercial banks in Vietnam over the past decade.

Suggestions and recommendations

Research from 2012 to 2022 identifies key factors influencing the financial performance of Vietnamese commercial banks, including bank size, equity-to-total assets ratio, technology investment, total deposits ratio, non-performing loans ratio, liquidity ratio, and GDP growth rate To enhance financial performance, particularly through technology investment, the author offers several recommendations for commercial banks and their management.

5.2.1.1 The bank should increase their scale and expand branches

The positive relationship between the size of commercial banks and their financial performance suggests that banks should consider scaling up to enhance their financial capacity, expand credit offerings, and boost competitiveness and customer trust Developing additional branches in strategic locations, such as densely populated areas and industrial zones, further increases a bank's visibility and appeal to customers Large commercial banks with robust financial resources can successfully expand their global presence However, it is crucial to ensure that the quantity and quality of human resources align with expansion efforts, as inadequate staffing can negatively impact business performance and profits Therefore, bank administrators must carefully evaluate expansion plans, taking into account operational costs and potential customer base.

5.2.1.2 Commercial banks should raise equity-to-total assets ratio

Research in Chapter 4 indicates that the equity ratio positively influences the financial performance of Vietnamese commercial banks To enhance equity, banks must establish a sound financial structure that aligns with Basel III standards Presently, banks can boost their capital through various methods.

Stock dividends enable banks to boost their capital by distributing shares or issuing common stock to shareholders This method effectively enhances shareholder awareness and accountability, highlighting their crucial role in the bank's financial success.

Commercial banks can enhance their charter capital by issuing additional shares to existing shareholders or foreign strategic investors, which boosts their financial capacity and supports the integration of information technology and management strategies To successfully implement this approach, it is crucial for banks to maintain transparency and openness regarding their financial information, thereby enhancing their credibility in the capital mobilization market.

5.2.1.3 Vietnames commercial banks needs to enhance the efficiency of technology investment and diversify technology products

The results show that technology investment has a positive impact on the financial performance of Vietnamese commercial banks Therefore, the author has the following recommendations for these banks as follows:

Firstly, administrators need to improve the efficiency of technology investment

Commercial banks must select a technology development platform that aligns with their strategic goals Given their financial constraints and human resource capabilities, it may not be feasible for all banks to implement every new technology at once Therefore, each bank should identify and adopt specific, essential technology platforms that correspond with their development strategy and digital banking model at various stages of growth To remain competitive, they should leverage diverse technologies, including advanced storage systems and state-of-the-art hardware and software, to enhance their information technology infrastructure and keep pace with other commercial banks.

Secondly, commercial banks need to diversify technology products and services

Commercial banks must implement a robust technology exploitation policy that leverages advanced technologies like Cloud computing, Big Data Analytics, Artificial Intelligence, and Blockchain By developing high-tech products, banks can enhance their competitiveness and offer a diverse range of product options, which facilitates effective cross-selling to customers Additionally, the diversification of products and services not only improves customer choice but also helps mitigate risks associated with operational processes.

Commercial banks should develop strategic partnerships with Fintech companies to enhance their service offerings In today's technology-driven landscape, Fintech facilitates easy and cost-effective access to financial services for consumers The rise of Fintech has transformed traditional banking distribution channels, enabling broader online transactions through platforms like Zalo Pay, Momo, and VNPay By collaborating with Fintech firms, banks can lower advertising expenses and encourage increased customer engagement in non-cash payment methods.

5.2.1.4 The bank should monitor the ratio of total deposits at appropriate levels

Research indicates a negative correlation between this ratio and the financial performance of commercial banks To mitigate the decline in bank profits attributed to interest expenses, the author proposes two key recommendations.

To enhance financial performance, administrators should lower deposit interest rates during periods of low credit demand This adjustment alleviates the financial burden on commercial banks, allowing them to manage incurred costs more effectively.

To foster fair competition, commercial banks should refrain from solely increasing deposit interest rates Instead, they can enhance their competitive edge through non-price strategies, such as improving customer service for both individual and corporate clients These initiatives can boost service revenue, enabling banks to fund deposit interest rates while also enhancing product quality and investing in advanced technology.

5.2.1.5 Commercial banks should supervise and minimize non-performing loans ratio at an acceptable level

Empirical findings indicate a negative correlation between the ratio of non-performing loans and the financial performance of commercial banks To enhance their financial health, it is crucial for commercial banks to implement strategies aimed at reducing non-performing loans Specifically, Vietnamese commercial banks must focus on effective measures to minimize this ratio.

To enhance credit quality, banks must refine their credit analysis processes by ensuring specificity, accuracy, and thorough appraisal This involves improving asset valuation and maintaining clear communication with customers to prevent ethical violations Additionally, it is crucial to assess customers both prior to and after loan disbursement, as effective business operations enable timely repayment of both principal and interest to the bank.

Commercial banks must maintain an acceptable level of non-performing loans by regularly monitoring and analyzing their status and causes To mitigate risks associated with these loans, banks should expedite the sale and management of collateral to recover capital When non-performing loans arise, it is essential for banks to engage with customers to assess the situation, identify underlying issues, and develop appropriate debt settlement plans.

5.2.1.6 The bank needs to utilize and maintain liquidity ratio according to the Central Bank

Empirical research in Chapter 4 indicates that the liquidity ratio positively influences the financial performance of commercial banks in Vietnam during the study period To enhance financial performance, the author proposes two key recommendations.

Thesis limitations and future research direction

Besides the obtained results, the study still has certain limitations as follows:

The study, spanning a decade from 2012 to 2022, analyzed research data from 26 Vietnamese commercial banks, highlighting the challenges in drawing definitive conclusions about the impact of technology investment on their financial performance.

According to Circular No 45/2018/TT-BTC, application software is categorized as intangible assets due to its minimal value Technology investment encompasses more than just computer software or banking systems; it also includes equipment and various technology products Consequently, research data alone is insufficient to capture the full scope of technology investment in commercial banks in Vietnam.

Thirdly, this thesis applies only one financial performance measure which is "Net

Return on Equity (ROE)", while there are two other measures which are Return on Assets (ROA) and Net Income Margin (NIM)

In addition to the identified micro and macro factors influencing financial performance, other significant elements such as interbank interest rates, operating costs, capital adequacy ratio, and credit risk provision ratio have not been included in the model.

In order to improve the above disadvantages, the author makes some suggestions for future research directions:

To enhance accuracy and practicality, the research will expand its scope by increasing the number of banks involved, thereby increasing the sample size Additionally, the upcoming study will extend the time frame of the research to provide a more comprehensive perspective.

Future research will incorporate additional metrics of technology investment and gather a wider range of input data to comprehensively assess the effects of technology investment on the financial performance of commercial banks in Vietnam, ultimately leading to more precise outcomes.

This study will incorporate additional factors to represent the dependent variable, enhancing the measurement of financial performance in a comprehensive and precise way Additionally, it will explore further explanatory variables to provide a deeper understanding of the mutual relationships between these factors and financial performance.

In conclusion, this chapter has concluded the research results obtained in Chapter

4 At the same time, the author also makes some suggestions and recommendations to contribute to improving financial performance in Vietnamese commercial banks to promote better banking business Finally, the author also indicates some limitations in this study, which is also the basis for the next development direction of the thesis in the future

The article explores the impact of technology investment on the financial performance of commercial banks in Vietnam, highlighting that these banks are increasingly prioritizing technological advancements to enhance profitability To meet customer demands and stay aligned with global trends, banks must adopt modern innovations Additionally, sustaining stable financial performance is crucial for maintaining business operations and mitigating the adverse effects of macroeconomic factors.

Based on secondary data from 26 Vietnamese commercial banks in the period

Between 2012 and 2022, the thesis identified key factors influencing the financial performance of commercial banks, including bank size, equity-to-total assets ratio, technology investment, total deposits, non-performing loans ratio, liquidity ratio, and GDP growth rate Positive contributors to financial performance were found to be bank size, technology investment, capital adequacy, liquidity, and GDP growth Conversely, total deposits and non-performing loans negatively impacted financial performance Notably, the effects of loans and inflation diverged from initial expectations and were not statistically significant The discussion in Chapter 4 elaborates on the statistically significant variables, most of which align with the initial hypotheses.

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APPENDIX 1 26 COMMERCIAL BANKS IN THE THESIS Number Stock Code Name of commercial bank

1 ABB An Binh Commercial Joint Stock Bank

2 ACB Asia Commercial Joint Stock Bank

3 BAB Bac A Commercial Joint Stock Bank

4 BID Joint Stock Commercial Bank for Investment and

5 BVB Viet Capital Commercial Joint Stock Bank

6 CTG Vietnam Joint Stock Commercial Bank of Industry and Trade

7 EIB Vietnam Export Import Commercial Joint Stock

8 HDB Ho Chi Minh city Development Joint Stock

9 KLB Kien Long Commercial Joint Stock Bank

10 LPB LienViet Commercial Joint Stock Bank

11 MBB Military Commercial Joint Stock Bank

12 MSB The Maritime Commercial Joint Stock Bank

13 NAB Nam A Commercial Joint Stock Bank

14 NVB National Citizen Commercial Joint Stock Bank

15 OCB Orient Commercial Joint Stock Bank

16 PGB Petrolimex Group Commercial Joint Stock Bank

17 SGB Saigon Bank For Industry And Trade

18 SHB Saigon – Hanoi Commercial Joint Stock Bank

19 SSB Southeast Asia Commercial Joint Stock Bank

20 STB Saigon Thuong Tin Commercial Joint Stock Bank

21 TCB Vietnam Technological and Commercial Joint

22 TPB Tien Phong Commercial Joint Stock Bank

23 VAB Vietnam - Asia Commercial Joint Stock Bank

24 VPB Vietnam Prosperity Joint Stock Commercial Bank

25 VCB Joint Stock Commercial Bank for Foreign Trade of

26 VIB Vietnam International Commercial Joint Stock

Code Year ROE SIZE CAP TE DEP LOANS NPL LIQ GDP INF

ABB 2012 0,08148 17,6444 0,10650 0,40417 0,62447 0,39862 0,02835 0,1894 0,05250 0,09095 ABB 2013 0,02447 17,8695 0,09968 0,36960 0,64486 0,39900 0,07631 0,0700 0,05422 0,06595 ABB 2014 0,02047 18,0271 0,08472 0,19032 0,66854 0,37791 0,04508 0,0599 0,05984 0,04085 ABB 2015 0,01576 17,9802 0,08995 0,16275 0,73833 0,47426 0,02424 0,0563 0,06679 0,00631 ABB 2016 0,04175 18,1219 0,07877 0,18781 0,69467 0,52863 0,02311 0,0691 0,06211 0,02668 ABB 2017 0,07989 18,2523 0,07241 0,19802 0,68516 0,55788 0,02770 0,0792 0,06812 0,03520 ABB 2018 0,10408 18,3153 0,07632 0,27728 0,69179 0,57265 0,01886 0,1083 0,07076 0,03540 ABB 2019 0,12759 18,4459 0,07647 0,39510 0,67840 0,54672 0,02310 0,1684 0,07020 0,02796 ABB 2020 0,12542 18,5723 0,07658 0,39773 0,62310 0,53785 0,02092 0,2274 0,02910 0,03221 ABB 2021 0,13300 18,6108 0,09699 0,38074 0,56095 0,56371 0,02062 0,1802 0,02580 0,01835 ABB 2022 0,14168 18,6835 0,09978 0,30540 0,64638 0,62225 0,02885 0,1814 0,08020 0,03150 ACB 2012 0,06380 18,9877 0,07160 0,01475 0,71031 0,57464 0,02501 0,1871 0,05250 0,09090 ACB 2013 0,06580 18,9311 0,07506 0,21443 0,82900 0,63411 0,03025 0,0647 0,05250 0,09095 ACB 2014 0,07640 19,0063 0,06902 0,44765 0,86083 0,63886 0,02178 0,0542 0,05422 0,06595 ACB 2015 0,08170 19,1211 0,06348 0,43719 0,86827 0,65766 0,01308 0,0664 0,05984 0,04085 ACB 2016 0,09870 19,2695 0,06018 0,42420 0,88604 0,69156 0,00869 0,0646 0,06679 0,00631 ACB 2017 0,14080 19,4656 0,05745 0,35645 0,84903 0,69173 0,00700 0,0672 0,06211 0,02668 ACB 2018 0,27730 19,6126 0,06382 0,37855 0,81983 0,69226 0,00727 0,0905 0,06812 0,03520

ACB 2019 0,21650 19,7649 0,07240 0,22134 0,80344 0,69402 0,00539 0,0945 0,07076 0,03540 ACB 2020 0,21670 19,9125 0,07974 0,23093 0,79454 0,69406 0,01442 0,1055 0,07020 0,02796 ACB 2021 0,23900 20,0842 0,08508 0,20566 0,71986 0,67463 0,00774 0,1584 0,02910 0,03221 BAB 2012 0,04691 17,3341 0,09328 0,48601 0,86041 0,65374 0,05661 0,0745 0,05250 0,09095 BAB 2013 0,01099 17,7337 0,06583 0,28839 0,84296 0,58132 0,02318 0,0251 0,05422 0,06595 BAB 2014 0,05806 17,8617 0,07209 0,24958 0,80991 0,63106 0,02154 0,0269 0,05984 0,04085 BAB 2015 0,06649 17,9659 0,07897 0,19511 0,83353 0,65036 0,00701 0,0241 0,06679 0,00631 BAB 2016 0,07191 18,1454 0,07647 0,03804 0,77901 0,62759 0,00811 0,0138 0,06211 0,02668 BAB 2017 0,08622 18,3349 0,06945 0,03133 0,69093 0,59788 0,00634 0,1247 0,06812 0,03520 BAB 2018 0,09451 18,3905 0,07299 0,02872 0,74755 0,65327 0,00763 0,1177 0,07076 0,03540 BAB 2019 0,09562 18,4966 0,07243 0,02965 0,70594 0,66993 0,00683 0,1217 0,07020 0,02796 BAB 2020 0,09590 18,5793 0,07137 0,03223 0,73763 0,67087 0,00791 0,1122 0,02910 0,03221 BAB 2021 0,07028 18,6013 0,07556 0,04765 0,78002 0,69736 0,00775 0,0211 0,02580 0,01835 BAB 2022 0,08025 18,6737 0,07609 0,04788 0,75240 0,72266 0,00546 0,0973 0,08020 0,03150 BID 2012 0,13008 19,9992 0,05508 0,06741 0,62514 0,68898 0,02695 0,0963 0,05250 0,09095 BID 2013 0,09632 20,1225 0,05889 0,09501 0,61800 0,70186 0,02261 0,0925 0,05422 0,06595 BID 2014 0,12545 20,2930 0,05167 0,07286 0,67729 0,67514 0,02032 0,0997 0,05984 0,04085 BID 2015 0,14836 20,5615 0,04978 0,11568 0,66369 0,69465 0,01680 0,0893 0,06679 0,00631 BID 2016 0,15062 20,7296 0,04384 0,13951 0,72142 0,70911 0,01994 0,0831 0,06211 0,02668 BID 2017 0,14110 20,9075 0,04062 0,12196 0,71529 0,71159 0,01622 0,0953 0,06812 0,03520

BID 2018 0,14223 20,9956 0,04155 0,10716 0,75382 0,74367 0,01689 0,1085 0,07076 0,03540 BID 2019 0,13825 21,1220 0,05212 0,08830 0,74778 0,73986 0,01745 0,1296 0,07020 0,02796 BID 2020 0,11008 21,1398 0,05251 0,06525 0,80879 0,78806 0,01760 0,0817 0,02910 0,03221 BID 2021 0,09070 21,2895 0,04900 0,07871 0,78356 0,75242 0,01000 0,1099 0,02580 0,01835 BID 2022 0,12558 21,4749 0,04913 0,09715 0,69489 0,69980 0,01158 0,1550 0,08020 0,03150 BVB 2012 0,08178 16,8442 0,15798 0,01022 0,49819 0,37289 0,01895 0,5091 0,05250 0,09095 BVB 2013 0,06308 16,9535 0,13959 0,27983 0,52224 0,42974 0,04108 0,5126 0,05422 0,06595 BVB 2014 0,03203 17,0652 0,12850 0,29870 0,56966 0,49838 0,02888 0,5872 0,05984 0,04085 BVB 2015 0,04893 17,1835 0,11417 0,31735 0,64178 0,54241 0,02888 0,6453 0,06679 0,00631 BVB 2016 0,01606 17,2932 0,10223 0,31131 0,75993 0,64267 0,02888 0,7413 0,06211 0,02668 BVB 2017 0,00081 17,5019 0,08380 0,20130 0,67724 0,62116 0,01800 0,6966 0,06812 0,03520 BVB 2018 0,01003 17,6561 0,07376 0,13967 0,71953 0,63048 0,02100 0,7035 0,07076 0,03540 BVB 2019 0,02744 17,7631 0,07210 0,14229 0,67978 0,64743 0,02509 0,7612 0,07020 0,02796 BVB 2020 0,03375 17,9280 0,06367 0,14670 0,67711 0,64256 0,02791 0,7777 0,02910 0,03221 BVB 2021 0,04135 18,1530 0,06063 0,19638 0,59134 0,59715 0,02535 0,7748 0,02580 0,01835 BVB 2022 0,05364 18,1858 0,06327 0,16775 0,63400 0,63386 0,40291 0,8290 0,08020 0,03150 CTG 2012 0,18580 20,0372 0,06721 0,06582 0,57416 0,65474 0,01467 0,8638 0,05250 0,09095 CTG 2013 0,21810 20,1723 0,09419 0,07504 0,63240 0,64714 0,01002 0,8456 0,05422 0,06595 CTG 2014 0,18230 20,3095 0,08357 0,08812 0,64149 0,65861 0,01115 0,8331 0,05984 0,04085 CTG 2015 0,10250 20,4741 0,07198 0,05913 0,63242 0,68447 0,00917 0,9087 0,06679 0,00631

CTG 2016 0,10370 20,6705 0,06358 0,03768 0,69058 0,69061 0,00904 0,8943 0,06211 0,02668 CTG 2017 0,10190 20,8141 0,05823 0,16088 0,68757 0,71447 0,01140 0,9108 0,06812 0,03520 CTG 2018 0,11230 20,8755 0,05793 0,14914 0,70929 0,73166 0,01563 0,9228 0,07076 0,03540 CTG 2019 0,11700 20,9390 0,06235 0,17676 0,71958 0,74338 0,01156 0,9331 0,07020 0,02796 CTG 2020 0,08030 21,0170 0,06367 0,16639 0,73822 0,74748 0,00938 0,9125 0,02910 0,03221 CTG 2021 0,12250 21,1496 0,06115 0,14029 0,75859 0,72139 0,01265 0,8725 0,02580 0,01835 CTG 2022 0,16110 21,3157 0,05981 0,12049 0,69075 0,68847 0,01239 0,8740 0,08020 0,03150 EIB 2012 0,13525 18,9522 0,09293 0,02590 0,41408 0,43675 0,01318 0,5830 0,05250 0,09095 EIB 2013 0,04487 18,9503 0,08644 0,01654 0,46794 0,48661 0,01982 0,5739 0,05422 0,06595 EIB 2014 0,00399 18,8975 0,08192 0,01187 0,63300 0,53779 0,02461 0,6119 0,05984 0,04085 EIB 2015 0,00304 18,6426 0,10528 0,01462 0,78839 0,67193 0,01859 0,7968 0,06679 0,00631 EIB 2016 0,02297 18,6738 0,10441 0,02078 0,79465 0,66633 0,02946 0,7983 0,06211 0,02668 EIB 2017 0,05774 18,8219 0,09541 0,01973 0,78691 0,67128 0,02268 0,7867 0,06812 0,03520 EIB 2018 0,04438 18,8437 0,09750 0,11442 0,77755 0,67455 0,01846 0,7723 0,07076 0,03540 EIB 2019 0,05499 18,9367 0,09400 0,10811 0,83132 0,66959 0,01707 0,7656 0,07020 0,02796 EIB 2020 0,06363 18,8934 0,10484 0,10331 0,83472 0,62011 0,02515 0,7171 0,02910 0,03221 EIB 2021 0,05428 18,9265 0,10725 0,09719 0,82839 0,68327 0,01960 0,7908 0,02580 0,01835 EIB 2022 0,14384 19,0362 0,11067 0,08306 0,80308 0,69815 0,01798 0,8130 0,08020 0,03150 HDB 2012 0,06052 17,7817 0,10219 0,60244 0,64911 0,39695 0,02353 0,5016 0,05250 0,09095 HDB 2013 0,02534 18,2725 0,09959 0,28446 0,72349 0,50255 0,03672 0,5967 0,05422 0,06595

HDB 2014 0,05184 18,4159 0,09243 0,22283 0,65724 0,41574 0,02271 0,4929 0,05984 0,04085 HDB 2015 0,06402 18,4835 0,09242 0,15636 0,70002 0,52451 0,01586 0,7241 0,06679 0,00631 HDB 2016 0,09198 18,8281 0,06615 0,02892 0,68732 0,54097 0,01458 0,6685 0,06211 0,02668 HDB 2017 0,13242 19,0590 0,07795 0,01718 0,63664 0,54579 0,01515 0,6624 0,06812 0,03520 HDB 2018 0,19025 19,1911 0,07789 0,01203 0,59271 0,56370 0,01531 0,7296 0,07076 0,03540 HDB 2019 0,19726 19,2513 0,08882 0,05181 0,54915 0,63056 0,01365 0,8322 0,07020 0,02796 HDB 2020 0,18811 19,5811 0,07741 0,22758 0,54718 0,55272 0,01322 0,7149 0,02910 0,03221 HDB 2021 0,20960 19,7414 0,08219 0,10333 0,48923 0,53587 0,01654 0,7009 0,02580 0,01835 HDB 2022 0,21053 19,8469 0,09368 0,07603 0,51840 0,62640 0,01669 0,8554 0,08020 0,03150 KLB 2012 0,10170 16,7377 0,18540 0,07499 0,57269 0,51351 0,02926 0,7094 0,05250 0,09095 KLB 2013 0,09060 16,8776 0,16263 0,05817 0,62248 0,56164 0,02471 0,7163 0,05422 0,06595 KLB 2014 0,05140 16,9555 0,14561 0,04641 0,71722 0,57955 0,01953 0,6989 0,05984 0,04085 KLB 2015 0,04900 17,0472 0,13322 0,04323 0,79301 0,63502 0,01126 0,7630 0,06679 0,00631 KLB 2016 0,03590 17,2316 0,11047 0,03221 0,75167 0,64355 0,01061 0,7496 0,06211 0,02668 KLB 2017 0,05830 17,4352 0,09515 0,03462 0,69988 0,65544 0,00839 0,7446 0,06812 0,03520 KLB 2018 0,06350 17,5605 0,08864 0,03250 0,69029 0,69054 0,00757 0,7922 0,07076 0,03540 KLB 2019 0,01790 17,7493 0,07420 0,04869 0,64422 0,64936 0,01021 0,7406 0,07020 0,02796 KLB 2020 0,03280 17,8635 0,06840 0,04096 0,73352 0,60094 0,05423 0,6788 0,02910 0,03221 KLB 2022 0,11040 18,2671 0,06044 0,06140 0,60863 0,51449 0,01890 0,5894 0,08020 0,03150 LPB 2012 0,07625 18,0114 0,11129 0,01139 0,62242 0,34012 0,02711 0,3990 0,05250 0,09095

LPB 2013 0,04974 18,1925 0,09135 0,01245 0,69795 0,36377 0,02482 0,4156 0,05422 0,06595 LPB 2014 0,04097 18,4287 0,07332 0,01367 0,77201 0,40491 0,01100 0,5117 0,05984 0,04085 LPB 2015 0,03073 18,4938 0,07065 0,15345 0,72154 0,51558 0,00966 0,6907 0,06679 0,00631 LPB 2016 0,09335 18,7704 0,05873 0,14336 0,78233 0,55479 0,01114 0,6334 0,06211 0,02668 LPB 2017 0,12016 18,9119 0,05741 0,20344 0,78488 0,60815 0,01067 0,7095 0,06812 0,03520 LPB 2018 0,08432 18,9808 0,05826 0,23864 0,71360 0,10115 0,01410 0,8430 0,07076 0,03540 LPB 2019 0,14056 19,1241 0,06226 0,22922 0,67727 0,68694 0,01445 0,9102 0,07020 0,02796 LPB 2020 0,16354 19,3059 0,05873 0,24102 0,72016 0,71946 0,01431 0,8242 0,02910 0,03221 LPB 2022 0,39615 19,6077 0,07340 0,20481 0,65871 0,70371 0,01455 0,9215 0,08020 0,03150 MBB 2012 0,17150 18,9838 0,07704 0,04918 0,67051 0,41664 0,01842 0,4985 0,05250 0,09095 MBB 2013 0,16250 19,0106 0,08708 0,03962 0,75458 0,47670 0,02446 0,5541 0,05422 0,06595 MBB 2014 0,10840 19,1163 0,08553 0,02630 0,83600 0,48933 0,02730 0,5782 0,05984 0,04085 MBB 2015 0,12560 19,2139 0,10488 0,06404 0,82141 0,54004 0,01607 0,6344 0,06679 0,00631 MBB 2016 0,11470 19,3617 0,10376 0,08629 0,76022 0,58022 0,01318 0,6762 0,06211 0,02668 MBB 2017 0,12320 19,5645 0,09431 0,15728 0,70147 0,58004 0,01204 0,6818 0,06812 0,03520 MBB 2018 0,19170 19,7081 0,09432 0,15939 0,66229 0,58366 0,01321 0,7069 0,07076 0,03540 MBB 2019 0,21130 19,8353 0,09693 0,18402 0,66274 0,60058 0,01158 0,7666 0,07020 0,02796 MBB 2020 0,18360 20,0200 0,10121 0,24644 0,62823 0,59385 0,02471 0,8234 0,02910 0,03221 MBB 2021 0,22560 20,2243 0,10292 0,38656 0,63361 0,58437 0,00899 0,8173 0,02580 0,01835 MBB 2022 0,24610 20,4065 0,10928 0,44604 0,60890 0,61576 0,01092 0,9053 0,08020 0,03150

MSB 2012 0,02440 18,5153 0,08269 0,01524 0,54207 0,25648 0,02645 0,3222 0,05250 0,09095 MSB 2013 0,03570 18,4894 0,08787 0,01893 0,61142 0,24904 0,02708 0,3049 0,05422 0,06595 MSB 2014 0,01510 18,4634 0,09050 0,25890 0,60573 0,22005 0,05159 0,2650 0,05984 0,04085 MSB 2015 0,01010 18,4629 0,13053 0,34192 0,60028 0,26354 0,03411 0,3510 0,06679 0,00631 MSB 2016 0,01030 18,3439 0,14686 0,41775 0,62185 0,37435 0,02364 0,5155 0,06211 0,02668 MSB 2017 0,00890 18,5361 0,12226 0,61248 0,50650 0,31882 0,02227 0,4192 0,06812 0,03520 MSB 2018 0,06310 18,7411 0,10031 0,68150 0,46113 0,34673 0,03006 0,4818 0,07076 0,03540 MSB 2019 0,07280 18,8716 0,09469 0,60717 0,51518 0,39947 0,02045 0,4972 0,07020 0,02796 MSB 2020 0,12670 18,9900 0,09550 0,64303 0,49525 0,44425 0,01963 0,5536 0,02910 0,03221 MSB 2021 0,20740 19,1320 0,10821 0,64143 0,46457 0,49039 0,01742 0,6249 0,02580 0,01835 MSB 2022 0,18960 19,1757 0,12527 0,72785 0,55044 0,56027 0,01715 0,7202 0,08020 0,03150 NAB 2012 0,05510 16,5886 0,20470 0,09118 0,54516 0,42344 0,02476 0,5785 0,05250 0,09095 NAB 2013 0,04140 17,1753 0,11321 0,08930 0,47527 0,39934 0,01477 0,5771 0,05422 0,06595 NAB 2014 0,05680 17,4343 0,08933 0,08169 0,54485 0,42129 0,01400 0,4737 0,05984 0,04085 NAB 2015 0,05760 17,3842 0,09627 0,07689 0,68701 0,58278 0,00913 0,6669 0,06679 0,00631 NAB 2016 0,00960 17,5733 0,08012 0,05554 0,79531 0,55190 0,01624 0,6236 0,06211 0,02668 NAB 2017 0,06740 17,8126 0,06736 0,16432 0,73219 0,65214 0,01948 0,7654 0,06812 0,03520 NAB 2018 0,13980 18,1338 0,05636 0,11335 0,72193 0,66671 0,01009 0,7642 0,07076 0,03540 NAB 2019 0,14750 18,3661 0,05239 0,08429 0,74713 0,70497 0,01975 0,8190 0,07020 0,02796 NAB 2020 0,12120 18,7157 0,04913 0,07574 0,73152 0,65756 0,02950 0,7636 0,02910 0,03221

NAB 2021 0,15480 18,8475 0,05237 0,04270 0,75255 0,66153 0,00834 0,7846 0,02580 0,01835 NAB 2022 0,17010 18,9949 0,07123 0,06389 0,70388 0,66615 0,01348 0,8168 0,08020 0,03150 NVB 2013 0,00064 17,1854 0,11018 0,01888 0,63207 0,45629 0,06067 0,5762 0,05422 0,06595 NVB 2014 0,00494 17,4220 0,08719 0,01575 0,66347 0,44643 0,02525 0,5021 0,05984 0,04085 NVB 2015 0,00218 17,6915 0,06671 0,02476 0,70560 0,41928 0,02150 0,4641 0,06679 0,00631 NVB 2016 0,00174 18,0498 0,04678 0,03206 0,60558 0,36316 0,01485 0,4057 0,06211 0,02668 NVB 2017 0,00290 18,0900 0,04479 0,02627 0,63640 0,44196 0,01533 0,5226 0,06812 0,03520 NVB 2018 0,00588 18,0980 0,04464 0,06972 0,65103 0,48716 0,01670 0,6268 0,07076 0,03540 NVB 2019 0,00970 18,2025 0,05357 0,06002 0,73507 0,46625 0,01926 0,5301 0,07020 0,02796 NVB 2020 0,01155 18,3109 0,04758 0,06866 0,80451 0,44480 0,01510 0,6072 0,02910 0,03221 NVB 2021 0,00032 18,1166 0,05779 0,04573 0,87446 0,55465 0,03002 0,8256 0,02580 0,01835 NVB 2022 0,00037 18,3136 0,06416 0,05725 0,79413 0,52047 0,17930 0,5975 0,08020 0,03150 OCB 2012 0,06070 17,1269 0,13928 0,12653 0,55686 0,61724 0,02800 0,8046 0,05250 0,09095 OCB 2013 0,06090 17,3058 0,12090 0,15234 0,58288 0,60904 0,02900 0,7445 0,05422 0,06595 OCB 2014 0,05530 17,4815 0,10277 0,20168 0,61130 0,53606 0,03000 0,7101 0,05984 0,04085 OCB 2015 0,04960 17,7164 0,08545 0,25432 0,59672 0,55519 0,01940 0,6864 0,06679 0,00631 OCB 2016 0,08650 17,9715 0,07390 0,34356 0,67482 0,59821 0,01754 0,7051 0,06211 0,02668 OCB 2017 0,13300 18,2499 0,07283 0,32451 0,63115 0,56677 0,01794 0,6931 0,06812 0,03520 OCB 2018 0,20020 18,4203 0,08800 0,42579 0,60384 0,55771 0,02288 0,7302 0,07076 0,03540 OCB 2019 0,22440 18,5875 0,09739 0,52245 0,58516 0,59551 0,01842 0,8123 0,07020 0,02796

OCB 2020 0,20270 18,8429 0,11431 0,51279 0,57151 0,57891 0,01690 0,8113 0,02910 0,03221 OCB 2021 0,22450 19,0331 0,11819 0,50695 0,53555 0,54710 0,01322 0,7813 0,02580 0,01835 OCB 2022 0,14910 19,0833 0,13027 0,50606 0,52684 0,60940 0,02229 0,9596 0,08020 0,03150 PGB 2012 0,08300 16,7733 0,16592 0,39334 0,64046 0,69949 0,08437 0,8748 0,05250 0,09095 PGB 2013 0,01190 17,0294 0,12903 0,28163 0,55722 0,54992 0,02980 0,6503 0,05422 0,06595 PGB 2014 0,04000 17,0651 0,12954 0,31303 0,69839 0,55603 0,02485 0,6579 0,05984 0,04085 PGB 2015 0,01220 17,0216 0,13665 0,25070 0,68330 0,63630 0,02754 0,7570 0,06679 0,00631 PGB 2016 0,03570 17,0273 0,14080 0,15550 0,73706 0,69926 0,02468 0,8340 0,06211 0,02668 PGB 2017 0,01830 17,1930 0,12150 0,16470 0,78085 0,72336 0,03225 0,8468 0,06812 0,03520 PGB 2018 0,03500 17,2134 0,12330 0,16344 0,78078 0,73000 0,03061 0,8911 0,07076 0,03540 PGB 2019 0,02000 17,2678 0,11910 0,14213 0,80408 0,74243 0,03159 0,8683 0,07020 0,02796 PGB 2020 0,04410 17,4033 0,10870 0,09810 0,79489 0,70391 0,02440 0,8130 0,02910 0,03221 PGB 2021 0,06370 17,5173 0,10318 0,28088 0,69284 0,67263 0,02245 0,3757 0,02580 0,01835 PGB 2022 0,09210 17,7071 0,09358 0,51299 0,63807 0,58721 0,02563 0,6884 0,08020 0,03150 SGB 2012 0,08690 16,5137 0,23831 0,04308 0,70370 0,72388 0,02930 0,9945 0,05250 0,09095 SGB 2013 0,04910 16,5429 0,23838 0,02305 0,73566 0,71969 0,02242 0,9333 0,05422 0,06595 SGB 2014 0,05180 16,6147 0,22030 0,01430 0,74846 0,70398 0,02080 0,8954 0,05984 0,04085 SGB 2015 0,01250 16,7257 0,19153 0,00710 0,74043 0,64907 0,01882 0,7899 0,06679 0,00631 SGB 2016 0,04040 16,7940 0,18453 0,00576 0,74386 0,65261 0,02631 0,7925 0,06211 0,02668 SGB 2017 0,01580 16,9033 0,16029 0,00396 0,47413 0,44664 0,02980 0,7735 0,06812 0,03520

SGB 2018 0,01220 16,8592 0,16860 0,00612 0,75048 0,66555 0,02201 0,7927 0,07076 0,03540 SGB 2019 0,04130 16,9691 0,15611 0,00812 0,68680 0,63308 0,01939 0,7466 0,07020 0,02796 SGB 2020 0,02700 17,0163 0,15125 0,00576 0,76113 0,64031 0,01440 0,7559 0,02910 0,03221 SGB 2021 0,03350 17,0186 0,15072 0,00723 0,73573 0,66399 0,01971 0,8074 0,02580 0,01835 SGB 2022 0,04990 17,1369 0,14077 0,01761 0,74010 0,66893 0,02125 0,8024 0,08020 0,03150 SHB 2012 0,00340 18,5737 0,08159 0,02709 0,66587 0,47787 0,08807 0,5716 0,05250 0,09095 SHB 2013 0,08560 18,7827 0,07212 0,02535 0,63193 0,52443 0,05663 0,6741 0,05422 0,06595 SHB 2014 0,07590 18,9456 0,06202 0,02033 0,72900 0,60963 0,02025 0,6867 0,05984 0,04085 SHB 2015 0,07320 19,1370 0,05500 0,01426 0,72704 0,63509 0,01722 0,7415 0,06679 0,00631 SHB 2016 0,07460 19,2706 0,05496 0,01331 0,69190 0,66699 0,01875 0,8114 0,06211 0,02668 SHB 2017 0,11020 19,4715 0,05137 0,00954 0,68141 0,68334 0,02332 0,7999 0,06812 0,03520 SHB 2018 0,10780 19,5940 0,05052 0,00457 0,69669 0,66193 0,02396 0,8031 0,07076 0,03540 SHB 2019 0,13880 19,7161 0,05067 0,00748 0,70974 0,71739 0,01907 0,8613 0,07020 0,02796 SHB 2020 0,12260 19,3881 0,05824 0,01172 0,73564 0,73228 0,01832 0,8931 0,02910 0,03221 SHB 2021 0,16810 20,0432 0,07014 0,02149 0,64586 0,70623 0,01687 0,8888 0,02580 0,01835 SHB 2022 0,19710 20,1271 0,07788 0,02368 0,65651 0,68719 0,02814 0,6129 0,08020 0,03150 SSB 2012 0,0052 18,1339 0,07436 0,00000 0,41892 0,21621 0,02969 0,2657 0,05250 0,09095 SSB 2013 0,0148 18,1958 0,07170 0,00000 0,45306 0,25572 0,06297 0,2885 0,05422 0,06595 SSB 2014 0,0085 18,1998 0,07086 0,00000 0,56159 0,39370 0,03111 0,6069 0,05984 0,04085 SSB 2015 0,0090 18,2552 0,06806 0,83190 0,67273 0,50072 0,03169 0,5682 0,06679 0,00631

SSB 2016 0,0112 18,4540 0,05688 0,87684 0,69783 0,56543 0,02967 0,6268 0,06211 0,02668 SSB 2018 0,0482 18,7620 0,05909 0,28246 0,60037 0,59086 0,02344 0,7305 0,07076 0,03540 SSB 2019 0,1125 18,8774 0,06942 0,29120 0,60819 0,61935 0,02312 0,7730 0,07020 0,02796 SSB 2020 0,1441 19,0114 0,07586 0,33763 0,62859 0,59798 0,01856 0,6971 0,02910 0,03221 SSB 2021 0,2545 19,1705 0,08817 0,35734 0,51868 0,59437 0,01650 0,7585 0,02580 0,01835 SSB 2022 0,3958 19,2598 0,11335 0,40704 0,49929 0,65474 0,01598 0,8784 0,08020 0,03150 STB 2012 0,07100 18,8347 0,09005 0,13854 0,70641 0,62378 0,02048 0,8618 0,05250 0,09095 STB 2013 0,14490 18,8917 0,10574 0,07818 0,81576 0,67676 0,01456 0,8119 0,05422 0,06595 STB 2014 0,12560 19,0556 0,09517 0,06229 0,85909 0,66725 0,01189 0,7664 0,05984 0,04085 STB 2015 0,03230 19,4866 0,07561 0,05941 0,89372 0,62890 0,01855 0,7066 0,06679 0,00631 STB 2016 0,00400 19,6121 0,06684 0,05606 0,87841 0,59161 0,06912 0,6680 0,06211 0,02668 STB 2017 0,04400 19,7127 0,06306 0,05236 0,86808 0,59760 0,04667 0,6781 0,06812 0,03520 STB 2018 0,07480 19,8116 0,06067 0,05970 0,86048 0,62334 0,02115 0,7262 0,07076 0,03540 STB 2019 0,09560 20,0283 0,05896 0,10708 0,88373 0,64390 0,01937 0,7384 0,07020 0,02796 STB 2020 0,09630 20,0028 0,05879 0,16122 0,86895 0,67989 0,01699 0,7882 0,02910 0,03221 STB 2021 0,10790 20,0715 0,06575 0,16109 0,82014 0,73115 0,01475 0,8784 0,02580 0,01835 STB 2022 0,13830 20,1989 0,06526 0,16454 0,76826 0,73153 0,02827 0,9078 0,08020 0,03150 TCB 2012 0,05930 18,9969 0,07386 0,87766 0,61946 0,37312 0,02696 0,4499 0,05250 0,09095 TCB 2013 0,04840 18,8732 0,08761 0,88011 0,75507 0,43480 0,03652 0,5175 0,05422 0,06595 TCB 2014 0,07490 18,9872 0,08520 0,93439 0,74866 0,45109 0,02383 0,5286 0,05984 0,04085

TCB 2015 0,09730 19,0749 0,08572 0,92998 0,74086 0,57534 0,01661 0,6792 0,06679 0,00631 TCB 2016 0,17470 19,2748 0,08322 0,39226 0,73694 0,59959 0,01575 0,7149 0,06211 0,02668 TCB 2017 0,27710 19,4078 0,09997 0,36304 0,63465 0,59009 0,01606 0,7364 0,06812 0,03520 TCB 2018 0,21560 19,5795 0,16132 0,33461 0,62748 0,49084 0,01753 0,6693 0,07076 0,03540 TCB 2019 0,18230 19,7546 0,16177 0,14409 0,60281 0,59392 0,01334 0,7856 0,07020 0,02796 TCB 2020 0,18030 19,9013 0,16973 0,16490 0,63116 0,62627 0,00467 0,8469 0,02910 0,03221 TCB 2021 0,21530 20,1589 0,16360 0,26805 0,55343 0,60416 0,00660 0,8135 0,02580 0,01835 TCB 2022 0,19520 20,3652 0,16226 0,43918 0,51271 0,59475 0,00721 0,7995 0,08020 0,03150 TPB 2012 0,04660 16,5316 0,21951 0,13548 0,61308 0,39618 0,03663 0,6063 0,05250 0,09095 TPB 2013 0,10870 17,2840 0,11533 0,21546 0,43314 0,35690 0,02325 0,4619 0,05422 0,06595 TPB 2014 0,13500 17,7567 0,08230 0,34528 0,42006 0,38152 0,01217 0,4237 0,05984 0,04085 TPB 2015 0,12440 18,1491 0,06296 0,45213 0,51830 0,36706 0,00807 0,4092 0,06679 0,00631 TPB 2016 0,10790 18,4769 0,05344 0,53489 0,52071 0,43707 0,00750 0,4840 0,06211 0,02668 TPB 2017 0,15590 18,6367 0,05379 0,61245 0,56638 0,50555 0,01086 0,5841 0,06812 0,03520 TPB 2018 0,20870 18,7295 0,07800 0,71497 0,55910 0,56026 0,01116 0,7040 0,07076 0,03540 TPB 2019 0,26110 18,9180 0,07951 0,89621 0,56215 0,57429 0,01291 0,7210 0,07020 0,02796 TPB 2020 0,23540 19,1449 0,08116 0,92356 0,56178 0,57235 0,01184 0,7810 0,02910 0,03221 TPB 2021 0,22600 19,4951 0,08875 0,98765 0,47660 0,47626 0,00819 0,6226 0,02580 0,01835 VAB 2012 0,03689 17,0186 0,14357 0,21640 0,07676 0,51582 0,04650 0,7633 0,05250 0,09095 VAB 2013 0,01352 17,1126 0,13275 0,23400 0,15835 0,52513 0,02880 0,6228 0,05422 0,06595

VAB 2014 0,01068 17,3876 0,10216 0,05141 0,55576 0,43925 0,02326 0,5036 0,05984 0,04085 VAB 2015 0,01843 17,5503 0,09359 0,13932 0,58359 0,47852 0,02264 0,5433 0,06679 0,00631 VAB 2016 0,02235 17,9340 0,06536 0,11078 0,52371 0,48817 0,02140 0,5429 0,06211 0,02668 VAB 2017 0,02221 17,9812 0,06389 0,06518 0,53392 0,52610 0,01950 0,5978 0,06812 0,03520 VAB 2018 0,02662 18,0823 0,05940 0,02043 0,58025 0,52633 0,01370 0,5923 0,07076 0,03540 VAB 2019 0,04663 18,1521 0,05857 0,00000 0,62041 0,55147 0,01180 0,6191 0,07020 0,02796 VAB 2021 0,14701 18,4310 0,06313 0,11026 0,66999 0,53303 0,01858 0,5984 0,02580 0,01835 VAB 2022 0,20023 18,4709 0,06906 0,14456 0,66771 0,58798 0,01532 0,6805 0,08020 0,03150 VPB 2012 0,10190 18,4461 0,06534 0,49200 0,57965 0,35572 0,02719 0,4332 0,05250 0,09095 VPB 2013 0,14170 18,6134 0,06372 0,48817 0,69141 0,42774 0,02810 0,5410 0,05422 0,06595 VPB 2014 0,15010 18,9107 0,05501 0,31988 0,66376 0,47326 0,02538 0,5824 0,05984 0,04085 VPB 2015 0,21420 19,0827 0,06906 0,51634 0,67193 0,59348 0,02693 0,7890 0,06679 0,00631 VPB 2016 0,25750 19,2482 0,07509 0,66102 0,54110 0,62326 0,02908 0,9479 0,06211 0,02668 VPB 2018 0,22830 19,5940 0,10749 0,88177 0,52847 0,67554 0,03499 0,9861 0,07076 0,03540 VPB 2019 0,21470 19,7483 0,11190 0,88233 0,56720 0,67099 0,03421 0,9711 0,07020 0,02796 VPB 2021 0,16860 20,1207 0,15761 0,90205 0,44178 0,63095 0,04572 0,9971 0,02580 0,01835 VPB 2022 0,19150 20,2628 0,16403 0,89944 0,48042 0,67299 0,05735 0,9885 0,08020 0,03150 VCB 2012 0,12530 19,8425 0,10060 0,13054 0,68852 0,56911 0,02403 0,7572 0,05250 0,09095 VCB 2013 0,10380 19,9661 0,09070 0,08128 0,70842 0,57114 0,02725 0,7290 0,05422 0,06595 VCB 2014 0,10650 20,1733 0,07534 0,04104 0,73173 0,54810 0,02308 0,6946 0,05984 0,04085

VCB 2015 0,12010 20,3293 0,06698 0,01530 0,74219 0,56131 0,01841 0,6760 0,06679 0,00631 VCB 2016 0,14650 20,4849 0,06110 0,03377 0,74937 0,57457 0,01502 0,6953 0,06211 0,02668 VCB 2017 0,18060 20,7580 0,05077 0,04291 0,68437 0,51707 0,01143 0,7007 0,06812 0,03520 VCB 2018 0,25460 20,7947 0,05789 0,02691 0,74666 0,57873 0,00985 0,7192 0,07076 0,03540 VCB 2019 0,25880 20,9243 0,06615 0,06707 0,75933 0,59236 0,00790 0,7331 0,07020 0,02796 VCB 2020 0,21090 21,0056 0,07095 0,22744 0,77823 0,61871 0,00623 0,7427 0,02910 0,03221 VCB 2021 0,21570 21,0702 0,90771 0,21901 0,80253 0,66077 0,00637 0,7681 0,02580 0,01835 VCB 2022 0,24430 21,3187 0,07478 0,15079 0,68555 0,61764 0,00683 0,7758 0,08020 0,03150 VIB 2012 0,06330 17,9904 0,12973 0,34256 0,60073 0,51232 0,02495 0,6736 0,05250 0,09095 VIB 2013 0,00610 18,1577 0,10384 0,75341 0,56247 0,44635 0,02821 0,5658 0,05422 0,06595 VIB 2014 0,06340 18,2058 0,10538 0,82877 0,60812 0,46230 0,02514 0,5548 0,05984 0,04085 VIB 2015 0,06090 18,2500 0,10213 0,59810 0,63225 0,55777 0,02070 0,7238 0,06679 0,00631 VIB 2016 0,06470 18,4649 0,08365 0,96090 0,56700 0,56607 0,02575 0,6504 0,06211 0,02668 VIB 2017 0,12830 18,6290 0,07135 0,98839 0,55520 0,64079 0,02488 0,7824 0,06812 0,03520 VIB 2018 0,22550 18,7512 0,07665 0,96943 0,60979 0,68451 0,02519 0,8413 0,07076 0,03540 VIB 2019 0,27110 19,0333 0,07278 0,09822 0,66307 0,69318 0,01963 0,8637 0,07020 0,02796 VIB 2020 0,29570 19,3154 0,07346 0,94239 0,61448 0,68569 0,01747 0,8814 0,02910 0,03221 VIB 2021 0,30330 19,5505 0,07848 0,99992 0,56076 0,64331 0,02318 0,8457 0,02580 0,01835

APPENDIX 3 DESCRIPTIVE STATISTIC AND CORRELATION

APPENDIX 4: RESULTS OF POOL-OLS, FEM, REM, FGLS, AND GMM

Table 4.1 Results of Pool-OLS model

Table 4.2 Results of FEM model

Table 4.3 Results of REM model

Table 4.4 Results of FGLS model

Table 4.5 Results of GMM model

Tiêu đề	The Impact Of Technology Investment On Financial Performance Of Commercial Banks In Vietnam
Tác giả	Nguyen Thi Mai Linh
Người hướng dẫn	Dr. Nguyen Minh Nhat
Trường học	Ho Chi Minh University of Banking
Chuyên ngành	Finance – Banking
Thể loại	Graduation Thesis
Năm xuất bản	2023
Thành phố	Ho Chi Minh City

Định dạng
Số trang	130
Dung lượng	2,33 MB