Model to evaluate factors affecting financial performance of Information and Technology enterprises in Vietnam in the period 2017-2022...38 3.2.1... Tables Table 1: Some studies on fina
Overview of financial performance
Concept of efficiency (performance) and financial performance
Efficiency is a key outcome that meets the expectations and goals of individuals across various sectors In manufacturing, it translates to enhanced productivity, while in the business realm, it relates to interest rates and profitability.
Labor efficiency, often referred to as labor productivity, is measured by the time invested in producing a single unit of a product or by the quantity of products generated within a specific timeframe (Pham, 2022).
1.1.1.2 The concept of financial performance
Financial performance fundamentally reflects the overall business performance of an enterprise It serves as a comprehensive economic indicator that assesses how effectively an enterprise utilizes its production factors Additionally, business efficiency highlights the adeptness of managers in bridging theory and practice to optimize the use of resources, including machinery, equipment, raw materials, and labor, ultimately aimed at maximizing profits Thus, business efficiency represents a holistic economic evaluation of resource utilization and financial management to achieve optimal outcomes.
Financial performance fundamentally involves assessing the relationship between a business's output results and its input factors over a specific period, tailored to the needs of business administrators This relationship can be encapsulated by the term financial efficiency, which reflects overall business efficiency.
H is expressed according to the following mathematical formula:
The formula illustrates the output generated, including revenue and profit, for every unit of input costs, such as capital, labor, raw materials, and machinery A higher value of this indicator during a business period signifies improved financial performance for the company.
This thesis will review existing research on metrics for assessing financial performance, highlighting the specific metrics that will be employed in the subsequent research and analysis The following sections will discuss various studies focused on measuring the financial performance of businesses.
Financial performance measures
Table 1: Some studies on financial performance
Dividend yield (DY) Ming et al (2008)
Ongore (2011) Return on assets (ROA) Hu et al (2008)
Return on equity (ROE) Hu et al (2008)
Return on sales (ROS) Le et al (2011)
Jenkins et al (2011) Return on investment (ROI) Shah et al (2011)
Market value coefficient (Marris and Tobin’s Q)
(Source: Author compiled from previous studies)
Evaluating corporate financial performance relies heavily on the selection of appropriate metrics Commonly used indicators can be categorized into two main types: accounting value coefficients, which assess profitability, and market value coefficients, which focus on asset growth.
According to Hu et al (2008), the most commonly used profit metrics include return on assets (ROA) and return on equity (ROE) In addition, two studies by Ming et al
Various studies have employed different financial metrics to assess business performance, including the dividend yield index (DY) by Ongore (2011) and others, the return on sales (ROS) ratio utilized by Le et al (2011), and the return on investment (ROI) ratio applied by Shah et al (2011).
For the market value coefficient group, according to research by Tian et al
In 2008, the Marris and Tobin's Q coefficients emerged as key metrics for assessing corporate financial performance The Marris coefficient compares the total market value of equity to its book value, while Tobin's Q evaluates the market value of equity and liabilities against the book value of total assets However, it's important to note that these coefficients primarily assess the effectiveness of state ownership capital, as they directly reflect the growth in equity value within a company's capital structure.
In evaluating the financial performance of businesses, the three most commonly used metrics are Return on Sales (ROS), Return on Assets (ROA), and Return on Equity (ROE) This thesis focuses on applying these three indicators to assess the financial performance of information technology enterprises listed on the Vietnamese stock market, as they provide critical insights into the companies' profitability and efficiency.
Key financial indicators reflect past performance, showcasing a company's profitability and operational success over recent accounting periods These metrics facilitate straightforward comparisons among businesses within the same industry Furthermore, the integration of these three groups of coefficients offers managers, company leaders, shareholders, and market analysts a holistic view of historical and potential financial performance, as well as insights into future profitability and growth prospects.
A business's long-term strategy for generating revenue is crucial for enhancing profits and overall financial performance However, the primary objective for administrators is to maximize profit after tax, which requires ensuring that revenue growth outpaces cost growth for sustainable development Additionally, this metric reflects the effectiveness of cost control measures implemented by management, contributing to increased competitiveness in the market.
This target indicates the profit earned after corporate income tax for every 1 dong of net revenue during a specific analysis period A higher indicator reflects effective utilization of port costs and improved financial efficiency, enabling administrators to expand markets and boost revenue Conversely, a lower indicator signifies reduced financial efficiency, highlighting the need for administrators to enhance cost control across departments.
Businesses aim to enhance production scale and market reach to achieve robust growth and improve financial performance Consequently, market administrators frequently assess the efficiency of invested assets using a specific evaluation formula.
This metric indicates the amount of after-tax profit generated for every 1 VND of total assets invested during a specific analysis period A higher value reflects greater efficiency in asset utilization and improved financial performance, while a lower value suggests the opposite.
The ability to generate profits from the equity that an enterprise uses for business activities is the goal of every administrator This target is determined as follows:
This indicator reveals the profit generated by a business for every 1 dong of equity invested after corporate income tax during a specified analysis period A higher value indicates effective use of equity capital and strong financial efficiency, enhancing the investment potential for business owners Conversely, lower values suggest a need for improvement, prompting administrators to focus on increasing equity for better business operations and financial investments.
Factors affecting financial performance
Capital structure
The capital structure of a business, which refers to the mix of debt and equity financing used to fund its operations, has a significant impact on its overall financial
A well-balanced capital structure is crucial for optimizing capital costs, maximizing shareholder returns, and minimizing financial risk While high debt levels can enhance financial leverage and potentially boost returns, they also increase the risk of financial distress during economic downturns Conversely, excessive reliance on equity financing may dilute ownership and lower earnings per share, negatively impacting investor perceptions and stock valuation Therefore, achieving the right balance in capital structure is vital for maintaining financial stability, flexibility, and long-term sustainability in business.
Capital structure has been proven to affect financial performance of enterprises Several studies have explored the relationship between capital structure and financial performance
Recent studies highlight the significant impact of capital structure on the financial performance of firms across various emerging markets Prekazi (2023) identified a strong relationship between assets and both total capital and liabilities in Kosovo's commercial sector Similarly, Dodoo (2023) found that both short-term and long-term debt adversely affect firm performance in Ghana's non-financial companies from 2008 to 2017 In Pakistan's engineering sector, Khan (2012) noted a reliance on short-term debt and its detrimental effects on performance Furthermore, Zeitun and Tian (2014) revealed that a higher debt ratio negatively influences return on assets (ROA), while growth in total assets, firm size, and tax ratios positively impact ROA Additionally, Onaolapo and Kajola (2010) found that both debt and fixed asset ratios negatively affect ROA and return on equity (ROE), whereas asset turnover has a positive effect on these performance indicators.
Firm’s size
Table 2: Some studies on company size
Simerly and Li (2000) Athanasoglou et al (2005) Papadognas (2007)
Amato and Burson (2007) Falope and Ajilore (2009) Amarjit et al (2010) Dr.Amal et al (2012)
(Source: Author compiled from previous studies)
The size of a company is a crucial indicator for investors, as larger firms typically enjoy a competitive edge in the marketplace Consequently, many businesses strive to grow in size to capitalize on the benefits of economies of scale In Vietnam, large publicly listed companies often have significant state ownership, which enhances their ability to attract external capital, particularly from state-owned financial institutions.
Numerous studies have explored the connection between company size and financial performance, leading to the emergence of two contrasting theories regarding how scale impacts a company's financial success.
The relationship between a company's size and its financial performance is notably positive Research by Simerly & Li (2000) indicates that larger companies can predict future stock prices more effectively, facilitating better management and risk mitigation, leading to enhanced financial outcomes Flamini et al (2009) further assert that larger firms possess greater competitiveness and profitability in the market Additionally, Athanasoglou et al (2005) found that expanding scale improves the financial efficiency of banks A study by Papadognas (2017) on 3,035 Greek businesses demonstrated that profits are significantly influenced by company size Amato et al (2007) highlighted that smaller firms in the financial services sector experience negative impacts on profitability due to their size In Vietnam, research by Truong et al (2015) revealed a positive correlation between ROE and company scale, with ROE increasing by 0.007% for each additional employee Similarly, Doan Ngoc Phi Anh (2010) showed that the scale of 428 listed enterprises in Vietnam positively affects their financial structure and performance.
Research indicates that there is no significant correlation between company size and financial performance Amarjit et al (2010) found no relationship between business size and profit margins, while Falope et al (2009) demonstrated that capital management and scale expansion did not significantly affect financial performance among 50 companies listed on the Nigerian stock exchange Additionally, Do (2011) concluded that business size does not influence financial performance.
For large-scale businesses, while profits may rise, the increase is often insufficient to significantly enhance the profit ratio relative to total assets Consequently, this indicates that larger businesses are not necessarily more efficient than their smaller counterparts.
Firm’s growth rate
The growth rate of a business is crucial for its financial performance and overall success, as rapid growth can lead to increased revenue, market share, and profitability However, maintaining high growth may necessitate significant investments in resources and talent, potentially straining cash flow and short-term profits Additionally, fast growth can create operational challenges like scalability issues and supply chain disruptions, which may affect efficiency and profit margins On the other hand, slow or stagnant growth can indicate market saturation or ineffective strategy, leading to declining revenues and profitability over time.
The relationship between firms’ growth rate and their financial performance has been shown in multiple studies Finally, two opposing views are raised
Research indicates that a high growth rate positively influences a business's financial performance According to Sawarni et al (2023), firms experiencing rapid growth demonstrate more efficient working capital management, leading to improved financial outcomes, particularly in high-growth companies However, Heck (2023) found that while company growth has a positive effect on financial performance, it is statistically insignificant in the context of Indonesian automotive firms.
Some experts argue that growth can adversely affect a company's financial performance According to Pernille (2012), significant early growth may hinder a firm's long-term survival and overall performance, as evidenced by research conducted in Denmark.
A recent study by Melinda (2023) highlights that a company's growth rate can negatively impact its overall performance, as demonstrated through simple regression model analysis Additionally, Onyekwelu et al (2018) found that growth indicators such as size and profitability significantly reduce Return on Assets, adversely affecting the financial performance of selected firms in Nigeria.
Firm’s operation year
Operating time significantly influences a business's financial performance, measured from its inception to the current date The connection between operating time and financial outcomes is well-documented in both theoretical frameworks and practical scenarios However, this relationship is subject to two contrasting perspectives, highlighting the complexity of how operating duration impacts financial success.
Long-term business operations can lead to a decline in financial performance due to a negative relationship between operating time and efficiency (Loderer et al., 2009) While uptime can enhance operational efficiency, businesses that have been established for extended periods may struggle to adapt to changing market conditions, resulting in diminished skills to identify trends and a slowdown in growth (Agarwal and Gort, 2002) Additionally, research by Sorensen & Stuart (2000) indicates that prolonged operation fosters organizational inertia, reducing flexibility and hindering accurate assessments during volatile economic periods.
In contrast to the previous perspective, research by Liargovas and Skandalis (2008) indicates a positive relationship between operating time and the financial performance of an enterprise, suggesting that larger companies tend to possess advanced skills and capabilities.
Larger companies tend to exhibit greater financial efficiency due to their extensive experience and lower debt levels compared to smaller businesses According to Malik's (2011) research on Pakistani companies, there is a proportional relationship between a company's size, age, and its financial performance, indicating that as companies grow older and larger, their financial outcomes improve.
In general, through past studies, the author hypothesizes that operating time and financial performance of companies have an unclear relationship, it can be in the same or opposite direction.
Literature review: factors affecting financial performances
Foreign researches
1.3.1.1 Studies about firm’s financial performance
Numerous studies have explored the financial performance of firms in diverse contexts Pertiwi et al (2011) demonstrated that Return on Assets (ROA) significantly impacts firm value, although Good Corporate Governance did not moderate this effect Wagner et al (2012) found that a higher supply chain fit correlates with improved ROA, while firms with negative misfits experience diminished performance Guo et al (2012) analyzed corporate governance structures in Sri Lanka, revealing their influence on firm performance through multiple regression analysis Wang et al (2014) highlighted that knowledge sharing, particularly explicit knowledge, positively affects financial performance more than operational performance Feng et al (2017) examined the relationship between Corporate Social Responsibility (CSR) and financial performance, noting varying impacts across industries and CSR categories Lastly, Kim et al (2018) offered insights into competitive actions and their implications for firm performance.
Research highlights the intricate relationship between Corporate Social Responsibility (CSR) activities and firm financial performance, emphasizing the role of competitive actions as a key factor Le et al (2018) found a positive correlation between effective working capital management and financial performance Tihanyi et al (2019) revealed that state ownership and political connections have a minor negative impact on financial performance Additionally, Santosa et al (2020) demonstrated that financial performance significantly influences firm value in large Indonesian companies, while its effect on dividend policy is comparatively less pronounced Collectively, these studies enhance our understanding of the complex dynamics between financial performance and firm value across various organizational contexts.
1.3.1.2 Studies about Information and Technology firms
Goswami et al (2022) examine the relationship between technological advancements, firm ownership, and productivity in Indian IT service firms from 2000 to 2016, revealing significant variations in total factor productivity linked to specific firm characteristics Amar (2021) emphasizes the importance of strategic capital allocation in optimizing revenue growth and performance for IT firms, highlighting the roles of R&D, capital expenditure, and operational expenses across various industries Utilizing multivariate data analysis, the study identifies key management decisions that drive growth, including investments in research & development and capital expenditures Emmanuel et al (2023) investigate the impact of strategic management on the operational performance of Nigerian IT firms, concluding that effective strategic management is crucial for enhancing operational outcomes Malik (2013) contributes qualitative insights through case studies, further enriching the discourse on firm performance in the IT sector.
A study involving 16 evidence cases from four IT-enabled organizations in India examined how external business networks and institutional ecosystems contribute to enhancing an organization's innovative capacity.
1.3.1.3 Studies about factors affecting firms’ financial performance
Numerous studies have identified various factors influencing firms' financial performance Liao (2006) found that corporate controls in conglomerates affect subsidiaries' human resource management and performance Soch et al (2008) analyzed the impact of Customer Relationship Management (CRM) activities on firm performance in India Yaldiz et al (2011) emphasized the roles of firm size, owners' gender, and location in firms' dependence on informal credit for fixed asset investments Mothilal et al (2012) explored critical success factors in the Indian Third-party Logistics (3PL) industry and their effects on operational and financial outcomes.
Research has shown that client-vendor relationships significantly impact the financial performance of outsourcing firms (2013) Factors influencing firm performance during financial crises were examined by Demirhan et al (2014), who used market-to-book ratios and financial metrics as indicators Hutchinson et al (2015) found that increased gender diversity on boards can mitigate excessive risk and enhance financial outcomes Tuân et al (2016) investigated the role of innovation in improving firm performance within supporting industries in Hanoi, Vietnam Raharjo (2017) analyzed how company characteristics affect financial reporting quality and investment efficiency Additionally, Salah (2020) conducted a systematic review on the effects of International Financial Reporting Standards (IFRS), revealing that IFRS's fair value orientation leads to volatility in financial performance metrics Collectively, these studies highlight the various factors influencing financial performance across different industries and contexts.
Domestic researches
1.3.2.1 Studies about firm’s financial performance
Nguyen (2023) conducted a study on the impact of intellectual capital (IC) on the financial performance of service firms in Vietnam Utilizing a two-step system GMM model, the research analyzed data from 2005 to 2014, highlighting the significant role of IC in enhancing financial outcomes within the service sector of this emerging economy.
The 2023 study examines the influence of capital investments on the financial performance of listed food and agriculture companies in Vietnam, revealing a significant positive effect on their long-term performance This research aims to inform future capital investment decisions by demonstrating the strong correlation between capital investments and enhanced firm performance Additionally, Vu (2022) investigates the relationship between public relations, innovation, and investment strategies and their positive impact on the financial performance of SMEs in Vietnam, utilizing quantitative methods and primary data for in-depth analysis Furthermore, Pham (2023) evaluates the financial performance of 11 Vietnamese textile and apparel companies through the Entropy-TOPSIS method, offering valuable insights for industry managers by assessing financial stability and security based on seven financial ratios from 2016 to 2018.
1.3.2.2 Studies about Information and Technology firms
Le (2022) investigates how intellectual property (IP) management influences firm performance within Vietnam's Information and Communication Technology (ICT) sector, emphasizing the significance of strategic IP management The study reveals that effective IP management positively affects firm performance across various levels Additionally, IPBES (2022) highlights the crucial role of foreign direct investment (FDI) in advancing digital technology and the ICT industry in Vietnam, suggesting that FDI is vital for technological progress and economic development The authors propose strategies to optimize capital and technology use to enhance the digital economy by leveraging advancements from other countries and major economic groups Furthermore, Pham (2017) examines the relationship between information technology and knowledge transfer in Vietnam's IT companies, demonstrating that the ease of use of IT tools correlates positively with the frequency of knowledge transfer, thereby improving organizational knowledge transfer processes.
1.3.2.3 Studies about factors affecting firms’ financial performance
Numerous studies have explored the factors impacting financial performance across diverse industries and regions Hong (2017) utilized multivariable regression analysis to assess the sustainable development factors of agricultural cooperatives in Vietnam's Mekong River Delta Similarly, Tran et al (2019) and Xuan et al (2020) focused on small and medium-sized enterprises (SMEs) in Vietnam, employing multivariate linear regression models to identify key factors affecting financial efficiency and performance Additionally, Tuan et al (2021) examined the financial performance determinants of manufacturing enterprises Collectively, these studies enhance the understanding of the various influences on financial performance across different sectors and geographical areas.
Research gap
Research on the factors influencing financial activities, particularly within information technology enterprises, is not a new topic However, the rapidly evolving landscape of the IT industry continuously presents new challenges and questions This dynamic environment necessitates that both researchers and businesses seek innovative solutions tailored to the current context.
The period from 2017 to 2022 has introduced significant changes and challenges for Information Technology enterprises, making it crucial to investigate the factors influencing their financial performance Understanding these factors will help identify key influences on business performance and develop effective strategies to enhance financial outcomes in the IT sector Consequently, this research addresses a gap by analyzing the determinants of financial performance for information technology companies in Vietnam during this transformative period.
The thesis titled "Factors Affecting the Financial Performance of Information Technology Enterprises in Vietnam" analyzes previously conducted research to explore the key factors influencing business performance during the period from 2017 to 2022, offering the latest insights and updates on the subject.
In the chapter's conclusion, the author discusses theories and measures of financial performance, identifying key variables for the model, including dependent variables such as ROA, ROE, and ROS, alongside independent variables like capital structure, regulations, business size, growth rate, and years of operation The research highlights a significant body of previous studies, both domestically and internationally, focused on the financial activities of businesses in general and information technology firms specifically However, given the industry's rapid evolution, there remains a pressing need for further research in this area.
20 factors affecting the financial performance of information technology businesses in the new era to draw conclusions and modern solution
RESEARCH METHOD AND MODEL
Research methods
To develop variables and hypotheses for a research model, the thesis reviews studies with similar frameworks, aiming to identify and clarify the factors influencing the financial performance of enterprises This analysis allows for a comprehensive comparison of existing research to construct an effective model Subsequently, the author gathers data and financial information from information technology companies listed on the Vietnam stock market, along with audited financial statements, to gain a holistic overview of the sector.
The thesis employs a panel data regression model analyzed using Stata 17 software, focusing on three approaches: Pooled OLS, Fixed Effects Model (FEM), and Random Effects Model (REM) The author tests variable relationships and utilizes the F-test and Hausman test to identify the most suitable model for the research data Subsequently, potential deficiencies in the chosen model are addressed using the Generalized Least Squares (GLS) method, leading to conclusions derived from the research findings.
The Pooled OLS regression model assumes that usage data is collected randomly and is independent across observations This approach synthesizes data from multiple observations or diverse sample groups, organizing it into a matrix format to effectively estimate regression parameters.
The Pooled OLS regression model has the form:
Yit is the dependent sea at time for observation i
Xit is the independent sea at time t for observation i
β1 is the coefficient of the independent variable
εit is the random error over time t for observation i
The Pooled OLS regression model operates under the assumption of uniform variation of independent variables across observations, utilizing the entire dataset If this assumption is violated, the resulting estimates may lack accuracy, making this model inappropriate for studies characterized by significant differences among groups or variables.
The Fixed Effects Model (FEM) operates under the premise that the potential of the dependent variable remains constant over time, emphasizing the differences between observations It incorporates several fixed effects to address variations among these observations A fixed effect is a factor that remains constant regardless of the explanatory variables, providing insight into the influence of external fixed factors on the dependent variable.
The FEM regression model has the form:
Yit is the dependent sea at time for observation i
Xit is the independent sea at time t for observation i
β1 is the coefficient of the independent variable
ai is the fixed effect of observation i
εit is the random error over time t for observation i
The FEM regression method is an effective estimation technique for time series data, particularly when certain influencing factors are omitted from the model This approach enhances the accuracy of model coefficient estimates and captures the impact of fixed factors, leading to a deeper understanding of variable correlations.
The Random Effects Model (REM) posits that fixed effects within a dataset are randomly distributed Additionally, REM suggests a linear relationship exists between independent and dependent variables, emphasizing the uniformity of these fixed effects.
The REM regression model has the form:
Yit is the dependent sea at time for observation i
Xit is the independent sea at time t for observation i
β1 is the coefficient of the independent variable
ai is the fixed effect of observation i with a random distribution
uit is the random error over time t for observation i
The Random Effects Model (REM) is typically employed when the fixed effects show little correlation with the independent variables or when there is significant variance among the fixed effects By averaging the fixed effects within a dataset, REM effectively accounts for their variance, leading to more reliable estimates.
Picture 1: Implementation steps of quantitative method
Research model
Based on previously conducted research, Le et al (2022) and Hoang et al
(2023), the author proposes a model to research the business performance of companies in the IT sector on the Vietnam stock exchange as follows:
The regression model that quantifies the relationship between the independent variables and the dependent variable is written as follows:
Yit = β0 + β1CSit + 2SIZEit + 3GROWTHit + 4AGEit + ε it
CS, SIZE, GROWTH, AGE are the independent variables
i is the financial statement number, running from 1 to 28
t is the number of years the financial statement, is taken, running from 1 to 6
Research variables and hypotheses
Table 3: Description of variables in the analysis
ROA Profit after tax/Total assets ROE Profit after tax/Equity
ROS Profit after tax/Total net revenue
CS1 Liabilities/Total Assets CS2 Short-term debt/Total assets CS3 Liabilities/Equity
Firm’s size SIZE1 Ln(Total assets)
GROWTH1 (Net revenue this year - Net revenue last year)/Net revenue last year
GROWTH2 (Total assets this year - Total assets last year)/Total assets last year
AGE Year of taking financial statements - Year of establishment
Four hypotheses are set out as follows:
Table 4: Expected signs of variables in the model
H1 Capital structure negatively affects business performance
H2 Enterprise size has a positive impact on business performance
H3 Growth rate negatively affects business performance
H4 Number of operation years has a positive or negative impact on business performance
Data
The thesis utilizes secondary data indicators to measure variables, which are sourced from published audited financial reports of various companies The list of publicly listed companies is compiled from reputable websites such as Vietstock, CafeF, and Investing.com.
The financial report data is first organized into an Excel table, followed by analysis and calculations using Stata software Various statistical methods, including descriptive, comparative, and synthetic techniques, are employed to compile both financial and non-financial indicators of the company, enabling an analysis of the trends in financial indicators and business results over time.
Data used in the study was collected from financial reports of 28 Information technology enterprises listed on the Vietnamese stock market from 2017 to 2022 with
In the chapter's conclusion, the author introduces a proposed model for study, drawing from prior research The discussion includes various analysis methods, data collection techniques, and the selection of models such as FEM, REM, and OLS, alongside verification models to facilitate thorough analysis.
ASSESSING FACTORS AFFECTING FINANCIAL
Current status of financial performance of IT enterprises in Vietnam
3.1.1.1 History of formation and development of the information technology industry in Vietnam
In Vietnam, information technology is defined by Government Resolution 49/CP, signed on August 4, 1993, as a comprehensive set of scientific methods, tools, and technologies, primarily focusing on computer and telecommunications techniques This framework aims to efficiently harness and utilize the abundant information resources available across various sectors of human activity and society.
The history of Vietnam's Information Technology (IT) industry dates back to the late 20th century, marked by significant developments that unfolded in several key stages.
In the 1980s and 1990s, Vietnam's IT industry began to take shape as the government invested in computer science education and infrastructure development Initiatives focused on enhancing computer literacy and establishing essential IT infrastructure, setting the stage for future growth in the sector.
In the late 1980s, Vietnam implemented market-oriented policies known as Doi Moi, which spurred significant economic reforms and attracted increased foreign investment, particularly from multinational IT companies, throughout the 1990s and early 2000s.
30 companies started to enter the Vietnamese market, bringing technology expertise, capital, and knowledge transfer
In the early 2000s, Vietnam emerged as a prominent destination for software outsourcing and offshore development services, driven by its abundant pool of young, educated talent and competitive labor costs This attracted international clients looking for cost-effective IT solutions, positioning Vietnam as a key player in the global outsourcing market.
Since the mid-2000s, the Vietnamese government has actively supported the IT industry, acknowledging its role in fostering economic growth and innovation Key initiatives have included tax incentives for IT firms, significant investments in infrastructure, the creation of technology parks, and the encouragement of research and development activities.
Since the 2010s, the Vietnamese startup ecosystem has experienced remarkable growth, driven by enhanced access to capital, a supportive regulatory framework, a burgeoning entrepreneurial culture, and technological advancements This growth has particularly benefitted tech startups in sectors such as e-commerce, fintech, and software-as-a-service (SaaS), significantly energizing the IT industry.
In recent years, the Vietnamese economy has seen a significant focus on innovation and digital transformation, with businesses increasingly embracing technologies like cloud computing, big data analytics, artificial intelligence, and the Internet of Things This shift aims to enhance efficiency, boost productivity, and strengthen competitiveness across various sectors.
Overall, the formation of Vietnam's IT industry has been shaped by a combination of government support, foreign investment, talent development, entrepreneurial activity, and technological advancement The sector continues to
31 evolve rapidly, positioning Vietnam as a significant player in the global digital economy
3.1.1.2 Current status of IT industry in Vietnam
Between 2017 and 2022, Vietnam's information technology (IT) industry experienced remarkable growth, fueled by government support, strong economic development, and digital transformation Initially accounting for just 0.5% of the country's GDP in the early 2000s, the IT sector's revenue surged over 400 times by 2022, averaging a 38% increase annually over the past decade In 2022, the estimated revenue reached approximately 148 billion USD, reflecting a 10% rise from 2021 Currently, the IT industry contributes 14.4% to Vietnam's GDP, marking a significant leap forward for the nation's economy.
IT industry has had a certain position in the world Not only that, with software services, Vietnam ranks first in Southeast Asia
Picture 3: Comparision between growth rate of GDP and ICT industry
The IT industry has experienced remarkable growth over the years and is projected to continue this upward trajectory due to the rapid pace of digital transformation Technology investments are essential for enhancing operational efficiency and reducing production costs across various sectors Over the past two decades, the integration of electronics, information technology, and communications has deepened, leading to the emergence of the ICT industry.
Vietnam's startup ecosystem is flourishing, with a significant rise in tech startups driven by strong support from incubators, accelerators, and venture capital funding This growth fosters innovation and technological progress, positioning Vietnam as a prime destination for outsourcing IT services, especially in software development and business process outsourcing (BPO) Consequently, the number of Information Technology businesses in Vietnam surged between 2017 and 2022, and this upward trend is expected to continue in the future.
Picture 4: Number of ICT firms 2017-2023
The ICT industry has experienced significant growth, leading Vietnam to transform from a war-torn nation into a formidable player in information technology This rapid development is complemented by the country's commitment to integrating information technology across various socio-economic sectors.
3.1.1.3 Opportunities of the Information Technology industry in Vietnam in the coming period
Digital transformation presents a significant opportunity for Vietnam to advance towards becoming a developed nation, yet challenges remain in political will, legal frameworks, human resources, and digital infrastructure Vietnam has emerged as a key player in the global software technology industry, ranking among the Top 30 in software outsourcing, with major export markets in the US and Japan Vietnamese IT companies are evolving from mere outsourcing to offering consulting services and content creation Additionally, the hardware sector, encompassing computer products, network equipment, and electronic components, has seen Vietnam attract substantial investment from global tech giants like IBM, Intel, and Samsung Furthermore, Vietnam has become a pioneer in telecommunications, mastering 5G technology and producing 5G infrastructure and devices, showcasing the country’s innovative capabilities.
The creative labor in Vietnam's information technology sector represents a significant aspiration, with strong growth potential in both the medium and long term Organizations involved in analysis and evaluation, along with distributors, agree that the retail segment of the IT product market remains promising.
3.1.1.4 Challenges of the Information Technology industry in Vietnam in the coming period
The information technology industry in Vietnam faces significant challenges, primarily due to supply chain disruptions exacerbated by the Covid-19 pandemic and the Russia-Ukraine war These events have led to congestion and delays, making it difficult for manufacturers and technology companies to source essential parts, particularly amid ongoing semiconductor shortages that affect various industries Additionally, the rising threat of cyber security is a pressing concern, as the frequency and sophistication of cyber attacks continue to escalate, resulting in trillions of dollars in global damages These cyber crimes impact organizations through data destruction, financial theft, intellectual property loss, fraud, system paralysis, and damage to reputations.
Picture 5: Chart comparing innovation capacity and information security level of
Note: The size of the ball in the graph represents the country's GDP
Source: ITU, WIPO and World Bank
Model to evaluate factors affecting financial performance of Information and
and Technology enterprises in Vietnam in the period 2017-2022
This thesis analyzes data from 28 Information and Technology enterprises listed on the Vietnam stock market over a six-year period from 2017 to 2022, comprising a total of 168 observations, as detailed in the accompanying table.
Table 5: Statistics describe the variables used in the model
Variable Obs Mean Std.dev Min Max
Source: Summary of results from Stata 17 software
From the table above we can make some general comments about the variables as follows:
ROA: The average value of the average net profit to asset ratio of businesses in the period 2017-2022 is 10.22% with a standard deviation of 16.57%, showing
The data reveals significant fluctuations in the Return on Assets (ROA) within the industry, with the highest ROA reaching 81.22% and the lowest plummeting to -4.03% This stark contrast highlights the disparity in net profit generation relative to assets among businesses, indicating that while some companies excel in profitability, others are struggling with losses.
The average return on equity (ROE) for businesses during the research period is 13.4%, with a significant standard deviation of 17.26%, indicating considerable variability in the data The highest recorded ROE is 129.06%, while the lowest is -10.73%, highlighting the substantial differences in net profit-to-equity ratios among companies in the industry over the years.
From 2017 to 2022, the average Return on Sales (ROS) for businesses was 12.76%, with a significant standard deviation of 18.3%, indicating considerable variability among companies The highest recorded ROS reached 85%, while the lowest dipped to -13.16%, reflecting a stark contrast in net profit to revenue ratios across the industry during this period.
Between 2017 and 2022, the financial performance of IT industry businesses varied significantly, largely due to fluctuations during this period Many companies experienced profit declines in 2019-2020 as a result of the pandemic, followed by a strong rebound in 2021-2022 Additionally, various objective and subjective factors, including socio-economic conditions, inflation, cost control capabilities, and strategic decisions to incur losses for expansion, also played a crucial role in influencing the financial outcomes of these enterprises.
Group of variables on capital structure
In the industry, the average ratio of liabilities to total assets for businesses stands at 0.58%, with a standard deviation of 1.16% This ratio varies significantly, with a maximum of 15.27% and a minimum of just 0.01%.
In the industry, the average ratio of short-term debt to total assets for businesses is 0.52%, with a standard deviation of 1.17% This ratio ranges from a minimum of 0.01% to a maximum of 15.27%.
CS3: The debt to equity ratio of businesses in the industry has an average value of 4.5% with a standard deviation of 11.45% The maximum value is 64.93% and the minimum value is 0.01%
The analysis of capital structure variables reveals that the standard deviation between observations of CS1 and CS2 indicates minimal differences among companies in the research dataset In contrast, the standard deviation for variable CS3 is considerably higher, highlighting significant fluctuations in the debt-to-equity ratios across businesses in the industry.
Group of variables on scale:
SIZE1: The average value of the indicator of total assets of businesses is 11.56 with a standard deviation of 2.74% The minimum value is 7.36 and the maximum value is 17.92
SIZE2: The average value of the net revenue indicator of businesses is 11.38 with a standard deviation of 2.65% The minimum value is 5.92 and the maximum value is 17.60
The standard deviation of the variables in the size variable group shows that the difference in size of businesses in the research data set is not large
Group of variables on growth rate:
GROWTH1: The average value of net revenue growth rate among businesses is 1.67% and the standard deviation is 17.11% The largest value is 220.39% and the smallest value is -0.96%
GROWTH2: The average value of total asset growth rate among businesses is 0.11% and the standard deviation is 0.36% The largest value is 2.69% and the smallest value is -0.38%
There is a large difference between net revenue growth rates among businesses in the industry However, in terms of total asset growth rate, the fluctuation is very small
Group of variables on year of operation:
The average operational age of IT businesses is 25.75 years, with a standard deviation of 12.26%, indicating considerable variability in experience levels The age range spans from a minimum of 9 years to a maximum of 68 years, highlighting the diverse maturity of companies within the industry.
3.2.2 Analyze correlation of variables in the model
The correlation coefficient matrix is utilized to assess the linear relationships among variables, treating both independent and dependent variables equally By analyzing the matrix, the author determines which variables from each group should be incorporated into the analysis model.
Table 6: Correlation between variables in the model
Source: Summary of results from Stata 17 software
The dependent variables Return on Assets (ROA), Return on Equity (ROE), and Return on Sales (ROS) exhibit a negative correlation with capital structure and operational years, while showing a positive correlation with company size Notably, the growth rate variables reveal varied relationships: the GROWTH2 variable positively correlates with all three dependent variables, whereas GROWTH1 negatively correlates with ROA and ROE but positively correlates with ROS.
The analysis reveals a strong correlation among the variables within each group, indicating that only one variable should be chosen for inclusion in analytical models Consequently, the authors selected the following independent variables for regression analysis: CS1, SIZE1, GROWTH1, and AGE.
Before conducting regression analysis using OLS, FEM, and REM models, the author assessed the presence of multicollinearity by employing the Variance Inflation Factor (VIF) test A VIF coefficient of less than 5 indicates the absence of multicollinearity, while a coefficient greater than 5 suggests its presence, necessitating corrective measures The hypothesis tested in this analysis is structured around these VIF results.
H0: The model does not have multicollinearity
H1: The model has multicollinearity phenomenon
Variable SIZE1 AGE CS1 GROWTH1
Source: Summary of results from Stata 17 software
The analysis indicates that the Variance Inflation Factor (VIF) coefficients are low, with an average of 1.04 and values between 1.00 and 1.09 Consequently, we accept the null hypothesis (H0) and reject the alternative hypothesis (H1), concluding that the model is free from multicollinearity.
3.2.4.1 Regression model results a, Dependent variable: ROA
Table 8: Regression model results: ROA
Coefficient [P-value] Coefficient [P-value] Coefficient [P-value]
CS1 -0.035787 0.012** -0.185681 0.028** -0.134567 0.001*** SIZE1 0.017623 0.082* 0.5634094 0.079* 0.0299317 0.069* GROWTH1 -0.040534 0.023** 0.0077563 0.018** -0.013855 0.047** AGE -0.006184 0.045** -0.175317 0.001*** -0.010886 0.044** _cons 10.87439 0.006 8.905454 0.006 10.84886 0.019
Source: Summary of results from Stata 17 software
In the model with the dependent variable ROA, from the results we see:
OLS model estimation results: The effects of variables CS1, GROWTH1 and
The AGE variable shows a statistically significant impact at the 5% level, while the SIZE1 variable is significant at the 10% level This model's independent variables collectively account for 73.1% of the variance in the dependent variable.
CONCLUSION AND RECOMMENDATIONS
Conclusion
This thesis investigates the factors influencing the financial performance of IT enterprises listed on the Vietnam Stock Exchange from 2017 to 2022 Utilizing theoretical frameworks and findings from both domestic and international research, the author develops a model that examines financial performance through three key dependent variables: Return on Assets (ROA), Return on Equity (ROE), and Return on Sales (ROS) The independent variables are categorized into four groups: the first group focuses on capital structure, including total debt to total assets, short-term debt to total assets, and total debt to equity; the second group addresses business size, represented by net revenue and total assets; the third group considers growth rates, encompassing net revenue growth and asset growth rates; and the final group examines the variable of years of operation.
The collected data includes 168 observations from 28 IT businesses listed on
Between 2017 and 2022, data from the three exchanges—HOSE, HNX, and UPCOM—was compiled from audited financial statements and reliable sources like Vietstock, Investing.com, and CafeF The synthesized data was processed using Excel, and a regression model was executed in Stata 17 After confirming the absence of multicollinearity, the regression analysis was conducted using Ordinary Least Squares (OLS), Fixed Effects Model (FEM), and Random Effects Model (REM) Following the F-test and Hausman tests, the REM model was determined to be the most appropriate, with Return on Assets (ROA) and Return on Equity (ROE) as dependent variables, and CS1, SIZE1, GROWTH1, and AGE as independent variables The model with Return on Sales (ROS) was excluded due to lack of statistical significance.
58 variables, the author only selected 1 variable from each group due to strong autocorrelation between variables in the group)
After addressing issues of heteroskedasticity in the REM model through GLS regression, the analysis revealed that the independent variables CS1 and GROWTH1 significantly affect both ROA and ROE at the 5% significance level Additionally, the SIZE1 variable shows statistical significance at the 10% level for ROA and at the 5% level for ROE Furthermore, the AGE variable demonstrates a strong influence on both ROA and ROE, achieving significance levels of 5% for ROA and 1% for ROE.
Capital structure significantly negatively affects a business's Return on Assets (ROA) and Return on Equity (ROE), indicating that a higher debt-to-asset ratio correlates with lower ROA and ROE, thus increasing the financial burden on companies and leading to adverse business outcomes Conversely, business scale positively influences ROE at a weak level while inversely affecting ROA at an average level; as total assets increase, ROE rises while ROA declines This unexpected negative relationship between total assets and ROA aligns with findings from previous studies.
The analysis reveals a negative correlation between growth rate and the financial indicators ROA (Return on Assets) and ROE (Return on Equity), indicating that higher net revenue growth rates are associated with lower ROA and ROE Additionally, the length of time a business has been operational negatively affects both ROA and ROE, suggesting that older companies may struggle with performance This finding aligns with the IT industry's need for innovation and adaptability, highlighting the importance of a youthful and dynamic business environment for success.
Despite providing statistically significant research results and testing the established hypotheses, the thesis cannot avoid having shortcomings
The research sample of the project is small and the number of years of research
The research, conducted over a six-year period, is limited in scope as it only includes data from 28 publicly listed IT companies, alongside a selection of other IT businesses Consequently, the findings may not accurately represent the entire IT industry.
The four independent variables in the model do not account for all the factors influencing the financial performance of businesses in the industry Numerous micro and macro factors, including governance activities and government economic policies, remain unaddressed Specifically, in the IT sector, various technology-related factors significantly impact financial performance; however, these factors are challenging to measure and collect data on, leading to their exclusion from the study.
Recommendations for businesses, government and further researches
The ratio of debt to total assets negatively impacts a business's Return on Assets (ROA) and Return on Equity (ROE), making it crucial to manage this ratio effectively and utilize liabilities wisely While debt can drive growth, IT companies must avoid over-leveraging, as excessive debt increases financial risk and interest expenses, potentially diminishing returns Maintaining a balanced debt-to-equity ratio is vital for risk mitigation and profitability preservation Additionally, IT firms should consider alternative financing options like venture capital, private equity, or strategic partnerships, which can provide funding without increasing debt, thereby enhancing ROE without incurring interest expenses.
Investing in research and development (R&D) is essential for IT businesses to foster innovation and drive revenue growth, ultimately enhancing asset value Prudent management of debt can also support expansion efforts, allowing companies to scale operations, acquire new technologies, or penetrate new markets By strategically leveraging debt, IT businesses can boost both assets and revenue, leading to improved return on assets (ROA) and return on equity (ROE).
To enhance ROA and ROE, companies must ensure efficient asset utilization by optimizing production processes, minimizing idle capacity, and maximizing equipment use Implementing new technology, effectively managing software licenses, and optimizing human resource deployment are crucial steps Utilizing asset management software and monitoring tools for infrastructure can identify improvement areas Additionally, focusing on increasing profitability per revenue dollar through pricing optimization, cost reduction, and operational efficiency will lead to higher profit margins and net income, boosting ROA and ROE Companies should also avoid investing in low-return or high-depreciation assets, conduct regular asset performance assessments, and consider divesting underperforming assets to reallocate capital to more profitable ventures.
The inverse correlation between revenue growth rate and both Return on Assets (ROA) and Return on Equity (ROE) highlights the critical need for effective cost management It is essential for companies to assess their operating expenses and pinpoint opportunities for cost reduction while maintaining operational efficiency.
61 compromising quality or performance This could involve renegotiating contracts with suppliers, streamlining processes, or leveraging technology to automate repetitive tasks
To operate effectively in the IT sector, businesses must prioritize technological advancements by staying updated on emerging technologies such as artificial intelligence, machine learning, blockchain, and the Internet of Things (IoT) Engaging with industry trends and market dynamics through conferences, forums, and networking with thought leaders is crucial Forming strategic partnerships with complementary companies, startups, or research institutions can foster innovation and provide access to new markets and resources Additionally, attracting and retaining top talent is vital; offering competitive compensation, professional development, and a stimulating work environment, along with investing in training and upskilling programs, ensures that employees are equipped to thrive in the rapidly evolving IT landscape.
Businesses, especially in the IT sector, must prioritize customer experience by creating user-friendly and intuitive products and services Regularly gathering customer feedback is essential for continuous improvement Additionally, emphasizing cybersecurity and data privacy is crucial for maintaining customer trust Companies should implement strong security measures, conduct regular vulnerability assessments, and adhere to relevant regulations and standards to protect sensitive information.
To support IT businesses and help them operate more effectively, governments can implement policies and initiatives that foster innovation, facilitate growth, and create a supportive business environment
To enhance the quality of the IT workforce, the government must promote STEM education and collaborate with educational institutions and industry leaders to create relevant curricula and training programs Additionally, fostering partnerships between academia, research institutions, and the industry is essential for facilitating knowledge transfer and technology commercialization Furthermore, establishing a supportive ecosystem for entrepreneurship is crucial, which includes incubation centers, co-working spaces, mentorship programs, and networking events Providing incentives such as tax breaks, access to funding, and regulatory relief will further stimulate innovation and entrepreneurship within the IT sector.
To foster innovation and growth in the IT sector, the government should provide financial incentives like tax breaks, grants, and subsidies specifically targeting startups and small businesses Additionally, creating venture capital and innovation funds will facilitate funding for research and development, product development, and expansion efforts Establishing R&D grants and collaborative funding programs will further support technology advancement within the industry.
To enhance support for IT businesses, the government should simplify regulatory processes and reduce administrative burdens by implementing digital services and e-governance initiatives This approach would facilitate online registration, licensing, and compliance, ultimately streamlining operations and fostering a more efficient business environment.
The government can enhance market access for IT businesses by negotiating trade agreements, lowering trade barriers, and offering export assistance and market intelligence Additionally, it is crucial to promote digital inclusion by ensuring that all societal segments, including rural and marginalized communities, have access to technology Implementing digital literacy programs, providing affordable internet connectivity, and encouraging the use of digital technologies for social and economic development are essential steps in this process.
Protecting intellectual property rights (IPR) is crucial in the IT industry, necessitating government action to enhance IPR protection and enforcement Strengthening legal frameworks and support services for patents, copyrights, trademarks, and trade secrets is essential for safeguarding innovations and intellectual assets, ultimately fostering industry growth and development.
To address the identified limitations, future research should focus on expanding the dataset concerning the businesses studied or conducting longitudinal experimental research This approach will help to more clearly identify the influencing factors at play.
To enhance the research model, it's essential to incorporate additional independent variables that reflect the unique characteristics of the IT industry, particularly technology-related factors Furthermore, including control variables such as macroeconomic indicators like inflation and interest rates will contribute to a more comprehensive analysis.
Chapter’s conclusion: The author summarizes the research process and results, thereby proposing solutions for businesses, the government and future research
[1] Kumar Sanjay Sawasrni, Working capital management, financial performance and growth of firms: Evidence from India, 2022
[2] Stenio Cristaldo Heck, Growth, Intellectual Capital, financial performance and firm value, 2023
[3] Uche Lucy onyekwelu e al., Evaluation of firms’coporate financial Indiactorsand operational performance of selected firms in Nigeria, 2018
[4] Prekazi, The impact of capital strucrure on financial performance, 2023
[5] Dodoo, The effect of capital structure on firm performance: empirical evidence from emerging economy, 2023
[6] Hoang, Factors that affect efficiency business of enterprises information technology industry, 2023
[7] Le, Analyze factors affecting management activities business results of information technology companies listed on Vietnam stock market, 2022
[8] Nguyen, Studying influential factors to business efficiency of manufacturing industry companies food processing listed above Vietnam stock market, 2013
[9] Pham, Analyzing factors affecting the financial performance of real estate joint stock companies listed on the Vietnamese stock market, 2015
[10] Associate Professor, Dr Nguyen Nang Phuc - Textbook of Financial Statement Analysis - National Economics University Publishing House (2013) Page 199
[11] Learn about efficiency, Dai Tu Dien, 2015
[12] Data from financial statements of 28 companies, 2017-2022
[13] Data from cafeF, Vietstock of 28 companies from 2017-2022
Table 18: List of Information Technology enterprises listed on the stock exchange in
1 FPT FPT Joint Stock Company
4 ICT Joint Stock Company For Telecom &
6 ST8 ST8 Invesment Development Joint Stock
9 CMT Information and Networking Technology
10 TST Telecomunication Technical Service JSC
12 POT Post And Telecommunication Equipment
14 UNI Sao Mai Viet Investment And
15 VLA Van Lang Technology Development &
20 KST Kasati Joint Stock Company
23 HPT HPT Viet Nam Corporation
24 PMJ Post And Telecommunications Material
25 VGI Viettel Global Investment JSC
26 PMT TELVINA Vietnam Communication Joint
27 HIG HIPT Group Joint Stock Company
28 SBD Sao Bac Dau Technologies Corporation
NHẬN XÉT CỦA GIẢNG VIÊN HƯỚNG DẪN
Đánh giá thái độ làm việc của sinh viên trong quá trình viết chuyên đề rất quan trọng, bao gồm việc xem xét nỗ lực và hiệu quả công việc của họ Sự thường xuyên liên lạc giữa sinh viên và giảng viên hướng dẫn (GVHD) cũng là một yếu tố then chốt, giúp nâng cao chất lượng chuyên đề và tạo điều kiện cho sự phát triển của sinh viên Việc duy trì giao tiếp thường xuyên không chỉ hỗ trợ sinh viên trong quá trình học tập mà còn góp phần vào sự thành công của dự án nghiên cứu.
(Ký & ghi rõ họ tên)