factors affecting firms performance a case study of listed manufacturing companies in vietnam

MASTER IN FINANCIAL MANAGEMENT MASTER THESIS Topic: Factors affecting firm’s performance: A case study of listed manufacturing companies in Vietnam... Therefore, the indicator that the

INTRODUCTION

Research problems

Globalization and technological advancements have intensified competition among businesses, demanding effective management, accurate predictions, and sustained performance to succeed To survive in this cutthroat landscape, companies must address challenges, identify opportunities, and maintain operational efficiency Maximizing profits and evaluating profitability factors are crucial for growth and innovation The performance evaluation of organizations has become a focal point for managers and researchers, with various methods developed to measure enterprise performance Researchers continuously seek to enhance concept expression, leading to ongoing debates and documentation on the effectiveness of enterprise performance evaluation practices.

Previous studies have found many factors that affect firm’s performance

Including factors derived from the external environment of the enterprise and also factors derived from the internal self of the business At the same time, each business line has its own characteristics, so businesses operating in different business lines are affected by different groups of factors To date, there have been many studies, both theoretical and empirical, examining the factors that affect firm’s performance For example, the capital structure of a business reflects the level of debt and equity used to finance the assets of the business Specifically, the extent to which debt is used will affect managers' behavior as well as their financial decisions; therefore, it greatly affects the results of operations of the business (Ben Said Hatem, 2014; Hoang Tung, 2016; Zahid Bashir et al 2013; Ho Xuan Thuy et al, 2020) Numerous empirical studies have demonstrated financial control as the backbone of organizational activity in the business world (Morley et al., 2016) In this view, financial control contributes to strengthening budget control for employees and bringing better benefits to employees and organizations in order to improve the performance of the organization Likewise, Ekadjaja et al, (2021)

2 emphasize that the importance of financial control over an organization's operations includes organizational effectiveness and firm’s performance

Meanwhile, previous studies on financial factors affecting the firm’s performance of enterprises listed on the Vietnam Stock Exchange have not received much attention An understanding of the determinants that affect the efficiency of a business is key to helping shareholders or investors better understand whether the capital they spend in a business will profit or lose; so, they can limit risks

Therefore, this study is carried out to study the factors affecting the efficiency of manufacturing companies listed on the Vietnamese stock market is necessary Previous studies have found models used to measure firm’s performance So far, there have been many studies both theoretical and empirical examining the model of measuring firm’s performance For example: ROE is the ratio of return on equity and is an index comparing operating results between businesses based on the size of invested capital, and ROA is different from ROE, which is an index of return on total assets, and comparing operating results between businesses based on the assessment of the use of money in business (Hoang Tung, 2016) Reinforcement, the scholar (Ekadjaja et al 2021) asserts that a high ROA indicates that a company's performance is strong, ROA is used to measure the performance of a business The researcher (Ben Said Hatem, 2014) also uses ROA, ROE is a measure of the firm’s performance in Europe from which to study the financial factors that strongly affect the firm’s performance

Therefore, the indicator that the author chooses to use in the model to measure business results of manufacturing enterprises is the indicator of firm’s performance in financial terms, most commonly the rate of return on assets (ROA) and return on equity (ROE)

The research problem has been widely addressed globally and domestically However, this study focuses on manufacturing enterprises listed on the Vietnamese stock market due to the industry's significant advantages and potential in Vietnam The manufacturing sector and its enterprises are experiencing heightened competition with the influx of foreign direct investment and expanding global supply chains.

3 a series of domestic and foreign investors In recent years, with the advantage of a young population country and strong urbanization, the Vietnamese market has become a target market for many retail giants from Japan, Korea and Thailand In this context, manufacturing enterprises are facing significant competitive pressures

In addition, concerns about being taken over and suppressed by foreign businesses are well founded From the perspective of manufacturing business managers in Vietnam, the issue of studying factors affecting the performance of manufacturing enterprises in the current period is very necessary

After the economic turmoil, the resurgence of the economy has paralleled an augmented demand for manufactured goods However, the indigenous manufacturing enterprises often portray a demeanor of restraint in comparison to their global counterparts Within the domestic landscape, there's a conspicuous scarcity of sizeable and fiercely competitive entities within the manufacturing sector It is evident that a considerable number of local companies fail to match the scale and influence wielded by foreign counterparts In this context, the manufacturing domain harbors a dearth of robust and commanding entities, with just a handful of players holding a significant sway over the market Names such as Vinamilk, Sabeco, Hoa Phat, Vietnam Rubber Group, Masan, and Bien Hoa stand out as prominent examples within manufacturing, exemplifying the limited scope of highly impactful indigenous players Despite the resurgent economy and an escalating demand for manufactured products, the landscape remains characterized by a marked absence of local entities on a scale comparable to the global behemoths that dominate this sector

In addition, Vietnamese consumers with a preference for foreign consumption (also known as foreign psychology) have contributed to the increase of more and more international consumer goods brands This will be an advantage to help foreign enterprises gradually dominate the domestic market if domestic enterprises do not promote brand promotion activities Therefore, in the current period, competitive pressure and the risk of capturing market share right at home of manufacturing enterprises is huge Therefore, the problem of finding the answer to the problem of improving the operating results of manufacturing enterprises is more urgent than ever In manufacturing in Vietnam, several factors can affect the

4 successful application of firm’s performance These factors include data availability and quality, and insufficient resources The successful application of firm’s performance in manufacturing in Vietnam depends on several factors and being able to address these factors can help improve the likelihood of the successful application of firm’s performance in Vietnamese manufacturing At the same time, from these indicators, it is possible to make judgments and factors affecting the firm’s performance of the enterprise, thereby making judgments and solutions to improve the operational efficiency of the enterprise, businesses, improve performance, contribute to help businesses maintain and expand in this competitive market

Stemming from the importance and necessity to understand the factors affecting performance to help manufacturing enterprises in Vietnam improve their competitiveness and operational results Author decided to choose the topic:

"Factors affecting firm’s performance: A case study of listed manufacturing companies in Vietnam." Thereby, in order to propose solutions to improve the competitiveness and operational results of manufacturing enterprises, helping enterprises promptly have appropriate policies for their business activities, avoiding the risk of being dominated by market share as well as being acquired by foreign enterprises.

Research Objectives and Research Questions

The objective of the study is to understand the factors affecting the performance of manufacturing enterprises listed on the Vietnamese stock market To address the above overview, author identify the following specific research objectives:

(1) Examine financial factors affecting the performance of manufacturing enterprises in Vietnam

(2) Analyze how financial factors affecting the performance of manufacturing enterprises

(3) Provide implications for companies in Vietnam manufacturing industry to improve their performance based on the research about the affecting of financial factors with listed manufacturing companies in Vietnam market

To address the above research objectives, author ask research questions

(1) What are financial factors effecting firm’s performance of listed manufacturing companies in Vietnam?

(2) How do these financial factors affect firm’s performance of listed manufacturing companies in Vietnam?

(3) What are the solutions and implications to enhance firm’s performance of listed manufacturing companies in Vietnam?

Scope of Research and Study Subjects

The research project investigates factors that influence the performance of manufacturing companies on the Vietnamese stock market The study focuses primarily on financial performance as a measure of overall enterprise effectiveness This target approach allows for a comprehensive analysis of factors impacting the financial success of manufacturing businesses.

- The scope of the research scope of the topic on time is in the period 2015 – 2022

- The scope of the research is manufacturing enterprises listed on the Vietnam stock market, including those on the Ho Chi Minh and Hanoi Stock Exchanges.

Research Methodology

The study used quantitative methods with the support of Microsoft Excel and Stata 13 software to examine the influence of factors on firm’s performance

This study employs secondary data from published annual financial statements of 160 Vietnamese manufacturing enterprises listed on the stock market, spanning 2015–2022 The data's year-over-year time series and spatial distribution necessitate the use of regression methods with tabular data Two models, the FEM (Fixed effects model) and the REM (Random effects model), are utilized for regression analysis, with tests conducted to determine their suitability.

Theoretical and practical contributions of the topic

In terms of theory, the topic synthesizes general knowledge as well as the results of research works in the world on issues related to business results and impact factors From there, providing readers with a theoretical foundation to explain the events and phenomena surrounding the performance of the business In addition, the results of the project can become the basis for further research to clarify the characteristics of business executives in relation to the performance of those enterprises in Vietnam

In practical, author hope that this research can help manufacturing enterprises in Vietnam in particular and listed enterprises in general realize the important role and effective use of financial factors affecting the operating results of enterprises and thereby improve the operating results of enterprises In addition, investors using business information can partially evaluate the performance of the business through information about financial factors in the income statement of the enterprise From there, investors can make the right one-wing investment decision, avoiding risks

This study's findings lay the groundwork for future research in data analysis and comparison using various experimental models For those in the financial sector, this paper provides insights into factors influencing enterprise performance, particularly in manufacturing industries.

Thesis structure

Chapter 1: An overview of the factors affecting firm's performance in the manufacturing industry This chapter summarizes previous studies related to factors affecting firm's performance in manufacturing firms that have been studied around the world and in Vietnam, thereby identifying gaps in research on factors affecting firm's performance in manufacturing

Chapter 2: General theory of factors affecting firm's performance in manufacturing This chapter introduces firm's performance and measurement methods by ROA, ROE; Factors affecting firm's performance and trends in the impact of factors affecting firm's performance in manufacturing

Chapter 3: Research methodology on factors affecting firm's performance in manufacturing This chapter specifically presents the research methodology used to study and measure factors affecting firm's performance in manufacturing

Chapter 4: Research results on factors affecting firm's performance in manufacturing in Vietnam This chapter presents the current situation of Vietnam's manufacturing industry; the results of the study and some discussion of the research findings on factors affecting firm's performance in manufacturing in Vietnam Chapter 5: Conclusion on factors affecting firm's performance in manufacturing in Vietnam This chapter summarizes the main findings in chapter 4 to draw conclusions, significance, and limitations of the thesis.

LITERATURE REVIEW AND THEORETICAL BACKGROUND 7 2.1 The concept and methods of measuring the performance of the enterprise

Internal factors affecting the firm’s performance

Firm’s performance is an important measure for the success and competitiveness of the business Various determinants affect the efficiency of the business, including leverage, liquidity, growth, enterprise size, and capital structure

A review of this literature aims to examine and synthesize existing research on the relationship between these determinants and business effectiveness, shedding light on their importance, interaction, and implications in a business context

Typically, there are three main sources that form capital in an enterprise, namely: own capital, State budget-allocated capital and borrowed capital In finance, the financial leverage ratio of the enterprise is the factor that characterizes the capital structure

Several studies show a positive relationship between leverage and corporate efficiency, suggesting the potential benefits of effective debt control

According to Hierarchical Order Theory, companies with high profits have lower levels of debt Leverage is a debt ratio factor used to measure based on an enterprise's ability to fulfill obligations with all of its liabilities to total assets and/or equity as collateral (Irfani, 2020) In general, this leverage ratio aims to measure a business's ability to meet its long- and short-term financial obligations This ratio is used to help measure the composition of capital coming from debt or loans In terms of analyzing corporate finances, this ratio plays an important role because it can provide information about the source of capital used to finance the operations of the business or activities that come from own capital or debt (Ekadjaja, 2021)

Other studies show a negative relationship due to the financial risk associated with rising debt ratios

The findings of Zeitun and Tian (2007) indicate that leverage has a significant and negative relationship with corporate performance Research results show that debt ratio has the strongest impact while growing total assets and enterprise size They used leverage, growth, size, taxes, risk, and tangibility as an independent variable to see their effect on the firm's performance

According to a study by Onaolapo and Kajola (2010) that surveyed 30 non- financial enterprises listed on the Nigerian Stock Exchange between 2001 and 2007, debt ratios and fixed asset ratios had a negative impact on corporate efficiency, while asset turnover showed a positive impact

According to Ekadjaja, Wijaya, and Vernetta (2021), if external funding is needed, the company will first choose the safest assets, such as the lowest risk debt, before moving to riskier debt such as hybrid securities

Empirical research has investigated the relationship between corporate efficiency and financial leverage, with notable findings Tan (2012) concluded that firms with low leverage outperformed those with high leverage during the Asian financial crisis Hossain and Nguyen (2016) established a negative correlation between leverage and performance in the Canadian oil and gas industry, irrespective of the crisis period Zahid Bashir (2013) identified a complex relationship, with short-term leverage negatively impacting efficiency, while long-term leverage had a positive influence, defying the initial hypothesis.

Hierarchical order theory dictates that companies prioritize debt repayment based on debt level Companies with higher profit margins tend to carry lower debt levels as a proportion of their equity due to the increased cash flow available for debt servicing This theory suggests that companies with strong financial health can manage lower debt levels, as they have the resources to cover any potential financial risks and maintain their investment grade status.

18 to The higher the level of leverage of the company, the greater the financial burden it faces It means, the higher the risk faced by the company Too high debt can reduce the company's free cash flow, as well as the waste of management, so leverage has an effect on company efficiency

However, research results on the relationship between financial leverage and firm’s performance of enterprises so far are still inconsistent Abor's (2005) estimates show the covariant effect of the debt-to-total assets ratio on the return on equity of 20 Ghanaian-listed companies between 1998 and 2002 In support of Abor's (2005) conclusion, Gill et al (2011) also observed a mutual relationship between debt ratio and ROE in a sample of 272 New York-listed manufacturing and service companies between 2005 and 2007 In stark contrast to these conclusions, a study by Gleason et al (2000) showed a negative effect of debt-to-total assets ratios on the ROA of retail companies in 14 countries in Europe Chinmoy Ghosh &; Raja Nag (2000) argue that financial leverage as a force acting on a business amplifies the financial viability of the business, but it is like a double-edged sword If you do not know how to use it at the right time and at the right time, it will cause businesses to face many financial risks Ghosh (2012) studied Indian companies that define leverage, and the results showed that less leveraged companies are more likely to grow Meanwhile, Ebaid (2009) and Muritala (2012) show a negative relationship between financial performance and leverage In summary, so far there are numerous studies looking at the impact of financial leverage on firm’s performance However, the results of the study have produced mixed results and remain controversial

Liquidity, defined as a business's ability to meet its short-term financial obligations, is critical to its financial health and firm’s performance Studies have shown a positive relationship between liquidity and firm’s performance, indicating that businesses with higher liquidity are better positioned to handle costs incurred, seize investment opportunities, and navigate economic downturns On the other

19 hand, insufficient liquidity can lead to financial constraints and hinder the overall performance of the business

Liquidity's impact on firm performance remains inconclusive Research by Astutik and Hutajulu (2015) suggests a positive and significant correlation, while Thaibah and Faisal (2020) found no significant impact However, Agustin Ekadjaja (2021) posits that liquidity positively influences efficiency, indicating that high liquidity may enhance firm performance.

According to Ho Xuan Thuy et al (2020), liquidity and firm's performance have a mutual effect and have a positive effect However, in the same study, the author mentioned that the impact of liquidity on firm's performance was relatively small and negligible In addition, the impact of liquidity variables that are too small can also be explained because when companies keep this index too high (lots of cash and bank deposits, high inventories) it wastes company resources when capital is not invested, causing firm's financial performance to be reduced This research is similar to the opinion expressed in research conducted by Zahid Bashir (2013), which shows that liquidity is positively correlated with firm's performance Chytis, Arnis, and Tasios (2018) study based on 13 regression-listed food companies explains seven variables: including company size, capital employed to net fixed assets ratio, financial leverage, liquidity, accounts receivables turnover, accounts payable turnover, and inventory turnover, use table data Descriptive statistics of the aforementioned variables reflect the significant impact of the financial crisis that has affected the performance indicators of food companies listed on ASE The table regression results demonstrate that profitability is significantly and positively correlated with company size and accounts payable revenue, thus indicating that companies that are larger in terms of total assets and have higher payables turnover days show increased profits during crises On the other hand, the findings on the impact on the profitability of capital used to calculate net fixed asset ratios, payables turnover, and inventory turnover give new insight into factors affecting the

During crises, the performance of listed food companies has been examined in numerous studies These studies suggest that short-term assets should not exceed short-term liabilities to avoid unnecessary waste Highly liquid assets constitute a small portion of a company's asset structure, minimizing the impact of liquidity on firm performance.

This is an important factor to decide the ability to operate as well as an indispensable criterion for assessing the liquidity of an enterprise Solvency includes the following indicators:

Research Hypotheses

The research model is built by author based on the following hypotheses:

First, economies of scale have been empirically studied by many authors around the world in different countries Firm size is one of the most important deciding factors for investors, suppliers and partners There are studies that prove the relationship between the size of the company and profitability Although there are many studies that give conflicting results, it is generally the same effect

Accordingly, the larger the scale, the more effective the business operates

Therefore, author propose the H1 hypothesis as follows:

H1: Firm size has a positive (+) relationship with the firm's performance of the business

Second, listed companies are mostly large-scale companies in the economy and have a long-standing firm The firm age of a company is determined by the number of years it has established Typically, established and established companies are capable of achieving optimal profitability and are more complex than new ones However, there have been many enterprises that prove the opposite theory, causing an inverse correlation between the number of years of operation and the profitability indicator Basically, the longer a business operates, the more inert and difficult it is to innovate technology Therefore, the longer a business has been in operation, the less likely it is to perform less efficiently than start-ups So, the H2 hypothesis is formulated as follows:

H2: Firm age has an inverse relationship (-) with the firm's performance of the business

Third, high liquidity helps businesses stay afloat in difficult situations, because all business operations will be halted if the amount of cash in the treasury is not enough to meet urgent needs such as paying workers, paying suppliers of raw materials and other expenses On the other hand, liquidity is also a factor to evaluate the financial management process of the business itself Strict governance will help increase liquidity and businesses will operate more efficiently Therefore, the liquidity of enterprises plays a very important role and has a positive influence on the performance of enterprises The author proposes the following hypothesis:

H3: Liquidity is correlated (+) with firm's performance

Fourth, based on the results of previous studies mentioned in the overview, the leverage ratio (which represents the capital structure of the business) can have a negative or positive impact on firm's performance This depends on the capital structure of the enterprise, its industry and business field of operation According to trade-off theory, at low debt ratios, capital structure can positively impact efficiency; If the debt ratio is high, the capital structure has the opposite effect Most studies in developing countries show that financial leverage negatively affects firm's performance, Vietnam is no exception At the same time, during the study period, from 2011 to 2016, the domestic economy with many macro fluctuations, many enterprises had huge fluctuations in debt ratio Therefore, the author proposes the H4 hypothesis as follows:

H4: Financial leverage has a negative impact (-) on firm's performance Fifth, based on previous studies on the correlation between the annual growth rate and firm's performance of the enterprise, the vast majority of people assume that the growth rate is in the same direction as the firm's performance of the enterprise Therefore, the author proposes the H5 hypothesis as follows:

H5: Annual revenue growth rate positively impacts (+) firm's performance Sixth, based on previous research articles on the correlation between annual asset turnover and firm's performance of the enterprise, it is determined that the number of asset turnovers in the same direction as the firm's performance of the

27 enterprise, that is, the more turnovers, the higher the firm's performance Therefore, the author proposes the H6 hypothesis as follows:

H6: Annual asset turnover positively impacts (+) firm's performance

RESEARCH METHODS AND MODELS

Research Process

The research process begins with the identification of the problem and research objectives and ends with the presentation of the final research results report The research process is described as a specific flowchart as follows:

Research methodology

To see the impact of these factors on firm's performance, the thesis uses quantitative research methods with the support of Stata 13 software and Microsoft

Excel software Specifically, author will use the Multiple Regression Model for analysis The sequence of data processing is carried out as follows: univariate analysis includes descriptive statistics, correlation analysis, and multivariate analysis

How to measure variables Determine the data that needs to be collected Collect necessary data

29 includes data processing model selection, regression equation suitability assessment, explanatory variable selection The steps are as follows:

After collecting data through Excel and Stata software, the author performed descriptive statistical analysis using tables to describe the collected data using basic data characteristics This analysis provided insights into the properties and basic statistical indicators of the data, including maximum value, minimum value, mean value, and standard deviation The analysis helped the author generalize the production and business situation and identify common features in the operation of manufacturing enterprises listed on the Vietnam stock market.

Correlation analysis is the process of setting up a correlation coefficient matrix between pairs of variables and considering pair correlation coefficients, between independent and dependent variables, and between independent variables with each other Correlation coefficients allow to find the correlation relationships between pairs of variables as well as indicate the strength of the linear correlation between these pairs of variables Typically, the correlation coefficient has values ranging from [-1.1] If the correlation coefficient has a value of 0, it means that the two variables have no relation to each other, conversely, the greater the absolute value if the correlation coefficient, the stronger the correlation between the two variables

However, too high a pair correlation coefficient between two variables can occur linear multi-additive If the correlation coefficient exceeds the value from the interval (-0.8,0.8), the regression equation is likely to have a serious linear multi- additive problem Therefore, in the analysis applied to the thesis, if the pair

30 correlation coefficient between 2 independent variables in the regression equation goes beyond the interval (-0.8,0.8), the author will remove a less important variable from the model

In the process of selecting data methods, with Unblanced - Panel data, commonly used to estimate regression models include the following methods: the first is regression according to the aggregation method i.e., the Pooled OLS model, the second is the Fixed Effects Model, FEM) and the third is the Random Effects Model

(REM) Each method has the following advantages and disadvantages:

- Pooled Ordinary Least Squares method (Pooled OLS): The pooling method is carried out under the assumption of a regression model in which all coefficients remain constant over time and space, that is, ignoring the spatial and temporal dimensions of the data This also means conducting conventional OLS regression estimates and stacking the observations of each cross-unit, i.e., considering the effect of each observation and the data is the same, with no difference Therefore, a disadvantage of the regressed model using this method is the possible distortion of the relationship between the dependent variable and the independent variables and the occurrence of positive self-correlation The fixed impact assessment model and the random impact assessment model will help overcome the above phenomenon

- Fixed Effects Model (Fixed Effects Model, FEM): The fixed impact assessment (FEM) model is a way to help researchers solve the above limitations of the pooled method FEM regression model estimation is done with the assumption that each observation can have its own characteristics, so the original level in the FEM regression model is allowed to vary between observations To look at different strain coefficients, the researcher can use dummy variables in the model The FEM regression model with pseudo-variables is called the Least Square Dummy Variable

(LSDV) model The greatest advantage of the FEM estimation model is that it is suitable in situations where the origin factor of each observation correlates with one

31 or more other independent variables Therefore, the FEM estimation model helps researchers solve the limitations of the pooling method mentioned above However, FEM also has disadvantages

- Random Effects Model (Random Effects Model, REM): As shown above, the FEM estimation model has advantages over the pooled OLS method However, FEM causes the estimation model to lose many degrees of freedom, especially in the case of data with a large number of observations Another solution to the drawback of pooled OLS is the random impact assessment (REM) model The REM model is made based on the assumption that the observations in the study sample are randomly selected from a larger population, i.e., random errors and independent variables do not correlate with each other Therefore, when analyzing table data, researchers need to make a choice between FEM and REM estimation methods to give unbiased estimation results

In this study, author used a sample of unbalanced tabular observational data Therefore, author will estimate the regression model with FEM and REM estimation methods, then will perform the necessary tests to select the most suitable model and interpret the research results To determine which research model is suitable, author use the Hausman test to determine whether the FEM or REM model is suitable for research

At the same time, besides using correlation analysis, author calculate VIF coefficients to detect multi-linear phenomena between variables In addition, in regression models using tabular data, self-correlation may occur, and variance changes will distort estimates Therefore, author use the Woolridge test to check for autocorrelation and the Wald test or the Breusch and Pagan Lagrangian

To assess the presence of variance errors, the multiplier test is employed If violations are identified, the Generalized Least Squares (GLS) method is utilized to rectify them GLS boasts significant advantages over the traditional OLS method, making it a preferred choice for addressing variance errors.

Research Model

Since the concept of firm’s performance, academics and business executives have been looking for ways to measure firm’s performance accurately However, finding a common measure for the firm’s performance of the enterprise allows comparing the real firm’s performance of the enterprise without consistency, there are differences between periods and concepts of each researcher Different approaches to PM have led to different definitions of it, and there is little consensus on its key factors and characteristics (Dumond, 1994) A firm’s performance measurement system is a set of fast, accurate measurement measures, one of them is financial measures Gimbert et al (2010)

In this thesis, in order to identify the factors affecting the firm’s performance of manufacturing enterprises listed on the Vietnamese stock market, author choose to measure firm’s performance in the direction of the target approach, firm’s performance is understood as financial performance Because for listed businesses, the goal of profit is the top goal in the operation of the business The rate of return on equity (ROE) reflecting capital efficiency is the ratio between after-tax profit and equity of the enterprise This indicator reflects how much profit is generated for every single dollar of capital or in other words, the ability to generate a profit of one dollar of equity Return on total assets (ROA) is also known as the indicator of return on total assets This ratio is the financial ratio used to measure the profitability per dollar of assets of a business If this ratio is greater than 0, then it means that the business operates profitably The higher this ratio is, the more efficient the business is So, ROA and ROE are chosen for dependent variables and shown as firm performance Zeitun and Tian (2007) conducted a study on the factors affecting firm’s performance and the market value of firms with research data collected from 167 companies listed on the Amman-Jordan Stock Exchange covering 16 different business sectors in the non-financial sector between 1989 and

2003 The dependent variable "firm’s performance of the enterprise" is measured by the ratio of "return to total assets – ROA", independent variables include: capital structure (financial leverage ratio), enterprise size (assets, revenue), business risk,

33 income tax, fixed asset ratio, political crises, business lines and financial leverage (total liabilities to total assets, total liabilities to total equity, long-term liabilities to total assets, current liabilities to total assets and total liabilities to total equity) From there, the study shows that the firm’s performance of dependent enterprises is evaluated largely on fixed financial events, thereby leading to the conclusion that enterprises with a high proportion of fixed assets have low firm’s performance of enterprises because enterprises invest too much in fixed assets without fully improving the firm's performance

Pouraghajan and Malekian (2012) studied the impact of capital structure on firm’s performance through 400 firms in 12 industries listed on the Tehran Stock Exchange between 2006 and 2010 Using qualitative research methods combined with quantitative methods, the author identifies dependent variables that represent firm’s performance of the business: ROA and ROE The independent variables are debt ratio, asset turnover, enterprise size (assets), tangible asset ratio, enterprise age, and growth rate The study results show a significant negative relationship between debt ratio and firm’s performance Asset turnover, business size, tangible asset structure, and growth rate have a positive and statistically significant relationship with the firm's performance (ROA and ROE) Quang Bui et al (2020) has systematically studied the theoretical basis of firm’s performance of enterprises such as the definition of firm’s performance of the enterprise, influencing factors, criteria for evaluating firm’s performance of the enterprise; from this comes the conclusion that the two variables AGE, and Inflation have no impact on profitability (based on ROA analysis) Hau Nguyen et al (2021) measured factors affecting the firm’s performance of F&B businesses listed on HNX from 2015 to 2015 research the impact of internal factors with the following observations: variables such as the ratio of current liabilities to total liabilities and total assets have an adverse effect on ROA and ROE; the ratio of total assets has an adverse effect on ROA; variables such as total asset growth of the growth factor positively impact ROA and ROE Ho Xuan Thuy et al (2020) Using the method of processing table data, the FEM model

34 studied on 180 joint stock companies listed on HNX in the period of 2013-2017 has shown that there are 4 factors affecting financial performance: CIT, company growth, liquidity and company size and the return on assets (ROA) variable was used to measure the financial performance of companies Based on the above theory and summary studies, the author proposes to build a research model with one dependent variable and several independent variables, specifically as follows:

- Dependent variable: firm's performance is evaluated through ROA, ROE indicators

- Independent variables: factors affecting the firm's performance of the business included in the model include size, firm age of the business, financial leverage, liquidity, annual growth rate, annual asset turnover

The dependent variables are calculated in the following manner:

ROA Return on total assets 𝑅𝑂𝐴 (%) =𝑃𝑟𝑜𝑓𝑖𝑡 𝑎𝑓𝑡𝑒𝑟 𝑡𝑎𝑥

From the overview of theoretical basis and empirical studies, firm's performance of the enterprise will be influenced by the following factors, and this is also an independent variable in the research model: firm size (SIZE), firm age (AGE), financial leverage (LEV), liquidity (LIQ)

The independent variables are calculated in the following manner:

SIZE Firm Size SIZE = Natural logarithm of book value of total assets

AGE Firm Age AGE = From listing year to present time LIQ Liquidity

The dependent variable "firm’s performance" is measured by the ratio of "return to total assets – ROA" with independent variables include: capital structure

(financial leverage ratio), enterprise size (assets, revenue), business risk, income tax, fixed asset ratio, political crises, business lines and financial leverage (total liabilities to total assets, total liabilities to total equity, long-term liabilities to total assets, short-term liabilities to total assets and total liabilities to total equity)

Multiple regression models have been employed to examine the impact of various factors on firm performance Pouraghajan and Malekian (2012) utilized the Pearson correlation and regression estimation to analyze the influence of size and debt Hau Nguyen et al (2021) applied the ordinary least squares (OLS) regression method to demonstrate the effects of internal factors Their findings suggest that the ratio of current liabilities to total liabilities and total assets negatively impact ROA and ROE, while total asset growth has a positive impact These prior studies inform the development of two multivariate regression models proposed by the author.

ROAi, t = β0+ β1*SIZEi, t + β2*AGEi, t + β3*LEVi, t + β4*LIQi, t + β5 *ASTOVi, t + β6*GROWTHi, t + εi, t (1)

ROEi, t = β0+ β1*SIZEi, t + β2*AGEi, t + β3*LEVi, t + β4*LIQi, t + β5 *ASTOVi, t + β6*GROWTHi, t + εi, t (2)

- ROA, ROE, SALE, AGE, LEV, LIQ, ASTOV, GROWTH: are the dependent and independent variables listed respectively in (Table 3.1) and (Table 3.2)

- i: is the i th year of the factor

- ε: is the random error of the model

Table 0.3 Summary of research hypotheses and sign predictions

Expectation sign (Correlation with dependent variable)

Source: Author summarizes and calculate

Research data and data collection sources

The sample includes manufacturing enterprises listed on the Vietnamese stock market, represented by the Ho Chi Minh City Stock Exchange and Hanoi Stock Exchange in the period 2015-2022 The research data used is collected in Vietstock

In addition, data is also checked and supplemented from other published information such as corporate websites as well as annual reports and management reports

According to statistics, in the period 2015-2022, there are 206 listed enterprises in the manufacturing industry However, there were also newly listed or delisted businesses during the study period, so after re-surveying the data, the study sample included 160 businesses with 1272 observations between 2015 and 2022 Therefore, the tabular data used in the study is unbalanced tabular data (table data is unbalanced when the cross units in the table do not have the same number of observations over time)

The data source used for the study is expected to be derived from the annual financial statements from annual financial reports of listed companies in the study sample

RESEARCH RESULTS

Current situation of firm's performance of manufacturing enterprises in

The manufacturing sector accounts for about 1/3 of Vietnam's GDP and 85% of Vietnam's merchandise exports The application of advanced technologies through the concept of Industry 4.0 has significantly enhanced the competitiveness of

Vietnam's manufacturing sector boasts five prominent players: Vinamilk, Sabeco, Hoa Phat, Masan, and Bien Hoa These established corporations have carved out significant market shares in Vietnam, representing both the manufacturing industry and Vietnamese enterprises in the competitive landscape against imported products Their longevity has allowed them to build strong reputations, brands, and a loyal customer base within the Vietnamese market.

The common characteristic of manufacturing enterprises is that their names, brands and products are associated with activities in daily activities and serving in labor and production business Products of manufacturing enterprises are extremely diverse and rich, including dairy products, alcoholic and non-alcoholic beverage products, sugarcane products, vegetables, cashews, rice, electronic products, garments, furniture products, daily items, etc According to published data compiled by Vietstock, in the period 2015-2022, the number of listed enterprises operating in the manufacturing industry in Vietnam stock market in both Ho Chi Minh City and Hanoi Stock Exchanges is 160 enterprises Most businesses have a long-standing firm age and a listing period of more than 8 years The list of manufacturing enterprises in the research sample is presented by the participant in Appendix 1

Manufacturing enterprises in Vietnam are relatively larger than other industries in terms of total assets Because manufacturing enterprises often require large

39 facilities, accompanied by a large amount of labor, a high level of investment in assets, especially fixed assets Typically, businesses that invest well and develop technology will bring firm's performance, investment efficiency and even increase business value higher than other businesses Graph 4.1 below presents data on total assets of listed enterprises and the average size of total assets of manufacturing enterprises over the years from 2015 to 2022 Data in the table shows that the scale of the manufacturing industry has grown steadily over the years in terms of the average total asset size of each enterprise While in 2015, the manufacturing industry of listed enterprises had a total asset value of about VND 473 trillion, by

2022 this figure has increased with the total asset value reaching about VND 1,024 trillion The same number of businesses, but over eight years, the value of total assets increased by about 1.16 times

Figure 0.1 Firm size of manufacturing enterprises

To analyze the firm's performance of manufacturing enterprises by operating size, author used the average annual total asset value to divide the study sample into two groups per year: small size enterprises and large size enterprises

To assess firm performance based on size, enterprises are divided into two groups: small and large Small enterprises comprise those with total assets below the average for the year, while large enterprises have total assets greater than or equal to the average This categorization allows for the calculation of average return on assets (ROA) and return on equity (ROE) for each group over the 2015-20XX period.

2022 The calculated figures are illustrated through Figure 4.2 and Figure 4.3 as shown below

Figure 0.2 Firm’s performance ROA by size

Figure 0.3 Firm’s performance ROE by size

The figures presented in Figure 4.2 show that the firm's performance (ROA) of large-scale businesses was significantly higher than that of small size businesses over the years from 2015 to 2022 In particular, in 2018 and 2022, the difference in firm's performance (ROA) of small and large size groups is significant, while the small size group only achieved an average ROA of 5.58% and 5.05%, the large size group achieved an average ROA of 7.76% and 6.96%, equivalent to about 30% more value This shows that the correlation is very clear between the scale and firm's performance of manufacturing enterprises, the larger the enterprise, the higher the firm's performance In addition, in terms of the number of listed enterprises in each group, Figure 4.2 also shows that the difference in operating scale between listed enterprises in the manufacturing industry is not much

Manufacturing enterprises exhibit significant diversity in firm size, with small enterprises averaging approximately 70 per year and large enterprises averaging around 90 per year This variation suggests a lack of intense competition within the industry, as companies can operate at different scales based on their product offerings.

Figure 4.3 presents the firm's performance ROE of manufacturing enterprises at size as well as similar results in terms of covariate correlation The larger the business, the higher the firm's performance ROE The difference in firm's performance ROE between the small and large size groups illustrated in Figure 4.3 is very high over the years In particular, in 2018 and 2021, the large size group achieved a higher ROE of 43.56% and 46.87% respectively compared to the small size group; the small size group achieved an ROE of 12.12% and 9.45% respectively while the large size group achieved an ROE of 43.56% and 46.87% respectively 17.4% and 13.88% This data shows the level of competition in the manufacturing industry, when enterprises with the advantage of operating size have higher profit margins than small size enterprises

In summary, by analyzing the data illustrated in Figure 4.2 and Figure 4.3 above, author come to the conclusion about the covariate relationship between scale and firm's performance of manufacturing enterprises listed on the Vietnamese stock market

Author analyzes the listing time of manufacturing enterprises through grouping as follows:

- Group 1: Listing period less than 10 years ()

The figures for the number of listed companies and the average firm's performance of each group are presented in Figure 4.4 and Figure 4.5 below

Figure 0.4 Firm’s performance ROA by age

Figure 0.5 Firm’s performance ROE by age

Author’s calculation Figure 4.4 illustrates the number of listed companies and firm's performance

ROA per group The figures in Figure 4.4 show that, between 2015 and 2021,

44 businesses with a listing period of 10 years or less had a lower firm's performance ROA than those listed for 10 years or more In 2022 alone, businesses with a listing period of 10 years or less have a higher firm's performance ROA Therefore, the participant came to the conclusion that the listing period has a covariate correlation with the firm's performance ROA of manufacturing enterprises in the period 2015-

In contrast, Figure 4.5 shows that, between 2017 and 2022, businesses with a listing period of 10 years or less had a higher ROE than listed businesses of 10 years or more Between 2015 and 2016, companies with listing periods of 10 years or less had lower ROE firm's performance In summary, through the above analysis from Figure 4.4 and Figure 4.5, author come to the conclusion that listing time is inversely correlated compared to firm's performance.

In general, the financial leverage of manufacturing enterprises is relatively large In other words, the capital structure of manufacturing enterprises is tilted towards debt use However, financial leverage has an inverse correlation with the firm's performance Author came to the above conclusion after dividing the businesses in the research sample into 2 groups and conducting analysis

Specifically, the grouping is as follows:

- Group 1: low financial leverage (leverage ratio P%)

The figures for the number of listed companies and firm's performance of each group are presented in Figure 4.6 and Figure 4.7 below:

Figure 0.6 Firm’s performance ROA by financial leverage

Figure 0.7 Firm’s performance ROE by financial leverage

First, Figure 4.6 shows that the firm's performance ROA of the Low Leverage Group is always significantly higher than that of the Firm's performance ROA of the

High Leverage Group Specifically, the Low Leverage Group has an average ROA of about 9% per year in 2015-2022, while this metric in the High Leverage Group is always lower than 6% per year, in 2022 the average ROA of the High Leverage Group is only 1.71% This shows that although manufacturing enterprises borrow more and more debts, they have not fully used the performance of assets in enterprises

Authentic verification of factors affecting firm's performance of

From the research data, the author conducts statistics describing firm's performance variables and factors affecting firm's performance of manufacturing companies listed on the Vietnam stock market in the period of 2015 – 2022 The descriptive statistics are presented in the following table in left-to-right presentation order, including the following content columns: variable name, number of observations, mean, standard deviation, minimum value, and maximum value

Variable Obs Mean Std Dev Min Max

Statistical results show that up to 2 variables have a larger standard deviation value than the mean, implying that these variables have a non-standard distribution and a fairly high deviation As follows:

- AGE: The firm age of the business is not long and uneven On average, manufacturing enterprises have a firm age of about 9 – 10 years In which, for enterprises with the shortest firm age is 1 year, and enterprises with the longest firm age is 23 years This shows the fierce competition in the manufacturing industry in Vietnam, when very few businesses survive with a long firm age In addition, the manufacturing industry is an industry with good potential and profit when continuously attracting more investment every year, there are many new enterprises entering the manufacturing industry every year

- GROWTH: Manufacturing enterprises have good resilience with good development capacity, this is reflected in enterprises in the manufacturing industry maintaining an average growth rate of 11.12% / year However, there is a big disparity between enterprises in the industry, when for businesses that are maintaining their dominance, and newly established, maintaining growth rates of up to 1100% / year, but those that lose competitiveness do not even maintain their operations, the revenue growth rate drops sharply and even the growth rate is negative (growth rate: -100%/year)

- Liquidity (LIQ): The liquidity of enterprises is quite good, on average reaching 2.2 times with a standard deviation of 2.6 times, the lothe thesisst value is 0.15 times, the largest value is 33.15 times Although the capital structure shows a high debt ratio, the liquidity of manufacturing enterprises shows a positive signal about the ability to pay debts and the initiative in the operation of enterprises to difficult situations that may arise However, too high liquidity ratio (some businesses up to more than 33 times) is not necessarily a good manifestation due to

56 too much investment in short-term assets, both limiting the profitability of enterprises and using capital (especially borrowed capital) inefficiently

- Financial leverage (LEV): Enterprises maintain a fairly good level of financial leverage, averaging 0.46 times with a standard deviation of 0.19 times, the lothe thesisst value is 0.02 times, the largest value is 0.97 times However, the average capital structure shows that the enterprise still maintains a debt ratio lower than 50% of the total operating capital of the enterprise and achieves maximum capital efficiency, avoiding waste and incurring unnecessary costs However, this is uneven, the debt ratio of some enterprises is very low (2%), there are enterprises with very high debt ratio (97%) is not necessarily a good manifestation when businesses become too dependent on borrowed capital or, unable to utilize borrowed capital effectively

Author conducts an assessment of the correlation between pairs of variables in the model The results of the correlation analysis are presented in the following statistical table

Table 0.2 Correlation coefficient matrix between variables

ROA ROE SIZE LIQ LEV AGE GROWTH ASTOV

Through the table of correlation analysis data shows The correlation between all variables has values in the range (-0.5; 0.5), so there is no basis to show that there is a multi-linear phenomenon with the survey model In particular, for the survey of firm's performance ROA and ROE, the variables of LEV and AGE are negatively correlated For the rest of the variables, the variable SIZE represents a positive correlation The GROWTH variable is positively correlated with ROE and negatively correlated with ROA The LIQ variable is positively correlated with ROA and negatively correlated with ROE

For models with ROA dependent variables:

To assess the linear multi-additivity of the model, a linear multi-additive test was conducted The calculated Variance Inflation Factor (VIF) coefficients were found to be minimal (both < 2), indicating the absence of evidence suggesting a linear multi-additive phenomenon within the model.

Table 0.3 The result of calculating the VIF coefficient for independent variables

Next, author undertake the Hausman test to choose between FEM and REM research methods The results show P-value < 0.05, so the FEM model is the most suitable model when considering the factors affecting the firm's performance ROA of the business in the study sample

ROA Model P-value = 0.0000 The FEM model is more suitable

ROE Model P-value = 0.0002 The FEM model is more suitable

Then, for common errors in the tabular data model, author use the Wald test to check for self-correlation and the Breusch and Pagan Lagrangian Multiplier test to check for variance change If a change in correlation or variance is detected in the study model, the author uses the GLS regression model (robust parameter for standard error) to correct the error with the selected model The results showed that the data in the study model existed variable variance and there was no self- correlation Therefore, author use the GLS regression model to overcome the variance of change in the study model

For models with ROE-dependent variables:

Author performs the same steps as above for the model with the dependent variable ROE

The results show that the Hausman test gives a p-value result of < 0.05, so choose a more suitable FEM model After that, the trainee checks the variance of change and self-correlation in the model, the test results show that there is the phenomenon of variance change and self-correlation Therefore, author use the GLS regression model to overcome the phenomenon of variance change and self- correlation in the study model with the dependent variable ROE

In summary, through the tests, author will proceed to build FEM models for both ROA and ROE dependent variables

4.2.4 Results of regression model estimation

4.2.4.1 The estimation result for the model with the dependent variable is

Table 0.5 Estimation results for the model with the dependent variable ROA

Dependent variable: ROA Estimation coefficient

Obs 1272 1272 t statistics in second column

Author run a linear regression model according to the FEM method for a model with an ROA dependent variable The regression model estimation results extracted from the above Stata software show that the independent variables (LEV), (SIZE) and (AGE) are statistically significant at 1% while the remaining variables were (LIQ), (GROWTH) and (ASTOV) which were not statistically significant Statistically significant variables have a negative correlation coefficient, and a positive correlation, which indicates the inverse impact relationship of liquidity variables on the firm's performance of the business and the mutually inverse relationship of growth variables and asset turnover The scale variable is positively correlated, which shows that firm size has a positive impact on firm's performance of manufacturing enterprises The variable leverage and firm age are negatively correlated, which indicates that leverage and firm age have an adverse effect on the firm's performance of the business

4.2.4.2 The estimation result for the model with the dependent variable is ROE

Table 0.6 Estimation results for model with dependent variable ROE

Dependent variable: ROE Estimation coefficient

Obs 1272 1272 t statistics in second column

The regression model estimation results showed that variables (LIQ),

(GROWTH), and (ASTOV) were not statistically significant at 10% in this study This suggests that liquidity, growth, and asset turnover factors do not affect firm's performance ROE.

The variable (LEV) has a negative correlation coefficient (-0.175) and is statistically significant at 1% The results show that financial leverage has an inverse effect on the firm's performance of manufacturing enterprises

The variable AGE exhibits a negative correlation coefficient (-0.012), indicating an inverse relationship with the firm's performance This correlation is statistically significant at a confidence level of 1%, suggesting that the listing time has a significant negative effect on the performance of manufacturing enterprises.

The variable (SIZE) also showed a positive correlation with the correlation coefficient (0.065) at a statistically significant level of 5% This shows Scale has a covariate correlation with firm's performance of manufacturing enterprises

In this study, author perform an analysis of two linear regression models to identify factors affecting the firm's performance of manufacturing enterprises listed on the

Vietnamese stock market in the period 2015-2022, one with ROA dependent variable and one with ROE dependent variable, both models adopt the same FEM methodology

Although different in how dependent variables are defined to measure firm's performance, the estimation results of both regression models are quite consistent The estimated results of both models compared to the original research hypothesis are presented in the statistical table below:

Table 0.7 Research Results vs Expectations

Sign prediction (correlation with dependent variable)

Author’s calculation 4.2.5.1 Analysis of factors that have no effect on firm's performance

Liquidity (LIQ): While previous studies have found either a covariate correlation between the size and firm's performance of the business as research by (Astutik et al, Hutajulu 2019), (Thaibah et al, 2020), (Agustin Ekadjaja, 2021), (Xuan Thuy et al., 2020), (Zahid Bashir, 2013), (Chytis, Arnis, and Tasios, 2018) However, in this study, author have not found evidence of a relationship between scale and firm's

62 performance of manufacturing enterprises This also contradicts the results of previous statistical analysis It can be explained that in the statistical analysis, only a very few enterprises belong to the group with low liquidity, the rest of the enterprises in the manufacturing industry are in the group with high liquidity The research results can be explained to the Vietnamese market because manufacturing enterprises have not made the most of short-term asset resources, liquidity and debt control Most manufacturing enterprises in Vietnam are borrowing more and more debt (the average financial leverage ratio is up to 46%), but the efficiency of using capital is not high, the ratio of cash holdings and short-term assets is large, manifested in high liquidity ratio Therefore The increase in liquidity has not shown an impact on firm's performance

CONCLUSIONS

Conclusions

5.1.1 Results achieved in theoretical research

- In terms of theoretical basis, the thesis synthesized theoretical and empirical studies on the factors that affect firm's performance In addition, the thesis also summarizes the business situation and analyzes the characteristics of manufacturing enterprises listed on the Vietnamese stock market in the period of

Empirical research has identified factors affecting the performance of manufacturing enterprises in Vietnam Through qualitative and quantitative analysis of 160 representative enterprises, the study assessed industry conditions and examined the influence of financial leverage, enterprise size, and operating time These findings contribute to existing empirical studies and provide valuable insights for future research on manufacturing industries in Vietnam.

5.1.2 Results achieved in terms of practical significance

The research topic of the thesis is "Factors affecting firm's performance: A case study of listed manufacturing companies in Vietnam."

The research results of the thesis have met the initial research objective of identifying factors affecting the firm's performance of enterprises listed in the manufacturing industry on the Vietnamese stock market Specifically, author conducted qualitative and quantitative analyses with a sample of research data including 160 listed companies corresponding to 1272 observations for the period

2015-2022 The results of quantitative research show that the main factors with positive dynamics are the size of the business The negative drivers are financial leverage and uptime

For manufacturing enterprises in particular, the research results are extremely practical in the current context When competitive pressure in the industry comes not only from domestic enterprises but also from foreign competitors entering the domestic market through bilateral and multilateral trade cooperation agreements.

Contribution

This research provides valuable insights into the intricate relationship between financial factors and a manufacturing company's overall performance Through empirical evidence and rigorous analysis, it contributes to the existing body of knowledge about the affecting of firm’s size, firm’s age and leverage on firm’s performance within the manufacturing sector By conducting empirical research and employing rigorous analytical methodologies, this thesis sheds light on the positive connection between size and firm’s performance and negative connection between age, leverage and firm’s performance of manufacturing sector Manufacturing firms are intricately entwined within complex financial landscapes, where the astute management of financial leverage stands as a linchpin for optimizing performance The research analysis amalgamates empirical research findings with theoretical frameworks to furnish actionable insights tailored for manufacturing firms striving to bolster their performance through adept management of financial leverage

Drawing from an exhaustive review of academic literature, we elucidate the nuanced interplay between financial leverage and firm performance within the manufacturing milieu Leveraging empirical methodologies such as regression analysis, we dissect the ramifications of financial leverage on performance metrics, illuminating the intricacies of this relationship Our findings underscore the imperative for manufacturing firms to embrace a balanced approach to financial leverage, fine-tuning their capital structure to ameliorate risk while accentuating returns Crucially, strategic endeavors such as debt restructuring, refinancing, and

67 capital allocation optimization surface as pivotal pathways for augmenting performance in the face of financial leverage challenges Furthermore, we probe into the role of financial risk management frameworks, encompassing hedging strategies and liquidity management, in assuaging the deleterious impacts of excessive leverage on firm performance By synthesizing these insights, we proffer manufacturing firms with actionable strategies to adeptly navigate the intricacies of financial leverage, thereby fostering sustainable enhancements in performance and fortifying long-term competitiveness within dynamic market milieus

Businesses striving to wield financial leverage judiciously while maintaining leverage below the 50% threshold to yield optimal outcomes must adopt a multifaceted strategic approach to debt management, intertwining debt and equity financing in line with overarching growth aspirations Continuous vigilance and assessment of leverage ratios are imperative, necessitating a robust risk evaluation regimen to pinpoint and preemptively mitigate risks inherent in leveraging

Employing stress tests and scenario analyses, businesses can fortify their capital structures against adversities, adjusting leverage as warranted to bolster financial resilience Thorough cost-benefit analyses for prospective investment ventures serve as a compass in determining the most advantageous financing blend, considering intricacies such as capital costs, tax implications, and risk-adjusted returns

Transparent, open communication channels with stakeholders are pivotal, fostering a climate of trust and assurance Furthermore, an overarching strategic vision, oriented towards the long term, ensures that financing strategies are crafted to underpin sustained growth and profitability, safeguarding against short-sighted, opportunistic leveraging practices that may jeopardize long-term stability

Conversely, synthesizing empirical research findings with theoretical underpinnings, our analysis delves into actionable insights tailored for manufacturing firms endeavoring to harness the positive impetus of business size on performance leveraging empirical The research findings underscore the strategic significance of organic growth initiatives, strategic mergers and acquisitions, and

68 symbiotic partnerships as conduits for augmenting business size and unlocking economies of scale Moreover, this research explored the catalytic role of market positioning strategies, inclusive of niche differentiation and product diversification, in harnessing business size to propound competitive advantages and amplify performance By synthesizing these insights, we equip manufacturing firms with actionable strategies to deftly navigate the complexities of scale expansion, thereby fostering sustained growth and fortifying competitiveness within dynamic market realms Furthermore, our findings underscore the criticality of strategic rejuvenation initiatives aimed at reinvigorating mature manufacturing firms and unearthing latent potential Strategic realignment endeavors, spanning product portfolio optimization and market resegmentation, surface as indispensable strategies for adapting to the fluidity of market dynamics and rekindling growth trajectories Additionally, organizational restructuring initiatives, such as operational streamlining and the cultivation of an innovative ethos, emerge as pivotal levers for bolstering agility and responsiveness in mature firms Furthermore, strategic alliances and ecosystem partnerships furnish avenues for accessing nascent markets, cutting-edge technologies, and invaluable resources, thereby fostering sustained growth and competitiveness Moreover, proactive talent management strategies, inclusive of knowledge transfer programs and leadership succession planning, stand as imperative for preserving institutional knowledge and fostering a culture of continual learning and innovation By synthesizing these insights, we furnish a holistic framework for enhancing performance in mature manufacturing firms, endowing them with actionable strategies to deftly traverse the intricacies of organizational maturity and perpetuate sustained growth and competitiveness within dynamic market environs

This thesis extends the current understanding of financial components' impact on manufacturing firms' operational performance By leveraging empirical data and rigorous analysis, it explores the complex interactions among these components The findings expand the existing body of knowledge by identifying novel relationships and providing insights into how financial strategies influence firm operations.

This research provides invaluable insights that directly benefit industry professionals and scholars Its practical implications guide strategic decision-making within firms, enhancing operational efficiency and financial performance By leveraging these insights, organizations can optimize their growth and competitiveness, solidifying their market position.

Limitation

One potential limitation of a financial master's thesis investigating factors influencing firm performance in manufacturing could be related to the scope and generalizability of the findings An inherent limitation that might arise within a financial master's thesis exploring the determinants impacting manufacturing firm performance lies in the scope and the expendability of its findings As the research is confined within a specific context or a delimited timeframe, it might encounter challenges in offering universally applicable conclusions or broader generalizations across different industrial settings or periods This limited scope might hinder the complete representation of the multifaceted dynamics that could influence firm performance, potentially restricting the transferability of its outcomes to diverse industrial landscapes or varying economic circumstances

This thesis focuses on manufacturing sector in Vietnam, and limited sample size of manufacturing firm listed on HNX and HOSE, which could impact the broader applicability of its conclusions

Moreover, the analysis might consider only a specific set of financial factors such as ROA, ROE, leverage, etc…influencing firm performance, potentially overlooking other critical variables that could play significant roles The exclusion of certain influential factors might limit the comprehensive understanding of how diverse financial elements collectively impact a company's performance within the manufacturing industry

Additionally, the thesis's reliance on historical data from 2015 to 2022 for the analysis might introduce limitations in capturing real-time market dynamics or

70 changes in financial policies and economic conditions This could affect the relevance and timeliness of the conclusions drawn from the study

Furthermore, the complexity and interplay of various internal and external factors affecting firm performance in the manufacturing sector might pose challenges in establishing direct causal relationships between financial variables and performance metrics, leading to potential limitations in establishing concrete cause- effect relationships

Addressing and acknowledging these limitations would be essential in ensuring the appropriate interpretation and application of the thesis findings within the context of the manufacturing sector's financial landscape

Agustin Ekadjaja, A W (2021) Factors Affecting Firm’s performance in manufacturing companies in Indonesia Stock Exchange Jurnal Akuntansi,

Ali Abbas, Z B (2013) Determinants of Firm’s Financial Performance: An

Empirical Study on Textile Sector of Pakistan Business and Economic

Andreas, C S (2021) Effect of Liquidity, Asset Management, Leverage and

Dividend Policy on Firm Value at Food and Beverages Companies Listed on the Indonesia Stock Exchange Budapest International Research and Critics

Institute-Journal, 4(2), 2695-2701 doi:https://doi.org/10.33258/birci.v4i2.1977

Evangelos Chytis, S T (2018) Factors affecting Firm’s performance in periods of

Financial Crisis: Evidence from the listed on the Athens Stock Exchange Food Companies MIBES Transactions, 12(1), 29-36 Retrieved 2023

Gary S Hansen, B W (1989) Determinants of Firm’s performance: The Relative

Importance of Economic and Organizational Factors Strategic Management

Journal, 10(5), 399-411 Retrieved from http://links.jstor.org/sici?sici43-

Gaurav Mitra, V G (2023) Impact of macroeconomic Factors on firm’s performance: Empirical evidence from India Investment Management and

Gul, F A (1999) Growth opportunities, capital structure and dividend policies in

Japan Journal of Corporate Finance, 5(2), 141-168 doi:10.1016/S0929- 1199(99)00003-6

Hatem, B S (2014) Determinants of Firm’s performance: A Comparison of

European Countries International Journal of Economics and Finance, 6(10), 243-249 doi:10.5539/ijef.v6n10p243

Hồ Xuân Thủy, Đ L (2020) Các nhân tố ảnh hưởng đến hiệu quả tài chính của các công ty niêm yết trên sở giao dịch chứng khoán Hà Nội Tạp chí Phát triển

Khoa học và Công nghệ – Kinh tế-Luật và Quản lý, 4(3), 948-962 doi:10.32508/stdjelm.v4i3.663

Huy Quang Bui, D V (2020) Determinants influencing the profitability of listed food processing firms in Vietnam Growing Science Canada, Accounting(6), 441-452 doi:10.5267/j.ac.2020.4.015

Inder Sekhar Yadav, D P (2022) The nexus between firm size, growth and profitability: new panel data evidence from Asia–Pacific markets European

Journal of Management and Business Economics, 31(1), 115-140 doi:10.1108/EJMBE-03-2021-0077

Lihard Stevanus Lumapow, R A (2017) The Effect of Dividend Policy, Firm Size, and Productivity to The Firm Value Research Journal of Finance and

Maja Pervan, J V (2012) INFLUENCE OF FIRM SIZE ON ITS BUSINESS

SUCCESS Croatian Operational Research Review, 3, 213-223 Retrieved

Malik, H (2011) DETERMINANTS OF INSURANCE COMPANIES

PROFITABILITY: AN ANALYSIS OF INSURANCE SECTOR OF

PAKISTAN Academic Research International, 1(3), 315-321 Retrieved

2023, from http://www.savap.org.pk/journals/ARInt./Vol.1(3)/2011(1.3- 32)stop.pdf

Nawaiseh, M A (2020) The Effect of Firm's Age, Size and Growth on Its

Profitability: Evidence from Jordan European Journal of Business and

R Charles Moyer, V S (1997) Performance, capital structure and home country:

An analysis of Asian corporations Global Finance Journal, 8(1), 129-143 Retrieved 2023, from https://www.academia.edu/29518682/Performance_capital_structure_and_ho me_country_An_analysis_of_Asian_corporations

Rajshree Agarwal, M G (2002) Firm Age and Performance Firm and Product Life

Cycles and Firm Survival American Economic Review, 92(2), 184-190 doi:10.1257/000282802320189221

Siti Nurlaela, B M (2019) Asset Turnover, Capital Structure and Financial

Performance Consumption Industry Company in Indonesia Stock Exchange

International Journal of Economics and Financial Issues, 9(3), 297-301 doi:https://doi.org/10.32479/ijefi.8185

Tian, R Z (2007) Capital Structure and Corporate Performance: Evidence from

Jordan The Australasian Accounting Business & Finance Journal, 1(4), 40-

Tùng, H (2016) Nghiên cứu các nhân tố ảnh hưởng đến hiệu quả kinh doanh của các doanh nghiệp dầu khí ở Việt Nam Tạp Chí Khoa học Công nghệ Việt

Van Hau NGUYEN, T T (2021) Internal Factors Affecting Firm’s performance: A

Case Study in Vietnam Journal of Asian Finance, Economics and Business,

Vedy, H S (2023) Asset Turnover, Profitability, Capital Structure, and Firm Value in Coal Subsector in Indonesia Pontianak: FEB UNTAN Works Press

Zahid Bashir, A A (2013) Empirical Investigation of the Factors Affecting Firm’s

Performance: a Study Based on Food Sector of Pakistan International

SAMANM Journal of Finance and Accounting, 1(2), 11-23 doi:ISSN 2308-

Appendix 1: List of enterprises in the study sample

1 ALT Tan Binh Culture Joint Stock Company

2 BBS VICEM Packaging But Son JSC

3 BCC Bim Son Cement JSC

4 BLF Bac Lieu Fisheries Joint Stock Company

5 BPC Vicem Packaging Bim Son JSC

6 BTS Vicem But Son Cement JSC

7 BXH Hai Phong Cement Packing JSC

8 CAN Ha Long Canned Food Joint Stock Corporation

Yen Bai Joint Stock Forest Agricultural Products And Foodstuff Company

10 CET HTC Holding Joint Stock Company

11 CJC Central Area Electrical Mechanical JSC

12 CLH VVMI La Hien Cement Joint Stock Company

13 CPC Can Tho Pesticides Joint Stock Company

14 CTB Hai Duong Pump Manufacturing JSC

15 CTT Vinacomin - Machinery Joint Stock Company

16 DHP Hai Phong Electromechanical JSC

17 DNP DNP Holding Joint Stock Company

18 DPC Da Nang Plastic Joint Stock Company

19 DZM Dzi An Mechanoelectric JSC

20 GKM Khang Minh Group Joint Stock Company

21 GMX My Xuan Brick Tile Pottery And Construction JSC

22 HAD Ha Noi - Hai Duong Beer JSC

23 HCC Intimex - Hoa Cam Concrete JSC

24 HDA Dong A Paint Joint Stock Company

26 HOM Vicem Hoang Mai Cement JSC

28 HVT Viet Tri Chemical Joint Stock Company

29 INN Agriculture Printing & Packing Joint Stock Company

30 ITQ Thien Quang Group JSC

32 KTS Kon Tum Sugar Joint Stock Company

34 MCC High Grade Brick Tile Corporation

35 NAG Nagakawa Group Joint Stock Company

36 NET NET Detergent Joint Stock Company

37 NFC Ninh Binh Phosphate Fertilizer Joint Stock Company

38 NHC Nhi Hiep Brick-Tile Co-Operation

39 NSH Song Hong Aluminum Shalumi Group Joint Stock Company

40 NST Ngan Son Joint Stock Company

41 NTP Tien Phong Plastic Joint Stock Company

43 PCE Central PetroVietnam Fertilizer And Chemicals JSC

44 PDB Din Capital Investment Group JSC

46 PMP Dam Phu My Packaging Joint Stock Company

47 PMS Petroleum Mechanical Stock Company

48 QHD Viet Duc The thesislding Electrode Joint Stock Company

49 SAF Safoco Foodstuff Joint Stock Company

50 SDG Sadico Cantho Joint Stock Corporation

51 SDN Dong Nai Paint Corporation

52 SFN Sai Gon Fishing Net Joint Stock Company

53 SGC Sa Giang Import Export Corporation

54 SHE Son Ha Development Of Renewable Energy Joint Stock Company

55 SJ1 Hung Hau Agricultural Corporation

56 SLS Son La Sugar JSC

57 SSM Steel Structure Manufacture Joint Stock Company

58 STP Song Da Industry Trade JSC

59 TBX Thai Binh Cement Joint Stock Company

60 TDT TDT Investment and Development Joint Stock Company

62 THB Ha Noi - Thanh Hoa Beer Joint Stock Company

63 TKU Tung Kuang Industrial JSC

64 TNG TNG Investment and Trading JSC

65 TPP Tan Phu Viet Nam Joint Stock Company

66 TSB Tia Sang Battery Joint Stock Company

67 TTC Thanh Thanh Joint Stock Company

68 VBC Vinh Plastic & Bags JSC

70 VDL Lam Dong Foodstuffs JSC

71 VGS Vietnam Germany Steel Pipe JSC

72 VHL Viglacera Ha Long JSC

73 VIT Viglacera Tien Son Joint Stock Company

74 VTH Viet Thai Electric Cable Corporation

76 AAA An Phat Bioplastics JSC

77 AAM Mekong Fisheries Joint Stock Company

78 ABT Bentre Aquaproduct Import And Export JSC

79 ACC ACC Binh Duong Investment and Construction JSC

80 ACG An Cuong Wood - Working Joint Stock Company

81 ACL Cuu Long Fish Joint Stock Company

82 ADS DamSan Joint Stock Company

84 APH An Phat Holdings Joint Stock Company

86 BFC Binh Dien Fertilizer Joint Stock Company

87 BHN Hanoi Beer Alcohol And Beverage Joint Stock Corporation

88 BMP Binh Minh Plastics Joint Stock Company

89 CAV Vietnam Electric Cable Corporation

90 CLC Cat Loi Joint Stock Company

92 CSM The Southern Rubber Industry JSC

93 CSV South Basic Chemicals JSC

94 CVT CMC Joint Stock Company

96 DAT Travel Investment And Seafood Development Corporation

98 DBD Binh Dinh Pharmaceutical and Medical Equipment JSC

99 DCL Cuu Long Pharmaceutical JSC

100 DCM Petro Viet Nam Ca Mau Fertilizer JSC

101 DGC Duc Giang Chemicals Group JSC

102 DHC Dong Hai Joint Stock Company of Bentre

103 DHG DHG Pharmaceutical Joint Stock Company

104 DLG Duc Long Gia Lai Group JSC

105 DMC Domesco Medical Import Export Joint Stock Corporation

106 DPM Petrovietnam Fertilizer & Chemicals Corporation

107 DPR Dong Phu Rubber Joint Stock Company

108 DQC Dien Quang Group Joint Stock Company

109 DRC Danang Rubber Joint Stock Company

110 DTL Dai Thien Loc Corporation

112 FIT F.I.T Group Joint Stock Company

113 FMC Sao Ta Foods Joint Stock Company

115 GIL Binh Thanh Import - Export Production & Trade JSC

117 GVR Vietnam Rubber Group - Joint Stock Company

118 HII An Tien Industries JSC

119 HPG Hoa Phat Group JSC

120 HRC Hoa Binh Rubber Joint Stock Company

122 HT1 Vicem Ha Tien Cement Joint Stock Company

123 IDI I.D.I International Development & Investment Corporation

126 LSS Lam Son Sugar Joint Stock Corporation

127 MSH Song Hong Garment Joint Stock Company

129 NAF Nafoods Group Joint Stock Company

130 NHH HaNoi Plastics Joint Stock Company

131 NKG Nam Kim Steel Joint Stock Company

132 OPC OPC Pharmaceutical Joint Stock Company

133 PAC Dry Cell And Storage Battery Joint Stock Company

134 PAN The Pan Group Joint Stock Company

135 PHR Phuoc Hoa Rubber Joint Stock Company

136 PLP Pha Le Plastics Manufacturing and Technology Joint Stock Company

137 PNJ Phu Nhuan Jethe thesislry Joint Stock Company

139 PTB Phu Tai Joint Stock Company

140 RAL Rangdong Light Source And Vacuum Flask JSC

141 RDP Rang Dong Holding Plastic JSC

142 SAB Saigon Beer - Alcohol - Beverage Corporation

144 SBT Thanh Thanh Cong - Bien Hoa JSC

145 SFG The Southern Fertilizer JSC

146 SHI Son Ha International Corporation

147 SPM SPM Joint Stock Company

148 STK Century Synthetic Fiber Corporation

149 TCM Thanh Cong Textile Garment Investment Trading Joint Stock Company

151 TDP Thuan Duc Joint Stock Company

152 THG Tien Giang Investment And Construction JSC

153 TLG Thien Long Group Corporation