Dissertation submitted in partial fulfillment of the Requirement for the MSc in Finance FINANCE DISSERTATION ON THE INFLUENCE OF CORPORATE SOCIAL RESPONSIBILITY ON CAPITAL STRUCTURE I
INTRODUCTION
Capital structure refers to the combination of liabilities and equity that businesses use to finance their assets and operations Choosing the right capital structure is crucial for optimizing the value of owner assets during commercial production To achieve this, businesses must implement various strategies, with selecting an effective financial framework being essential for informed decision-making An optimal capital structure not only reduces capital costs but also enhances the overall value of the business.
Previous research has focused on the factors influencing capital structure, including profitability, company size, and tangible assets However, capital structure is flexible and varies based on individual company characteristics, industry context, and broader macroeconomic, cultural, and religious influences Therefore, it is essential to thoroughly investigate the factors affecting capital structure across different industries In Vietnam, numerous studies have targeted specific sectors, such as cement, manufacturing, tourism, construction, and minerals.
Le, 2014; Phan, 2016; Nguyen, 2017; Pham, 2017) Despite this, an explicit inquiry into the interplay between Corporate Social Responsibility (CSR) and corporate capital structure remains conspicuously absent
Social Responsibility explores strategies to balance economic growth with societal progress at both macro (international, regional, national, local) and micro (enterprise) levels Within this field, Corporate Social Responsibility (CSR) specifically examines what defines a socially responsible business and the necessary actions for companies to embody this commitment This area includes a wide range of practical applications and initiatives.
Corporate Social Responsibility (CSR) represents a continuous commitment by businesses to foster economic growth while enhancing the welfare of their employees, communities, and the environment This involves implementing ethical business practices, improving corporate governance, formulating social policies, and promoting fair trade and responsible investment strategies (Nguyen Hoang Tien, 2015).
Corporate Social Responsibility (CSR) has emerged as a significant organizational trend with extensive implications for practitioners, scholars, and society (Kot, 2014) Its roots can be traced back to community involvement initiatives that gained traction in developed countries during the 1960s and 70s This transformation was driven by changes in how companies understand their roles within different societal contexts (Herbuś & Ślusarczyk).
Corporate Social Responsibility (CSR) has become a widely recognized managerial concept globally and is gradually gaining traction in Vietnam, particularly among businesses in the food industry This shift is largely driven by increasing pressure from multinational corporations from developed countries operating in Vietnam However, many believe that Vietnamese companies have yet to fully adopt CSR practices A survey by Social Responsibility Initiatives Vietnam found that 90% of respondents had misconceptions about CSR and related topics Overall, the exploration of CSR within the Vietnamese context remains limited, with few studies addressing these important issues.
This study examines the influence of Corporate Social Responsibility (CSR) on the capital structure of companies in the Vietnam stock market Utilizing secondary data from the annual financial statements of 80 manufacturing firms listed on the HOSE and HNX exchanges between 2016 and 2021, the research aims to enhance both theoretical and practical insights into financial architecture The findings are expected to provide valuable guidance for financial managers in making informed capital management decisions.
The research paper features essential chapters, with Chapter II providing a comprehensive literature review that investigates Corporate Social Responsibility (CSR) and Capital Structure, summarizing current knowledge and research Chapter III then shifts its focus to
This article explores the formulation of hypotheses regarding the impact of Corporate Social Responsibility (CSR) on a firm's capital structure Chapter IV details the research objectives, data collection methods, scope, and methodology Chapter V focuses on data collection, research models, and the primary research emphasis Chapters VI to VIII present research results and discussions that reveal significant insights into the relationship between CSR and financial leverage (LEV) among listed companies on HNX and HOSE from 2016 to 2021 The study highlights CSR's influence on capital structure decisions and emphasizes the importance of tailored strategies and deeper exploration of underlying mechanisms, while taking into account local contexts and stakeholder engagement.
LITERATURE REVIEW
The concept of social responsibility
Corporate social responsibility (CSR) in developing nations is a significant topic in business literature, encompassing various definitions that highlight its extensive societal impact, particularly regarding the state's economic role Both large corporations and smaller businesses have responsibilities that extend beyond generating shareholder value, emphasizing the need to benefit all stakeholders, including managers, owners, suppliers, employees, and local communities As a result, the interpretation of CSR is influenced by geographical and temporal contexts, with the World Business Council for Sustainable Development's definition currently being the most widely accepted.
Corporate Social Responsibility (CSR) refers to the continuous dedication of businesses to uphold ethical practices and foster economic development, all while improving the welfare of employees, their families, and the wider community.
The concept of Corporate Social Responsibility (CSR) in developing countries diverges significantly from its established definitions in European and American markets Visser (2008) highlights that the unique internal and external drivers of CSR in these nations influence local dynamics, underscoring the need to recognize the distinct agenda and scope of CSR in emerging economies.
The literature indicates that corporations operate within diverse economic, political, social, and cultural contexts in different countries, influencing their interactions with
In developing countries, unique conditions reshape the challenges businesses face, intertwining social and business considerations (Visser, 2008; Lindgreen et al., 2009; Muthuri & Gilbert, 2011) Corporate social responsibility (CSR) themes in these regions do not follow a uniform pattern but instead reflect diverse, country-specific dynamics Current literature primarily describes CSR practices, with limited focus on conceptual frameworks, often utilizing case studies and interviews to explore the 'why' and 'how' of these practices This research design indicates an initial exploration of the driving forces behind CSR in developing nations, highlighting the challenges of empirical research and the need for innovative research approaches in these contexts.
The adoption of Corporate Social Responsibility (CSR) principles for sustainable development is gaining momentum in developed countries, yet it remains relatively underexplored in developing nations like Vietnam Despite a lack of extensive CSR studies in the Vietnamese context, Nguyen Dinh Cung & Luu Minh Duc (2008) provided foundational insights into theoretical concerns and international practices, assessing the understanding of CSR among Vietnamese companies and the government's supportive role Research has also highlighted CSR's integration within Vietnam's garment and textile industry, with the Dap Cau joint-stock company's case offering recommendations for enhancing sustainable practices Additionally, Tran Thi Minh Hoa and Nguyen Thi Hong Ngoc (2014) examined CSR's impact on employees, the environment, and the community through case studies of Sofitel hotels, analyzing various stakeholders and proposing strategies to improve CSR efforts However, a comprehensive exploration of CSR in Vietnam remains significantly limited.
In Vietnam, the relationship between Corporate Social Responsibility (CSR) and capital structure is crucial for sustainable business practices CSR not only offers development advantages for enterprises in compliance with existing laws but also fosters a connection among businesses for collective growth and progress.
5 benefits for the community through social cooperation and interaction (Tran Anh Phuong
The concept of capital structure
Capital structure remains a debated topic in finance, with differing opinions among managers, researchers, and academics It is a complex subject requiring knowledge in econometrics, microeconometrics, accounting, and mathematics The foundational theories of capital structure significantly impact both academia and the business world, as highlighted by key studies from Modigliani and Miller, Jensen and Meckling, and Myers.
Trade theory, pecking order theory, and representation theory are key frameworks for understanding capital structure in imperfect markets The Modigliani and Miller Propositions (M&M) are foundational to finance, examining how a firm's capital structure affects its value M&M Proposition I, known as the "irrelevance proposition," states that under ideal conditions—such as the absence of taxes, transaction costs, and information asymmetry—a firm's capital structure does not influence its overall value This implies that investors can achieve their desired returns through their preferred mix of debt and equity without affecting the firm's total value M&M Proposition II further explains that in the presence of corporate taxes, a leveraged firm may be more valuable than an unleveraged one due to the tax deductibility of interest payments However, it warns that excessive debt can lead to financial distress and bankruptcy costs, impacting the optimal capital structure While these propositions offer valuable theoretical insights, they often oversimplify the complexities of real-world financial decisions, where factors like taxes, bankruptcy costs, and market imperfections significantly influence firms' financing choices.
The pecking order theory, introduced by Myers and Majluf in 1984, suggests that high-growth companies primarily rely on retained earnings for financing new projects instead of pursuing external funding As a result, these firms often exhibit lower debt ratios When internal funding falls short, they may consider external financing options.
Established businesses often utilize debt to support new ventures, as suggested by agency theory, which highlights the importance of an optimal capital structure that includes various financing options like equity and debt to benefit stakeholders Additionally, trade theory emphasizes the need for managers to balance the advantages and costs of debt to achieve this ideal capital structure The attractiveness of such a structure is further enhanced by the tax benefits linked to interest payments, which can help mitigate income fluctuations.
No single theory fully captures the complexities of capital structure factors due to the limitations of their underlying assumptions This gap highlights the challenge of applying these theories to the nuanced realities of the business environment Many studies concentrate on particular countries, taking into account unique determinants, which leads to varying outcomes that reflect the diverse characteristics of financial markets worldwide.
Before the Doi Moi movement in 1986, Vietnam's economy was predominantly state-owned, leading to minimal concern for capital structure However, post-Doi Moi, Vietnam has rapidly evolved into a dynamic Southeast Asian economy, marked by significant privatization and globalization efforts The establishment of a stock market in 2000 increased focus on capital structure, enabling companies to access external funding through share issuance Despite these advancements, the World Bank (2014) still classifies Vietnam as an emerging market, lacking sophisticated financial instruments, corporate transparency, and a robust regulatory framework for securities As a result, the capital structure of listed companies in Vietnam is shaped by unique factors that differ from those in more developed financial markets, prompting new research into capital structure in small transition economies like Vietnam.
The link between CSR and Capital structure
a Theories Exploring the Link between Corporate Social Responsibility and Capital Structure
As corporations increasingly value moral and environmental practices, corporate social responsibility (CSR) has grown significantly in recent years Capital structure decisions,
The relationship between Corporate Social Responsibility (CSR) and capital structure is a complex and evolving area of corporate finance, significantly influencing how companies fund their operations and growth This article explores various theories that analyze this intricate relationship, shedding light on the interplay between CSR initiatives and financial strategies.
The Pecking Order Theory, proposed by Myers and Majluf in 1984, suggests that businesses prioritize internal resources, followed by debt, and finally equity for financing Companies engaged in Corporate Social Responsibility (CSR) may allocate retained earnings to fund these initiatives, potentially leading to a conflict between CSR and leverage as they might favor equity over debt to maintain financial flexibility The trade-off theory of capital structure indicates that firms weigh the benefits of debt, such as tax advantages, against risks like financial distress CSR can enhance a company's reputation and reduce financial crisis risks, but funds used for CSR could alternatively service debt This dynamic influences a firm's risk profile and financial flexibility, affecting the relationship between CSR and capital structure Additionally, agency theory highlights conflicts between stakeholders, where CSR initiatives can align management and shareholder interests, thereby lowering agency costs and facilitating debt financing as creditors gain confidence in effective management.
The stakeholder thesis asserts that businesses have responsibilities beyond just their shareholders, highlighting the importance of corporate social responsibility (CSR) initiatives These initiatives aim to exceed stakeholder expectations and maintain goodwill, which can lead to a lower debt ratio as stakeholders, including consumers, employees, and communities, may prefer reduced leverage for ethical or risk-related reasons In this context, a company's dedication to stakeholder satisfaction integrates CSR as a fundamental element of its strategy.
Businesses should align their capital structure decisions with market conditions, opting for equity issuance during high stock price periods and debt financing during downturns Corporate Social Responsibility (CSR) initiatives can influence stock prices and market perception, thereby impacting capital structure choices When stock prices are elevated, the positive sentiment from CSR efforts can enhance equity issuance Additionally, companies focused on environmental sustainability may benefit from issuing green bonds or utilizing sustainable finance options, as these instruments are often tied to specific sustainability goals, further shaping their capital structure decisions.
The relationship between Corporate Social Responsibility (CSR) and capital structure is complex and varies by context Different theories highlight how CSR practices influence financing choices, including preferences for debt versus equity This intricate connection emphasizes the necessity for further research and tailored strategies that account for industry specifics, geographic factors, and the distinct CSR initiatives of companies As CSR increasingly impacts corporate priorities, comprehending its effect on capital structure decisions is crucial for sustainable and responsible financial management.
Numerous studies highlight the positive impact of Corporate Social Responsibility (CSR) on financial performance, revealing that companies that disclose CSR information tend to incur lower financial costs (Goss and Roberts, 2011) This transparency reduces information asymmetry between management and creditors, offering valuable insights to stakeholders Jang and Ardichvili (2020) found that engaging in CSR activities can lead to improved credit ratings and reduced financial expenses Consequently, it can be inferred that companies with robust CSR disclosures face fewer financial constraints, establishing a favorable correlation between CSR disclosure and debt ratios.
Previous research indicates a positive correlation between a company's CSR disclosure and its cost of equity capital and debt (Raimo, 2020) Firms that adopt CSR strategies address non-financial elements of their operations and manage stakeholder conflicts, ultimately reducing business risks and alleviating information asymmetry for lenders and investors in the financial market (Lopatta, 2016).
9 efficiency of a firm can reduce agency costs that arise when searching for funding sources
Market perception of a company's social efficiency can motivate firms to disclose non-financial information, as noted by Healy and Palepu (2001) Additionally, stakeholder concerns play a significant role in prompting companies to actively share information related to sustainable development (Raimo, 2020).
According to Yang (2017), effective corporate social responsibility (CSR) strategies play a significant role in diminishing information asymmetry between companies and their creditors, resulting in firms with robust CSR practices achieving higher financial leverage Additionally, CSR disclosure slows the adjustment of a firm's capital structure and leverage towards target levels, particularly when leverage is below the target Moreover, CSR reports equip creditors with long-term forecasts about companies, enabling firms that engage in CSR to sustain elevated long-term leverage compared to those that do not.
A study by Chang (2019) revealed a correlation between corporate social responsibility (CSR) and a company's debt structure, indicating that firms with stronger CSR practices typically have a higher ratio of public debt to total debt However, this positive impact of CSR diminishes for companies operating in "sin" industries, such as alcohol, tobacco, and gambling, as well as those in sectors with low trust In these instances, CSR appears to function primarily as a tool for image enhancement rather than delivering tangible benefits.
Focusing on stakeholders and enhancing transparency through Corporate Social Responsibility (CSR) can help firms ease capital access constraints (Cheng, 2017) Institutional investors are likely to favor companies with high social efficiency, as this reduces information costs In contrast, banks often rely on specific information gathered from lending relationships rather than assessing firms based solely on their social efficiency.
Harjoto (2016) posits that corporate social responsibility (CSR) serves as a strategic approach for firms to internalize costs associated with implicit contracts with non-investing stakeholders, influencing their operations and financial leverage The study reveals a positive correlation between CSR and firms' operating costs, suggesting that CSR can substitute for a firm's debt tax shield, particularly when financial leverage is low.
The author aims to enhance the theory of capital structure by sharing research findings, offering financial managers a more scientific understanding of the factors influencing capital decisions within the company.
Building effective policies is essential for maximizing enterprise value and meeting investors' expectations, which in turn contributes to the growth of the stock market and the overall Vietnamese economy This approach also provides investors with a solid theoretical foundation for making informed investment decisions, ultimately leading to increased income and an improved quality of life in a more developed society.
HYPOTHESIS
Capital structure has remained a key research focus in recent decades, with empirical studies consistently highlighting the relationship between internal company factors—such as firm size, sales growth rate, and financial leverage—and a company's capital structure Researchers have developed multiple regression models incorporating diverse independent and explanatory variables, utilizing various sample sizes and correlation coefficients tailored to their specific study objectives.
This research examines the impact of key factors on the capital structure of non-financial listed companies on the Hanoi Stock Exchange (HNX) and the Ho Chi Minh Stock Exchange (HOSE), focusing on Company Size (SIZE), Asset Structure (AST), After-tax Profit/Total Assets (ROA), State Ownership Ratio (GOV), Proportion of Independent Members in the Board of Directors (IND), and Board Size (BSIZE), due to data limitations and research capabilities.
Null Hypothesis (H0): Corporate Social Responsibility does not have a significant influence on Capital Structure
The relationship between Corporate Social Responsibility (CSR) and a company's financial structure has garnered significant attention in corporate finance and strategic management This study seeks to systematically explore how CSR activities may impact Capital Structure, which is a crucial element of a firm's financial framework.
The null hypothesis suggests that corporate social responsibility (CSR) does not significantly impact business decisions regarding capital structure This theory asserts that ethical and socially responsible activities have little to no effect on how companies choose to finance their operations, investments, and growth.
This theory suggests that while corporate social responsibility (CSR) may hold value, it does not influence a firm's capital structure decisions regarding debt and equity ratios Instead, it emphasizes the importance of focusing on traditional financial metrics and market conditions when determining the optimal balance of debt and equity financing, irrespective of any CSR efforts undertaken by the business.
To test the null hypothesis, we will analyze the CSR policies and financial structures of a diverse range of businesses across multiple industries Our evaluation will determine if CSR initiatives independently influence organizations' financial leverage decisions by examining historical data, employing advanced statistical methods, and considering various confounding factors.
This investigation enhances understanding of the intricate relationship between corporate social responsibility (CSR) and financial decision-making The null hypothesis suggests that, although CSR holds undeniable ethical and reputational benefits, its direct influence on capital structure decisions is only marginally significant This finding may encourage additional research into the broader economic and strategic factors that shape businesses' financial frameworks.
This inquiry starts with the possibility that the null hypothesis may be disproven, revealing a complex relationship between Corporate Social Responsibility (CSR) and Capital Structure This initial assumption facilitates a thorough analysis of the data, leading to clearer conclusions regarding the impact of CSR on the financial strategies employed by contemporary corporations.
Alternative Hypothesis (HA): Corporate Social Responsibility has a significant influence on Capital Structure
Corporate social responsibility (CSR) has become increasingly vital in shaping corporate decision-making in today's business landscape This study aims to explore the significant link between CSR initiatives and the capital structure of organizations, highlighting the essential role this relationship plays in contemporary finance.
The alternative hypothesis suggests that corporate social responsibility (CSR) initiatives significantly affect a company's capital structure, leading to changes in the balance between debt and equity financing (Ahmad, N., Salman, A., & Shamsi, A F., 2015) This theory posits that CSR practices are expected to play a crucial role in shaping financial decision-making within organizations.
12 decisions, ultimately leading them to either increase or decrease their reliance on debt financing
This hypothesis suggests that corporate social responsibility (CSR) initiatives can enhance a company's reputation and transform its financial structure by focusing on key areas such as environmental stewardship, community engagement, and ethical business practices Companies engaged in CSR are expected to leverage their ethical image to optimize their financial strategies, either by reducing financial risk through lower leverage or by securing favorable capital terms through increased leverage.
This article investigates the relationship between corporate social responsibility (CSR) initiatives and financial structures across diverse organizations By analyzing extensive historical data and employing advanced econometric techniques, we aim to determine if a firm's level of CSR engagement influences its capital structure decisions.
If the alternative theory holds true, CSR policies significantly influence business financial organization, extending beyond mere moral and ethical implications This suggests that the corporate landscape is in constant flux, with sociological and environmental factors playing a crucial role in shaping financial and strategic decisions.
Our investigation into this alternative hypothesis highlights the complex relationship between Corporate Social Responsibility (CSR) and Capital Structure While we acknowledge the potential for different outcomes, this hypothesis paves the way for an in-depth analysis of the financial impacts of CSR initiatives, enhancing our comprehension of how ethical business practices influence corporate financial strategies.
RESEARCH METHODS
Research objectives
Firstly, the purpose of this study is to determine The Influence of Corporate Social
Responsibility on Capital Structure in Vietnam for 6 years from 2016-2021
Second, measure and analyze the influence of quantitative factors on capital structure Including the following factors: Asset structure, Enterprise size, Profit after tax,
Financial leverage, Total assets, State ownership ratio, Percentage of independent members in the Board of Directors and Board size
This article discusses the implications of capital structure planning for enterprises, investors, and the State, emphasizing the importance of appropriate financial leverage to enhance business value Additionally, it addresses the study's limitations and offers suggestions for future research in this area.
Research data
Eighty manufacturing enterprises are listed and traded on HNX and HOSE, with their financial data sourced from business financial statements Sustainable development data is obtained from sustainable development reports and annual reports available on www.Vietstock.com and the companies' websites The sustainable development scores are calculated using a set of indicators based on the GRI standards, which include 30 environmental criteria and 34 social criteria, ensuring comprehensive evaluation.
- Enterprises have full information on financial structure for the period from 2016-2021
To ensure the integrity of the regression model and the collection of unbiased panel data, it is essential to exclude enterprises lacking sufficient financial statement data from 2016 to 2021.
- Eliminate companies specializing in the financial sector (because this sector has completely different characteristics of financial statements and capital structure).
Research scope
- Space: The study is limited to only 80 manufactures companies whose shares are listed and traded on HNX and HOSE
- Time: Data is collected in 6 years from 2016-2021, the period of data collection is year
- Content: Data on asset structure, enterprise size, profit after tax, financial leverage, total assets, state ownership ratio, percentage of independent members in the board of directors and board size
The sustainable development score is calculated as the following formula
Xj: jth criterion (takes value of 1 if there is information, 0 if there is no information)
X: maximum number of criteria is 64
Research method
This study employs secondary data in tabular form to assess the impact of independent variables on a dependent variable, focusing on both the direction and magnitude of this impact To achieve unbiased results, the research utilizes quantification through regression models while controlling for all relevant observable and unobserved variables The Pooled OLS model, or multivariable linear regression model using Ordinary Least Squares, is commonly applied for observed variables For unobserved variables, the choice between the fixed-effects model (FEM) and the random-effects model (REM) is determined by the characteristics of the subjects and time The FEM is utilized when differences exist solely between objects, whereas the REM is appropriate when variations occur across both objects and time.
This study examines the relationship between financial leverage (LEV) as the dependent variable and social responsibility (CSR) as the explanatory variable It also considers several independent variables that may impact this relationship, including firm size (SIZE), asset structure (AST), return on assets (ROA), state ownership ratio (GOV), the percentage of independent directors on the board (IND), and board size (BSIZE).
As of now, various statistical software tools like STATA, SPSS, and EVIEWS are widely used for quantitative research This article focuses on using Stata 16 for analyzing tabular data due to its efficient data processing capabilities and user-friendly interface Stata 16 offers comprehensive functions, including descriptive statistics, model suitability testing, and the ability to identify and correct model deficiencies, ensuring the development of the most appropriate model for the study.
RESEARCH CONTENT
Data Collection
This study investigates the influence of quantitative factors on the capital structure of Non-Financial Listed Companies (NFLCs) on the Hanoi Stock Exchange (HNX) and the Ho Chi Minh Stock Exchange (HOSE).
The author analyzed 15 secondary data points over a 6-year period from 2016 to 2021, as the business cycle of a company generally spans 3-6 years This timeframe was selected to ensure the research data is relevant to the current context, utilizing the most recent audited financial statements available on legitimate financial websites Additionally, the study considers the significant impacts of the COVID-19 pandemic, which began at the end of 2019, on companies' operational performance, as reflected in their financial statements The primary objective is to assess the influence of the pandemic on the dividend payout policies implemented by financial managers during this period.
This study utilizes panel data, specifically cross-sectional time-series data, collected from 80 manufacturing companies over a 6-year period The analysis focuses on annual data, resulting in a comprehensive sample size for the research.
The data for this study is collected from audited financial statements sourced from the website: vietstock.vn.
Research models
On panel data, consider the model of the form: yit = a + bxit + czi + eit
In which: Z is the variable representing the idiosyncratic characteristics (here we consider the i-th company's idiosyncratic characteristics)
Depending on the effect of Zi eigenvectors on the model, different patterns will be formed on the panel data
Ignoring Zi's unique characteristics or assuming it has no effect on the model leads to the use of OLS regression on panel data, known as Pool Regression A significant drawback of this approach is that the Durbin-Watson coefficient often falls below 1, indicating the presence of autocorrelation, a frequent issue in such models However, both the Fixed Effect Model and Random Effect Model effectively address this problem.
- If we consider the impact of the eigenvalues Zi on the model to be meaningful, we use the Fixed Effect Model and Random Effect Model methods:
In statistical analysis, when the effect is unpredictable and varies randomly, the Random Effect Model is employed Conversely, if the effect can be accurately measured and is consistent, the Fixed Effect Model is utilized.
The details of each model are as follows:
16 a Pooled OLS (Ordinary Least Square) model:
Pooled Ordinary Least Squares (OLS), a widely used least squares regression method, effectively estimates linear regression models by determining the correlation between independent and dependent variables Despite its popularity in research, the OLS model can yield biased estimates when applied to panel data, as it aggregates individual observations Additionally, issues such as missing or limited variable values during data collection can obscure the true impact of independent variables on dependent variables, resulting in inaccurate model outcomes in real-world scenarios.
To address the limitations of the Ordinary Least Squares (OLS) model when analyzing tabular data, the author employs both the Random Effects Model (REM) and the Fixed Effects Model (FEM) The suitability of each method is assessed using the Hausman test (1978) Once the most appropriate method is determined, a comprehensive regression model is constructed based on that method and subsequently compared to the OLS results.
Fixed Effects Model (FEM) examines the unique characteristics of each entity to analyze the correlation between residuals and explanatory variables By controlling for individual, time-constant characteristics, FEM effectively isolates the true impact of regressors on the dependent variable, allowing for a clearer estimation of their effects.
The FEM model has the following form:
Y it = β 1 X it + β 2 X it + μ i t it with i = 1, 2, …, N and t = 1, 2, …, T (N - object, T - time)
The model introduces an index "i" for the intercept coefficient "a" to distinguish between the varying intercepts of different businesses, reflecting their unique characteristics and management policies This analysis involves panel data comprising N entities over T time periods Two methods are utilized to estimate the parameters of the fixed effects model: the first involves least squares regression with dummy variables for each observed entity, while the second method focuses on estimating the fixed effects directly.
Hence, this model can be examined similarly to an OLS model using dummies, where these dummies act as fixed factors:
The Fixed Effects Model (FEM) highlights that each firm has unique characteristics that can impact explanatory variables By isolating these firm-specific, time-invariant traits, FEM allows for accurate estimation of the effects of explanatory variables on the dependent variable These characteristics are unique to each firm, remain constant over time, and are uncorrelated with those of other firms.
The FEM is also known as a one-way effects model since the different slope coefficients exist between entities but remain constant over time c REM model (random effect model):
The primary distinction between the Random Effects Model (REM) and the Fixed Effects Model (FEM) lies in how they handle variation among entities In the FEM, variation between entities is correlated with the independent or explanatory variables, while the REM assumes that this variation is random and uncorrelated with the explanatory variables.
If the differences between entities significantly impact the dependent variable, the Random Effects Model (REM) is preferable to the Fixed Effects Model (FEM) In this context, the residuals of each entity, which are uncorrelated with the explanatory variable, serve as new explanatory variables The foundational concept of the Random Effects Model is rooted in this approach.
Y it = a Xit + b X it + e it with i = 1, 2, …, N and t = 1, 2, …, T;
In the Random Effects Model (REM), the variable is treated as a random variable with a mean, contrasting with the Fixed Effects Model (FEM) where it is considered fixed The intercept in REM is represented by the equation ai = a + εi, highlighting the incorporation of random variation.
In the model, the random error (εi) is characterized by a mean of 0 and a variance of σ² Additionally, the errors (eit) arise from a combination of subject-specific and time-specific characteristics It is assumed that these errors (eit) are not correlated with any of the explanatory variables included in the model.
The analysis utilizes panel data comprising N subjects across T time points, where the classical error is divided into two components The first component, ai, accounts for unobservable factors that differ between subjects but remain constant over time, while the second component, eit, captures unobserved factors that vary both between subjects and over time This model assumes that the individual characteristics among entities are random and uncorrelated with the explanatory variables.
The selection between Fixed Effects Model (FEM) and Random Effects Model (REM) for research hinges on the correlation between the error term (εi) and the explanatory variables (X) If no correlation is assumed, REM is preferred; otherwise, FEM is more appropriate The subsequent section will evaluate which of the three models—Pooled Ordinary Least Squares (OLS), FEM, or REM—is the most suitable for the analysis.
Main research
Based on the results of previous research models, the author proposes a multivariable linear regression model for 80 non-financial companies listed on HNX and HOSE in the period 2016-2021 as follows:
LEVi,t = β0 + β1CSRi,t + β2 LNAi,t + β3 AST i,t + β4 ROAi,t +β5 AGEi,t + β6 INDi,t+ β7 BSIZEi,t + Ɛi,t
LEVi,t : is the dependent variable used to measure financial leverage
The independent variables analyzed in this study include SIZE, CSR, LNA, AST, ROA, AGE, IND, and BSIZE, which are utilized to evaluate how various internal factors influence the capital structure of an enterprise.
The author anticipates that the research findings will demonstrate the impact of social responsibility on the capital structure of companies listed on the Hanoi Stock Exchange (HNX) and the Ho Chi Minh Stock Exchange (HOSE), building on the proposed model and previous studies.
Table 1: expects the research results
CSR - Average score of 64 environmental and social criteria
AST - Fixed assets/Total assets
3 Profit after tax/Total assets
ROA - Profit after tax/Total assets
GOV + Number of shares owned by the State / Total number of outstanding shares of the company
5 Proportion of independent members in the Board of
BOD members/ Total number of BOD members
6 Board size BSIZE - Total number of members of the Board of Directors
Note: The plus sign (+) has a positive impact on capital structure, minus sign (-) has negative effect on capital structure
RESEARCH RESULTS AND DISCUSSION
Descriptive statistics of the variables used in the model
The table below presents descriptive statistics for both dependent and explanatory variables utilized in the research model, focusing on the factors influencing the capital structure of publicly listed companies on HNX and HOSE during the period from 2016 to 2021.
Between 2016 and 2021, key traits of firms listed on the Hanoi Stock Exchange (HNX) and Ho Chi Minh City Stock Exchange (HOSE) were analyzed, revealing an average financial leverage (LEV) of 0.1585 This indicates that most companies maintain a moderate level of leverage, despite some outliers with very low (0.0003) or significantly high (1.2123) leverage The low standard deviation of 0.0897 suggests that the financial leverage of the majority of firms is closely aligned with the average.
The average corporate social responsibility (CSR) score is 0.0907, reflecting a generally low level of engagement in CSR initiatives among businesses With a standard deviation of 0.0614, the dataset highlights a range of CSR practices, showcasing the diversity in approaches The presence of minimum (0) and maximum (1) values underscores the broad spectrum of CSR involvement, where some organizations exhibit no commitment to social responsibility, while others demonstrate a significant dedication to it.
The analysis reveals a standard deviation of 0.5843 and an average size of 12.2359 among companies, indicating notable variance in firm sizes, with a range from 10.9502 to 14.1190 Asset turnover (AST) averages at 0.4001 with a standard deviation of 0.0646, reflecting how effectively companies utilize their assets to generate revenue Profitability, measured by return on assets (ROA), shows an average of 0.1324 and a standard deviation of 0.0916, with values ranging from -0.3212 to 0.5111, highlighting diverse profitability levels The state ownership ratio (GOV) averages 0.1659 and exhibits a high standard deviation of 0.2583, indicating significant variability in governance practices The Board of Directors' independence (IND) averages 0.7334 with a standard deviation of 0.2002, suggesting variability in independent membership across companies Additionally, company size (BSIZE) has an average of 4.9333 and a standard deviation of 1.3903, further illustrating the diversity in business sizes These descriptive statistics provide a foundation for exploring factors influencing capital structure among Vietnamese listed companies during the specified period and offer valuable insights into the dataset's characteristics.
Comparing financial measures across Southeast Asia, Asia, and globally reveals significant economic differences among these regions Southeast Asian companies exhibit varying levels of financial leverage influenced by sector dynamics and local factors, while Asia's financial system showcases a broad spectrum reflecting regional economic diversity Corporate social responsibility (CSR) initiatives are increasingly prevalent in Southeast Asia, with varying engagement levels across Asia, particularly in countries like Japan The coexistence of large conglomerates and SMEs in Southeast Asia highlights the region's economic complexity, similar to the global business ecosystem which includes both multinational corporations and local startups Asset turnover is affected by industry types in Southeast Asia, with Asia's diverse economy leading to differing efficiency levels across sectors Return on Assets (ROA) in Southeast Asia indicates regional economic disparities, and Asia's ROA is shaped by economic conditions, particularly in technology hubs Globally, ROA is influenced by economic stability and corporate strategies Governance practices, such as the State Ownership Ratio and the proportion of independent board members, vary regionally, underscoring the need for detailed data for accurate comparisons Overall, these financial metrics provide insights into the economic landscapes of Southeast Asia, Asia, and the world, emphasizing the importance of regional data for understanding unique business dynamics.
Correlation between variables
LEV CSR SIZE AST ROA GOV IND BSIZE
IND -0,167 -0,0185 0,0136 0,0193 -0,0392 -0,0142 1 BSIZE 0,0737 0,0228 -0,0256 0,0074 0,0553 -0,0001 -0,8973 1 Source: Author’s calculation
Correlation values illuminate potential connections within datasets, offering crucial insights into the relationships between various financial indicators The correlation coefficient, ranging from -1 to 1, serves as a reliable measure, with values near 1 indicating a strong positive correlation, values near -1 signifying a strong negative correlation, and values close to 0 reflecting weak or nonexistent connections Additionally, correlation matrices display 1s along the diagonal, representing the perfect correlation of a variable with itself.
This analysis explores the relationships between financial leverage (LEV) and various factors, revealing key insights Notably, corporate social responsibility (CSR) shows a weak negative correlation with LEV (-0.19), suggesting that companies prioritizing social responsibility tend to have lower financial leverage Conversely, firm size (SIZE) has a positive association with LEV (0.2388), indicating that larger firms often leverage more due to better access to funding sources Additionally, asset structure (AST) presents a weak negative relationship with LEV (-0.1343), implying that firms with more structured assets may maintain lower financial leverage Profitability, measured by return on assets (ROA), also displays a negative correlation with LEV (-0.1184), indicating that more profitable companies tend to have lower leverage In contrast, state ownership ratio (GOV) shows a positive association with LEV (0.1127), suggesting that firms with higher state ownership are likely to exhibit more financial leverage Furthermore, the percentage of independent directors (IND) negatively correlates with LEV (-0.167), indicating that companies with more independent directors typically maintain lower leverage Lastly, board size (BSIZE) has a slight positive correlation with LEV (0.0737), suggesting that firms with larger boards tend to have higher financial leverage.
The correlation coefficient of -0.19 indicates a weak inverse relationship between corporate social responsibility (CSR) and financial leverage (LEV), suggesting that as CSR scores increase, financial leverage tends to decrease slightly Conversely, lower CSR ratings are associated with a slight increase in financial leverage Despite this relationship, the absolute value of the correlation coefficient remains below 0.3, highlighting the tenuous nature of this association.
The tenuous negative link between social responsibility and financial leverage can be attributed to ethical principles, resource allocation, and investor perceptions Companies prioritizing social responsibility often choose lower financial leverage to uphold their moral stance and ensure financial stability Furthermore, funds allocated to CSR projects may detract from debt repayment, affecting leverage levels Positive stakeholder perceptions of socially conscious businesses can also lead to reduced borrowing rates, decreasing the need for significant leverage to demonstrate financial stability.
Correlation does not imply causality, and while there is a negative correlation between corporate social responsibility (CSR) and financial leverage, it remains unclear whether stronger CSR leads to reduced financial leverage or the opposite Both factors may be influenced by various external variables, such as market conditions, corporate strategy, management's risk preferences, and industry norms To better understand this relationship, further statistical analysis, including regression modeling, is needed to isolate CSR's impact on financial leverage while accounting for other independent variables Ultimately, although the observed negative correlation is intriguing, comprehensive research and analysis are necessary to fully uncover the dynamics at play.
Multicollinearity test
Variable VIF SQRT VIF Tolerance R- Squared
Multicollinearity occurs when independent variables in a regression model exhibit a high correlation, complicating the interpretation of their individual significance To identify multicollinearity, the Variance Inflation Factor (VIF) is commonly employed, with a high VIF indicating that a variable is strongly correlated with other variables in the model.
The LEV variable demonstrates minimal multicollinearity, as indicated by a Variance Inflation Factor (VIF) of 1.24 and a tolerance value of 0.8095 Additionally, the R-squared score of 0.1905 reveals that other independent variables in the model explain 19.05% of the variation in LEV.
Corporate Social Responsibility (CSR) exhibits minimal multicollinearity, evidenced by a Variance Inflation Factor (VIF) of 1.05 and a tolerance level of 0.9568 With an R-squared value of 0.0432, only 4.32% of its variance is accounted for by other variables in the model, indicating that CSR operates largely independently and lacks significant correlation with other factors.
The SIZE variable exhibits a low level of multicollinearity, as indicated by a Variance Inflation Factor (VIF) of 1.07 and a tolerance value of 0.9306 Additionally, the R-squared value of 0.0694 reveals that only 6.94% of its variance is accounted for by other variables in the model.
Asset Turnover (AST) demonstrates a low Variance Inflation Factor (VIF) of 1.03 and a high tolerance level of 0.9722, suggesting minimal multicollinearity The R-squared value of 0.0278 indicates that only 2.78 percent of AST's variation is influenced by other variables in the model.
The Return on Assets (ROA) variable shows low multicollinearity, with a Variance Inflation Factor (VIF) of 1.03 and a tolerance of 0.97 Additionally, its R-squared value of 0.03 reveals that only 3% of its variance is accounted for by other variables in the model.
The GOV (State Ownership Ratio) exhibits minimal multicollinearity, indicated by a Variance Inflation Factor (VIF) of 1.01 and a high tolerance level of 0.9854 Furthermore, its R-squared value of 0.0146 implies that merely 1.46% of its variance can be accounted for by other variables.
The proportion of independent members in the board of directors (IND) exhibits a high level of multicollinearity, as indicated by a variance inflation factor (VIF) of 5.44 and a tolerance value of 0.184 Additionally, its high R-squared value of 0.816 suggests that a significant portion of the variance in IND is accounted for by other variables in the model.
BSIZE, representing firm size in a binary format, exhibits considerable multicollinearity, evidenced by a VIF of 5.31 and a tolerance of 0.1885 Additionally, its R-squared value of 0.8115 suggests that a substantial part of its variance is affected by other variables within the model.
The analysis reveals that most variables in the model demonstrate low multicollinearity, indicated by VIF values near 1 and high tolerance levels However, the IND and BSIZE variables show significant multicollinearity, implying they share considerable variance with other variables Therefore, caution is advised when interpreting the coefficients of these highly correlated variables, as their individual impacts on the dependent variable may be challenging to distinguish due to this shared variance.
When dealing with variables that have high multicollinearity, considering techniques like variable selection, regularization, or further domain-specific analysis can aid in making more accurate and interpretable regression models.
Suitable Model Selection
a Pooled OLS model estimation results:
Table 5: Pooled OLS model estimation results
The Pooled OLS regression analysis reveals important insights, with an R-squared value of 0.1905, indicating that approximately 19.05% of the variability in the dependent variable "LEV" is explained by the model Additionally, the adjusted R-squared is 0.1785, offering a more conservative estimate of explanatory power by considering the number of independent variables and the sample size.
The coefficient for the "CSR" variable is estimated at -0.2773692, indicating that for every one-unit increase in "CSR," the dependent variable "LEV" is expected to decrease, assuming all other factors remain constant This negative coefficient highlights a significant inverse relationship between CSR and LEV.
"CSR" and "LEV," implying that as the level of Corporate Social Responsibility (CSR) increases, financial leverage (LEV) tends to decrease
The p-value for the "CSR" coefficient is nearly 0, indicating a very high level of statistical significance With the common significance threshold (alpha) set at 0.05, the considerably lower p-value confirms that the "CSR" variable significantly influences "LEV."
Furthermore, the model includes other independent variables, such as "SIZE," "AST,"
The variables "ROA," "GOV," "IND," and "BSIZE" each have specific coefficients, standard errors, t-values, and p-values that must be analyzed in relation to the "CSR" variable By examining the coefficients, their signs, and significance levels, we can evaluate the influence of these variables on "LEV."
The Pooled OLS regression analysis indicates a statistically significant negative relationship between Corporate Social Responsibility (CSR) and financial leverage (LEV), suggesting that higher CSR scores are associated with lower LEV However, it is important to note that regression analysis identifies associations rather than causation, necessitating further analysis and context-specific considerations to draw meaningful conclusions about the relationship between CSR and LEV.
Table 6: FEM model estimation results
Fixed-effects (within) regression Number of obs = 480
Group variable: mck Number of groups = 80
R-sq: Obs per group: within = 0.1692 min = 6 between = 0.2652 avg = 6 overall = 0.1861 max = 6
LEV Coef Std Err t P>t [95% Conf Interval]
CSR -0,2584518 0,0657846 -3,93 0,000 -0,3877855 -0,1291181 SIZE 0,0447223 0,0104396 4,28 0,000 0,0241978 0,0652468 AST -0,1582405 0,0623557 -2,54 0,012 -0,2808331 -0,035648 ROA -0,1699171 0,0540865 -3,14 0,002 -0,2762522 -0,063582 GOV 0,0398032 0,0234443 1,7 0,090 -0,0062887 0,0858952 IND -0,2138377 0,0455901 -4,69 0,000 -0,3034687 -0,1242068 BSIZE -0,0231105 0,0066698 -3,46 0,001 -0,0362234 -0,0099977 _cons -0,0152422 0,1446266 -0,11 0,916 -0,2995807 0,2690964 sigma_u 0,03563669 sigma_e 0,08047532 rho 0,16394686 (fraction of variance due to ui)
The FEM model analysis indicates that the component 'ui' captures the fixed factors influencing the i-th observation in the research sample The results show an F-statistic of F(7,393) = 11.43 with a p-value of 0.000, leading to the rejection of the null hypothesis (H0) Additionally, the R-squared value of 18.61% supports the conclusion that the FEM model is more suitable than the OLS model for this study.
Conclusion: Between the OLS and FEM models, the FEM model is the most suitable
The Fixed Effects Model regression analysis offers significant insights, particularly through its R-squared values, which measure the explanatory power of independent variables on the dependent variable's variance The Within R-squared is 0.1692, indicating the model's effectiveness at the within-group level, while the Between R-squared is 0.2652, reflecting its performance across groups Overall, the model achieves an R-squared of 0.1861, showcasing its overall explanatory capability.
The "CSR" coefficient is estimated at -0.2584518, indicating that for each one-unit increase in the "CSR" variable, the dependent variable "LEV" is expected to decrease This estimation incorporates fixed effects while controlling for other variables, and the negative sign of the coefficient suggests an inverse relationship between "CSR" and "LEV."
30 relationship between "CSR" and "LEV," suggesting that as a company's Corporate Social Responsibility (CSR) score increases, its financial leverage (LEV) tends to decrease
The "CSR" coefficient exhibits a p-value close to 0, demonstrating a high level of statistical significance With the significance threshold (alpha) typically set at 0.05, the p-value's substantial deviation from this limit confirms that the "CSR" variable has a strong impact on "LEV."
Various other independent variables, including "SIZE," "AST," "ROA," "GOV,"
The variables "IND" and "BSIZE" are integrated into the model, each accompanied by a coefficient, standard error, t-value, and p-value It is essential to analyze the coefficients, signs, and significance levels of these variables in a manner akin to the interpretation of the "CSR" variable to assess their influence on "LEV."
Both "sigma_u" (the random effects' standard deviation) and "sigma_e" (the error term's standard deviation) serve as indicators for random effects These variables shed light on the model's internal variability
The F-test for Fixed Effects was performed to evaluate the overall significance of fixed effects, yielding a p-value of 0.2289, which exceeds the common significance threshold of 0.05 Consequently, this suggests that the fixed effects may not significantly impact the variance in "LEV."
In summary, the Fixed Effects Model regression study reveals a statistically significant negative relationship between Corporate Social Responsibility (CSR) and financial leverage (LEV), indicating that as a company's CSR score increases, its LEV tends to decrease However, it is important to note that regression analysis identifies correlations rather than causation, necessitating further research and context-specific analysis to draw definitive conclusions about the causal relationship between CSR and LEV.
Table 7: REM model estimation results
GLS regression Number of obs = 480
Group variable: mck Number of groups = 80
R-sq: Obs per group: within = 0.1665 min = 6 between = 0.2845 avg = 6 overall = 0.1904 max = 6
The Random Effects GLS regression analysis presents a detailed summary of variable relationships, highlighted by R-squared values that indicate how well independent variables account for the variance in the dependent variable The Within R-squared is 0.1665, the Between R-squared is 0.2845, and the Overall R-squared is 0.1904, collectively demonstrating the model's explanatory strength.
The analysis reveals a "CSR" coefficient value of -0.2752366, indicating that for each unit increase in Corporate Social Responsibility (CSR), the financial leverage (LEV) decreases, suggesting a negative relationship between the two variables This relationship is statistically significant, with a p-value of 0.000, well below the typical alpha threshold of 0.05.
"CSR" variable has a considerable impact on "LEV" because the p-value is significantly below this threshold
Other independent variables included in the model include "SIZE," "AST," "ROA,"
The analysis of the variables "GOV," "IND," and "BSIZE" reveals their respective coefficients, standard errors, z-values, and p-values, which are crucial for understanding their impact on "LEV." By evaluating these metrics, including the significance levels, we can interpret how these variables influence leverage Additionally, the random effects are analyzed through "sigma_u," representing the standard deviation of random effects, and "sigma_e," the standard deviation of the error term The assumption that the correlation between random effects and independent factors is zero suggests that these random effects are uncorrelated with the independent variables Ultimately, the Random Effects GLS regression analysis indicates a statistically significant negative relationship between "CSR" and "LEV," suggesting that an increase in a company's Corporate Social Responsibility is associated with a decrease in leverage.
0,03054454(fraction of variance due to ui)
DISCUSSION OF RESULTS
The Random Effects Model (REM) regression results provide valuable insights into the relationship between Corporate Social Responsibility (CSR) initiatives and a company's Capital Structure This study is based on a substantial dataset of 480 observations from 80 different corporate entities, establishing a strong analytical foundation for exploring the proposed relationship.
The coefficient for the CSR variable is -0.2752366, with a standard error of 0.0604433, indicating that increased engagement in CSR activities is linked to a statistically significant decrease in a company's Capital Structure This finding is supported by a z-score of -4.55 and a p-value of 0.000, both of which exceed conventional thresholds Additionally, the confidence interval for the coefficient ranges from -0.3937033 to -0.15677, reinforcing the validity of this relationship.
Corporate Social Responsibility (CSR) is increasingly important, but understanding the broader context influenced by control variables is essential Firm size (SIZE) plays a crucial role, showing a positive correlation with capital structure, suggesting that larger companies often prefer higher capitalization ratios Furthermore, factors such as asset turnover (AST), return on assets (ROA), government ownership (GOV), industry classification (IND), and firm size (BSIZE) also demonstrate significant relationships with capital structure, as indicated by their statistically significant coefficients.
The analysis of variance distribution highlights the R-squared values, with the within-group R-squared at 0.1665 indicating that 16.65% of the variation in Capital Structure is attributable to characteristics within corporate groups In contrast, the between-group R-squared of 0.2845 shows that 28.45% of the variability is due to differences among various groups The overall R-squared value of 0.1904 combines these insights, offering a comprehensive view of the model's explanatory capabilities The model's overall significance is evaluated through the Wald test.
37 chi-squared test, which yields a statistically significant test statistic of 108.77, accompanied by a remarkably low p-value of 0.000
The dissertation and related research articles investigate the link between Corporate Social Responsibility (CSR) and financial outcomes, utilizing empirical analysis methods such as regression models and statistical significance measures to assess CSR's impact on financial variables (Raimo, 2020; Soebin Jang & Alexandre Ardichvili, 2020; Goss & Roberts, n.d.).
The article examines the impact of Corporate Social Responsibility (CSR) on a company's capital structure, highlighting variations in focus among different studies While the dissertation utilizes a Random Effects Model (REM) regression to quantitatively analyze this relationship, presenting key statistical findings such as coefficients and p-values, other research articles explore diverse aspects like equity capital costs and human resources Despite differing sample sizes and methodologies, these studies collectively enhance our understanding of CSR's financial implications, demonstrating its influence on various financial outcomes.
Research conducted in 2009 and referenced by Hương (2021) explores a wide range of questions and methodologies related to financial structures and the influence of Corporate Social Responsibility (CSR) on capital costs, particularly in contexts like Vietnamese firms and developing nations such as Botswana and Malawi Each study provides unique insights into the relationship between CSR and financial factors, enhancing our understanding of this intersection from various perspectives.
The findings from the Random Effects Model (REM) regression clearly indicate that Corporate Social Responsibility (CSR) significantly influences Capital Structure The negative coefficient suggests that CSR initiatives may lead companies to pursue lower financial leverage This relationship is linked to the idea that strong CSR practices can create valuable intangible assets, positively affecting a firm's reputation and financial health Future research should explore the intricate mechanisms that drive this relationship further.
38 accounting for potential endogeneity and unobserved variables that could provide richer context.
CONCLUSIONS
The author effectively synthesizes theoretical foundations of capital structure and previous research to explore the influence of corporate social responsibility (CSR) on capital structure in listed companies on the Hanoi and Ho Chi Minh City Stock Exchanges from 2016 to 2021 The study's objectives are clearly outlined in the initial chapters, and this chapter concludes with research findings and recommendations for stakeholders, including businesses, investors, and the general public, regarding the importance of CSR.
Research indicates that listed companies on the Hanoi and Ho Chi Minh City Stock Exchanges have capital structures, represented by financial leverage (LEV), that are influenced by several factors Corporate social responsibility (CSR) serves as a key explanatory variable, while additional independent variables affecting this relationship include Firm Size (SIZE), Asset Structure (AST), Return on Assets (ROA), Government Ownership Ratio (GOV), Percentage of Independent Directors (IND), and Board Size (BSIZE).
The author utilized panel regression to conduct a multivariate analysis of data from 80 manufacturing companies listed on HNX and HOSE between 2016 and 2021 The findings reveal that the Random Effects Model (REM) effectively accounts for the variations in the dependent variable, LEV.
The analysis reveals three key factors positively influencing the financial leverage (LEV) ratio: Firm Size (SIZE), Government Ownership Ratio (GOV), and Return on Assets (ROA) Conversely, Corporate Social Responsibility (CSR), Asset Structure (AST), Percentage of Independent Directors (IND), and Board Size (BSIZE) negatively impact the LEV ratio Notably, while Firm Size (SIZE) consistently shows positive correlations in the models, it is deemed statistically insignificant by the author.
Corporate Social Responsibility (CSR) has been found to reduce financial leverage, which contradicts previous studies suggesting that CSR increases it In Vietnam's manufacturing sector, the disclosure of CSR information by listed companies does not diminish the level or asymmetric cost of information between creditors and businesses This is attributed to the recent implementation of CSR practices, initiated by Circular No 155/2015-BTC on information disclosure in the securities market Due to the limited timeframe and a lack of deep understanding and investment in CSR activities, the impact of CSR-related information on creditors remains insufficient.
Banks prioritize the feasibility of a company's borrowing projects, its ability to repay loans and interest punctually, and the availability of adequate assets to secure those loans, rather than depending on the company's published Corporate Social Responsibility (CSR) information.
Research findings suggest that policies should support banking sector companies in adopting CSR practices by offering green credit packages This approach would incentivize businesses to participate in CSR activities, facilitating their access to loans at preferential rates.
Tailoring CSR Integration for Optimal Capital Structure
In today's business environment, the interplay between Corporate Social Responsibility (CSR) and capital structure has transformed from a theoretical concept into a crucial strategy Companies are increasingly focused on aligning their financial goals with ethical responsibilities, making the integration of CSR initiatives and capital structure choices essential for sustainable growth This article explores how effectively combining CSR with capital structure decisions can enhance both financial performance and social impact.
A key element of effective CSR integration is aligning initiatives with a company's core values Understanding the fundamental principles that drive an organization's mission and vision is crucial This alignment fosters authenticity, which is vital for positively influencing stakeholder perceptions and, consequently, capital structure considerations.
To enhance their corporate social responsibility (CSR) efforts, companies must align their initiatives with industry-specific values and stakeholder expectations A deep understanding of the unique opportunities and challenges within their sector is essential for a tailored approach By addressing key issues relevant to their industry, firms demonstrate a proactive commitment to ethical business practices, which can influence their capital structure and establish them as thought leaders in the field.
Engaging stakeholders is crucial for the successful development of Corporate Social Responsibility (CSR) projects By actively gathering feedback from employees, clients, communities, and investors, organizations can ensure that their initiatives are more relevant and foster a sense of ownership among stakeholders CSR programs informed by stakeholder insights can significantly enhance a company's reputation and influence its capital structure by reshaping perceptions of risk.
A company's societal impact is shaped by its local environment, making it essential for CSR projects to be tailored to address specific community needs This localized approach not only enhances civic virtue but also fosters support from local stakeholders, potentially influencing capital structure decisions.
Setting ambitious Corporate Social Responsibility (CSR) goals is commendable, but they must be realistic and scalable based on the company's operational and financial capabilities A tailored CSR strategy that aligns with the business's growth trajectory fosters long-term commitment This strategic approach not only cultivates a consistent and positive reputation but also influences the perceptions of investors and capital providers.
Integrating business functions is essential for effective Corporate Social Responsibility (CSR) implementation By embedding ethical considerations into product development, supply chain management, and marketing, companies can create a comprehensive strategy This alignment not only reflects a commitment to ethical practices but also influences capital structure decisions, demonstrating a genuine dedication to moral behavior beyond mere symbolism.
Integrating Corporate Social Responsibility (CSR) into a company's identity and industry context is essential for sustainable growth By aligning CSR with stakeholder expectations and local circumstances, businesses can enhance their financial performance while meeting ethical obligations This tailored approach positions companies as responsible corporate citizens and catalysts for positive change within their ecosystems, effectively merging business goals with social and environmental commitments.
41 complex intersection of CSR and capital structure through these tailored efforts, developing a success strategy that resonates with stakeholders and society as a whole
Unveiling Long-Term Value Creation through CSR Integration and Capital Structure