With the assistance of specialized software Stata, the results show a negative relationship between average customer receivable period ARP, average inventory period AIP, average payables
INTRODUCTION
Overview
Effective working capital management is essential for a business's financial health and sustainability, focusing on the efficient handling of current assets and liabilities to ensure smooth operations and support growth Defined as the difference between current assets and current liabilities, working capital is critical for fulfilling short-term obligations and enhancing overall business performance across various industries It involves careful planning and control to mitigate the risk of failing to meet short-term commitments while avoiding excessive investment in assets Furthermore, proficient working capital management not only reflects a company's financial standing but also serves as a key performance indicator, prompting managers to optimize its use Despite the importance of this area, empirical studies indicate a lack of comprehensive research across different business sectors and economic markets globally.
This research article focuses specifically on listed manufacturing companies in the Vietnam Stock Markets, addressing a gap in previous studies that often examined a broad range of industries By narrowing the scope, the study aims to provide an in-depth analysis of working capital management, which is vital for manufacturing firms where current assets represent over half of total assets Effective working capital management is crucial, as both excessively high and low levels can disrupt business operations Given the growing recognition of the manufacturing sector's contribution to developing economies, this research seeks to explore the relationship between working capital management and profitability among listed manufacturing enterprises on the Ho Chi Minh Stock Exchange Ultimately, the study aims to offer valuable insights for domestic businesses, emphasizing the importance of profitability as a primary goal for all enterprises.
Manufacturing Companies Listed in Vietnam Stock Markets" emerges as a pertinent and indispensable subject, offering practical reference value to enterprises.
Research objectives
This research investigates the relationship between working capital management (WCM) and profitability in Vietnam's manufacturing sector It aims to provide valuable insights and effective financial management strategies for analysts and businesses By analyzing key financial indicators, including the average receivable period (ARP), average inventory period (AIP), average payables period (APP), and cash conversion cycle (CCC), the study seeks to determine the optimal working capital levels that can enhance the profitability of manufacturing companies in Vietnam.
Subject and Scope
Research object: The relationship between working capital management and profitability of manufacturing enterprises in Vietnam.
This research focuses on manufacturing firms listed on the Ho Chi Minh Stock Exchange (HOSE) and the Ha Noi Stock Exchange (HNX) Data was gathered from FiinGroup, utilizing annual reports, management reports, and audited financial statements from 171 manufacturing enterprises over the period from 2008 to 2019, resulting in a total of 2,052 observations.
Research questions
This research will analyze and address the following questions:
• What is the current status of working capital management practices among manufacturing enterprises in the Vietnamese market?
• What is the relationship between working capital management and the profitability of manufacturing businesses in Vietnam?
Research contributions
Effective working capital management is essential for businesses, especially during financial crises, as it directly influences short-term assets, liabilities, revenue, and operating costs During challenging times, companies often encounter obstacles such as limited access to credit, declining sales, and heightened uncertainty, making efficient management of working capital crucial for maintaining operations and financial stability (Akgiin and Karatas, 2020; Baveld).
This study explores the significance of working capital management (WCM) on the return on investment of manufacturing companies in Vietnam, highlighting its influence on short-term assets, liabilities, revenue, and operating costs It identifies key WCM variables, including average customer receivable period (ARP), average inventory period (AIP), average payables period (APP), and cash conversion cycle (CCC), while emphasizing their negative impact on profitability The research aims to provide businesses with insights to develop effective WCM policies, allowing them to analyze the impact of each component, address real issues, and enhance their overall financial performance.
Structure
The research is structured into five chapters, along with the respective sections and reference materials:
• Chapter 1: Introduction: Introducing to the research purpose, topic context, subject, scope, and significance of the research;
• Chapter 2: Theoretical Framework: Discussing relevant concepts, theoretical foundations and hypotheses;
• Chapter 3: Research Methodology: Presenting and analyzing variables, model and research methods;
• Chapter 4: Analysis and Research Findings: Analyzing research findings and providing observations;
• Chapter 5: Conclusion and Policy Implications: Providing final conclusions and offering recommendations and practical suggestions.
THEORETICAL FRAMEWORK
Theoretical Literature
Working capital refers to the total funds a company utilizes to finance its short-term assets essential for production and business activities It represents the money spent on current assets that support operations at a given moment Typically, this capital circulates within a single business cycle and is fully recovered, marking the completion of its circulation.
Working capital management involves overseeing a business's short-term assets and making decisions regarding their quantity and composition, as well as their financing This process is crucial for maintaining liquidity and meeting short-term obligations while ensuring efficient investment of the company's assets Effective working capital policies differ based on profitability and risk tolerance, requiring managers to carefully weigh the trade-off between profitability and risk when determining the optimal level of working capital.
Effective working capital management is crucial for enhancing operational efficiency in production and business activities It encompasses various forms of capital, including inventory, accounts receivable, and accounts payable This study will examine four key aspects of working capital management.
Effective accounts receivable management is crucial for businesses, as it encompasses various types of receivables, including customer receivables, prepayments to suppliers, internal receivables, deductible value-added tax receivables, and others Among these, customer receivables typically represent the largest share While the presence of receivables is a natural aspect of business operations, the extent to which they are managed—whether at a high or low level—largely depends on the subjective decisions made by corporate management.
Effective inventory management is crucial for businesses as it involves overseeing assets like raw materials, tools, unfinished products, and finished goods Proper inventory levels ensure that production runs smoothly without interruptions due to shortages, while also preventing the financial strain caused by excess inventory that ties up capital and increases costs The primary objective of inventory management is to maintain adequate reserves for ongoing production and business needs while minimizing working capital tied up in storage, thereby enhancing overall efficiency and profitability.
Effective accounts payable management is crucial for businesses, as it involves managing capital amounts owed to suppliers, employees, and the state budget Supplier payments often represent a significant portion of a company's liabilities While leveraging outside capital can enhance production and operations, excessive payables can harm a firm's reputation by raising concerns about its financial stability To maintain a healthy accounts payable system, businesses should consistently monitor and reconcile their payables with suppliers, ensuring timely debt repayment and minimizing the risk of overdue obligations.
- Cash Management: Cash connects all financial-related activities of a business
Effective cash capital management involves overseeing cash flow, funds, and payment accounts, while controlling expenditures and forecasting cash requirements for the business It addresses budget deficits and balances short- and long-term cash surpluses or shortages Cash reserves are essential for conducting business transactions, including purchasing raw materials, covering necessary expenses, and repaying debts and loans.
Profit, as defined by Pandey (1980), reflects an enterprise's ability to generate earnings and assess performance based on profits and capital utilization Profitability is a critical objective in financial management (Malik, 2011), and profit ratio indicators are commonly employed to evaluate a business's profitability, illustrating the relationship between profits and production costs These indicators highlight the effectiveness of a business in managing its resources Additionally, the profit margin serves as a key indicator of a company's financial health and asset management efficiency (Lesakova, 2007).
Two key profitability ratios are return on assets (ROA) and return on equity (ROE) ROA measures a company's profitability relative to its assets, indicating how effectively the organization manages production and business activities It reveals the profit generated per dong of assets utilized, highlighting asset efficiency in profit generation Conversely, ROE assesses the ability to generate profit from equity, demonstrating how much profit a dollar of equity produces after corporate income tax Analyzing ROE helps determine whether a company's leadership prioritizes profit maximization or scaling operations.
Empirical Literature
2.2.1 Empirical Studies in the world
Deloof (2003) examined the influence of working capital management on profitability within the Belgian market, utilizing the cash conversion cycle method The research analyzed data from 1,009 non-financial firms spanning from 1992 to 1996, revealing significant insights into the relationship between effective working capital management and enhanced financial performance.
A 1996 study highlights that minimizing cash turnover by shortening receivables and inventory periods can enhance profitability It reveals a negative correlation between cash cycle components—such as average collection period, daily inventory turnover, and accounts payable cycle—and gross profit margin Deloof recommends that managers focus on reducing these components to increase profits.
A study conducted by Adeel Mumtaz and colleagues in 2011 examined the financial statements of chemical industry companies in Pakistan from 2005 to 2009, revealing a significant relationship between working capital management and company performance The findings indicate that effective management of working capital directly enhances company performance To boost working capital efficiency and overall performance, companies should prioritize improving debt collection, managing inventory, optimizing production processes, and enhancing cash flow management.
A study by Vural, Sokmen, and Ọetenak (2012) analyzed 75 manufacturing companies listed on the Istanbul Stock Exchange from 2002 to 2009, revealing a negative relationship between cash conversion cycle (CCC) and accounts payable with company profitability The findings suggest that managers can boost profits by minimizing the time taken to collect accounts payable and reducing CCC Additionally, the study indicated no significant correlation between days of inventory, accounts payable, and profitability.
A study by Bolek & Wilinski (2012) examined the influence of internal and external economic factors on the profitability of construction firms listed on the Warsaw Stock Exchange from 2000 to 2010 The findings revealed that both company size and GDP growth rate positively affect business profitability, measured by Return on Assets (ROA) Conversely, asset structure, capital structure, average cash collection, and quick ratio were found to have negative effects on profitability.
Aregbeyen's (2013) research on the impact of working capital management on the profitability of 48 large manufacturing firms listed on the Nigerian Stock Exchange from 1993 to 2005 revealed a significant negative correlation between working capital management and profitability The study emphasizes the necessity for manufacturing companies in Nigeria to enhance their working capital efficiency by reducing the Average Collection Period, Average Payment Period, Daily Inventory Turnover, and Cash Conversion Cycle to boost profitability.
A 2013 study by Sajid Gul examined small and medium-sized enterprises (SMEs) in Pakistan from 2006 to 2011, revealing that effective working capital management can significantly enhance profits, improve debt collection, reduce inventory, and optimize overall working capital However, it cautioned against overly tight management of working capital, which could harm liquidity and diminish customer confidence These insights are valuable for business managers in Pakistan seeking to enhance the efficiency of their working capital management practices.
Rehman & Khidmat (2014) conducted a study on nine chemical companies listed on the Pakistan Stock Exchange from 2001 to 2009, focusing on Return on Assets (ROA) as a measure of profitability The research examined various factors influencing profitability, including the quick ratio, current ratio, debt-to-equity ratio, and debt-to-total assets ratio The findings from the regression model analysis revealed that solvency ratios positively impact business profitability, while the other factors examined have a negative effect.
A study by Oladipupo, Adekanbi, and Oluwadare (2019) revealed that in Nigerian Stock Exchange-listed companies, cash receipts and expenditures negatively affect asset profitability, while the current ratio and inventory age positively influence it They recommended that businesses maintain a shorter collection period and avoid extending payment terms to benefit from cash discounts Additionally, proactive management based on material principles is essential to prevent idle resources that could detrimentally impact financial performance.
2.2.2 Empirical Studies in Viet Nam
A significant study on working capital management and profitability in Vietnam was conducted by Huynh Phuong Dong and Jyh-tay Su (2010), analyzing data from 130 companies on the Vietnamese stock exchange between 2006 and 2008 The study excluded companies in finance, insurance, and banking, as well as those with insufficient data The authors used the gross operating profit ratio as the dependent variable to represent profitability, while the cash conversion cycle served as the independent variable to assess its impact on profitability The findings of this research provide valuable insights into the relationship between working capital management and company profitability in Vietnam.
Research from 2010 confirms that an extended cash conversion cycle negatively impacts company profits, particularly highlighting a negative correlation between the receivable period and inventory with profitability In contrast, the payable period shows a positive correlation with profitability The study's variables demonstrate a high level of explanatory significance, ensuring both reliability and notable statistical significance.
Another study in Vietnam by Tu Thi Kim Thoa and Nguyen Thi Uyen Uyen
In a study conducted from 2006 to 2012, a multivariate regression model was employed to analyze the impact of working capital management on the profitability of 208 companies in Vietnam The research utilized Pooled OLS, FEM, and GLS models to assess how various factors, including receivables management, inventory management, cash flow management, and production process optimization, influence profitability The findings indicate a positive relationship between effective working capital management and company profitability, revealing that companies with strong management practices are more profitable The study emphasizes the need for businesses to enhance their debt collection abilities, manage inventory effectively, improve cash flow management, and optimize production processes to boost working capital efficiency and profitability.
A study by Duong Thi Hong Van and Tran Phuong Nga (2018) utilized multivariate regression analysis on a sample of 324 companies listed on the Vietnamese stock market from 2011 to 2015 The research focused on key indicators of working capital management, including the capital collection period, inventory turnover cycle, and receivable turnover cycle The findings indicate that effective working capital management positively influences the business performance of these companies Specifically, improved management practices lead to shorter capital recovery times, enhanced inventory turnover, and reduced receivable turnover, ultimately boosting financial performance and increasing profits.
Research Hypotheses
Accounts receivable play a crucial role in a business's short-term assets, with the collection period heavily influenced by the company's policies A longer average receivable period indicates less efficient capital use, as more capital remains tied up Conversely, a shorter average receivable period suggests better capital utilization and a higher ability to recover funds.
Numerous studies indicate that a longer average receivable period negatively impacts business profitability (Adeel, 2011; Sajid Gul, 2013; Rezarl Demiraj et al., 2022) This inverse relationship suggests that quicker collection of payments from customers enhances cash recovery, which improves liquidity Increased liquidity allows for faster inventory investment, boosts sales, and ultimately enhances the overall efficiency of the business Based on these findings, the author proposes the following hypothesis:
Hl: The average receivable period has a negative effect on profitability
The average inventory period is a key metric for assessing a business's inventory management efficiency Excessive inventory can lead to stagnation and potential losses, while insufficient inventory may hinder the ability to meet customer demands promptly Balancing inventory levels is crucial for optimizing operations and ensuring customer satisfaction.
A shorter inventory turnover period is associated with higher business profitability, as it leads to reduced costs related to storage and management, ultimately enhancing operational efficiency This relationship supports the hypothesis proposed by the author.
H2: The average inventory period has a negative effect on profitability.
The average payable period is a crucial metric in assessing working capital, as a longer period allows businesses to retain capital from suppliers for extended durations This relationship between average payables period and profitability is debated among experts Sajid Gul (2013) and Malhuva (2015) argue for a positive correlation, suggesting that extended payable periods enable businesses to reinvest capital, leading to increased profits Conversely, Rezart Demiraj et al (2022) contend that shorter payable periods correlate with enhanced business performance and profitability Thus, the author proposes the following hypothesis:
Hỉ: The average payable period has a negative effect on profitability.
The cash conversion cycle is a crucial metric for evaluating the efficiency of working capital management, as defined by Richards and Laughlin (1980) It represents the duration from the acquisition of raw materials and goods to the collection of payment, as noted by Ghosh (2010) and Chasha, Kavele, & Kamau (2022) This cycle is calculated by adding the inventory conversion period to the accounts receivable collection period and subtracting the payables deferral period Additionally, it serves as a tool for assessing the need for external capital, according to Tong & Wei (2011).
A longer cash conversion cycle can initially enhance a company's profitability through increased sales; however, as the costs of investing in working capital rise more rapidly than the advantages of maintaining higher inventory levels, overall profitability declines Conversely, a shorter cash conversion cycle is associated with improved profitability, as supported by research (Raheman & Nasr, 2007; Sajid Gul, 2013; T.T.K Thoa et al., 2014) Consequently, the author posits the following hypothesis.
H4: The cash conversion cycle has a negative effect on profitability.
In a concentrated market, larger firms tend to achieve higher profitability due to the economic advantages associated with their scale, such as lower interest and discount rates from bulk trading (Malik, 2011; Yazdanfar, 2013; Vătavu, 2014; Alghusin, 2015; Alarussi and Alhaderi, 2018) These economies of scale enable better specialization and labor division, ultimately reducing costs However, as firms grow, they may face increased management challenges and costs, leading to a point where further expansion can exceed market demand and fail to enhance profits (Goddard et al., 2005).
- Financial Leverage: Pecking order theory and trade-off theory explain the relationship between financial leverage and profitability in two different dimensions.
The capital structure order theory posits an inverse relationship between leverage and profitability, a finding supported by research from various authors including Goddard et al (2005), Malik (2011), Vătavu (2014), Alghusin (2015), Odusanya et al (2018), and Alarussi and Alhaderi (2018) Conversely, trade-off theory suggests that managers weigh the tax shield benefits against the risks of financial distress, which can affirm the connection between profits and leverage.
The current ratio measures a company's ability to cover short-term liabilities, with a low ratio indicating potential financial challenges and reduced debt repayment capacity This metric serves as a control variable in assessing its influence on business profitability (Islam et al., 2018).
Sales growth is a crucial indicator of business development, reflecting increased revenue, assets, and profits A higher growth rate signifies that a business is accumulating more assets, which in turn leads to greater revenue generation and enhanced profitability (Agiomirgianakis et al., 2006; Yazdanfar, 2013).
Research by Yazdanfar (2013) and others in 2006 suggests a positive correlation between growth rates and profitability However, contrary evidence from Glancey (1998) indicates that growth can negatively impact profits This occurs when the costs associated with growth outpace the actual growth rate, leading to a decline in profitability.
The Gross Domestic Product (GDP) growth is a crucial indicator that enhances business attractiveness to investors, elevates production levels, and raises individual incomes This increase in income stimulates consumer spending and boosts market demand, ultimately leading to higher business profits Consequently, GDP growth has a significant positive effect on the profitability of businesses.
High inflation leads to increased input prices and fluctuating output, resulting in instability in production processes and potential bankruptcy for businesses with profit margins lower than inflation This negative impact on production is supported by Odusanya et al (2018) and Vătavu (2014) Conversely, Keynesian theory suggests that moderate inflation can positively influence production, particularly in times of high unemployment, as rising prices can enhance profit expectations when they outpace production costs.
RESEARCH MODELS AND METHODOLOGY
Data and Sample
The population of this research comprised all the manufacturing firms listed on
The study analyzed data from the Ho Chi Minh Stock Exchange (HOSE) and the Ha Noi Stock Exchange (HNX), utilizing samples from FiinGroup, which included annual reports, management reports, and audited financial statements of 171 manufacturing enterprises from 2008 to 2019, totaling 2,052 observations Additionally, the research incorporated GDP and inflation rate data for Vietnam during the same period to assess the macroeconomic impacts on manufacturing enterprises.
Between 2008 and 2019, research focused on enterprises listed on the Ha Noi Stock Exchange (HNX) and Ho Chi Minh City Stock Exchange (HOSE), collecting annual data to reduce the effects of short-term shocks and market price fluctuations The study also considered the indirect impact of the US-China trade war from 2017 to 2019 on the Vietnamese economy, given that both countries are key trading partners Additionally, the data was gathered before the COVID-19 pandemic in 2020 to maintain accuracy and avoid epidemic-related disruptions The dataset samples were selected based on two specific criteria.
1 Enterprises do not operate in the fields such as insurance, banking, or securities which have industry-specific financial structures.
2 Enterprises are listed corporations that have disclosed their financial statements for 12 years (2008-2019).
Variables
This study explores the connection between working capital management and the profitability of manufacturing firms, utilizing return on assets (ROA) as the key metric for profitability ROA, defined as net income divided by total assets, assesses how effectively management utilizes a company's assets to generate earnings Due to its ability to relate a firm's profitability to its overall asset base, ROA is a preferred measure among researchers Previous studies, including those by Samiloglu and Demirgunes (2008), Garcia Teruel and Martinez Solano (2007), Nazir and Afza (2009), and Mathias (2012), have consistently employed ROA as a proxy for assessing the profitability of manufacturing firms.
In this study, we analyze key explanatory variables related to working capital management, specifically the Average Receivable Period (ARP), Average Inventory Period (AIP), Average Payable Period (APP), and Cash Conversion Cycle (CCC) ARP measures the time taken to collect cash from customers, AIP indicates how long it takes to convert inventory into sales, and APP reflects the duration for settling payments to suppliers The CCC, introduced by Richards and Laughlin in 1980, represents the total time from acquiring inventory to collecting receivables after sales A shorter CCC allows companies to minimize working capital investments, leading to lower borrowing costs and enhanced profitability, as noted by Deloof in 2003 The CCC is calculated by adding ARP and AIP, then subtracting APP.
In addition to the previously mentioned explanatory variables, the research team incorporated several control variables that influence a manufacturing firm's profitability, including firm size, sales growth, current ratio, and financial leverage Furthermore, the study also considered macroeconomic factors such as the inflation rate and GDP index in Vietnam.
From 2008 to 2019, this study examines macroeconomic factors as representative variables, utilizing control variables aligned with established empirical literature, including works by Deloof (2003), Garcia Teruel and Martinez Solano (2007), Nazir and Afza (2009), Raheman and Nasr (2007), Huang et al (2009), and Shin and Soenen.
1998 and Mathis, 2012) Table 1 below shows the variables used in this research: variable name, variable abbreviation and variable measurement.
Table 1: Variable Measurements and Abbreviations Variable Variable name Abbreviation Measurement
Variable Return on Assets ROA Net income/Total Assets
Inventory Period AIP (Inventory/Cost of Goods
Period APP (Account Payable/Cost of
Cycle ccc ARP + AIP - APP
Firm Size SIZE Natural Logarithm of total assets
Leverage LEV Total Debt/Total Assets
Sales Growth SALESGR Salest+1 — Salest
Inflation rate INFL Data from World Bank
Product GDP Data from World Bank
Model Specifications
This study investigates the correlation between working capital management—specifically Accounts Receivable Period (ARP), Accounts Inventory Period (AIP), Accounts Payable Period (APP), and Cash Conversion Cycle (CCC)—and the profitability of manufacturing firms, as indicated by Return on Assets (ROA), utilizing four distinct regression models.
(/): ROAit = a + pỊ * ARPit + p2 * Controlsit + 8it (2): ROAi t = a + Pl * AIPit + p2* Controlsit + 8it (3): ROAit = a + P j * APPit + 02 * Controlsit + 8it (4): ROAit = a A- Pl * cccit A- 02 * C(mtrolsi t A- 8i t
- i: refers to the firm number
- ROAit: denotes profitability of manufacturing firm i in year t
- ARPit, AIPit>APPit:, cccit: four core working capital management activities
- Controls! t\ set of control variables that are listed in Table 1
- 8if:: errors are assumed to be independently random and normally distributed with zero mean and constant variance.
3.4.1 Methods of Collecting Documents and Data
The research team analyzes scientific articles from both domestic and international specialized journals, as well as textbooks, to identify research gaps and conduct comparisons This approach allows for the evaluation of empirical research findings against previous studies in the field.
For research data, the author collected secondary data from financial reports of
171 manufacturing enterprises listed on the Vietnam stock exchange.
3.4.2 Data Processing and Analysis Methods
The research team employs Stata 14 software for statistical analysis and data comparison Utilizing various methodologies, the study incorporates descriptive statistics alongside pooled regression models, including Pooled OLS, Random Effects Model (REM), Fixed Effects Model (FEM), and Generalized Least Squares (GLS) models to derive comprehensive results.
- GLS) to estimate the model.
The research employs a Pooled OLS model, analyzing data from manufacturing enterprises listed on the Ho Chi Minh City Stock Exchange (HOSE) and the Ha Noi Stock Exchange (HNX) This model regresses all stacked data without accounting for individual or cross-sectional variations, treating enterprises as homogeneous entities However, this approach overlooks the unique characteristics of each business, which can significantly impact transparency and lead to biased estimates, as it fails to control for unobservable factors that are specific to each individual and remain constant over time (Gujarati & Porter, 2004).
Due to the limitations of Pooled OLS, this study utilized two additional regression models: the Random Effects Model (REM) and the Fixed Effects Model (FEM) REM assumes that individual business characteristics are random and uncorrelated with the explanatory variables, treating firm-specific residuals as new explanatory variables In contrast, FEM accounts for unique characteristics that may influence the explanatory variables, allowing for the separation of time-invariant effects from the explanatory variables This enables a clearer estimation of the net effects of the explanatory variable on the dependent variable (Gujarati & Porter, 2004) Unique characteristics, such as management style or philosophy, are specific to each business and do not correlate with those of other firms, highlighting the diverse origins and traits of the companies involved.
The Hausman test is utilized to determine the most suitable model for reinforcing the study's argument Following the test's results, the fixed effects model (FEM) was selected, prompting the research to conduct various regression assumption tests These tests included verifying that the average error value equals zero, assessing multicollinearity, checking for autocorrelation, examining heteroskedasticity, testing for endogeneity, and ensuring the normal distribution of residuals to identify any potential flaws in the regression model.
The research employed the generalized least squares (GLS) method for regression analysis to address model deficiencies, particularly when the assumption of homogeneity of variance among observation units is violated.
ANALYSIS OF RESULTS AND DISCUSSION
Descriptive statistics
Utilizing STATA software, the statistical analysis reveals key variables in the model: return on assets (ROA) indicates the profitability of manufacturing firms, while the average receivable period (ARP), average inventory period (AIP), average payable period (APP), and cash conversion cycle (CCC) are essential for assessing working capital management Additionally, control variables such as the ratio of total debts to total assets (financial leverage or LEV), current ratio (CR), firm size (SIZE), revenue growth rate (SALESGR), gross domestic product index (GDP), and inflation rate (INFL) are detailed in Table 2.
Variable Obs Mean Std Dev Min Max
The research analyzed 2052 observations from 171 listed manufacturing enterprises on the HNX and HSX exchanges between 2008 and 2019 Data was sourced from audited financial statements, annual reports, and the World Bank.
The descriptive statistics reveal that the average Return on Assets (ROA) in the manufacturing industry stands at 7.25%, indicating a moderate level of profitability compared to other sectors However, the profitability varies significantly among enterprises, with values ranging from -1.69 to 0.78, highlighting the diverse financial performance within the industry.
The average collection period (ARP) across companies is 76.02 days Notably, Dien Quang Bulb Joint Stock Company (DQC) recorded the longest ARP at 900.57 days in 2008, whereas Phu Nhuan Jewelry Joint Stock Company achieved the shortest ARP with just 1.44 days.
The average inventory turnover period (AIP) stands at 109.53 days, with a significant standard deviation of 98.06 days, indicating a range from 0 to 2213.69 days A high AIP can expose businesses to liquidity risks, while low inventory levels may suggest understocking, potentially leading to lost sales Conversely, shorter average inventory periods are often viewed as a sign of effective management.
The average payment period (APP) for companies is 179.5 days, with a standard deviation of 161.02 days On average, companies take 76.02 days to collect payments, 109.53 days to sell their inventory, and face a waiting period of 179.5 days to settle their bills.
The average cash conversion cycle (CCC) was 6.05 days, reflecting the time needed for working capital to be converted into cash However, the maximum CCC reached approximately 693 days, indicating potential mismanagement of the cash conversion process.
The firm size, measured by the natural logarithm of total assets within the industry, exhibits a consistent range from 23.55 to 32.25, with an average of 27.05 Additionally, the average financial leverage stands at 0.48, indicating that, on average, companies finance half of their assets through debt.
The companies studied exhibit a generally strong liquidity position, with an average current ratio of 2.13, aligning closely with the ideal benchmark of 2 Notably, one company recorded a maximum current ratio of 43.02, suggesting inefficiencies in utilizing current assets Conversely, a minimum current ratio of 0.045 highlights a company's struggle to meet its current liabilities.
The analysis reveals that sales growth (SALESGR) in Vietnamese manufacturing enterprises has a mean value of 0.154 and a standard deviation of 1.04, indicating significant variability with a range from -1 to 42.22 This substantial difference highlights the high dispersion in revenue growth among these organizations, which is influenced by both external environmental factors and internal company dynamics.
The inflation rate (INFL) has shown significant variability over the years, with an average of 0.0755 and a standard deviation of 0.065 In comparison, the gross domestic product index (GDP) has an average value of 0.064, fluctuating between 0.054 and 0.075.
Correlation Analysis
Using the commands "pwcorr" to determine the correlation coefficient based on the following table that displays the pairwise correlation between the study variables.
ROA ARP AIP APP ccc SALESGR LEV CR SIZE GDP INFL
The correlation coefficient indicates the strength of the relationship between pairs of variables, with values approaching 1 signifying a strong correlation and values near 0 indicating no correlation The correlation matrix reveals a significant linear relationship between variables such as ARP, AIP, APP, ccc, SALESGR, LEV, CR, SIZE, GDP, and INFL with ROA Notably, the correlation between ARP and ccc is 0.8521, AIP and ccc is 0.723, and APP and ccc is 0.8764 This high correlation can be attributed to the equation ccc = ARP - AIP - APP, which demonstrates the interconnectedness of these variables.
To mitigate the risk of serious multicollinearity in the estimated model, four models will be utilized The remaining correlation pairs exhibit low correlation coefficients (less than 0.5), indicating a lack of strong correlation among the variables Consequently, it is likely that autocorrelation will not arise within the model.
Regression Results
The Pooled Ordinary Least Squares (OLS) model often fails to address endogeneity, a common issue in financial research, despite yielding statistically significant results for some variables This endogeneity arises from unobserved heterogeneity, measurement errors, and simultaneity among variables Due to the potential for inconsistent estimation outcomes with the Pooled OLS approach, this research employed additional analyses using alternative methodologies.
*** ** an(Ị ^ cỊenOfe significance at 1%, 5% and 10% levels, respectively.
To enhance the accuracy of research results in the financial sector, the team opts for panel data over cross-sectional data, as the latter fails to capture business fluctuations over time By employing regression analysis with panel data, they can effectively control unobservable variables that introduce noise and bias in the model This approach allows for a deeper understanding of the factors influencing business operating profits Additionally, the team incorporates analytical methods such as the fixed effects model (FEM) and random effects model (REM) to further refine the model's precision and reliability.
*** ** ancỊ * denOỊe significance at 1%, 5% and 10% levels, respectively.
*** ** ancỊ * ^enote significance at J %, 5% and 10% levels, respectively.
The regression analysis results using fixed effects (FEM) and random effects (REM) are detailed in Tables 4 and 5 FEM results indicate that the variables ARP and ccc significantly negatively affect ROA at the 1% and 5% levels, respectively, while AIP and APP show positive coefficients but lack statistical significance In contrast, REM analysis reveals that ARP, APP, and ccc also exhibit negative coefficients with statistical significance at the 1% and 5% levels, except for AIP, which remains statistically insignificant.
To improve the precision of our analysis and reduce data noise, our team opted to use the FEM model This model emphasizes the factors influencing variations among observation units within the sample data and effectively manages unobservable elements by controlling for them.
To identify the optimal model between the Fixed Effects Model (FEM) and the Random Effects Model (REM), our team utilized the Hausman test This econometric test assesses the correlation between the model's residuals and independent variables to ascertain the most suitable model The hypothesis formulated for this analysis guided our evaluation.
HO: Choose the REM modelHI: Choose the FEM model
The Hausman test results from Stata in both 4 models were less than 0.05 (Table
6) This result indicates that the regression model with fixed effects (FEM) is more appropriate Therefore, our team opted for the FEM model, which ensures consistent and effective results.
Table 6: Results of Hausman Test
Hausman Test Appropriate model Model 1 Chi2 = 59.93 Prob>chi2=0.0000 < 0.05 FEM
Model 2 Chi2 = 67.16 Prob>chi2=0.0000 < 0.05 FEM
Model 3 Chi2 = 65.47 Prob>chi2=0.0000 < 0.05 FEM
Model 4 Chi2 = 48.01 Prob>chi2=0.0000 < 0.05 FEM
Regression Assumptions Test
Following the selection of the FEM model based on Hausman test outcomes, the research team validated the reliability of the regression estimations by examining key assumptions of the regression model This process aimed to identify potential model defects, including ensuring that the average error value is zero, assessing for multicollinearity, heteroskedasticity, autocorrelation, endogeneity, and confirming a standard normal distribution of residuals.
4.4.1 The average value of the errors is equal to 0
For most tests, we cannot observe disturbances and it is therefore difficult to perform tests on residuals But because the average value of the residual will always be
In this study, we ensure that the intercept coefficient remains constant within the regression model, thereby satisfying the assumption that the average value of the errors equals zero.
The linear regression model assumes that the independent variables do not have an exact linear relationship, or in other words, the model does not have multicollinearity.
Multicollinearity is the phenomenon in which independent variables in the model depend on each other and can be expressed as a function.
Variable VIF 1/VIF Variable VIF 1/VIF
Variable VIF 1/VIF Variable VIF 1/VIF
The Variance Inflation Factor (VIF) is a crucial tool for identifying multicollinearity in statistical models, as high correlation among variables can lead to significant multicollinearity issues To address this, the research team performed a detailed analysis using VIF, employing the command "vif" to calculate the variance magnification coefficients for four different models.
Table 7 shows that there is no multicollinearity phenomenon between explanatory variables (VIF coefficient runs from 1.01 - 1.72, VIF < 10) according to Hoang Trong and Chu Nguyen Mong Ngoc (2017).
Autocorrelation is a statistical occurrence where the residual errors in a model show correlation with one another, potentially resulting in inflated statistics, biased parameter estimate variances, and ineffective forecasting.
A key assumption of the classical linear regression model is the presence of constant error variance among residuals When this assumption is violated, resulting in heteroscedasticity, the reliability of statistical inferences may be compromised, potentially leading to incorrect conclusions.
A statistical test known as the Woolridge test was conducted using the "xtserial" command to determine the presence of autocorrelation in the model Two hypotheses were considered:
HO: the model does not exhibit autocorrelation, HI: the model exhibits autocorrelation.
The Stata test results indicated that all four models demonstrated P-values below 0.05, allowing for the rejection of the null hypothesis (HO) and suggesting the existence of autocorrelation within the model.
To determine the presence of heteroscedasticity, the ”xttest3" command was employed, and two hypotheses were considered:
HO: the model does not exhibit heteroscedasticity, HI: the model exhibits heteroscedasticity.
The test results from Stata showed that both 4 models have P-values less than 0.05, indicating that the alternative hypothesis (Hl) can be accepted, implying that the model exhibits heteroscedasticity.
Table 8: Results of Heteroscedasticity and Autocorrelation Test
Wooldridge test Modified Wald statistic
In econometric models, the assumption that independent variables are uncorrelated with errors is crucial to avoid endogeneity; however, this phenomenon often arises due to missing variables When important factors are excluded from the model, it creates a correlation between the independent variables and the omitted variables, diminishing the explanatory power of the independent variables on the dependent variable Consequently, this can lead to biased estimation results, further complicating the issue of endogeneity in the analysis.
4.4.5 Standard Normal Distribution of Residuals Test
In classical linear regression models, it is crucial to assume that residuals follow a normal distribution However, factors like model misspecification, heteroscedasticity, or insufficient data can lead to deviations from this assumption To test this hypothesis, we utilized the "histogram" command to create a frequency histogram of the residuals and the "sktest" command to perform the Jarque-Bera test, which assesses Skewness and Kurtosis values The hypotheses for the Jarque-Bera test are outlined accordingly.
HO: The residuals have a normal distribution,
HI: The residuals have a non-normal distribution.
Table 9: Skewness/Kurtosis tests for Normality
Skewness/Kurtosis tests for Normality Residual Obs Pr(Skewness) Pr(Kurtosis) adj chi2 (2) Prob>chi2 ut_l 1,873 0.0000 0.0000 • 0.0000 ut_2 1,873 0.0000 0.0000 • 0.0000 ut_3 1,873 0.0000 0.0000 • 0.0000 ut_4 1,873 0.0000 0.0000 • 0.0000
Figure 2: Normal Distribution of Residuals of Model 1
Figure 3: Normal Distribution of Residuals of Model 2
Figure 4: Normal Distribution of Residuals of Model 3
Figure 5: Normal Distribution of Residuals of Model 4
The analysis of Figures 2 to 5 indicates that the model's residuals do not follow a normal distribution Statistical tests for Skewness and Kurtosis yield P-values below 0.05 across all four models, leading us to reject the null hypothesis and accept the alternative hypothesis of non-normal distribution This finding suggests that, despite having over 2000 observations, the model's distribution remains non-normal.
Correcting Model Defects
The Hausman test indicates that the fixed effects model (FEM) is suitable; however, further analysis has uncovered issues such as autocorrelation, heteroskedasticity, and non-normality in the residual distributions, which compromise the reliability of the estimated results.
To enhance the reliability of our assessment, we re-evaluated the research model utilizing the Generalized Least Squares (GLS) method to address previous limitations The findings, along with the estimation results, are detailed in Table 10.
*** ** and * denote significance at 1%, 5% and 10% levels, respectively.
The GLS regression analysis employing the cross-sectional time-series method yielded Wald chi2 values of 875.81 for model 1, 781.52 for model 2, 845.33 for model 3, and 787.78 for model 4 With p-values of 0.0000, all results are statistically significant, confirming that the GLS regression effectively mitigates heteroskedasticity and autocorrelation concerns within the models.
The statistical analysis indicates that the four variables associated with working capital management exhibit a negative correlation with Return on Assets (ROA) Furthermore, these variables are statistically significant in all four models at the 1% and 5% significance levels.
The regression results will be mentioned and explained more thoroughly in section 4.6 below.
Discussions
- 0.00251 * CRi>c + 0.00421 * SIZEiit + 0.0574 * GDPi>t + 0.00793 * INFLiit + siit
Model 1 shows that the average collection period (ARP) has a regression coefficient of-0.000134 and the p value is -7.65, confirming the negative relationship between collection period (ARP) and profitability of manufacturing businesses in the research sample from the period 2008-2019 with a statistical significance level of I % This result indicates that manufacturing enterprises can increase profitability by shortening credit terms for customers or applying effective debt collection methods In the working capital structure of these companies, receivables account for a large proportion This also shows that listed companies in the research sample often prioritize granting credit to customers to increase revenue However, to sell more products, business departments of businesses tend to loosen their credit policies On the other hand, if there are no timely debt collection measures, extended late payment periods from customers will affect businesses' cash accounts, leading to misappropriation of capital and even the possibility of not being able to pay debt recovery, causing reduced profitability Therefore, the hypothesis Hl is completely consistent and consistent with previous results of Adeel Mumtaz et al (2011), Mustafa Afeef (2011) and Sajid Gul (2013).
The GLS regression analysis of model 2, based on 1,873 observations from 2008 to 2019, reveals a statistically significant negative relationship between the average inventory period (A1P) and the profitability of manufacturing enterprises, with a correlation coefficient of -0.0000121 and a p-value of -1.53 at the 5% significance level Specifically, a one-day reduction in the AIP correlates with an increase in Return on Assets (ROA) by 0.00121% This finding underscores that shorter inventory periods enhance profitability by lowering storage and warehousing costs and mitigating risks associated with product damage These results support hypothesis H2 and align with previous studies, including those by Adeel Mumtaz et al (2011) and Mustafa Afeef.
The results from the third model indicate that the Average Payment Period (APP) is a significant determinant of Return on Assets (ROA), with a negative regression coefficient of -0.0000425 and a p-value of -4.74, demonstrating statistical significance at the 1% level This finding supports hypothesis H3 and suggests that reducing the time taken to pay creditors can enhance profitability Specifically, a one-day decrease in the average payment period is associated with an increase in ROA of 0.00425% A common explanation for this relationship is that early payments to suppliers can lead to improved product quality and services, thereby boosting a firm's overall profitability These results align with the findings of Tu Thi Kim Thoa and Nguyen Thi Uyen Uyen (2014), while differing from those of Sajid Gul (2013) and Mathuva (2015).
The cash conversion cycle (CCC) is a critical metric that reflects how effectively a manufacturing firm manages its working capital, with a negative impact on profitability According to model 4, the estimated coefficient of -0.00000754 and a p-value of -0.6 indicate statistical significance at the 5% level, suggesting that a one-day reduction in CCC leads to a 0.000754% increase in Return on Assets (ROA) CCC is defined as the duration between the purchase of raw materials and the receipt of payment (Ghosh, 2010; Chasha, Kavele, & Kamau, 2022) Shortening the CCC enhances business efficiency, prompting companies to prioritize reducing inventory and customer collection times This strategic focus aids in making better financial decisions and boosting overall profitability The negative correlation between CCC and profitability supports hypothesis H4 and aligns with previous research findings by Sajid Gul (2013).
Tu Thi Kim Thoa and Nguyen Thi Uyen Uyen (2014).
In the analysis of control variables across four models, all were found to be highly significant at the 1% level, except for inflation (INFL) The results indicate an inverse relationship between the current ratio (CR) and profitability, suggesting that improved liquidity negatively impacts a firm's profitability Conversely, sales growth (SALESGR) demonstrates a significant positive effect on return on assets (ROA), highlighting the importance of business opportunities Additionally, firm size (SIZE) is positively correlated with performance, indicating that larger firms benefit from economies of scale, leading to greater profitability Interestingly, contrary to theoretical expectations, increased debt financing, as indicated by the financial leverage (LEV) variable, negatively affects profitability.
This study introduces two key macroeconomic variables, inflation rate (INFL) and GDP index (GDP), to examine their impact on the profitability of manufacturing enterprises The findings reveal that both indicators positively influence profitability, with GDP showing strong statistical significance at the 1% level across all models As GDP rises, economic sectors expand, leading to lower unemployment rates and increased income and purchasing power This growth enables investments in infrastructure, fostering sustainable economic development A country with a high GDP gains a stronger position internationally, enhancing opportunities for cooperation and boosting import and export activities Consequently, increased purchasing power and a stable economy allow businesses to grow revenue, reduce costs, and achieve higher operating profits.
Research indicates that the inflation rate (INFL) does not consistently negatively affect a firm's profitability When inflation rises, consumers often hoard goods and convert cash into tangible items, which can lead to increased business profits Additionally, for export-focused businesses, a high domestic inflation rate may still be advantageous if it remains lower than the inflation rates of trading partner countries, giving exported products a competitive edge However, it is important to note that INFL shows weak statistical significance (only at 10%) across all four models, suggesting caution in drawing definitive conclusions about the relationship between inflation rate and the profitability of manufacturing enterprises.
APP — 1% Accept Hypothesis H3 ccc — — 5% Accept Hypothesis H4
The research findings on the influence of working capital management on the profitability of manufacturing enterprises are summarized in Table 11 The study discusses various variables that measure the effectiveness of working capital management and its subsequent effects on a firm's profitability Results indicate multiple pathways through which working capital management affects the profitability of manufacturing firms Additionally, the research utilizes foundational theories and prior studies to support its conclusions The upcoming chapter will summarize these findings and offer recommendations for manufacturing enterprises to enhance their working capital management, ultimately aiming to boost profitability.
CONCLUSIONS AND RECOMMENDATIONS
Findings
Effective working capital management is essential for operational efficiency in businesses, particularly in the Vietnamese manufacturing sector, where current assets represent a substantial portion of total assets This study analyzes listed manufacturing companies on the Vietnamese stock exchange over a 12-year period from 2008 to 2019, highlighting the importance of working capital management amidst regulatory and economic challenges The research reveals significant negative relationships between four independent variables—Average Receivable Period (ARP), Average Inventory Period (AIP), Average Payable Period (APP), and Cash Conversion Cycle (CCC)—and profitability, as measured by Return on Assets (ROA), indicating that these factors adversely affect the financial performance of manufacturing firms.
The study reveals that as the duration for ARP, AIP, APP, and CCC extends to 5% and 10% levels, the profitability of these companies declines Additionally, it highlights the influence of various factors on company profitability, including company size, financial leverage, current payment ratio, revenue growth rate, inflation rate, and GDP.
Managerial Implications
Based on identifying elements of working capital management policy that affect the profitability of manufacturing enterprises, the article proposes some following recommendations to improve corporate financial efficiency Specifically:
To optimize inventory management, it is essential to establish proactive inventory policies and select diverse supply sources to maintain appropriate stock levels that meet customer demands efficiently Regular organization and classification checks will enhance tracking capabilities in storage and inventory records Additionally, managing and rotating inventory frequently will help minimize associated costs, ultimately driving higher profitability.
- Second, develop appropriate sales policies for each stage, suitable to business products and market economic conditions Balance factors in credit policy: purchase volume, product types, business risks, credit term,
To optimize financial management, businesses should negotiate favorable payment terms with suppliers and actively monitor payables By leveraging modern technologies to reduce production cycles and costs, companies can enhance the efficiency of working capital turnover Additionally, shortening the average payment period fosters trust with suppliers, enabling them to deliver better products and services.
To enhance financial stability and seize profitable investment opportunities, businesses should establish a robust cash flow management system This involves maintaining adequate cash reserves and making timely investments, such as loans and capital contributions Additionally, companies must prioritize reducing inventory turnover and accelerating customer payment collection, ensuring a steady capital flow that facilitates informed financial decisions and boosts overall profitability.
Expanding business scale and driving revenue growth are crucial for enhancing profitability Companies should integrate these factors into their working capital policies Additionally, staying attuned to market economic conditions and regularly monitoring GDP trends will enable businesses to adjust their strategies effectively.
Limitations
The research still has some limitations:
- First, the scope of the research only focuses on manufacturing enterprises listed on the stock market.
- Second, the research has not identified and measured different measures of profitability, and has not measured and identified the impact of each individual industry.
- Third, limitations relate to the accounting practices used by businesses, as the value of inventory and receivables can be significantly affected.
The research may be affected by endogeneity, and while the Generalized Least Squares (GLS) model addresses certain limitations, it does not fully resolve this issue To effectively tackle endogeneity, a higher-quality model is necessary Additionally, when analyzing over 2,000 observations, the data distribution tends to approximate a normal distribution.
The research was conducted within a limited timeframe and faced constraints in resources and data Additionally, the use of measurement scales and theories from foreign studies may not fully align with the Vietnamese context, potentially leading to overlooked variables Consequently, the topic could not be developed in a comprehensive and flawless manner.
Recommendations
The current study analyzed data from 171 enterprises in Vietnam's manufacturing sector over a span of 12 years, resulting in a limited sample size of only 2,052 observations This constraint affects the representativeness of the findings for the entire manufacturing industry While the research primarily measures business profitability through return on assets (ROA), it does not explore the influence of working capital management on return on equity (ROE) or enterprise value as indicated by Tobin's Q index.
The author suggests broadening the research sample to enhance representativeness across the entire manufacturing industry Additionally, future studies could examine the influence of working capital management on profitability across various economic sectors Furthermore, research could assess business profitability through return on equity (ROE) or evaluate business value using Tobin’s Q index.
This article primarily examines research conducted from 2008 to 2019, constrained by limited time and resources Future studies could broaden their scope by incorporating a wider range of businesses and extending the timeline, particularly to include the significant impacts of the Covid pandemic Additionally, exploring various industries and fields would yield more comprehensive insights Employing different metrics would also facilitate easier comparisons of the effects of working capital on profitability across each measure.
The business environment, encompassing the political system, economic laws, policies, social culture, and infrastructure, plays a crucial role in determining a company's profitability Expanding operations to additional countries can further diversify market institutions and enhance business opportunities.
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