VIETNAM NATIONAL UNIVERSITY, HANOI INTERNATIONAL SCHOOL STUDENT RESEARCH REPORT HOW DOES WORKING CAPITAL MANAGEMENT AFFECT THE FINANCIAL PERFORMANCE: A COMPARISON OF F&B AND RETAIL...
INTRODUCTION
Rationale of the study
Financial performance plays an important role in the management activities of corporations Financial performance activities help to ensure capital for enterprises, to take measures to elevate operation efficiency, and to control the business operation of firms Lamberson (1995) stated that the management of working capital is a very important issue within a company, some financial managers seek to identify the concept of controlling working capital and working capital adequacy level The adequacy of working capital will affect the activities of the company, and more broadly the business activities With sufficient working capital, then management can more freely create products the market needs
Working capital management (WCM) is a crucial aspect of corporate finance, directly impacting a company's financial health By effectively managing current assets (inventory, receivables, cash) and current liabilities (accounts payable), businesses can influence their liquidity, profitability, and overall financial performance Working capital measures a company’s efficiency and represents the liquid assets that are available to a firm It also indicates the firm’s short-term financial health and its capacity to meet day-to-day operating expenses
Therefore, working capital management has a significant impact on company performance However, in reality, for Vietnamese Enterprises in general and F&B and Retail Enterprises in particular, how to manage working capital to impact financial performance is still a significant issue considered
In the context of many market fluctuations, data from the 2022 Socio-Economic Situation Report of the General Statistics Office estimates that GDP in 2022 will increase by 8.02% compared to 2021 due to the economic recovery and reaching the highest increase in 2011-2022 Of which, the retail industry grew significantly (10.15%), contributing greatly to the growth rate of the total value of the entire economy Meanwhile, the food and beverage industry had the highest growth rate in the region (40.61%)
In addition, according to the Report on industrial production and commercial activities in November 2023 of the Ministry of Industry and Trade, the total scale of Vietnam's retail market reached more than 140 billion USD and is forecast to increase to about
350 billion USD by 2025 This number will contribute about 59% of the country's GDP The Retail industry is expected to be a high-growth industry, contributing significantly to the country's GDP growth and presenting economic restructuring towards increasing the proportion of industry and services Expanding the scale of the retail market in Vietnam always comes with challenges in managing the working capital of businesses, as this industry often has short business cycles, high inventory levels and high turnover rates Profit margins are low, so the use of working capital has an immediate impact on the financial performance of Retail corporations
Besides, the F&B industry is considered to have more positive changes than the Retail industry as in the first months of 2024, Vietnam's F&B market is quieter, and consumer spending has decreased significantly Difficulties in the economy directly impact people's income The sentiment of tightening spending is reflected in the Retail and Consumer markets in general and the F&B industry market in particular However, the overall picture of Vietnam's F&B industry is not all gray Despite the global economic recession, the Food and Beverage (F&B) market in Vietnam is having rapid expansion of leading chains The F&B industry market is forecast to grow 18% this year and reach 1 million billion VND by 2026 F&B industry trends in
2024 in Vietnam will witness fierce competition between large chains to gain market share, while small chains will become more cautious Vietnam's F&B market also requires business owners to solve many challenges First is the pressure on capital and the ability of businesses to mobilize finance In particular, with small corporations, cash flow management is still unprofessional, which causes losses A significant amount of money has been and continues to be lost because businesses have not optimized the effective use of this capital source
Effective working capital management brings many benefits to F&B and Retail businesses The first is to increase liquidity: ensure enough cash to pay loans, obligations and bills on time, maintain credit and avoid late penalties Next is profit optimization: using working capital effectively helps businesses free up capital to invest in other profitable activities, increasing profits Besides, good working capital management helps businesses have a competitive advantage over other competitors in the industry Thus, the research team has found a direct impact on the financial performance, especially important in the F&B and retail industries with high capital turnover In addition, the differences between the F&B and retail industries such as business characteristics and working capital management activities need to be compared to evaluate the impact and come up with appropriate solutions.
Research objectives
Working capital management plays a pivotal role in determining the financial performance of businesses This study aims to investigate the impact of working capital management on the financial performance of Food & Beverage (F&B) and Retail corporations in Vietnam By comparing these two industries, the study seeks to shed light on the specific factors and strategies that contribute to effective working capital management and its subsequent impact on overall financial health.
By analyzing and comparing the working capital practices of these corporations, valuable insights can be gained into their financial health, efficiency, and profitability This study aims to provide concrete evidence and data-driven conclusions that can guide strategic decision-making for businesses operating in these industries
Understanding how working capital management affects financial performance is critical for companies in F&B, Retail as well as other industries looking to optimize operations, enhance liquidity, and maximize profitability
The group's research brings scientific significance: Adding more knowledge about the impact of working capital management on financial performance Provides comparative information on the effectiveness of working capital management between the food, beverage, and retail industries in Vietnam Helping business administrators better understand the importance of working capital management as well as providing solutions for businesses to improve their performance Improve working capital management efficiency and improve financial efficiency The research team hopes to uncover key findings and implications from this insightful study that can revolutionize financial strategies in the F&B and Retail industry in Vietnam.
Research questions
This study aims to evaluate the impact of working capital management on financial performance and compare management efficiency between the food beverage and retail industries in Vietnam We have posed the following research questions:
How does working capital management specifically affect the financial performance of a business?
Is the level of influence of working capital management factors different between the food and beverage and retail industries?
What solutions help improve working capital management efficiency for each industry?
From those questions, we hope to find research benefits That is to provide scientific evidence about the impact of working capital management on financial performance Next is to identify similarities and differences in working capital management between the two industries The team will then propose appropriate solutions for each industries, helping businesses improve capital efficiency and optimize profits.
Research method
Data is collected from Vietstock Stock Exchange, financial statements and annual reports of the corporations F&B and Retail industries in Vietnam
To serve the research objectives, the topic uses research methods below:
Data collection method: data is collected on Vietstock Stock Exchange and audited financial reports and annual reports of businesses The data is quantitative The study is based on the results of analyzing statistical data information to compare and contrast, then synthesize into tables to analyze and evaluate the impact of working capital management on the financial performance of F&B and Retail industries listed on the Vietnam stock market
The research employs statistical techniques for data analysis, including descriptive statistics, correlation analysis, and exploratory analysis The findings are then analyzed using regression models constructed specifically for the research sample The coefficients of the regression model variables serve as the basis for the author's analysis of the results.
Structure of the study
In addition to the introduction, conclusion, list of tables, figures, list of references, appendices, the topic is structured into 5 chapters:
LITERATURE REVIEW AND HYPOTHESIS DEVELOPMENT10 2.1 Working Capital concept
DATA & METHODOLOGY
Analytical Frame work
Variables Reflecting Factors
The study uses data to measure working capital management activities to achieve financial efficiency, in other words, finds the relationship between ROA and the 12 factors below:
Debt to assets equity to assets Equity Turnover
Acrual Ratio CF Cash Ratio
Short-term assets/Total assets
Short-term Liabilities to Equity Short-term Ratio
Short-term Receivebles/Short- term assets
Short-term liabilities/Total Liabilities
Table 3-1: Summary table of how to measurement variables
Dependent variables Return on assets Net income/Total assets %
(Net profit – Cash flow from Operating Activities - Adjusted cash flow from Investment Activities) / (Average Net Operating Assets)
Cash Ratio Cash + Cash Equivalents / Current
Debt To Assets Total Debt/Total Assets %
Quick Ratio (Current assets - Inventory)
Receivable Turnover Net Credit Sales/Average
Short-term Liabilities to Equity %
Short-term Receivables/Short- term Assets
Equity to Asseta Net worth/Total assets %
Equity Turnover Net Revenue/Average
Inventory Turnover COGS/Average Value of Inventory
Short-term liability to total liabilities
Hypothesis development
Based on actual research and expectations about the relationship between the dependent variable and the independent variable, the author hypothesizes for each variable in the following research:
H1.1: There is a significant relationship between Liquidity ratio and Return on Assets (ROA) of the corporations in Retail industry
H1.2: There is a significant relationship between Liquidity ratio and Return on Assets (ROA) of the corporations in F&B industry
H2.1: There is a significant relationship between Leverage ratio and Return on Assets (ROA) of the corporations in Retail industry
H2.2: There is a significant relationship between Leverage ratio and Return on Assets (ROA) of the corporations in F&B industry
H3.1: There is a significant relationship between Accrual ratio CF and Return on Assets (ROA) of the corporations in Retail industry
H3.2: There is a significant relationship between Accrual ratio CF and Return on Assets (ROA) of the corporations in F&B industry
H4.1: There is a significant relationship between Short-term receivables/Short-term Assets and Return on Assets (ROA) of the corporations in Retail industry
H4.2: There is a significant relationship between Short-term receivables/Short-term Assets and Return on Assets (ROA) of the corporations in F&B industry.
Methodology
To evaluate the impact of working capital management on the financial performance of businesses in the F&B and Retail industries in Vietnam, the team will conduct research on the topic according to the following steps:
First, Descriptive Statistics and Correlation Analysis were used to describe the basic quantitative characteristics of the data in this study It includes the following steps: Step 1: Calculate the mean, median, maximum, minimum, and standard deviation values to make a basic conclusion and assessment of the sample
Step 2: We calculate the correlation between variables to ensure the meaning of the regression analysis and find the relationship between the independent variables and the dependent variable
Second, we decided to come up with a plan to run KMO, Variance Explained, and Rotated Component Matrix on SPSS software to build a regression model In particular, use the average method to select the variables with the most optimal values
After completing the regression model for each industry, our team will perform the regression model analysis process and provide comparative comments between the Retail and F&B industries, then come up with solutions.
RESULTS & DISCUSSIONS
Descriptive Statistics
Table 4-1: Descriptive Statistics of Sample
N Minimum Maximum Mean Std Deviation
Whole sample Retail F&B Whole sample Retail F&B Whole sample Retail F&B Whole sample Retail F&B Whole sample Retail F&B Accrual ratio CF 264 81 183 -0.695 -0.193 -0.695 0.658 0.658 0.543 0.036 0.031 0.038 0.140 0.101 0.154
Short-term assets/Total assets 264 81 183 0.170 0.170 0.170 0.936 0.936 0.889 0.617 0.615 0.617 0.178 0.219 0.157
Short-term liabilities to equity 264 81 183 0.051 0.084 0.051 16.662 16.662 8.161 1.089 1.740 0.801 1.493 2.270 0.825
Short-term receivables/Short- term assets
Short-term liabilities to total liabilities 264 81 183 0.057 0.057 0.095 1.000 1.000 1.000 0.841 0.785 0.865 0.223 0.299 0.175
The descriptive statistics results show that the variables in the estimated model all collect enough data with 264 observations:
ROA dependent variable represents the ability to generate profits from total assets of listed businesses in the sample with an average value of 0.082 (8.2%), the level of fluctuation is relatively large for the lowest ROA was -0.190 (-19%) belonging to Long An Export Processing Joint Stock Company in 2018, compared to the highest ROA of 0.722 (72.2%) in 2015 achieved by KIDO Group The standard deviation of ROA is 0.085
Accrual ratio CF with a standard deviation of 0.140 shows that operating cash flow to net revenue of the two industries is quite similar
Cash ratio with the mean is 0.507 and the maximum and minimum values are 8.240 and 0.000, respectively The standard deviation is 0.920
Debt to Assets has the highest value of 0.766 (76.6%) while the lowest value is 0.000
(0%), meaning there is a significant difference between businesses in the level of debt usage
Quick ratio: the independent variable representing quick ratio has the highest value of
14.010 times while lowest value of 0.080 times, which means firms in the sample have very high variability that indicates a strong ability to pay off short-term debts
Receivables turnover varies significantly among the sample firms, ranging from 0 to 3577.110 days, indicating a wide range of receivables management practices On average, the receivables turnover is over 100 days, approximately equivalent to three months This extended turnover period reflects a notable deviation of 306.599 days from the mean, suggesting potential inefficiencies or inconsistencies in credit and collection strategies.
Short-term assets/Total assets have an average value of 0.617, which indicates that most listed firms in the sample don't have good short-term liquidity abilities
Short-term liabilities to equity: the average value is 1.089, meaning that the firms in the sample have a high ratio of short-term debt, relying heavily on short-term funding to maintain operations
Short-term ratio with an average of 2.255 times, it suggests a decent level of short- term liquidity for the firms in the sample
Short-term receivables/Short-term assets with the largest being 93.6%, which indicates the structure of short-term assets is also at a high level
Equity to assets with the mean is 0.534 and the maximum and minimum values are
0.951 and 0.056, respectively The standard deviation is 0.193
Short-term liability to total liability ranges from 0.057 to 1.000 The mean is 0.841 and the standard deviation is quite small (0.223)
Inventory turnover in the sample rage from 0 days to 938.680 days, which means that these firm has wide Inventory turnover variance, the average Receivables turnover is more than 35 days (approximately 1 months) with a standard deviations of 112.776 days
Equity turnover in the sample ranges from 0 days to 51.970 days, which indicates that these firms had a quite large Equity cycle The average Equity turnover is 6.301 days with a standard deviation of 7.672 days.
Correlation Analysis
Table 4-2 shows that ROA negatively correlation with variables which are Debt to
Assets, Short-term liabilities to equity, Short-term receivables/Short-term assets and Equity turnover Moreover, they have positively correlation with variables that are liquidity ratio, cashflow ratios and short-term asset structure
The correlation matrix also indicates that correlation coefficients between variables are almost less than 0.7 and the independent variables have the moderately impacts on ROA, including Receivables turnover, Short-term assets/Total assets, Short-term ratio, Short-term receivables/Short-term assets and Equity turnover are no correlation Therefore, it is concluded that there is no strong correlation among variables in the model and the multicollinearity problem will not occur
Table 4-2: Correlation Matrix among Variables
Short-term assets/Total assets
Short- term liabilitie s to equity
Short- term receivable s/Short- term assets
Short- term liabilitie s to total liabilitie s
Short-term assets/Total assets -0.163 -0.157 0.150 -0.190 -0.127 -0.137 1.000
Short-term liabilities to equity -0.103 -0.235 0.399 -0.299 -0.067 -0.031 0.318 1.000
Short-term receivables/Short- term assets 0.033 -0.235 0.298 -0.088 -0.084 -0.272 -0.060 0.028 -0.195 1.000
Short-term liabilities to total liabilities 0.013 -0.390 0.045 -0.528 -0.176 -0.151 0.651 0.242 -0.431 0.024 0.209 0.059 1.000
EFA (Exploratory Factor Analysis)
Use the Kaiser-Meyer-Olkin Coefficient (KMO) to evaluate the appropriateness of the dataset and the magnitude of the correlation coefficient between two variables with the magnitude of their partial correlation coefficient The value of KMO must reach a value of 0.5 or higher (0.5 ≤ KMO ≤ 1), which is a sufficient condition for factor analysis to be appropriate
The KMO of Retail and F&B are 0.668 and 0.612, respectively, more than 0.5 and the Sig Bartlett's Test both are 0.000, less than 0.05 Therefore, the EFA is relevant
Table 4-3: KMO and Bartlett's Test of Sample
Kaiser-Meyer-Olkin Measure of Sampling Adequacy 0.668 0.612 Bartlett's Test of
Sphericity Approx Chi-Square 898.37 1411.41 df 66 66
Total Variance method
After using the Kaiser-Meyer-Olkin Coefficient (KMO) to assess the suitability of the data for factor analysis, the Total Variance method (Table Variance Explained) is used to determine the number of factors to extract This method calculates the proportion of variance in the observed variables that will be explained by each extracted factor.
Eigenvalues in Variance Explained table is the criterion to determine the total number of factors in EFA, the standard value of the Eigenvalue is 1 This means that only factors with an Eigenvalue ≥ 1 will be retained in the factor analysis model
According to Merenda (1997), the number of factors extracted needs to achieve a cumulative variance percentage of at least 50% Meanwhile, Hair et al (2009) said the number of extracted factors explaining 60% of the total variance is positive
For the Retail industry, the Variance Explained table shows the first four factors extracted based on the criterion of eigenvalue greater than 1, therefore, these factors best summarized the information of the 12 observed variables included in the EFA
The Cumulative Percentage index reaches over 60% from the second factor onwards Thus, according to Hair et al (2009) opinion, the number of factors extracted should be from 2 onwards
Combined with the eigenvalue criterion, the optimal number of extracted factors should be 4 at a cumulative variance of 79,172 > 50% Thus, the 4 extracted factors can explain (condense) 79.172% of the data variation of the 12 observed variables participating in EFA
Besides, for the F&B industry, there are five factors to summarize the information of the 12 observed variables included in the EFA
The Cumulative Percentage index surpassed 60% from the third factor onward, indicating the optimal number of factors extracted should be at least three The five extracted factors collectively account for 82.40% of the data variation observed in the 12 variables included in the EFA, exceeding the recommended threshold of 50%.
Initial Eigenvalues Extraction Sums of Squared
Loadings Initial Eigenvalues Extraction Sums of Squared
Rotated Component Matrix
Rotated Component Matrix helps to explain the relationship between observed variables and factors after rotation, thereby better understanding the structure of the data The higher the absolute value of the loading factor in the Rotated Component matrix table the stronger the relationship between the observed variable and the factor The Rotated Component matrix of the Retail dataset shows the ratio below could be combined with 4 factors However, the Rotated Component matrix of the F&B dataset shows the ratio below could be combined with 5 factors and the KMO indexes of Retail and F&B are approximately the same Therefore, to facilitate comparison between the two industries, we will evaluate F&B based on the factors analyzed in the Rotated Component matrix of Retail, including:
Liquidity Ratio: Quick ratio and Short-term ratio Since Quick ratio and Cash ratio have the same properties as two indicators of liquidity and should be priority to higher factor loading, thus, select Quick ratio)
Liquidity ratio = (Quick ratio + Short-term ratio)/2
Leverage Ratio: Short-term liabilities to equity, Short-term assets/Total assets and
Leverage ratio = (Short-term liabilities to equity + Short-term assets/Total assets + Debt to assets)/3
Short-term receivables/Short-term assets
Short-term ratio 0.944 Cash ratio 0.905
Cash ratio 0.855 Short-term ratio 0.900
Short-term liabilities to total liabilities -0.778 Short-term liabilities to equity 0.905
Equity to assets -0.917 Equity turnover 0.824
Short-term liabilities to equity 0.815 Equity to assets -0.730
Short-term assets/Total assets 0.699 Short-term liabilities to total liabilities 0.878
Debt to assets 0.667 Short-term assets/Total assets 0.845
Accrual ratio CF 0.871 Accrual ratio CF 0.890
Equity turnover Debt to assets
Short-term receivables/Short-term assets 0.888 Receivables turnover 0.814
Short-term receivables/Short-term assets
Hence, the following general regression model was used to investigate the relationship between working capital management and financial performance of Retail and F&B industries as:
ROA = β0 + β1Liquidity ratio + β2Leverage ratio + β3Accrual ratio CF + β4Short-term receivables/Short-term assets + μ
Regression Analysis
Short-term receivables/Short- term assets
ROA = 0.000000 + 0.012786Liquidity ratio + 0.000014Leverage ratio + 0.210227Accrual ratio CF + 0.000452Short-term receivables/Short-term assets
ROA = 0.000002 + 0.160049Liquidity ratio + 0.003710Leverage ratio + 0.001134Accrual ratio CF + 0.695779Short-term receivables/Short-term assets
Regression analysis is applied to find the relationship between working capital management and financial performance of Retail and F&B industries in Vietnam We base the results on using ROA as the measure of WCM effect to financial performance
The result for the relationship between Accrual ratio CF and ROA in Retail industry is not statistically significant Moreover, for F&B industry, the relationship between Liquidity ratio, Short-term receivables/Short-term assets for ROA is also not statistically significant
According to the result in Retail industry, Liquidity ratio has positive impact on ROA The coefficients of Liquidity ratio for ROA is 0.012786 with the significant level of 10% This indicates that, if Liquidity ratio increases by 1 unit, ROA will increase by 0.012786, under the condition that the other factors are holding the same The results lead to a conclusion that the higher Liquidity ratio would somehow lead to higher ROA for corporation in Retail industry Hence, the same is true for Quick ratio and Short-term ratio, as they both have positive impact for ROA
Besides, the coefficient of Short-term receivables/Short-term assets for ROA is 0.000452 with the significant level of 10% This means that Short-term receivables/Short-term assets has positive impact on ROA The small coefficient suggests the impact might be minimal and might not be practically significant However, Short-term receivables/Short-term assets ratio measures the proportion of a company's short-term assets that are tied up in customer receivables, when Short-term receivables/Short-term assets increase, ROA also increases
On the other hand, for the regression results of F&B industry, the coefficient of Accrual ratio CF for ROA is 0.001134 with the significant level of 10% This means there's a less than 10% chance the observed positive trend is due to random chance However, the coefficient itself (0.001134) is very small This suggests that even if there's a positive relationship, the magnitude of the impact of Accrual Ratio CF on ROA might be weak Accrual Ratio CF measures the relationship between a company's net operating cash flow and net profit A more positive Accrual Ratio CF for ROA indicates a corporation in F&B industry not relying on accruals (non-cash expenses) to generate reported profits
In addition, based on the results in both industries, Leverage ratio has both positive impacts on ROA These relationships are statistically significant at 10% level This indicates that, if Leverage ratio increases by 1 unit, ROA will increase by 0.000014 for Retail and 0.003710 for F&B, respectively, under the condition that the other factors are holding the same, thus, corporations with higher leverage ratios tend to have higher ROA on average However, the positive coefficients are very small While statistically significant, the practical impact on ROA might be minimal Besides, Leverage ratio is the only factor that affects both industries.
CONCLUSION
Conclusion
Working capital management is one of the crucial financial management activities that contributes to the business efficiency of an enterprise And this is also an issue that is always of top concern to business administrators This research used data from 46 companies in the Retail and F&B industries listed on the Vietstock Stock Exchange during 2015-2023 The study is empirical evidence of the impact of working capital management on the financial performance of Retail and F&B businesses in Vietnam The study has systematized empirical studies on the relationship between working capital and corporate financial performance Next, the authors presented data analysis and used different methods to build appropriate regression models
The research results have answered the question of the impact of working capital management expenditures on the financial performance of corporations, particularly the indicators of liquidity ratios including quick ratio, short-term ratio, leverage ratios including short-term liabilities to equity, short-term assets/total assets, debt to assets, accrual ratio cash flow and short-term receivables/total assets Besides, the accrual ratio cashflow variable has no impact on the financial performance of the Retail corporation and the short-term receivables/total assets variable does not influence the financial performance of the F&B corporation
However, the research scale is still small, only surveying F&B and Retail corporations listed on the Vietnamese stock market, not unlisted corporations, making it difficult to accurately reflect fluctuations of the study variables The study does not consider several other indicators that also reflect the profitability of the corporation such as ROS, ROI, Tobin"Q and several factors (control variables) that can affect the effectiveness of the business Financial performance of corporations such as inflation rate, unemployment rate and other factors outside the industry This is also a gap for further research.
Implication
With the research results obtained from the regression model, the research team recommends several solutions to improve the efficiency of working capital management in enterprises as follows:
Corporations of Retail should develop a monitoring policy on the ratio of Short-term receivables/Short-term assets to improve the profit margin on assets The return on assets ratio is positively related to the liquidity of the business, including Quick ratio and Short-term ratio Therefore, to improve the financial efficiency of businesses, administrators in the Retail industry need to ensure high liquidity in the business process Besides, the corporations of F&B industry with higher Accrual Ratio CF might have slightly higher ROA, if they should invest in growth, might use accruals (e.g., depreciation) to reflect the cost of investments that will generate cash flows and might have higher accruals due to lower operating expenses, leading to higher profitability (ROA)
In addition, research results show that businesses in the two industries should consider monitoring Leverage ratio based on three factors: Short-term liabilities to equity, Short-term assets/Total assets and Debt to assets The F&B industry has short business cycles but profit margins are higher than the Retail industry and has a higher debt/total asset ratio than the Retail industry due to its ability to generate higher cash flow Overall, it is necessary to ensure the ability to generate cash flow from business activities to pay loans and interest rates, advised to closely monitor this ratio and ensure inventory is not too high to avoid waste and obsolescence risks and pay attention to raw material price fluctuations on both industries
From here, corporations in the F&B and Retail industries can rely on them to improve financial efficiency in their corporation
4 KMO Kaiser-Meyer-Olkin Coefficient
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