To help in testing our research, the study will use balance sheets and income statements from the financial statements for 24 manufacturing companies were collected for periods between 2014 and 2020. Research methodology section provides an analysis of the methods used in data collection as well as data analysis used by the researcher. This research will use a cross-sectional and descriptive method. This research will use multiple case study research method. The method was preferred for its ability to illuminate that casual bond between various factors in different business concepts. By studying multiple cases, the validity might increase since it was collected creates more solid empirical evidence that will be used to generate a comprehensive picture of the risks and countermeasures for e-commerce company’s sustainable development.
The purpose of this study is to investigate the relationship between profitability and firm size in manufacturing companies in China. In this regard, the proposed study will adopt quantitative research method to collect as well as analyze the data. Quatitative research method is described as a scientific method of observations that is used to collect non-numerical data through the focus of meaning-making (Creswell and Poth, 2016). The reason why qualitative research method is appropriate for this study is because it can be used to answer questions about perspective, meaning as well as experience. In this case, the researcher will analyze the perspective and meaning of some of the potential risks and countermeasures for company’s sustainable development.
4.2. Data Collection Method
In relation to the proposed study, the researcher will make use of secondary sources to collect the necessary study that will be used to answer the research question. Secondary research or desk research is a type of research method that make use of already existing data (Saunders, Lewis and Thornhill, 2012). In this view, the proposed study will make use of organization websites, government publications, journal articles, company’s annual reports and books. As the proposed study attempts to investigate the potential risks and countermeasures for company’s sustainable development, there are numerous secondary data from different company’s annual report, and articles.
The data gathered will later be used to compare with the empirical findings during analysis and discussion phase. Additionally, company websites, news and journals about the companies were used to present policies, strategies and tolls used by the companies. The materials were used to benchmark the companies and provide a comprehensive conclusion. The methods and sources triangulation was used to ensure that there was a high level of validity. China is one of the leading companies in manufacturing. Analyzing companies in the country would provide insightful information about the effect of firm size on profitability. Data collection involved calculations and data cleaning. Formulas have been presented on table 1 bellow.
Number of firms Number of firms in the industry grouping.
Beta Average regression beta across companies in the group.
ROE Aggregated Net Income , across all firms in group, using trailing 12 month data/ Aggregated Book Value of equity, across all firms in group, using most recent balance sheet.
Cost of Equity Risk free Rate + Beta * Equity Risk Premium
(ROE – COE) ROE – Cost of Equity, across the sector
BV of Equity Aggregated Book Value of Equity, in most recent balance sheet, across all firms in the group (in million of dollars)
Equity EVA (ROE – Cost of Equity)* BV of Equity, defined as above (in $ millions)
ROC Aggregated Operating income , across all firms in group, using trailing 12 month data (1- Effective Tax Rate)/ (BV of Equity + BV of Debt – Cash), across all firms in group, using most recent balance sheet.
Cost of Capital Cost of Equity * (Equity/ (Debt + Equity)) + Cost of Debt (1- Marginal tax rate) *(Debt/ (Debt + Equity)), with aggregated debt and market equity values across all companies in the sector, using most recent balance sheet for debt and most recent year-end for equity.
(ROC – Cost of Capital) ROE – Cost of Equity, across the sector
BV of Capital (Book Value of Equity + BV of Debt – Cash), aggregated across all firms in the group, in most recent balance sheet.
EVA (ROE – Cost of Equity)* BV of Equity, defined as above (in $ millions)
Table 1: Variable Definition
The methodology is a research that is based on a review of science and more details drawn from business valuation methodology. It emphasizes the importance of improving the code of conduct and valuation standards. The aim of this paper is to explore the effect of research and development expenses on financial reports. A literature review was conducted to establish the approach of research and development measurements. A sample was later selected of companies from various industries to provide a wider scope of research.
Regression Model Analysis
The analysis analyzed the effect of firm size on company profitability. The analysis only analyzed manufacturing companies. All data was collected from official websites. Multiple regression and correlation was used in the analysis. Additionally, empirical analysis and variance inflation factors were conducted (Does Firm Size Affect The Firm Profitability? Evidence from , 2013).
Model development is a common method of employing model benchmark to study the equity value as a linear function of book value and equity earnings. The benchmark also works together with the constant term to capture the effect of variables that were omitted (Golberg & Cho, 2020). This leads to development of an empirical specification in the in a linear regression. In order to examine the internal and external factors that affect the proﬁtability of manufacturing in China, the following model has been developed:
According to Issah, (2015), firm profitability is measured by return on assets, net profits. Firm size is measured by total assets and total sales. Correlation and regression methods will be deployed for empirical analysis. Additionally, age of the firms, leverage ratio and liquidity ratio have been used as control variables. Research methods to be used in this research are based on statistical analysis. (Hu, 2014, Pg 5) emphasized on the importance of using regression as a research methodology. The study also confirmed the impact computers have had in changing research methods. Additionally, Zsuzsannaa & Marian, (2012) studied the use of quantitative analysis using regression analysis method. This method is used to model and analyse several variables (Taherizadeh, 2010). A basic form of regression model includes dependent and independent variables (X and Y variables). The regression model basically specifies the combination of the two variables and unknown parameter β. Thus, a regression analysis formula may be used to predict the y values if provided with x and y and the two sets of measures of n sample size. Therefore, the regression analysis formula will be
NP = Alpha + β1*X1 + β2*X2 + ε Model I
NP = alpha+ β1X1 + β2*X3 + ε Model II
ROA =alpha + β1*X1 + β2*X2 + ε Model III
ROA = Alpha + β1*X1 + β2*X3 + ε Model IV
Where: X1= Asset Turnover. X2= Logarithm of Total Assets. X3= Logarithm of Total Sales. NP= Net Profit. ROA = Return on Assets. β0= Constant. ε= Error term
As firm profitability aged , profitability estimates was estimated by the static panel model such as use of random effects, fixed effects and OLS (Isik, Unal, & Una, 2017)
In the paper, there is the use of samples of 24 manufacturing companies in the CHINA. They will include 5 top manufacturing from 15 CHINA countries. There will be reliance on manufacturing whose information is accessible in the internet. The analysis will be based on a 10-year period from 2009- 2018. Data will be obtained from the various financial websites, government records and respective financial statements. Based on literature, profitability for the bank is measured using the ROE and ROA to ensure there is a uniform analysis based on the different sizes of the manufacturing. Company beta was used to measure company’s systematic risk (Leister, 2015). It was used to test the ability of the company to adjust to the overall market change. This represented the share market of the companies being studied.
For the independent variables, there is the consideration of qualitative and quantitative factors that affect profitability for the commercial manufacturing. Hence, there are cyclic factors (macroeconomic) and structural factors (internal). In cyclic factors there will be consideration of various aspects. First, to determine between lending and profitability the loan ratio (net loans/total assets) will be used. Secondly, there is interest rate spread where there will be the use of market interest rates. Third, there will be the comparison between the inflation rates of the specific country where the bank is located. Fourth, there will be the consideration of GDP growth for the specific country. Structural factors will include various aspects. They include the extent of digitization for the manufacturing, non-performing loans, diversification and mergers and acquisitions. These factors will be determined using a qualitative analysis because they are relative. Moreover, there is the cost of funding that include the lending rates for manufacturing.
As compared to other parts of the world, Net interest margins are lower in the China manufacturing. However, a small difference of 1 percentage in net interest margins leads to a major competition among the China manufacturing. A decrease in net interest margin has been as a result of central bank actions, competition, and low-interest rates. Low-interest rates may prompt depositors to withdraw their savings and invest in other platforms. Financial innovations have also affected the net interest margins. The emergence of new manufacturing and other money lending institutions are a major threat to main manufacturing. With more favorable lending terms and payment services, these new manufacturing and money lending institutions have gained a large customer following. Therefore, those manufacturing that depend on high-interest rates are affected when the above factors come along.
This section analyzes the tests conducted and the descriptive statistic to either accept or reject the hypothesis presented in 3.1 above. Descriptive analysis was aimed at understanding the data and variables used in the study.
A descriptive statistics summary using large sample statistics was presented. The valuation models were used as input variables. The valuation models include forecasted earnings, market value of shares and the book value of shares. They will later be presented in this study. Besides having relevant information between industries and groups from the statistical properties, the data was trimmed to remove 1% on tails of each variable. Later a descriptive analysis model was to find prediction error of the data. The data will then be summarized and commented.
Results and Discussion
The descriptive analysis conducted on the data shows the average and standard deviations of company data. The results shows several outliers, which are excluded from the study sample. The standard deviation of the data represents a relative low dispersion. The low standard deviation indicate that the data points had a low variability. This study uses two sets of variables to represent firm efficiency and market structure. The companies average ROE were 0.077488, 0.1202 for ROA, Asset turnover of -0.04278 and a net profit of 145875.133. The standard deviation for the variables were 0.4005, ROE of 0.061, Asset Turnover of 0.0603. The results shows that the variables did not fluctuate much from their means. Table 2 shows descriptive analysis results. The result shows that there is a
Beta ROA Asset Turnover ROE Net Profit (NP)
Mean 1.715882 7.7% 12.0% -4.3% 145875.1331
Standard Error 0.08955 1.4% 0.5% 1.3% 84743.68572
Median 1.777278 7.5% 12.4% -5.9% 59801.13
Standard Deviation 0.40048 6.1% 2.4% 6.0% 378985.2839
Sample Variance 0.160384 0.4% 0.1% 0.4% 143629845390.49
Kurtosis 5.537557 2.97 5.54 1.05 18.78551099
Skewness -1.91782 1.33 -1.92 0.95 4.283949656
Range 1.827493 27.3% 10.8% 25.4% 1734145.1
Minimum 0.394922 -1.7% 4.2% -13.9% 537.9
Maximum 2.222414 25.7% 15.0% 11.4% 1734683
Sum 34.31763 1.55 2.41 -0.86 2917502.661
Count 150 150 150 150 150
Table 2: Descriptive Analysis
Correlation analysis was conducted to find the relationship between variables. It showed that there was little correlation between return on equity and company beta. This research used Itaoka, (2012) concept to find the relationship between the variables. Analysis showed that there was a close relationship between company beta and return on asset, and asset turnover with ROA. There was no relationship between company net profit ROA, ROE, beta and asset turnover (Isik, Unal, & Una, 2017).
The Pearson connection results uncover that there are noteworthy relationships between benefit measure and firm size indicators. Likewise, similar holds for control factors. The connection coefficients among our autonomous and control factors utilized in productivity condition are not greater than the edge estimation of 0.80 (Nimresh, 2014). According to Golberg & Cho, (2020) aftereffects of connection examination, the research can therefore can infer that there exists no genuine multicollinearity issue in our model particulars. As detailed in Table 4, the connection coefficient among ROA and slacked ROA is sure and critical at 1% level, affirming that dynamic board information estimation strategy ought to be utilized to assess benefit model.
ROE Cost of Equity ROA Cost of Debt
ROE Pearson Correlation 1 .217 .924** -.221
Sig. (2-tailed) .358 .000 .350
N 20 20 20 20
Cost of Equity Pearson Correlation .217 1 -.173 .319
Sig. (2-tailed) .358 .467 .170
N 20 20 20 20
ROA Pearson Correlation .924** -.173 1 -.348
Sig. (2-tailed) .000 .467 .133
N 20 20 20 20
Cost of Debt Pearson Correlation -.221 .319 -.348 1
Sig. (2-tailed) .350 .170 .133
N 20 20 20 20
Table 3: Correlation Analysis
Regression analysis was used to test the hypothesis that firm size affect profitability in manufacturing companies in China. The regression analysis conducted was aimed at finding if there was any relationship between the variables. Company net profit was set as the dependent variable. Taherizadeh, (2010) noted that companies with high net profits were big. Independent variables were profitability measure such as return on asset, return on equity and the asset turnover.
Table 4 bellow shows the coefficients of NP, and ROA variables. The variables were first examined for appropriateness in the regression model. The variables comprised of firm size being estimated by net profit and return on assets (ROA). The two variables were thus set as dependent variables(Leister, 2015). The NP coefficient showed a positive coefficient (coefficient =0.0627) indicating that factors of production such as number of employee would affect profitability. The t-statistics for net profit 0.000 means that the significance level is less than 0.01. we can therefore regard the null hypothesis as disproved. The return on asset had a negative coefficient in the regression model (B = -0.11026) indicating that the firm size will have a negative effect on return on asset (ROA). The t-statistics was 0.000 which was also not significant level. The result indicates that the size of a firm is not a significant of ROA. Similarly, there was a negative coefficient on asset turnover with a negative value (B= -0.0023) and a negative t test (Britto & Paranhos, 2019).
Variables Coefficient SE t Prob.>|t|
NP 0.06274 0.019158 4.21 0.0000
ROA -0.11026 0.01238 -0.934 0.0000
Asset Turnover -0.0023 0.00124 -0.23 0.6120
Constant -0.35241 0.10322 -3.41 0.0020
Table 4: Coefficient
The regression analysis results has been represented in table 5 bellow. Itaoka, (2012) defines the r squared as a statistical measure that represents the variane propostion for a dependent variable. It represents the strength of the relationship between dependnt and indepent variables. Hayes, (2020) explained r squire as the extent at which the variance of one variable is to the second one. Thus, if the r square is 0.50, then the variables are approximately half of the observed variance by the input model. Table 5 shows that only asset turnover had a close relationship with the net profit. The r square for return on asset was 0.051, 0.0047, 0.0405 and 0.9895 for ROA, Net Profit, ROE and Asset Turnover respectively. The result also showed that there was no relationship between net profit and ROA, ROE and Asset Turnover. This basically mean that there was no close relationship between firm size and profitability.
The regression was used to test the hypothesis. p value is one of the most commonly used method of testing the hypothesis. the p value helps to ditermine the significance of the test. The hypothesis test is a method used to test the validity of a claim about the sample (Marasini, Quatto, & Ripamonti, 2016). The p value indicates the incapabiity of data with a statical model. The significance level was set at <0.05 indicating that all reults bellow 0.05 means that the null hypothesis should be rejected. The results from table 2 show no relationship between firm size and return on equity. The p Value was 0.8565 which is above the significance value. The significance level was also low with 0.3377. The regression analysis showed no relatinship between firm size and ROA, ROE and Asset Turnover. The p values from the analysis were 0.858, 0.997, 0.09, and 0.289 which are all >0.05. This indicate that the the null hypothesis should be accepted. The study thus suggests that there is no relationship between firm size and profitability.
Model Sum of Squares Df Mean Square F Sig.
1 Regression .062 3 .021 50.934 .000b
Residual .007 16 .000
Total .069 19
Table 5: Regression analysis results
Profitability of a company depend on many factors. There is no specific measure that leads to a firm to make profit. The combinations of inputs include management skills, market share, ability to reach to potential clients, marketing etc. However, big firms often have the advantage of having resources at their disposal compared to small one. A big company is able to employ the best human and machinery resource in production (Rumsey, 2019). This gives them the economic advantage in that they can produce large quantities at a lower cost. This is not the main reason for company profitability.
The control variables for growth, asset structure and liquidity have positive relationship with company performance. However, the study showed a significant negative coefficient for firm size and profitability. Table 3 on the correlation indicated that firm size does not affect profitability. The correlation results confirmed that firm size does not necessarily mean that it is profitable. Further research should be conducted to link to understand what affects profitability apart from firm size. Additionally, this study used one industry (manufacturing industry), multi-industry analysis would provide a broader point of view. Based on the experiment result, firm size has a significant influence of profitability. The positive influence shows that a manufacturing company profitability would be affected by size of the firm. This is so since big companies are more efficient in production and distribution compared to small firms.
Britto, D., & Paranhos, R. (2019). When is statistical significance not significant? Brazilian Political Science Review, 1.
Does Firm Size Affect The Firm Profitability? Evidence from . (2013). Research Journal of Finance and Accounting, 4(4), 5.
Golberg, M., & Cho, H. (2020). Introduction to Regression Analysis. Wessex Institute of Technology,: WIT press.
Hayes, A. (2020). Investopedia. Retrieved July 24, 2020, from https://www.investopedia.com/terms/r/r-squared.asp
Isik, O., Unal, E. A., & Una, Y. (2017). THE EFFECT OF FIRM SIZE ON PROFITABILITY: EVIDENCE FROM TURKISH MANUFACTURING SECTOR. Journal of Business, Economics and Finance (JBEF), 6(1), 308.
Issah, O. (2015). AN EMPIRICAL STUDY OF THE RELATIONSHIP BETWEEN PROFITABILITY RATIOS AND MARKET SHARE PRICES OF PUBLICLY TRADED BANKING FINANCIALINSTITUTIONS IN GHANA. International Journal of Economics, Commerce and Management, 2.
Itaoka, K. (2012). Regression and interpretationlow R-squared! Mizuho Information & Research Institute, Inc, I, 2.
Leister, F. (2015). The CAPM Beta Factor: A Critical Investigation of the Beta Factor as a Mean to Assess the Risk of Equity Investments and Presentation of an Alternative. IUBH School of Business and Management , 1(3), 4.
Marasini, D., Quatto, P., & Ripamonti, E. (2016). The use of p-values in applied research: Interpretation and new trends. STATISTICA, 5.
Nimresh, J. (2014). Firm Size and Profitability: A Study of Listed Manufacturing Firms ed Manufacturing Firms in Sri Lanka. Journal of Business and Management.
Rumsey, D. J. (2019). What a p-Value Tells You about Statistical Data. Statistics Journals, 4.
Taherizadeh, S. (2010). Research Methodology- Regression analysis. Research Methodology, 1(2), 2.