# The Impact of Capital Structure on the Performance of Chinese E-Commerce Industry Companies (2013–2019 Panel Data)

Authors: Meng-Yao Zhang; Li-Wei Lin; Huei-Ping Chen; Xuan-Ze Zhao
Journal: Journal of Mathematics
 (2022)

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## Abstract

This research examined how to estimate performance in China’s e-commerce industry, by taking 2013–2019 e-commerce listed companies as the research samples and using an empirical research method to study the relationship between capital structure and firm performance. The empirical results show that the debt-to-asset ratio and the long-term interest-bearing debt ratio have a negative impact on firm performance, but the impact of the long-term interest-bearing debt ratio is not significant. Moreover, the short-term interest-bearing debt ratio and the total asset turnover ratio have a positive impact on firm performance, while the impacts of the current assets ratio and intangible assets ratio on firm performance are not significant. The following suggestions are drawn from the results in order of importance. (1) Enterprises should maintain a reasonable debt-to-assets ratio and a stable capital structure and appropriately increase the proportion of short-term liabilities. (2) Firms should moderately invest to expand their scale and improve their profitability, so as to increase retained earnings and develop internal financing channels.


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## Body

## 1. Introduction

A company’s operating performance refers to the operating efficiency of the company and the performance of the operator during a certain period of operation. The level of business efficiency is mainly reflected in the profitability, asset operation level, debt repayment ability, and follow-up development ability of the enterprise. The performance of the operator is mainly reflected by the achievements and contributions made by the operator to the operation, growth, and development of the enterprise in the process of operating and managing the enterprise.Company performance is one of the most important factors for the operation of e-commerce in China. This research, therefore, investigated the financial statement data of Chinese listed e-commerce companies, collecting the data from 2013 to 2018 for panel data observation and analysis, which can then be provided to executives of e-commerce companies to make decisions and forecast their overall operating strategy. We mainly fill in the concept of corporate performance that many scholars have done but do not deeply explore the meaning behind corporate performance. We use the decision theory to provide executives with the ability to accurately determine and accomplish company performance goals. Our research combines the fields of marketing, financial management, and information management to finally solve the problem of executive decision-making. Asifa and Sheikh presented the impact of financial decisions on firm The main point is that the decision will affect the overall company performance. The decisions of the executives of many e-commerce companies can affect the company's performance throughout the following year [1]. Wang noted that the decision of company performance mainly comes from three directions: capital structure, investment, and dividend payment [2]. Lin et al. discussed intelligent capital, organisational learning, and corporate performance influence relationship [3]. We clearly understand that these issues are critical success factors for company performance.Since Modigliani and Miller first proposed the theory of capital structure in 1958, both domestic and international scholars have conducted extensive studies on the relationship between capital structure and firm performance [4]. However, as the selected samples vary from country to country and industry to industry, scholars have not reached a unified conclusion on whether the impact of capital structure on enterprise performance is positive or negative.E-commerce enterprises in recent years are relying on the power of network technology and growth under the dual promotion of the market and the government and have gradually become an important driving force for the transformation and upgrading of China’s industrial structure and a new engine of economic expansion. According to the Report on The Development of the Internet in China released by the Internet Society of China, the transaction volume of e-commerce in China reached 31.6 trillion yuan in 2018, an increase of 8.5% over the previous year, making it the most important component of the digital economy [5]. However, in contrast to the booming development of the e-commerce industry, some e-commerce giants that were once very popular have fallen one after another, such as Jumei Youpin, Wangjiu.com, Yunhou Global Network, and Pet Bear. Actually, this is related to the industry in which the company is located, its development strategy, its operating model, its financing policy, and its capital structure.This research focuses on developing a company performance model that supports and predicts China’s e-commerce industry. We combine tangible and intangible assets to predict future firm performance in China’s e-commerce industry. We know that many senior executives often ignore the value of intangible assets and only see tangible assets when discussing company performance. The main purpose of our research design herein is to combine tangible and intangible assets and to see the relationship between the company’s overall liabilities and profits.This study takes listed e-commerce companies as research samples to discuss how the performance of enterprises will be affected under the capital structure and whether the capital structure can help improve the performance of the enterprise. At present, there are few studies on the capital structure of listed e-commerce companies, and the research results are still inconclusive. This study expects to draw some enlightening conclusions from the research and to put forward some targeted and constructive suggestions, which have positive theoretical and practical significance.Table1 presents the decision-making literature to sort out the findings and results of past and present decision-making values [6–13].Table 1
Illustrative research summarizing the antecedents to enterprise performance in e-commerce industry companies.
Illustrative researchContextTheoretical basisAntecedents to purchase intention in e-commerce industry companiesKey findings or propositionsChen et al. (2021)Between capitalising upon new insights into demandDecision-making theoryPredictive analyticsThe findings suggest how big data analytics affect the supply chain decision-making.Kaen (2002)Between managing risk prior to the financial crisisDecision-making theoryRisk managementThe findings suggest that risk management is a central corporate governance task as it sustains value creation.Jeremy Alan Woods et al. (2019)Between the family business decision-making processesDecisions theoryDecision-making biasesThe findings suggest the impact of customer orientation and family decision-making style on family business performance.Certo (2012)Between the quality of business decisionDecisions theoryDecision-making theoryThe results show that decision-making is the process of choosing the best alternative for reaching objectives.DeSanctis et al. (1994)Between virtual team processes and performanceGrounded theoryDecision makingThe results build a grounded theory of global virtual team processes and performance over time.Lam et al. (2001)Between qualitative and quantitative concepts exhibitsDecision-making theoryA management decision problemThe findings suggest an integration of the fuzzy reasoning technique and the fuzzy optimisation method in construction project management decision-making.Ali Intezari (2018)Between 37 CEOs, top managers, and senior executives who were interviewed about their perspectivesDecision-making theoryPerceived valueThe results Conceptualizing Wise Management Decision-Making: A Grounded Theory ApproachVânia Moreira Costa et al. (2021)Between financial decision-makingDecision-making theoryDecision problemThe results provide consumers with the knowledge and confidence required to take part in financial markets.


## 2. Literature Review

### 2.1. Decision-Making Theory

“Decision-making is generally defined as a process or series of activities including problem identification, information search, the definition of alternatives, and selection of a participant from two or more alternatives aligned with ranking preferences.”Keeney (1996, p. 537) mentioned values as a way of making decisions in an organisation [13]. Kassel noted that the value assets of an organisation can affect the decision-making of managers [14]. We know that the decision theory refers to the concept of decision optimisation in economics, financial management, and management science. We thus study the company performance of China’s e-commerce industry and predict company performance from the overall asset structure.Many organisations are now experiencing rapid changes in the overall environment, leading to transformations in company performance. Abbasi et al. proposed that the analysis and prediction of big data can affect the decision-making of the company as a whole [15]. Galbraith mentioned that companies provide relevant supply chain data to senior executives for decision-making [16]. Droge et al. presented their study of firm time and firm performance [17]. We took Chinese e-commerce data from 2013 to 2018 in order to clearly understand the importance of the data to the decision-making of a company’s senior executives, which will affect subsequent company performance.


### 2.2. Enterprise Performance

The theory of capital structure is the basis for analysing the relationship between capital structure and enterprise performance. Domestic and foreign scholars have carried out a large number of empirical studies by taking enterprise data from different regions and industries as samples. Guo and Sun classified A-share listed companies in Shanghai and Shenzhen and conducted an empirical study on the industry characteristics of the capital structure. The results showed that the capital structure of listed companies in different industries is significantly different, the optimal capital structure does exist in a company, and the industry is one of the important influencing factors [18].Nawaz et al. utilised 173 listed companies in the textile industry in Pakistan as research objects and used the single-factor regression analysis method to confirm the positive correlation between capital structure and enterprise performance [19]. Saeed et al. used the short-term interest-bearing debt ratio, long-term interest-bearing debt ratio, and asset-liability ratio as samples to measure capital structure, while performance was measured by ROA, ROE, and EPS. Using multiple regression analysis, they found that banking performance positively correlated with capital structure, but the results were not significant [20]. Yu and Li took 109 listed real estate companies in China as examples. Empirical analysis showed that the value of real estate enterprises positively correlated with the capital structure, and there was an optimal capital structure to maximise the enterprise value [21].Due to the differences in variables, industry sample data, and other factors, some scholars have drawn opposite conclusions through empirical studies. Hossain and Dao Xuan applied the data of the top ten oil and gas companies in Canada from 2004 to 2014 as samples, showing research results that there was a negative correlation between leverage ratio and corporate performance [22]. This is because Canadian oil and gas companies are mature and have accumulated huge profits from their own operating advantages, and so they prefer to finance with retained earnings rather than debt. Wang et al. employed 54 Shanghai and Shenzhen A-share listed companies as an example in the food industry and used principal component analysis (PCA) to measure the profitability of the company’s level. Through the multiple linear regression method, they analysed the key factors that affect the level of the industry’s profitability. The empirical results showed that the company’s profitability level and the asset-liability ratio have a negative correlation [23].Chinese scholars have made new discoveries by studying the capital structure of listed companies in specific industries. Long (2011) made an empirical analysis of the relationship between capital structure and corporate performance by using the mixed data analysis method based on the data of 39 power companies listed in Shanghai and Shenzhen from 2008 to 2010. Research showed an inverted U-shape relationship between capital structure and corporate performance. When the asset-liability ratio is between 0% and 57%, capital structure is positively correlated with corporate performance. When the asset-liability ratio exceeds 57%, the two are negatively correlated [24]. Liu and Li took small- and medium-sized board manufacturing companies as an example and found that the asset-liability ratio and long-term interest-bearing liability ratio have a negative effect on enterprise value. However, the short-term interest-bearing debt ratio has a positive promotion effect on enterprise value [25]. Li and Zhao applied 690 private listed companies in Shanghai and Shenzhen as examples, analysed the debt structure and performance of private enterprises against the background of debt diversification, and found that the asset-liability ratio of private enterprises was in an inverted U-shape relationship with performance indicators. The short-term debt ratio is positively correlated with performance. The ratio of loans and liabilities of financial institutions is negatively correlated with corporate performance [26].It can be found from the research of the above scholars that based on different industries, capital structure has different influences on enterprise performance. However, as an emerging industry, empirical studies on the capital structure of e-commerce in the existing literature are still limited. By taking Suning Yunshang as an example, Yao and Cong found that industrial competition intensity negatively correlated with the asset-liability ratio of enterprises through empirical research [27]. Before fierce industrial competition arises, the low financial leverage of private enterprises is not only the result of financial discrimination but also a financial strategy chosen by enterprises to establish competitive advantages and obtain financial flexibility. As e-commerce enterprises are still in the growth stage, they need to maintain a low debt ratio to accumulate potential energy before the fierce competition comes. In order to come in the competition, quickly raise the debt ratio, release potential energy, and obtain a competitive advantage, Zhang took three typical e-commerce companies in China as the research object and analysed the use of financial leverage and the composition of capital by employing the calculation results of financial indicators in the past four years [28]. The research found that the capital structure of e-commerce companies had a large proportion of debt capital, and most of them were short-term liabilities. E-commerce enterprises can use financial leverage to adjust their capital structure and play a positive role, but due to the unique operation mode of their industry, the regulatory role of financial leverage is not obvious.Although the above literature has carried out in-depth studies on the capital structure of the e-commerce industry, it is not widely representative due to the small number of research samples. In addition, in recent years, the caller BUSINESS industry has been booming, and the operation mode has also undergone great changes. The competition among enterprises has become increasingly fierce, and existing research fails to reflect the characteristics of the rapid change in the e-commerce industry. Therefore, this study takes panel data of A-share listed e-commerce enterprises as the research sample to discuss how the capital structure of this industry influences enterprise performance, so as to obtain some enlightening conclusions and put forward some suggestions on the current situation of e-commerce development.


### 2.3. Capital Structure

Capital structure refers to the decisions made by enterprises to raise funds for their daily operations and future development, including debt financing and equity financing. Generally speaking, debt financing is riskier than equity financing, and creditors have the right to take the company into bankruptcy if the company fails to repay the principal and interest at maturity. However, the cost of equity financing is higher than that of debt financing, because equity owners can only get the residual value after debt repayment after the company goes bankrupt. They take higher risks than creditors, and therefore, they demand a higher return on investment. It can be seen that the capital structure decision has an important impact on the profitability of enterprises.Modigliani and Miller first put forward the perfect capital market (the perfect capital market refers to the capital market with no transaction costs and no tax costs, and the information is completely symmetrical [4]. The theory of irrelevance under the assumption holds that the market value of an enterprise depends on investment decisions and has nothing to do with capital structure [4]. Taking tax factors into consideration, they found a positive correlation between capital structure and enterprise performance—that is, there is an optimal capital structure, and enterprises can increase value by paying tax-deductible interest [29]. This is the tax shield effect. The real world is much more complex than the perfect capital market assumed by Modigliani and Miller. Companies face financial difficulties, and if they do not pay the principal and interest, they may be forced into bankruptcy. There is a trade-off between the tax advantages of debt financing and the costs of bankruptcy.Kraus and Litzenberger further took the cost factor of the corporate financial crisis into consideration and pointed out that corporate profitability positively correlated with its leverage level, and there was an optimal capital structure [30], which are consistent with the research conclusions of Modigliani and Miller. In addition to the cost of a financial crisis, an enterprise may also incur agency costs, including supervision costs, constraint costs, and residual losses [31]. Jensen and Meckling pointed out that issuing debt could reduce the waste of management resources, thus alleviating the agency problem to a certain extent. The bankruptcy crisis caused by failure to repay principal and interest on time is an effective driving force for management to improve efficiency, which means that debt financing has a positive impact on enterprise value [32]. However, Jensen and Meckling also pointed out that the positive impact of debt financing did not have much effect on those companies with high-speed development and investment projects with very large and considerable returns. On the basis of considering the agency problem and information asymmetry factor, Myers and Majluf put forward the theory of optimal order financing in 1984. They believed that there was no optimal capital structure, and compared with equity financing, companies were more inclined to use debt for financing [33]. The above capital structure theory takes microeconomics as the theoretical basis and conducts empirical research by building models to illustrate the essential relationship of enterprises in capital structure arrangement to a certain extent.


## 2.1. Decision-Making Theory

“Decision-making is generally defined as a process or series of activities including problem identification, information search, the definition of alternatives, and selection of a participant from two or more alternatives aligned with ranking preferences.”Keeney (1996, p. 537) mentioned values as a way of making decisions in an organisation [13]. Kassel noted that the value assets of an organisation can affect the decision-making of managers [14]. We know that the decision theory refers to the concept of decision optimisation in economics, financial management, and management science. We thus study the company performance of China’s e-commerce industry and predict company performance from the overall asset structure.Many organisations are now experiencing rapid changes in the overall environment, leading to transformations in company performance. Abbasi et al. proposed that the analysis and prediction of big data can affect the decision-making of the company as a whole [15]. Galbraith mentioned that companies provide relevant supply chain data to senior executives for decision-making [16]. Droge et al. presented their study of firm time and firm performance [17]. We took Chinese e-commerce data from 2013 to 2018 in order to clearly understand the importance of the data to the decision-making of a company’s senior executives, which will affect subsequent company performance.


## 2.2. Enterprise Performance

The theory of capital structure is the basis for analysing the relationship between capital structure and enterprise performance. Domestic and foreign scholars have carried out a large number of empirical studies by taking enterprise data from different regions and industries as samples. Guo and Sun classified A-share listed companies in Shanghai and Shenzhen and conducted an empirical study on the industry characteristics of the capital structure. The results showed that the capital structure of listed companies in different industries is significantly different, the optimal capital structure does exist in a company, and the industry is one of the important influencing factors [18].Nawaz et al. utilised 173 listed companies in the textile industry in Pakistan as research objects and used the single-factor regression analysis method to confirm the positive correlation between capital structure and enterprise performance [19]. Saeed et al. used the short-term interest-bearing debt ratio, long-term interest-bearing debt ratio, and asset-liability ratio as samples to measure capital structure, while performance was measured by ROA, ROE, and EPS. Using multiple regression analysis, they found that banking performance positively correlated with capital structure, but the results were not significant [20]. Yu and Li took 109 listed real estate companies in China as examples. Empirical analysis showed that the value of real estate enterprises positively correlated with the capital structure, and there was an optimal capital structure to maximise the enterprise value [21].Due to the differences in variables, industry sample data, and other factors, some scholars have drawn opposite conclusions through empirical studies. Hossain and Dao Xuan applied the data of the top ten oil and gas companies in Canada from 2004 to 2014 as samples, showing research results that there was a negative correlation between leverage ratio and corporate performance [22]. This is because Canadian oil and gas companies are mature and have accumulated huge profits from their own operating advantages, and so they prefer to finance with retained earnings rather than debt. Wang et al. employed 54 Shanghai and Shenzhen A-share listed companies as an example in the food industry and used principal component analysis (PCA) to measure the profitability of the company’s level. Through the multiple linear regression method, they analysed the key factors that affect the level of the industry’s profitability. The empirical results showed that the company’s profitability level and the asset-liability ratio have a negative correlation [23].Chinese scholars have made new discoveries by studying the capital structure of listed companies in specific industries. Long (2011) made an empirical analysis of the relationship between capital structure and corporate performance by using the mixed data analysis method based on the data of 39 power companies listed in Shanghai and Shenzhen from 2008 to 2010. Research showed an inverted U-shape relationship between capital structure and corporate performance. When the asset-liability ratio is between 0% and 57%, capital structure is positively correlated with corporate performance. When the asset-liability ratio exceeds 57%, the two are negatively correlated [24]. Liu and Li took small- and medium-sized board manufacturing companies as an example and found that the asset-liability ratio and long-term interest-bearing liability ratio have a negative effect on enterprise value. However, the short-term interest-bearing debt ratio has a positive promotion effect on enterprise value [25]. Li and Zhao applied 690 private listed companies in Shanghai and Shenzhen as examples, analysed the debt structure and performance of private enterprises against the background of debt diversification, and found that the asset-liability ratio of private enterprises was in an inverted U-shape relationship with performance indicators. The short-term debt ratio is positively correlated with performance. The ratio of loans and liabilities of financial institutions is negatively correlated with corporate performance [26].It can be found from the research of the above scholars that based on different industries, capital structure has different influences on enterprise performance. However, as an emerging industry, empirical studies on the capital structure of e-commerce in the existing literature are still limited. By taking Suning Yunshang as an example, Yao and Cong found that industrial competition intensity negatively correlated with the asset-liability ratio of enterprises through empirical research [27]. Before fierce industrial competition arises, the low financial leverage of private enterprises is not only the result of financial discrimination but also a financial strategy chosen by enterprises to establish competitive advantages and obtain financial flexibility. As e-commerce enterprises are still in the growth stage, they need to maintain a low debt ratio to accumulate potential energy before the fierce competition comes. In order to come in the competition, quickly raise the debt ratio, release potential energy, and obtain a competitive advantage, Zhang took three typical e-commerce companies in China as the research object and analysed the use of financial leverage and the composition of capital by employing the calculation results of financial indicators in the past four years [28]. The research found that the capital structure of e-commerce companies had a large proportion of debt capital, and most of them were short-term liabilities. E-commerce enterprises can use financial leverage to adjust their capital structure and play a positive role, but due to the unique operation mode of their industry, the regulatory role of financial leverage is not obvious.Although the above literature has carried out in-depth studies on the capital structure of the e-commerce industry, it is not widely representative due to the small number of research samples. In addition, in recent years, the caller BUSINESS industry has been booming, and the operation mode has also undergone great changes. The competition among enterprises has become increasingly fierce, and existing research fails to reflect the characteristics of the rapid change in the e-commerce industry. Therefore, this study takes panel data of A-share listed e-commerce enterprises as the research sample to discuss how the capital structure of this industry influences enterprise performance, so as to obtain some enlightening conclusions and put forward some suggestions on the current situation of e-commerce development.


## 2.3. Capital Structure

Capital structure refers to the decisions made by enterprises to raise funds for their daily operations and future development, including debt financing and equity financing. Generally speaking, debt financing is riskier than equity financing, and creditors have the right to take the company into bankruptcy if the company fails to repay the principal and interest at maturity. However, the cost of equity financing is higher than that of debt financing, because equity owners can only get the residual value after debt repayment after the company goes bankrupt. They take higher risks than creditors, and therefore, they demand a higher return on investment. It can be seen that the capital structure decision has an important impact on the profitability of enterprises.Modigliani and Miller first put forward the perfect capital market (the perfect capital market refers to the capital market with no transaction costs and no tax costs, and the information is completely symmetrical [4]. The theory of irrelevance under the assumption holds that the market value of an enterprise depends on investment decisions and has nothing to do with capital structure [4]. Taking tax factors into consideration, they found a positive correlation between capital structure and enterprise performance—that is, there is an optimal capital structure, and enterprises can increase value by paying tax-deductible interest [29]. This is the tax shield effect. The real world is much more complex than the perfect capital market assumed by Modigliani and Miller. Companies face financial difficulties, and if they do not pay the principal and interest, they may be forced into bankruptcy. There is a trade-off between the tax advantages of debt financing and the costs of bankruptcy.Kraus and Litzenberger further took the cost factor of the corporate financial crisis into consideration and pointed out that corporate profitability positively correlated with its leverage level, and there was an optimal capital structure [30], which are consistent with the research conclusions of Modigliani and Miller. In addition to the cost of a financial crisis, an enterprise may also incur agency costs, including supervision costs, constraint costs, and residual losses [31]. Jensen and Meckling pointed out that issuing debt could reduce the waste of management resources, thus alleviating the agency problem to a certain extent. The bankruptcy crisis caused by failure to repay principal and interest on time is an effective driving force for management to improve efficiency, which means that debt financing has a positive impact on enterprise value [32]. However, Jensen and Meckling also pointed out that the positive impact of debt financing did not have much effect on those companies with high-speed development and investment projects with very large and considerable returns. On the basis of considering the agency problem and information asymmetry factor, Myers and Majluf put forward the theory of optimal order financing in 1984. They believed that there was no optimal capital structure, and compared with equity financing, companies were more inclined to use debt for financing [33]. The above capital structure theory takes microeconomics as the theoretical basis and conducts empirical research by building models to illustrate the essential relationship of enterprises in capital structure arrangement to a certain extent.


## 3. The Research Methods

### 3.1. Sample Selection and Data Sources

This study’s research scope is A-share e-commerce listed companies, and the time range is set from 2013 to 2019. However, since there is no separate e-commerce industry in the industry classification standards issued by CSRC, different databases have different standards for e-commerce industry classification. For example, there are only 5 A-share listed e-commerce enterprises classified by Citic Securities. In general, the larger the sample size is, the more broadly representative and reliable the results are. Therefore, e-commerce concept stocks listed in the A-share market are selected in this study, which include all enterprises involved in the e-commerce business. The industry covers cosmetics, textile, clothing, medicine, tourism, community, logistics e-commerce, and other fields. On the basis of considering the data availability, 149 e-commerce companies exported from the Gutai database, 7 companies of ST and ∗ST, and 3 companies with incomplete data were excluded. In total, there are 139 research samples. The collected original data were sorted out using Excel2010, and Eviews 10.0 was used to analyse 6811 panel data using the multiple linear regression method.


### 3.2. Variable Selection

For the explained variable, the measurement methods in the existing literature can be divided into two categories, including the measurement based on accounting value and the measurement based on market value. Common measures based on accounting value include return on equity, return on total assets, and net interest rate on sales, while measures based on market value include Tobin’s Q value, price-earnings ratio, and price-book ratio. Considering the characteristics of China’s capital market and the internal structure of listed companies and other factors, this study adopts the rate of return on total assets (ROA) as the performance measurement index, which is consistent with the performance measurement method adopted by CAI Chun, CAI Li, Tian Qiulong (2011), Qiuchao (2016), and Li Chuanxian and Zhao Zilin (2020).As for the capital structure of explanatory variables, considering the market economic environment and the characteristics of enterprise operation in China, this study adopts the short-term interest-bearing debt ratio (SDBR), long-term interest-bearing debt ratio (LDBR), and asset-liability ratio (TDBR) to measure the capital structure. A short-term interest-bearing debt repayment period with low interest, to a certain extent, can produce a tax shield effect, promote corporate management to improve the efficiency of governance, and promote the improvement of enterprise value. When the long-term interest-bearing debt repayment period is long, the interest is relatively high. Due to the high amount of capital involved in long-term liabilities, creditors usually restrict the scope of capital investment projects to ensure the safety of capital. Therefore, the development of enterprises is restricted to a certain extent and is not conducive to the improvement of enterprise value. The asset-liability ratio is the ratio of total liabilities to total assets, representing the total liabilities level of an enterprise. This index is too high, representing the company’s financial risk and capital turnover pressure, and high-interest costs will inhibit the development of the enterprise.To ensure the validity of the empirical results, we adopted total asset turnover, current asset ratio (CAR), and intangible asset ratio (IAR) as control variables. The turnover ratio of total assets measures the operating capacity of the overall assets of an enterprise. The higher this index is, the higher the asset management efficiency of an enterprise will be, and the higher benefits that the invested assets will bring to the enterprise are conducive to the improvement of enterprise value. The ratio of current assets is the proportion of current assets in total assets. The higher this index is, the stronger the liquidity and the stronger the liquidity of corporate assets are, but the lower the profitability is compared with fixed assets. The ratio of intangible assets is the proportion of intangible assets in total assets. For the online operation mode of the e-commerce industry, intangible assets are an important factor for enterprises to gain competitive advantages, which are conducive to enhance the competitiveness of enterprises and ultimately translate into corporate profits.


### 3.3. Hypothesis

Based on the above literature and the analysis of variables, this study proposes the following hypotheses:Research Hypothesis 1: Short-term interest-bearing debt ratio positively correlates with corporate performance.Research Hypothesis 2: Long-term interest-bearing debt ratio negatively correlates with corporate performance.Hypothesis 3: Asset-liability ratio negatively correlates with corporate performance.Hypothesis 4: Total asset turnover positively correlates with corporate performance.Research Hypothesis 5: The current capital ratio positively correlates with enterprise performance.Hypothesis 6: Intangible assets ratio positively correlates with enterprise performance.


### 3.4. Model Specification

Based on the hypotheses proposed in this study, the following multiple regression linear model is established:(1)ROAit=α+β1SDBRit+β2LDBRit+β3TDBRit+β4TATOit+β5CARit+β6IARit+ε.Here,α is the constant term, βi (i = 1, 2, 3, 4, 5, 6) is the estimated parameter of the corresponding variable, and ε is the error term.


## 3.1. Sample Selection and Data Sources

This study’s research scope is A-share e-commerce listed companies, and the time range is set from 2013 to 2019. However, since there is no separate e-commerce industry in the industry classification standards issued by CSRC, different databases have different standards for e-commerce industry classification. For example, there are only 5 A-share listed e-commerce enterprises classified by Citic Securities. In general, the larger the sample size is, the more broadly representative and reliable the results are. Therefore, e-commerce concept stocks listed in the A-share market are selected in this study, which include all enterprises involved in the e-commerce business. The industry covers cosmetics, textile, clothing, medicine, tourism, community, logistics e-commerce, and other fields. On the basis of considering the data availability, 149 e-commerce companies exported from the Gutai database, 7 companies of ST and ∗ST, and 3 companies with incomplete data were excluded. In total, there are 139 research samples. The collected original data were sorted out using Excel2010, and Eviews 10.0 was used to analyse 6811 panel data using the multiple linear regression method.


## 3.2. Variable Selection

For the explained variable, the measurement methods in the existing literature can be divided into two categories, including the measurement based on accounting value and the measurement based on market value. Common measures based on accounting value include return on equity, return on total assets, and net interest rate on sales, while measures based on market value include Tobin’s Q value, price-earnings ratio, and price-book ratio. Considering the characteristics of China’s capital market and the internal structure of listed companies and other factors, this study adopts the rate of return on total assets (ROA) as the performance measurement index, which is consistent with the performance measurement method adopted by CAI Chun, CAI Li, Tian Qiulong (2011), Qiuchao (2016), and Li Chuanxian and Zhao Zilin (2020).As for the capital structure of explanatory variables, considering the market economic environment and the characteristics of enterprise operation in China, this study adopts the short-term interest-bearing debt ratio (SDBR), long-term interest-bearing debt ratio (LDBR), and asset-liability ratio (TDBR) to measure the capital structure. A short-term interest-bearing debt repayment period with low interest, to a certain extent, can produce a tax shield effect, promote corporate management to improve the efficiency of governance, and promote the improvement of enterprise value. When the long-term interest-bearing debt repayment period is long, the interest is relatively high. Due to the high amount of capital involved in long-term liabilities, creditors usually restrict the scope of capital investment projects to ensure the safety of capital. Therefore, the development of enterprises is restricted to a certain extent and is not conducive to the improvement of enterprise value. The asset-liability ratio is the ratio of total liabilities to total assets, representing the total liabilities level of an enterprise. This index is too high, representing the company’s financial risk and capital turnover pressure, and high-interest costs will inhibit the development of the enterprise.To ensure the validity of the empirical results, we adopted total asset turnover, current asset ratio (CAR), and intangible asset ratio (IAR) as control variables. The turnover ratio of total assets measures the operating capacity of the overall assets of an enterprise. The higher this index is, the higher the asset management efficiency of an enterprise will be, and the higher benefits that the invested assets will bring to the enterprise are conducive to the improvement of enterprise value. The ratio of current assets is the proportion of current assets in total assets. The higher this index is, the stronger the liquidity and the stronger the liquidity of corporate assets are, but the lower the profitability is compared with fixed assets. The ratio of intangible assets is the proportion of intangible assets in total assets. For the online operation mode of the e-commerce industry, intangible assets are an important factor for enterprises to gain competitive advantages, which are conducive to enhance the competitiveness of enterprises and ultimately translate into corporate profits.


## 3.3. Hypothesis

Based on the above literature and the analysis of variables, this study proposes the following hypotheses:Research Hypothesis 1: Short-term interest-bearing debt ratio positively correlates with corporate performance.Research Hypothesis 2: Long-term interest-bearing debt ratio negatively correlates with corporate performance.Hypothesis 3: Asset-liability ratio negatively correlates with corporate performance.Hypothesis 4: Total asset turnover positively correlates with corporate performance.Research Hypothesis 5: The current capital ratio positively correlates with enterprise performance.Hypothesis 6: Intangible assets ratio positively correlates with enterprise performance.


## 3.4. Model Specification

Based on the hypotheses proposed in this study, the following multiple regression linear model is established:(1)ROAit=α+β1SDBRit+β2LDBRit+β3TDBRit+β4TATOit+β5CARit+β6IARit+ε.Here,α is the constant term, βi (i = 1, 2, 3, 4, 5, 6) is the estimated parameter of the corresponding variable, and ε is the error term.


## 4. The Empirical Analysis

### 4.1. Descriptive Statistical Analysis

After descriptive statistics of each variable through Eviews, the data in Table2 are obtained. From the analysis results, the short-term interest-bearing debt ratio, long-term interest-bearing debt ratio, and asset-liability ratio of e-commerce enterprises are all significantly different, indicating that the capital structure level of each enterprise is very different, and there is a large gap in the degree of use of financial leverage. In addition, the total asset return rate, total asset turnover rate, and current asset ratio of e-commerce listed companies differ greatly, indicating that there are also certain differences in the performance level, asset operation ability, and asset composition of each enterprise.Table 2
Descriptive statistics of variables.
ROASDBRLDBRTDBRTATOCARIARMean0.0399770.1055010.047710.4395970.9132670.5988450.0433Median0.0398680.0801660.0099660.4280960.66850.6061090.031773Maximum0.4495690.6975290.4822461.23789512.37290.9818680.389841Minimum−0.909104000.015610.06480.067290Std. dev.0.0792510.1094010.0727180.2094391.028740.1904230.044481


### 4.2. Correlation Analysis

As can be seen from the results of correlation analysis in Table3, the absolute value of correlation coefficients among these variables is basically lower than 0.5, indicating that there is no serious multicollinearity among all variables. Thus, the panel data model is suitable for multiple regression analysis.Table 3
Variable correlation analysis.
CorrelationROASDBRLDBRTDBRTATOCARIARROA1.000000SDBR−0.3447661.000000LDBR−0.1657750.1360261.000000TDBR−0.3235460.4752670.4482401.000000TATO−0.0078760.117398−0.1377320.2928791.000000CAR0.1015110.028100−0.1486560.0658270.2781221.000000IAR0.0309360.0635040.018608−5.61E − 05−0.074606−0.4215301.000000


### 4.3. Regression Analysis

In order to further test the relationship between capital structure and enterprise performance, it is necessary to construct a multiple regression linear model for estimation. However, before building the model, the first thing to determine is whether to use a fixed effect or a random effect model. In this study, Eviews is used to perform the Hausman test on panel data, and the test results are listed in Table4. According to the data in the table, the chi-square value of the Hausman test was 34.405410, and the P value was ≤0.01. Thus, we reject the null hypothesis at the significance level of 1% and select the fixed-effect model for estimation.Table 4
Hausman test result.
Test cross-section random effectsTest summaryChi-sq. statisticChi-sq. d.f.Prob.Cross-section random34.40541060.0000According to the results of the regression analysis of the fixed-effect model in Table5, the regression equation between different variables can be obtained as follows:(2)RAOit=0.105446+0.116828SDBRit−0.056984LDBRit−0.229207TDBRit+0.023503TATOit+0.030533CARit+0.118875IARit+ε.Table 5
Results of regression analysis of fixed effects model.
VariableCoefficientStd. errort-statisticProb.C0.1054460.0210215.0161860.0000SDBR0.1168280.044310−2.6366150.0085LDBR−0.0569840.0526731.0818380.2796TDBR−0.2292070.030793−7.4434190.0000TATO0.0235030.0052074.5140150.0000CAR0.0305330.0261581.1672400.2434IAR0.1188750.0943041.2605550.2078Effects specificationCross-section fixed (dummy variables)R-squared0.483859Mean dependent var.0.039977AdjustedR-squared0.394096SD dependent var.0.079251SE of regression0.061689Akaike info criterion−2.596742Sum squared resid.3.150994Schwarz criterion−1.869449Log likelihood1408.315Hannan–Quinn criter.−2.319953F-statistic5.390373Durbin–Watson stat.1.655726Prob (F-statistic)0.000000From the results of regression analysis, the effect of capital structure on enterprise performance is more complex. The regression coefficient of the short-term interest-bearing debt ratio is 0.116828, and theP value is 0.0085. Therefore, we can judge that the short-term interest-bearing debt ratio has a positive impact on enterprise performance at the 1% significance level. The regression coefficient of the long-term interest-bearing debt ratio is −0.056984, which negatively correlates with corporate performance. However, its P value is 0.2796, which rejects the null hypothesis at the significance level of 5%, indicating that the long-term interest-bearing debt ratio has no significant impact on corporate performance. The regression coefficient of the asset-liability ratio was −0.229207 and the P value was ≤ 0.01 and passed the 1% significance level test, indicating that this explanatory variable had a strong negative correlation with enterprise performance. For the three control variables, the total asset turnover ratio, the proportion of current assets, and the proportion of intangible assets, their regression coefficient is positive, indicating that the impact on enterprise performance is positive. However, only total asset turnover passed the significance test at the 1% level, and the other two variables had no significant impact on enterprise performance. The adjusted R2 value of this model is 0.3941, indicating that the part of the dependent variable that can be explained by this model accounts for 39.41%. The regression equation has a good degree of fit, and the regression analysis results have a certain accuracy.


## 4.1. Descriptive Statistical Analysis

After descriptive statistics of each variable through Eviews, the data in Table2 are obtained. From the analysis results, the short-term interest-bearing debt ratio, long-term interest-bearing debt ratio, and asset-liability ratio of e-commerce enterprises are all significantly different, indicating that the capital structure level of each enterprise is very different, and there is a large gap in the degree of use of financial leverage. In addition, the total asset return rate, total asset turnover rate, and current asset ratio of e-commerce listed companies differ greatly, indicating that there are also certain differences in the performance level, asset operation ability, and asset composition of each enterprise.Table 2
Descriptive statistics of variables.
ROASDBRLDBRTDBRTATOCARIARMean0.0399770.1055010.047710.4395970.9132670.5988450.0433Median0.0398680.0801660.0099660.4280960.66850.6061090.031773Maximum0.4495690.6975290.4822461.23789512.37290.9818680.389841Minimum−0.909104000.015610.06480.067290Std. dev.0.0792510.1094010.0727180.2094391.028740.1904230.044481


## 4.2. Correlation Analysis

As can be seen from the results of correlation analysis in Table3, the absolute value of correlation coefficients among these variables is basically lower than 0.5, indicating that there is no serious multicollinearity among all variables. Thus, the panel data model is suitable for multiple regression analysis.Table 3
Variable correlation analysis.
CorrelationROASDBRLDBRTDBRTATOCARIARROA1.000000SDBR−0.3447661.000000LDBR−0.1657750.1360261.000000TDBR−0.3235460.4752670.4482401.000000TATO−0.0078760.117398−0.1377320.2928791.000000CAR0.1015110.028100−0.1486560.0658270.2781221.000000IAR0.0309360.0635040.018608−5.61E − 05−0.074606−0.4215301.000000


## 4.3. Regression Analysis

In order to further test the relationship between capital structure and enterprise performance, it is necessary to construct a multiple regression linear model for estimation. However, before building the model, the first thing to determine is whether to use a fixed effect or a random effect model. In this study, Eviews is used to perform the Hausman test on panel data, and the test results are listed in Table4. According to the data in the table, the chi-square value of the Hausman test was 34.405410, and the P value was ≤0.01. Thus, we reject the null hypothesis at the significance level of 1% and select the fixed-effect model for estimation.Table 4
Hausman test result.
Test cross-section random effectsTest summaryChi-sq. statisticChi-sq. d.f.Prob.Cross-section random34.40541060.0000According to the results of the regression analysis of the fixed-effect model in Table5, the regression equation between different variables can be obtained as follows:(2)RAOit=0.105446+0.116828SDBRit−0.056984LDBRit−0.229207TDBRit+0.023503TATOit+0.030533CARit+0.118875IARit+ε.Table 5
Results of regression analysis of fixed effects model.
VariableCoefficientStd. errort-statisticProb.C0.1054460.0210215.0161860.0000SDBR0.1168280.044310−2.6366150.0085LDBR−0.0569840.0526731.0818380.2796TDBR−0.2292070.030793−7.4434190.0000TATO0.0235030.0052074.5140150.0000CAR0.0305330.0261581.1672400.2434IAR0.1188750.0943041.2605550.2078Effects specificationCross-section fixed (dummy variables)R-squared0.483859Mean dependent var.0.039977AdjustedR-squared0.394096SD dependent var.0.079251SE of regression0.061689Akaike info criterion−2.596742Sum squared resid.3.150994Schwarz criterion−1.869449Log likelihood1408.315Hannan–Quinn criter.−2.319953F-statistic5.390373Durbin–Watson stat.1.655726Prob (F-statistic)0.000000From the results of regression analysis, the effect of capital structure on enterprise performance is more complex. The regression coefficient of the short-term interest-bearing debt ratio is 0.116828, and theP value is 0.0085. Therefore, we can judge that the short-term interest-bearing debt ratio has a positive impact on enterprise performance at the 1% significance level. The regression coefficient of the long-term interest-bearing debt ratio is −0.056984, which negatively correlates with corporate performance. However, its P value is 0.2796, which rejects the null hypothesis at the significance level of 5%, indicating that the long-term interest-bearing debt ratio has no significant impact on corporate performance. The regression coefficient of the asset-liability ratio was −0.229207 and the P value was ≤ 0.01 and passed the 1% significance level test, indicating that this explanatory variable had a strong negative correlation with enterprise performance. For the three control variables, the total asset turnover ratio, the proportion of current assets, and the proportion of intangible assets, their regression coefficient is positive, indicating that the impact on enterprise performance is positive. However, only total asset turnover passed the significance test at the 1% level, and the other two variables had no significant impact on enterprise performance. The adjusted R2 value of this model is 0.3941, indicating that the part of the dependent variable that can be explained by this model accounts for 39.41%. The regression equation has a good degree of fit, and the regression analysis results have a certain accuracy.


## 5. Conclusions

Based on the above empirical analysis, this study verifies that the capital structure of listed e-commerce companies has a significant impact on corporate performance, and the empirical conclusions are as follows:(1)
The short-term interest-bearing debt ratio positively correlates with corporate performance, which supports Hypothesis 1. This is consistent with the research conclusions of Saeed, Gull, and Rasheed that the short-term interest-bearing debt ratio of listed banks has a positive impact on corporate performance [34], and li Chuanxian and Zhao Zilin conclude that the short-term debt ratio has a significantly positive correlation with corporate performance in private enterprises with diversified debts. Short-term borrowings have a shorter repayment term, lower interest rate, and more flexible capital dispatching, which can produce a tax shield effect to a certain extent (Modigliani and Miller, 1963), improving the efficiency of management in corporate governance and thus promoting the improvement of enterprise value. Therefore, e-commerce enterprises can improve their credit and gain the trust of creditors through repeated timely repayment and flexible capital turnover, so as to obtain timely financing for future production and operation and promote their long-term stable and efficient operation.(2)
The long-term interest-bearing debt ratio negatively correlates with corporate performance, but the results are not significant. The empirical results show that e-commerce enterprises use less long-term debt. Compared with short-term borrowing, long-term debt has longer maturity and higher interest rate and generally involves a large amount of capital, which will bring higher financial risk to enterprises. This is similar to Zhang Yuernan who studied three typical e-commerce companies in China and found that their capital structure is mainly based on short-term debt and the use of long-term debt is relatively limited. In addition, Liu Quanxiu and Li Xinglin took small- and medium-sized board manufacturing companies as an example and found that the long-term interest-bearing debt ratio had a negative effect on enterprise value.(3)
There is a negative correlation between the asset-liability ratio and corporate performance, which supports Hypothesis 3. Excessive debt will lead creditors to make certain restrictions on an enterprise’s investment behaviour, limit the development of the enterprise, be not conducive to the improvement of enterprise value, and even increase the risk of enterprise bankruptcy. E-commerce companies can adopt more conservative financing strategies to ensure their capital security and enrich their capital flow with their own profits. The empirical results support the theory of optimal order financing to some extent, and enterprises prefer to adopt internal financing rather than external financing.(4)
Total asset turnover positively correlates with corporate performance, which supports Hypothesis 4. The higher the total asset turnover rate is, the better the benefit of the enterprise’s asset investment and the stronger its operating capacity are. E-commerce enterprises should invest in an efficient way to expand the scale of sales and use the economies of scale effect to reduce costs and increase profits, so as to improve their overall performance level.(5)
The proportion of current assets positively correlates with corporate performance, but the result is not significant. E-commerce enterprises still need to hold certain current assets in order to improve the liquidity and solvency of their assets and ensure smooth daily operations.(6)
The proportion of intangible assets positively correlates with enterprise performance, but the result is not significant, which may relate to the small proportion of intangible assets in total assets. With the adjustment of industrial structure, intangible assets are also playing a more and more important role, and China’s awareness of trademark rights, patent rights, and other protections is becoming stronger and stronger. This means that in the future development of e-commerce enterprises, they should pay attention to investment in intangible assets, which is an important factor affecting their core competitiveness.Based on the above empirical research and conclusions, this study, therefore, proposes several suggestions for listed e-commerce companies. (1) The degree of liabilities of an enterprise will have a significant impact on the overall value of the enterprise. Thus, an enterprise should maintain a reasonable asset-liability ratio and a relatively stable capital structure and appropriately increases the proportion of short-term liabilities among overall liabilities, so as to reduce financing costs and promote the improvement of enterprise value. However, e-commerce companies should not rely too much on short-term financing and should maintain rational short-term liabilities to prevent excessive financial pressure from breaking the daily operating capital chain. (2) On the one hand, enterprises should make appropriate investments to expand their scale, such as developing more product categories, looking for new markets to increase sales, making full use of the economies of scale effect, reducing marginal costs, and improving their profitability. On the other hand, the improvement of profitability also means that enterprises can retain more profits, which are conducive to internal financing, reducing financing costs, and ensuring the smooth turnover of funds.


### 5.1. Limitation and Future Research

We encountered some difficulties in doing this research, and we address the following two important issues. First, some listed companies will have missing values, but we have overcome difficulties to solve this problem by manpower. Second, with the changes in time and policies, we need to make adjustments to accurately predict the final performance results of e-commerce companies. We suggest that follow-up researchers can compare the e-commerce industry in different countries, just like what we have done this time about China’s e-commerce industry. Following researchers can also include the e-commerce industry in the United States or the e-commerce industry in the United Kingdom. If the business industry can be compared, we believe it will increase the value of this research.


## 5.1. Limitation and Future Research

We encountered some difficulties in doing this research, and we address the following two important issues. First, some listed companies will have missing values, but we have overcome difficulties to solve this problem by manpower. Second, with the changes in time and policies, we need to make adjustments to accurately predict the final performance results of e-commerce companies. We suggest that follow-up researchers can compare the e-commerce industry in different countries, just like what we have done this time about China’s e-commerce industry. Following researchers can also include the e-commerce industry in the United States or the e-commerce industry in the United Kingdom. If the business industry can be compared, we believe it will increase the value of this research.


## 6. Managerial and Practical Implications

The results of this study have important research and management implications. Our research contributions are mainly in accurately predicting the future development of the overall Chinese e-commerce industry and providing a set of decision-making solutions for managers. We can see that the overall asset structure has an impact on company performance. We have broken the traditional managers who only do their research on tangible assets. Our research this time increases their intangible assets to look at the company performance of the entire Chinese e-commerce industry.We have provided a set of asset structures to senior executives of China’s e-commerce industry, hoping that they can make further accurate predictions and decisions from different perspectives. Lin and Wei refer to the impact of the internationalization of China’s new retail industry on corporate performance [35]. We mentioned the importance of intangible assets to company performance earlier. It can be clearly seen that many e-commerce industries ignore this aspect, resulting in the failure of subsequent company performance to arise. Our management design can clearly see that managers need to consider intangible assets in the future, which is of great significance for future company development indicators.



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*Source: 7612349-2022-07-14.xml*