# Factor Analysis for Financial Performance Evaluation of Listed Chinese Medicine Companies

Authors: Jin Wang
Journal: Mobile Information Systems
 (2022)

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## Abstract

Coastal city enterprises are directly related to people’s health and are considered to be a sunrise industry that does not fall, while the 14th Five-Year Plan proposes to vigorously develop coastal city enterprises. In this paper, the financial data of 20 coastal city enterprises in 2019 were selected as samples, and an index system reflecting the characteristics of coastal city enterprises was constructed and analyzed by factor analysis to obtain objective enterprise performance scores and rankings. The results show that the Chinese medicine industry has large disparities at different levels but relatively small disparities in the overall ranking. The highest overall score is Yunnan Baiyao, the highest profitability and shareholder return score is Jichuan Pharmaceutical, Ealing Pharmaceutical has the strongest solvency, Baiyunshan is the best at corporate asset inventory management, Yunnan Baiyao has the greatest asset expansion, and the Zhongheng Group has the best quality of accounts receivable. It is recommended that Chinese medicine companies should take into account their own situation, strengthen their core competitive ability, and improve their corporate performance.


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## Body

## 1. Introduction

The world is currently facing huge challenges and changing international forms that pose a considerable challenge to major companies. Most companies are gradually resuming production and sales and preparing for possible future opportunities and threats according to their own situation, when effective corporate performance assessment is particularly important. Many scholars have conducted research on how to measure corporate performance in order to examine the strength of companies in their own environment. Domestic scholars mostly use hierarchical analysis, balanced scorecard, economic value added, and factor analysis to study corporate performance. For example, in [1], the authors adopted the combination of balanced scorecard and EVA to conduct a study on corporate performance evaluation of the GP enterprise group, providing new ideas for performance evaluation. At the same time, the authors in [2] used the entropy value method to evaluate the performance of GEM companies. The relationship between corporate growth and corporate performance was studied. The “14th Five-Year Plan” emphasizes the comprehensive promotion of the construction of a healthy China, adheres to the equal importance of Chinese and Western medicine, and vigorously develops the Chinese medicine industry. The development of the pharmaceutical industry is increasingly favored by capital, and the authors in [3] conducted a more comprehensive analysis and comparison of the subsectors under the pharmaceutical industry as a whole based on the value of enterprise performance evaluation criteria but did not take specific enterprises as examples. In [4], the authors constructed an enterprise performance evaluation model based on BP neural network, but the indicators used were generic and lacked in evaluating the specificity of Chinese medicine enterprises. Overall, there is little research in the segmentation of coastal city enterprises in the pharmaceutical market, and at the same time, there is a lack of references to the special indicators needed for the pharmaceutical industry, and it is hoped that the research in this paper can fill some of the research gaps in this field.


## 2. Indicator System Establishment

With the development of China’s economy, the existing enterprise legal regulations in China have been gradually improved, the evaluation index system of enterprises has been gradually improved, and many scholars have carried out research from many angles for the analysis of enterprise performance. In [5], the authors combined the characteristics of GEM SMEs with good growth but high P/E ratio and high investment risk as well as the industry background and evaluated 129 GEM SMEs by selecting indicators from four aspects: profitability, asset quality, debt risk, and business growth. In [6], the authors constructed an index system suitable for state-owned capital investment enterprises from two aspects: “value creation orientation” and “value-driven orientation.”At present, although in [7] used hierarchical analysis to analyze the performance of 30 listed pharmaceutical enterprises from the four perspectives of profitability, debt servicing, operation, and development, there are still few financial performance evaluations about the pharmaceutical industry, and the indicators selected for the study of the pharmaceutical industry are mostly general indicators, lacking indicators that reflect the characteristics of the pharmaceutical industry, especially those of enterprises in coastal cities. Combining the actual research and knowledge experience, this paper will select indicators from four perspectives: profitability, risk and asset quality, growth capacity, and operational capacity. Profitability reflects the ability of enterprises to obtain profits and achieve the goal of maximizing shareholder value, and the stronger the ability, the better the enterprise performance. Based on the polarization phenomenon of low gross margin for general drugs and high gross margin for special drugs in the pharmaceutical industry, this paper selects return on net assets (ROE) [8], operating profit margin, net sales margin, and earnings per share to reflect profitability.Risks and assets reflect whether the enterprise can effectively and reasonably use operating leverage, the quality of the enterprise’s accounts receivable, and the enterprise’s solvency. The pharmaceutical industry mostly relies on offline network and cooperation with major hospitals and has weak bargaining power with intermediaries, which makes pharmaceutical companies adopt “rebate” and loose accounts receivable policies to protect sales, resulting in increased costs and lower revenue quality [9]. Therefore, this paper selects the gearing ratio, current ratio, and cash flow ratio to reflect the “risk and asset quality” of Chinese medicine enterprises.Growth capacity reflects whether a company has future prospects. The development of the pharmaceutical industry relies heavily on the development of new drugs, which requires a large amount of capital investment. Due to the geographical and quantitative constraints of raw materials, many companies in coastal cities choose to build plants close to raw material production areas or build their own production areas, which also require asset investment and corporate earnings to support. Therefore, this paper selects the growth rate of total assets and the proportion of R&D expenses to operating revenue (hereinafter referred to as “R&D ratio”) to reflect the growth capability.Operating capacity reflects a company’s ability to sell products, control costs, and fully manage assets in the course of business, and the stronger the capacity, the more profitable the company. For pharmaceutical companies, the use of “rebates” and other forms of sales makes sales expenses that have been the most important part of their expenses, testing their ability to manage inventory, accounts receivable, and assets. This paper selects inventory turnover, total assets turnover, and accounts receivable turnover to reflect the operating ability of enterprises. At the same time, this paper selects 20 listed companies in the Chinese medicine segment of the pharmaceutical and biological field for analysis based on net profit ranking, where the data are all from the 2019 annual reports published by each company.


## 3. Performance Evaluation Based on Factor Analysis

### 3.1. Research Ideas

After calculating the values of each indicator, we face problems such as too much content of the indicator system, complex processing, whether the existing indicators meet the requirements for calculating performance, and difficulty in determining the indicator weights. Determining which indicators to use and assigning weights to each indicator are highly subjective and do not objectively reflect the performance of the company and analyze objective facts. The factor method is based on the indicators, from the correlation matrix, through the algorithm to discover the intrinsic relationship, extract information, streamline the indicators, and reduce the dimension, which can retain the maximum information and get objective indicator weights.


### 3.2. Research Methodology

In this paper, we use theR-type factor analysis method of the factor analysis method, in which the R factors exist objectively, but the common influencing factors cannot be measured directly. Significant relationships occur in R-type factor analysis when participants who rate well on variable 1 usually score well on variable 2 and vice versa. When there are significant relationships among groupings of variables, factors arise.Each variable can be expressed in the following equation form, i.e., eq. (1):
(1)Xi=ai1F1+αi2F2+⋯+αimFm+εi,i=1,2,⋯,ρ,where F1, F2,…, Fm are a common factor, and εi is a special factor of Xi as shown in eqs. (2) and (3).The matrix form is(2)X=AF+ε,where(3)A=a11a12⋯a1ma21a22⋯a2m⋮⋮⋮ap1ap2⋯apm=A1,A2,⋯Am,X=X1X2⋮XP,F=F1F2⋮Fm,ε=ε1ε2⋮εP.


### 3.3. Result Analysis

#### 3.3.1. Model Test

SPSS software is used to analyze the feasibility of the index system [10]. Because the KMO value is 0.535, greater than 0.5, and the significance of Bartlett sphericity test is less than 0.05, it shows that the model data structure is good and can be analyzed in the next step as shown in Table 1.Table 1
1 KMO and Bartlett test.
Cut quantity of KMO joint sample0.535Bartlett sphericity testApproximate chi square185.898Freedom66Significance0.000


#### 3.3.2. Common Factor Extraction

The common factor extraction is shown below in Table2.Table 2
Common factor variance table.
Indicator nameInitialExtractReturn on net assets (ROC)1.0000.868Net profit margin on sales1.0000.979Net profit margin on sales1.0000.851Asset liability ratio1.0000.863Current ratio1.0000.849Full flow ratio1.0000.769Current profit margin1.0000.847Accounts receivable turnover1.0000.735Proportion of R&D1.0000.868The “extracted” column in the common factor analysis of variance table indicates the percentage of data information extracted under each index. For example, in a row of ROE, the extracted data in a column is 0.868, which means that 86.8% of the ROE data is extracted. The information extraction rate of most of the overall indicators is greater than 80%, which shows that the data structure of the model is better.The gravel line chart is shown in Figure1. The gravel chart demonstrates the variation of feature value with respect to the component number. In the total variance interpretation table (Table 3), the total column is the eigenvalue of the principal factors (components). In this paper, five principal factors with eigenvalues greater than 1 are selected. The cumulative extracted information of the five main factors is greater than 80%, and the data information is well retained. Among them, the contribution of component 1 is 25.071%, the contribution of component 2 is 18.051%, the contribution of component 3 is 15.834%, the contribution of component 4 is 15.388%, the contribution of component 5 is 11.098%, and the contribution of component 1 is the largest.Figure 1
Gravel diagram.Table 3
Component score coefficient matrix.
Inventory turnover0.0400.0860.491-0.052-0.133Total asset turnover-0.109-0.0730.3470.1390.120Then, the comprehensive performance score formula is shown using eq. (4):
(4)F=0.293F1+0.211F2+0.185F3+0.180F4+0.130F5.


## 3.1. Research Ideas

After calculating the values of each indicator, we face problems such as too much content of the indicator system, complex processing, whether the existing indicators meet the requirements for calculating performance, and difficulty in determining the indicator weights. Determining which indicators to use and assigning weights to each indicator are highly subjective and do not objectively reflect the performance of the company and analyze objective facts. The factor method is based on the indicators, from the correlation matrix, through the algorithm to discover the intrinsic relationship, extract information, streamline the indicators, and reduce the dimension, which can retain the maximum information and get objective indicator weights.


## 3.2. Research Methodology

In this paper, we use theR-type factor analysis method of the factor analysis method, in which the R factors exist objectively, but the common influencing factors cannot be measured directly. Significant relationships occur in R-type factor analysis when participants who rate well on variable 1 usually score well on variable 2 and vice versa. When there are significant relationships among groupings of variables, factors arise.Each variable can be expressed in the following equation form, i.e., eq. (1):
(1)Xi=ai1F1+αi2F2+⋯+αimFm+εi,i=1,2,⋯,ρ,where F1, F2,…, Fm are a common factor, and εi is a special factor of Xi as shown in eqs. (2) and (3).The matrix form is(2)X=AF+ε,where(3)A=a11a12⋯a1ma21a22⋯a2m⋮⋮⋮ap1ap2⋯apm=A1,A2,⋯Am,X=X1X2⋮XP,F=F1F2⋮Fm,ε=ε1ε2⋮εP.


## 3.3. Result Analysis

### 3.3.1. Model Test

SPSS software is used to analyze the feasibility of the index system [10]. Because the KMO value is 0.535, greater than 0.5, and the significance of Bartlett sphericity test is less than 0.05, it shows that the model data structure is good and can be analyzed in the next step as shown in Table 1.Table 1
1 KMO and Bartlett test.
Cut quantity of KMO joint sample0.535Bartlett sphericity testApproximate chi square185.898Freedom66Significance0.000


### 3.3.2. Common Factor Extraction

The common factor extraction is shown below in Table2.Table 2
Common factor variance table.
Indicator nameInitialExtractReturn on net assets (ROC)1.0000.868Net profit margin on sales1.0000.979Net profit margin on sales1.0000.851Asset liability ratio1.0000.863Current ratio1.0000.849Full flow ratio1.0000.769Current profit margin1.0000.847Accounts receivable turnover1.0000.735Proportion of R&D1.0000.868The “extracted” column in the common factor analysis of variance table indicates the percentage of data information extracted under each index. For example, in a row of ROE, the extracted data in a column is 0.868, which means that 86.8% of the ROE data is extracted. The information extraction rate of most of the overall indicators is greater than 80%, which shows that the data structure of the model is better.The gravel line chart is shown in Figure1. The gravel chart demonstrates the variation of feature value with respect to the component number. In the total variance interpretation table (Table 3), the total column is the eigenvalue of the principal factors (components). In this paper, five principal factors with eigenvalues greater than 1 are selected. The cumulative extracted information of the five main factors is greater than 80%, and the data information is well retained. Among them, the contribution of component 1 is 25.071%, the contribution of component 2 is 18.051%, the contribution of component 3 is 15.834%, the contribution of component 4 is 15.388%, the contribution of component 5 is 11.098%, and the contribution of component 1 is the largest.Figure 1
Gravel diagram.Table 3
Component score coefficient matrix.
Inventory turnover0.0400.0860.491-0.052-0.133Total asset turnover-0.109-0.0730.3470.1390.120Then, the comprehensive performance score formula is shown using eq. (4):
(4)F=0.293F1+0.211F2+0.185F3+0.180F4+0.130F5.


## 3.3.1. Model Test

SPSS software is used to analyze the feasibility of the index system [10]. Because the KMO value is 0.535, greater than 0.5, and the significance of Bartlett sphericity test is less than 0.05, it shows that the model data structure is good and can be analyzed in the next step as shown in Table 1.Table 1
1 KMO and Bartlett test.
Cut quantity of KMO joint sample0.535Bartlett sphericity testApproximate chi square185.898Freedom66Significance0.000


## 3.3.2. Common Factor Extraction

The common factor extraction is shown below in Table2.Table 2
Common factor variance table.
Indicator nameInitialExtractReturn on net assets (ROC)1.0000.868Net profit margin on sales1.0000.979Net profit margin on sales1.0000.851Asset liability ratio1.0000.863Current ratio1.0000.849Full flow ratio1.0000.769Current profit margin1.0000.847Accounts receivable turnover1.0000.735Proportion of R&D1.0000.868The “extracted” column in the common factor analysis of variance table indicates the percentage of data information extracted under each index. For example, in a row of ROE, the extracted data in a column is 0.868, which means that 86.8% of the ROE data is extracted. The information extraction rate of most of the overall indicators is greater than 80%, which shows that the data structure of the model is better.The gravel line chart is shown in Figure1. The gravel chart demonstrates the variation of feature value with respect to the component number. In the total variance interpretation table (Table 3), the total column is the eigenvalue of the principal factors (components). In this paper, five principal factors with eigenvalues greater than 1 are selected. The cumulative extracted information of the five main factors is greater than 80%, and the data information is well retained. Among them, the contribution of component 1 is 25.071%, the contribution of component 2 is 18.051%, the contribution of component 3 is 15.834%, the contribution of component 4 is 15.388%, the contribution of component 5 is 11.098%, and the contribution of component 1 is the largest.Figure 1
Gravel diagram.Table 3
Component score coefficient matrix.
Inventory turnover0.0400.0860.491-0.052-0.133Total asset turnover-0.109-0.0730.3470.1390.120Then, the comprehensive performance score formula is shown using eq. (4):
(4)F=0.293F1+0.211F2+0.185F3+0.180F4+0.130F5.


## 4. Research Design

### 4.1. Data and Variables

Considering the availability and consistency of research data, this paper selects 2011-2015 as the research period. Here, the samples of enterprises and St enterprises with obvious lack of R&D investment and other data are eliminated, and finally, the listed enterprises in 29 smart cities in mainland China are taken as the research object. The basic data involved in the research are mainly from the wind consulting financial terminal database, and some are collected from the annual reports of relevant enterprises over the years. In order to objectively reveal the impact of R&D investment on the financial performance of Chinese smart city enterprises from the perspective of heterogeneous threshold effect, specific variables are set here.


#### 4.1.1. Explained Variable

The financial performance of smart city enterprises is the explanatory variable. In the measurement of enterprise financial performance, the existing literature usually adopts market performance indicators or accounting performance indicators. The indicators of market dimension are ideal indicators to measure enterprise performance under the conditions of developed and fully circulated capital market. However, there are a large number of nontradable shares in China’s capital market, and the academic and industrial circles do not have a reasonable standard for the value of nontradable shares. There is still an obvious gap between the effectiveness of China’s capital market and the mature western market. At present, there are still big defects in using market performance indicators to measure enterprise performance [11]. Therefore, this paper uses accounting performance indicators to measure the financial performance of enterprises. Referring to the practices of Tao Wenjie and Jin Zhanming (2013), this paper selects the rate of return on total assets (ROA) as an alternative indicator to measure the financial performance of enterprises [12]. At the same time, in order to make the research conclusion more stable, the research of Liu Zhen (2014) is also used for reference, and the return on net assets (ROE) is selected as another alternative index of enterprise financial performance to further confirm [13].


#### 4.1.2. Core Variable

Independent research and development (RD) is the core explanatory variable of this paper. As for the measurement of enterprises’ independent R& D investment level, the current academic circles mostly use two kinds of indicators: one is the relative indicator represented by the ratio of R&D investment level to output indicators such as sales output value, and the other is the absolute indicator represented by the amount of internal R&D investment. Drawing on the existing common practices based on relevant research literature at the microlevel [14, 15], and considering that the relative indicators are more targeted and comparable, the relative indicators are used to measure the independent R&D investment level of smart city enterprises. Therefore, the independent R&D index selected in this paper is expressed by the ratio of enterprise R&D investment to total operating income.


#### 4.1.3. Threshold Variable

In order to reveal more deeply the possible constraining effects and moderating effects of other factors acting on R&D investment on financial performance of smart city firms, this paper also further selects equity concentration, firm size, operational capacity, capital density, financial support, and capital structure as threshold variables based on different dimensions to be examined. Here, the above variables are defined as follows: equity concentration (gq): different equity concentration may lead to different shareholder behavior of smart city enterprises [16], which in turn may have certain impact on their financial performance; here, referring to the research method of Chen Deping and Chen Yongsheng (2011), the shareholding ratio of the top ten shareholders of smart city enterprises is selected as a proxy variable for equity concentration; enterprise size (fs): as smart city enterprises of different sizes have different shareholder behavior, which in turn may have certain impact on their financial performance. Since smart city enterprises of different sizes have different resource advantages, which may also have an important impact on their financial performance [17], total assets are used to measure the size level of smart city enterprises and are logarithmically processed; operating capacity (opc) reflects the operation level of listed smart city enterprises and thus plays a supportive role in the improvement of their financial performance level, and total assets are selected turnover ratio to reflect; capital density (capi), referring to Dong Mingfang and Han Xianfeng (2016), is selected to characterize the ratio of fixed assets to total assets of smart city enterprises; financial support (fin) is defined as the ratio of outstanding shares to total equity of each smart city listed enterprise; capital structure (caps), drawing on the study of Tian Lihui and Ye Yao (2013), adopts smart city enterprises. The capital structure (caps), borrowed from Tian, Li-Hui and Ye, Yao (2013), is used to reflect the capital structure variables.


### 4.2. Model Construction

In order to objectively reveal the heterogeneous nonlinear effect of independent R&D on the financial performance of Chinese smart city enterprises, based on the results of Hansen (1999, 2000), this paper constructs the following threshold regression model as shown in eq. (5):
(5)ROAit=c+α1contror+β1RDitIRDit≤γ1+β2RDitIRDit>γ1+⋯+βnRDitIRDit≤γn+βn+1RDitIRDit>γn+eit.In the above equation,i is the study sample number of listed smart city enterprises in China, and t denotes the year. i∗ is an indicator function that takes the value of 1 when the conditions in parentheses are met, and 0 otherwise denotes the level of financial performance of smart city enterprises, γ is the threshold value, and RDit is both the threshold variable and the core explanatory variable.The model (1) is simplified and can be expressed in matrix form as shown in eq. (6):
(6)ROA=Xγα+ε.Given anyγ, the least squares estimate of the coefficients α⌢ is shown in eq. (7):
(7)α^=X∗γ·X∗γ−1X∗γ′ROA.Correspondingly, the sum of squared residuals of the regression equation is shown in eq. (8):
(8)SSE1γ=ε^γε^γ=ROA′1−ROAγ−X∗γX∗γ−1×X∗γ∗ROA.The estimated amount of the threshold is shown in eq. (9):
(9)γ^=argminSSE1γ,σ∧2γ^=1nT−1e∧∗·γ^e∧∗γ^=1nT−1SSE1γ.After obtaining the estimates of the parameters, two more tests are needed to determine whether the threshold effect is significant and whether the estimate of the threshold is equal to its true value. The specific can be judged by constructing LM statistics. The original hypothesisH0:α1=α2 of the threshold effect existence test and the alternative hypothesis H0:α1≠α2 construct the statistic that is shown in eqs. (10) and (11):
(10)F1=SSE0γ−SSE1γ^σ∧2,(11)LR1γ=SSE1γ−SSE1γ^σ∧2.The distribution of the LR statistic is nonstandard, its asymptotic distribution satisfiescα=−2ln when LR1≤cα is satisfied, the original hypothesis does not hold, and thus the confidence interval of the threshold estimator can be obtained.In addition, in order to further investigate the heterogeneous nonlinear and moderating effects of the impact of independent R&D investment on the financial performance of smart city firms under the effect of other factors such as equity concentration, firm size, operational capacity, capital intensity, financial support, and capital structure, the nonlinear panel model constructed here on the basis of Hansen’s basic model is as follows shown in eq. (12):
(12)ROAit=c+β1contror+α1RDitIqit≤γ1+α2RDitIqit>γ1+⋯+αnRDitIqit≤γn+αn+1RDitIqit>γn+eit.In eq. (12), qit is the threshold variable, which is a proxy for equity concentration, firm size, etc. The nonlinear model with the above factors as threshold variables can be obtained by expanding.The panel threshold model constructed with equity concentration as the threshold variable is represented in eq. (13):
(13)ROAit=c+β1contror+α1RDitIgqit≤γ1+α2RDitIgqit>γ1+⋯+αnRDitIgqit≤γn+αn+1RDitIgqit>γn+eit.The panel threshold model constructed with firm size as the threshold variable as shown in (14):
(14)ROAit=c+β1contror+α1RDitIfsit≤γ1+α2RDitIfsit>γ1+⋯+αnRDitIfsit≤γn+αn+1RDitIfsit>γn+eit.The panel threshold model constructed with operational capability as the threshold variable is shown in eq. (15):
(15)ROAit=c+β1contror+α1RDitIopcit≤γ1+α2RDitIopcit>γ1+⋯+αnRDitIopcit≤γn+αn+1RDitIopcit>γn+eit.The panel threshold model constructed using capital intensity as the threshold variable is shown in eq. (16):
(16)ROAit=c+β1contror+α1RDitIopcit≤γ1+α2RDitIopcit>γ1+⋯+αnRDitIopcit≤γn+αn+1RDitIopcΩit>γn+eit.The panel threshold model constructed with financial support as the threshold variable is shown in eq. (17):
(17)ROAit=c+β1contror+α1RDitIfinit≤γ1+α2RDitIfinit>γ1+⋯+αnRDitIfinit≤γn+αn+1RDitIfinit>γn+eit.The panel threshold model constructed with capital structure as the threshold variable is shown in eq. (18):
(18)ROAit=c+β1contror+α1RDitIcapsit≤γ1+α2RDitIcapsit>γ1+⋯+αnRDitIcapsit≤γn+αn+1RDitIcapsit>γn+eit.


## 4.1. Data and Variables

Considering the availability and consistency of research data, this paper selects 2011-2015 as the research period. Here, the samples of enterprises and St enterprises with obvious lack of R&D investment and other data are eliminated, and finally, the listed enterprises in 29 smart cities in mainland China are taken as the research object. The basic data involved in the research are mainly from the wind consulting financial terminal database, and some are collected from the annual reports of relevant enterprises over the years. In order to objectively reveal the impact of R&D investment on the financial performance of Chinese smart city enterprises from the perspective of heterogeneous threshold effect, specific variables are set here.


### 4.1.1. Explained Variable

The financial performance of smart city enterprises is the explanatory variable. In the measurement of enterprise financial performance, the existing literature usually adopts market performance indicators or accounting performance indicators. The indicators of market dimension are ideal indicators to measure enterprise performance under the conditions of developed and fully circulated capital market. However, there are a large number of nontradable shares in China’s capital market, and the academic and industrial circles do not have a reasonable standard for the value of nontradable shares. There is still an obvious gap between the effectiveness of China’s capital market and the mature western market. At present, there are still big defects in using market performance indicators to measure enterprise performance [11]. Therefore, this paper uses accounting performance indicators to measure the financial performance of enterprises. Referring to the practices of Tao Wenjie and Jin Zhanming (2013), this paper selects the rate of return on total assets (ROA) as an alternative indicator to measure the financial performance of enterprises [12]. At the same time, in order to make the research conclusion more stable, the research of Liu Zhen (2014) is also used for reference, and the return on net assets (ROE) is selected as another alternative index of enterprise financial performance to further confirm [13].


### 4.1.2. Core Variable

Independent research and development (RD) is the core explanatory variable of this paper. As for the measurement of enterprises’ independent R& D investment level, the current academic circles mostly use two kinds of indicators: one is the relative indicator represented by the ratio of R&D investment level to output indicators such as sales output value, and the other is the absolute indicator represented by the amount of internal R&D investment. Drawing on the existing common practices based on relevant research literature at the microlevel [14, 15], and considering that the relative indicators are more targeted and comparable, the relative indicators are used to measure the independent R&D investment level of smart city enterprises. Therefore, the independent R&D index selected in this paper is expressed by the ratio of enterprise R&D investment to total operating income.


### 4.1.3. Threshold Variable

In order to reveal more deeply the possible constraining effects and moderating effects of other factors acting on R&D investment on financial performance of smart city firms, this paper also further selects equity concentration, firm size, operational capacity, capital density, financial support, and capital structure as threshold variables based on different dimensions to be examined. Here, the above variables are defined as follows: equity concentration (gq): different equity concentration may lead to different shareholder behavior of smart city enterprises [16], which in turn may have certain impact on their financial performance; here, referring to the research method of Chen Deping and Chen Yongsheng (2011), the shareholding ratio of the top ten shareholders of smart city enterprises is selected as a proxy variable for equity concentration; enterprise size (fs): as smart city enterprises of different sizes have different shareholder behavior, which in turn may have certain impact on their financial performance. Since smart city enterprises of different sizes have different resource advantages, which may also have an important impact on their financial performance [17], total assets are used to measure the size level of smart city enterprises and are logarithmically processed; operating capacity (opc) reflects the operation level of listed smart city enterprises and thus plays a supportive role in the improvement of their financial performance level, and total assets are selected turnover ratio to reflect; capital density (capi), referring to Dong Mingfang and Han Xianfeng (2016), is selected to characterize the ratio of fixed assets to total assets of smart city enterprises; financial support (fin) is defined as the ratio of outstanding shares to total equity of each smart city listed enterprise; capital structure (caps), drawing on the study of Tian Lihui and Ye Yao (2013), adopts smart city enterprises. The capital structure (caps), borrowed from Tian, Li-Hui and Ye, Yao (2013), is used to reflect the capital structure variables.


## 4.1.1. Explained Variable

The financial performance of smart city enterprises is the explanatory variable. In the measurement of enterprise financial performance, the existing literature usually adopts market performance indicators or accounting performance indicators. The indicators of market dimension are ideal indicators to measure enterprise performance under the conditions of developed and fully circulated capital market. However, there are a large number of nontradable shares in China’s capital market, and the academic and industrial circles do not have a reasonable standard for the value of nontradable shares. There is still an obvious gap between the effectiveness of China’s capital market and the mature western market. At present, there are still big defects in using market performance indicators to measure enterprise performance [11]. Therefore, this paper uses accounting performance indicators to measure the financial performance of enterprises. Referring to the practices of Tao Wenjie and Jin Zhanming (2013), this paper selects the rate of return on total assets (ROA) as an alternative indicator to measure the financial performance of enterprises [12]. At the same time, in order to make the research conclusion more stable, the research of Liu Zhen (2014) is also used for reference, and the return on net assets (ROE) is selected as another alternative index of enterprise financial performance to further confirm [13].


## 4.1.2. Core Variable

Independent research and development (RD) is the core explanatory variable of this paper. As for the measurement of enterprises’ independent R& D investment level, the current academic circles mostly use two kinds of indicators: one is the relative indicator represented by the ratio of R&D investment level to output indicators such as sales output value, and the other is the absolute indicator represented by the amount of internal R&D investment. Drawing on the existing common practices based on relevant research literature at the microlevel [14, 15], and considering that the relative indicators are more targeted and comparable, the relative indicators are used to measure the independent R&D investment level of smart city enterprises. Therefore, the independent R&D index selected in this paper is expressed by the ratio of enterprise R&D investment to total operating income.


## 4.1.3. Threshold Variable

In order to reveal more deeply the possible constraining effects and moderating effects of other factors acting on R&D investment on financial performance of smart city firms, this paper also further selects equity concentration, firm size, operational capacity, capital density, financial support, and capital structure as threshold variables based on different dimensions to be examined. Here, the above variables are defined as follows: equity concentration (gq): different equity concentration may lead to different shareholder behavior of smart city enterprises [16], which in turn may have certain impact on their financial performance; here, referring to the research method of Chen Deping and Chen Yongsheng (2011), the shareholding ratio of the top ten shareholders of smart city enterprises is selected as a proxy variable for equity concentration; enterprise size (fs): as smart city enterprises of different sizes have different shareholder behavior, which in turn may have certain impact on their financial performance. Since smart city enterprises of different sizes have different resource advantages, which may also have an important impact on their financial performance [17], total assets are used to measure the size level of smart city enterprises and are logarithmically processed; operating capacity (opc) reflects the operation level of listed smart city enterprises and thus plays a supportive role in the improvement of their financial performance level, and total assets are selected turnover ratio to reflect; capital density (capi), referring to Dong Mingfang and Han Xianfeng (2016), is selected to characterize the ratio of fixed assets to total assets of smart city enterprises; financial support (fin) is defined as the ratio of outstanding shares to total equity of each smart city listed enterprise; capital structure (caps), drawing on the study of Tian Lihui and Ye Yao (2013), adopts smart city enterprises. The capital structure (caps), borrowed from Tian, Li-Hui and Ye, Yao (2013), is used to reflect the capital structure variables.


## 4.2. Model Construction

In order to objectively reveal the heterogeneous nonlinear effect of independent R&D on the financial performance of Chinese smart city enterprises, based on the results of Hansen (1999, 2000), this paper constructs the following threshold regression model as shown in eq. (5):
(5)ROAit=c+α1contror+β1RDitIRDit≤γ1+β2RDitIRDit>γ1+⋯+βnRDitIRDit≤γn+βn+1RDitIRDit>γn+eit.In the above equation,i is the study sample number of listed smart city enterprises in China, and t denotes the year. i∗ is an indicator function that takes the value of 1 when the conditions in parentheses are met, and 0 otherwise denotes the level of financial performance of smart city enterprises, γ is the threshold value, and RDit is both the threshold variable and the core explanatory variable.The model (1) is simplified and can be expressed in matrix form as shown in eq. (6):
(6)ROA=Xγα+ε.Given anyγ, the least squares estimate of the coefficients α⌢ is shown in eq. (7):
(7)α^=X∗γ·X∗γ−1X∗γ′ROA.Correspondingly, the sum of squared residuals of the regression equation is shown in eq. (8):
(8)SSE1γ=ε^γε^γ=ROA′1−ROAγ−X∗γX∗γ−1×X∗γ∗ROA.The estimated amount of the threshold is shown in eq. (9):
(9)γ^=argminSSE1γ,σ∧2γ^=1nT−1e∧∗·γ^e∧∗γ^=1nT−1SSE1γ.After obtaining the estimates of the parameters, two more tests are needed to determine whether the threshold effect is significant and whether the estimate of the threshold is equal to its true value. The specific can be judged by constructing LM statistics. The original hypothesisH0:α1=α2 of the threshold effect existence test and the alternative hypothesis H0:α1≠α2 construct the statistic that is shown in eqs. (10) and (11):
(10)F1=SSE0γ−SSE1γ^σ∧2,(11)LR1γ=SSE1γ−SSE1γ^σ∧2.The distribution of the LR statistic is nonstandard, its asymptotic distribution satisfiescα=−2ln when LR1≤cα is satisfied, the original hypothesis does not hold, and thus the confidence interval of the threshold estimator can be obtained.In addition, in order to further investigate the heterogeneous nonlinear and moderating effects of the impact of independent R&D investment on the financial performance of smart city firms under the effect of other factors such as equity concentration, firm size, operational capacity, capital intensity, financial support, and capital structure, the nonlinear panel model constructed here on the basis of Hansen’s basic model is as follows shown in eq. (12):
(12)ROAit=c+β1contror+α1RDitIqit≤γ1+α2RDitIqit>γ1+⋯+αnRDitIqit≤γn+αn+1RDitIqit>γn+eit.In eq. (12), qit is the threshold variable, which is a proxy for equity concentration, firm size, etc. The nonlinear model with the above factors as threshold variables can be obtained by expanding.The panel threshold model constructed with equity concentration as the threshold variable is represented in eq. (13):
(13)ROAit=c+β1contror+α1RDitIgqit≤γ1+α2RDitIgqit>γ1+⋯+αnRDitIgqit≤γn+αn+1RDitIgqit>γn+eit.The panel threshold model constructed with firm size as the threshold variable as shown in (14):
(14)ROAit=c+β1contror+α1RDitIfsit≤γ1+α2RDitIfsit>γ1+⋯+αnRDitIfsit≤γn+αn+1RDitIfsit>γn+eit.The panel threshold model constructed with operational capability as the threshold variable is shown in eq. (15):
(15)ROAit=c+β1contror+α1RDitIopcit≤γ1+α2RDitIopcit>γ1+⋯+αnRDitIopcit≤γn+αn+1RDitIopcit>γn+eit.The panel threshold model constructed using capital intensity as the threshold variable is shown in eq. (16):
(16)ROAit=c+β1contror+α1RDitIopcit≤γ1+α2RDitIopcit>γ1+⋯+αnRDitIopcit≤γn+αn+1RDitIopcΩit>γn+eit.The panel threshold model constructed with financial support as the threshold variable is shown in eq. (17):
(17)ROAit=c+β1contror+α1RDitIfinit≤γ1+α2RDitIfinit>γ1+⋯+αnRDitIfinit≤γn+αn+1RDitIfinit>γn+eit.The panel threshold model constructed with capital structure as the threshold variable is shown in eq. (18):
(18)ROAit=c+β1contror+α1RDitIcapsit≤γ1+α2RDitIcapsit>γ1+⋯+αnRDitIcapsit≤γn+αn+1RDitIcapsit>γn+eit.


## 5. Experimental Results and Analysis

The scores of each Chinese medicine company are shown in Figure2.Figure 2
Visualization of different enterprise analysis.From Figure2, there are 10 enterprises with a composite score greater than 0, which means that these 10 enterprises have a higher score than the average score of the selected TCM enterprises.The F1 factor reflects the size of the profitability of the enterprise and the ultimate ability to bring returns to shareholders and is the factor that reflects the performance of the enterprise to the greatest extent. And the high weight of F1 on the three indicators of net sales margin, operating profit margin, and return on net assets indicates that Chinese medicine enterprises show great differences in this profitability and return to shareholders.F2 reflects an enterprise’s ability to repay its debts and is a factor to measure the ability of an enterprise to protect the interests of its creditors, with the highest weighting given to the asset-liability ratio. Among the F2 scores of each company, Ealing Pharmaceuticals scored 1.507, the highest score, and Tianshi Li scored -1.431, the lowest score, with a relatively small difference of 2.938 points between them. Nine companies have positive scores, six of which have scores greater than 1, and four have scores less than -1. The relative gap between companies is large, indicating that companies in coastal cities have different approaches to corporate debt policies, but overall performance is good.F3 reflects the ability of Chinese medicine enterprises to manage the assets they own. The stronger the management ability, the better the performance of the enterprise, with the highest weighting on inventory turnover. Other enterprises score average and show a stepwise distribution. It indicates that the level of asset management of Chinese medicine enterprises, especially the management of inventory, is uneven, and the overall performance is weak.F4 reflects the extent of asset expansion, future growth ability, and shareholder return from expansion, with the highest weighting on total asset growth rate. Among the F4 scores, Yunnan Baiyao has the highest score of 3.139, and Kang’anbei has the lowest score of -0.915, with a large difference of 4.054 between them. Among them, 9 enterprises haveF scores greater than 0, 2 enterprises have scores greater than 1, and the others are mostly in the range of -0.6 ~ 0.8, indicating that the overall degree of external expansion of TCM enterprises is relatively small.F5 reflects the quality of the enterprises’ accounts and the amount of capital invested in R&D, among which the accounts receivable turnover ratio has the greatest weight. There are 11 enterprises with positive scores, mostly in the range of 0.5 to 0.9, which are better, while those with negative scores are in the range of -0.7 to -0.1, which are worse, indicating the polarization of Chinese medicine enterprises in terms of accounts management and R&D investment.The difference between the scores of F2 and F4 is small, while the differences between the scores of F1, F3, and F5 are large, indicating that the differences in the ability to repay debts, the size of asset expansion, and the shareholder’s income from expansion are small, but the differences in profitability, the ability to bring returns to shareholders, the ability to manage assets, the quality of accounts, and the future growth of Chinese medicine companies are uneven. However, there is a wide range in profitability, return to shareholders, asset management, account quality, and future growth. In addition, none of the companies have positive scores on all factors under the model, indicating that all companies in coastal cities have shortcomings below the industry average.


## 6. Conclusion

This paper selects the 2019 financial data of 20 coastal city enterprises as samples, constructs an index system reflecting the characteristics of coastal city enterprises, and analyzes them using factor analysis to obtain objective enterprise performance scores as well as rankings. The results show that the Chinese medicine industry has large disparities at different levels but relatively small disparities in the overall ranking. The highest overall score is Yunnan Baiyao, the highest profitability and shareholder return score is Jichuan Pharmaceutical, and Ealing Pharmaceutical has the strongest solvency.In future, we aim to look at the impact of three external influences on businesses: the economy, strategy, and locality.



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*Source: 2446779-2022-05-20.xml*