Whether the CEO Turnover Can Improve the Conversion of Enterprise’s New and Old Driving Force?

Abstract

After 2014, China focused on supply-side reform and put forward the concept of replacing old driving with new driving force, which can create a new round of economic improvement. From the perspective of the conversion of old and new driving forces of enterprises, this paper takes the listed enterprises in China’s a-share manufacturing industry from 2016 to 2018 as samples. This paper also discusses the “Net Effect” of CEO turnover on the conversion of new and old driving forces of enterprises. The results reflect that CEO turnover has a negative effect on the conversion of old and new driving force. Moving forward, when the CEO and chairman changes at the same time or the ownership of the enterprise turns to state-owned, the change of CEO has a higher degree of prevention on the conversion of old and new force. The number of board’s conferences and the degree of equity balance will increase the negative effect of CEO turnover on the conversion of old and new driving force.

1. Introduction

After 2014, China’s entered the “New Normal” in terms of economic development. Under the situation of the “New Normal”, China’s economic environment is not so optimistic. The country put forward the concept of supply-side reform and emphasized replacing old driving force with new ones to create a new round of economic development. At present, the decline in the growth rate of fixed asset investment and macro-capacity in China is reflected in the micro-enterprise level, which is a question of investment efficiency and innovation efficiency, and then a systemic issue of corporate governance and equity structure. The so-called supply-side reform is to use the reform method to promote structural adjustment, reduce inefficient and low-end supply, expand effective and mid-to-high-end supply, and improve total factor productivity. One of the focuses is to resolve excess capacity and improve corporate efficiency. Romer [1] pointed out that if you want to create competitive advantages, it is necessary to realize innovation. The enterprise from the old state to the new state needs to rely on more advanced capital investment and human capital investment, thus it can cause favored capital and technological progress. Besides, from the perspective of corporate strategy on capital, manpower, technology investment, it accelerates the enterprise to experience the conversion between the old and the new driving force. From his research, the concept of elasticity is used for solving the degree of the conversion of old and new driving forces among enterprises, and the degree of this conversion is required to be studied.

CEO turnover is an essential part of corporate governance [2], which has a long-term impact on the enterprise’s investment decisions, business performance and market performance. As with CEO turnover, the business activities and strategic decisions of the enterprise are influenced by both the departure CEO and the new CEO. Even if CEO turnover happens at the end of the year, the new CEO may still have an impact on the enterprise’s business activities and strategic decisions in the same year [3]. For example, the new CEO was hired from the enterprise’s internal management and was involved in making strategic decisions or the new CEO makes a major adjustment to the enterprise’s business strategy at the end of the year. Therefore, CEO turnover on the information presented in the annual report from the enterprise in the current year has interactive impact on both the departure CEO and the new CEO.

The overall decisions from the CEO have a direct impact on the conversion of old and new business driving force. If the CEO will change, the enterprise’s strategy will definitely be affected. Is there not any existence of its discussions that promote a conversion among businesses in a dynamic way or inhabiting way?

According to the mentioned definition, the conversion of old and new business driving force is mainly based on the new output generated by new input. The “three new” economic value-added refers to the final results of the “three new” economic production activities of all resident units in a country (or region) within a certain period. A resident unit refers to an economic unit that has a center of economic interest in China’s economic territory. Production refers to activities that use labor, capital, goods, and services inputs to create new goods and services output under the control and organization of institutional units. On November 22, 2018, the National Bureau of Statistics of China announced that, according to the “Statistical Classification of New Industries, New Formats, and New Business Models (2018)” and the “New Industry, New Formats, and New Business Models Value-added Accounting Methods”, after accounting, the national “three new” economic-added value was 129,578 billion yuan, equivalent to 15.7% of GDP, an increase of 0.4 percentage points from the previous year. The growth rate at current prices is 14.1%, which is 2.9 percentage points higher than the growth rate of GDP in the same period.

In addition, the strategy of these investments is what the CEO does. Previous studies were mainly based on the impact of CEO turnover on Research & Development investment [4,5,6,7,8,9] their studies show that the CEO’s tenure is correlated with Research & Development. Bushee [5] also observed that CEOs have a strong incentive to reduce Research & Development expenditure when they are close to leaving or retiring period. Dechow [10], from the perspective of the “Horizon Problem”, took American technology-intensive listed enterprises as research samples. He found that CEOs inclined to reduce Research & Development expenditure to increase short-term earnings in the year before leaving office. Due to the reduction of Research & Development expenditure, enterprises’ changes of old and new driving forces would be reduced. Murphy [3], after controlling the endogenous problem of CEO turnover before the departure time, believes that the decline of Research & Development expenditure, advertising expenditure and other capital expenditure is not for the purpose of earnings management from the CEO, but because the enterprise’s current performance is too poor to afford more long-term capital investment projects. Kalyta [11] took Fortune Top-100 enterprises as research samples to study the impact of the CEO’s retirement. Moreover, he found that the CEO’s pension is matched with the enterprise performance during the CEO’s tenure. The CEO has a stronger incentive to realize upward earnings management activities by cutting R&D expenditure and other long-term expenditures before retirement. Jing [12], taking listed enterprises in the United States as research samples, found the new CEO was based on the purpose of taking a big bath on profit. In the initial stage, the CEO tends to increase the intensity of R&D investment and put more investment in R&D projects. Even if the enterprise will experience the short-term decline for it, it results from the former CEO’s responsibility, and it will also lay low benchmark performance measurement in the future. Du [13] also took American enterprises as samples and reached similar research conclusions. Moving forward, he found that when the current CEO was forced to leave, the new CEO’s higher degree of option incentive and short-term tenure tended to have stronger motivation to manipulate earnings and increase R&D investment at the beginning of his tenure. Bernstein [14], from the perspective of innovation performance, took the number of patents and patent citations to enterprise as a measure of innovation performance, and found that the new CEO in his initial stage of the R&D input is in a higher level. Enterprise’s innovation performance is significantly improved in a short-term. The improvement is influenced by the CEO overconfidence, options, and the impact of information asymmetry. Chen [15] suggested director abnormal compensation has a negative impact on the likelihood of CEO turnover and reduces the sensitivity of CEO turnover to poor innovation performance. Orekhova [16] demonstrates that boards of directors should not make hasty decisions to switch the CEO after a short-term poor performance.

To sum up, previous studies only focused on R&D investment influenced by the impact of CEO turnover, and no one studied the conversion of old and new driving forces among enterprises influenced by the impact of CEO turnover. Moreover, there is lacking in research and discussion on the input and output of enterprises influenced by the overall impact of CEO turnover.

Due to the differences between the industry and the internal characteristics of the enterprise, there may be some endogenous issues caused by missing variables using ordinary regression methods [17]. Even if there is no CEO turnover, the conversion of the old and new driving force of the enterprise will be affected by other factors. Existing studies cannot distinguish the influence of CEO turnover and the conversion of the old and new driving force of the enterprise. This article uses the PSM-DID model, which can evaluate the effect of policy implementation or personnel changes. Compared with other data analysis models, this model does not directly compare the average change of the sample before and after the policy, but uses individual data for regression and judgment, and assesses whether the impact of the policy has significant statistical significance. Based on the propensity score matching method, according to the CEO turnover, the sample was divided into the processed group and unprocessed group. This paper controls CEO turnover and the old and the new conversion with the key and observable variables. Then, it matches with the dual difference method to erase the endogenous issues resulting from the regression model and control the difference among groups influenced by an unobservable variable. Then, this paper obtains more accurate “Net Effect” of the conversion of old and new force among enterprises influenced by CEO turnover.

2. Analysis on Theory and Hypothesis

2.1. CEO Turnover and the Conversion of Enterprise New and Old Driving Force

The establishment of a modern enterprise system brings enterprises a series of advantages such as risk sharing and professional management. However, at the same time, the separation of corporate ownership and control rights also causes agency problems [18]. Although enterprise managers have full responsibility for the shareholders among an enterprise, managers should take to maximize shareholder’s wealth as the decision objective during the process of experiencing entrusting. However, personal “bounded rationality” and shareholders with high supervision costs drive managers to have the motivation and ability to achieve personal goals and manipulation of the enterprise’s short-term inputs and outputs [19]. For example, in order to obtain higher salary, managers are motivated to ignore the investment among the new capital and human resources on a large scale, which is based on job security and business flexibility. In a short period of time, enterprises are unable to realize the conversion among old driving forces and new ones. China is still in the period of system transformation. The instability and imperfection of the system lead to a higher degree of information asymmetry between the owners and managers of enterprises than that of in western developed countries, which also provides better objective conditions for managers to manipulate short-term profit. At the same time, executive compensation contracts are still not perfect. The CEO’s compensation plans and short-term accounting for profits make the departure CEO and new CEO have a stronger motivation to manipulate earnings, and then, choose profitable projects in a quick way not to increase the investment for long-term profit, and enterprises will not realize the conversion between the new and the old driving force in the short-term [20]. The new CEO lays a more likely goal for achieving performance for the future, realizing the continuous improvement of enterprise’s future earnings, and advocating a series of activities on profit management at the beginning. Because of the performance of the improvement in the enterprise’s business and long-term strategic adjustment, the new CEO can transfer his mistakes during his term to the former CEO on the profits at the initial period [21]. According to the hypothesis of earnings management, the CEO has a dominant right to make major investment decisions of the enterprise, and adjusting investment decisions is his direct way to manipulate earnings [21].

According to the principal-agent theory and information asymmetry theory, in the separation of ownership and management rights in modern enterprises [22], there is information asymmetry between principals and agents on the earnings level of enterprises, and managers have information advantages. When the goal of the principal and the agent are not consistent, in order to pursue personal interests, the agent sacrifices the interests of the principal by virtue of information advantages, and adjusts or lowers the earnings of the enterprise by selecting appropriate accounting policies and other means to realize earnings manipulation. Under the agency theory, the remuneration of the management is usually related to the profit of the enterprise. Therefore, driven by their own interests, the executives may manipulate the earnings management to maximize their personal interests. In the year of the turnover of senior managers, the attribution of corporate performance is not clear in terms of responsibility. The new senior manager takes the opportunity to realize the negative earnings management and attribute the poor performance to the previous managers in the year of succession, which can create space for the improvement of performance after succession and cause a decrease in the input during the change period. The authors in references (Elliott, J.A. et al. 1988 [23]; Godfrey, J.et al.2003 [24]; Graham, J.R.et al. 2005 [25]) and other scholars found that, in order to stabilize their positions and avoid their responsibility, new executives would reduce profits in the year of succession through earnings management, and blame the poor performance on former executives. In the following year, the CEO proves their ability through positive earnings management adjustment. Through earnings management, executives can improve their monetary remuneration income, establish their own good reputation, and even provide opportunities for promotion.

The conversion of the new and the old driving force in the enterprise generates a new profit growth point and make the profit from the old phase to the new stage mainly through the capital and manpower input. It is a long-term investment of enterprises with many characteristics, such as large amount, elastic measuring, and the CEO has the core leadership in decision-making, especially when it comes to innovation. The CEO can adjust the speed and the quality of the conversion of the old and the new driving force based on his purpose. The conversion between old and new driving force is characterized by long-term and uncertain performance in the short-term. Furthermore, the CEO’s compensation is matched with performance, so they have little incentive to switch the old and new driving forces.

Moving forward, some studies divide CEO turnover into two cases: CEO turnover alone, and CEO turnover and chairman turnover at the same time. They found that there are differences in the degree of earnings management in enterprises.

The research demonstrates that if the chairman of the board also changes in the year of CEO turnover, the listed enterprise will use accruals to reduce profits to a greater extent [26]. The chairman decides on the appointment, dismissal and the level of the salary of the enterprise’s senior management group, responsibility for the operation of financial funds and governance of the directors. Therefore, the chairman has the superior power over the investment decisions from the CEO. When there is CEO turnover but the chairman does not change, the chairman can restrain the CEO’s motivation to pursue his own interests to some extent, which can help to reduce the degree of earnings management [27]. When the CEO and the chairman turnover at the same time, the new chairman also has the motivation to realize the earnings management. Although the ultimate interests of the chairman and CEO both are different, they agree on the short-term goals, prompting the CEO and the chairman to cooperate, which can reduce the resistance of the CEO’s earnings management, and increase the space and degree of earnings management. The difference of the ownership may also lead to differences in the impact of CEO turnover on earnings management. Kato [28] found that if the ownership of an enterprise is a non-state-owned enterprise, senior executives have stronger motivation to invest more in innovation than ideas from state-owned enterprises when senior management changes. With the existence of the administrative appointment system in China’s state-owned enterprises and the political pursuit of senior executives, they are generally unwilling to take risks to increase the investment in innovation. As a result, newly appointed executives of enterprises are less inclined to invest more in the conversion of old and new driving forces than those seniors of non-state-owned enterprises. Therefore, Girma [29] listed enterprises with state-owned shares that may make relatively low decisions to invest in new products in the year of CEO turnover. However, in non-state-owned enterprises, managers have a stronger incentive to obtain a higher salary, and may increase investment to create new products, thus generating profits from the conversion of old and new driving forces, which is a tool to realize their own interests.

H1: 

Compared with enterprises without CEO turnover, enterprises with CEO turnover have a lower degree of old and new driving forces.

H1a: 

In the year of CEO turnover, if the chairman of the board turnover is at the same time, the turnover of the CEO will lead to a low degree of conversion of old and new driving forces.

H1b: 

Compared with state-owned enterprises, the change of CEO in non-state-owned enterprises leads to a higher degree of conversion of old and new driving forces.

2.2. Internal Governance and the Conversion of Enterprise Old and New Driving Forces

The board of directors is an essential means of corporate internal governance in China and the system of the share also plays an important role in the internal governance [30]. During the process of CEO turnover, the reshuffle of senior management will lead to that the internal supervision tends to be strict, and the conversion of old and new driving forces needs to use various inputs to generate new profit growth. As for CEO turnover caused by the conversion of the old and new driving forces, internal supervision has a certain inhibiting effect. Firstly, in terms of board supervision, the board exercises strategy of decision-making power, appoints and supervises the senior management [31,32]. Based on the principal-agent theory, in a short period, the board of directors will be cautious about the decisions of the newly appointed CEO after CEO turnover. During CEO turnover, the more frequent meeting the board has, the more decisions the board makes on behalf of the new CEO [33]. Then, moving to explain in the short-term, CEO turnover leads to the frequency of the board meeting [34]. The former CEO and the new CEO are not better to focus on profitable projects for a long time, and thus, will not spend a lot of resources on innovation—Research & Development of new products. Then, the more conferences the board has, CEO turnover has a stronger driving force to limit the conversion between the old and the new driving forces.

Secondly, equity balance refers to a number of major shareholders realizing the mutual interaction through the sharing of controlling power. More and more scholars have shown that the use of equity balance mechanism can benefit the governance of listed enterprises [35]. According to the theory of equity balance, large-power shareholders tend to have more control over the remaining, easier to plunder interests of less-power shareholders. However, when the enterprise is no longer “a dominant control”, equity balance among multiple shareholders can effectively avoid big shareholders to reduce the second category of agency cost through high dividend policies of listed enterprises [36]. Meanwhile, by participating in corporate governance and much supervision over the management, the probability of management level will be replaced when the enterprise’s business can be enhanced as the situation deteriorates [37] and reduces the agency cost of the first category. Therefore, the structure of equity balances will encourage more big-power shareholders to participate in corporate governance and strengthen the supervision over the management. In the short-term, based on the principal-agent theory, during the period of CEO turnover, shareholders need time to precipitate on the CEO’s cognition. Moving forward, the higher degree of equity balance, the more shareholders will doubt the capacity of the CEO, which leads to the negative effect on the enterprise strategy and resources to realize new product innovation. Therefore, in the short-term, the greater degree of equity balance, the stronger the inhibiting effect of CEO turnover on the conversion of old and new driving forces.

Based on the mentioned analysis, this paper puts the following hypothesis 2.

H2: 

The stronger the internal supervision, the stronger the inhibiting effect of CEO turnover on the conversion of old and new driving forces.

H2a: 

The more board meetings there are, the stronger the inhibiting effect of CEO turnover on the conversion of old and new driving forces.

H2b: 

The greater degree of equity balance, the stronger the inhibiting effect of CEO turnover on the conversion of old and new driving forces.

3. Research Design

3.1. Selection of Samples and Source of Data

According to the CSRC industry code, we identified 3659 listed manufacturing enterprises that went public between 2016 and 2018. Enterprises of ST and *ST categories and enterprises with missing data are excluded. A unique dataset of the 2447 firms was compiled from two databases: CSMAR (China Stock Market and Accounting Research) and WIND (Wind information financial terminal and economic database). The former is developed by Guo Tai An Information Technology (GTA) in collaboration with the University of Hong Kong and the China Accounting and Finance Research Center of Hong Kong Polytechnic University; this database covers the ownership, board of directors, and financial data of all listed firms in China since 1992. It has been widely used in finance and economics research [38,39]. The WIND database is produced by WIND Information and provides detailed information on R&D expenditure and property. Accounting information in the WIND database is similar to that in the CSMAR database, which enabled us to cross-check and patch our data for completeness. The reason for choosing from 2016 to 2018 is that China is carrying out supply-side structural reform, and corresponding state-owned and private enterprises need to realize the conversion of the old and new driving forces. In addition, enterprises may have multiple CEO turnovers of CEOs in long-term samples. Therefore, in order to better use the PSM-DID method to accurately test the mentioned hypothesis of this paper, the time span of the research samples focuses on from 2016 to 2018. When using PSM-DID to estimate the processing effect for CEO turnover in the t year, the data of t − 1 year and the data of t + 1 year are treated as the panel data of two periods.

It can be seen from

Table 1. Distribution of manufacturing industries in Chinese listed companies.

Industry	Number of Companies	Proportion	Grand Total	Scale (100 Billion yuan)	Proportion	Grand Total	Average Scale (100 Billion yuan)
Metal, non-metal	213	8.70%	8.70%	1319.84	11.66%	11.66%	6.2
Machinery, equipment, instruments	1040	42.50%	51.21%	4747.19	41.93%	53.59%	4.56
Petroleum, chemical, plastic, plastic	515	21.05%	72.25%	2289.14	20.22%	73.81%	4.44
food and drink	120	4.90%	77.16%	626.24	5.53%	79.34%	5.22
Medicine, Biopharmaceutical	252	10.30%	87.45%	912.26	8.06%	87.40%	3.62
Papermaking, printing	49	2.00%	89.46%	231.85	2.05%	89.44%	4.73
Textile, clothing and clothing products	104	4.25%	42.50%	492.83	4.35%	40.21%	4.74
Furniture, wood	88	3.60%	9.85%	537.98	4.75%	11.15%	6.11
Cultural and educational sports goods	27	1.10%	98.41%	54.51	0.48%	99.03%	2.02
other	39	1.59%	100%	109.74	0.97%	100%	2.81
total	2447	100%	-	11321.58	100%	-	4.63

that the listed companies are involved in all aspects of the 10 major subdivision industries of the manufacturing industry. Among them, the machinery, equipment, and instrument industries are the most concentrated, with a total of 1040 companies, accounting for 42.50% of the total number of enterprises in the industry. The industry’s total scale is 41.93%, with an average asset size of 4.56 billion yuan, ranking 6th in the industry. Although the enterprises in the metal and non-metal industries are more dispersed, with a total of 213 companies, their asset scale accounts for 11.66% of the entire industry, the average size reached 6.20 billion yuan, ranking first in the industry. Papermaking, printing and textiles, clothing and products, cultural and educational sports goods, and other manufacturing industries, and its asset scale accounts for only 2.05%, 4.35%, 0.48% and 0.97% of the entire industry. The overall scale is small, and overall, the distribution of the manufacturing industry has obvious characteristics of heavy chemical industry, to a certain extent, it also shows that the development of heavy chemical industry in China is relatively large, which also reflects the exposure of manufacturing companies The traditional driving forces problem will also be more serious, so how to cultivate and develop new driving forces, transform and upgrade traditional driving forces, and accelerate the realization of the conversion of old and new driving forces in manufacturing enterprises studies have certain necessity. In addition, Stata13.0 was used for quantitative analysis.

3.2. Research Method and Model Design

This paper, by comparing the differences between CEO turnover and the change of old and new driving force of an enterprise, studies the relationship between the CEO turnover and the change of old and new driving force of an enterprise. However, a simple comparison of the difference between the turnover and non-turnover of CEO turnover cannot exclude the influence of other factors on the conversion of old and new driving forces of an enterprise. Additionally, it is impossible to observe the two states in the same enterprise at the same time. Therefore, this paper uses the method of PSM and DID to eliminate the influence of confounding variables and solve the problem of data loss caused by “counterfactual factor”. Then, this paper can obtain the “net effect” of CEO turnover on the conversion of old and new driving forces. The PSM method can achieve the goal of “dimensionality reduction” by changing the control of several indistinct variables to the control of propensity value, thus solving the interference problem of confounding variables. The method of PSM’s first choice is to build a propensity score included in the calculation model. Then, Logit regression calculates each sample of the probability of event occurrence. The selection close to the propensity of the CEO turnover is considered to be the sample of the control group and finally, this paper focuses on the test of the processed group and the control group. After matching, there was no significant difference between the processed group and the control group.

As is known to all, the CEO is the most senior manager in the enterprise and has the final decision-making power, which determines the management and operation of the enterprise. Therefore, CEO turnover is an important decision of the enterprise, which has important influence on the daily operation of the enterprise. The selection of the dependent variable mainly refers to the articles [1,40]. As for the control variables of the article, based on the literature of research on the influential factors of CEO turnover [41,42,43], this article mainly focuses on the influential factors from the perspective of the scale, performance, price-to-book ratio, asset-liability ratio, audit opinion, ownership concentration and equity balance degree, the board of directors characteristics (board size, board independence, board meeting times), and the turnover of chairman.

Firstly, Logit regression was carried out on the factors of CEO turnover, and on the basis of it, the enterprise with CEO turnover finds the matching samples. The definition of the samples and the measurement are shown in

Table 2. Definition on Variables of CEO turnover.

Name of the Variable	Matching Variable	Definition of Variable
The conversion of the enterprise new and old driving force	dep	Flexibility of R&D expenditure on operating income
CEO turnover	CEO_Turnover	If the CEO was changed in the previous year and has been in the position this year, the assignment value is 1; Otherwise the assignment value is 0.
Chairman turnover	Chairman_Turnover	If the chairman was changed in the previous year and has been in the position this year, the assignment value is 1; Otherwise the assignment value is 0
Together turnover	Together_Turnover	If the chairman and CEO are changed at the same time, the assignment value is 1; Otherwise the assignment value is 0
Nature of ownership	OWN	If the enterprise is state-owned, the assignment value is 1; otherwise the assignment is 0
Equity balance	Z	The ratio of the proportion of shareholders by the enterprise’s second to tenth largest shareholders to that of the largest shareholders
The scale of board	Board	The number of the directors
Board independence	Ind	The proportion of the number of independent directors in the total number of directors
Number of board meetings	Meeting	The number of board meetings
The size of enterprise	Size	The natural logarithm of the enterprise’s total assets at the end of the period
The level of performance	ROA	The ratio of an enterprise’s net profit to its average total assets
Price-to-book	PB	The ratio of the share price to the enterprise’s net assets
Asset-liability ratio	LEV	The ratio of an enterprise’s total liabilities to its total assets
Audit opinion	Audit	If the audit opinion type is standard unreserved opinion, the assignment value is 1; otherwise the assignment value is 0

in detail.

Logit regression results from

Table 3. Logit regression results from CEO turnover factors.

CEO_Turnoverit	2016	2017	2018
	(1)	(2)	(3)
Chairman_Turnoverit	1.819 ***(8.17)	1.842 ***(9.29)	1.944 ***(10.06)
Zit-1	−0.024(−0.21)	−0.045(−0.42)	0.095(1.06)
Boardit-1	0.081(1.17)	−0.067(−0.92)	−0.051(−0.79)
Indit-1	2.140(1.13)	−1.646(−0.83)	−1.851(−1.02)
Meetingit-1	0.029(1.21)	0.068 ***(2.91)	0.098 ***(4.66)
Sizeit-1	−0.081(−0.66)	−0.016(−0.13)	−0.025(−0.25)
ROAit-1	−0.554(−0.30)	−3.563 *(−1.82)	1.654(0.83)
PBit-1	−0.030(−0.97)	0.016(0.41)	−0.087 **(−2.06)
LEVit-1	0.476(0.73)	−0.460(−0.73)	0.170(0.28)
Auditit-1	0.005(0.01)	−0.327(−0.58)	−0.079(−0.12)
_cons	1389	1575	1965
Prob > chi2	0.000	0.000	0.000

show that when the chairman turnover in t year, the probability of CEO turnover is higher; the more meetings in board, the more likely CEO turnover are; the worse the performance level, the higher the possibility of CEO turnover; the lower the price-to-book ratio, the more likely the CEO is to change. All other variables correlated with CEO turnover, but the results were not significant.

The propensity score of each sample in an enterprise was calculated on the basis of Logit regression. Kernel matching was adopted to select the enterprise with the closest propensity score for each sample enterprise with CEO turnover, and to test whether the variables were well balanced after the matching process. According to the research, if the absolute value of the standard deviation of the matched variable is greater than 10%, the matching effect is not good, and the matching method or matching variable needs to be changed. The smaller the standard deviation is, the better the matching effect is.

The test results from Figure 1 show that the standard deviation of all variables is less than 10% after the PSM matching process. By comparing the results before and after the matching process, the standard deviation of most variables shrinks to different degrees, indicating that there is no significant difference in the samples between the processed group and the control group, and the matching effect is very good. Since the PSM method cannot control the non-observable factors of samples, this paper combines the PSM method and DID method for analysis. The DID method is widely applicable to quantitative analysis of the net effect of public policy or intervention effect, and the implementation effect of the policy can be obtained by comparing the difference of the influence of public policy on the processed group and the control group in different periods. The DID model can make up for the defect that the PSM method cannot control the unobservable factors of samples and can more clearly explain the relationship between CEO turnover and the conversion of old and new driving forces of enterprises.

When using the DID method to calculate the average processing effect, for the CEO turnover in the t year, the data of the t-1st year and the data of t+1 are processed as the panel data of the two periods. The data of these two periods contain unmeasurable variables that do not change with time. The panel data can be written as follows:

In the mentioned formula, Dp is the dummy variable. When p = 1, it means it is at t-1 year, and then Dp is 0. When p = 2, it means it is at t+1 year, and then Dp = 1. ui in the formula is an unmeasurable characteristic variable. xip indicates whether the dummy variable of the experiment has taken place. When i belongs to the processed group and p = 2, xip = 1; otherwise, it is 0. According to the above formula and the above variable definitions, the change value of yit explained before and after CEO turnover in the processed group and control group is respectively obtained:

Δy_treat = y_treat,₂-y_treat,1 = (α + γ+β + u + ε)-(α + u + ε) = γ + β

Δy_control = y_control,2-y_control,1 = (α + γ + u + ε)-(α + u + ε) = γ

Then, the second difference between the processed group and the control group is used to obtain the processed effect after removing the unobservable variables:

βˆols = Δy_treat-Δy_control = β

3.3. Variable Measurement

The dependent variable: the conversion of the enterprise from old driving force to new driving force is represented by the elasticity of R&D expenditure to operating income [1].

The independent variables: the value is 1 if CEO turnover happens [40,41]. Otherwise, the value is 0. If simultaneous turnover for CEO and chairman, the value is 1. Otherwise, the value is 0. If the OWN means the state-ownership, the value is 1. Otherwise, the value is 0. The number of conferences means the times of meeting. Z means the ratio of form the second shareholder to the tenth largest shareholder to the largest shareholder [33].

4. Process and Result of Empirical Analysis

4.1. Descriptive Statistics and Correlation Analysis

Table 4. The descriptive statistics of variables.

Variable	N	mean	sd	min	p50	max
dep	5039	1.463	8.330	−29.47	0.814	54.89
CEO_Turnover	7340	0.175	0.380	0	0	1
Chairman_Turnover	7340	0.155	0.362	0	0	1
Together_Turnover	7340	0.102	0.303	0	0	1
Meeting	7340	9.742	3.914	2	9	46
OWN	7340	0.239	0.427	0	0	1
Z	7340	1.018	0.855	0.011	0.790	7.191
Board	7340	8.372	1.586	4	9	18
Ind	7340	0.376	0.056	0	0.333	0.800
Meeting	7340	9.742	3.914	2	9	46
Size	7340	22.01	1.181	17.64	21.87	27.39
ROA	7340	0.043	0.070	−1.057	0.043	0.381
PB	7340	4.954	20.37	0.178	3.286	1404
LEV	7340	0.380	0.190	0.010	0.364	1.352
Audit	7340	0.977	0.150	0	1	1

;

Table 5. Correlation Coefficient.

	dep	CEO_Turnoveit	Chairman_Turnoverit	Zit-1	Boardit-1	Indit-1	Meetingit-1	Sizeit-1	ROAit-1	PBit-1	LEVit-1	Auditit-1
dep	1
CEO_Turnoverit	−0.018	1
Chairman_Turnoverit	−0.040	0.548	1
Zit-1	−0.005	0.002	−0.004	1
Boardit-1	0.016	0.007	0.029	0.006	1
Indit-1	−0.001	−0.003	0.003	−0.023	−0.570	1
Meetingit-1	0.027	0.087	0.054	0.029	−0.015	0.049	1
Sizeit-1	0.008	0.032	0.037	−0.119	0.268	−0.011	0.258	1
ROAit-1	0.016	−0.043	−0.071	0.018	−0.024	−0.016	−0.135	−0.087	1
PBit-1	0.006	−0.008	0.026	0.053	−0.139	0.056	−0.025	−0.500	0.123	1
LEVit-1	0.008	0.038	0.056	−0.117	0.171	−0.019	0.298	0.550	−0.403	−0.085	1
Auditit-1	0.006	−0.011	−0.067	−0.009	−0.029	0.012	0.004	0.047	0.180	−0.194	−0.153	1
Lower-triangular cells report Pearson’s correlation coefficients

respectively show the descriptive statistics and correlation analysis of the main variables. It can be seen from Table 4 that the mean value of the enterprise’s conversion of the new and the old driving force (dep) is 1.463, and the maximum value is 54.89. According to Table 5, the correlation coefficients of all variables are less than 0.6, and there is no multi-collinearity. Moreover, the correlation between CEO turnover and the conversion of old and new driving forces is significantly negative at the level of 5%, and the correlation between chairman turnover and the conversion of new and old driving force is significantly negative at the level of 5%, which preliminarily proves the correlation hypothesis of this paper.

4.2. The Analysis on the CEO Turnover and the Enterprise New and Old Driving Force

4.2.1. The Effect of CEO Turnover

In this paper, the PSM method is used to analyze the differences in the conversion of old and new driving forces between the enterprises with CEO turnover (processed group) and those without CEO turnover (control group) under different circumstances, such as whether the CEO and chairman change at the same time, whether the ownership nature is state-owned, and whether the internal supervision is strong or weak.

The results of

Table 6. DID analysis results of CEO turnover impact on the conversion of enterprise’s new and old driving force.

CEO and Chairman Turnover	Outcome var	Base Line			Follow Up			DID
		Control	Treated	Diff (T-C)	Control	Treated	Diff (T-C)
CEO turnover	Dep	1.842	2.184	0.342	1.934	0.890	−1.045	−1.387
	S.Err.			0.564			0.548	0.786
	t			0.61			1.91	1.76
	P > |t|			0.544			0.057 *	0.078 *
Together turnover	Dep	1.866	2.941	1.075	1.786	−0.544	−2.330	−3.405
	S.Err.			1.210			0.859	1.484
	t			0.89			2.71	2.29
	P > |t|			0.375			0.007 ***	0.022 **

* p < 0.05, ** p < 0.01, *** p < 0.001

show that, after two differences, the difference between the two sample groups with and without CEO turnover is −1.387, which is significantly negative at the level of 10%, indicating that CEO turnover can inhibit the conversion of old and new driving force. Hypothesis 1 is verified. When the CEO and chairman change at the same time, the difference is larger, reaching −3.405, and significantly negative at the 5% level. It indicates that in the year of CEO turnover, if the chairman of the board changes at the same time, the degree of conversion of old and new driving forces of the enterprise caused by CEO turnover is lower. Hypothesis 1a is verified.

4.2.2. The Effect of Ownership

According to the nature of ownership, it is divided into state-owned enterprise group (Panel A) and non-state-owned enterprise group (Panel B). In

Table 7. DID analysis results of the ownership.

Ownership	Outcome var	Base Line			Follow Up			DID
		Control	Treated	Diff (T-C)	Control	Treated	Diff (T-C)
Panel A State-owned	Dep	1.055	3.049	1.995	2.288	1.342	−0.946	−2.941
	S.Err.			1.118			1.137	1.594
	t			1.78			0.83	1.84
	P > |t|			0.075 *			0.405	0.066 *
Panel B Non-state-owned	Dep	2.070	1.770	−0.300	1.840	0.646	−1.194	−0.893
	S.Err.			0.656			0.608	0.895
	t			−0.46			1.96	1.00
	P > |t|			0.647			0.050 **	0.318

* p < 0.05, ** p < 0.01

, in the sample group of state-owned enterprises, the difference is −2.941, which is significant at the level of 10%. In the sample group of non-state-owned enterprises, the difference was −0.893, which was not significant. Therefore, in state-owned enterprises, CEO turnover has a stronger inhibiting effect on the conversion of old and new driving force. Hypothesis 1b is verified.

4.3. The Analysis on the Effect of the Internal Supervision

4.3.1. The Effect of the Supervision from the Board

According to the median number of board meetings that the value is 8.5, it can be seen from the results that in the sample group with more board meetings from

Table 8. DID analysis results of the board of directors’ supervision influence.

Number of Board Meetings	Outcome var	Base Line			Follow Up			DID
		Control	Treated	Diff (T-C)	Control	Treated	Diff (T-C)
Panel A More meetings	Dep	2.591	2.535	−0.056	1.531	−0.681	−2.212	−2.156
	S.Err.			1.098			0.964	1.461
	t			−0.05			2.30	1.48
	P > |t|			0.959			0.022 **	0.141
Panel B Less meetings	Dep	1.076	1.600	0.525	1.310	1.935	0.625	0.100
	S.Err.			1.006			1.021	1.434
	t			0.52			0.61	0.07
	P > |t|			0.602			0.541	0.944

** p < 0.01

, the difference is −2.156; in the sample group with fewer board meetings, the difference value is 0.100. This shows that when the board of directors is more frequently meeting, CEO turnover has a stronger inhibiting effect on the old and new driving forces. Hypothesis 2a is verified.

4.3.2. The Effect of the Equity Balance

According to the results from

Table 9. DID analysis results of equity balance.

Equity Balance	Outcome var	Base Line			Follow Up			DID
		Control	Treated	Diff (T-C)	Control	Treated	Diff (T-C)
Panel A Higher Equity balance	Dep	1.791	2.165	0.374	2.275	0.020	−2.255	−2.628
	S.Err.			0.861			0.742	1.137
	t			0.43			3.04	2.31
	P > |t|			0.664			0.002 ***	0.021 **
Panel B Lower Equity balance	Dep	1.887	2.640	0.753	1.632	1.679	0.047	−0.706
	S.Err.			0.877			0.937	1.283
	t			0.86			0.05	0.55
	P > |t|			0.391			0.960	0.582

** p < 0.01, *** p < 0.001

, in the sample group with a large degree of equity balance, the difference value is −2.628, which is significant at the level of 5%; in the sample group with small degree of equity balance, the difference value is −0.706, which is not significant. This shows that when the degree of equity balance is large, CEO turnover has a stronger inhibiting effect on the conversion of old and new driving forces. Hypothesis 2b is verified. In conclusion, hypothesis 2 is verified.

4.4. The Test for Robustness

In order to verify the robustness of the mentioned analysis results, the PSM, in this paper, uses kernel matching methods. In the robustness test, one-to-three matches in the nearest neighbor matches are used to re-match the samples, and the results are in line with the above results. Therefore, the conclusion of the study on the relationship between CEO turnover and the conversion of old and new driving forces of enterprises obtained by PSM and DID method in this paper, which is stable to some extent. The results are shown in

Table 10. The robustness test of CEO turnover.

CEO and Chairman Turnover	Outcome var	Base Line			Follow Up			DID
		Control	Treated	Diff (T-C)	Control	Treated	Diff (T-C)
Panel A CEO turnover	Dep	1.793	2.187	0.394	2.067	0.912	−1.155	−1.549
	S.Err.			0.563			0.55	0.787
	t			0.7			2.1	1.97
	P > |t|			0.484			0.036 **	0.049 **
Panel B Together turnover	Dep	1.83	2.952	1.122	1.885	−0.466	−2.352	−3.474
	S.Err.			1.199			0.857	1.474
	t			0.94			2.75	2.36
	P > |t|			0.35			0.006 ***	0.018 **

** p < 0.01, *** p < 0.001

,

Table 11. The robustness test of ownership.

Ownership	Outcome var	Base Line			Follow Up			DID
		Control	Treated	Diff (T-C)	Control	Treated	Diff (T-C)
Panel A Stat eowned enterprise	Dep	0.996	3.04	2.044	2.6	1.373	−1.227	−3.271
	S.Err.			1.113			1.153	1.602
	t			1.84			1.06	2.04
	P > |t|			0.067 *			0.287	0.042 **
Panel B Non-state enterprise	Dep	2.025	1.779	−0.246	1.918	0.639	−1.279	−1.033
	S.Err.			0.656			0.611	0.896
	t			−0.37			2.09	1.15
	P > |t|			0.708			0.036 **	0.249

* p < 0.05, ** p < 0.01

,

Table 12. The robustness test of number of board meetings.

Number of Board Meetings	Outcome var	Base Line			Follow Up			DID
		Control	Treated	Diff (T-C)	Control	Treated	Diff (T-C)
Panel A More meetings	Dep	2.591	2.535	−0.056	1.793	−0.515	−2.308	−2.252
	S.Err.			1.098			0.98	1.472
	t			−0.05			2.35	1.53
	P > |t|			0.959			0.019 **	0.127
Panel B Less meetings	Dep	0.924	1.601	0.676	1.333	1.935	0.602	−0.074
	S.Err.			1.005			1.02	1.432
	t			0.67			0.59	0.05
	P > |t|			0.501			0.555	0.959

** p < 0.01.

and

Table 13. The robustness test of equity balance.

Equity Balance	Outcome var	Base Line			Follow Up			DID
		Control	Treated	Diff (T-C)	Control	Treated	Diff (T-C)
Panel A Higher Equity balance	Dep	1.791	2.165	0.374	2.291	0.052	−2.238	−2.612
	S.Err.			0.861			0.741	1.136
	t			0.43			3.02	2.3
	P > |t|			0.664			0.003 ***	0.022 **
Panel B Lower Equity balance	Dep	1.778	2.635	0.857	1.805	1.714	−0.092	−0.949
	S.Err.			0.873			0.937	1.281
	t			0.98			0.1	0.74
	P > |t|			0.327			0.922	0.459

** p < 0.01, *** p < 0.001

.

4.4.1. The Effect of CEO Turnover

The robustness test results show that in the two sets of samples of CEO turnover or not, the difference between the conversion of the old and new driving forces and the enterprise is −1.549, and it is significantly negative at the level of 5%; it shows that CEO turnover can inhibit the conversion of the old and new driving forces of the enterprise. Verification is established; when the CEO and the chairman change at the same time, the difference reaches −3.474, and is significantly negative at the level of 5%. This shows that if the CEO and the chairman change at the same time, the conversion of the new and old driving forces of the enterprise will be lower. Assumption 1a is valid.

4.4.2. The Effect of Ownership

In the sample of state-owned enterprises, the difference between the old and new driving forces conversion of the enterprise under CEO turnover is −3.271, and it is significantly negative at the level of 5%; while in the non-state-owned sample, the difference is −1.033, but the difference is not significant In comparison, in state-owned enterprises, CEO turnover is more restrictive to the conversion of old and new driving forces. Hypothesis 1b is valid.

4.4.3. The Effect of the Supervision from the Board

In the sample group with a large number of board meetings, the difference between the old and new driving forces conversion of the enterprise under CEO turnover is −2.252; in the sample with a small number of board meetings, the difference is −0.074, although the difference between the two samples is not significant, but in comparison, in the case of a large number of board meetings, CEO turnovers are more restrictive of the conversion of old and new driving forces, assuming that 2a is valid.

4.4.4. The Effect of the Equity Balance

In the sample with high equity checks and balances, the difference between the old and new driving forces conversion of the enterprise under CEO turnover is −2.612, and it is significantly negative at the level of 5%; while in the sample group with low equity checks and balances, the difference is −0.949; it is not significant, so in comparison, when the degree of equity checks and balances is greater, CEO turnovers are more restrictive to the conversion of old and new driving forces, assuming that 2b verification is established.

In summary, except for a few core variables that become more significant, Hypothesis 1 and Hypothesis 2 are both validated, and the test results are basically stable, so the results of this paper have a certain degree of credibility.

5. Conclusions and Suggestion

In this paper, the methods of PSM and DID are used to analyze the impact of CEO turnover on the conversion of old and new driving forces in enterprises. The research samples are A-share listed enterprises in China’s manufacturing industry from 2016 to 2018, and the research data are derived from the CSMAR database and WIND database. Through comparative analysis on CEO turnover in an enterprise of the conversion of old and new driving force, the CEO turnover on the conversion of old and new driving force, namely “net effect”, can be obtained. In addition, at the same time, the together turnover and without together turnover, the nature of the ownership on state-owned enterprises and non-state-owned enterprises, internal supervision and the differences between the strong and the weak, the contrast analysis differences of CEO turnover impact on the conversion of old and the new driving force. Among them, internal supervision includes the board of directors’ supervision and the degree of equity balance, which are measured by the number of board meetings and the ratio of the shareholding of the enterprise’s second to tenth largest shareholders to that of the largest shareholder. In this paper, the elasticity of R&D expenditure to operating income is used to measure the conversion of old and new driving forces, using kernel matching method, and the robustness test of research results is carried out by kernel matching method. The results show that: (1) The influence of the factors of CEO turnover after Logit regression, in t years, the chairman of the board change, the possibility of CEO turnover is higher; the more meetings there are, the more likely CEO turnover is; the worse the performance level, the higher the possibility of CEO turnover; the lower the price-to-book ratio, the more likely the CEO turnover is. (2) The comparative analysis of the results of CEO turnover and non-CEO turnover shows that the conversion degree of new and old driving force of the enterprises with CEO turnover is significantly lower than that of the enterprises without CEO turnover. (3) Moving forward, analysis shows that when together turnover happens and the enterprise is a state-owned enterprise, CEO turnover has a higher inhibiting effect on the conversion of old and new driving forces. (4) After analyzing the role of internal supervision, this paper found internal supervision for CEO turnover caused by the conversion of old and new driving forces has certain inhibition. Among them, supervision of the board and equity balance degree has played a significant role. When the board of directors equity balance degree is higher, the more CEO turnover is a higher level of inhibition on conversion between the old and the new.

This study enriches the literature on the conversion of old and new driving forces of listed enterprises. Based on the mentioned research conclusions, it can be known that the CEO turnover has an inhibiting effect on the conversion of old and new driving forces of listed enterprises. Then, CEO turnover is not conducive to the conversion of old and new driving forces of enterprises in the short-term. This paper puts forward suggestions from the following three aspects, in order to provide certain reference for the long-term development of listed enterprises, the rights and interests of stakeholders and the maintenance of capital market operation: (1) Further improvement on the incentive mechanism of the CEO. At present, the main characteristic of the incentive mechanism of listed enterprises in China is characterized by the emphasis on monetary incentives while despising equity incentive, attaches great importance to the short-term incentive and despises long-term incentive, the CEO succession focuses on individual interests pursuit of short-term behavior, not enough on the strategy and the development of new products, using the original product’s profits to boost its currency salary income. Therefore, the enterprise should combine monetary incentives with equity incentives, short-term incentives with long-term incentives, and the long-term development of the enterprise with the interests of the CEO, which can improve the degree of the conversion of old and new driving forces. (2) Improvement on internal supervision, making internal supervision play an effective role. Listed enterprises need to improve their own governance level, strengthen internal supervision, give full play to the role of board’s supervision and equity balance, effectively avoid the influence of reducing the conversion of old and new driving forces in the process of CEO turnover, and promote the long-term development of the enterprise.