Research on the Influence of Technological Innovation Enthusiasm on Innovation Performance from the Perspective of Nonlinearity—Empirical Evidence from Chinese Listed Firms

Abstract

Taking Chinese listed companies from 2010 to 2019 as research samples, in this paper, the authors empirically analyze the impact of technological innovation enthusiasm on innovation performance from a nonlinear perspective. The research finds that an inverted, U-shaped relationship exists between technological innovation enthusiasm and innovation performance, that is, to a certain extent, the improvement of the enthusiasm for technological innovation can improve the innovation performance of companies. However, when the enthusiasm for technological innovation reaches a certain degree, the innovation performance declines with the improvement of technological innovation enthusiasm. In addition, the moderating effect of CEO succession on the relationship between technological innovation enthusiasm and innovation performance is investigated from the perspective of corporate governance, and the research conclusions show that CEO succession strengthens the inverted, U-shaped relationship between technological innovation enthusiasm and innovation performance. This study further enriches the theoretical framework of technological innovation and corporate governance, and the relevant conclusions can provide certain theoretical reference for the innovation performance improvement of listed companies in China.

1. Introduction

According to the three time points of China’s economic development laid out at the 19th National Congress of the Communist Party of China (CPC), China will basically realize socialist modernization by 2035, and its economy will continue to move forward. Development of science and technology, and the sustainable development of China’s economy, cannot be achieved without innovation. Innovation has become the main driving force for the sustainable development of China’s economy. Through innovation and green development, we will promote industrial transformation and upgrading, new urbanization, industrialization and digital construction and improve the efficiency of resource allocation so as to achieve sustainable economic development in China. Therefore, in order to achieve economic sustainability and achieve the goal of operating sustainability of enterprises, the enhancement of innovation ability and innovation performance of enterprises is one of the most important paths.

According to the Global Innovation Index 2020 report released by the World Intellectual Property Organization and others, China ranks 14th among 131 economies in terms of innovation capacity, making it the only middle-income economy in the overall top 30. The report of the 19th CPC National Congress puts forward: “On the basis of market-oriented, deep integration of production, education and research establishing a technological innovation system with enterprises as the main body”. The Fifth Plenary Session of the 19th CPC Central Committee held in 2020 also proposed to “strengthen the key leading role of enterprises in innovation and promote the agglomeration of all kinds of innovative elements in enterprises. We will promote deeper integration between enterprises, universities, and research institutes, and support enterprises in leading the formation of innovation consortia to undertake major national science and technology projects. Give full play to the important role of entrepreneurs in technological innovation and encourage enterprises to increase various investment in research and development”. It means that, in a long period in the future, continuously strengthening innovation input and striving to improve innovation performance will become an important means for Chinese enterprises to cope with environmental uncertainty and shape their competitive advantages.

Innovation performance directly reflects the innovation ability of an enterprise and is crucial to obtaining economic sustainability. Therefore, in the context of “mass innovation”, the promotion path of innovation performance has become the focus of academic and practical circles [1,2,3]. Combing through the academic research about technological innovation enthusiasm, innovation performance and the internal logical relationship between the two, it can be seen that most of the existing literature verifies the positive impact of innovation input on innovation performance from the perspective of resource-based theory. For example, the research of scholars such as Xu et al. (2021) [4], Yi et al. (2017) [5], Ramadani (2019) [6] and Su and Li (2021) [7] shows that the greater the investment in technological innovation, the better the innovation performance from a different perspective. In other words, the innovation performance of enterprises is continuously improved with the enhancement of technological innovation enthusiasm. However, the paradox of R&D growth proposed by some scholars shows that ever-increasing R&D input does not necessarily translate into ideal innovation output. Problems existing in the theory of resource redundancy and the practice of enterprises’ technological innovation make the monotonous relationship between technological innovation enthusiasm and innovation performance questionable [8,9].

Based on this, combined with the innovation practice of Chinese listed companies, this paper investigates the complicated logical influence of technological innovation enthusiasm on innovation performance. Compared with previous studies, possible contributions include: Firstly, an exploration of the relationship between company innovation enthusiasm and innovation performance from a nonlinear research perspective, answering the question of whether investment in technological innovation is “too much is better” or “too much is not good” for the development of enterprises and clarifying the long-standing research debates in this field. Secondly, the consideration of how CEO replacement changes the internal governance situation and greatly influences the efficiency and effect of enterprise strategy execution [10,11,12]. This paper investigates the moderating effect of CEO succession on the relationship between technological innovation enthusiasm and innovation performance and reveals the complexity and weight variation of the relationship between the two at a deeper level. It is very important to make reasonable use of CEO succession and to adopt appropriate management measures to improve the sustainability of an enterprise using the appropriate innovation enthusiasm. This study tries to provide scientific advice and references for enterprise governance structure design and the system optimization of listed companies in practice based on the improvement of innovation performance.

2. Theoretical Analysis and Hypothesis Development

2.1. Literature Review

The economist Schumpeter first proposed the concept of innovation in 1934 in his book «The Theory of Economic Development: An Inquiry into Profits, Capital, Credit, Interest and the Business Cycle» emphasizing that innovation is the main source of capitalist economic growth. At present, performance is defined from three perspectives: behavior, ability and result. However, based on realistic considerations, the definition of the result perspective is the most intuitive, and all behavioral abilities can be reflected through results. This paper chooses the result perspective of performance to measure innovation performance.

After collecting and sorting out the existing literature, the factors affecting enterprise innovation performance can be divided into macro and spectator levels. At the macro level, scholars have focused on the impact of market change, economic development level, national culture, policy system and other factors on enterprise innovation performance. For example, Zhang and Duan (2010) [13] took manufacturing companies in mainland China as samples to study the influence of market orientation and enthusiasm on innovation. Disoska et al. (2020) [14] found that, after the economic and financial crisis, the reduction of public funds in R&D budgets led to a significant decline in enterprises’ innovation activities and innovation performance. Kostis et al. (2018) [15] found that the positive effects of discovery culture on innovation came from the positive effects of trust, control, professional ethics and honesty. Wang and Zou (2018) [16] pointed out that there are significant differences in the impact of different types of policy tool on enterprise innovation. Demand-side policies and environment-side policies inhibit the improvement of enterprise innovation performance, while supply-side policies obviously promote the improvement of enterprise innovation performance. At the micro level, scholars have mainly studied M&A, analyst tracking, financing constraints, leadership structure, firm size, board independence and many other aspects. For example, Howell (2020) [17] found that cross-border mergers and acquisitions of enterprises significantly promote the quantity of innovation performance of enterprises and have a certain inhibitory effect on the quality of enterprise innovation performance due to the lack of experience or technical barriers faced by China’s transition economies. Guo (2019) [18] found that financial analysts may encourage enterprises to make more effective, innovation-related investments, which improves the quality and quantity of enterprises’ innovation performance and affects the novelty of their innovations. Hovakimia (2011) [19] found through research that financing constraints are conducive to easing the organizational inertia constraints of enterprises, giving play to the guiding role of entrepreneurship and encouraging enterprises to search for innovation opportunities, thus, improving innovation performance. Tan et al. (2001) [20] believed that when external risk degree is high, CEO duality can effectively reduce the uncertainty of decision making and improve the possibility of successful technology creation and, thus, contribute to the improvement of enterprise innovation performance. Wakasugi and Koyata (1997) [21] believed that scale expansion provides sufficient R&D funds for the technological innovation of enterprises and increases their ability to undertake technological innovation risks, so large enterprises have stronger innovation motivation. Based on the panel data of American listed companies, Lu and Wang (2018) [22] proved that the independence of the board of directors has a positive impact on a company’s innovation by promoting the risk-taking ability of the management. This is also a basis for the selection of control variables in this paper.

The endogenous growth theory based on R&D emphasizes the important role of R&D investment in the process of economic growth and technological progress and lays a theoretical foundation for the relationship between R&D investment and innovation performance. However, innovation is a process full of risks. Innovation risks are the risks that enterprises are willing to take to a certain extent in order to achieve expected returns, among which R&D investment is the most prominent. Since the investment of R&D funds into the output of new technology or new products is not achieved overnight and requires a long cycle of operation, R&D investment may not play a prominent role in the current period and can only be converted into operating profit when it enters the sales stage, so there may be a lag effect. How to achieve a balance between strictly regulated “risk control” and improvisational “innovation” is a common concern in academia and practice. Therefore, many scholars have studied the optimal relationship between R&D investment and innovation performance in an attempt to understand the balance between risk and innovation. For example, Beneito (2003) [23] proposed that R&D investment can improve innovation performance by guiding original technology innovation, enterprise process reengineering and promoting new products to enter the market. Graham (2013) [24] believed that the blind expansion of science and technology expenditure distorts the allocation of innovation resources, and excess R&D investment is difficult to absorb and digest, leading to low resource utilization and then hinders the improvement of enterprise performance and productivity development. Therefore, the existence of an optimal level of R&D intensity promotes the maximization of innovation performance [25].

Based on the above literature review, we found that most of the existing studies studied the impact of innovation performance from the perspectives of the external environment and the internal nature of enterprises, not from the macro or micro perspective. At present, the research on the optimal relationship between R&D investment and innovation performance, based on the initiative of technological innovation influencing the innovation activities of enterprises, is a hot research topic in academic circles. Most of the current research focuses on the positive impact of technological innovation enthusiasm on enterprise innovation performance [20,21,22,23,24,25,26], but is it really the case? Based on the perspective of CEO change, this paper studies the nonlinear impact of technological innovation enthusiasm on firm innovation activities.

2.2. Technological Innovation Enthusiasm and Innovation Performance

As mentioned, the enthusiasm for technological innovation, innovation performance and the relationship between them have always been hot topics for domestic and foreign scholars. Most researchers believe that enthusiasm for technological innovation is positively correlated with innovation performance. The specific logic is as follows: on the one hand, based on the resource-based view, enterprises develop competitive advantages through their unique resources and knowledge. As the R&D investment of enterprises increases, the internal resources, new knowledge and new technology created by the enterprise increase, and the innovation ability is also improved, thus leading to an increase in enterprise innovation output [26,27,28]. On the other hand, R&D investment encourages enterprises to develop, learn and absorb external technical knowledge and creates favorable conditions for enterprises’ technological innovation [29,30,31,32]. However, based on practical considerations, we believe that the monotonous positive relationship between innovation enthusiasm and innovation performance is not sustainable, because, in the process of continuous enhancement of technological innovation enthusiasm, enterprises may face two problems.

First, due to the limitation of enterprises’ ability to utilize innovation resources, excessive enthusiasm for technological innovation is likely to lead to a large amount of resource aggregation and redundancy. When enterprises fail to properly absorb and utilize resources, innovation performance hardly improves with the increase in technological innovation input [33,34,35]. Even though, in some cases, excessive investment in technological innovation encourages managers to use redundant organizational resources to pursue private interests, such as high salary and high authority, it makes it difficult to continuously improve the innovation performance of enterprises and may inhibit enterprise innovation [36,37,38].

Second, in addition to the internal resource utilization ability of adaptation, continuous improvement of innovation performance requires innovation investment and the institutional environment and social norms which correspond to the external conditions, such as cognitive conditions and demand [39,40,41]; enterprise innovation motivation means that, compared with other companies in the industry, high technology and the development to a more radical degree is strong, the technology developed or product created faces strong legitimacy challenge and the improvement of innovation performance faces great uncertainty [42,43,44]. Based on the above logic, it is known that the increase in innovation enthusiasm of listed companies does not necessarily lead to the continuous improvement of innovation performance and should be viewed from the perspective of nonlinear analysis to explain the influence mechanism of innovation enthusiasm on innovation performance. That is, the impact mechanism of innovation enthusiasm on innovation performance should be analyzed and explained from a nonlinear perspective. Therefore, we propose the following hypothesis:

Hypothesis 1 (H1).

An inverted, U-shaped correlation exists between technological innovation enthusiasm and innovation performance, that is, with the improvement of technological innovation enthusiasm, innovation performance shows a curve evolution trend of first rising and then falling.

2.3. The Moderating Role of CEO Succession

As the highest member of the administrative personnel of an enterprise, the CEO plays a core role when making and implementing strategic decisions [45] which ultimately affects the business performance of the company’s strategic decisions [46]. CEO succession is a complex process, including the departure of the former CEO, the selection of the new CEO and the evaluation of the new CEO [47]. Previous studies consistently demonstrated that low performance is significantly positively correlated with forced CEO turnover. In order to make the company better adapt to the ever-changing environment, the board of directors has the responsibility and obligation to replace the CEO with low performance so as to enhance the company’s value and protect the interests of shareholders [48]. The deterioration of the company’s performance reflects the mismatch between the company’s internal conditions and the external environment, which leads to the change of CEO.

First of all, under the leadership of the former CEO, the company has huge path dependence and inertia [49]. To some extent, the board of directors expects the CEO successor to lead the enterprise to innovate and improve the company’s performance, so it has a higher tolerance for the innovation input of the CEO successor after taking office. Therefore, the new CEO successor can demonstrate his ability to the board of directors and senior management team members by investing in innovation and achieving significant innovation performance (more patent applications, new product launches). Secondly, after the former CEO demission, the board of directors strictly selects the CEO who matches the requirements of the listed company in all aspects, regardless of whether the reason for the resignation is normal or not, and tends to strengthen the assessment intensity of the new CEO in the early stage of succession so as to make the new CEO meet the urgent need of the board of directors to improve performance. Therefore, at the beginning of CEO succession, considering the risk of short-term resignation, CEOs tend to pursue short-term performance to gain the trust of the board of directors. Based on the short-sighted theory of managers, in order to maximize current benefits, CEOs sacrifice the future development potential of enterprises. Therefore, when choosing innovation activities, they tend to invest in activities with a short period of return [50,51,52], and “comfortable” innovation projects tend to be their first choice.

Taking the above analysis into consideration, we argue that, after CEO succession, for the consideration of his career, in the process of making a series of decisions, the new CEO usually does not plan for the long-term development of the company until the elimination of career threats. Eliminating career threats means earning the trust of the board by “delivering quick results”. Therefore, with the improvement of the company’s enthusiasm for technological innovation, the CEO uses his decision-making power and applies his limited resources to his “handy” innovation activities. Therefore, with regard to the influence of the path of technological innovation enthusiasm on innovation performance, when technological innovation enthusiasm does not reach the ultra-high level, the emergence of CEO succession reinforces the positive impact of technological innovation enthusiasm on innovation performance. With the continuous strengthening of the enthusiasm for technological innovation, the resources available in the enterprise become increasingly numerous. At this time, the CEO who has just taken office has less relevant work experience or lacks the understanding of the company’s business and organizational relationship, so he cannot reasonably allocate the various resources in the enterprise. Therefore, the negative influence of technological innovation enthusiasm on innovation performance level is strengthened. Therefore, we propose the following hypothesis:

Hypothesis 2 (H2).

CEO succession reinforces the inverted, U-shaped relationship between technological innovation enthusiasm and innovation performance.

3. Research Design

3.1. Sample Selection and Data Collection

In this study, the sample observation time was limited to 2010–2019. Based on Carney et al.’s (2009) [53] classification of companies affiliated to enterprise groups, the sample included the companies in China listed on the Shanghai and Shenzhen stock market in the preliminary selection, and we carried out the processing according to the following criteria: (1) we excluded samples from financial and insurance industries, (2) excluded the samples that are specially treated (ST); and (3) eliminated samples with missing relevant data.

In addition, the variable IS in this paper was processed with a lag of one period to prevent the endogeneity problem caused by the inversion of causality and mutual causality; meanwhile, for fear of the interference of extreme value, 1% level of tail reduction processing was carried out. From the sample, data reflecting technological innovation enthusiasm of listed companies were selected from the annual R&D expenses of the listed companies disclosed in the Wind database; related data reflected innovation performance, and data of the control variables were all obtained from the CSMAR database.

3.2. Variable Definition and Measurement

1. Technological innovation enthusiasm (IS): In past studies, the enthusiasm of technological innovation was mostly measured by the proportion of the enterprise’s R&D investment in the operating cost or revenue. This measurement method can reflect the enterprise’s R&D investment, but it is difficult to reflect the target enterprise’s enthusiasm of technological innovation relative to the enterprises in the same industry. For this reason, this paper designed the following formula to evaluate the enthusiasm of technological innovation by referring to the research of Xu Peng et al. (2019) [54]:

$$I{S}_{j,t}=T{I}_{j,t}/\frac{{\displaystyle \sum _{j=1}^{n}T{I}_{j,t-1}}}{n}$$

where, TI_j,_t denotes the input of listed companies numbered j in the t year in technological innovation in an industry, ${\displaystyle \sum _{j=1}^{n}T{I}_{j,t-1}}/n$ reflects the average level of technological innovation input in the industry in which the enterprise is located in the t − 1 year and the ratio of the two represents the technological innovation decision (i.e., IS_j,_t) made by the listed company numbered j in the t year based on the average level of technological innovation in the industry in the t − 1 year. The higher the value of IS_j,_t, the higher the enterprise’s enthusiasm for technological innovation. This measurement method can reflect the technological innovation enthusiasm of the target enterprise compared with the average level of the industry, which is more objective and reasonable.

2. Innovation Performance (IP): Referring to the practice of Lin et al. [55], we adopted the number of patent applications as the measurement index of enterprise innovation performance. The reason is that, as long as an enterprise chooses to apply for a patent to protect the innovation achievements, public patent data are generated. Therefore, from the perspective of the feasibility of performance measurement, the feasibility of measuring the innovation performance of an enterprise with the number of patent applications is usually stronger than other methods. From the perspective of the reliability of the variable measure, the patent standard has not changed much in recent years and is relatively objective. Therefore, it is more reliable to express the explained variable with the number of patent applications. The number of patent applications is a direct measure of an enterprise’s innovation output and reflects its technological innovation performance.

3. CEO Succession (SUCC): The occurrence of CEO succession in the sample company in the same year was marked as 1, while the absence of CEO succession was marked as 0.

4. Control variables: Referencing the existing literature, this paper took on board independence (BI), company size (CS), leverage level (LL), return on equity (ROE), leadership structure (LS), board size (BS) and other factors that may have an impact on the innovation performance of listed companies as the control variables of this study.

Table 1. Definition and measurement of variables.

Variable Name and Code	Index
Innovation performance (IP)	Number of patents filed by listed companies in the current year
Technological innovation enthusiasm (IS)	Based on the research and development cost, the calculation is carried out according to the formula designed
CEO succession (SUCC)	In the current year, if CEO succession occurs in listed companies, a value of 1 is assigned, otherwise assignment is 0
Board independence (BI)	The proportion of independent directors in the total number of directors of the listed company in the current year
Company size (CS)	Logarithm of the total assets of the listed company in the current year
Leverage level (LL)	Ratio of total liabilities to total assets of listed companies at the end of the current year
Return on equity (ROE)	The ratio of current year profit to net assets of a listed company
Leadership structure (LS)	If the general manager concurrently holds the post of chairman, it is assigned to 1, otherwise, it is assigned to 0
Board size (BS)	Total number of directors of listed companies in the current year

shows the specific definitions and measurement methods of each variable.

3.3. The Empirical Model

In order to validate the proposed hypothesis, we designed the following multiple regression model:

$$IP=C+{{\displaystyle \sum }}_{j=1}^n{b}_{j}Control+\epsilon$$

(1)

$$IP=C+{{\displaystyle \sum }}_{j=1}^n{b}_{j}Control+aIS+\epsilon$$

(2)

$$IP=C+{{\displaystyle \sum }}_{j=1}^n{b}_{j}Control+a_{1}IS+a_{2}IS\ast IS+\epsilon$$

(3)

$$IP=C+{{\displaystyle \sum }}_{j=1}^n{b}_{j}Control+a_{1}IS+a_{2}IS\ast IS+a_{3}IS\ast SUCC+a_{4}IS\ast IS\ast SUCC+\epsilon$$

(4)

Control group represents the control variable, c means the intercept term, epsilon ε represents the random disturbance term, j stands for each control variable number, b_j represents the regression coefficient of the control variables and a is representative of each explanatory variable regression coefficient. Model 1 is the basic regression model of the control variable and explains variable innovation performance (IP). In Model 2, the explanatory variable IS is added on the basis of Model 1. On the basis of Model 2, the square term of technological innovation enthusiasm (IS2) is added in Model 3 to test the nonlinear correlation between innovation enthusiasm and innovation performance, that is, hypothesis H1. In Model 4, the product term of the enthusiasm of technological innovation and CEO succession (IS × SUCC) and the product term of the square term of the enthusiasm of technological innovation and CEO succession (IS2 × SUCC) are added on the basis of Model 3 to test Hypothesis H2: the moderating effect of CEO succession.

4. Data Analysis

4.1. Descriptive Statistics and Correlation Analysis

This paper firstly conducted both descriptive statistics analysis and correlation analysis on the main variables and control variables involved, such as enthusiasm for technological innovation, innovation performance, CEO succession, etc., and the correlation coefficient, mean value and standard deviation of each variable are shown in

Table 2. Descriptive statistics and correlation analysis.

	IP	IS	SUCC	BI	CS	LL	ROE	LS	BS	Mean	Standard Deviation
IP	1.000									45.552	237.491
IS	0.395 ***	1.000								1.343	4.181
SUCC	−0.024 ***	−0.012 *	1.000							0.213	0.410
BI	0.010	0.023 ***	0.000	1.000						37.194	4.858
CS	0.284 ***	0.334 ***	−0.058 ***	0.006	1.000					21.948	1.323
LL	0.101 ***	0.127 ***	−0.026 ***	−0.019 ***	0.373 ***	1.000				44.093	22.119
ROE	0.052 ***	0.068 ***	−0.018 ***	−0.021 ***	0.080 ***	−0.191 ***	1.000			0.055	0.169
LS	−0.002	−0.042 ***	−0.012 **	0.116 ***	−0.167 ***	−0.153 ***	0.016 ***	1.000		0.263	0.440
BS	0.128 ***	0.106 ***	−0.026 ***	−0.480 ***	0.259 ***	0.149 ***	0.037 ***	−0.180 ***	1.000	8.683	1.734

Note: ***, **, * indicate the significance level of 1%, 5% and 10%, respectively, and the t-values are in parentheses.

. The coefficient of correlation between innovation enthusiasm and innovation performance was positive, and the nonlinear relationship between the two needed to be further verified by multiple regression analysis. The absolute value of correlation coefficients among all variables was less than 0.5; it was visible that, between all variables, serious multicollinearity problems did not exist. Therefore, we could conduct the next step of data analysis.

4.2. Multiple Regression Analysis

According to the above model, this paper used the software Stata to conduct regression analysis, and

Table 3. Regression analysis results.

Model	(1)	(2)	(3)	(4)
Variables	IP	IP	IP	IP
SUCC	0.008 (0.37)	−0.002 (−0.08)	−0.001 (−0.03)	0.010 (0.38)
BI	0.033 *** (3.09)	0.023 ** (2.18)	0.022 ** (2.11)	0.023 ** (2.16)
CS	0.340 *** (27.73)	0.163 *** (12.00)	0.139 *** (9.67)	0.132 *** (9.18)
LL	−0.056 *** (−4.61)	−0.019 (−1.51)	−0.016 (−1.27)	−0.015 (−1.22)
ROE	0.062 *** (4.67)	0.032 ** (2.32)	0.025 * (1.84)	0.023 * (1.65)
LS	0.125 *** (5.82)	0.086 *** (4.09)	0.082 *** (3.95)	0.083 *** (4.00)
BS	0.082 *** (7.29)	0.058 *** (4.89)	0.057 *** (4.81)	0.059 *** (4.96)
IS		0.239 *** (26.79)	0.319 *** (17.79)	0.343 *** (18.33)
IS × IS			−0.004 *** (−5.12)	−0.004 *** (−5.93)
SUCC × IS				−0.001 (−0.01)
SUCC × IS × IS				−0.022 *** (−2.94)
CON	−0.008 (−0.63)	−0.027 ** (−2.25)	−0.026 ** (−2.12)	−0.026 ** (−2.18)
R2	0.094	0.163	0.166	0.169
F	173.73	187.94	170.52	142.50

Note: ***, **, * indicate the significance level of 1%, 5% and 10%, respectively, and the t-values are in parentheses.

shows the regression results. The regression results of Model 1 showed that there was a significant relationship between corporate innovation performance and variables such as company size, board size and leverage level, which supports the existing studies to a certain extent and also shows that the selection of control variables was more appropriate. The regression results of Model 3 showed that: The regression coefficient of the primary item of technological innovation enthusiasm was 0.319, and the regression coefficient of the square item of technological innovation enthusiasm was −0.004. Both of them passed the significance test at the 1% level, indicating that there is a significant inverted, U-shaped relationship between the technological innovation enthusiasm of listed companies and innovation performance, that is, moderate technological innovation enthusiasm promotes the improvement of innovation performance. However, excessive enthusiasm for technological innovation inhibits the improvement of innovation performance. Hypothesis H1 was proven. Model 4 tested the succession to the company regulation effect of technology innovation motivation and innovation performance; the results showed that the succession and the technology innovation motivation of listed companies had a significantly negative secondary interaction coefficient (“coefficient of 0.022”, p < 0.01), and the CEO succession, enthusiasm and innovation of technological innovation performance of the inverted, U-shaped relationship was improved. We concluded that H2 was proven.

4.3. Robustness Test

The empirical research results of this study showed that there is an inverted, U-shaped relationship between innovation motivation and innovation performance which rises first and then falls, and the emergence of CEO succession strengthens this inverted, U-shaped relationship. To guarantee the reliability of the research, in addition to the regression analysis mentioned above, this paper also used a variety of methods for robust estimation:

1. The “number of patents applied by the company in that year and the number of patents authorized by the company in that year” was used to measure innovation performance and conduct a robustness test. Regression analysis was conducted according to the model set above, and the specific operation results are shown in

Table 4. Robustness test of main effects.

Variables	Replacement Lag	Add Control Variables	Change the Time
SUCC	−0.010 (−0.34)	0.000 (0.01)	0.001 (0.04)
BI	0.024 ** (1.98)	0.021 * (1.93)	0.022 * (1.90)
CS	0.157 *** (9.66)	0.173 *** (9.96)	0.145 *** (8.96)
LL	−0.014 (−0.98)	−0.013 (−0.93)	−0.024 * (−1.68)
ROE	0.037 ** (2.43)	0.026 * (1.80)	0.022 (1.49)
LS	0.075 *** (3.09)	0.061 *** (2.83)	0.085 *** (3.70)
BS	0.057 *** (4.17)	0.076 *** (6.14)	0.048 *** (3.64)
IS	0.275 *** (14.23)	0.310 *** (16.14)	0.345 *** (17.28)
IS × IS	−0.004 *** (−5.21)	−0.003 *** (−4.63)	−0.005 *** (−5.35)
state		−0.056 ** (−2.29)
old		0.003(1.40)
Time fixation effect		Control
Industry fixation effect		Control
CON	−0.024 * (−1.73)	−0.157 (−0.82)	−0.029 ** (−2.21)
R2	0.147	0.204	0.159
F	116.89	23.70	138.50

Note: ***, **, * indicate the significance level of 1%, 5% and 10%, respectively, and the t-values are in parentheses.

. The regression coefficient of the square term of technological innovation enthusiasm in Model 3 was −0.007, which was significant at the 1% level;

2. In order to prevent the wrong estimation caused by the setting error of the lag period, this paper further introduced the regression results of two lag periods based on the previous lagging period. The results showed that the regression coefficient of the square term of technological innovation enthusiasm was −0.004, which was significant at the 1% level; the regression coefficient of CEO succession and the square term of technological innovation enthusiasm was −0.008 and was significant at the 1% level, which is consistent with the previous conclusion;

3. Based on the original data, the property right nature and age of the company were added as the control variables, and the time fixed effect and industry fixed effect were controlled at the same time. The results showed that the regression coefficient of the square term of technological innovation enthusiasm was −0.003, which was significant at a 1% level; the regression coefficient of CEO succession and the square term of technological innovation enthusiasm was −0.025, which was significant at the 1% level; 4. In 2014, Premier Li Keqiang issued the call of “mass entrepreneurship and innovation” at the Summer Davos Forum and then the country issued a series of policies on innovation. This paper holds that the implementation of relevant policies may have a certain impact on the relationship between innovation enthusiasm and innovation performance. In order to exclude the policy effect, the data from after 2014 were adopted to test.

Table 5. Robustness test of the moderating effects.

Variables	Replacement Lag	Add Control Variables	Change the Time
SUCC	0.003 (0.09)	0.013 (0.51)	0.014 (0.50)
BI	0.024 ** (2.00)	0.021 ** (1.99)	0.023 ** (1.97)
CS	0.161 *** (9.94)	0.162 *** (9.24)	0.136 *** (8.37)
LL	−0.014 (−0.98)	−0.011 (−0.83)	−0.023 (−1.63)
ROE	0.039 ** (2.55)	0.023 (1.62)	0.019 (1.29)
LS	0.076 *** (3.17)	0.061 *** (2.85)	0.086 *** (3.75)
BS	0.056 *** (4.10)	0.078 *** (6.35)	0.051 *** (3.83)
IS	0.257 *** (12.59)	0.339 *** (16.87)	0.375 *** (17.92)
IS × IS	−0.001 ** (−1.98)	−0.004 *** (−5.56)	−0.005 *** (−6.12)
IS × SUCC	0.043 (0.89)	0.025 (0.37)	−0.000 (−0.00)
IS × IS × SUCC	−0.008 *** (−4.70)	−0.025 *** (−3.36)	−0.024 *** (−3.03)
state		−0.060 ** (−2.46)
old		0.003 (1.41)
Time fixation effect		Control
Industry fixation effect		Control
CON	−0.026 * (−1.93)	−0.168 (−0.87)	−0.029 ** (−2.23)
R2	0.157	0.207	0.163
F	103.50	23.59	116.63

Note: ***, **, * indicate the significance level of 1%, 5% and 10%, respectively, and the t-values are in parentheses.

showed the robustness test of the moderating effects. The results showed that the regression coefficient of the square term of innovation enthusiasm was −0.005, which was significant at the 1% level; the regression coefficient of CEO succession and the square term of technological innovation enthusiasm was −0.024, which was significant at the 1% level. The above robustness test shows that the enthusiasm of technological innovation still has an inverted, U-shaped influence on innovation performance, and the empirical results of this paper are relatively robust.

5. Research Conclusions and Management Implications

5.1. Research Conclusions

Taking listed companies in China as samples, this paper conducted an empirical analysis on the relationship between technological innovation enthusiasm and innovation performance from a nonlinear perspective and further investigated the moderating effect of CEO succession on the relationship. The following conclusions were drawn: (1) the technological innovation enthusiasm and innovation performance of listed companies rise after falling in an inverted, U-shaped relationship, namely, the augmentation of technology innovation motivation improves innovation performance, and when technology innovation enthusiasm reaches a certain level, it reaches the highest level of innovation performance and enhances technology innovation motivation. It means that there is an optimal relationship between technological innovation enthusiasm and innovation performance, and a too high a level of technological innovation enthusiasm cannot bring about the growth of innovation performance. So, there is an appropriate technological innovation enthusiasm level under which enterprises can obtain high innovation performance and realize enterprise operating sustainability; (2) CEO succession has a significant positive moderating effect on the relationship between technological innovation enthusiasm and innovation performance. Specifically, when CEO succession occurs in listed companies, the impact of technological enthusiasm on innovation performance is strengthened.

5.2. Management Implications

From the above conclusions, we draw the following management implications: First of all, the conclusion that there is an inverted, U-shaped correlation between innovation enthusiasm and innovation performance indicates that neither insufficient nor excessive R&D investment can maximize innovation performance. Therefore, on the one hand, listed companies should correctly understand the non-monotone relationship between the two and appropriately adjust the intensity of R&D investment in a timely manner to avoid a lack of R&D investment leading to the level of innovation performance not being in the best state. On the other hand, in order to construct a more sustainable enterprise and to realize economic sustainability, enterprises should attach importance to innovation and R&D activities. In other words, the sustainable development of enterprises requires scientific and reasonable R&D expenditure. It should be understood that more R&D investment is not “more is better”, that is, too much R&D investment may lead to resource redundancy, making the organization unable to efficiently absorb and digest resources, resulting in resource waste and the decline of innovation performance level. On the other hand, listed companies should constantly improve the ability to allocate R&D resources and improve the use efficiency of R&D funds to avoid the possible negative effects of technological innovation enthusiasm.

Second, in the process of CEO succession, the board of directors plays a crucial role. Therefore, before CEO succession, the board of directors should adopt a variety of ways and use a variety of network resources to obtain the relevant information about CEO candidates as fully as possible so as to select the CEO who fits the enterprise in all aspects. After the new CEO succession, the new CEO faces the evaluation of the board of directors, and, in order to reduce short-term turnover risk, the new CEO adopts methods for quick results to obtain the trust of the board of directors; the new CEO wants result as the only goal, thus this causes the enthusiasm and innovation “enterprise technology innovation performance has an inverted, U-shaped relationship” problem mentioned above. Therefore, the board of directors must concentrate on the diversification of the assessment methods and add indicators such as the CEO’s operation mode and decision-making ability into the assessment, rather than just taking the company’s short-term financial performance as the assessment method, so as to give the CEO enough space and sense of security. At the same time, the successor CEO should also actively identify potential opportunities or problems in the enterprise according to the changes of the internal and external environment and make reasonable decisions to keep the innovation performance of the enterprise at a higher level; then, the sustainability goal of enterprise operation can be realized.