Operating Leverage, Equity Incentive, and Enterprise Research and Development Investment

Abstract

Science and technology innovation plays a vital role in the sustainable development of enterprises, and even in the security and sustainable development of a nation. Against the background of China’s structural “deleveraging” macro policy, the following two aspects are considered in this research: First, should operating leverage be removed, and how does it affect the innovation investment of enterprises? Second, what will be the impact of the implementation of equity incentives on the relationship between operating leverage and innovation investment? Using a longitudinal panel dataset of Chinese A-share listed companies from 2010 to 2020, this study empirically tested the impact and mechanism of operating leverage on enterprise innovation investment. The findings show that operating leverage significantly contributes to an increase in enterprise innovation investment in general, but the positive correlation trend decreases with the increase in operating leverage. The implementation of equity incentives plays a positive role in moderating the relationship between operating leverage and innovation investment. Further heterogeneity analysis shows that the promotion effect of operating leverage on innovation investment is significant only in non-state owned enterprises (SOE), and the positive regulating effect of equity incentives in non-SOEs is more significant than that of the overall sample.

1. Introduction

Science and technology innovation plays a vital role in the sustainable development of enterprises, and even in the security and sustainable development of a nation. Enterprises are one of the main implementation subjects of scientific and technological innovation and the last foothold of the transformation of innovation achievements. The 20th National Congress of the Communist Party of China proposed to “adhere to the core position of innovation in the overall modernization of our country, strengthen the dominant position of scientific and technological innovation of enterprises”. It can be said that the level of enterprise innovation capability is related to the success or failure of the national scientific and technological innovation strategy, and research and development (R&D) is the source of sustainable competitive advantages and development for enterprises [1]. In 2019, China’s R&D investment reached CNY 2.21 trillion, with the country ranking second in the world and the number of invention patent applications and authorizations ranking first [2]. Therefore, the question of how to encourage enterprises to increase R&D investment and gather strength to obtain key core technologies and competitiveness has become a prominent practical problem to be solved urgently.

Since the supply side structural reform in 2016, China has taken multiple measures to engage in deleverage. China’s overall leverage ratio grew by 23.6 percentage points in 2020, down from 31.8 percentage points in 2009. In recent years, scholars have studied this subject extensively. The essence of structural deleveraging is to adjust the leverage ratios of enterprises to reasonable levels, which not only reduces the “bad leverage” of over-indebted enterprises but also preserves the “good leverage” of high-quality enterprises [3]. It can be seen that the research on leverage in “deleveraging” mainly focuses on financial leverage. On the one hand, many scholars believe that for innovation, excessive financial leverage will produce high financial risks, which has an inhibitory effect on innovation investment [4,5,6]; Wang et al. (2021) argue that a capital structure with a zero interest-bearing debt ratio is conducive [7]. On the other hand, some scholars have also pointed out that corporate leverage has a significant driving effect on enterprise innovation investment and output [8], and listed companies can find the lowest agency cost by finding the balance point between equity cost and debt cost, so as to improve the return on R&D investment [9]. However, compared with the research on the impact of financial leverage on enterprise innovation, the impact of operating leverage is often ignored, and there is little literature on the impact of operating leverage on enterprise innovation investment. As an important part of corporate leverage, operating leverage plays a very important role in the sustainable development of enterprises. In the context of structural “deleveraging” macro policy, does “deleveraging” only refer to deleveraging financial leverage? What is the impact of operating leverage on enterprise innovation investment, and is it promoting, inhibiting, or neutral? In order to stimulate the endogenous power of enterprises, a growing number of listed companies have implemented an equity incentive [10]. What is the impact of equity incentives on the relationship between operating leverage and innovation investment? Does the nature of firm ownership and the degree of operating leverage lead to a difference in the innovation-driven effect of enterprises? Based on these questions, this paper takes listed companies from 2010 to 2020 as research samples to empirically test the impact of operating leverage on enterprise innovation investment and the impact of equity incentives on the relationship between operating leverage and enterprise innovation investment. In addition, from the perspective of different property rights and operating leverage, and combined with China’s unique institutional background, this paper examines whether there are differences in the impact on enterprise innovation investment and the effectiveness of equity incentive.

2. Theory and Hypotheses

2.1. Operating Leverage and Enterprise Innovation

Operating leverage refers to the phenomenon of the profit change rate being greater than the change rate of sales revenue due to the existence of fixed costs [11]. The principle of operating leverage is that, because an enterprise has fixed costs, when there are small changes in production and sales volume there will be a large change in profits. The fixed cost ratio of an enterprise with low operating leverage is relatively low. When the income declines, the profit decreases more slowly along with the income. Enterprises with high operating leverage have high input fixed costs, and the same degree of changes in incomes will lead to greater profit fluctuations [12], which are also accompanied by higher enterprise risks and higher systemic risks [13,14]. At present, the research on operating leverage focuses on two aspects. On the one hand, research focuses on the consequences of operating leverage, such as its impact on operating risk, corporate decision-making, and corporate performance. Operating leverage is a sharp, double-edged sword that may not only bring a significant profit increase but also cause a company to suffer significant losses, and so decision-makers should be cautious [15]. Wu found that operating leverage and financial leverage amplify the inherent business risk of common stock, and they pointed out that operating leverage is significantly positively correlated with the risk of corporate bankruptcy [16,17]. Therefore, management adjusts the operating leverage to achieve the earnings forecast of analysts [18]. Other research focused on the driving factors affecting operating leverage [19] and put forward countermeasures to control operating leverage risks caused by fixed costs and expenses from the perspective of econometric analysis. Aboody et al. (2018) found that, when the intensity of equity incentives decreases, managers have insufficient risk-taking tendency and will reduce operating leverage to avoid the risk of an accelerated decline in corporate profits under operating difficulties [20].

At present, how to improve the level of enterprise innovation is a issue much studied by scholars at home and abroad, and a large amount of literature has also formed. According to the leverage effect theory in the field of innovation, enterprises often make decisions on innovation investment according to their current leverage level and risk status. According to managerial risk aversion theory and strategic choice theory, when financial leverage is high, firms will reduce R&D investment [21]. However, corporate leverage here mainly refers to the influence of financial leverage on innovation, and enterprise risk also mainly refers to financial risk. The existing literature largely discusses the relationship between enterprise financial leverage and innovation, but there is little research on the impact of operating leverage on innovation, and academia has not yet formed a unified view.

2.1.1. Operating Leverage Is Positively Related to Enterprise Innovation

Chen et al. (2019) pointed out that for enterprises, operating leverage is significantly positively correlated with profitability [17]. When the profitability of a firm increases, it will be motivated to invest more funds in R&D, and the operating leverage will drive the innovation effect of the enterprise [4] so as to obtain a corresponding innovation output [22] and achieve corporate sustainable development. For enterprises in fierce competition or enterprises with poor management, high operating leverage means that firms also invest fixed costs, such as manpower, which increases the uncertainty faced by such enterprises in the processes of their operations [23]. This may force firms to accelerate innovation activities under competitive pressure to ensure the stable growth of sales revenues and avoid elimination [24].

2.1.2. Operating Leverage Is Negatively Correlated with Enterprise Innovation

An operating leverage coefficient is highly correlated with operating risk [15]. The independent innovation of an enterprise has a large risk of failure [25] and requires long-term financial support [26]. Once financial support is interrupted, it will bring great losses. The effect of a significant decline in the potential performance of an enterprise is more obvious in a company with high operating leverage [27]. Zhu Lin et al. (2021) pointed out that operating leverage is significantly negatively correlated with enterprise innovation [28], which will greatly increase the risk exposure of an enterprise. Managers tend to be more inclined to engage in short-sighted behaviors of risk avoidance due to concerns about career planning, property risks, and reputations [29,30,31].

In summary, operating leverage, on the one hand, helps to promote enterprise innovation, and on the other hand, it may crowd out innovation investment, which is a topic to be tested. Based on this, this paper puts forward the following competing hypotheses:

Hypothesis 1a (H1a). 

Operating leverage is significantly positively correlated with enterprise innovation.

Hypothesis 1b (H1b). 

Operating leverage is significantly negatively correlated with enterprise innovation.

2.2. The Impact of Equity Incentives on the Relationship between Operating Leverage and Enterprise Innovation

Equity incentives are a long-term incentive method [32,33] that allocate part of the shares of a company to specific employees for the long-term development of that company to share profits and risks so as to alleviate principal–agent conflicts [34]. There is an abundance of literature on the impact of enterprise innovation activities, but there remain great differences in the research conclusions on the effectiveness of its implementation in China. One view is that an appropriate proportion of equity incentives is an effective mechanism for enterprises to promote innovation and development [35], which can motivate risk-taking behavior [36]. Studies have found that the implementation of stock options for executives can promote the innovation output of enterprises [37,38,39,40,41]. Similarly, employee stock ownership has a significant innovation incentive effect [42,43], which can effectively solve the principal–agent problem, give better play to the ownership consciousness of the incentive object, and increase R&D investment to obtain innovation output [44]. One view holds that the implementation of an equity incentive has no incentive effect, and it may even have negative effects. The existing literature shows that the executive equity incentives of listed companies in China cannot effectively play an incentive role, and it even has a negative impact, resulting in damage to the value of listed companies or to an increase in earnings management [45,46]. It can be seen that scholars do not have a unified view on the effect of equity incentives on innovation activities. Therefore, this paper takes equity incentive as a dummy variable (the value is zero when equity incentives are not implemented, and the value is one when equity incentives are implemented), and it puts forward the following competing hypotheses:

Hypothesis 2a (H2a). 

After the implementation of an equity incentive, it plays a positive moderating role in the relationship between operating leverage and enterprise innovation.

Hypothesis 2a (H2b). 

After the implementation of an equity incentive, it plays a negative moderating role in the relationship between operating leverage and enterprise innovation.

3. Methods

3.1. Data and Sampling Approach

In order to study the impact of operating leverage on corporate enterprise innovation investment, we selected A-share listed companies from 2010 to 2020 as initial research samples, and the data were acquired from the CSMAR and Wind databases, following the existing studies from Zheng and Wen [21]. CSMAR and Wind are the two largest databases in China’s financial field and are also selected by many authors for their research. The CSMAR and Wind databases cover comprehensive data, featuring timely information updates and high data accuracy. The data used to conduct the research in this paper came mainly from CSMAR. The reason for this is that CSMAR data is easier to obtain and more convenient to organize. The Wind database is used as a supplement to obtain R&D investment data. On this basis, referring to the practices of Quan et al. (2015) and Xu et al. (2020), we further screened the samples to ensure the comparability and similarity between them [47,48]. First, the financial industry, especially banks, has a special financial structure as well as special accounting methods, audit requirements, and regulatory provisions, which will skew the research results. Therefore, the financial industry is excluded from this paper. Second, as securities of ST (special treatment) companies cannot be circulated and traded normally in the market, data on ST companies were thus omitted. Third, we omitted samples with missing data. In order to eliminate the influence of outliers, referring to the practice of Zhang and Huang (2009) [49], outliers outside the 1% and 99% quantiles of relevant continuous variables were eliminated (Winsorize); that is, we assigned values at 1% quantiles to numbers less than 1% and 99% quantiles to numbers greater than 99% [50]. Finally, 11,946 company-year observation samples of A-share listed companies from 2010 to 2020 were obtained. In addition, in order to eliminate the impact of industry and year differences, we controlled industry and year fixed effects. The data processing and statistical software used were Excel, from Microsoft Office 2019, and Stata16.0.

3.2. Variables Definition

Table 1. Variables and definitions.

Variables	Definition
RDIN	The proportion of R&D investment in operating revenue
Dol	Dol = (EBIT + F)/EBIT
Post	The value is 1 for the implementation year of equity incentives and 0 otherwise
ROA	The numerator is net profit and the denominator is the average balance of the total assets
ROE	The numerator is net profit and the denominator is the average balance of the net assets
CI	The numerator is the total assets and the denominator is the operating income
Tobin	The market value of a company’s liabilities and equity divided by the replacement value of its assets
Share	The proportion of shares held by a firm’s largest shareholder
Solid	The net value of fixed assets scaled by the total assets
Size	Logarithm of the total assets

shows the main variables and definitions.

3.2.1. Dependent Variable: R&D Investment (RDIN)

R&D investment is the source of energy for scientific and technological innovation. Drawing on the methods in the existing literature [51,52,53], we used R&D investment intensity as the variable to represent enterprise innovation investment. There are many ways to measure the variable of R&D investment intensity, such as the proportion of R&D investment to operating revenue and the proportion of R&D investment to corporate value. Because the enterprise value of listed companies is difficult to accurately measure, the first measurement method was adopted in this paper.

3.2.2. Independent Variable: Operating Leverage (Dol) [54]

As an important part of corporate leverage, operating leverage will affect enterprise innovation. We used the degree of operating leverage (DOL) as the independent variable. Drawing on the methods in the existing literature [11], the formula for operating leverage is as follows:

DOL = (revenue − variable cost)/(revenue − variable cost − fixed cost)

Since earnings before interest and tax (EBIT) are equal to revenue minus variable and fixed costs (F), the formula is as follows:

Dol = (EBIT + F)/EBIT

EBIT = net profit + income tax + financial cost

F = depreciation + intangible assets amortization + amortization of long-term unamortized expenses

where depreciation represents the depreciation of fixed assets, depreciation of oil and gas assets, and depreciation of productive biological assets. Earnings before interest and tax are abbreviated as EBIT, and fixed cost is abbreviated as F.

3.2.3. Dummy Variable: Equity Incentives (Post)

In order to study the impact of equity incentives, an internal incentive mechanism, on enterprise innovation, we manually organized the list of listed companies’ equity incentives from the CSMAR database to obtain the dummy variable of whether the enterprise implemented equity incentives. The value was 1 for the year when the equity incentive plan was implemented and after, and it was 0 for the year when the equity incentive plan was not implemented [55].

3.2.4. Control Variables

In order to mitigate the endogenous problem caused by missing variables as much as possible, and to obtain more accurate empirically tested results by drawing on existing research, we selected return on total assets (ROA), return on equity (ROE), asset intensity (CI), Tobin’s Q value (Tobin), ownership concentration (Share), fixed asset ratio (Solid), and company size as the control variables [53].

3.3. Analysis Model

Based on the theoretical analyses and research hypotheses of this paper, according to the research of Zhou and Zhang (2016) [52] and Hu et al. (2019) [53], the following ordinary least square regression analysis model was designed to verify the hypotheses.

In order to test Hypotheses H1a and H1a, this paper first set regression Model (1) with R&D investment intensity as the dependent variable and operating leverage as the independent variable to verify the relationship between operating leverage and enterprise innovation, as follows:

RDIN = α₀ + α₁Dol + α∑Controls + ∑Industry/Year + ε

(1)

where RDIN is the R&D investment intensity, Dol is the operating leverage, Controls is the control variable, and ε is the random error. At the same time, we controlled for the Industry and Year variables.

In order to test Hypotheses H2a and H2b, on the basis of Model (1), dummy variables of equity incentives were set and the cross-multiplication term between dummy variables of equity incentives and the continuous variable operating leverage was added to construct Model (2):

RDIN = β₀ + β₁Dol + β₂Post + β₃Dol × Post + β∑Controls + ∑Industry/Year + ε,

(2)

where Post represents whether the enterprise implemented an equity incentive and is a dummy variable. “Dol × Post” is the interaction term which was used to examine the moderating effect of equity incentives on the relationship between operating leverage and enterprise R&D investment intensity. Controls refers to the defined control variables and ε is the disturbance term. We also controlled for the Industry and Year variables to ensure that the results were more robust.

4. Results

4.1. Descriptive Statistics

Table 2. Descriptive statistics of the main variables.

Variables	Sample Size	Median	Average	Standard Deviation	Min	Max
RDIN	11,946	3.660	4.635	4.492	0.030	26.240
Dol	11,946	1.356	1.579	0.719	1.024	5.749
Post	11,946	1.000	0.195	0.396	0.000	1.000
ROA	11,946	4.988	6.000	4.701	−0.420	23.304
ROE	11,946	0.086	0.099	0.071	0.001	0.361
CI	11,946	1.769	2.114	1.337	0.424	8.526
Tobin	11,946	1.660	2.063	1.261	0.871	7.957
Share	11,946	32.573	34.409	14.797	8.544	73.820
Solid	11,946	0.189	0.213	0.141	0.004	0.62668
Size	11,946	5.620	5.703	0.566	4.694	7.450

shows the descriptive statistics of the main variables. The median and average values of the R&D investment intensity were 3.660 and 4.635, respectively, the maximum value was 26.240, and the standard deviation was 4.492, indicating that the average proportion of innovation investment in the operating revenue of listed companies was 4.635%, among which 50% of the companies were lower than 3.66%, and the overall distribution of innovation investment was uneven. The mean value of R&D investment intensity was significantly higher than the median, and the sample distribution was right-skewed. In general, the level of innovation investment of the listed companies in China was low, and there were large differences among the different companies, which is consistent with the existing literature [56]. The median Dol of operating leverage was lower than the average, and these values were 1.356 and 1.579, respectively. The sample distribution was skewed to the right, indicating that there were more companies with low operating leverage.

At the same time, we also conducted a multicollinearity test on the research variables, and carried out regression on all the variables. After regression, the variance inflation factors (VIF) were as shown in

Table 3. VIF analysis of the study variables.

Variables	VIF	1/VIF
Dol	1.53	0.655
ROA	5.66	0.177
ROE	5.61	0.178
CI	1.10	0.913
Tobin	1.34	0.748
Share	1.05	0.952
Solid	1.18	0.847
Size	1.41	0.708
Mean VIF	2.360

. The VIF values of the variables were all lower than the key threshold of 10, among which the VIF values of Dol, Size, Tobin, Solid, CI, and Share were also less than 2.

4.2. Regression Analysis of Operating Leverage and R&D Investment Intensity

In order to test Hypothesis H1, multiple regression tests were conducted on the R&D investment intensity and operating leverage according to Model (1), as shown in

Table 4. Regression results of Model (1).

Variables	Coefficient	p > |t|
Dol	0.293 ***	0.000
ROA	0.113 ***	0.000
ROE	−5.918 ***	0.000
CI	0.908 ***	0.000
Tobin	0.686 ***	0.000
Share	−0.018 ***	0.000
Solid	−3.865 ***	0.000
Size	−0.567 ***	0.000
Industry	control
Year	control
_cons	2.094 ***	0.001
Sample	11946
Adjusted r-square	0.417

Notes: *** p < 0.01.

. The results show that the regression coefficient of the independent variable was 0.293, which is significant at the 1% level. Operating leverage has a significant positive impact on R&D investment intensity, which supports Hypothesis H1a. The reason may be that, with the deepening of reform during the 11 years from 2010 to 2020, China’s economy achieved sustained growth, and most listed companies expanded their production and sales, which brought an operating leverage effect, and the growth of corporate profits was higher than the growth of sales revenue. Therefore, firms were willing and able to invest more funds in R&D, which is consistent with the findings of Chen et al. (2019) [17].

By testing the regression results of control variables, we could see that they were significantly correlated with enterprise innovation investment. Among them, the ROA, CI, and Tobin regression coefficients were significantly positive at the level of 1%, which is consistent with theoretical expectations.

4.3. The Moderating Effect of Equity Incentives on the Relationship between Operating Leverage and R&D Investment Intensity

In order to test Hypothesis H2, we conducted regression according to Model (2), and the regression results of the interaction between the operating leverage and equity incentives on innovation investment are shown in

Table 5. Regression results of Model (2).

Variables	Coefficient	p > |t|
Dol	0.268 ***	0.000
Post	0.380 *	0.070
Dol × Post	0.324 ***	0.014
ROA	0.114 ***	0.000
ROE	−6.338 ***	0.000
CI	0.918 ***	0.000
Tobin	0.670 ***	0.000
Share	−0.016 ***	0.000
Solid	−3.748 ***	0.000
Size	−0.582 ***	0.000
Industry	control
Year	control
_cons	2.257 ***	0.000
Sample	11946
Adjusted r-square	0.422

Notes: *** p < 0.01, * p < 0.1.

. The results show that the regression coefficient of Dol was 0.268, which was significant at the level of 1%. The regression coefficient of “Dol × Post” was 0.324, which was significant at the 1% level, and both the benchmark regression coefficient and the interaction coefficient were positive, which had a positive moderating effect, indicating that the implementation of equity incentives by the listed companies would further strengthen the relationship between operating leverage and R&D investment intensity. In the listed companies without equity incentives (Post = 0), the regression coefficient of operating leverage and R&D investment intensity was 0.268. There was a difference in the slope between those with equity incentives (Post = 1) and those without (Post = 0) of 0.324. In the listed companies that implemented equity incentives (Post = 1), their regression coefficients were 0.592 (0.268 + 0.324), which was significant at the level of 1% after the joint test. The economic significance is as follows: for listed companies that implemented equity incentives, when other conditions remained unchanged, the difference in average R&D investment intensity between the listed companies that implemented equity incentives (Post = 1) and those that did not (Post = 0) was 2.744% (4.635% × 59.2%), indicating that the implementation of equity incentives played a significant role in increasing R&D investment. In general, the regression results in Table 5 support Hypotheses H1a and H2a.

4.4. Analysis of Heterogeneity

4.4.1. The Impact of Different Property Rights

Theoretically, a different nature of corporate ownership may affect the relationship between operating leverage and R&D investment intensity. To explore this, we classified those companies containing state-owned shares as SOEs (SOE = 1), and the rest as non-SOEs (SOE = 0), and the two groups of SOEs and non-SOEs were tested for regression, as shown in

Table 6. Regression results of Model (1) grouped by the nature of property rights.

Variables	SOE = 0		SOE = 1
	Coefficient	p > |t|	Coefficient	p > |t|
Dol	0.386 ***	0.000	0.106	0.183
ROA	0.104 ***	0.000	0.137 ***	0.000
ROE	−4.841 ***	0.001	−7.435 ***	0.000
CI	1.076 ***	0.000	0.484 ***	0.000
Tobin	0.755 ***	0.000	0.435 ***	0.000
Share	−0.016 ***	0.000	−0.022 ***	0.000
Solid	−4.027 ***	0.000	−3.482 ***	0.000
Size	−0.634 ***	0.000	−0.426 ***	0.000
Industry	control		control
Year	control		control
_cons	1.551 ***	0.058	4.002 ***	0.000
Sample	8582		3364
Adjusted r-square	0.394		0.345

Notes: *** p < 0.01.

. The results show that in the samples of SOEs (SOE = 1), the regression coefficient of operating leverage was 0.106 and did not pass the significance test, while in the samples of non-SOEs (SOE = 0), the regression coefficient of operating leverage was 0.386, which was significant at the level of 1%. The reason for this difference may be that SOEs are more vulnerable to various policies due to their natural advantages in political connections, such as external financing, implicit guarantee, and tax preferences, and so the risk sensitivity of SOE innovation behavior is relatively weak. Therefore, although operating leverage was positively correlated with R&D investment intensity in the SOEs, it was not significant, while in non-SOEs, it was relatively more sensitive and significantly positive.

According to Model (2), the two groups of SOEs and non-SOEs were tested for regression, as shown in

Table 7. Regression results of Model (2) grouped by the nature of the property rights.

Variables	SOE = 0		SOE = 1
	Coefficient	p > |t|	Coefficient	p > |t|
Dol	0.340 ***	0.000	0.102	0.204
Post	0.178	0.457	1.960 ***	0.000
Dol × Post	0.420 ***	0.005	−0.180	0.543
ROA	0.105 ***	0.000	0.140 ***	0.000
ROE	−5.038 ***	0.000	−7.880 ***	0.000
CI	1.093 ***	0.000	0.499 **	0.000
Tobin	0.736 ***	0.000	0.415 ***	0.000
Share	−0.015 ***	0.000	−0.020 ***	0.000
Solid	−3.940 ***	0.000	−3.252 ***	0.000
Size	−0.723 ***	0.000	−0.485 ***	0.000
Industry	control		control
Year	control		control
_cons	2.148 ***	0.009	4.237 ***	0.000
Sample	8582		3364
Adjusted r-square	0.424		0.355

Notes: *** p < 0.01, ** p < 0.05.

. The results show that the sample size of the SOEs was 3364, and the regression coefficient of the operating leverage was 0.102, which failed to pass the significance test. The sample size of the non-SOEs was 8582, and the regression coefficient of the operating leverage was 0.340, which was significant at the level of 1%. This indicates that operating leverage was positively correlated with R&D investment intensity in the SOEs, but it was not significant, while it was significantly positive in the non-SOEs. For the SOEs, the regression coefficient of “Dol × Post” was −0.180, which did not pass the significance test and was negatively correlated. For the non-SOEs, the regression coefficient of “Dol × Post” was 0.42, which was significant at the 1% level. The results show that: (1) Equity incentives do not play an effective role in SOEs. (2) In non-state-owned listed companies, the implementation of equity incentives will promote a positive correlation between operating leverage and R&D investment, and the coefficient of the interaction term between the operating leverage and equity incentives is greater than that without grouping. Therefore, the moderating effect of equity incentives on the non-SOEs was stronger than that of the whole. One possible reason is that the incentive ratio and incentive income of the SOEs were subject to excessive policy restrictions, resulting in insufficient incentives. Moreover, due to internal control and other problems, the equity incentive design tended to be of a welfare type. On the contrary, the equity incentive plan of private holding companies was more reasonable and tended to be of the incentive type. This is consistent with the conclusions of Shao et al. (2014) [57] and Jiang and Yu (2017) [32].

4.4.2. The Impacts of Different Operating Leverage Values

Theoretically, the degree of a firm’s operating leverage coefficient may affect the relationship between operating leverage and R&D investment intensity. The median operating leverage value of all samples was 1.356. Based on this boundary, all samples were divided into a low operating leverage group and a high operating leverage group. Grouping regression tests were conducted according to Model (1), as shown in

Table 8. Regression results of Model (1) grouped by operating leverage values.

Variables	Dol =< 1.356 (Low)		Dol > 1.356 (High)
	Coefficient	p > |t|	Coefficient	p > |t|
Dol	4.758 ***	0.000	0.437 ***	0.000
ROA	0.165 ***	0.000	0.262 ***	0.000
ROE	−6.329 ***	0.000	−3.759 **	0.041
CI	0.961 ***	0.000	0.924 ***	0.000
Tobin	0.607 ***	0.000	0.845 ***	0.000
Share	−0.022 ***	0.000	−0.015 ***	0.000
Solid	−6.126 ***	0.000	−5.074 ***	0.000
Size	−0.719 ***	0.000	−0.254 ***	0.009
Industry	control		control
Year	control		control
_cons	−1.825	0.182	−0.843	0.329
Sample	5976		5970
Adjusted r-square	0.420		0.420

Notes: *** p < 0.01, ** p < 0.05.

. The results were all significant at the level of 1%, and the regression coefficient of the low operating leverage group was 4.758, which was significantly higher than that of the high operating leverage group (0.437), indicating that the positive effect of the low operating leverage group was more obvious. From the perspective of all samples, there was a positive correlation between operating leverage and R&D investment, but, with a general increase in operating leverage, the trend of this positive impact declined.

In order to further analyze its internal logic, the operating leverage of the whole sample was subdivided into five groups from low to high for regression analysis, as shown in

Table 9. Regression results of Model (1) grouped by operating leverage values.

Variables	Dol = 1		Dol = 2		Dol = 3		Dol = 4		Dol = 5
	Coefficient	p > |t|	Coefficient	p > |t|	Coefficient	p > |t|	Coefficient	p > |t|	Coefficient	p > |t|
Dol	13.368 ***	0.000	3.780 *	0.096	3.617 ***	0.006	1.724 **	0.017	0.316 ***	0.000
ROA	0.147 *	0.066	0.250 ***	0.000	0.251 ***	0.000	0.273 ***	0.000	0.590 ***	0.000
ROE	−4.125 ***	0.000	−9.205 ***	0.000	−3.627	0.105	−1.669	0.545	−3.786	0.332
CI	1.074 ***	0.000	0.945 ***	0.000	0.927 ***	0.000	0.754 ***	0.000	1.134 ***	0.000
Tobin	0.621 ***	0.000	0.558 ***	0.000	0.714 ***	0.000	0.797 ***	0.000	0.992 ***	0.000
Share	−0.026 ***	0.000	−0.022 ***	0.000	−0.014 ***	0.002	−0.012 ***	0.008	−0.018 ***	0.000
Solid	−8.774 ***	0.000	−6.650 ***	0.000	−6.746 ***	0.000	−5.470 ***	0.000	−5.514 ***	0.000
Size	−1.350 ***	0.000	−0.317 *	0.066	−0.258 *	0.078	−0.468 **	0.002	0.125	0.419
Industry	control		control		control		control		control
Year	control		control		control		control		control
_cons	−0.735 **	0.027	−2.788	0.374	−5.706 **	0.015	−2.067	0.232	−3.681 **	0.023
Sample	2390		2389		2389		2389		2389
Adjusted r-square	0.421		0.422		0.418		0.401		0.460

Notes: *** p < 0.01, ** p < 0.05, * p < 0.1.

. The results still support the conclusion of Table 8, and, from group 1 to group 5, the value of the operating leverage gradually increased and its regression coefficient gradually decreased. It is speculated that firms with lower operating leverages took a smaller proportion of the fixed costs and had greater risk-bearing capacity than those with higher operating leverage, and had more funds to participate in and invest in R&D under asset-light operations. Under the dual effects of higher risk-bearing capacity and more capital flow, enterprises had a stronger willingness to invest in R&D innovation. With the increase in operating leverage, the proportion of fixed costs increased, the financial pressures of the enterprises increased, and the operating risk also increased. Although the lever effect brought by the operating leverage still existed and enterprises were still willing to increase innovation inputs, enterprises would adopt more cautious attitudes and the promotion degree of operating leverage on R&D investment was weakened compared with the period of low operating leverage.

5. Robustness Tests

The selection of indicators may have led to variable measurement errors that affected the research results. To test the robustness of the conclusion, a sensitivity test of enterprise innovation indicators was planned.

5.1. Lagged Dependent Variable Analysis

Operating leverage is the result of the operation of the current period, and its impact on innovation investment will be delayed to a certain extent. In order to further improve the robustness, we used Model (1) to further test the impact of operating leverage on the innovation of the listed companies in the t + 1, t + 2, and t + 3 periods [37]. As shown in

Table 10. Regression results of Model (1) with lagged innovation investment as the dependent variable.

Variables	t + 1		t + 2		t + 3
	Coefficient	p > |t|	Coefficient	p > |t|	Coefficient	p > |t|
Dol	0.252 ***	0.000	0.172 **	0.015	0.129 *	0.085
ROA	0.074 ***	0.000	0.047 **	0.038	0.028	0.260
ROE	−4.610 ***	0.000	−3.356 **	0.021	−3.055 **	0.054
CI	0.795 ***	0.000	0.707 ***	0.000	0.651 ***	0.000
Tobin	0.706 ***	0.000	0.693 ***	0.000	0.700 ***	0.000
Share	−0.022 ***	0.000	−0.024 ***	0.000	−0.025 ***	0.000
Solid	−3.497 ***	0.000	−3.428 ***	0.000	−3.274 ***	0.000
Size	−0.547 ***	0.000	−0.581 ***	0.000	−0.505 ***	0.000
Industry	control		control		control
Year	control		control		control
_cons	2.683 ***	0.000	3.956 ***	0.000	3.890 ***	0.000
Sample	8251		6733		5376
Adjusted R-square	0.431		0.409		0.402

Notes: *** p < 0.01, ** p < 0.05, * p < 0.1.

, the regression coefficients of operating leverage were 0.252, 0.172, and 0.129, respectively, all of which were significantly positively correlated, and the significance was the highest when the R&D investment intensity lagged one period. This shows that the main results of this paper are consistent, which again verifies Hypothesis H1a.

5.2. Regression Results of Model (1) Replacing Operating Leverage

In order to avoid the mechanical correlation between calculated operating leverage and R&D investment intensity, referring to the practice of Liu and Zhang (2022) [4], the ratio of operating income to operating cost was used to recalculate the operating leverage of the enterprises. The research conclusions remained unchanged and are shown in

Table 11. Regression results of Model (1) with Replaced Operating Leverage as the independent variable.

Variables	Coefficient	p > |t|
Dol	0.480 ***	0.000
ROA	0.027 *	0.097
ROE	−4.299 ***	0.000
CI	0.814 ***	0.000
Tobin	0.612 ***	0.000
Share	−0.018 ***	0.000
Solid	−3.343 ***	0.000
Size	−0.616 ***	0.000
Industry	control
Year	control
_cons	2.884 ***	0.000
Sample	11946
Adjusted r-square	0.441

Notes: *** p < 0.01, * p < 0.1.

.

5.3. Economic Consequences Test

Innovation quality is the most important dimension of innovation [58]. Patents are an important embodiment of innovation quality, which include invention patents, invention authorizations, utility models, and appearance designs, among which invention patents are the most recognized by investors [52]. In recent years, more and more attention has been paid to invention patents in China. Therefore, this paper added 1 to the number of invention patent applications and took the logarithm as the dependent variable into Model (1) for the regression test [53]. The results are shown in

Table 12. The influence of operating leverage on invention patent (Model (1)).

Variables	Coefficient	p > |t|
Dol	0.109 ***	0.000
ROA	−0.002	0.785
ROE	0.424	0.299
CI	−0.122 ***	0.000
Tobin	0.091 ***	0.000
Share	−0.004 ***	0.000
Solid	−1.050 ***	0.000
Size	0.948 ***	0.000
Industry	control
Year	control
_cons	−3.798 ***	0.000
Sample	12248
Adjusted r-square	0.245

Notes: *** p < 0.01.

. The regression coefficient of operating leverage had a significant positive correlation at the level of 1%, and the research conclusion remained unchanged. This further indicated that operating leverage also contributed to the increase in innovation output in the context of promoting innovation input, which also provided evidence for the mechanism of action in this paper.

5.4. Equity Incentives Are Grouped to Detect the Impact

We regression-tested Model (1) according to whether the listed companies implemented equity incentive grouping. The operating leverage regression coefficients were all significant at the 1% level. As seen in

Table 13. Regression results of Model (1) grouped by equity incentives.

Variables	Post = 0		Post = 1
	Coefficient	p > |t|	Coefficient	p > |t|
Dol	0.267 ***	0.000	0.659 ***	0.000
ROA	0.127 ***	0.000	0.042	0.312
ROE	−6.042 ***	0.000	−5.660 **	0.043
CI	0.905 ***	0.000	1.089 ***	0.000
Tobin	0.531 ***	0.000	1.063 ***	0.000
Share	−0.014 ***	0.000	−0.020 ***	0.001
Solid	−3.084 ***	0.000	−7.208 ***	0.000
Size	−0.706 ***	0.000	−0.375 *	0.059
Industry	control		control
Year	control		control
_cons	3.058 ***	0.000	−0.135	0.957
Sample	9613		2333
Adjusted r-square	0.388		0.481

Notes: *** p < 0.01, ** p < 0.05, * p < 0.1.

, the operating leverage regression coefficient of the group without the implementation of equity incentives was 0.267, and that of the group with the implementation of equity incentives increased to 0.659. As seen in Table 4, the regression coefficient for all samples was 0.293. It indicates that operating leverage was positively correlated with innovation investment and that equity incentives played a positive regulating role in the relationship between operating leverage and innovation investment. This verified the validity of Hypotheses H1a and H2a.

6. Conclusions and Future Research

As an important part of corporate leverage, operating leverage will have a significant impact on enterprise innovation investment decisions, and will consequently affect the entire field of social, scientific, and technological innovation. However, previous studies have paid more attention to the role of financial leverage in innovation investment, but ignored the important role of operating leverage in innovation investment. Based on this, in this study, we theoretically analyzed and empirically tested the influence path of operating leverage on R&D investment, as well as the influence of the interaction effect of equity incentives and operating leverage on R&D investment, and we conducted heterogeneity analyses according to property rights and operating leverage groups, respectively. The results showed that operating leverage significantly promoted the increase in enterprise innovation investment in general, but the positive correlation trend declined with the increase in operating leverage. The implementation of equity incentives played a positive moderating role in the relationship between operating leverage and innovation investment. Further heterogeneity analyses showed that the promotion effect of operating leverage on innovation investment was significant only in non-SOEs, and the positive regulating effect of equity incentives on non-SOEs was more significant than that of the overall sample. Differing from the research perspective of financial leverage, this paper not only has important practical significance for decision-makers to correctly understand and reasonably use operational leverage to play an innovative role in enterprises, but also provides new inspiration for the functional departments within governments to distinguish between financial leverage and operational leverage, and targeted “deleveraging” from the perspective of operational leverage.

The future work is as follows: First, we will further collect more complete sample data. As the information disclosure of listed companies becomes more standardized, the problem of missing and inconsistent data published in the past can be effectively solved, and further research can be carried out in this area in the future. Second, research should be carried out on different industries. Third, the impact of different equity incentive methods can be studied.