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Abstract

Gender differences affect corporate decision making. In view of the importance of corporate innovation in a country’s development, scholars have increasingly focused on the gender of chief executive officers (CEOs) and corporate innovation; although, their findings have been mixed. In lieu of the lack of consensus in the extant literature, this study explores the impact of CEO gender on corporate innovation. In addition, it analyzes the moderating effects of firm size and age on the relationship between CEO gender and corporate innovation. An empirical analysis of 13,000 firm-year observations of manufacturing companies in the Shanghai and Shenzhen stock markets from 2013 to 2022 shows that companies with male CEOs are more innovative than those with female CEOs. The moderation analysis suggests that the positive impact of male CEOs on corporate innovation diminishes for larger firms. Additionally, the results reveal that firm age does not affect the positive relationship between male CEOs and corporate innovation.

Keywords

gender differences CEO gender firm size firm age corporate innovation

Introduction

Cultivating corporate innovation is important for improving the competitiveness of the manufacturing industry and achieving China’s technological independence (X. Zhao et al., 2022). Given the importance of corporate innovation, scholars have explored the factors that make enterprises innovative and the reasons why some enterprises are more innovative (Prasad & Junni, 2017). Research shows that among the organizational determinants, decision-making centralization (Cardinal, 2001), employee connectivity (Atuahene-Gima, 2003), and specialization (Damanpour, 2018) promote corporate innovation. In addition, there is substantial evidence dating back to the introduction of the upper echelon theory (UET; Hambrick & Mason, 1984) that executives’ personal characteristics, in addition to being management determinants, significantly influence corporate decisions and outcomes (Q. Wu et al., 2021). However, current research focuses on board diversity and top management team characteristics (Belderbos et al., 2022; Firk et al., 2022; Y. x. Li & He, 2023; Makkonen, 2022), and research examining the impact of individual executives on innovation remains limited (Q. Wu et al., 2021; T. Zhang, Sharon, & Yin, 2022).

Recent research reveals that CEOs can make crucial decisions regarding organizational change and the distribution of resources to pursue innovation (Kong et al., 2021; Prasad & Junni, 2017). Therefore, CEOs play a pivotal role in the corporate innovation process (Hou et al., 2021; Ren et al., 2021). In addition, organizations face many ambiguities and complexities in the innovation process, and CEOs can provide support for resource allocation and decision execution to promote corporate innovation capabilities (C.-W. Wu, 2013). Given the importance of the CEOs, this study selects the CEO as a representative of senior executives and examines the CEO’s role in corporate innovation.

In China, the number of female CEOs has been increasing (Lam et al., 2013). In 2016, data from the World Development Indicators reveal that 18% of corporations in China had the highest proportion of female managers, surpassing the Organization for Economic Cooperation and Development (OECD) average of 15.8% (Javaid et al., 2021). In addition, China is ranked second globally in terms of female CEOs between 2004 and 2013, and forecasts suggest that one-third of CEOs by 2040 will be women (Hu, 2014). The increasing number of female CEOs provides an excellent opportunity to examine the differences between male and female CEOs regarding their usage of corporate innovation resources. Therefore, this study investigates the influence of CEO gender on corporate innovation.

Prasad and Junni (2017) insist that aside from understanding the influence of CEO characteristics on corporate innovation, the organizational background should also be considered. In some cases, CEO characteristics may be effective, whereas in others, specific CEO characteristics do not affect organizational decisions (Owens & Hekman, 2012). Therefore, this study uses contingency theory to expand our understanding of how CEOs influence organizational outcomes (Javed et al., 2023; Yan et al., 2016). According to contingency theory, the influence of an independent variable on a dependent variable can differ among organizations (Donaldson, 2001). The divergent outcomes can be primarily attributed to country-, industry-, or organization-specific factors (Donaldson, 2001). The contingency perspective assumes that the alignment between situational contingencies and CEO gender is the determinant of corporate innovation. Consequently, this study investigates the effects of two contingencies (firm age and size).

Considering the current research gaps, this study uses an unbalanced panel dataset of 2,555 listed manufacturing companies from 2013 to 2022 to explore the impact of CEO gender on corporate innovation and the moderating role of contingencies (firm age and size) in the relationship between CEO gender and corporate innovation. This study, which measures corporate innovation based on the total number of patent applications per year, finds that male CEOs can influence corporate innovation more positively than female CEOs. Additionally, the moderation analysis suggests that the positive impact of male CEOs on corporate innovation diminishes for larger firms.

This study makes several key contributions. First, existing studies regarding the impact of CEO gender on corporate innovation have different findings (Javaid et al., 2021; Javed et al., 2023; Strohmeyer et al., 2017). Our study provides a new perspective on the relationship between CEO gender and corporate innovation. Specifically, by combining UET and contingency theory, we extend the view on how gender differences affect corporate innovation. Second, previous research on CEO gender and corporate innovation mainly focuses on Western countries (Expósito et al., 2021; Galbreath, 2019; Strohmeyer et al., 2017; Q. Wu et al., 2021). Since China is the world’s largest developing economy (Javed et al., 2023), our study provides strong guidance for corporate innovation in developing countries.

Third, this study considers the influence of organization-specific factors (i.e., firm size and firm age) while examining the relationship between CEO gender and corporate innovation. It supports the contingency theory view that corporate innovation is the result of a match between the CEO’s gender and situation-specific factors. Finally, by incorporating a gender perspective, this study enhances our understanding of potential gender differences in corporate innovation and helps challenge existing social gender-based stereotypes that may disadvantage women.

Literature Review and Hypothesis Development

CEO Gender and Corporate Innovation

The term “corporate innovation” in manufacturing refers to the integration of technology, equipment, or significant software upgrades into manufacturing or logistics systems to significantly enhance production (Rogers, 2003). This innovation must replace existing production or service capabilities, thereby adding value to the firm and its value chain (Mamasioulas et al., 2020). Following UET, Hambrick and Mason (1984) argue that top managers influence strategic decisions. This view is further confirmed by the fact that top managers’ demographic characteristics influence their decisions, which further affects organizational results (Finkelstein et al., 2009; S. Wu, 2021). In this regard, CEO gender, as a demographic characteristic, impact corporate innovation (Han et al., 2019; Javaid et al., 2021; Q. Wu et al., 2021).

The literature shows that women face a glass ceiling effect in the corporate world, in which they experience bias and discrimination (J. Wang et al., 2022; Q. Wu et al., 2021). They must overcome discrimination to become CEOs (Prabowo & Setiawan, 2021). Therefore, to ensure the corporation benefits from innovation, female CEOs exert additional effort to resolve misunderstandings and foster corporate innovation at the organizational level (Javed et al., 2023). Additionally, recent studies show that female CEOs are more active in promoting corporate innovation than male CEOs (Javaid et al., 2021; J. Wang et al., 2022). J. Wang et al. (2022) state that female CEOs tend to work harder to prove their leadership status due to unfair evaluation standards and a discriminatory environment. They contend that women holding dominant roles in businesses are forced to generate more innovative ideas to maintain their positions in the business. Overall, biased practices at the organizational level prompt female CEOs to adapt to uncertain environments and devise innovative ideas (Jones & Clifton, 2018). Compared to male CEOs, female CEOs possess unique qualities and characteristics such as excellent communication skills, strong intuition, and a strong sense of responsibility, which are more likely to foster a positive and open discussion environment within the company, leading to the generation of innovative ideas (Eagly, 2007; Han et al., 2019; Javed et al., 2023).

However, the Chinese context cannot be ignored as it is the focus of this study. China has a patriarchal society (Cho et al., 2015). Thus, Chinese men are expected to exhibit masculine behaviors such as dominance, leadership, and success, while Chinese women are expected become mothers and housewives (Javed et al., 2023; Liu, 2013). Social presentist and poststructuralist feminist theories argue that cultural values, education, and the outcomes of social interactions create similarities and differences between men and women (Ahl, 2006; Fischer et al., 1993). Gender is a construct that results from the structure of society, leading to stereotypes regarding the differences in attitudes, abilities, and behavioral patterns between men and women (Ahl, 2006; Expósito et al., 2021; Fischer et al., 1993). Therefore, Entrepreneurship is a gendered process that shapes individual expectations regarding the behavior of men and women (Eddleston & Powell, 2012). In particular, women’s roles in China are related to the family, and women internalize these values, leading to male and female entrepreneurs operating their corporations differently (Javed et al., 2023; Liu, 2013). Consequently, social presentist and poststructuralist feminist theories contend that the decision to engage in innovative activities is gendered, resulting in a lower probability of innovation introduction by female-led businesses than by their male-led counterparts.

In summary, according to social presentist and poststructuralist feminist theories, gender, as a social construct, profoundly affects management’s cognition, behavior, and business decisions (Ahl, 2006). Therefore, gender differences in the propensity to innovate may persist, even after accounting for specific differences at the managerial and firm levels. Not only do individual attributes contribute to this difference, but social roles and expectations of gender in the workplace also play a significant role. Therefore, this study proposes the following hypothesis:

H1: Firms with a male CEO are more inclined toward corporate innovation than those with a female CEO.

CEO Gender and Corporate Innovation: Contingency Factors

According to contingency theory, organizational outcomes (e.g., corporate innovation) are the result of multiple factors working together and matching each other (Donaldson, 2001; Hambrick, 1983). The effect of independent variables on dependent variables differ depending on organizations, mainly because of the influence of specific organizational, industrial, or national factors (Donaldson, 2001). From a contingency perspective, this study assumes that corporate innovation is the result of the interaction between CEO gender and situation-specific factors. Therefore, this study tests organization-specific factors (i.e., firm size and firm age) as contingencies.

The Moderating Role of Firm Size

In line with contingency theory, Child (1975) insists that additional research is necessary to fully understand how organization-specific factors moderate the impact of CEO gender on corporate innovation. Firm size is an important variable that affects corporate innovation by influencing organizational characteristics (Javed et al., 2023; Prasad & Junni, 2017), while firm size determines how easily a company can undergo internal change (Walter & Bruch, 2010). Organizations often require substantial internal changes to develop new products and services (C.-W. Wu, 2013). In smaller firms, the organizational structure tends to be less complex, making it easier for their CEOs to introduce changes and inspire innovation (Papadakis, 2006; Prasad & Junni, 2017). These firms typically have more direct communication, fewer hierarchical layers, and less bureaucracy, which reduce barriers to change (Vaccaro et al., 2012).

By contrast, large firms, with their complex structures, decentralized power, and extensive hierarchies, often face greater resistance to change (Nahavandi & Malekzadeh, 1993; Prasad & Junni, 2017). In large firms, even if CEOs strongly identify with the organization and is committed to innovation, their ability to drive change may be hindered due to multiple layers of decision-making and formalized systems (Koene et al., 2002; Xie, 2014). As such, CEOs in large firms are likely to be ineffective in motivating staff members to embrace new methodologies for modifying established products and services due to the more complex organizational structures and entrenched processes (Prasad & Junni, 2017). Therefore, based on contingency theory, we propose the following hypothesis:

H2: Firm size weakens the positive effect of CEO gender and corporate innovation.

The Moderating Role of Firm Age

According to the contingency theory, firm age is an organization-specific factor that influences corporate decisions such as company performance, R&D investment, company ownership choices, and innovation value (Lin et al., 2023; Messeni Petruzzelli et al., 2018; Pandey et al., 2022; Xie, 2014). From an organizational perspective, firm age serves as a proxy for internal inertia (Xie, 2014). Older firms have accumulated internal inertia, which often leads them to rely heavily on established practices and years of experience in making decisions (Van de Wal et al., 2020). Research suggests that as a firm ages, the influence of the CEO on its behavior and outcomes diminishes (Beckman & Burton, 2008). Thus, older firms, with their internal inertia, experience greater interference in CEO decision-making, whereas younger firms, with less inertia, face fewer constraints (T. Zhang, Sharon, & Yin, 2022).

Economically, older firms tend to adapt to their environments and have stable structures (Hannan & Freeman, 1977). They prioritize stability and are risk averse (Hannan & Freeman, 1977; Messeni Petruzzelli et al., 2018). Moreover, their CEOs face more constraints and have limited decision-making power, reducing the impact of CEO gender on corporate innovation. By contrast, younger firms, which focus on gaining market share, are more inclined to disrupt the status quo through innovation, enabling them to shape new market conditions and compete with established players (Bouncken et al., 2021; Hill & Rothaermel, 2003). Furthermore, their CEOs face fewer constraints and greater decision-making authority, increasing the influence of CEO gender on corporate innovation. Thus, we propose the following hypothesis:

H3: Firm age weakens the positive effect of CEO gender and corporate innovation.

Methodology

Sample Data

This study uses data from all A-share manufacturing companies listed in the Shanghai and Shenzhen stock exchanges between 2013 and 2022. The 2013 to 2022 period is selected as the study period for two reasons: First, the Chinese government implemented the innovation-driven development strategy in 2013 (Tong et al., 2021; L. Zhao, 2016); hence, the R&D investment of each company and the number of related patent applications have changed (Xiao et al., 2022). Second, the updates for the two databases selected for this study ended in 2022.

The data on CEO personal characteristics, corporate governance data, and company financial data are obtained from the China Stock Market and Accounting Research database. In addition, patent data are obtained from the Chinese Research Data Services Platform database. This study merges all datasets by year and stock code and removes missing data, and thus obtaining a final sample of 13,000 firm-year observations.

Measurement of Main Variables

This study uses data on company-related patents to measure a company’s innovation capabilities. Following previous research, this study uses PATENT, which represents the total number of patent applications annually filed by a company, to measure corporate innovation (Javaid et al., 2021; Kong et al., 2021). In addition, this study uses patents granted at the end of each year (PATENT1) to measure corporate innovation and check for robustness (He & Tian, 2013; Q. Wu et al., 2021).

The independent variable in this study is CEO gender (Male CEO). Male CEO is a binary variable that takes the value of 1 if he is CEO of a company, and 0 otherwise. The moderating variables are firm size (SIZE) and firm age (AGE).

The control variables are divided into several categories. First, since the demographic characteristics of CEOs have a greater impact on corporate innovation (Javed et al., 2023; Prasad & Junni, 2017; Ricotta et al., 2021; C. Zhang, Li, et al., 2022). we control for CEO education (CEOE) and age (CEOA). Second, since a negative relationship exists between financial leverage and corporate innovation (H. Li & Chen, 2018; S. Wu, 2021), and tangibility and capital assets are related to corporate innovation (Cho et al., 2016; Kong et al., 2021; San, 2021; Q. Wu et al., 2021), we control for the following financial variabes: financial leverage (LEV), tangibility (TAN), and capital assets (CA). Third, we follow Yong et al. (2021), T. Wang and Cheng (2022), and Kang et al. (2018) and control for the following corporate governance factors: board independence (BI), institutional holding (IH), and top holding (TOP). Finally, since R&D is closely related to corporate innovation (Cho et al., 2016; W. Huang et al., 2019; Loukil & Yousfi, 2022), we include R&D as a control variable. Table 1 shows how each variable is measured.

Table 1.

Description of Measurement Variables.

Variables	Measurement
Dependent variables
Enterprise innovation (PATENT)	The total number of patent applications filed by an enterprise each year
Enterprise innovation (PATENT1)	The total number of patent applications filed by an enterprise each year are granted.
Independent variable
CEO gender (Male CEO)	Equals one if the CEO is a male and zero if female.
Moderator variables
Firm size (SIZE)	The number of employed people/1,000.
Firm age (AGE)	The number of years since the firm was founded for every year end.
Control variables
CEO age (CEOA)	The CEO’s age in that year.
CEO education (CEOE)	Equals one if the CEO has a master or doctoral degree, and zero otherwise.
R&D capital (R&D)	The ratio of R&D expenditures to the book value of total assets.
Financial leverage (LEV)	The ratio of the total debt to total assets at every year end.
Tangibility (TAN)	The ratio of fixed assets to total assets at every year end.
Capital assets (CA)	The ratio of capital expenditure to total assets at every year end.
Board independence (BI)	The ratio of independent directors to the number of directors on the board at the year end
Institutional holding (IH)	The percentage ownership of institutional owners at every year end
Top holding (TOP)	The percentage of total shares outstanding held by the largest shareholder

Estimation Model

In this study, PATENT is a non-negative integer. This often contradicts the underlying normality assumption (Cameron & Trivedi, 2013; Gardner et al., 1995). Therefore, ordinary least squares cannot be used to analyze this data type (Miaou & Lum, 1993). Based on the literature, this study uses Poisson regression to examine the count data (Dritsaki et al., 2023; Q. Wu et al., 2021). Before conducting the regression analysis, we use the Hausman test to decide on whether to use a random- or fixed-effects model (Chu et al., 2022; Wicaksono & Setiawan, 2022). The Hausman test results indicate that the fixed effects model is more suitable for this study. Therefore, we adopt a panel fixed-effect Poisson regression model to address the research questions.

The main empirical model is as follows:

$Innovatio n_{it} = β_{o} + δ_{i} + ω_{t} + β X_{it} + μ_{it}$

where ω_t represents time series fixed effects at period t, and µ_it represents the error term. In addition, δi represents the fixed effects of the i-th firm, and X_it comprises a collection of explanatory variables that are linked to the i-th firm at period t. Some firm-level characteristics depend on years of observation. Thus, firm-level standard errors are clustered.

Results

Descriptive Statistics

Table 2 presents the descriptive statistics. The sample of companies in this study comprised 93.1% male CEOs. Additionally, the median value of patents (PATENT) is 31. In comparison to the mean value of 85.618, this indicates a large gap in the number of patents between different firms.

Table 2.

Descriptive Statistics of All Variables Used in This Study From 2013 to 2022.

Variable	Mean	Std. Dev.	Min	Median	Max	N
PATENT	85.618	183.553	1.000	31.000	1,336.000	13,000
Male CEO	0.931	0.253	0.000	1.000	1.000	13,000
SIZE	4.393	7.848	0.224	1.890	55.762	13,000
AGE	8.051	6.903	0.000	6.000	26.000	13,000
CEOA	50.497	6.916	32.000	51.000	67.000	13,000
CEOE	0.473	0.499	0.000	0.000	1.000	13,000
R&D	0.026	0.018	0.001	0.023	0.101	13,000
CA	0.053	0.045	0.002	0.040	0.220	13,000
LEV	0.376	0.185	0.055	0.366	0.829	13,000
TAN	0.214	0.125	0.017	0.192	0.582	13,000
BI	0.376	0.053	0.333	0.333	0.571	13,000
IH	0.402	0.252	0.002	0.408	0.902	13,000
TOP	0.330	0.140	0.086	0.308	0.719	13,000

Note. All continuous variables are winsorized at the level of 1% and 99% levels to minimize the influence of outliers.

Table 3 presents the pairwise correlations between the variables. The results reveal that Male CEO and PATENT are positively correlated. Moreover, the results indicate significant correlations between several control variables and PATENT. On the one hand, SIZE, AGE, CEOE, CEOA, R&D, LEV, TAN, IH, BI, and TOP are significantly positively correlated with PATENT. On the other hand, CA and TAN are significantly negatively correlated with PATENT. These results provide preliminary evidence for this study. Then, a variance inflation factor (VIF) test is performed before estimating the models. The VIF test results suggest that the multicollinearity problem does not exist since the VIF values for all variables are between 1.01 and 1.88, which is well below the threshold value of 10.

Table 3.

The Pairwise Correlations of the Variables.

Variable	PATENT	Male CEO	CEOA	CEOE	SIZE	AGE	R&D	CA	LEV	TAN	BI	IH	TOP	VIF
PATENT	1.000
Male CEO	.008 (.36)	1.000												1.01
CEOA	.023 (.01)	.058 (.00)	1.000											1.02
CEOE	.144 (.00)	.026 (.00)	−.032 (.00)	1.000										1.04
SIZE	.669 (.00)	−.005 (.56)	.059 (.00)	.121 (.00)	1.000									1.30
AGE	.213 (.00)	.032 (.00)	.061 (.00)	.114 (.00)	.313 (.00)	1.000								1.43
R&D	.170 (.00)	.037 (.00)	.029 (.00)	.080 (.00)	.008 (.37)	−.144 (.00)	1.000							1.08
CA	−.017 (.06)	−.004 (.69)	−.013 (.14)	−.031 (.00)	.026 (.00)	−.227 (.00)	.064 (.00)	1.000						1.17
LEV	.257 (.00)	.048 (.00)	−.001 (.94)	.073 (.00)	.343 (.00)	.373 (.00)	−.111 (.00)	−.019 (.03)	1.000					1.27
TAN	−.059 (.00)	.006 (.50)	−.001 (.92)	−.043 (.00)	.129 (.00)	.161 (.00)	−.188 (.00)	.240 (.00)	.176 (.00)	1.000				1.18
BI	.039 (.00)	−.051 (.00)	.004 (.65)	.003 (.76)	.036 (.00)	−.032 (.00)	.027 (.00)	.030 (.00)	−.006 (.53)	−.015 (.08)	1.000			1.02
IH	.212 (.00)	.037 (.00)	.078 (.00)	.103 (.00)	.302 (.00)	.205 (.00)	−.019 (.03)	.014 (.11)	.147 (.00)	.109 (.00)	−.066 (.00)	1.000		1.41
TOP	.041 (.00)	−.025 (.00)	.003 (.71)	−.014 (.10)	.114 (.00)	−.109 (.00)	−.033 (.00)	.033 (.00)	−.028 (.00)	.049 (.00)	.058 (.00)	.417 (.00)	1.000	1.30
Mean VIFs														1.19

Note. p-values are reported in parentheses.

Testing the Hypotheses

Table 4 reports the fixed-effect Poisson regression results for the relationship between CEO gender and corporate innovation. Coefficient estimates and robust z-statistics (in parentheses) are based on robust standard errors clustered at the firm-level. Model 1 presents the results for basic regression, including control variables and moderating variables. Model 2 presents the fixed-effect Poisson regression results if independent variables are included. Models 3 and 4 present the results when the interaction terms “Male CEO*SIZE” and “Male CEO*AGE” are added, respectively.

Table 4.

Relationship Between CEO Gender and Corporate Innovation, and the Moderating Role of Firm Size and Age on the Association Between CEO Gender and Corporate Innovation.

Variables	Model 1	Model 2		Model 3	Model 4
	PATENT	PATENT		PATENT	PATENT
	Coefficient	Coefficient	Incidence rate ratio	Coefficient	Coefficient
Male CEO		0.156** (2.15)	1.169	0.250*** (2.87)	0.095 (0.68)
Male CEO*SIZE				−0.013** (−2.27)
Male CEO*AGE					0.005 (0.47)
SIZE	0.017*** (3.20)	0.017*** (3.13)	1.017	0.029*** (4.27)	0.017*** (3.07)
AGE	−0.108** (−2.14)	−0.110** (−2.17)	0.896	−0.108** (−2.16)	−0.116** (−2.27)
CEOA	0.001 (0.43)	0.001 (0.28)	1.001	0.001 (0.30)	0.001 (0.28)
CEOE	0.134*** (3.04)	0.133*** (3.05)	1.142	0.130*** (2.97)	0.134*** (3.07)
R&D	5.078*** (2.65)	5.142*** (2.69)	170.969	5.011*** (2.61)	5.159*** (2.70)
CA	0.804** (2.24)	0.816** (2.28)	2.262	0.822** (2.30)	0.818** (2.28)
LEV	0.351** (2.23)	0.362** (2.29)	1.437	0.372** (2.34)	0.364** (2.31)
TAN	−0.368 (−1.30)	−0.373 (−1.31)	0.689	−0.363 (−1.28)	−0.377 (−1.32)
BI	−0.333 (−1.22)	−0.315 (−1.15)	0.730	−0.318 (−1.17)	−0.318 (−1.16)
IH	0.392* (1.88)	0.399* (1.90)	1.490	0.381* (1.81)	0.397* (1.89)
TOP	0.093 (0.31)	0.103 (0.34)	1.108	0.084 (0.28)	0.102 (0.34)
Year dummies	Included	Included		Included	Included
Individual dummies	Included	Included		Included	Included
Observations	12,688	12,688		12,688	12,688
Wald Chi²	400.41	400.21		413.63	401.97
Prob > Chi²	.000	.000		.000	.000

Note.***, **, and * indicate significance at the 1%, 5%, and 10% levels, respectively. The () represents robust z-statistic value. For detailed descriptions of variables, please refer to Table 1.

Model 2 of Table 4 presents the results for the entire sample, which indicate that the coefficient of Male CEO (β = .156, p < .05) is both positive and highly significant. Thus, Hypothesis 1 is supported. In addition, the Male CEO variable incidence rate ratio is 1.169, indicating that the number of patent applications filed by male CEOs is 1.169 times that of female CEOs and that male CEOs add impetus to corporate innovation.

The results also show that control variables CEOE, SIZE, R&D, CA, LEV, and IH have a positive and significant relationship with corporate innovation, indicating that highly educated CEOs, large firms, firms with large R&D investments, riskier firms, firms with high capital assets and firms with high institutional holdings are more inclined to corporate innovation. Moreover, the results also indicate that older firms are less prone to corporate innovation. The results of this study on the control variables are consistent with those of previous studies (Cid-Aranda & López-Iturriaga, 2023; Javaid et al., 2021; Javed et al., 2023; Q. Wu et al., 2021).

In Model 3, the regression coefficient for Male CEO*SIZE is −.013 and is statistically significant at the 5% level. This indicates that the positive impact of male CEOs on corporate innovation is lower in larger firms. Thus, Hypothesis 2 is supported. In Model 4, the coefficient of Male CEO*AGE is positive but lacks statistical significance. Therefore, Hypothesis 3 is not supported.

Robustness Test

This study adopts three methods to test the robustness of the results. First, following existing studies, we replace the dependent variable measurement indicators and use PATENT1 to measure corporate innovation (He & Tian, 2013; Q. Wu et al., 2021). Second, we exclude sample data from 2020 crisis period—the height of the COVID-19 pandemic (D. Huang & Chen, 2022). The reason for this is that during the COVID-19 pandemic, the economies of various countries declined, as governments adopted anti-flow policies that paralyzed business operations and faced financial risks (Adam & Alarifi, 2021; Azoulay & Jones, 2020; Caballero-Morales, 2021). Thirdly, according to previous literature, CEO gender affects R&D, which in turn affects corporate innovation (Pan et al., 2022; Saggese et al., 2021). In this study, R&D as a control variable may inadvertently control for part of the impact of CEO gender on corporate innovation. Therefore, this study removes the control variable R&D to test the robustness of the research results.

Models 1 to 3 of Table 5 present the robustness test results, with PATENT1 as the dependent variable. Model 1 indicates that the regression coefficient of Male CEO is significantly positive (β = .162) at the 10% level, while Model 2 indicates that the regression coefficient of Male CEO*SIZE is significantly negative (β = −.013) at the 10% level. Moreover, Model 3 demonstrates that the male CEO*AGE regression coefficient is positive but not statistically significant. These findings are consistent with our main results. Models 4 to 6 of Table 5 show the robustness test results after excluding the crisis sample data. The results remind consistent with our main research results. Finally, Models 1 to 3 in Table 6 represent the robustness test results after excluding the control variable R&D data, and their results are consistent with our main research results. This shows that the results of this study are not sensitive to whether R&D is a control variable and the results of the study are robust.

Table 5.

Robustness Test by Using an Alternative Indicator to Measure the Dependent Variable and Excluding the Sample of Crisis Period.

Variables	PATENT1			PATENT
Variables	Model 1	Model 2	Model 3	Model 4	Model 5	Model6
Male CEO	0.162* (1.67)	0.259*** (2.81)	0.093 (0.63)	0.142* (1.95)	0.242*** (2.75)	0.070 (0.49)
Male CEO*SIZE		−0.013* (−1.70)			−0.013** (−2.27)
Male CEO*AGE			0.006 (0.38)			0.006 (0.50)
SIZE	0.017*** (3.05)	0.029*** (3.65)	0.017*** (3.01)	0.017*** (3.26)	0.029*** (4.44)	0.017*** (3.20)
AGE	−0.077** (−2.05)	−0.075** (−2.00)	−0.084* (−1.88)	−0.108** (−2.20)	−0.106** (−2.19)	−0.115** (−2.31)
CEOA	0.004 (1.30)	0.004 (1.32)	0.004 (1.30)	0.002 (0.64)	0.002 (0.65)	0.002 (0.64)
CEOE	0.144*** (2.90)	0.141*** (2.84)	0.145*** (2.94)	0.148*** (3.25)	0.145*** (3.18)	0.149*** (3.28)
R&D	4.009** (2.01)	3.870* (1.93)	4.025** (2.02)	5.244*** (2.61)	5.147** (2.55)	5.262*** (2.62)
CA	1.071*** (3.03)	1.079*** (3.07)	1.071*** (3.03)	0.703** (2.02)	0.700** (2.03)	0.705** (2.03)
LEV	0.379** (2.30)	0.390** (2.35)	0.381** (2.31)	0.375** (2.31)	0.385** (2.35)	0.378** (2.33)
TAN	−0.467* (−1.93)	−0.458* (−1.89)	−0.473* (−1.94)	−0.292 (−0.97)	−0.284 (−0.95)	−0.297 (−0.99)
BI	−0.210 (−0.68)	−0.212 (−0.68)	−0.213 (−0.69)	−0.284 (−0.98)	−0.289 (−1.00)	−0.286 (−0.99)
IH	0.362* (1.71)	0.341 (1.60)	0.360* (1.70)	0.495** (2.25)	0.476** (2.15)	0.491** (2.23)
TOP	0.337 (1.08)	0.316 (1.01)	0.336 (1.08)	0.054 (0.17)	0.035 (0.11)	0.053 (0.17)
Year dummies	Included	Included	Included	Included	Included	Included
Individual dummies	Included	Included	Included	Included	Included	Included
Observations	12,664	12,664	12,664	11,036	11,036	11,036
Wald Chi²	345.66	355.16	348.95	390.92	403.63	394.66
Prob > Chi²	.000	.000	.000	.000	.000	.000

Note.***, **, and * indicate significance at the 1%, 5%, and 10% levels, respectively. The () represents robust z-statistic value. For detailed descriptions of variables, please refer to Table 1.

Table 6.

Robustness Test by Excluding the Control Variable of R&D.

Variables	PATENT
Variables	Model 1	Model 2	Model 3
Male CEO	0.149** (1.99)	0.253*** (2.90)	0.101 (0.72)
Male CEO*SIZE		−0.014** (−2.40)
Male CEO*AGE			0.004 (0.37)
SIZE	0.017*** (3.30)	0.031*** (4.47)	0.017*** (3.25)
AGE	−0.106** (−2.17)	−0.104** (−2.16)	−0.110** (−2.26)
CEOA	0.001 (0.17)	0.001 (0.20)	0.001 (0.17)
CEOE	0.132*** (3.03)	0.129*** (2.95)	0.133*** (3.04)
CA	0.903** (2.49)	0.909** (2.51)	0.905** (2.49)
LEV	0.311* (1.95)	0.323** (2.01)	0.313** (1.97)
TAN	−0.293 (−1.00)	−0.284 (−0.98)	−0.296 (−1.01)
BI	−0.258 (−0.94)	−0.263 (−0.96)	−0.260 (−0.95)
IH	0.402* (1.90)	0.381* (1.79)	0.400* (1.89)
TOP	0.124 (0.40)	0.101 (0.32)	0.123 (0.39)
Year dummies	Included	Included	Included
Individual dummies	Included	Included	Included
Observations	12,688	12,688	12,688
Wald Chi²	414.68	429.23	415.62
Prob > Chi²	.000	.000	.000

Note.***, **, and * indicate significance at the 1%, 5%, and 10% levels, respectively. The () represents robust z-statistic value. For detailed descriptions of variables, please refer to Table 1.

Discussion of the Results

This study uses UET and contingency theory to examine how CEO gender affects innovation in Chinese manufacturing firms. It measures innovation using the number of patent applications filed annually. Our results show that male CEOs positively impact innovation, supporting UET’s idea that CEO characteristics such as gender influence business decisions (Hambrick & Mason, 1984). These results differ from those of other studies and offer a new perspective on CEO gender and corporate innovation. Existing studies based on liberal feminist theory argue that male and female executives have equal management abilities and innovation tendencies (Ahl, 2006; Fischer et al., 1993). However, studies based on a sociological perspective believe that discrimination and unfair treatment may drive women to perform better than men and reach top management positions, thereby promoting innovation and enhancing corporate value (Han et al., 2019; Lam et al., 2013; Prabowo & Setiawan, 2021). These perspectives are valid in certain cultural and societal contexts.

The Chinese cultural context is the key to understanding these findings. According to social presentist and poststructuralist feminist theories, Chinese cultural values, educational systems, and social interactions significantly shape the perception of women’s roles, which are traditionally associated with the family (Ahl, 2006; Expósito et al., 2021; Fischer et al., 1993; Javed et al., 2023). These values have been largely internalized, leading to different management styles between male and female executives in Chinese firms (Lashitew, 2023). This cultural and social context likely explains why male CEOs have a stronger influence on innovation in Chinese manufacturing companies (Expósito et al., 2021).

Furthermore, this study finds that the positive correlation between male CEOs and corporate innovation tends to weaken in large organizations. This could be attributed to the more intricate and formal organizational structures prevalent in larger firms (Papadakis, 2006; Prasad & Junni, 2017). CEOs of large firms may not have access to members at all levels of the organization, thereby preventing changes in the company (Prasad & Junni, 2017). These results advance the corporate innovation literature by enhancing our understanding of top managers’ influence on corporate innovation in light of contingency theory. It also responds to the call for research on the boundary conditions of top management’s impact on corporate innovation (Vaccaro et al., 2012).

Moreover, this study also finds that firm age has no significant impact on the relationship between male CEOs and corporate innovation, supporting Xie’s (2014) findings. The main reason for this is that firm age may simultaneously increase or decrease executives’ management discretion (Xie, 2014), making the moderating effect of firm age insignificant.

Conclusion and Limitations

Using 13,000 firm-year observations data from China’s manufacturing industry, this study investigates the relationship between CEO gender and corporate innovation. The study’s results show that male CEOs are more inclined to engage in corporate innovation than female CEOs. Additionally, the results show that the positive role of male CEOs in corporate innovation weakens in large firms.

The study’s findings have theoretical and practical implications. First, this study confirms UET through empirical analysis; that is, CEO gender influences corporate strategic decisions. Second, this study comprehensively explores the relationship between CEO gender and corporate innovation by exploring the moderating effects of firm size and age. According to contingency theory, the direct correlation between CEO gender and corporate innovation is contingent on organization-specific variables, and the study’s results provide support for this perspective.

Third, this study shows that male CEOs in the manufacturing industry are more inclined to engage in corporate innovation than female CEOs in the Chinese context. To eliminate inherent gender stereotypes, Chinese corporate managers should focus on individual capabilities, experience, and innovative vision when formulating recruitment policies. Fourth, this study finds that the positive impact of male CEOs on corporate innovation is lower in large firms. This may be the result of organizational intransigence that impedes change, the incapability of organizational members to operationalize an abstract CEO vision, or the challenge of reaching company members at all levels. These results emphasize the significance of CEO signals, organizational member actions that support corporate innovation, and CEO endeavors to convey a coherent and unambiguous vision. Company managers must understand that innovation is important for large firms, and they need to help reduce corporate complexity and formalities. This can ease organizational rigidity and promote the corporate changes envisioned by the CEO. For example, providing organizational members with access to new ideas and making collaborations between teams easier can create an environment that is more receptive to innovation (Poskela & Martinsuo, 2009).

Despite the study’s numerous theoretical and practical contributions, it has several limitations that can be explored in future research. First, most enterprises in this study are led by male CEOs, resulting in a highly imbalanced distribution of observations. Future studies should consider running more advanced statistical techniques (such as propensity score matching or difference-in-differences) to make the results more comprehensive. Second, China currently lacks patent citation data; therefore, measurement standards for innovation quality are lacking. If citation data for Chinese patents could eventually be accessed, it could be a valuable topic for further research. Thirdly, in this study, no strict boundaries were made regarding firm size or firm age. Follow-up research can strictly define the size of the firm or the age of the firm, and then use difference-in-differences for group analysis to make the research results more comprehensive. Finally, this study explores the relationship between CEO gender and corporate innovation through contingency factors at the organizational level. Future research could examine the role of CEO gender in promoting corporate innovation by integrating institutional contingencies within countries, such as ownership concentration and regional development, and distinguishing between publicly and privately listed companies.

Footnotes

ORCID iDs

Tingting Zhang

Jing Chen

Author Contributions

Conceptualization,methodology,formal analysis,writing—original draft preparation,review and editing—T.T. Zhang;data curation,software—J. Chen. All authors have read and agreed to the published version of the manuscript.

Funding

The author(s) disclosed receipt of the following financial support for the research,authorship,and/or publication of this article: This research is funded by Jiangxi Provincial Humanities and Social Science Research projects for Colleges and Universities (Research on CEO demographic characteristics and corporate innovation,Grant no. GL22238).

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research,authorship,and/or publication of this article.

Data Availability Statement

The data presented in this study are available on request from the corresponding author.

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A Contingency Model of CEO Gender and Corporate Innovation: The Moderating Effects of Firm Age and Firm Size

Abstract

Keywords

Introduction

Literature Review and Hypothesis Development

CEO Gender and Corporate Innovation

CEO Gender and Corporate Innovation: Contingency Factors

The Moderating Role of Firm Size

The Moderating Role of Firm Age

Methodology

Sample Data

Measurement of Main Variables

Estimation Model

Results

Descriptive Statistics

Testing the Hypotheses

Robustness Test

Discussion of the Results

Conclusion and Limitations

Footnotes

ORCID iDs

Author Contributions

Funding

Declaration of Conflicting Interests

Data Availability Statement

References