Female Corporate Leadership and Firm Growth Strategy: A Global Perspective

Abstract

This study aims to understand the relationship between female corporate leadership and firm performance based on exploration and exploitation strategies using a global enterprise data set. Previous studies report conflicting evidence of female corporate leadership on firm performance. This study applies an exploration–exploitation framework and suggests that the relative advantage of female corporate leadership relies upon certain aspects of the firm’s growth strategy. The empirical evidence confirms that the relative advantage of female corporate leadership resides more in the exploitation than the exploration aspect of a firm’s growth strategy compared to male corporate leadership. The study thus offers important implications for broader business practices when considering the alignment between the choice of corporate leadership and firm growth strategy.

1. Introduction

The world has been moving towards greater gender equality. However, the proportion of women in top management positions remains low compared to that of men in all global regions. According to World Bank Enterprise Surveys, only 18.5% of firms in the world have female top managers. Even in the most women-friendly regions, East Asia and Pacific, females represent 31.9% of the top management in companies. More research on the impact of managerial gender differences is required to help us understand the phenomenon of the uneven proportion of female and male managers.

Insufficient research has been carried out on the impact of female top managers on a firm’s strategic activities, particularly from a global point of view. Previous research has studied managerial impact on firm exploitation and exploration activities from various perspectives, such as leadership style [1,2,3], age-biased leadership [4], entrepreneurship experience [5], the extensiveness of CEO networks [6], and the regulatory focus of CEOs [7]. Within the field of managerial impact, the literature regarding gender differences has emphasized several aspects of firm behavior and performance, such as strategic changes [8], a firm’s risk level [9], innovation facilitation through management task performance [10], and a firm’s financial performance [11,12,13]. However, few of these studies examined the impact of managerial gender differences on the exploitation and exploration activities of firms. To answer this question, we build the argument for how managerial gender differences affect a firm’s growth strategy and test the hypothesis on a global dataset.

The present study uses a wide sample of countries represented in the World Bank Enterprise Surveys database. The empirical portion of the study uses publicly available data extracted from the World Bank Enterprise Surveys (WBES) and from the World Development Indicators (WDI) databases. The relationship between female top managers and organizational performance is tested on a sample of 129 countries and 99,633 enterprises using five different measures (sales growth, employment growth, buying fixed assets, productivity growth, and capacity utilization), which are grouped into two categories to differentiate between exploitation- and exploration-related outcomes. The study aims to contribute to the existing literature by using cross-country comprehensive data to reveal gender differences related to managerial decisions with regard to differentiating types of exploitation and exploration measurements. We found, in general, that the relative advantage of female corporate leadership resides more on the exploitation than the exploration aspect of a firm’s growth strategy compared to male corporate leadership. The study thus offers important implications for broader business practices when considering the alignment between the choice of corporate leadership and firm growth strategy.

2. Theory and Hypothesis

Various research has suggested that the demographic diversity of management teams can predict firm performance [14,15]. The female top manager representation is associated with financial performance [16,17], mergers and acquisitions, research and development investment [18], and a wide range of outcomes [15]. The relationship between gender diversity and firm-level outcomes has been well-approached, but how gender diversity affects firm performance has remained inconclusive. The mixed evidence of gender effect may be attributed to different dimensions of firm-level outcomes, such as value, productivity, and efficiency. In our study, we examine the impact of female leadership on firm performance based on the exploitation and exploration activities of firms. Our paper confirms that female leaders may affect the firm’s growth strategy from the perspective of unique resource portfolios [19]. Female leaders can bring distinct risk-taking attitudes, leadership styles, and knowledge that influences the decision-making process of firms.

2.1. The Exploitation and Exploration Activities of Firms

The exploration-exploitation framework has been applied in a series of research to study innovation and firm performance [20]. Organizations learn through exploring new possibilities and exploiting old certainties [21]. Exploration uses the knowledge that shifts away from a firm’s existing knowledge base [22,23] and includes search, discovery, experimentation, and innovation activities [21,24]. According to March [21], the essence of exploration is experimentation with new alternatives, and the returns are uncertain, distant, and often negative; meanwhile, the essence of exploitation is the refinement and extension of existing competencies, technologies, and paradigms. Exploitation is also associated with utilizing an organization’s existing knowledge base [23] and includes activities such as the refinement, implementation, and execution of a particular action [21,24].

Due to the opposing nature of exploitation and exploration, firms may need to make trade-offs between exploration and exploitation [23]. These trade-off decisions bear uncertainty in terms of variability and time [21]. Exploration generates larger performance variations, while exploitation generates a more stable performance for firms [25]. Compared to exploitation, exploration is vulnerable because of its systematic uncertainty, long time span, long distance from locus action, and adaption [21,25]. The self-reinforcing nature of organizational routines is a potential cause of rigidity within a firm’s current capabilities [26]. Previous research supported the critical role played by the senior executives on organizational learning [27,28,29], and the present study connects individual-level and firm-level research by examining the impact that female top managers have on a firm’s exploitation and exploration activities.

To study the impact of gender differences on organizational learning in terms of exploration and exploitation, we argue that the gender-associated characteristics of top executives may affect organizational strategies. Post et al. [18] provided evidence that female top management team (TMT) appointments contribute to shaping firms’ strategic decisions. Cognitive perspectives may explain the gender difference in TMT strategic decision-making, but recent studies in psychology argue that gender differences in cognitive performance are small or trivial [30,31]. We try to understand how female leaders’ unique characteristics are distributed to affect firms’ growth strategies. In the following sections, we mainly discuss the differences in risk-averse attitude, leadership style, and knowledge-obtaining channels for top executives and the way they affect how firms perform exploration and exploitation strategies. From the perspective of unique resource portfolios [19], female executives bring distinct risk attitudes and a unique cache of knowledge to TMTs that influence firm strategic decisions. In general, exploration strategies require risk-taking and a drive for growth [32], while exploitation requires being innovative, specialized knowledge, and analytical decision-making.

2.2. Risk-Aversion and Organizational Strategy

Managers who hold risk-averse attitudes are more likely to facilitate exploitation because the consequences of the exploitation process are more certain and immediate [21,33]. Hence, risk-averse managers are more concerned with survival or aspiration [34]. Manager bias toward risk-aversion may lead to firms placing too much focus on exploitation [23]. The reliable feedback of exploitation routines reinforces manager confidence in exploitation and further leads the firm into a “success trap” [35,36]. However, when organizational performance decreases after the exploitation process, managers shift towards exploration [21]. The failure trap of an “endless cycle of failure and unrewarding change” is possible if firms are committed to excessive exploration [36]. Pursuing either exploration or exploitation may end with firms being in “competency traps” [23,37].

Different risk preferences and attitudes between female and male top executives affect organizational strategies [19,38] and risky activities [39]. When it comes to managerial gender differences in risk taking, female CEOs tend to take significantly less risks compared to their male counterparts [40]. Khan and Vieito [9] analyzed the risk behavior of female leaders from a panel of U.S. companies in terms of financial performance and investments between 1992 and 2004. They found that male executives made more acquisitions and issued greater debts, while female executives had a wider range of earnings estimates and used stock options earlier than men. Huang and Kisgen [41] investigated the effect of executives on a firm’s financial and investment decisions and found a negative relationship between female representation on boards and firm acquisitions in terms of both number and size. For R&D investments, some studies showed that female directors can improve board effectiveness in terms of risk management [42]. However, the lower risk-taking behavior of female leaders might be a disadvantage in cases where a company has a heavy focus on growth through acquisitions [18]. Moreover, female-owned businesses were found to face discrimination when trying to have a loan approved and are charged with higher interest rates [43,44,45]. The difficulty in obtaining loans might affect the risk-taking behavior of female leaders, as opportunities for both genders are not equal when it comes to external financing. Men, on the other hand, have better access to financing, which might make them feel more confident when expanding into new markets, allowing them to purchase more assets and hire extra employees.

2.3. Leadership Style and Organizational Strategy

The particular leadership style of managers affects the exploitative and explorative innovation of firms [20,46]. Jansen, Vera, and Crossan [2] found that transformational leadership facilitates exploratory innovation, while transactional leadership is positively associated with exploitative innovation. When there is a mismatch between leadership type and learning outputs, transformational leadership suppresses exploitative innovation, and transactional leadership suppresses exploratory innovation [2]. In transition economies, when firms have the chance to break with administrative heritage, during the initial stages, an authoritarian leadership style is positively associated with exploitative organizational learning; however, when leaders take on a participatory leadership style later, explorative organizational learning is encouraged [1].

Transformational leaders can influence employee identification through their organization mission [47]. Reuvers et al. [48] revealed a positive relationship between transformational leadership and innovative behavior, but employees report more innovative behavior when transformational leadership is displayed by male managers. Additionally, Becic and Vojinic [49] indicated that firms innovate more in terms of goods and services when they have a male leader. Females have been found to have a more democratic or participatory style, while men prefer a more autocratic or direct style [50]. Eagly and Johnson [50] found that females involve more people and spend more time on decision-making. Moreover, female leaders are found to bring different viewpoints and skills to the board, which helps to analyze relevant information and improves the general decision-making outcomes [51,52]. Therefore, females involve more people in the decision-making process and consider more alternatives, while males like to make decisions themselves. Compared to males, experiments have found that females are generally more patient [53], long-term oriented [54], and altruistic [55].

2.4. Knowledge-Obtaining Channel and Organizational Strategy

How knowledge flows within firms also affect the exploration and exploitation activities of managers. There are multiple knowledge-obtaining channels, such as horizontal knowledge inflows, geographical clustering, and headquarter–subsidiary links. Mom et al. [56] found that top-down knowledge inflows are positively associated with the exploitation activities of managers, whereas bottom-up and horizontal knowledge inflows are positively associated with the exploration activities of managers. Furthermore, exploration and exploitation are not mutually exclusive at the individual manager level [56]. Geographical clustering builds connections that enable interfirm knowledge exchanges and that enhance the knowledge creation of member firms. A cluster with more firms and that follows an explorative strategy is more likely to increase its knowledge creation capability. Although there are external exchange methods such as exchanging or purchasing, internal exploitation by developing knowledge inside the firm can also be a preferred choice when the firms within the cluster can readily access complementary knowledge both internally and externally [57]. For multinational corporations, managers in head offices and subsidiaries can facilitate subsidiary market learning capabilities in exploration and exploitation activities by transferring and managing resources that are inherent to the parent firm, subsidiary firm, and the parent–subsidiary relationship [58]. After the firm obtains external knowledge, multiple organizational conditions also affect knowledge exploration and exploitation [59]. Besides the context of the information channel, the internal characteristics of firms, such as knowledge distance, a firm’s prior experience with university research centers, technological capabilities, and the tacitness of knowledge can affect the application of external knowledge [59].

Female leadership may provide unique knowledge-related resources and viewpoints to TMTs. Female directors bring strategic resources that improve board governance and performance [60]. Female leaders outperform male leaders due to their market orientation or through being innovative and dynamic. Davis et al. [61] analyzed SMEs in the U.S. service sector and found that female top executives outperform male leaders due to their higher market orientation. However, Munoz and Saran [62] studied Mexican small businesses owners and found that both genders have similar market orientations and showed that women are more important for their firms to be innovative and dynamic. Dezsö and Ross [10] also found that female executives only tend to improve firm performance if the company focuses on innovation as a part of its strategy.

In summary, the risk-averse attitude, leadership style, and knowledge-obtaining channels of female managers affect their decision making in exploration and exploitation strategies. Based on the previous discussion, we proposed that the relative advantage of female top managers compared to their male counterparts varies across the exploitation and exploration measures of firms. More specifically:

H1. 

Female top managers perform worse on exploration measures compared to their male counterparts.

H2. 

Female top managers perform better on exploitation measures compared to their male counterparts.

3. Methods

3.1. Data

A standardized data set for all countries was obtained from the WBES web page. The original data set included 160,456 observations and 148 countries. Surveys without the variable indicating whether an establishment had a female top manager or not were eliminated. The final sample included 99,366 observations across 129 countries from 2008 to 2016. WBES data set were used as they provide firm-level information that is suitable for investigating the relationship between female corporate leadership and firm performance globally. Standardized data ensure consistency in terms of the measures across countries.

The original World Bank data were collected through interviewing top managers in the private sector in different countries. Every year, new surveys are carried out and added to the WBES web page. The number of companies interviewed depends on the size of the country. In large economies, the number of surveyed firms is around 1200–1800; in the medium countries, around 360 firms are surveyed; and in small countries, around 150 firms are surveyed. Stratified random sampling is used to make sure that all groups within the economies are represented.

Country-level data such as GDP per capita were added to the data set and were extracted from the World Development Indicators, and values were set in accordance with the year in which the survey was conducted.

3.2. Dependent Variables

Exploration is measured based on sales growth [63], employment growth, and the buying of fixed assets. Exploitation is measured based on productivity growth and capacity utilization [63]. Sales growth facilitates demand-pull innovation, which further contributes to knowledge creation in exploration strategies [64]. At the industry level, productivity growth measures labor exploitation [65], and capacity utilization measures resource exploitation [66].

Sales growth (%) measures the change in the annual sales of the current fiscal year (time of the survey) from a previous period and is expressed as a percentage. According to the WBES, the interval between comparable years is usually two fiscal years; however, in some cases, a three-year period is used instead. To make the results fit together, an annualized measure is used. All of the sales values were converted to U.S. dollars using the exchange rates from the years when surveys were conducted. Then, the values were deflated to 2009 using the USD deflator.

Employment growth (%) represents the changes in full-time employment between the current fiscal year and the previously measured period and is expressed as a percentage. Similar to real annual sales growth, the annualized measure is used to stabilize the differences between different time intervals between the current and previous year of measurements used, according to the WBES.

Buying fixed assets is a binary variable that shows whether a firm bought fixed assets in the last fiscal year. According to the WBES, fixed assets could be new or used equipment, machinery, land, or buildings.

Productivity growth (%) is the change in the labor productivity between the current fiscal year and the previous period. Similar to the previous measures, the change between the current and previous years varies between two and three years, and annual measurements are used. It is measured by dividing real annual sales by full-time permanent workers. All sales values were converted to USD, just as they were for the first performance measure, and sales were deflated to 2009 levels using the USD deflator, according to WBES.

Capacity utilization (%) shows how well the firm utilizes its production capacity. It is measured by comparing the current output with the maximum possible output using all of the resources in the WBES possible. This measure is available for manufacturing firms only.

3.3. Independent Variables

Female top manager is a binary variable that shows whether the highest-ranked manager or CEO of an establishment is female. According to the World Bank Enterprise Survey, the term “female top manager” indicates that a firm’s top decision maker is a woman, regardless of her title or ownership; she can be a CEO, CFO, part of the top management team, or owner if she works as the manager of the firm. The variable coded 1 indicates that the top manager is female, and this variable is 0 otherwise.

3.4. Control Variables

Female ownership is a binary variable that shows whether women are represented in the firm’s ownership. An outcome of 1 indicates that at least one of the firm owners is female and 0 otherwise. Female-owned businesses are often described as underperforming male-owned firms [67]. Marlow and McAdam [68] criticized calling female-owned firms underperforming but admitted that gendered socio-economic positioning results in women-owned firms experiencing constrained performance.

Top manager’s experience is a continuous variable that is measured in years. It shows how long the top manager of the firm has worked in the sector that the firm operates in. The upper echelons theory suggests that the strategic choices that managers make are influenced by their background, including past experience [69]. Various authors [70] have related managers previous experience to firm performance.

Firm size is a continuous variable that is measured using the average number of permanent full-time workers. Previous literature indicates an existing relationship between firm size and performance [71,72,73].

Firm age is a continuous variable that represents how long a firm has legally existed. It is measured in years and is calculated by deducting the time of legal establishment from the current year (i.e., 2017). Newer companies are often found to lack legitimacy and under-perform compared to older companies [74,75,76].

Access to finance is a binary variable that shows whether a firm has a bank loan or line of credit. A variable outcome 1 indicates that the firm has access to finance, and the value is 0 otherwise. It is an important measure for developing countries because the lack of opportunities for external funding can slow firm growth and innovation and make it hard to survive difficult situations such as changes in the economy, natural disasters, or armed conflicts. Moreover, female-owned businesses are found to face discrimination when attempting to have a loan approved and are charged with higher interest rates [43], which might also apply to female managed firms.

GDP per capita (current USD) is used to test the effect of a country’s economic situation on firm performance. GDP indexes were extracted from the WBDI and set in accordance with the years of data used for the research. Values presented in USD were recalculated into log values.

Business sector fixed-effect dummies show whether a firm belongs to the manufacturing or service sector. As industries are not consistently represented in the surveys from all of the countries, basic sector measurements were used instead. Women are more likely to be top managers in the service sector than in the manufacturing sector [77].

Region fixed-effect dummies were created to test the effect of different geographical locations. The old WBES classification of regions was used, which divides regions into six subgroups: the Africa region (AFR), the East Asia and Pacific region (EAP), the Eastern European and Central Asia region (ECA), the Latin America and the Caribbean region (LAC), the Middle East and North Africa region (MNA), and the South Asia region (SAR). Within a region, development, political stability, geographic location, climate, and other factors might be more similar than between regions. Therefore, there are characteristics in every region that make it unique from the rest. This might create advantages or disadvantages for its companies and female leaders.

3.5. Empirical Model

Based on the analysis of the previously published literature about female executives and firm performance relationships, and according to the upper echelons theory, five models could test the main effect of the key independent variable on all performance measures. These models are specified as follows:

$$\begin{array}{c}\hfill {Performance}_{1,2,3,4,5 }={\beta }_0+{\beta }_1 FTM+{\beta }_2 Top Manager’s Experience+\hfill \\ \hfill {\beta }_3 Female Owner+{\beta }_4 Firm Size+{\beta }_5 Firm Age+\hfill \\ \hfill {\beta }_6 Access to Finance+{\beta }_7 GDP per capita+{\beta }_8 Busines Sector+\hfill \\ \hfill {\beta }_9 Region+\epsilon \hfill \end{array}$$

where:

• Performance₁ = sales growth;
• Performance₂ = employment growth;
• Performance₃ = buying fixed assets;
• Performance₄ = productivity growth;
• Performance₅ = capacity utilization.

As the dependent variable of Model 5 is only measured for manufacturing companies, the business sector dummies were removed.

As the dependent variables included both continuous and binary variables, both the Ordinary Least Squares (OLS) and logit estimation methods were used for the analysis. Based on the characteristics of the dependent variable, OLS was used for sales growth, employment growth, productivity growth, and capacity utilization as the dependent variable, while Logit regression was used for buying fixed assets, which was the dependent variable.

4. Results

4.1. Descriptive Statistics

From the sample of 99,366 firms, 53.95% belonged to the manufacturing sector, and 45.05% belonged to the service sector. Out of 53,606 manufacturing companies, 13.78% had top female leaders, and out of 44,760 service companies, 18.28% had female top managers. This shows that the service sector has slightly more women in high positions than the manufacturing industry, but in general, the gap between genders in top management positions is wide in both sectors.

From all of the respondents, 27.15% belonged to the Eastern European and Central Asia region (ECA), 17.51% belonged to the African Region (AFR), 15.44% belonged to the Latin America and the Caribbean region (LAC), 14.85% belonged to the East Asia and Pacific region (EPA), 14.69% belonged to the South Asia region (SAR), and 8.70% belonged to the Middle East and North Africa region (MENA). The highest percentages of female managers compared to men were found in the EAP (27.12%), ECA (18.50%), LAC (15.74%), and AFR (13.93%) regions. The smallest percentages of female leaders were found in the SAR (7.67%) and in the MENA (6.63%) regions, indicating that all regions have a substantive gender gap in terms of top management positions.

The exploitation and exploration measures such as sales growth, employment growth, productivity growth, and capacity utilization show a wide distribution, ranging from 100 to −100 percent. An even greater distance between minimum and maximum values comes up in the data related to firm size and firm age. The firm size, which is measured by the average number of permanent full-time workers, ranges from 0 to 5075, which reflects the inclusion of small micro-firms as well as very large enterprises. Firm age varies between 0 to 214, which means that the sample includes both newly established startups as well as mature enterprises that are a couple of centuries old. This means that the selected sample of companies includes a wide range of different characteristics and exploitation and exploration results. The means of the performance measures show that in general, the sales growth, employment growth, and capacity utilization are positive throughout the sample, while productivity growth and buying fixed assets are rather negative.

The Pearson’s correlation matrix seen in

Table 1. Mean, Standard Deviation and Pearson Correlation.

		Mean	S.D.	1	2	3	4	5	6	7	8	9	10	11	12
1	Sales Growth	1.302	29.184	1
2	Employment Growth	4.357	17.092	0.22 *	1
3	Buying Fixed Assets	0.429	0.495	0.09 *	0.10 *	1
4	Productivity Growth	−2.421	29.472	0.87 *	−0.26 *	0.03 *	1
5	Capacity Utilization	75.562	21.793	0.06 *	0.10 *	0	0.01	1
6	Female Top Manager	0.332	0.471	0.01	−0.02 *	−0.04 *	0.02 *	0.03 *	1
7	Female Ownership	0.332	0.471	0.02 *	−0.02 *	0.05 *	0.03 *	−0.02 *	0.41 *	1
8	Top Manager’s Experience	17.105	10.794	−0.02 *	−0.09 *	0.07 *	0.02 *	−0.05 *	−0.06 *	0.06 *	1
9	Firm Size	78.907	201.389	0.03 *	0.03 *	0.13 *	0	0.08 *	−0.03 *	0.03 *	0.07 *	1
10	Firm Age	17.962	14.817	−0.04 *	−0.13 *	0.05 *	0.02 *	0.04 *	−0.04 *	0.05 *	0.38 *	0.18 *	1
11	Access to Finance	0.357	0.479	0.05 *	0.03 *	0.24 *	0.02 *	−0.02 *	−0.01 *	0.08 *	0.12 *	0.14 *	0.12 *	1
12	GDP per capita (log)	7.998	1.087	0	−0.02 *	0.08 *	0.01	0.01 *	0.00 *	0.06	0.15 *	0.05 *	0.10 *	0.16 *	1

Note: Significance level * p < 0.05.

reveals significant correlations between most of the variables used for the study. There are significant but weak correlations between the independent and dependent variables. Sales growth, productivity growth, and capacity utilization have a positive relationship with FTM, while employment growth and buying fixed assets have a rather negative effect. The control variables have many significant relationships with the independent and dependent variables, showing the importance of including them in models to predict the effect of the explanatory variable on the dependent variables.

4.2. Regression Analysis

In the following, we present the regression results of the OLS and logit models.

Table 2. Regression Results.

	Exploration			Exploitation
	Sales Growth	Employment Growth	Buying Fixed Assets	Productivity Growth	Capacity Utilization
Independent Variable:
Female Top Manager	−1.092 **	−0.888 ***	−0.304 ***	0.021	1.740 ***
	(0.364)	(0.188)	(0.023)	(0.372)	(0.352)
Control Variables:
Female Ownership	0.855 **	−0.347 *	0.196 ***	0.859 **	−1.721 ***
	(0.275)	(0.144)	(0.018)	(0.282)	(0.253)
Top Manager’s Experience	−0.015	−0.069 ***	0.003 ***	0.049 ***	−0.019
	(0.011)	(0.006)	(0.001)	(0.012)	(0.011)
Firm Size	0.003 ***	0.005 ***	0.001 ***	−0.001 *	0.007 ***
	(0.001)	(0.000)	(0.000)	(0.001)	(0.000)
Firm Age	−0.105 ***	−0.150 ***	−0.002 ***	0.022 **	−0.072 ***
	(0.008)	(0.005)	(0.001)	(0.008)	(0.008)
Access to finance	1.867 ***	1.840 ***	0.901 ***	0.137	−1.152 ***
	(0.237)	(0.128)	(0.016)	(0.244)	(0.225)
GDP per capita (log)	0.702 ***	0.078	0.016 *	0.475 ***	1.142 ***
	(0.138)	(0.068)	(0.008)	(0.140)	(0.132)
Region Fixed Effect	Yes	Yes	Yes	Yes	Yes
Business Sector Fixed Effect	Yes	Yes	Yes	Yes	—
Constant	−1.229	7.979 ***	−1.441 ***	−7.144 ***	72.263 ***
	(1.053)	(0.540)	(0.063)	(1.072)	(1.005)
N	60941	76170	82228	59493	40869
R-sq	0.024	0.029		0.015	0.052
Adj. R-sq	0.024	0.029		0.014	0.051
Pseudo R-sq			0.072

Notes: Standard errors in parentheses; * p < 0.05, ** p < 0.01, *** p < 0.001, two-tailed test.

presents the regression results for all five models. The Breusch–Pagan/Cook–Weisberg test detected heteroskedasticity in all of OLS models, and robust standard errors were used to assure homoskedasticity. The variance inflation factor (VIF) detected no multicollinearity problem, as the VIF factors were all below 10.

Table 2 reveals the relationships between the key independent variable and the five dependent variables, testing the hypotheses. Firstly, a significant negative relationship was noticed between the top female manager variable and the three dependent variables measuring firm exploration activities (sales growth, employment growth, and buying fixed assets), supporting Hypothesis 1. Secondly, the effect of the top female manager variable on productivity growth is positive but statistically insignificant. Additionally, a significant positive relationship was noticed between the female top manager variable and firm capacity utilization. Combining the latter two findings supports Hypothesis 2. In summary, the results show that the overall relative advantage of female top managers compared to their male counterparts vary across exploitation and exploration measures.

The significant negative results found in the models using annual sales growth, annual employment growth, and buying fixed assets as the dependent variables support H1, which states that “Female top managers perform worse on exploration measures compared to their male counterparts”. Expansion measures were expected to require more risk-taking and drive for growth, which is associated with more masculine personality traits. The current results are consistent with Huang and Kisgen [41], who found a negative association between female representation on boards and firm acquisitions both in terms of number and size. Although their results indicate exploration through acquisitions, the current findings suggest that women also negatively affect other exploration measures such as sales growth, employment growth, and buying fixed assets.

The significant positive result in the model using capacity utilization (%) as the dependent variable supports H2, which states that “Female top managers perform better on exploitation measures compared to their male counterparts”. Exploitation strategies are expected to require more innovative, careful, and analytical decision-making. Previous studies found female leaders to be associated with more democratic and participatory leadership styles than their male counterparts [50], indicating that they involve more people in the decision-making process. In addition, the different viewpoints and skills of female leaders were found to improve decision-making [51,52]. Dezsö and Ross [10] found that female executives only tend to improve firm performance if the company focuses on innovation as a part of its strategy, and current results confirm that female top managers do have a positive effect on exploitation measures such as capacity utilization that require innovative, careful, and analytical decision-making.

It is worth noting that we found positive significant relationships between the control variables of female ownership and the dependent variables of real annual sales growth, buying fixed assets, and annual productivity growth. The dependent variables of annual employment growth and capacity utilization (%), however, are significantly negatively related to female ownership. Different from having females in managerial roles, having females as owners generates mixed effects on firm exploration and exploitation. We infer that the difference is caused by both gender differences and the differences in functional roles. The owners are less involved in operational decision making, and female owners are less likely to point out operational growth issues such as employee recruitment and capacity utilization. In terms of strategic decision-making issues, in which owners are more involved, female owners contribute more to resource connection and endorsement for the firms to obtain financial resources for buying fixed assets and are less risk-averse and have a drive for growth.

5. Discussions and Conclusions

5.1. Theoretical Contributions

The present study generates a threefold contribution to the existing literature by using cross-country comprehensive data sets. A sample of 129 countries of which the greater part consists of developing nations, provides new evidence about the relationship between female top managers and firm exploitation and exploration strategies. While previous research was heavily reliant on developed countries such as the U.S., the current analysis provides a broader understanding of the topic. The results indicate that female top managers exert differential influence over firm performance according to the firm’s strategic focus of exploration and exploitation.

Although the vast majority of previous findings indicated a positive relationship between female top managers and firm performance, some other papers found the relationship to be negative or insignificant. Therefore, it was hypothesized that the relative advantage of female top managers compared to their male counterparts varies across performance measures. Mixed results that are dependent on the choice of performance measures are consistent with Carter, Simkins, and Simpson [78], who also found that female leaders have a positive effect on some exploitation and exploration measures and have a negative effect on others. Therefore, it is important to consider different types of performance measures when evaluating whether female top managers perform better than their male counterparts.

As previous studies from literature have criticized the use of financial performance alone to identify advantage of female corporate leadership, the current study tested the effect of female top managers on five different performance measures provided by the WBES. These performance measures were grouped into two different categories. It was hypothesized that female top managers would perform better on exploitation measures (H2) and worse on exploration measures (H1) compared to their male counterparts. Both H1 and H2 were supported by empirical findings.

In conclusion, the relative advantage of female top managers compared to their male counterparts was found to be different across exploitation and exploration measures. More specifically, female top managers were found to perform better on exploitation measures and worse on exploration measures compared to their male counterparts. This research fills the research gap on the impact of female top managers on the exploitative and explorative growth strategies of firms from a global perspective. The study provided exploratory results for an important aspect to consider in future research on the relationship between gender and the exploitation and exploration strategies of firms.

5.2. Implications

This research generates both research and practical implications. In the present research, the findings enrich our knowledge of managerial gender difference and firm’s growth strategy from a global perspective. In practice, the research findings offer guidance for electing top managers. On the one hand, female top managers were found to have a significant positive effect on the exploitation measures of firms, such as capacity utilization. Therefore, firms who wish to improve their overall capacity utilization could benefit from female top executives. On the other hand, female top executives were found to have a significant negative effect on exploration measures such as sales growth, employment growth, and buying fixed assets. Therefore, if a firm’s strategy is physical expansion, it is more likely that the firm selects men as its top managers. This might be related to risk aversion that female managers have [41] or gender-based discrimination regarding access to finance [43,44], which is an important element for firm exploration. While female leaders have more difficulties obtaining loans and credit, they might have developed skills to use existing resources more efficiently but hold back from growth that requires more financial resources.

5.3. Limitations and Future Research

Although the current research makes valuable contributions to the existing literature, its findings might suffer from some limitations. The WBES dataset includes mainly developing countries. Some of these countries might suffer from higher levels of gender inequality or lower performance measures because of the country’s economic situation, natural disasters, or political instability. In addition, the data set is cross-sectional in nature; therefore, there is a potential endogeneity issue that is difficult to address in the present context. Despite these limitations, the comprehensive data set provides an overview of the situation of female executives around the world and their effect on firm performance. Overall, the sample supports the premise that females are better in some performance measures and that men are better in others.

Future research should conduct more cross-country analysis to further investigate the relationship between female executives and firm performance, as it provides a broader view of the topic with mixed results. Focusing only on big and successful firms in developed countries might provide us with somewhat biased results. Therefore, future research could test the effect of other country-specific moderators to identify important factors that could strengthen or weaken the positive or negative effects of female executives on firm performance outcomes. For instance, future research could combine culture with other country-level characteristics such as religion, laws, and politics that might have a direct or indirect effect on gender equality in different countries towards career opportunities as well as the perceptions of females and their roles in society.