Introduction

Over the past decade, there has been a growing need to improve corporate board effectiveness, as evidenced by the prevalence of high-profile financial scandals and a significant number of corporate failures. To address this concern, there has been a recognition of the significance of board diversity (BD) as a mechanism for enhancing boardroom effectiveness (Ain et al., 2022; Reguera-Alvarado et al., 2017). As a result, BD has become a prominent research area that has attracted significant attention from scholars. Corporate governance consistently deals with diversity, and as such, BD has emerged as an important domain within the corporate governance literature. BD in terms of demographic attributes has been shown to enhance financial decision-making, creativity, monitoring, and competitiveness within organizations (Ali et al., 2021; Bin Khidmat et al., 2020; Hsu et al., 2019; Ikbal Tawfik et al., 2022; Simionescu et al., 2021, and others). The top management team plays a crucial role in connecting an organization with its environment within a hierarchical structure. Their decision-making effectiveness and the performance of the organization are closely tied to the board’s operations (Adams and Ferreira, 2009). The relationship between BD and firms’ financial performance (FP) is grounded in three important management perspectives: Upper Echelons Theory (UET), Managerial Network Theory (MNT), and Resource-Based Theory (RBT). UET highlights the crucial role played by the top management team in the success of an organization (Finkelstein, 1990; Hambrick and Mason, 1984). RBT suggests that the effective utilization of human resources is key to achieving higher performance, gaining a competitive advantage, and achieving success (Pohjola, 2002). MNT argues that managers’ social and political ties can reduce transaction costs through expert knowledge and strategic resource exchange.

The board of directors is central to an organization’s internal governance mechanism and is responsible for upholding shareholder interests (Roberson and Hyeon Jeong, 2007). Therefore, a well-composed board can enhance the accuracy of a company’s decisions, reduce risk, and improve its FP. Despite the efforts to validate the relationship between BD and FP, researchers have arrived at varying and inconclusive conclusions (Khan et al., 2017; Solakoglu and Demir, 2016). For example, in Asia and Africa, the inclusion of directors with diverse educational and professional backgrounds has been found to generate unique perspectives that can lead to improved FP (Morales and Marquina, 2009; Tanikawa and Jung, 2016). Therefore, it is crucial to encourage diversity in board members to enhance performance and the decision-making process. The literature on BD-FP lacks a clear consensus and has primarily focused on developed countries, with limited attention given to emerging countries.

On the other hand, the strategic change (SC) plays a critical role in the study’s analysis as it is influenced by BD, with the level of environmental changes outside the organization being a crucial determinant of SC. Given that SC may require changes in the corporate boardroom structure to achieve strategic objectives, it is crucial for board members and other stakeholders to adopt a long-term perspective and make pragmatic and applicable adjustments to ensure beneficial outcomes (Hsu et al., 2019; Triana et al., 2013). The relationship between BD and FP is intricately linked to the degree of SC, underscoring the need to consider SC when examining the BD-FP nexus. Prior research has also emphasized the role of SC in this relationship (Hsu et al., 2019; Triana et al., 2013).

Building on the existing knowledge gap in related literature, this article investigates the impact of BD on FP and how this impact is influenced by SC, using a six-dimensional index to measure both. The study uses a dataset of 240 non-financial firms listed on the Moscow, Shanghai, Bombay, and Pakistan stock exchanges over a 13-year period (2008–2020) and employs both fixed-effect models and the two-step system GMM approach to analyze the data. The findings indicate that BD has a positive impact on FP, while SC weakens this relationship. The robustness of these results is confirmed through various estimation methods, highlighting valuable policy implications for managers and practitioners.

This study makes several contributions to the existing literature. Firstly, it provides recent empirical evidence on the relationship between BD and FP by considering four emerging markets, namely China, India, Pakistan, and Russia. These markets provide a unique context to test this relationship, given their distinctive institutional settings and varying levels of economic development. Secondly, the study focuses on the role of SC, an important yet understudied factor that may moderate the BD-FP relationship. Thirdly, the study considers multiple attributes of BD beyond gender, which has been the primary focus of prior research on this topic. Fourthly, the study employs a rigorous econometric approach that allows for the simultaneous control of individual effects, time periods, and potential endogeneity issues. Overall, this study enriches the literature by shedding light on how BD and SC jointly influence FP in non-financial firms across multiple emerging markets. Specifically, the study suggests that BD positively impacts FP, but this relationship is weakened by the presence of SC. The findings have important implications for corporate governance, as they highlight the importance of promoting board diversity and effective strategic planning to enhance organizational performance. Finally, this study adds to the growing body of research on the outcomes of board diversity and the boundary conditions that shape its relationship with FP, thereby advancing our understanding of this important topic.

This article is structured as follows: The next section presents the literature review, theoretical framework, and hypotheses. Section “Data and methodology” describes the research methodology, data description, and measurement of variables. The empirical results and discussion are presented in section “Empirical results and discussion”. The conclusion summarizes the study’s findings and suggests potential areas for future research.

Literature review, theoretical foundation, and hypothesis development

Theoretical literature

Previous empirical studies have investigated the relationship between BD and FP, but have produced mixed results, with some studies finding a positive association, others suggesting a negative relationship, and others failing to establish a significant link between the two (Adams and Ferreira, 2009; Ali et al., 2021; Bernile et al., 2018; Erhardt et al., 2003; Niesche and Haase, 2012; Talke et al., 2010; Zahra, 2007). The association between BD and FP is based on three important management perspectives: UET, RBT, and MNT. According to UET, the top management team plays a crucial role in organizational success (Finkelstein, 1990; Hambrick and Mason, 1984). RBT argues that effective utilization of human resources can lead to higher performance, competitive advantage, and success (Pohjola, 2002). MNT proposes that managers’ political and social ties reduce transaction costs through expert knowledge and the exchange of strategic resources. The board of directors is at the core of an organization’s internal governance mechanism and is responsible for upholding shareholders’ interests (Roberson and Hyeon Jeong, 2007). A board’s suitable composition can improve the accuracy of a company’s decisions and FP while reducing risk. Studies on BD and FP have generated ambiguous results, as validated by business requirement documents (Khan et al., 2017; Solakoglu and Demir, 2016). For instance, in Asia and Africa, recruiting diverse directors with unique perspectives can improve FP. Therefore, it is essential to promote educational and professional diversity among board members, which can enhance performance and the decision-making process (Morales and Marquina, 2009; Tanikawa and Jung, 2016). Given the theoretical foundation, Fig. 1 illustrates the theoretical framework to examine impact of BD on FP within the moderating role of SC.

Fig. 1
figure 1

Theoretical framework.

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Emprical literature

Gender diversity and nationality diversity are most researchable attributes of board (Bin Khidmat et al., 2020; Zahra & Stanton, 1988) studied the association between ratio of minority representation and FP, i.e., several accounting measures and documented insignificant linkage between the variables. Rose (2007) by taking a sample of listed companies in case of Danish firms carried cross-sectional analysis also reported insignificant association between proportion of foreigners and firms performance. Carter, D’Souza, Simkins, and Simpson (2010) by taking a sample of fortune 1000 firms documented significant association between board diversity and performance of selected firms. Rampling (2011) conducted study in context of UK, US and Australia and observed direct association between EBIT measures and gender and ethnicity and low effect on ROA and ROE. Rose et al. (2013) studied the linkage between foreign independent directors (FID) and firms performance in case of US corporation and reported mixed results. Rose et al. (2013) probed the connectedness between female board representation and FP and citizenship in case of Nordic countries and captured positive and significant linkage between the variables.

Cook and Glass (2014) using panel data documented positive relationship between product development, ethnic minority and corporate governance. Terjesen et al. (2016) taking sample of 3876 firms form 47 countries reported positive linkage between return on assets, Tobin’s Q and greater % of independent directors. Ciavarella (2017) in case of Europe studied the connotation between BR and FP and reported mixed association between board diversity and FP. Green and Homroy (2018) explored the linkage between BR and firms performance in perspective of EU firms and reported positive and meaningful association between female representation on board and FP. Some of the studies tried to model the impact of board diversity using composite index of board attributes for example, Hsu et al. (2019) carried out a research by taking strategic change into consideration found that SC has negative correlation between boardroom diversity and on firms’ FP while the linkage between diversity in board and strategic consideration is positive in case of smaller firms. Bin Khidmat et al. (2020) by employing Both FE model and dynamic panel GMM conducted study to check the impact of boardroom on firms performance listed on Shanghai stock exchange. To measure Board diversity they used Blau index and confirmed positive and significant association between the attributes of boardroom diversity and FP.

Ali et al. (2021) using panel data two steps system GMM, empirically probed the linkage between the board diversity attributes, i.e., expertize, tenure, nationality, gender, education age) on the foreign institutional investors in case of China and reported mixed association. Ikbal Tawfik et al. (2022) conducted a study recently to capture the impact of boar diversity on financial reporting and reported negative linkage between the variables. SC are affected by the BD. As far as the degree of environment changes outside the organization boundary is considered to be an important determinant. The greater the organization outside environment changes, the larger the SC. The corporate boardroom structure conceivably needs to be changed to attain companies strategic objectives (Tanikawa and Jung, 2016). The board members are responsible for strategic formulations and their implementation. Strategic change takes time to observe the impact of change. For example, the magnitude and effect of SC implemented by a company are generally linked to the product design as well as the advertising of a new product in the market; thus, it may take some time to reflect the increase in a company’s sales. Accordingly, BODs and others involved in the incorporation of change must make pragmatic and applicable adjustments by considering a long-term (tactical) perspective so as to ascertain the beneficial aspects of SC (Hsu et al., 2019). So, linkage between BD and FP is related to the magnitude of the SC. Another important aspect of this study is strategic change (SC) as suggested in related literature (Hsu et al., 2019; Triana et al., 2013).

One of the proponents, Williams and O’Reilly Iii (1998), stated that social identity theory (SIT) embraces the ideology that people like to socialize with like-minded people who embrace the same or similar ideas; consequently, this may tend to create conflict, which could have a crippling influence on teamwork within the organization. Jianakoplos and Bernasek (1998) reported that male and female counterparts tend to have different risk preferences and that the attitude of females is more pragmatic when considering or evaluating risks when compared to males. They also added that females may tend to follow conservative strategies, which can certainly influence a company’s overall performance. Another important BD attribute is age. For example, a decade ago, Li (2010) reported that strategy formulation and organization performance are affected by the age of the board members. Zhang and Qu (2015) stated that diversity in age has a significant influence on company performance. Furthermore, they argued that older managers tend to be less open and adoptive to change or accept new ideas than young managers. Herrmann and Datta (2005) posited that increased age translates into a reduced amount of energy and lowers various strength coping mechanisms, which can lead to a decline in organizational performance.

Conversely, older or senior individuals have more experience and maintain a solid social circle, providing considerable social and networking resources. Additionally, tenure is another notable characteristic that impacts board members. Finkelstein (1990) explained that a board member’s tenure is a crucial determining factor directly associated with an organization’s success. Bergh (2001) stipulated that older board members can craft applicable strategies at the right moment by using their experience, which can lead to superior performance and competitiveness. Accordingly, Boeker (1992) documented a positive connection in this regard. However, some other literature documented a negative association; for example, Gottesman and Morey (2006) argued that managers with strong educational backgrounds are valued as important intellectual capital. Hambrick and Mason (1984) asserted that a manager must have a strong educational upbringing to take care of complex information processing in the business world. Consequently, highly educated board members can apply their knowledge to corporate decisions and, thereby, restore performance.

H1: There is a positive relationship between BD and a company’s financial performance.

Moderating role of strategic change

According to Mintzberg (1978) and Zahra (2007) SC involves the reallocation or restructuring of the primary (key) resources of an organization after a specified period. Hsu et al. (2019) stipulated that both BD and performance association are impacted by organizational strategies because, by law, the BOD has the authority, power, and command over the control function of the enterprise. Hsu et al. (2019) an organization confronts ups and downs in the marketplace, the BODs make decisions concerning the reallocation of the company’s primary strategic resources in an effort to enhance its competitive advantages and achieve its set objectives. The process of formulating series of strategies for the reallocation of internal and external resources is called strategic change (SC). Triana et al. (2013) suggested that SC are influenced by the demography of the BODs. They also highlighted that the degree of outside organizational and environmental changes contributes to an important determinant concerning its strategy selections. It would seem that after adapting SC for a period of time, its effect on company performance is generally manifested. Hsu et al. (2019) argued that BD and the way in which it interacts with strategic change (SC), in the long run, impacts FP. Geletkanycz and Hambrick (1997) argued that the board members are responsible for strategic formulations and their implementation. When confronting rapid changes within the business environment, diversity among the board is validated through enhanced knowledge, understanding and exposure, which also supports businesses in making SC that positively influence performance. According to resource dependence theory, BD could help companies identify difficult markets, especially when board members exchange views. This process helps to create more ways in which to resolve problems by timely evaluating information and acquiring critical resources, enriching the legality associated with the target market as well as making pragmatic decisions, thereby providing better competitive leads and improved performance. Hsu et al. (2019) carried out a research by taking strategic change into consideration found that SC has negative correlation between boardroom diversity and on firms’ FP while the linkage between diversity in board and strategic consideration is positive in case of smaller firms.

Overall, the literature on the relationship between BD and FP remains inconclusive. While some studies have found a positive association, others have found negative or no meaningful association (Ali et al., 2021; Bin Khidmat et al., 2020). Adams and Ferreira (2009) suggest that the impact of BD on FP is likely to vary across firms. Moreover, prior studies have mainly focused on developed countries, with limited attention given to emerging economies. Therefore, this study addresses the gap in the literature by investigating the impact of BD on FP in emerging economies. In addition, given the emphasis being placed on corporate BD and FP has become one of the important research areas investigated in the economic literature. To the best of our knowledge, mainly with reference to emerging countries, a limited studies have investigated the impact of BD on FP and how this impact is influenced by the SC. The motivation of this study is to fill the the illustrated gap in the literature regarding the impact of boardroom attributes (Gender, Age, Expertize, National, Education and Tenure Diversity) on FP and how this impact is influenced by the SC.

Based on the above scholarly grounded constituent of literature, the study’s second hypothesis is as follows:

H2: Strategic changes moderate the relationship between boardroom diversity and FP.

Data and methodology

Sample and data description

A quantitative research design was employed in this study to investigate the impact of BD on firm FP in emerging economies. The sample was composed of 240 non-financial firms, with 60 selected from each of the four emerging markets based on market capitalization. The markets selected were the Moscow Exchange in Russia, the Shanghai Stock Exchange in China, the Bombay Stock Exchange in India, and the Pakistan Stock Exchange. The study utilized a balanced panel data approach and covered a 13-year period from 2008 to 2020. To collect the data, the top 100 firms from each stock exchange were selected based on their market capitalization, following the method in Shehata et al. (2017). A total of 240 firms were selected based on data availability. Strategic change data were collected from the official websites of the selected companies and from China Stock Market & Accounting Research (CSMAR). Accounting data were sourced from Bloomberg, while data on director characteristics were obtained from the official websites of the companies and data streams. To reduce the potential impact of outliers, the study employed winsorization of the variables under consideration at the 5 and 95% level. Additionally, to address the reverse causality issue, the study utilized time-lag dependent variables as well as independent variables, as recommended by Hsu et al. (2019). The use of time-lag dependent variables helps to deal with endogeneity problems that may arise between the variables being studied.

Measurement of variables

This section provides the detail of variables and their measurement utilized in the economic modeling to test our study hypothesis. In particular, consideration is devoted to the theoretical support.

Financial performance

This study uses accounting-based measures of FP, specifically return on assets (ROA) and return on equity (ROE), as these measures have been previously used in studies Ararat et al. (2015) and Low et al. (2015). ROA is calculated as net income divided by total assets, while ROE is calculated as net income divided by equity. ROA is used as the main analysis, while ROE is used in the robustness check analysis, which is consistent with studies (Ali et al., 2021; Bin Khidmat et al., 2020). Variable measurements are summarized in Table 1 in the next section.

Table 1 Variables measurement.
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Boardroom diversity

We introduced lag value of BD attributes (1) Gender Diversity-D_Gen (2) Age Diversity-D_Age (3) Expertize diversity-D-Exp (4) National Diversity-D-Nat (5) Education Diversity-D_Edu (6) Tenure Diversity-D_Ten as independent variables as used by Ali et al. (2021). Hafsi and Turgut (2013) and Hoang et al. (2017) utilized comprehensive board diversity indexes construct a comprehensive point of reference or benchmark for the four parameters to clarify the problem related to dissimilar estimation standards or criteria. This involved dividing the estimates into four quartiles per component: “low 25%”, “median 25%”, “median 75%”, and the “highest 25%”. These four quartile estimates involved changing each variable to a group variable and adding them, thus generating a composite benchmark. According to the extant literature, the defined parameters were based on BD as the independent variable. For BD, a composite index was used by considering the six dimensions developed by Hsu et al. (2019).

We measured BD attributes as measured by (Ali et al., 2021; Bin Khidmat et al., 2020) using (Hoang et al., 2017) Blau index as stated in the Eq. 1.

$$D = 1 - \mathop {\sum}\nolimits_{i - 1}^k {pi^2}$$
(1)

In Eq. 1, p represents the % of individuals in a category and k represents number of categories whereas i signifies the number of categories. In addition, the value of index 0 represents the population is homogenous while index value 1 indicates perfect heterogeneity. For instance, if there are 4 categories (with equal proportion) the BD index will be 0.75[1–0.252]1-(0.252 + 0.252 + 0.252 + 0.252) (Ali et al., 2021).

BD-independent variable, a composite index has been used by considering 6 dimensions (Hsu et al., 2019). (1) Gender Diversity-D-Gen is an index calculated by using two categories (i.e., male and female as employed by (Ararat et al., 2015).(2) Age Diversity-D_Age index is calculated by considering five categories (i.e., up to 70, 60–70, 50–59, 41–48 and under 40 as used by (Ararat et al., 2015). (3) Expertize diversity-D-Exp has been constructed by using two categories financial those having expertize and those who don’t as used by Bernile et al. (2018). (4) National Diversity-D_Nat is calculated by considering two categories (i.e., Pakistani national and non-Pakistani in line with Ararat et al. (2015). (5) Education Diversity-D_Edu index is computed by considering Five educational levels (i.e., for PhD level 5 for masters 4, bachelors 3, associate degree 2 and for technical education level as used by Ararat et al. (2015). (6) Tenure Diversity-D-Ten index is computed by considering 06 tenures (i.e., 3 years or less, 4 to six years, 7 to 9, 10 to 2, 3 to fifteen and more than 15 years) as used by Harjoto et al. (2015). Table 1 in the next section presents a detailed summary of the variable measurements.

Strategic change (moderator variable)

SC is a composite variable derived from six resource allocation profiles (AIN + R&DI + PMI + NPU + IL + FL divided by 6), as used by Harjoto et al. (2015). Its impact may take time to observe due to factors such as product design and advertising, and the effects may impose a 2-year lag time Triana et al. (2013). In this study, the time-lag dependent variable helps resolve reverse causality as proposed by Bin Khidmat et al. (2020), and recent data from 2008–2020 was used to test the effect of BD on firm performance and to demonstrate whether SC moderates the relationship between BD and firm performance. Advertising intensity (AIN) is calculated as advertising expenditures divided by net operating revenue (Hsu et al., 2019). SC was further quantified by considering the six resource allocation profile variables (Finkelstein, 1990), and the return on assets was calculated as net income divided by total assets (Hsu et al., 2019). Table 1 in the next section presents a detailed summary of the variable measurements.

Control variables

We included five control variables in our study. Firm size was measured by taking logarithmic samples of the company’s total revenues at year-end. Organization growth (OG) was defined as the sales growth rate at year-end. Financial leverage was measured using the debt ratio. Panel size was the number of executives in the panel of each sample firm at year-end. State ownership was measured as a dummy parameter (1 for state-owned corporations and 0 otherwise), reflecting the administrative resources available to the firm (Simionescu et al., 2021). See Table 1 for details.

Statistical tools for analysis

We used diagnostic tests as a prerequisite before conducting the panel data analysis to assess the compliance of the dataset’s validity. To diagnose the panel dataset, we used the descriptive pairwise correlation as well as the panel unit root test results at levels and first difference, multicollinearity—variance inflation factor (VIF), White’s test, Cameron & Trivedi’s decomposition of IM-test, and the Wald test as well as the Wooldridge test for heteroscedasticity and autocorrelation.

Econometrics models specification and its constituents

$$Y_{i,t} = \alpha _0 + \beta _1 + \beta _2 \ldots \ldots \ldots \ldots \ldots \ldots .. + \varepsilon _{i,t}$$
(2)

Y = return on assets (a measure of FP)

X1 = BD

X2 = Strategic change-moderator parameter

X3 = Firm size

X4 = Growth

X5 = Financial leverage

X6 = Board size

X7 = State owned

Ssc = Dummy variable

ε = the random error term

The regression direct effect model Eq. 3 was applied in order to capture the impact of corporate BD and its effect on a firm’s FP. Consequently, the moderating effect model Eq. 4 was used to measure the moderation effect.

$$\begin{array}{l}ROA_{i,t} = \alpha _0 + \beta _1BRD_{i,\left( {t - 2} \right)} + \beta _2FL_{i,\left( {t - 1} \right)} + \beta _3FS_{i,\left( {t - 1} \right)} \\\qquad\qquad\, +\, \beta _4OG_{i,\left( {t - 1} \right)} + \beta _5BS_{i,\left( {t - 1} \right)}\,\beta _6SO_{i,\left( {t - 1} \right)} \\\qquad\qquad\, +\,{\sum} {\beta _7State\,own_{i,t} + \varepsilon _{i,t}} \end{array}$$
(3)
$$\begin{array}{l}ROA_{i,t} = \alpha _0 + \beta _1BRD_{i,\left( {t - 2} \right)} + \beta _2SC_{i,\left( {t - 1} \right)} + \beta _3BRD_{i,\left( {t - 2} \right)}\\\qquad\qquad\, \times SC_{i,\left( {t - 1} \right)} + \beta _4FL_{i,\left( {t - 1} \right)} + \beta _5FS_{i,\left( {t - 1} \right)} + \beta _6OG_{i,\left( {t - 1} \right)} \\\qquad\qquad\, +\,\beta _7BS_{i,\left( {t - 1} \right)} + \beta _8SO_{i,\left( {t - 1} \right)} + {\sum} {\beta _9State\,own_{i,t} + \varepsilon _{i,t}} \end{array}$$
(4)

The dynamic panel data fixed-effect model (DPFE) is one of the popular techniques for conducting a quantitative analysis. This method permits the simultaneous ability to address individual effects, various periods and, in turn, the endogeneity of the model or independent regressors in the model. According to Sarafidis et al. (2009), the inclusion of LDV (lag dependent variable in an econometrics model (the same as a regressor) provides the DA (dynamic adjustment in an econometric model. Moreover, this endogeneity problem proposes that the LS-least squares-based estimators may be unpredictable and inconsistent. In this scenario, the use of contributory or influential components can be viewed as instrumental variable econometrics techniques or the generalized method of moments (GMM) that produces consistent parameter estimations for the quantitative data with finite time periods as well as large cross-section dimensions of constructs. In this study, we used two approaches: linear DPD estimation (longitudinal dynamic panel data) methods of movements and the Arellano–Bond estimator of generalized methods of movement since we are dealing with a dynamic panel model. The Eqs. 6 and 7 were designed to empirically encapsulate the FEM and DPAE outcomes.

$$\begin{array}{l}ROA_{i,t} = \alpha _0 + \beta _1BRD_{i,\left( {t - 2} \right)} + \beta _2SC_{i,\left( {t - 1} \right)} + \beta _3BRD_{i,\left( {t - 2} \right)} \ast SC_{i,\left( {t - 1} \right)} \\\qquad\qquad\, +\,\beta _4FL_{i,\left( {t - 1} \right)} + \beta _5FS_{i,\left( {t - 1} \right)} + \beta _6OG_{i,\left( {t - 1} \right)} + \beta _7BS_{i,\left( {t - 1} \right)} \\\qquad\qquad\, +\,\beta _8SO_{i,\left( {t - 1} \right)} + {\sum} {\beta _9State\,own_{i,t} + \eta _i + \varepsilon _{i,t}} \end{array}$$
(5)

Dynamic panel model

$$\begin{array}{l}Y_{i,t} = \alpha _0 + \beta _{i,L}Y_{i,\left( {t - 2} \right)} + \beta _2X_{i,\left( {t - 2} \right)}\left( {I.V} \right) + \beta _3\left( {Moderator\,Variable} \right.\\\qquad\;\; +\, \beta _4BRD_{i,\left( {t - 2} \right)} \ast SC_{i,\left( {t - 1} \right)}\left( {Interaction\,term} \right)\\\qquad\;\; +\, \beta _5{{{\mathrm{Control}}}}_{i,\left( {t - 1} \right)} + {\sum} {\beta _{10}State\,own_{i,t} + \eta _i + \varepsilon _{i,t}} \end{array}$$
(6)

Based on the above-generalized equation, the following precise equation was utilized (country-wise).

$$\begin{array}{l}ROA = \alpha _0 + \beta _{1,L}ROA_{i,\left( {t - 2} \right)} + \beta _2BRD_{i,\left( {t - 2 + } \right)}\beta _3SC_{i,\left( {t - 1} \right)}\\\qquad\quad\;\; +\,\beta _4BRD_{i,\left( {t - 2} \right)} \ast SC_{i,\left( {t - 1} \right)} + \beta _5FL_{i,\left( {t - 1} \right)} + \beta _6FS_{i,\left( {t - 1} \right)}\\\qquad\quad\;\; +\,\beta _7OG_{i,\left( {t - 1} \right)} + \beta _8BS_{i,\left( {t - 1} \right)} + \beta _9SO_{i,\left( {t - 1} \right)}\\\qquad\quad\;\; +\,{\sum} {\beta _{10}State\,own_{i,t} + \eta _i + \varepsilon _{i,t}} \end{array}$$
(7)

where ROA represents the firm’s FP, LROA denotes the lagged value of a (DV) dependent variable in the model, while the BD index signifies the BD index, SC denotes the strategic change index and (BDit∗SCit) the dummy variable. Interaction term i means 1,2,3,4, ……., n (number of firms), and t = 2008 to 2020. Furthermore, α denotes the intercept, while the β values represent the regression coefficients of the IVs and control variables, while ƞi as well as εit constitute the unobserved firm-specific effects and εit error terms, respectively.

Empirical results and discussion

The section provides empirical results pertaining to the hypothesis. Besides it offers detail discussion as well as different robust check to test the validity of study model.

The baseline results

Descriptive statistics

Table 2 reports the descriptive statistics, including the mean, standard deviation, minimum, median, and maximum values, as well as observation to evaluate the behavior of the data before moving on to other statistical calculations. On average, the ROA for China’s firms was 0.035%, while the minimum ROA was −0.107, with a maximum of 18.1%. The average BD score was 7.01, with the highest being 13.1 and the minimum 0.01. Similarly, the SC index was, on average. 25.931. This BD indicates that the average board is diverse in financial expertize, gender, and age overall. Moreover, the strategic change and control variables’, descriptive statistics are also presented. Tables 2, 3, 4, and 5 presents the descriptive statistics to evaluate the data by reviewing the mean, standard deviation, and minimum, median and maximum values and observation before pursuing other statistical calculations pertaining to cases of the Moscow Exchange, Russia; Shanghai Stock Exchange (SSC), China; Bombay Stock Exchange (BSE), India; as well as the Pakistan Stock Exchange (PSX) for non-financial firms.

Table 2 Descriptive statistics—China’s firms.
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Table 3 Descriptive statistics—Russia’s firms.
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Table 4 Descriptive statistics-India’s firms.
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Table 5 Descriptive statistics-Pakistan’s firms’.
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Pairwise correlations (country-wise)

Tables S1, S2, and S3 (Supplementary Information) provide the correlation coefficients for other dimensions related to BD. Multicollinearity was taken into account, and the estimated pairwise correlation results indicate that the dataset is free from multicollinearity issues. However, the pairwise correlation results do not provide sufficient evidence to accept or reject the hypothesis. Thus, a more detailed analysis will be presented in subsequent discussions to gather more evidence and assess model compliance.

Table 6 presents the estimated panel unit root test results at levels and first difference, which were conducted to ensure stationary data prior to employing panel data techniques such as the LLC, IPS, ADF, and PP-Fisher panel unit tests. The results showed that all variables in the study were stationary at both the level and first difference, indicating that they have the same order of integration.

Table 6 Panel unit root test results at levels and first difference.
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Table 7 presents the multicollinearity diagnostics that include the variance inflation factor (VIF). The VIF of more than 5 or 1/VIF that is lower than 0.2 is a sign of high collinearity with other explanatory variables. The VIF values listed in the table illustrate that there were no multicollinearity issues, and this infers that it was acceptable to use all the variables together.

Table 7 Multicollinearity diagnostics—variance inflation factor (VIF).
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Table 8 presents the results of White’s and Cameron & Trivedi’s decomposition of the IM-test, which were conducted to assess model compliance by testing for homoscedasticity. Homoscedasticity occurs when the variance of the error term in a regression model is constant, indicating a well-formulated model. Heteroscedasticity, on the other hand, occurs when the variance of the error term is not constant and can lead to issues in the model. To ensure model compliance, this study utilized popular tests for homoscedasticity, namely White’s and Cameron & Trivedi’s decomposition of IM-test, and found heterogeneity issues in the data. To eradicate this issue, we use the fixed-effect robust.

Table 8 White’s test Cameron & Trivedi’s decomposition of IM-test.
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Regression results direct and moderating effect

Table 9 presents the results of the direct effect between BD and FP, indicating a positive and significant association between the two variables for all cases. The R-squared values also show a strong link between BD and FP, explaining 13.5%, 16.5%, 22.0%, and 10.4% of the variation in FP for the input variables BD, FC, GRW, DB, BS, and STOW, respectively. The F-value confirms that the model is a good fit. However, FL has a negative and meaningful impact on FP, while FC, BS, STOW, and growth have a negative and insignificant association with FP for Pakistani firms. For Chinese, Indian, and Russian firms, FL and SO also have a negative and significant impact on their firms’ FP. The moderating effect of the BD Index*SC Index (Interaction term) on FP is also shown in Table 9, with positive and significant results for Chinese, Indian, and Russian firms, but an insignificant positive impact on FP for Pakistani firms. SC has a positive and significant impact on FP in all cases. The R-square values (16.5%, 18.2%, 13.2%, and 10.9%, respectively) further explain the systematic differences in the dependent variable. The results confirm the first hypothesis, suggesting that the greater the diversity in the corporate boardroom, the better the organization’s performance, in line with previous research (Hsu et al., 2019). The study also indicates that SC weakens this relationship, in agreement with prior research. Overall, the findings suggest that BD positively impacts FP, and SC plays a crucial role in enhancing the relationship between the two variables.

Table 9 Regression results direct and moderating effect estimated results.
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Fixed-effect Robust model and two-step system GMM estimation

Table 10 showed that the FEE was efficient in all cases after the Hausman test rejected the null hypothesis. Table 11 presented the FEM for China, Russia, India, and Pakistan, confirming balanced panel data. BD index and SC had a positive and significant impact on FP in all cases, while the interaction term had a negative impact on Chinese, Indian, and Russian firms, but not on Pakistani firms. FL and SO had an adverse and significant effect on ROA in all cases. BS, OG, and FS had a positive and statistically significant impact on FP for Chinese, Indian, and Russian firms, while OG had a negative impact on Pakistani firms’ ROA. All variables exhibited a logical relationship with FP, except for the interaction term for Pakistan. The F-test value indicated that all models were significant. Overall, the study used the two-step system GMM dynamic panel estimation to account for endogeneity preconceptions, over-identifying restrictions, measurement errors, omitted constructs or variables, and controlling autocorrelations.

Table 10 Estimation-hausman test specification.
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Table 11 Fixed-effect model and two-step system GMM estimation results.
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Robustness check

This study conducted various robustness analyses to ensure the validity of the results. First, robust regression was performed to avoid potential outlier biases, and the results in Table 12 were similar to the baseline results in Table 10, indicating that the baseline results are robust. Tobit regression was also used to verify the robustness of the study results, and it did not modify the linkage between BD and FP. Furthermore, board independence (BI) was added as an independent variable and an alternative proxy of FP return on Equity (ROE) to the baseline equation as additional variables to check for sensitivity. The study confirmed that the primary results are robust to the inclusion of additional variables.

Table 12 Robustness check.
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Conclusion

This article delves into the influence of diversity in boardroom attributes, such as gender, age, expertize, national, education, and tenure, on FP. It also investigates how SC can impact this relationship, using a six-dimensional index to measure BD and SC. The study employs both fixed-effect model and the two-step system GMM dynamic panel approach and utilizes data from 240 non-financial firms listed on four stock exchanges (Moscow, Shanghai, Bombay, and Pakistan) over a 13-year period (2008–2020). The study’s findings indicate that BD has a positive impact on firms’ FP across all cases. However, the study also reveals that this relationship is weakened by strategic change. The results are robust under different estimation methods and provide valuable policy implications for managers and practitioners.

Despite these limitations, the present study provides important insights into the relationship between BD and FP in emerging markets. Policymakers and practitioners should consider the findings of this study when developing policies and practices aimed at promoting diversity in the boardroom. Specifically, efforts should be made to implement policies and practices that encourage diversity in terms of gender, age, expertize, nationality, education, and tenure. Additionally, companies should consider providing training and development opportunities to their board members to improve their ability to effectively work in a diverse boardroom. This can help ensure that all board members feel empowered to contribute to the company’s strategic decision-making process. Overall, the limitations of this study suggest avenues for future research, while the insights provided have important implications for policymakers and practitioners seeking to promote diversity and improve FP in emerging markets.

Policy implications

The findings of this study have important policy implications for managers and practitioners in non-financial firms operating in China, India, Pakistan, and Russia. Given the positive relationship between BD and FP, companies should prioritize efforts to promote diversity in the boardroom. This can be achieved by implementing policies that encourage diversity in terms of gender, age, expertize, nationality, education, and tenure. Furthermore, the study’s results suggest that strategic change can weaken the relationship between BD and FP. Therefore, companies should be mindful of how strategic change initiatives may impact the diversity of the boardroom and the subsequent FP of the organization.

To promote BD, companies may consider adopting affirmative action policies, setting diversity targets, and creating diversity committees responsible for monitoring and reporting on progress. Companies may also prioritize the development and recruitment of diverse talent for senior management positions, which can lead to a more diverse boardroom over time. Additionally, companies should consider providing training and development opportunities to their board members to improve their ability to effectively work in a diverse boardroom. This can help ensure that all board members feel empowered to contribute to the company’s strategic decision-making process.

In summary, policy implications include promoting diversity in the boardroom, being mindful of how strategic change can impact diversity and FP, adopting affirmative action policies and setting diversity targets, prioritizing the recruitment and development of diverse talent, and providing training and development opportunities to board members. By implementing these policies, companies can create a more diverse and effective boardroom, leading to improved FP and ultimately benefiting the organization as a whole.