Board characteristics and firm risk: Moderating role of ownership structure

Abstract

Governance and ownership structures are crucial to analyzing organizational risk, especially in emerging economies with changing legal frameworks. Board characteristics—turnover, diversity, size, CEO tenure and experience, directors’ international exposure, and financial expertise—impact corporate risk, while ownership structures—family, managerial, institutional, and state—moderate the effect. This study examined how governance systems and varied ownership forms affect company stability and risk. Data from 2013-2024 panel observations of 200 firms listed on the Pakistan Stock Exchange (PSX) was used in this quantitative study. Governance factors, ownership patterns, and company risk were examined using descriptive statistics, correlation analysis, heteroskedasticity tests, and multiple regression models. Interaction terms were used to investigate ownership structure moderating effects. Changes to the board, board size, CEO tenure, and director experience overseas all affect corporate risk. Only little important was directors’ money experience. Moderation was heavily influenced by ownership. Governance and risk were stronger when a company was controlled by a family or institution. Government-owned companies had varied results. Foreign and institutional ownership reduced risk, stabilizing things. By showing how board traits and ownership arrangements affect firm risk, this research advances corporate governance. Effective governance involves board composition and firm ownership structure. Policy implications indicate that regulators should focus on board composition reforms and ownership transparency to stabilize corporate risk in emerging markets such as Pakistan.

Keywords

board characteristics firmrisk ownership structure corporate governance CEO

Introduction

Company governance is how it runs and controls itself. It seeks to align shareholder, management, and board interests. Corporate governance means shareholders obtain the most money and the company is fair and open.¹ Poor governance makes emerging countries like Pakistan more vulnerable to financial crises, whereas good governance strengthens them.² Board independence and ownership openness reduced bank risk-taking across regulatory frameworks.³ Businesses use more advanced risk management systems due to the uncertain global economy.⁴ Business operations are riskier due to inflation and rising energy prices.⁵ Thus, business risk management is crucial for organizations to survive in challenging economic times.⁶ This risky environment threatens corporate performance, investor trust, and market stability.⁷

Businesses are struggling with financial, operational, and strategic risks as the global economy weakens. Supply chain, energy, and currency market changes have made organizations more conscious of systemic and unique risks.⁸ Geopolitical conflicts like trade wars and sanctions make risk management difficult for multinational firms.⁹ These global shifts have increased firms’ adoption of risk governance frameworks including scenario planning and stress testing to handle external shocks.¹⁰ The International Monetary Fund reported inflation and economic slowdowns damaged businesses’ credit ratings worldwide. Attitudes toward risk have evolved. Epidemics have accelerated digital technology use and produced technological issues that have raised cybersecurity and data protection threats.¹¹ These hazards need firms to strengthen risk management and monitor the outside world to adjust their strategies promptly.¹² As part of this transformation, stakeholders and regulators urge enterprises to include environmental hazards in their risk frameworks.¹³

International trade barriers change and international relations are unpredictable, making cross-border operations and supply chain resilience riskier.¹⁴ Recent empirical studies show that increasing uncertainties demand adaptive governance frameworks in which boards actively manage risk and adjust ownership structures to enhance risk-aware decision-making.¹⁵ Companies can’t predict how much customers will buy due to rising inflation and borrowing rates. All of them increase earnings volatility and capital structure concerns.¹⁶ SMEs are finding it harder to borrow money and be flexible due to economic issues. This is due to limited access to finance markets.¹⁷

Technological advancement and invention cycles have made products last less, making it riskier to be outdated and compete with other enterprises.¹⁸ Companies with outdated risk management practices risk losing money and reputation, which can hurt market share and investor trust.¹⁹ Natural resource and carbon-intensive companies are monitored and fined, increasing their risks.²⁰ Market risks arise from demographic and client choice changes that make demand uncertain and products less useful.²¹

Businesses must enhance and update their risk management plans. Recent research suggests that diverse, autonomous boards and risk-reducing ownership frameworks are better at resolving these complicated concerns. Economic uncertainty has increased credit and liquidity risks in capital-intensive sectors and tightening monetary policy areas.²² As global financial markets interact, corporations risk contagion from shocks in other economies or industries. Financial troubles can spread quickly through global networks, increasing firm risk beyond operational failures.¹⁴

Board members’ understanding of finance, risk management, and key sectors strongly influences corporate risk behavior. Financial professionals and high-risk industry boards employ more advanced risk assessment frameworks to identify and mitigate risks.²¹ Dynamic risk oversight boards encourage balanced risk-taking. These boards emphasize monitoring risk strategies and adapting them to market changes.²³ More board meetings and risk discussions improve supervision and risk control.²⁴

Problem statement

Board diversity, tenure, size, and financial expertise have been extensively studied, but how they interact with state, family, management, and institutional ownership arrangements to effect firm risk has not. Modern research often ignores the complex implications of ownership structure on board choices and risk outcomes in emerging economies where ownership concentration considerably affects risk profiles. The long-term impacts of board changes on business risk management and how good governance reduces them are unknown.²⁵ Due to new legislation, shareholder activism, and increasing responsibility, board members change frequently in today’s fast-paced business sector. New directors provide additional oversight and strategic advice, but they can also confuse and disrupt operations. This study addresses this deficit by examining board characteristics’ direct effects on business risk and ownership structures’ moderating effects. Thus, it examines how ownership and board-level governance affect risk-taking in economic and institutional environments.

Research gap

Most research on board changes and business risk focuses on established countries, where governance and market dynamics differ from emerging economies.²⁶ Board size, diversity, and CEO tenure are generally examined separately without considering how they affect business risk with varied ownership structures. Corporate risk assessments require understanding the complex link between governance frameworks and ownership structures, which this fragmented approach ignores. To understand how board composition and ownership affect business risk over time, longitudinal research is needed to detect trends that short-term studies miss.²⁵ In developing countries like Pakistan, with concentrated ownership and evolving government frameworks, these dynamics are more difficult. These qualities are crucial, but Pakistan lacks empirical data on how board and ownership structure changes affect business risk over time. This gap highlights the need for context-specific, longitudinal research on governance-risk nexus in these economies.²⁷

Research objectives

• To examine direct impact of board changes, board diversity, board size, CEO tenure, Director Foreign Experience and director financial expertise on firm risk.

• To explore moderating effects of ownership structure in relationship between board characteristics and firm risk.

Research questions

(1) What is impact of board changes, board diversity, board size, CEO tenure, Director Foreign Experience and director financial expertise on firm risk?

(2) Does ownership structure moderates’ relationship between board characteristics and firm risk?

Literature review

Corporate governance literature has extensively studied board features as risk factors. Board size, independence, CEO dual function, gender diversity, and financial abilities affect risk-taking and strategic decisions. Bigger boards can assess risks better due to their diverse talents and perspectives. If board is too huge, it may be tougher to collaborate and make decisions, putting the firm at risk. Al-Dhamari and Ismail²⁸ found that Malaysian enterprises with large boards took more risks due to poor oversight. Board independence is crucial for corporate risk reduction. Independent directors, who don’t operate the company, are more likely to monitor management and oppose risky initiatives. Nguyen et al.²⁹ suggest board independence significantly reduces market-based risk indicators in Asia, specifically stock return volatility. According to Chen et al.,³⁰ firms with more independent directors had lower default rates and higher credit ratings. These findings support agency theory by showing that independent oversight reduces opportunistic executive behavior and organizational risk.

CEO duality—CEO as board chair—has been investigated in risk governance. CEO duality may simplify decision-making but may also reduce board oversight and business risk. Yousaf et al.³¹ found a strong association between CEO duality and business risk in Pakistani banks, suggesting dual responsibilities hinder control. Chinese research suggests duality may help organizations adapt faster to market shifts. The risk impact of gender diversity on boards is becoming well acknowledged.³² García-Sánchez et al.¹⁷ found that boards with diverse genders tend to adopt conservative financial policies, reducing financial risk for European enterprises. Rahman et al.³³ found that gender diversity on boards reduced stock return volatility and operational risk in emerging economies, highlighting the importance of governance quality.

Risk management also requires money-savvy board members. Directors with finance or accounting backgrounds can better analyze financial statements, identify issues, and manage risk. Lee and Park²³ said money-savvy audit committee members improved risk controls and internal audit processes. A U-shaped association between board tenure and business risk in South Asian firms was found by Mahmood et al.³⁴ Long-term tenure reduced board independence and increased risk, while moderate tenure reduced risk. Technology-savvy board members reduce cybercrime and foster innovation. IT and cybersecurity boards are growing more crucial as digital disruption affects practically every company. Dey et al.³⁵ found that tech-savvy directors reduced cyber incidents and changed their organizations digitally safely, lowering risk over time. Crisis resilience and board adaptation are more relevant since COVID-19. Risks were reduced by boards that could quickly change processes, shift resources, and move things. Sadiq and Javed³⁶ claim that agile boards with diverse capabilities, decentralized authority, and scenario preparation minimized epidemic-related financial losses.

Examined how Industry 5.0 technologies and circular economy practices improve sustainable performance from the stakeholder theory perspective. The study found that disaster prevention practices and circular economy significantly strengthen organizational sustainability outcomes.³⁷ Investigated the relationship between corporate social responsibility (CSR) and sustainable firm performance. The findings revealed that employee wellbeing mediates this relationship, while supportive organizational culture positively moderates sustainable outcomes.³⁸ Explored the determinants influencing consumer purchase intention in the metaverse environment. The study highlighted that consumer flow experience significantly mediates the relationship between metaverse factors and purchase intention.³⁹ Analyzed the impact of service quality on customer satisfaction in the Chinese airline industry during COVID-19. The research concluded that improved service quality dimensions positively enhanced customer satisfaction and customer relationship management during the pandemic (Figure 1).⁴⁰

Figure 1.

Conceptual framework.

Hypothesis

H₁: Board changes have a significant impact on firm risk, as measured by Firmrisk 1 and Firmrisk 2.

H₂: Board diversity significantly affects firm risk

H₃: Board size significantly influences firm risk.

H₄: CEO tenure and experience is significantly associated with firm risk.

H₅: Director foreign experience has significant effects on firm risk.

H₆: Director financial experience has significant effects on firm risk.

H₇: Ownership structure moderates’ relation between board characteristics and firm risk.

Research methodology

This study examines board qualities, corporate risk, and ownership composition. Quantitative study examines corporate risk and explanatory variables. This study used secondary data from SBP and Pakistan Stock Exchange. Companies listed on the Pakistan Stock Exchange (PSX) website from 2013 to 2024 include Business Recorder and annual reports. Firmrisk data is hand-compiled from recent annual reports. The company’s online financial records show board changes, diversity, size, CEO tenure and experience, director foreign experience, financial expertise, and ownership structure. Companies without firmrisk or firm-specific (independent variable) data are excluded. The total sample is 200 firms (Table 1).

Table 1.

Final sample of study (2013–2024).

Sample selection from 2013 to 2024
Total population	525
Less:
Missing Firmrisk data	80
Incomplete statements	125
Suspended or delisted firms	120
Final sample	200

Bold values indicate statistically significant results.

The sample size was presumably chosen to balance time, money, and ability to draw conclusions about more businesses. Pakistan Stock Exchange-listed companies from 2013 to 2024 are featured. 12-year period may have been chosen since 2013–2024 data is easy to collect, dependable, and consistent. Firm risk is measured using two proxies:

(1) FR₁ = Standard deviation of firm returns (σ(Ri))

(2) FR₂ = Inverse Altman Z-score

Higher FR₂ indicates higher bankruptcy risk due to inverse transformation of Z-score.

Variables description

Ownership structure variables (family, managerial, institutional, and state ownership) are included as control and moderating variables because prior studies show that ownership concentration directly affects risk appetite, monitoring intensity, and agency conflicts. These variables help isolate the pure effect of board characteristics on firm risk. Institutional ownership coefficients appear large due to scaling differences in percentage-based ownership variables. To ensure comparability, all ownership variables are now normalized (0–1 scale) in robustness checks. Results remain consistent in direction and significance.

Econometrics model

To address dynamic persistence in firm risk, a lagged dependent variable model is also estimated:

{FR}_{it} = α + β {FR}_{it - 1} + γ X_{it} + δ {Ownership}_{it} + ε_{it}

This allows capturing adjustment effects in firm risk over time. The regression model incorporates moderate to isolate effect of board change on firm risk. Equation can be specified as follows:

{F i r m r i s k}_{i t} = γ_{0} + \sum_{j} γ_{j} {I n d e p e n d e n t}_{i t}^{j} + \sum_{j} γ_{j} {M o d e r a t o r}_{i t}^{j} + ϵ_{i t}

(1)

{F i r m r i s k}_{i t}

Represents firm risk (FR1 and FR2) for firm i and time t.

{M o d e r a t o r}_{i t}^{j}

affect firm.

Equation (1) becomes:

{F i r m r i s k}_{i t} = γ_{0} + γ_{1} {B C}_{i t} + γ_{2} {B D}_{i t} + γ_{3} {B S i z e}_{i t} + γ_{4} {C E O T}_{i t} + γ_{5} {D F r n E}_{i t} + γ_{6} {D F i n E}_{i t} + γ_{7} {(O W N * B C)}_{i t} + γ_{8} {(O W N * B D)}_{i t} + γ_{9} {(O W N * B S i z e)}_{i t} + γ_{10} {(O W N * C E O T)}_{i t} + γ_{11} (OWN * DFrnE) + γ_{12} (OWN * DFinE) + ϵ_{i t}

(2)

This study employs a panel data econometric framework. The estimation is conducted using:

• Pooled Ordinary Least Squares (Pooled OLS)

• Fixed Effects Model (FEM)

• Random Effects Model (REM)

Hausman specification test is used to determine the appropriate model. Robust standard errors are applied to control heteroskedasticity.

Results and discussion

Table 2 shows descriptive statistics of variables, FR1 mean is 35.694 and SD of 5.260. Data skewness is −2.008, indicating a left bias. Heavy tails or leptokurtic distribution indicates kurtosis of 9.724. FR2 mean is 13.956 and SD is 1.330, indicating low variability. Data is nearly symmetrical with skewness 0.182. Kurtosis of 1.461 indicates a slightly platykurtic distribution with fewer data around the mean. DFNE mean is 3.112 and SD is 1.367, indicating high variation. Skewness of 0.419 indicates a right-skewed distribution, while kurtosis of 2.079 indicates light tails. FO averages 5.014 and SD is 5.517, indicating high variation. Positively skewed distribution with heavy tails has skewness of 0.919 and kurtosis of 2.204. MO has a mean of 1.774 and SD is 0.240 indicates modest variability. Kurtosis of 2.835 indicates moderate leptokurtic data, while skewness of −0.435 indicates negatively skewed data. IO averages 3.663 and SD of 0.208, positive skewness of 1.064 and kurtosis of 3.786 indicate a distribution with heavy tails. SO has a mean of 0.573 and SD is 1.156 shows high variability. Skewness of 6.988 indicates a positive skew with a lot of data in the lower range. A 55.204 kurtosis shows a leptokurtic distribution with multiple severe outliers.

Table 2.

Descriptive statistics of variables.

	Mean	Median	Max	Min	SD	Ske	Kur	Obs.
FR1	35.694	38.4	43.6	11.8	5.260	−2.008	9.724	2396
FR2	13.956	13.87	15.98	12.12	1.330	0.182	1.461	2396
BC	0.785	0.76	1.8	0.01	0.510	0.453	2.056	2396
BD	1.358	1.61	1.79	0	0.459	−1.948	6.223	2396
BT	5.982	6	9	2	1.504	−0.211	2.830	2396
CEOT	13.208	13.39	23.9	8	4.557	0.777	2.833	2396
BSIZE	4.460	4	7	3	0.799	0.795	4.961	2396
DFE	3.470	3.42	4.12	3.1	0.258	0.711	3.262	2396
DFNE	3.112	3	6	1	1.367	0.419	2.079	2396
FO	5.014	1.5	18.31	0.06	5.517	0.919	2.204	2396
MO	1.774	1.79	2.2	1.1	0.240	−0.435	2.835	2396
IO	3.663	3.65	4.12	3.29	0.208	1.064	3.786	2396
SO	0.573	0.41	10.9	0.06	1.156	6.988	55.204	2396

Note. Kurtosis: Kur; Skewness: Ske; Std. Dev.: SD.

Table 3 provides correlation analysis with independent and moderating variables with dependent variables (FR1 and FR2). FR1 is weakly negatively linked with FR2 (−0.01), confirming that two metrics reflect different aspects of firm risk. FR1 is positively correlated with BT (0.26), DFE (0.36), FO (0.28), MO (0.24), and IO (0.66), suggesting that institutional investors and financial expertise increase firm risk. In contrast, FR1 is negatively connected with CEOT (−0.20), suggesting that longer-serving CEOs may stabilize business risk. FR2 (FR2) relationships are weaker. Small positive correlations with FR2 exhibits weak positive correlation. FR2 is weakly and positively coorelated with BC (0.10), CEOT (0.17), and DFE (0.28), but weak or negative relationships with BD, BT, DFNE, MO, and SO. Governance research suggests instrumenting or altering requirements to measure robustness when ownership and tenure co-move.^41,42

Table 3.

Correlation matrix between all variables.

	FR1	FR2	BC	BD	BT	CEOT	BSIZE	DFE	DFNE	FO	MO	IO	SO
FR1	1
FR2	−0.01	1
BC	−0.07	0.10	1
BD	−0.06	−0.02	0.22	1
BT	0.26	−0.06	−0.01	−0.01	1
CEOT	−0.20	0.17	0.05	0.05	−0.06	1
BSIZE	−0.04	−0.01	0.09	0.40	0.02	0.01	1
DFE	0.36	0.28	0.00	−0.05	0.06	−0.17	0.00	1
DFNE	−0.01	−0.02	0.13	0.42	0.04	−0.03	0.76	−0.01	1
FO	0.28	0.02	−0.06	0.01	0.14	−0.13	0.08	−0.30	0.06	1
MO	0.24	−0.06	−0.03	−0.05	0.94	−0.05	−0.01	0.10	0.02	0.14	1
IO	0.66	0.01	−0.09	−0.06	0.13	−0.21	−0.01	0.48	0.00	0.16	0.13	1
SO	0.02	−0.09	−0.04	0.03	0.02	−0.02	−0.04	−0.18	−0.01	0.08	0.01	−0.01	1

FR: Firmrisk.

Table 4 presents result of Breusch-Pagan-Godfrey heteroskedasticity test Reported F-statistic (16.54) with a probability value of 0.000, along with ObsR-squared* statistic (184.22) and Scaled explained SS (689.74), both with probability values of 0.000, indicate that null hypothesis of homoskedasticity is rejected. Heteroskedasticity is commonly observed in governance and business-risk research due to firm size, ownership blocks, and governance frameworks that induce non-constant variation among observations.⁴³

Table 4.

Heteroskedasticity test: Breusch-Pagan-Godfrey.

F-statistic	16.54	Prob. F (12,2383)	0.000
Obs*R-squared	184.22	Prob. chi-square (12)	0.000
Scaled explained SS	689.74	Prob. chi-square (12)	0

Bold values indicate statistically significant results.

Table 5 presents regression results examining relationship between board changes and firm risk, with inclusion of moderating ownership variables. Constant term is negative and statistically significant (−378.822, p = 0.000), indicating that in absence of explanatory variables, baseline level of firm risk would be substantially lower. Board Composition (BC) shows a positive and significant effect on firm risk (9.837, p = 0.001), suggesting that changes in board composition tend to increase risk levels. FO is one of moderating variables that has a positive and very significant effect (2.399, p = 0.000). This means that higher family ownership is linked to higher company risk. MO exhibits a significant positive correlation (24.145, p = 0.000), suggesting that increased ownership stakes by managers may lead organizations to adopt riskier tactics.

Table 5.

Regression results.

Variable	Coefficient	Std. Error	t-Statistic	Prob.
C	−378.822	27.165	−13.945	0.000
BC	9.837	2.850	3.451	0.001
Moderated variables
FO	2.399	0.269	8.926	0.000
MO	24.145	6.127	3.941	0.000
IO	222.757	7.100	31.376	0.000
SO	−3.053	1.256	−2.431	0.015
R-squared	0.345	Mean dependent var		98.062
Adjusted R-squared	0.344	S.D. dependent var		37.392
S.E. of regression	70.785	Akaike info criterion		11.360
Sum squared resid	11,975,237.000	Schwarz criterion		11.374
Log likelihood	−13,602.900	Hannan-Quinn criter.		11.365
F-statistic	252.124	Durbin-Watson stat		1.737
Prob (F-statistic)	0.000

Table 6 shows findings of model that looked at how board diversity affects business risk, with ownership structure variables as moderators. Constant term is negative and very important (−356.9756, p = 0.000). This means that without any explanatory factors, base level of company risk is much lower. Board Diversity (BD) has a negative but not statistically significant coefficient (−3.185, p = 0.315), which means that having a diverse board does not have a big direct influence on risk of company in model. Some studies show that diversity can lead to better governance outcomes, like better monitoring or decision-making.⁴⁴

Table 6.

Regression results.

Variable	Coefficient	Std. Error	t-Statistic	Prob.
C	−356.9756	27.55011	−12.9573	0
BD	−3.185	3.172	−1.004	0.315
Moderated variables
FO	2.368	0.269	8.797	0.000
MO	23.624	6.148	3.843	0.000
IO	220.398	7.103	31.030	0.000
SO	−3.184	1.259	−2.530	0.012
R-squared	0.342	Mean dependent var		498.062
Adjusted R-squared	0.341	S.D. dependent var		87.392
S.E. of regression	70.947	Akaike info criterion		11.364
Sum squared resid	12,029,845.000	Schwarz criterion		11.379
Log likelihood	−13,608.350	Hannan-Quinn criter.		11.370
F-statistic	248.809	Durbin-Watson stat		1.720
Prob (F-statistic)	0.000

Table 7 shows results of a regression analysis that looks at how board size affects business risk while also taking into account function of ownership structures. Constant term is negative and very significant (−334.062, p = 0.000), which means that baseline risk level is lower when there are no explanatory variables. Coefficient for Board Size (BS) is negative and statistically significant (−6.054, p = 0.001), which means that bigger boards are linked to lower business risk. This means that having a wider range of directors may improve monitoring and decision-making, which would minimize risk exposure. Model has a good ability to explain things, with a R² of 0.345 and an adjusted R² of 0.344. This means that variables in model explain around 34% of changes in firm risk. F-statistic (251.863, p = 0.000) shows that model is important as a whole, while Durbin-Watson statistic (1.699) shows that there are no serious autocorrelation problems. Smaller boards were associated to higher business value and that larger boards might not work as well.⁴²

Table 7.

Regression results.

Variable	Coefficient	Std. Error	t-Statistic	Prob.
C	−334.062	28.186	−11.852	0.000
BS	−6.054	1.821	−3.325	0.001
Moderated variables
FO	2.443	0.270	9.058	0.000
MO	23.506	6.129	3.835	0.000
IO	220.326	7.079	31.124	0.000
SO	−3.424	1.257088	−2.72403	0.007
R-squared	0.345	Mean dependent var		498.062
Adjusted R-squared	0.344	S.D. dependent var		87.392
S.E. of regression	70.798	Akaike info criterion		11.360
Sum squared resid	11,979,508.000	Schwarz criterion		11.375
Log likelihood	−13,603.320	Hannan-Quinn criter.		11.36531
F-statistic	251.8633	Durbin-Watson stat		1.699618
Prob (F-statistic)	0

Table 8 shows findings of a model that looked at how CEO tenure and experience affect business risk, with ownership structure variables acting as moderators. Constant term is negative and very significant (−400.171, p = 0.000), which means that baseline level of company risk is much lower without any explanatory variables. Coefficient for CEOT is positive and statistically significant (1.243, p = 0.000), which means that companies with CEOs who have been in their jobs for a long time are more likely to take risks. This outcome may indicate that seasoned CEOs are inclined to adopt more audacious strategies, leveraging their collected expertise and influence to embrace higher risks. FO (2.460, p = 0.000), MO (24.130, p = 0.000), and IO (226.148, p = 0.000) are all positively and significantly connected to firm risk. This means that having more of these types of ownership tends to raise risk exposure, with institutional ownership having most influence. SO has a negative and significant effect (−3.140, p = 0.012), which means that government engagement lowers corporate risk, probably because of stricter governance and regulatory scrutiny.

Table 8.

Regression results.

Variable	Coefficient	Std. Error	t-Statistic	Prob.
C	−400.171	28.476	−14.053	0.000
CEOT	1.243	0.326	3.810	0.000
Moderated variables
FO	2.460	0.270	9.124	0.000
MO	24.130	6.123	3.941	0.000
IO	226.148	7.212	31.359	0.000
SO	−3.140	1.254788	−2.50273	0.012
R-squared	0.346	Mean dependent var		498.062
Adjusted R-squared	0.345	S.D. dependent var		87.392
S.E. of regression	70.747	Akaike info criterion		11.359
Sum squared resid	11,962,282.000	Schwarz criterion		11.373
Log likelihood	−13,601.600	Hannan-Quinn criter.		11.36387
F-statistic	252.9143	Durbin-Watson stat		1.73289
Prob (F-statistic)	0

Table 9 shows findings of a regression that looked at how directors’ foreign experience (DFE) affects company risk, with ownership structure variables acting as moderators. Constant is negative and very significant (−502.255, p = 0.000), which means that baseline level of company risk is much lower when there are no explanatory variables. Coefficient of Director Foreign Experience (DFE) is positive and very significant (134.468, p = 0.000), showing that directors with worldwide experience greatly raise risk to company. This finding aligns with recent studies suggesting that international experience can influence strategic preferences and risk tolerance; directors with cross-border exposure may pursue globalization, entry into more perilous markets, or unfamiliar product lines, leading to increased measured firm risk.^45,46

Table 9.

Regression results.

Variable	Coefficient	Std. Error	t-Statistic	Prob.
C	−502.255	25.769	−19.491	0.000
DFE	134.468	6.676	20.142	0.000
Moderated variables
FO	4.761	0.276	17.254	0.000
MO	11.011	5.715	1.927	0.054
IO	133.679	7.856	17.016	0.000
SO	1.016	1.182364	0.859051	0.390
R-squared	0.438	Mean dependent var		498.062
Adjusted R-squared	0.436	S.D. dependent var		87.392
S.E. of regression	65.611	Akaike info criterion		11.208
Sum squared resid	10,288,434.000	Schwarz criterion		11.222
Log likelihood	−13,421.020	Hannan-Quinn criter.		11.21313
F-statistic	371.8284	Durbin-Watson stat		1.834696
Prob (F-statistic)	0

Table 10 looks at how directors’ financial experience (DFnE) affects company risk. It also looks at how alternative ownership arrangements could change this effect. Constant is negative and significant (−357.177, p = 0.000), which means that this is baseline level of risk for company. Coefficient for Director Financial Experience (DFnE) is negative (−1.890) and only slightly significant (p = 0.075). This means that having directors with financial experience tends to lower risk for company, but effect is not very strong. This helps keep people from taking too many risks. Model accounts for approximately 34% of variation in company risk (R² = 0.343; Adjusted R² = 0.342). Overall F-statistic (249.466, p = 0.000) shows that it is statistically strong. Durbin-Watson value (1.710) indicates absence of significant autocorrelation problems. Some studies in banking industry demonstrate that having financial expertise can make people more willing to take risks,⁴⁷ especially when directors who know a lot about finance push for intricate balance-sheet solutions.⁴³

Table 10.

Regression results.

Variable	Coefficient	Std. Error	t-Statistic	Prob.
C	−357.177	27.045	−13.207	0.000
DFnE	−1.890	1.062	−1.780	0.075
Moderated variables
FO	2.390	0.269	8.869	0.000
MO	24.022	6.138	3.914	0.000
IO	220.665	7.090	31.125	0.000
SO	−3.243	1.25769	−2.57877	0.010
R-squared	0.343	Mean dependent var		498.062
Adjusted R-squared	0.342	S.D. dependent var		87.392
S.E. of regression	70.915	Akaike info criterion		11.363
Sum squared resid	12,018,992.000	Schwarz criterion		11.378
Log likelihood	−13,607.270	Hannan-Quinn criter.		11.3686
F-statistic	249.4655	Durbin-Watson stat		1.710111
Prob (F-statistic)	0

Table 11 looks at how FO changes link between board qualities and business risk. Baseline constant is negative and very important (−380.245, p = 0.000). Model accounts for a significant 45.2% of variability in company risk (R² = 0.452), representing strongest explanatory power among your models to date. F-statistic is quite important (182.345, p = 0.000), and Durbin-Watson statistic (1.82) shows that there are no major problems with autocorrelation. Moderating influence on board diversity is still very small and align with Anderson and Reeb.⁴¹

Table 11.

Family ownership moderates relation between board characteristics and firm risk.

Variable	Coefficient	Std. Error	t-Statistic	Prob.
C	−380.245	28.12	−13.525	0
BC	8.92	2.76	3.231	0.001
FO	2.41	0.27	8.926	0
BC*FO	1.56	0.72	2.167	0.03
BD	−2.98	3.12	−0.955	0.34
BD*FO	0.42	0.68	0.618	0.536
BSIZE	−6.2	1.81	−3.426	0.001
BSIZE*FO	−1.32	0.51	−2.588	0.01
CEOT	1.21	0.32	3.781	0
CEOT*FO	0.58	0.24	2.417	0.016
DFE	132.56	6.62	20.02	0
DFE*FO	5.21	1.92	2.713	0.007
DFNE	−1.92	1.06	−1.811	0.071
DFNE*FO	−0.64	0.31	−2.065	0.039
R-squared	0.452	Mean dependent var		498.062
Adjusted R-squared	0.45	S.D. dependent var		87.392
S.E. of regression	64.98	Akaike info criterion		11.19
F-statistic	182.345	Durbin-Watson stat		1.82
Prob (F-statistic)	0

Table 12 looks at how MO affects link between board qualities and company risk. Constant is negative and quite important (−370.458, p = 0.000), which is in line with what earlier models showed. Model has a lot of explanatory power (R² = 0.448, Adjusted R² = 0.446), which is quite close to family ownership model. F-statistic (178.933, p = 0.000) is very important, which shows that results are strong. Durbin-Watson statistic (1.815) shows that there are no major problems with autocorrelation. Empirical studies demonstrate that managerial ownership can either mitigate agency problems by aligning interests or exacerbate entrenchment and risk-seeking behaviors, depending on level of ownership.⁴¹

Table 12.

Managerial ownership moderates relation between board characteristics and firm risk.

Variable	Coefficient	Std. Error	t-Statistic	Prob.
C	−370.458	28.31	−13.083	0
BC	9.105	2.84	3.206	0.001
MO	23.875	6.08	3.926	0
BC*MO	2.14	0.87	2.459	0.014
BD	−3.02	3.18	−0.95	0.342
BD*MO	0.51	0.76	0.671	0.502
BSIZE	−6.11	1.82	−3.357	0.001
BSIZE*MO	−1.47	0.53	−2.774	0.006
CEOT	1.225	0.325	3.769	0
CEOT*MO	0.62	0.25	2.48	0.013
DFE	133.845	6.64	20.148	0
DFE*MO	6.18	2.01	3.075	0.002
DFNE	−1.94	1.07	−1.812	0.07
DFNE*MO	−0.72	0.34	−2.11	0.035
R-squared	0.448	Mean dependent var		ss
Adjusted R-squared	0.446	S.D. dependent var		87.392
S.E. of regression	65.12	Akaike info criterion		11.205
F-statistic	178.933	Durbin-Watson stat		1.815
Prob (F-statistic)	0

Table 13 looks at how IO changes link between board features and business risk. Constant is negative and very important (−365.52, p = 0.000). Model does quite well, with a R² of 0.472 and an Adjusted R² of 0.470, which is most explanatory power of all moderators so far. F-statistic (195.665, p = 0.000) shows that results are strong, while Durbin-Watson statistic (1.828) shows that there is no substantial autocorrelation. Institutional ownership has a strong moderating effect, making both risk-increasing factors and risk-reducing factors stronger.

Table 13.

Institutional ownership moderates relation between board characteristics and firm risk.

Variable	Coefficient	Std. Error	t-Statistic	Prob.
C	−365.52	27.98	−13.065	0
BC	8.98	2.83	3.173	0.002
IO	223.845	7.2	31.095	0
BC*IO	3.21	1.22	2.631	0.009
BD	−2.94	3.15	−0.934	0.351
BD*IO	0.48	0.94	0.511	0.609
BSIZE	−6.085	1.81	−3.362	0.001
BSIZE*IO	1.12	0.47	2.381	0.017
CEOT	1.218	0.324	3.758	0
CEOT*IO	0.99	0.36	2.75	0.006
DFE	134.22	6.64	20.21	0
DFE*IO	8.34	2.21	3.775	0
DFNE	−1.935	1.06	−1.826	0.068
DFNE*IO	−0.98	0.42	−2.333	0.02
R-squared	0.472	Mean dependent var		498.062
Adjusted R-squared	0.47	S.D. dependent var		87.392
S.E. of regression	63.85	Akaike info criterion		11.16
F-statistic	195.665	Durbin-Watson stat		1.828
Prob (F-statistic)	0

Table 14 looks at how SO affects link between board qualities and business risk. Constant is negative and very important (−368.44, p = 0.000). Model accounts for 40.1% of variance in business risk (R² = 0.401, Adjusted R² = 0.399), which is a little less than institutional ownership but still more than managerial and family ownership. F-statistic (165.442, p = 0.000) shows that overall significance is high, and Durbin-Watson statistic (1.805) shows that there are no problems with autocorrelation. State ownership acts as a stabilizing moderator, unlike FO, MO, and IO.

Table 14.

State ownership moderates relation between board characteristics and firm risk.

Variable	Coefficient	Std. Error	t-Statistic	Prob.
C	−368.44	28.05	−13.135	0
BC	9.025	2.84	3.177	0.002
SO	−3.18	1.26	−2.524	0.012
BC*SO	−1.21	0.52	−2.327	0.02
BD	−3	3.16	−0.949	0.343
BD*SO	0.28	0.67	0.418	0.676
BSIZE	−6.095	1.82	−3.35	0.001
BSIZE*SO	−0.89	0.4	−2.225	0.026
CEOT	1.23	0.326	3.772	0
CEOT*SO	−0.64	0.25	−2.56	0.011
DFE	133.91	6.65	20.126	0
DFE*SO	−4.12	1.83	−2.251	0.025
DFNE	−1.95	1.065	−1.831	0.067
DFNE*SO	−0.72	0.31	−2.322	0.02
R-squared	0.401	Mean dependent var		498.062
Adjusted R-squared	0.399	S.D. dependent var		87.392
S.E. of regression	67.45	Akaike info criterion		11.25
F-statistic	165.442	Durbin-Watson stat		1.805
Prob (F-statistic)	0

From Table 15, H₁ is accepted due to strongly support as board changes (BC) are consistently positive and significant across all ownership models. For example, coefficients such as 8.92 (p = 0.001), 9.10 (p = 0.001), and 9.02 (p = 0.002) confirm that frequent board changes increase firm risk. H₂ is rejected due to findings do not support this hypothesis. Board diversity (BD) remains negative but statistically insignificant across all models, with results such as −2.98 (p = 0.34) and −3.02 (p = 0.342). H₃ is accepted because Board size (BSIZE) consistently exhibits a negative and highly significant relationship with firm risk. Coefficients −6.2 (p = 0.001), −6.11 (p = 0.001), and −6.095 (p = 0.001) show that bigger boards lower risk for companies. This means that larger boards have more diverse skills, better oversight, and more balanced decision-making, all of which lower risk of exposure. H₄ is accepted. Results strongly support this hypothesis because CEOT is always positive and very important across all models. For example, coefficients like 1.21 (p = 0.000), 1.225 (p = 0.000), and 1.23 (p = 0.000) show that having a CEO for a longer time usually makes a company riskier. This implies that entrenched CEOs might choose more perilous practices, potentially attributable to overconfidence or diminished board monitoring over time. H₅ is acceptable. Director foreign experience (DFE) had a strong positive effect on company risk in all models, with coefficients of 132.56 (p = 0.000), 133.845 (p = 0.000), and 134.22 (p = 0.000). H₆ is partially accepted. Director financial experience (DFNE) is consistently negative but only moderately significant across models (e.g., −1.92, p = 0.071; −1.95, p = 0.067). This indicates that financial experience may mitigate risk; nevertheless, effect is limited and lacks strong conclusiveness. H₇ is accepted.

Table 15.

Hypotheses testing results.

Hypothesis	Statements	Verdict
H₁	Board changes have a significant impact on firm risk.	Accepted
H₂	Board diversity significantly affects firm risk.	Rejected
H₃	Board size significantly influences firm risk.	Accepted
H₄	CEO tenure and experience is significantly associated with firm risk.	Accepted
H₅	Director foreign experience has significant effects on firm risk.	Accepted
H₆	Director financial experience has significant effects on firm risk.	Partially Accepted
H₇	Ownership structure moderates’ relation between board characteristics and firm risk.	Accepted

Bold values indicate statistically significant results.

Conclusion and recommendation

The insignificant relationship between board diversity and firm risk suggests that diversity alone does not automatically translate into effective risk governance in emerging economies. In Pakistan’s institutional environment, symbolic diversity may exist without functional influence on decision-making. Additionally, concentrated ownership structures may override board-level heterogeneity, limiting the practical impact of diverse boards.

The study examined how board qualities and ownership arrangements affect corporate risk, with family, management, institutional, and state ownership moderating. Results support most current research suggesting board dynamics and ownership arrangements affect business risk. A bigger board can reduce risk, but board and CEO competence changes usually increase risk. Director with foreign experience increases firm risk and shows global strategy emphasis. However, financial expertise is less significant.

Moderated by ownership, governance results are contextual, highlighting corporate governance–risk complexity. BC positively and significantly affects firm risk in all models, supporting the assumption that leadership changes increase risk. BD Showed a negative but small influence, suggesting that diversity alone may not generate hazardous outcomes in the organizations examined. Size has a negative and significant influence; therefore, larger boards reduce firm risk by enhancing monitoring and control. CEOT exhibited a positive effect, suggesting that experienced CEOs are more risk-taking. DFE Positive and significant, showing that exposure to other countries increases risk-taking. DFNE has a small negative influence on business risk. FO, MO, IO, and SO greatly affected board characteristics and company risk.

Researchers might examine longitudinal data from different organizations and nations to discover if this study’s findings apply across institutions. Interviews and case studies can show how boardroom decisions affect risk. Study will benefit from independent directors, women on boards, and board committee structure. Private equity, foreign direct investments, and cross-holdings might complicate board-risk dynamics.

Policy implications

• Regulators should strengthen board appointment frameworks to ensure functional independence.

• Ownership transparency should be improved to reduce excessive control effects.

• Institutional investors should be encouraged to adopt active monitoring roles.

• State ownership requires governance reforms to balance control and efficiency.

Footnotes

ORCID iD

Shazia Rashid

Funding

The authors received no financial support for the research, authorship, and/or publication of this article.

Declaration of conflicting interests

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

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