Establishing the Relationship Between the Capital Structure, Intellectual Capital, and Financial Performance of SSA Insurance Companies

Abstract

This research examines the relationship between capital structure, intellectual capital, and financial performance among insurance companies in Sub-Saharan Africa (SSA). Anchored in a positivist paradigm, the study employed descriptive and quantitative methodologies, leveraging secondary panel data spanning from 2010 to 2022 across 122 insurance firms sampled from a population of 178 companies across 46 SSA countries. Utilizing a Panel Vector Error Correction Model (P-VECM), the analysis explored long-term equilibrium relationships and dynamic interactions among variables, including return on assets (ROAs), debt-to-equity ratio (DER), long-term debt (LTD), short-term debt (STD), Value-Added Intellectual Coefficient (VAIC™), and firm size (SIZE). Optimal lag lengths were determined through robust statistical criteria, ensuring model precision. The impulse response analysis revealed significant findings: variations in ROA negatively impacted intellectual capital (VAIC), leverage indicators (DER, LTD, and STD), and positively influenced firm size over a ten-period horizon. Specifically, decreases in ROA were consistently associated with reduced intellectual capital effectiveness and adverse financial liquidity conditions, while increased firm size correlated positively with improved financial performance.

1. Background

The capital structure (CS) of a firm, defined as the proportionate mix of debt and equity financing utilized to fund its operations, constitutes a foundational pillar of corporate financial management. This mix is instrumental in influencing a firm’s financial stability, risk profile, and growth trajectory (Amin & Cek, 2023). An optimal capital structure is critical as it ensures not only the minimization of the cost of capital but also the maximization of shareholder value (Obadire et al., 2023). In the insurance industry, which operates within highly regulated environments and underpins the broader financial ecosystem, achieving an appropriate CS is particularly vital (Mbonu & Amahalu, 2021). Khan et al. (2021) opined that it is essential for meeting stringent regulatory capital requirements, which safeguard the solvency and sustainability of insurance firms. Furthermore, a well-calibrated capital structure enhances an insurer’s ability to underwrite risks effectively, absorb shocks from adverse events, and honor policyholder obligations, thus fostering trust and stability in the market.

The structure of a firm’s liabilities and equity can also affect how effectively intellectual capital (IC) is leveraged (Asutay & Ubaidillah, 2024). X. Liu et al. (2014) observed that high levels of debt can impose financial discipline, encouraging firms to maximize IC productivity to ensure profitability and meet obligations. However, excessive debt may hinder innovation and long-term investment in IC due to the pressure of debt servicing. Conversely, equity financing provides flexibility for intellectual capital development but risks ownership dilution and reduced incentives for aggressive utilization (Ahmad, 2024). Financial liquidity, a vital aspect of CS, ensures the availability of liquid assets to meet obligations and seize opportunities (D’Amato, 2021). In the insurance sector, robust liquidity is critical for underwriting, prompt claims settlement, and regulatory compliance to maintain solvency and policyholder trust.

In parallel, the concept of IC has gained increasing recognition as a critical determinant of organizational performance, particularly in knowledge-intensive sectors such as insurance (Khan et al., 2021). IC encompasses three interrelated components: human capital, structural capital, and relational capital (Shahdadi et al., 2020). Human capital refers to the knowledge, skills, and competencies of an organization’s workforce, which are essential for innovation, decision-making, and service delivery. Structural capital includes the systems, processes, databases, and intellectual property that enable an organization to function efficiently and retain knowledge independent of individual employees. Relational capital pertains to the quality and value of an organization’s relationships with external stakeholders, including customers, suppliers, and regulators.

In the insurance industry, where the intangible aspects of service delivery and risk management predominate, intellectual capital plays a pivotal role in sustaining competitive advantage and driving financial performance (FP) (Alipour, 2012). Skilled human capital is essential for actuarial precision, effective risk assessment, and tailored product offerings (Zakery & Afrazeh, 2015). Robust structural capital enables streamlined operations, scalability, and compliance with complex regulatory frameworks (Andreeva et al., 2021). Strong relational capital fosters customer loyalty, brand equity, and market penetration, which are indispensable in an industry heavily reliant on trust and reputation (Asare et al., 2017). The interplay between CS and IC becomes particularly significant in the insurance sector, as both elements collectively shape a firm’s operational and strategic capabilities. While CS determines the financial capacity of insurers to underwrite risks and invest in growth initiatives, IC ensures the effective utilization of these resources to generate sustainable value (Ayuba et al., 2019).

In Sub-Saharan Africa (SSA), the insurance sector presents a unique landscape characterized by significant growth potential yet hindered by various structural challenges. According to Atlas Magazine (2022), the report noted that insurance penetration in SSA remains notably low compared to global standards. The average insurance penetration rate in the region is approximately 0.9% of the GDP, which is the lowest among all global regions when excluding South Africa, which significantly skews the overall figures due to its higher penetration rate of about 14.2%. This low penetration indicates untapped markets but also reflects issues, such as limited consumer awareness, economic instability, and regulatory hurdles (KPMG, 2014). Moreover, Abrego-Perez et al. (2023) noted that SSA insurance companies often grapple with undercapitalization. A report by the KPMG (2023) and the Swiss Re Institute (2024) highlighted most insurers recorded improvements in solvency, liquidity, and profitability at the beginning of 2024 due to higher interest rates, higher premium income, lower dividend payments, and an upturn in financial markets. These capital constraints limit their capacity to expand operations, invest in new technologies, and enhance service delivery. Intellectual capital deficiencies further exacerbate these challenges. The World Bank’s Human Capital Index 2020 revealed that SSA countries, on average, score 0.4 out of a possible 1, indicating that the future productivity of the workforce is less than half of what it could be if people enjoyed complete education and full health (International Monetary Fund, 2021). In the context of insurance companies, this translates to a scarcity of skilled professionals, inadequate knowledge management systems, and weak customer relationships, which all impede financial performance.

Empirical studies have established a positive relationship between IC and FP in various sectors globally. For instance, Chen et al. (2005) found that IC significantly enhances firm value and profitability in the financial sector of emerging economies. Recent studies have examined the relationship between IC, CS, and FP in insurance companies. Several papers found a positive impact of IC on FP (Nguyen, 2023; Ali et al., 2022; Suryani & Nadhiroh, 2020). Human capital and structural capital were identified as significant components of IC (Umar & Dandago, 2023). However, some studies reported mixed or insignificant effects (Yusuf et al., 2022; Acuña-Opazo & González, 2021). Regarding CS, findings were inconsistent, with some studies showing a negative effect on FP (Marzo & Bonnini, 2023; Anik et al., 2021) and others reporting no significant impact (Ronoowah & Seetanah, 2024; Dalwai & Salehi, 2021; Abdullah & Tursoy, 2021). Leverage was found to have a significant effect on FP in some studies (Bansal, 2025; Arhinful & Radmehr, 2023).

Most of the literature considers CS and IC as separate constructs, and there are few integrative studies that analyze how IC moderates the CS–FP relationship especially in emerging markets. Traditional theories like Modigliani and Miller (1958) are mostly abstract and not well-suited for knowledge-based, service-oriented industries such as insurance in developing countries. Though IC is commonly accepted as a source of firm performance (Pulic, 2004; Bontis, 1998), there is a general lack of a strong conceptual framework that connects CS and IC, particularly in financial institutions where intangible assets have a considerable bearing on profitability. Empirical investigations of IC–FP relations, including Jordão et al. (2025) and Bhatti et al. (2023), tend to concentrate on banks or manufacturing firms, overlooking the insurance sector, which is guided by different risk profiles and business models. In addition, how IC components interact with CS decisions is not well investigated, especially in SSA, where institutional gaps and resource limitations might influence IC use.

Most evidence is biased towards developed economies (e.g., D’Amato, 2021; Suryani & Nadhiroh, 2020), with few cross-country studies in SSA. Although studies like Zeitun and Tian (2014), and Doorasamy (2021) examine CS–FP relations in SSA, they exclude IC considerations, whereas African IC studies (e.g., Ulum & Jati, 2016; Nimtrakoon, 2015) concentrate on non-financial sectors or single-country settings, constraining regional generalizability. More noteworthy, empirical studies that investigate IC’s moderating influence on the CS–FP relationship in SSA are practically non-existent. This research bridges this gap by incorporating CS and IC within a combined analytical framework and presenting new cross-country evidence from 31 SSA nations’ insurance industries. It also innovates methodologically. Existing studies tend to use simplistic specifications, e.g., OLS, fixed-effects, or static panel models that cannot account for long-run relationships, endogeneity, and feedback effects. Pulic’s VAIC™ model, for example, despite its popularity, is routinely estimated in settings that neglect dynamic interactions (e.g., Joshi & Mehta, 2024). African research (e.g., Olarewaju & Msomi, 2021; Ogbulu & Emeni, 2012) seldom considers problems of non-stationarity or cointegration.

This research employs a dynamic panel methodology using the Panel Vector Error Correction Model (P-VECM) in combination with tests for examining lag effects, responses to shocks, and decomposition of variance, as well as reliability tests (such as the Hansen J-test and Arellano–Bond test). This methodology allows us to gain a clearer picture of how CS, IC, and FP relate to each other in the short and long run, in addition to enhancing the validity and reliability of results in the relatively under-researched insurance setting of SSA.

2. Literature Review

2.1. Review of Variables and Prior Studies

2.1.1. Intellectual Capital (IC)

IC has increasingly been recognized as a strategic intangible asset that drives firm competitiveness, especially in knowledge-intensive and service-oriented industries like insurance. It is widely conceptualized using three dominant components, human capital, structural capital, and relational capital, which cumulatively enhance operational efficiency, innovativeness, and stakeholder relations (Ren et al., 2021; Ayuba et al., 2019). In the insurance sector, where trust, service delivery, and compliance are paramount, IC is both a strategic resource and performance driver (Pulic, 2004; Kuttu et al., 2024). Because of IC’s intangible nature, its direct measurement has historically been elusive. In response, Pulic (1998, 2004) formulated the Value-Added Intellectual Coefficient (VAIC™) model, which has been used worldwide as an empirical instrument of choice in measuring IC efficiency. The VAIC™ framework analyzes how efficiently a firm is using its intellectual resources by estimating the contribution of human capital efficiency, structural capital efficiency, and capital-employed efficiency towards value creation. It captures the capacity of a firm to create economic value from tangible and intangible inputs and provides a strong proxy for strategic capability in knowledge-based contexts.

Financially, high VAIC scores are often correlated with improved profitability, asset turnover, and return on equity, especially in the insurance and financial industries where innovation, client services, and expert judgment are at the core of success (Ahmad, 2024; Setianto & Sukmana, 2016). Companies with higher VAIC usually have better return on assets (ROAs) and return on equity (ROE) since they are better able to convert intellectual inputs into operational outputs. Empirical research (e.g., Alipour, 2012; Jin & Xu, 2022) has proved that insurance companies with strong IC management, as manifested through high VAIC figures, perform better than counterparts with less robust IC structures. Such a positive correlation is due to more effective claim management, improved risk underwriting, customer loyalty, and strategic responsiveness.

Aside from performance, VAIC also affects a firm’s CS decisions. Companies with high intellectual capital efficiency tend to be viewed as more resilient and strategically viable, and this can enhance investor confidence and ease access to equity finance (Pulic, 2004; Handayani & Arrozi, 2023). The impact of VAIC on debt finance, however, is less straightforward. On the one hand, companies with strong IC profiles and high VAIC may enjoy easier access to favorable loan terms, especially if they show stable cash flows and effective risk management skills (Dalwai & Sewpersadh, 2023). On the other hand, because intellectual assets are not readily collateralizable, lenders might still exercise caution, especially in emerging markets such as SSA where credit evaluation frameworks are underdeveloped (Honjo, 2021; Troise et al., 2022). This can prompt firms with high VAIC to take on more conservative capital structures, with a preference for equity over debt in order to maintain financial flexibility and avoid restrictions that come with high leverage (Zareian et al., 2024).

In addition, VAIC is also able to affect the debt maturity structure. High IC companies would likely favor long-term debt to fund innovation and strategic investments, matching financing horizons with value creation cycles (G. Liu et al., 2021). But lenders may only provide short-term facilities owing to perceived risks related to intangible-heavy balance sheets. This has the potential to create a mismatch between a company’s strategic intention and financing constraints. Thus, while VAIC positively affects firm value and operational efficiency, it also creates capital sourcing challenges, highlighting the need for transparent reporting and investor communication in markets where IC is yet to be fully incorporated in financial valuation models.

2.1.2. Capital Structure (CS)

CS is the strategic mix of debt and equity employed to fund a firm’s assets, operations, and growth opportunities. It directly affects a company’s financial flexibility, risk profile, cost of capital, and overall performance. For insurance companies, CS decisions are important owing to strict regulatory capital requirements, high operating uncertainty, and the necessity to ensure solvency and liquidity (Li et al., 2023; Ahmed & Bhuyan, 2020). An optimal CS not only guarantees sufficient funding but also balances risk and return trade-offs, allowing firms to absorb shocks and take advantage of profitable investment opportunities. In this research, capital structure is operationalized with three major indicators: debt-to-equity ratio (DER), short-term debt (STD), and long-term debt (LTD). Each of these measures captures a different aspect of financial leverage and maturity structure that informs the firm’s financing strategy and its relation to IC and firm performance.

Debt-to-Equity Ratio (DER)

The DER indicates the percentage of a company’s funding that is financed through debt compared to equity. A higher DER means higher financial leverage, which can amplify returns but also increases default and insolvency risk. For insurance companies, having a sustainable DER is important for regulatory solvency margin requirements and to prevent capital adequacy penalties. Intellectual capital is a key driver of DER levels. Companies with quality human capital in the form of experienced actuaries, underwriters, and financial managers have better internal capital generation capacity, with less reliance on external debt (Supriati et al., 2019). Similarly, structural capital in the form of sophisticated IT infrastructure, standardized underwriting processes, and embedded risk evaluation models can minimize operating volatility and increase investor confidence (Pulic, 2004; Milanda et al., 2022). Consequently, such companies tend to receive greater equity investment and have lower DER ratios. In addition, good intellectual capital management minimizes information asymmetry and facilitates transparent reporting, which enhances firms’ attractiveness to equity markets further (Handayani & Arrozi, 2023). Nevertheless, in those markets with less developed equity infrastructures such as most SSA economies, firms can still use debt financing, which underlines the context-contingent nature of DER-IC relations.

Short-Term Debt (STD)

STD is an obligation falling due within one year and is generally employed for the management of working capital, liquidity, and operational contingency necessities. Though STD may provide low-cost liquidity in the short term, its overdependence enhances rollover risk as well as financial vulnerability particularly in economic downturns or when credit tightening occurs. IC has a direct impact on a firm’s dependence on and management of STD. For instance, its relational capital, in the form of long-term relationships with bankers, suppliers, and customers, assists firms in securing favorable terms for short-term borrowing, which lowers interest expenses and enhances liquidity (Phurahong et al., 2023). In addition, structural capital through effective data analytics, forecasting systems, and internal controls facilitates accurate cash flow planning, which assists firms in refraining from unnecessary short-term borrowing (Stropnik et al., 2017). Companies with well-developed IC systems are more able to match liquidity requirements and reduce short-term obligations, thereby improving operating resilience. Those without these capabilities might overuse STD to finance operating obligations, which raises their vulnerability to liquidity shocks. This correlation is particularly strong in the environment, where cash flow predictability and short-term liquidity buffers are extremely important for claim settlement and regulatory compliance.

Long-Term Debt (LTD)

LTD has maturities of more than one year and is often used to finance strategic investments, including digital infrastructure, market development, acquisitions, and innovation projects. Though LTD offers a more permanent source of capital and matches long-term planning horizons, it also comes with interest requirements and covenant limitations that can restrict managerial flexibility. Companies with strong intellectual capital are likely to have better access to LTD on favorable terms because of increased credibility, effectiveness, and governance. Human capital empowers companies with the strategic vision and risk management competencies necessary to take credible long-term investment choices and uphold credit discipline (Pradana & Chalid, 2023; Wu et al., 2022). Likewise, relational capital, like confidence with institutional lenders and regulators, decreases perceived credit risk, allowing access to patient capital. However, the abstract nature of intellectual capital can be a double-edged sword. According to G. Liu et al. (2021), most lenders are unwilling to extend long-term funding to companies without adequate tangible collateral, especially in developing markets. This translates into shorter maturity profiles, interest rate premiums, or restrictive covenants. Therefore, although IC reinforces a company’s strategic and reputational footprint, it might not necessarily offset the absence of hard assets during debt negotiations, indicating of a major limitation for insurance companies. In addition, the interplay between LTD and IC can influence the financial strategy of the company. Firms with high IC can strategically restrict LTD use to ensure financial flexibility, prevent over-leverage, and sustain a high innovation and growth focus (Zareian et al., 2024). But where LTD is undertaken, such companies are generally well-placed to mitigate related risks and utilize the financing for long-term value creation.

2.1.3. Financial Performance (FP)

FP is measured using return on assets (ROAs), a robust indicator that reflects how effectively a firm utilizes its total assets to generate profits. ROA is particularly relevant in the insurance sector, where profitability hinges not only on premium income but also on prudent underwriting, investment strategy, claims management, and cost control. Insurance companies with high ROAs are generally better at aligning risk exposure with returns, optimizing operational efficiency, and managing reserve levels for claims and regulatory requirements. A critical determinant of financial performance is IC, particularly when measured through the Value-Added Intellectual Coefficient (VAIC™). Firms with high VAIC scores typically achieve superior performance outcomes due to their ability to leverage intangible assets, like employee expertise, proprietary systems, and client relationships. These firms benefit from better customer service, process innovation, and strategic agility, all of which contribute to profitability and long-term competitiveness (Alipour, 2012; Ben Dhiab, 2021; Ahmad, 2024). In the insurance industry, where customer loyalty and trust are essential, relational capital and service reputation can translate into sustained premium income and reduced policyholder churn (Setianto & Sukmana, 2016).

The link between FP and CS is equally critical. While moderate levels of debt may enhance performance by providing capital for expansion and investment, excessive reliance on debt raises financial risk, elevates interest obligations, and increases vulnerability to external shocks. In underdeveloped capital markets, high debt levels may also restrict operational flexibility and weaken solvency positions. Importantly, intellectual capital serves as a moderating force in this relationship. Firms with strong IC are better positioned to utilize borrowed funds efficiently, enhance productivity, and mitigate agency costs associated with leverage (Zareian et al., 2024; Sitompul et al., 2020). Moreover, IC enhances internal control systems and risk management practices, allowing firms to maintain financial discipline while pursuing strategic growth.

An often overlooked but significant factor in the performance equation is firm size. Larger insurance firms generally have access to more diverse funding sources, benefit from economies of scale, and are better equipped to absorb operational shocks. Their established brand presence and wider client base enhance revenue stability and bargaining power in financial markets. However, firm size can also bring complexity and bureaucracy, which may reduce operational responsiveness. The interplay between firm size and IC is particularly important; large firms with well-developed intellectual capital frameworks are likely to experience compounding performance benefits, while those without strong IC systems may struggle with inefficiency despite their scale.

In the SSA insurance, where firms operate in challenging environments marked by regulatory volatility, limited access to long-term capital, and low financial inclusion, the interaction among IC, capital structure, and firm size is especially consequential. Firms that effectively manage intellectual capital not only enhance ROAs but also build resilience against systemic risks, strengthen their reputation among investors and regulators, and unlock more strategic financing options. Thus, financial performance is not merely a function of resource availability but a product of how well those resources, both tangible and intangible, are deployed and aligned with the firm’s structural and strategic realities.

2.2. Theoretical Model

This study is founded on two theories that complement each other very well: the resource-based view (RBV) and Signaling Theory. Combined, they offer a robust model for examining how CS, IC, and FP are interconnected. Barney (1991) developed the RBV, which posits that a firm can only achieve and sustain a competitive advantage if it has access to valuable, rare, unique, and non-substitutable resources. For insurance firms in institutional environments that are not well developed, IC is a strategic asset that underpins operating success, innovation, and long-term value creation. Since the insurance business is so information-dependent and intangible resources are increasingly critical to generating income, IC has emerged as a valuable internal competency. The RBV posits that firms with superior IC efficiency can extract more value from their financing arrangements since they are able to convert money from loans or retained earnings into such things as innovation, customer loyalty, and productivity gains. IC also explains why the operating success of firms that appear similar in terms of the structure of their finances is not always equal, particularly in environments with constrained resources such as SSA.

Signaling Theory complements the RBV and explains how firms convey to the market attributes that may not be observable by examining such things as their capital structure decisions. Signaling Theory was originally developed by Spence in 1973 and subsequently applied to corporate finance by Ross in 1977. It states that the financing choice of a firm, whether debt or stock, communicates to investors and other stakeholders how much confidence the firm has in its future performance. Here, capital structure decisions reflect how reliable and healthy a firm is financially. Yet, the interpretation of these signals is contingent on the development of the market, quality of institutions, and absence of equal access to information. All these issues are prevalent in SSA nations. Intellectual capital matters also because it can serve as a secondary signal. That is, when a firm invests in individuals, information systems, and relationships with stakeholders, it indicates that it has a long-term orientation and that it is healthy.

Thus, Signaling Theory supports the argument that IC not only enhances the direct performance of the firm but also alters the perception of its capital structure and the efficiency of its utilization. All these scholarly perspectives support one fundamental notion: how a firm’s capital structure decisions influence its success is a function of its IC profile. That is, firms with superior IC are more capable of managing their indebtedness, obtaining external financing for new ideas, and demonstrating to potential investors that they are creditworthy. Conversely, firms with poor IC might find that assuming more debt makes them inefficient or results in incorrect inferences by the market, particularly where there are institutional voids.

2.3. Hypothesis Development

Taking the RBV and Signaling Theory as a foundation and borrowing from the practical experiences of the insurance sector in Sub-Saharan Africa, the study derives a set of testable hypotheses to examine how capital structure, intellectual capital, and firm performance relate. The RBV illustrates how vital intangible assets, such as intellectual capital, are to a company’s long-term economic advantage and improved outcomes. Meanwhile, Signaling Theory emphasizes how decisions regarding a company’s capital structure convey valuable information to the market regarding its quality and financial health. Taking perspectives from these angles, the study proposes that intellectual capital not only enhances the performance of a business but also alters how effective and visible decisions to take on debt are. There are five hypotheses that attempt to describe the anticipated direction of relationships among some key concepts. These entail the use of return on assets (ROAs) as a measure of how well a business is performing financially, VAIC as a means of expressing intellectual capital, and various components of a business’s financial structure, including DER, STD, LTD, and SIZE.

H1. 

VAIC has a significant positive impact on FP of insurance firms.

H2. 

DER has a significant negative effect on FP of insurance firms.

H3. 

STD is negatively associated with FP of insurance firms.

H4. 

LTD negatively affects FP of insurance firms.

H5. 

SIZE has a significant positive relationship with FP of insurance firms.

3. Methodology

This research employed a positivist paradigm and adopted a descriptive and quantitative methodology, with a focus on the insurance sector within Sub-Saharan Africa. From this list, a sample of 122 companies was selected, calculated through a modified iteration of Cochran’s formula (Mahdi & Hussein, 2023). This statistical formula calculates an ideal sample size for a survey or study when the population is large or unknown, ensuring that the sample accurately represents the population with a given confidence level and margin of error. The sampled companies were from 31 Sub-Saharan African countries: Angola, Botswana, Cameroon, Côte d’Ivoire, Gabon, Kenya, Lesotho, Mauritania, Mauritius, Namibia, Nigeria, South Africa, Zambia, Burkina Faso, Burundi, Central African Republic, Chad, Congo, Democratic Republic of Congo, Ethiopia, Madagascar, Malawi, Mali, Mozambique, Niger, Rwanda, Senegal, Sierra Leone, Tanzania, Togo, and Uganda.

These countries were categorized into 18 low-income and 13 middle-income countries. The low-income countries were Burkina Faso, Burundi, Central African Republic, Chad, Sierra Leone, Congo, Democratic Republic of Congo, Ethiopia, Malawi, Madagascar, Mali, Mozambique, Niger, Rwanda, Senegal, Tanzania, Togo, and Uganda. The middle-income countries were Zambia, Angola, Botswana, Côte d’Ivoire, Cameroon, Gabon, Kenya, Lesotho, Mauritius, Mauritania, Namibia, Nigeria, and South Africa. Secondary data were collected from reliable sources, including Wharton Research Data Services (WRDS), S&P CapitalIQ, and Refinitiv Eikon. The choice of 2010 as the starting year was deliberate, allowing for the analysis of trends and transformations in the insurance industry following the Great Financial Crisis. These observations provided insights into the industry’s resilience and adaptive strategies to economic challenges.

The study utilized panel data with 1464 observations over 12 years, ensuring a comprehensive dataset that captured the nuances of insurance companies’ performance across the region. A Panel Vector Error Correction Model (P-VECM) was employed, requiring at least two cointegrated variables, as per Amaluddin (2019). Unlike static models, the P-VECM simultaneously models short-run adjustments and long-run causality, making it well-suited for understanding how deviations from long-run equilibrium correct over time. The P-VECM is appropriate when the variables under study are non-stationary at level (I(1)) but cointegrated, indicating a long-run equilibrium relationship. Optimal lag selection was conducted using criteria such as the corrected Akaike Information Criterion (CAIC), Akaike Information Criterion (AIC), Schwarz Bayesian Criterion (SBC), and Hanna–Quinn Criterion (HQC). The primary model estimated relationships between key variables, including return on assets (ROAs), debt-to-equity ratio (DER), long-term debt (LTD), short-term debt (STD), Value-Added Intellectual Coefficient (VAIC™), and firm size (SIZ), with the latter serving as a control variable. The model incorporated lagged Error Correction Terms (ECTs) to account for the speed of adjustment toward long-run equilibrium relationships.

First, to test for autocorrelation, the Arellano–Bond test was considered valid and thus employed.

Assuming G was a data matrix, $G^{-1}$ was defined as its $j$ lag with zeroes for $t\le j$. The Arellano–Bond test was based on $\left(1/N\right)\sum _i{\widehat{E}}_i^{-1}{\widehat{E}}_i$. Assuming errors were sufficiently uncorrelated across individuals, a central limit theorem implied that the statistics are asymptotically normally distributed as shown below:

$$\sqrt{N}{\displaystyle \frac{1}{N}}\sum _i{\widehat{E}}_i^{-1}{\widehat{E}}_i={\displaystyle \frac{1}{\sqrt{N}}}{\widehat{E}}^{-1}\widehat{E}$$

Second, to test the over-identification of instruments, the Hansen (1982) J-test of over-identifying restrictions was considered valid and was utilized. Additionally, the instruments employed in the analysis were tested for their validity. Another robustness test that was conducted was the predictive marginal test, which was used to assess the predictive marginal mean.

Notably, the main contribution of the model, to the best of the researchers’ knowledge, was that this study was the first to investigate the moderating effect of IC on the relationship between CS and FP. The research highlighted that achieving an optimal CS, which significantly impacted the FP of any organization, was influenced by the HC of that organization. IC, as conceptualized in the study, comprised three main components: RC, SC, and HC.

Relational capital encompassed the external relationships of the organization; SC referred to its internal processes and systems; and HC included the skills, knowledge, and experiences of employees. These components functioned collectively to drive the economic value of the organization, particularly through policies related to CS. According to Bhatti et al. (2023) and Jordão et al. (2025), IC played a critical role in developing a company’s competitive advantage, which in turn boosted its performance.

The HC component of IC was especially significant, as it included the collective skills, knowledge, and experiences of employees that contributed to economic value creation, including the formulation and implementation of CS policies. The study’s model and the choice of dependent variables were aligned with the work. However, the current research introduced a moderating variable, IC, which was the primary innovative contribution of the objective. This addition aimed to deepen the understanding of how IC influenced the relationship between CS and FP, thus providing new insights into strategic financial decision-making.

The following models will be estimated:

$$RO{A}_{it}={\theta }_0+{\partial }_1{DER}_{it}+{\alpha }_2{LTD}_{it}+{\gamma }_3{STD}_{it}+{\phi }_4{VAIC}_{it}^{TM}+{\vartheta }_5{SIZ}_{it}+{\mu }_{it}$$

(1)

$$\left[\begin{array}{c}\mathit{\Delta }RO{A}_{it}\\ \mathit{\Delta }{DER}_{it}\\ \mathit{\Delta }{LTD}_{it}\\ \begin{array}{c}\mathit{\Delta }{STD}_{it}\\ \mathit{\Delta }{VAIC}_{it}^{TM}\\ ∆{SIZ}_{it}\end{array}\end{array}\right]=\left[\begin{array}{c}{\theta }_0\\ {\partial }_0\\ {\alpha }_0\\ \begin{array}{c}{\gamma }_0\\ {\phi }_0\\ {\vartheta }_0\end{array}\end{array}\right]{\sum }_{p=1}^m⌈\begin{array}{c}\mathit{\Delta }{\theta }_{1i}\\ \mathit{\Delta }{\partial }_{1i}\\ \mathit{\Delta }{\alpha }_{1i}\\ \begin{array}{c}\mathit{\Delta }{\gamma }_{1i}\\ \mathit{\Delta }{\phi }_{1i}\\ ∆{\vartheta }_{1i}\end{array}\end{array}\begin{array}{c}\mathit{\Delta }{\theta }_{2i}\\ \mathit{\Delta }{\partial }_{2i}\\ \mathit{\Delta }{\alpha }_{2i}\\ \begin{array}{c}\mathit{\Delta }{\gamma }_{2i}\\ \mathit{\Delta }{\phi }_{2i}\\ ∆{\vartheta }_{2i}\end{array}\end{array}\begin{array}{c}\mathit{\Delta }{\theta }_{3i}\\ \mathit{\Delta }{\partial }_{3i}\\ \mathit{\Delta }{\alpha }_{3i}\\ \begin{array}{c}\mathit{\Delta }{\gamma }_{3i}\\ \mathit{\Delta }{\phi }_{3i}\\ ∆{\vartheta }_{3i}\end{array}\end{array}\begin{array}{c}\mathit{\Delta }{\theta }_{4i}\\ \mathit{\Delta }{\partial }_{4i}\\ \mathit{\Delta }{\alpha }_{4i}\\ \begin{array}{c}\mathit{\Delta }{\gamma }_{4i}\\ \mathit{\Delta }{\phi }_{4i}\\ ∆{\vartheta }_{4i}\end{array}\end{array}\begin{array}{c}\mathit{\Delta }{\theta }_{5i}\\ \mathit{\Delta }{\partial }_{5i}\\ \mathit{\Delta }{\alpha }_{5i}\\ \begin{array}{c}\mathit{\Delta }{\gamma }_{5i}\\ \mathit{\Delta }{\phi }_{5i}\\ ∆{\vartheta }_{5i}\end{array}\end{array}\begin{array}{c}\mathit{\Delta }{\theta }_{6i}\\ \mathit{\Delta }{\partial }_{6i}\\ \mathit{\Delta }{\alpha }_{6i}\\ \begin{array}{c}\mathit{\Delta }{\gamma }_{6i}\\ \mathit{\Delta }{\phi }_{5i}\\ ∆{\vartheta }_{6i}\end{array}\end{array}⌉\left[\begin{array}{c}\mathit{\Delta }{ROA}_{i(t-p)}\\ \mathit{\Delta }{DER}_{i(t-p)}\\ \mathit{\Delta }{LTD}_{i(t-p)}\\ \begin{array}{c}\mathit{\Delta }{STD}_{i(t-p)}\\ \mathit{\Delta }{VAIC}_{it}^{TM}\\ ∆{SIZ}_{i(t-p)}\end{array}\end{array}\right]+\left[\begin{array}{c}{\theta }_6\\ {\partial }_6\\ {\alpha }_6\\ \begin{array}{c}{\gamma }_6\\ {\gamma }_6\\ {\gamma }_0\end{array}\end{array}\right]EC{T}_{t-1}+\left[\begin{array}{c}{\mu }_{1it}\\ {\mu }_{2it}\\ {\mu }_{3it}\\ \begin{array}{c}{\mu }_{4it}\\ {\mu }_{5it}\\ {\mu }_{6it}\end{array}\end{array}\right]$$

Robustness tests were conducted to validate the model, including lag selection, lag exclusion, stability, impulse response, variable decomposition, and block endogeneity Wald tests. The impulse response and variable decomposition analyses provided insights into the dynamic interrelations and impacts of random shocks on the variables. The Wald test established causality between variables, using chi-square or F-test distributions for validation, in line with the methodologies proposed by Sun (2013). This study’s use of P-VECM is a novel approach in the Sub-Saharan African insurance context. By addressing potential multicollinearity inherent in time-series data and summarizing long-run effects in an interpretable level matrix, the model contributes to understanding the causal relationships among key financial variables. To make sure the model is valid and reliable, tests have been performed on cross-sectional dependence (Pesaran’s CD test) along with heteroskedasticity (modified Wald test). Missing data were treated using listwise deletion, which excludes any firm-year observation with missing values across the required variables. This approach ensures consistency in the panel dataset and avoids biases introduced through imputation in financial performance metrics.

4. Analysis and Discussion

4.1. Panel Vector Autoregressive Model (ROA)

The presence of unit root and stationarity at level and cointegration among the financial performance measured by return on assets (ROA), intellectual capital (VAIC), financial liquidity (DER, STD, and LTD), and SIZE were established, thus emphasizing the short- and long-run equilibrium relationship or stability among the variables. Based on this evidence, the panel vector autoregressive (PVAR) model was employed to examine the interrelationship among the financial performance, intellectual capital, and financial liquidity identified for this study. The stability condition check and the variance decomposition and impulse response on the financial variables are carried out to validate and justify the appropriateness of the fitted PVAR model. Thus, the results for the stability condition check are presented in

Table 1. Panel vector autoregressive model stability condition check.

Roots of Characteristic Polynomial
Endogenous variables: ROA, VAIC, DER, STD, LTD, SIZE
Root	Modulus
1.14 × 10−13	1.14 × 10−13
8.28 × 10−15	8.28 × 10−15
−2.05 × 10−15	2.05 × 10−15
1.51 × 10−15	1.51 × 10−15
−3.54 × 10−16	3.54 × 10−16
−9.19 × 10−17	9.19 × 10−17

Source: Author’s own computation (2025).

.

Table 1 showed the results of the panel vector autoregressive model stability condition for the effect of the VAIC, DER, STD, LTD, and SIZE on the ROA of the insurance firms. Here, the rule of thumb is that if any root or eigen value is greater than one, then the PVAR model fails the stability test. However, it was found from the results in Table 1 that all the roots or the eigen values are less than one. Thus, the eigen values were within the unit circle and as such indicated that the PVAR model satisfied the stability condition. Thus, it can be used for policy formation and implementation with respect to the financial performance of the insurance firms. The graphical representation of the PVAR model stability condition check with the same results in Table 1 are shown in Figure 1.

Having examined the PVAR model in relation to the intellectual capital, financial liquidity, and financial performance variables such as VAIC, DER, STD, LTD and SIZE on ROA, the financial performance measured for the firms under investigation, and having established the stability of the fitted PVAR model, it is imperative to further carry out another evaluation to better enhance the stability of the fitted PVAR model using the variance decomposition and analysis of impulse responses.

4.2. Variance Decomposition of the Financial Variables

Following from the stability condition check, the extent at which the intellectual capital and financial liquidity under consideration shocks affect return on asset among the firms are examined using forecast error variance decompositions. Variance decomposition breaks down variation in an endogenous variable into component shocks to the endogenous variables in the PVAR model. This helps in evaluating the proportionality of the forecast error variation in a variable, which is described by innovations and the other variables. Moreso, variance decompositions show the significance of variance in the projection of each variable in the system, which is assigned to its own shocks in other variables in the system. The method decomposes the variance of the forecast error for every variance resulting from a shock to a specific variable, thereby identifying variables that are greatly impacted by the shocks, as presented in

Table 2. Variance decomposition of return on assets (ROA).

Period	S.E.	ROA	VAIC	DER	STD	LTD	SIZE
1	72,434.70	100.0000	0.0000	0.0000	0.0000	0.0000	0.0000
		(0.0000)	(0.0000)	(0.0000)	(0.0000)	(0.0000)	(0.0000)
2	73,143.26	100.0000	1.26 × 10−28	8.26 × 10−31	4.09 × 10−28	2.24 × 10−29	3.75 × 10−29
		(0.0000)	(1.6 × 10−28)	(8.5 × 10−30)	(4.0 × 10−28)	(5.4 × 10−29)	(6.8 × 10−29)
3	73,157.12	100.0000	1.28 × 10−28	8.42 × 10−31	4.17 × 10−28	2.28 × 10−29	3.82 × 10−29
		(0.0000)	(1.7 × 10−28)	(8.7 × 10−30)	(4.1 × 10−28)	(5.6 × 10−29)	(7.0 × 10−29)
4	73,157.39	100.0000	1.28 × 10−28	8.42 × 10−31	4.17 × 10−28	2.28 × 10−29	3.82 × 10−29
		(0.0000)	(1.7 × 10−28)	(8.7 × 10−30)	(4.1 × 10−28)	(5.6 × 10−29)	(7.0 × 10−29)
5	73,157.40	100.0000	1.28 × 10−28	8.42 × 10−31	4.17 × 10−28	2.28 × 10−29	3.82 × 10−29
		(0.0000)	(1.7 × 10−28)	(8.7 × 10−30)	(4.1 × 10−28)	(5.6 × 10−29)	(7.0 × 10−29)
6	73,157.40	100.0000	1.28 × 10−28	8.42 × 10−31	4.17 × 10−28	2.28 × 10−29	3.82 × 10−29
		(0.0000)	(1.7 × 10−28)	(8.7 × 10−30)	(4.1 × 10−28)	(5.6 × 10−29)	(7.0 × 10−29)
7	73,157.40	100.0000	1.28 × 10−28	8.42 × 10−31	4.17 × 10−28	2.28 × 10−29	3.82 × 10−29
		(0.0000)	(1.7 × 10−28)	(8.7 × 10−30)	(4.1 × 10−28)	(5.6 × 10−29)	(7.0 × 10−29)
8	73,157.40	100.0000	1.28 × 10−28	8.42 × 10−31	4.17 × 10−28	2.28 × 10−29	3.82 × 10−29
		(0.0000)	(1.7 × 10−28)	(8.7 × 10−30)	(4.1 × 10−28)	(5.6 × 10−29)	(7.0 × 10−29)
9	73,157.40	100.0000	1.28 × 10−28	8.42 × 10−31	4.17 × 10−28	2.28 × 10−29	3.82 × 10−29
		(0.0000)	(1.7 × 10−28)	(8.7 × 10−30)	(4.1 × 10−28)	(5.6 × 10−29)	(7.0 × 10−29)
10	73,157.40	100.0000	1.28 × 10−28	8.42 × 10−31	4.17 × 10−28	2.28 × 10−29	3.82 × 10−29
		(0.0000)	(1.7 × 10−28)	(8.7 × 10−30)	(4.1 × 10−28)	(5.6 × 10−29)	(7.0 × 10−29)

Source: Author’s own computation (2025).

.

The variance decomposition of the ROA presented in Table 2 shows that own shocks cause the major source of variation for the variable. The shock result-based ROA was 100% in all ten periods under the insurance firms. Apart from the ROA own shocks, the VAIC, DER, STD, LTD, and SIZE also accounted for variation in the ROA of the firms. Specifically, the shocks in these variables never contributed initially to the shocks in the ROA in the first period, but the contribution of the VAIC, DER, STD, LTD, and SIZE started in the second period, with 1.26 × 10⁻²⁸%, 8.26 × 10⁻³¹%, 4.09 × 10⁻²⁸%, 2.24 × 10⁻²⁹%, and 3.75 × 10⁻²⁹% shocks, respectively, on the ROA. The shocks as a result of the VAIC, DER, STD, LTD, and SIZE on the ROA increased to 1.28 × 10⁻²⁸%, 8.42 × 10⁻³¹%, 4.17 × 10⁻²⁸%, 2.28 × 10⁻²⁹%, and 3.82 × 10⁻²⁹%, respectively, in the third period and the shocks remained at the same level till the tenth period considered for this study.

The variance decomposition of the VAIC presented in

Table 3. Variance decomposition of intellectual capital (VAIC).

Period	S.E.	ROA	VAIC	DER	STD	LTD	SIZE
1	2.66 × 10−13	4.82 × 10−5	99.9999	0.0000	0.0000	0.0000	0.0000
		(0.0542)	(0.0542)	(0.0000)	(0.0000)	(0.0000)	(0.0000)
2	2.66 × 10−13	0.1714	99.8285	1.51 × 10−27	7.65 × 10−26	1.77 × 10−26	1.94 × 10−26
		(0.2137)	(0.2137)	(6.3 × 10−28)	(6.6 × 10−27)	(1.5 × 10−27)	(1.5 × 10−27)
3	2.66 × 10−13	0.1748	99.8251	1.51 × 10−27	7.65 × 10−26	1.77 × 10−26	1.94 × 10−26
		(0.2171)	(0.2171)	(6.3 × 10−28)	(6.6 × 10−27)	(1.5 × 10−27)	(1.5 × 10−27)
4	2.66 × 10−13	0.1749	99.8250	1.51 × 10−27	7.65 × 10−26	1.77 × 10−26	1.94 × 10−26
		(0.2171)	(0.2171)	(6.3 × 10−28)	(6.6 × 10−27)	(1.5 × 10−27)	(1.5 × 10−27)
5	2.66 × 10−13	0.1749	99.8250	1.51 × 10−27	7.65 × 10−26	1.77 × 10−26	1.94 × 10−26
		(0.2171)	(0.2171)	(6.3 × 10−28)	(6.6 × 10−27)	(1.5 × 10−27)	(1.5 × 10−27)
6	2.66 × 10−13	0.1749	99.8250	1.51 × 10−27	7.65 × 10−26	1.77 × 10−26	1.94 × 10−26
		(0.21718)	(0.2171)	(6.3 × 10−28)	(6.6 × 10−27)	(1.5 × 10−27)	(1.5 × 10−27)
7	2.66 × 10−13	0.1749	99.8250	1.51 × 10−27	7.65 × 10−26	1.77 × 10−26	1.94 × 10−26
		(0.2171)	(0.2171)	(6.3 × 10−28)	(6.6 × 10−27)	(1.5 × 10−27)	(1.5 × 10−27)
8	2.66 × 10−13	0.1749	99.8250	1.51 × 10−27	7.65 × 10−26	1.77 × 10−26	1.94 × 10−26
		(0.2171)	(0.2171)	(6.3 × 10−28)	(6.6 × 10−27)	(1.5 × 10−27)	(1.5 × 10−27)
9	2.66 × 10−13	0.1749	99.8250	1.51 × 10−27	7.65 × 10−26	1.77 × 10−26	1.94 × 10−26
		(0.2171)	(0.2171)	(6.3 × 10−28)	(6.6 × 10−27)	(1.5 × 10−27)	(1.5 × 10−27)
10	2.66 × 10−13	0.1749	99.8250	1.51 × 10−27	7.65 × 10−26	1.77 × 10−26	1.94 × 10−26
		(0.2171)	(0.2171)	(6.3 × 10−28)	(6.6 × 10−27)	(1.5 × 10−27)	(1.5 × 10−27)

Source: Author’s own computation (2025).

shows that own shocks cause the major source of variation for the variable. The shock result-based VAIC was 99.9999% in the first period. The shock result-based VAIC declined to 99.8285% and 99.8251% in the second and third period, respectively. The shocks in the VAIC further declined to 99.8250% in the fourth period and remained stable till the tenth period under the insurance firms. Apart from the VAIC own shocks, the ROA, DER, STD, LTD, and SIZE also accounted for the variation in the VAIC of the firms. Specifically, the shocks in the ROA initially contributed to the shocks in the VAIC, which was 4.82 × 10⁻⁵% in the first period and increased to 0.1749% in the fourth period and remained stable at the same level till the tenth period considered for this study. Other variables such as the DER, STD, LTD, and SIZE never contributed initially to the shocks in the VAIC in the first period, but the contribution of the DER, STD, LTD, and SIZE rose in the second period, with 1.51 × 10⁻²⁷%, 7.65 × 10⁻²⁶%, 1.77 × 10⁻²⁶%, and 1.94 × 10⁻²⁶% shocks, respectively, on the VAIC. The shocks as a result of the DER, STD, LTD, and SIZE on the VAIC remained at the same level till the tenth period considered for this study.

The variance decomposition of the DER presented in

Table 4. Variance decomposition of financial liquidity (DER).

Period	S.E.	ROA	VAIC	DER	STD	LTD	SIZE
1	3.43 × 10−14	1.39 × 10−6	1.5731	98.4268	0.0000	0.0000	0.0000
		(0.0509)	(0.5851)	(0.5843)	(0.0000)	(0.0000)	(0.0000)
2	3.45 × 10−14	0.8375	1.5599	97.6024	5.09 × 10−26	1.26 × 10−26	1.36 × 10−26
		(0.4464)	(0.5798)	(0.7239)	(5.0 × 10−27)	(1.2 × 10−27)	(1.1 × 10−27)
3	3.45 × 10−14	0.8539	1.5597	97.5863	5.09 × 10−26	1.26 × 10−26	1.36 × 10−26
		(0.4536)	(0.5796)	(0.7279)	(5.0 × 10−27)	(1.2 × 10−27)	(1.1 × 10−27)
4	3.45 × 10−14	0.8542	1.5597	97.5860	5.09 × 10−26	1.26 × 10−26	1.36 × 10−26
		(0.4538)	(0.5796)	(0.7280)	(5.0 × 10−27)	(1.2 × 10−27)	(1.1 × 10−27)
5	3.45 × 10−14	0.8542	1.5597	97.5860	5.09 × 10−26	1.26 × 10−26	1.36 × 10−26
		(0.4538)	(0.5796)	(0.7280)	(5.0 × 10−27)	(1.2 × 10−27)	(1.1 × 10−27)
6	3.45 × 10−14	0.8542	1.5597	97.5860	5.09 × 10−26	1.26 × 10−26	1.36 × 10−26
		(0.4538)	(0.5796)	(0.7280)	(5.0 × 10−27)	(1.2 × 10−27)	(1.1 × 10−27)
7	3.45 × 10−14	0.8542	1.5597	97.5860	5.09 × 10−26	1.26 × 10−26	1.36 × 10−26
		(0.4538)	(0.5796)	(0.7280)	(5.0 × 10−27)	(1.2 × 10−27)	(1.1 × 10−27)
8	3.45 × 10−14	0.8542	1.5597	97.5860	5.09 × 10−26	1.26 × 10−26	1.36 × 10−26
		(0.4538)	(0.5796)	(0.7280)	(5.0 × 10−27)	(1.2 × 10−27)	(1.1 × 10−27)
9	3.45 × 10−14	0.8542	1.5597	97.5860	5.09 × 10−26	1.26 × 10−26	1.36 × 10−26
		(0.4538)	(0.5796)	(0.7280)	(5.0 × 10−27)	(1.2 × 10−27)	(1.1 × 10−27)
10	3.45 × 10−14	0.8542	1.5597	97.5860	5.09 × 10−26	1.26 × 10−26	1.36 × 10−26
		(0.4538)	(0.5796)	(0.7280)	(5.0 × 10−27)	(1.2 × 10−27)	(1.1 × 10−27)

Source: Author’s own computation (2025).

shows that own shocks cause the major source of variation for the variable. The shock result-based DER was 98.4268% in the first period. The shock result-based DER declined to 97.6024% and 97.5863% in the second and third period, respectively. The shocks in the DER further declined to 97.5860% in the fourth period and remained stable at the same level till the tenth period for this study. Apart from the DER own shocks, the ROA, VAIC, STD, LTD, and SIZE also accounted for variation in the DER of the firms. Specifically, the shocks in the ROA and VAIC initially contributed to the shocks in the DER, which were 1.39 × 10⁻⁶% and 1.5731%, respectively, in the first period and increased to 0.8542% and 1.5597%, respectively, in the fourth period and remained stable at the same level till the tenth period considered for this study. Other variables such as STD, LTD, and SIZE never contributed initially to the shocks in the DER in the first period, but the contribution of the STD, LTD, and SIZE rose in the second period, with 5.09 × 10⁻²⁶%, 1.26 × 10⁻²⁶%, and 1.36 × 10⁻²⁶% shocks, respectively, on the DER. These shocks because of the STD, LTD, and SIZE on the DER remained at the same level till the tenth period.

The variance decomposition of the STD presented in

Table 5. Variance decomposition of financial liquidity (STD).

Period	S.E.	ROA	VAIC	DER	STD	LTD	SIZE
1	6.13 × 10−11	1.33 × 10−8	10.6451	0.0015	89.3533	0.0000	0.0000
		(0.1008)	(1.3803)	(0.0655)	(1.3734)	(0.0000)	(0.0000)
2	6.16 × 10−11	1.2352	10.5136	0.0015	88.2496	1.14 × 10−26	1.18 × 10−26
		(0.4292)	(1.3671)	(0.0646)	(1.3809)	(1.1 × 10−27)	(1.0 × 10−27)
3	6.16 × 10−11	1.2592	10.5110	0.0015	88.2281	1.14 × 10−26	1.18 × 10−26
		(0.4373)	(1.3669)	(0.0646)	(1.3820)	(1.1 × 10−27)	(1.0 × 10−27)
4	6.16 × 10−11	1.2596	10.5110	0.0015	88.2277	1.14 × 10−26	1.18 × 10−26
		(0.4375)	(1.3669)	(0.0646)	(1.3821)	(1.1 × 10−27)	(1.0 × 10−27)
5	6.16 × 10−11	1.2596	10.5110	0.0015	88.2277	1.14 × 10−26	1.18 × 10−26
		(0.4375)	(1.3669)	(0.0646)	(1.3821)	(1.1 × 10−27)	(1.0 × 10−27)
6	6.16 × 10−11	1.2596	10.5110	0.0015	88.2277	1.14 × 10−26	1.18 × 10−26
		(0.4375)	(1.3669)	(0.0646)	(1.3821)	(1.1 × 10−27)	(1.0 × 10−27)
7	6.16 × 10−11	1.2596	10.5110	0.0015	88.2277	1.14 × 10−26	1.18 × 10−26
		(0.4375)	(1.3669)	(0.0646)	(1.3821)	(1.1 × 10−27)	(1.0 × 10−27)
8	6.16 × 10−11	1.2596	10.5110	0.0015	88.2277	1.14 × 10−26	1.18 × 10−26
		(0.4375)	(1.3669)	(0.0646)	(1.3821)	(1.1 × 10−27)	(1.0 × 10−27)
9	6.16 × 10−11	1.2596	10.5110	0.0015	88.2277	1.14 × 10−26	1.18 × 10−26
		(0.4375)	(1.3669)	(0.0646)	(1.3821)	(1.1 × 10−27)	(1.0 × 10−27)
10	6.16 × 10−11	1.2596	10.5110	0.0015	88.2277	1.14 × 10−26	1.18 × 10−26
		(0.4375)	(1.3669)	(0.0646)	(1.3821)	(1.1 × 10−27)	(1.0 × 10−27)

Source: Author’s own computation (2025).

shows that own shocks cause the major source of variation for the variable. The shock result-based STD was 89.3533% in the first period. The shock result-based STD declined to 88.2496% and 88.2281% in the second and third period, respectively. The shocks in STD further declined to 88.2277% in the fourth period and remained stable at the same level till the tenth period. Aside from the STD own shocks, the ROA, VAIC, DER, LTD, and SIZE also accounted for variation in the STD of the firms. Specifically, the shocks in the ROA, VAIC, and DER initially contributed to the shocks in the STD, which were 1.39 × 10⁻⁶%, 0.6451%, and 0.0015%, respectively, in the first period and increased to 1.2596%, 10.5110%, and 0.0015%, respectively, in the fourth period and remained stable at the same level till the tenth period. Other variables such as the LTD and SIZE never contributed initially to the shocks in the STD in the first period, but their contribution of LTD and SIZE rose in the second period, with 1.14 × 10⁻²⁶% and 1.18 × 10⁻²⁶% shocks, respectively, on the STD. These shocks because of the LTD and SIZE on the STD remained at the same level till the tenth period.

The variance decomposition of LTD presented in

Table 6. Variance decomposition of financial liquidity (LTD).

Period	S.E.	ROA	VAIC	DER	STD	LTD	SIZE
1	4.02 × 10−9	8.76 × 10−6	6.8403	0.8297	43.5024	48.8274	0.0000
		(0.0860)	(0.9806)	(0.4013)	(1.7579)	(1.5041)	(0.0000)
2	4.02 × 10−9	0.0324	6.8381	0.8295	43.4883	48.8116	1.47 × 10−26
		(0.1420)	(0.9808)	(0.4011)	(1.7532)	(1.5043)	(1.3 × 10−27)
3	4.02 × 10−9	0.0330	6.8380	0.8295	43.4880	48.8113	1.47 × 10−26
		(0.1436)	(0.9808)	(0.4011)	(1.75317)	(1.5043)	(1.3 × 10−27)
4	4.02 × 10−9	0.0330	6.8380	0.8295	43.4880	48.8113	1.47 × 10−26
		(0.1437)	(0.9808)	(0.4011)	(1.7531)	(1.5043)	(1.3 × 10−27)
5	4.02 × 10−9	0.0330	6.8380	0.8295	43.4880	48.8113	1.47 × 10−26
		(0.1437)	(0.9808)	(0.4011)	(1.7531)	(1.5043)	(1.3 × 10−27)
6	4.02 × 10−9	0.0330	6.8380	0.8295	43.4880	48.8113	1.47 × 10−26
		(0.1437)	(0.9808)	(0.4011)	(1.7531)	(1.5043)	(1.3 × 10−27)
7	4.02 × 10−9	0.0330	6.8380	0.8295	43.4880	48.8113	1.47 × 10−26
		(0.1437)	(0.9808)	(0.4011)	(1.7531)	(1.5043)	(1.3 × 10−27)
8	4.02 × 10−9	0.0330	6.8380	0.8295	43.4880	48.8113	1.47 × 10−26
		(0.1437)	(0.9808)	(0.4011)	(1.7531)	(1.5043)	(1.3 × 10−27)
9	4.02 × 10−9	0.0330	6.8380	0.8295	43.4880	48.8113	1.47 × 10−26
		(0.1437)	(0.9808)	(0.4011)	(1.7531)	(1.5043)	(1.3 × 10−27)
10	4.02 × 10−9	0.0330	6.8380	0.8295	43.4880	48.8113	1.47 × 10−26
		(0.1437)	(0.9808)	(0.4011)	(1.7531)	(1.5043)	(1.3 × 10−27)

Source: Author’s own computation (2025).

shows that own shocks serve as the major source of variation for the variable. The shock result from LTD was 48.8274% in the first period. The shock result from LTD declined to 48.8116% and 48.8113% in the second and third period, respectively, and remained stable at the same level till the tenth. Aside from the LTD own shocks, the ROA, VAIC, DER, STD, and SIZE also accounted for variation in the LTD of the firms. Specifically, the shocks in ROA, VAIC, DER, and STD initially contributed to the shocks in LTD, which were 8.76 × 10⁻⁶%, 6.8403%, 0.8297%, and 43.5024%, respectively, in the first period and increased to 0.0330%, 6.8380%, 0.8295%, and 43.4880%, respectively, in the third period and remained stable at the same level till the tenth period. Other variables such as SIZE do not contribute initially to the shocks in LTD in the first period, but the contribution of SIZE rose in the second period, with 1.47 × 10⁻²⁶% shocks on LTD. These shocks because of the SIZE on LTD remained at the same level till the tenth period.

The variance decomposition of SIZE presented in

Table 7. Variance decomposition of control variable (SIZE).

Period	S.E.	ROA	VAIC	DER	STD	LTD	SIZE
1	3.43 × 10−14	8.23 × 10−6	24.1845	0.0190	50.5862	12.0449	13.1652
		(0.0678)	(1.7587)	(0.0823)	(1.7647)	(0.5692)	(0.4636)
2	3.43 × 10−14	0.0275	24.1778	0.0190	50.5722	12.0416	13.1615
		(0.1243)	(1.7573)	(0.0822)	(1.7657)	(0.5696)	(0.4626)
3	3.43 × 10−14	0.0280	24.1777	0.0190	50.5720	12.0415	13.1615
		(0.1255)	(1.7573)	(0.0822)	(1.7658)	(0.5697)	(0.4625)
4	3.43 × 10−14	0.0280	24.1777	0.0190	50.5720	12.0415	13.1615
		(0.1255)	(1.7573)	(0.0822)	(1.7658)	(0.5697)	(0.4625)
5	3.43 × 10−14	0.0280	24.1777	0.0190	50.5720	12.0415	13.1615
		(0.1255)	(1.7573)	(0.0822)	(1.7658)	(0.5697)	(0.4625)
6	3.43 × 10−14	0.0280	24.1777	0.0190	50.5720	12.0415	13.1615
		(0.1255)	(1.7573)	(0.0822)	(1.7658)	(0.5697)	(0.4625)
7	3.43 × 10−14	0.0280	24.1777	0.0190	50.5720	12.0415	13.1615
		(0.1255)	(1.7573)	(0.0822)	(1.7658)	(0.5697)	(0.4625)
8	3.43 × 10−14	0.0280	24.1777	0.0190	50.5720	12.0415	13.1615
		(0.1255)	(1.7573)	(0.0822)	(1.7658)	(0.5697)	(0.4625)
9	3.43 × 10−14	0.0280	24.1777	0.0190	50.5720	12.0415	13.1615
		(0.1255)	(1.7573)	(0.0822)	(1.7658)	(0.5697)	(0.4625)
10	3.43 × 10−14	0.0280	24.1777	0.0190	50.5720	12.0415	13.1615
		(0.1255)	(1.7573)	(0.0822)	(1.7658)	(0.5697)	(0.4625)

Source: Author’s own computation (2025).

shows that own shocks serve as the source of variation. The shock result from the SIZE was 13.1652% in the first period. The shock result from the SIZE declined to 13.1615% and 48.8113% in the second period and remained stable at the same level till the tenth period. Aside from the SIZE own shocks, the ROA, VAIC, DER, STD, and LTD also accounted for variation in the SIZE of the firms. Specifically, the shocks in the ROA, VAIC, DER, STD, and LTD initially contributed to the shocks in SIZE, which were 8.23 × 10⁻⁶%, 24.1845%, 0.0190%, 50.5862%, and 12.0449%, respectively, in the first period and increased to 0.0280%, 24.1777%, 0.0190%, 50.5720%, and 12.0415%, respectively, in the third period and remained stable at the same level till the tenth period.

4.3. Impulse Response Functions

Impulse response functions offer relevant information to examine the dynamic behavior of a variable because of innovation or a random shock in other variables. The cross effects of shocks on the present and future values of the endogenous variables of one standard deviation shock to the variables are traced by the impulse response. Therefore, for every variable from each equation, a unit of shock to the error is explored to establish their impact upon the PVAR model over time, with the use of cholesky decomposition. Nevertheless, it is noteworthy that with this approach, the ranking of the variables in the PVAR model is important. In this study, ROA responses to a shock in VAIC, DER, STD, LTD, and SIZE were observed as shown in Figure 2.

The responses of the VAIC, DER, STD, LTD, and SIZE to variations in the ROA were recognized in the ten periods as shown in Figure 2. The response of the VAIC to changes in the ROA was negative throughout the ten periods. The responses of the VAIC to variations in the ROA for the first, second, third, fourth, fifth, sixth, seventh, eighth, ninth, and tenth periods were 0.0000%, −8.20 × 10⁻¹¹%, −1.15 × 10⁻¹¹%, −1.61 × 10⁻¹²%, −2.26 × 10⁻¹³%, −3.17 × 10⁻¹⁴%, −4.45 × 10⁻¹⁵%, −6.23 × 10⁻¹⁶%, −8.74 × 10⁻¹⁷%, and −1.23 × 10⁻¹⁷%, respectively. The results indicate that a decline in the ROA was because of the low quality of the VAIC engaged by the firms. Also, the response of the DER to changes in the ROA was negative in all ten periods, with an impulse response effect to the tune of 0.0000%, −6.65 × 10⁻¹²%, −9.32 × 10⁻¹³%, −1.31 × 10⁻¹³%, −1.83 × 10⁻¹⁴%, −2.57 × 10⁻¹⁵%, −3.60 × 10⁻¹⁶%, −5.05 × 10⁻¹⁷%, −7.08 × 10⁻¹⁸%, and −9.93 × 10⁻¹⁹%, respectively. The response effect of the STD to changes in the ROA was also negative for the entire ten periods considered in this study to the tune of 0.0000%, −1.48 × 10⁻¹⁰%, −2.07 × 10⁻¹¹%, −2.91 × 10⁻¹²%, −4.08 × 10⁻¹³%, −5.72 × 10⁻¹⁴%, −8.02 × 10⁻¹⁵%, −1.12 × 10⁻¹⁵%, −1.58 × 10⁻¹⁶%, and −2.21 × 10⁻¹⁷%, respectively. The response of the LDT to changes in the ROA was also negative all through the ten periods. Thus, the negative impact of financial liquidity on the financial performance of the insurance firms was emphasized. However, the response of the SIZE to variations in the ROA was positive for all ten periods, with an impulse response effect to the tune of 0.0000%, 4.48 × 10⁻¹¹%, 6.28 × 10⁻¹²%, 8.80 × 10⁻¹³%, 1.23 × 10⁻¹³%, 1.73 × 10⁻¹⁴%, 2.43 × 10⁻¹⁵%, 3.40 × 10⁻¹⁶%, 4.77 × 10⁻¹⁷%, and 6.69 × 10⁻¹⁸%, respectively. Thus, it can be stated that the greater the size of the firms, the better the financial performance of the firms. Hence, it can be stressed that all the financial variables examined reacted to shocks in financial performance of the firms in an inverse or a direct manner during the period under investigation.

4.4. Discussion of P-VAR Results

The characteristic-root test and its visual confirmation reveal that every eigen value is comfortably inside the unit circle, demonstrating a dynamically stable six-equation system whose parameters can be employed confidently for forward-looking policy simulations without fear of explosive behavior; recent African evidence echoes this finding, including Fosu (2013) for South African industrials, Essel (2024) who document similar stability for West-African micro-finance banks, collectively suggesting that the profitability–capital structure nexus in the region has a well-behaved time-path. Turning to forecast error variance decomposition, the results underscore that, across a ten-period horizon, virtually the entire volatility in return on assets (ROA) is self-generated own shocks accounting for ≈100% of its variance while innovations in VAIC and liquidity ratios (debt-to-equity, DER; short-term debt, STD; and long-term debt, LTD) contribute only infinitesimal fractions (≤1.3 × 10⁻²⁸); this heavy dominance of own shocks is consistent with earlier findings by Bui et al. (2023) for Asia.

VAIC displays a mirror image: ≈99.8% of its own variance is self-driven, with a modest yet meaningful feedback (≈0.17%) from the ROA, supporting the resource-based view that knowledge resources both influence and are influenced by firm performance; the direction aligns with Firer and Mitchell Williams (2003) for South African corporates and Smriti and Das (2018) for Indian firms but contrasts with the stronger profitability-to-VAIC spill-overs observed by Sardo and Serrasqueiro (2018) in European firms and the amplified bidirectional linkages reported by Odongo (2021) for Kenyan insurers, hinting at contextual differences in intellectual capital deployment across markets. Regarding leverage, the DER, STD, and LTD are each overwhelmingly driven by their own histories (97–49% of variance); notably, STD explains roughly 43% of LTD’s fluctuations, signaling active maturity transformation, an interdependency corroborated by Abor (2005) for emerging-market corporates highlights the strategic rollover of short-term funding into longer horizons under liquidity pressure.

SIZE emerges as a joint product of short-term funding capacity (≈50% of its variance attributed to STD) and intellectual capital intensity (≈24% linked to VAIC), supporting the growth of the firm thesis and aligning with prior and recent evidence that scale economies in African insurers are largely achieved through effective knowledge asset utilization and prudent liquidity management (Kipngetich, 2019). Impulse response diagnostics reinforce these insights: a one-standard-deviation shock to VAIC or any of the liquidity ratios (DER, STD, and LTD) triggers a small yet persistent negative response in the ROA, while an analogous shock to SIZE elicits a sustained positive reaction; the adverse profitability impact of leverage aligns with Meckling and Jensen’s (1976) agency-cost framework and remains consistent with empirical results from Fosu (2013), caution against excessive gearing in African corporates. The negative ROA response to VAIC is more counterintuitive as most extant studies, from Pulic (2000) to the broader meta-analysis by Olarewaju and Msomi (2021), document a positive effect but may be explained by the relatively nascent stage of intellectual capital management in Sub-Saharan African insurers; qualitative work by Ndegwa (2025) suggest that knowledge resources remain under-utilized, generating initial costs that outweigh productivity gains.

According to the RBV, firms with high levels of intellectual capital are better positioned to mobilize and deploy valuable, rare, and inimitable resources for competitive advantage. In our context, the observed positive long-run impact of VAIC on firm performance aligns with RBV’s assertion that intangible assets such as human capital, organizational routines, and relational networks contribute to sustained profitability. Moreover, the negative short-run impact of VAIC on the ROA, initially counterintuitive, can be understood through the RBV lens reflecting a gestation period during which firms invest in IC without immediate returns. This is consistent with Ren et al. (2021), who find that intellectual capital can reduce the perceived riskiness of firms, thereby improving access to debt financing (

Table 8. Summary of hypothesis testing.

Hypothesis	Statement	Expected Direction	Empirical Result	Support Status
H1	VAIC positively influences FP	Positive	Minimal effect; short-run response negative (impulse response), long-run impact negligible (variance decomposition ≈ 1.28 × 10−28).	Partially Supported
H2	DER negatively affects FP	Negative	Consistent negative response across 10 periods; supported by IRF and supported in variance decomposition (~0.8542% effect on ROA).	Supported
H3	STD negatively affects FP	Negative	Strong negative response; IRF shows persistent decline in ROA; significant in decomposition (~1.2596% effect).	Supported
H4	LTD negatively affects FP	Negative	Negative but small effect; IRF shows steady decline; minor variance explained in ROA fluctuations.	Supported (Weak)
H5	SIZE positively influences FP	Positive	Positive response in IRF across periods; SIZE accounts for ~50% of its own variance and ~13% in ROA’s variance decomposition.	Supported

).

5. Conclusions and Recommendations

This study set out to unravel the dynamic interaction between firm profitability (ROA), VAIC, liquidity structure (DER, STD, and LTD), and SIZE among Sub-Saharan African insurers using a P-VAR model. The system satisfies all stability conditions, and variance decomposition results reveal that short-run fluctuations in ROA and VAIC are predominantly self-generated, while leverage metrics are likewise driven mainly by their own past shocks. Nevertheless, meaningful cross-variable linkages emerge: short-term debt materially shapes the path of long-term leverage and firm size, VAIC receives feedback from profitability, and size-related scale effects exert a positive and persistent influence on profitability. Impulse response diagnostics further underscore the nuanced nature of these relationships, showing that shocks to leverage and knowledge assets currently impose a modest drag on ROA, whereas shocks to firm size enhance it. Collectively, the findings paint a picture of insurers whose short-term performance is largely internally determined but whose medium-term trajectory can be steered through judicious capital structure management and more effective deployment of intellectual capital.

For insurance executives the evidence points to three complementary priorities. First, formal intellectual capital strategies need to be embedded across functional areas; structured knowledge management systems, continuous staff development, and incentive schemes tied to innovation outputs will help convert IC spending into productivity gains, mitigating the muted or even negative short-run impact of VAIC on profitability. Second, liquidity structures should be rebalanced by reducing over-reliance on both short- and long-term debt, substituting retained earnings and, where feasible, fresh equity to lower agency costs and interest-burden drag. Third, managers should exploit economies of scale responsibly through carefully vetted mergers, geographic expansion, and product diversification, always pairing growth initiatives with robust risk management frameworks to avoid diseconomies.

For regulators and policy-makers, the results underscore the need to strengthen intellectual capital disclosure requirements so that markets can price knowledge assets accurately and exert pressure for efficient utilization. Balanced capital structure guidelines are also vital: differentiated reserve requirements and risk-weighted leverage caps that penalize maturity mismatches would discourage excessive short-term borrowing. Finally, sector-wide capacity building via collaborative training programs and shared research initiatives can accelerate the maturation of intellectual capital management throughout the insurance industry. Regulators (e.g., insurance commissions) should introduce mandatory intellectual capital disclosure frameworks to improve transparency and investor confidence. Governments and actuarial bodies should fund training and certification programs to boost human capital in actuarial science, risk analysis, and compliance. They should also encourage equity-based funding mechanisms through tax incentives or credit ratings tied to IC reporting quality. They should also support the development of a Sub-Saharan Intellectual Capital Index to benchmark and monitor IC development across firms and countries. For investors, screening criteria should privilege scalable insurers that maintain prudent leverage profiles and articulate credible growth strategies. Beyond headline VAIC scores, due diligence should examine how effectively knowledge investments translate into new products, process improvements, and customer retention, as these are the channels through which IC ultimately drives value.

This study is subject to several limitations that should be acknowledged. First, important contextual variables such as governance quality, digital infrastructure, and customer trust were excluded due to data availability constraints. These factors may significantly influence both IC efficiency and financial performance, especially in the SSA insurance context. Second, while the VAIC™ model is a widely accepted proxy for IC efficiency, it primarily captures the output-based value creation from IC components and does not directly measure IC investment or development efforts, potentially overlooking important qualitative or strategic aspects of intellectual capital. Third, the sample was restricted to listed insurance firms, which limits the generalizability of findings to non-listed or mutual insurers, or firms operating in fragile or conflict-prone regions where institutional and operational dynamics may differ significantly.

To address these limitations, future studies are encouraged to adopt a mixed-methods approach, integrating qualitative insights through interviews or case studies to capture the depth of IC development practices. Additionally, researchers should seek to incorporate governance indicators, digital capability metrics, and trust-based customer relationship variables where possible. Expanding the sample to include unlisted or cooperative insurance firms and conducting country- or region-specific analyses in conflict-affected or low-institutional-capacity SSA contexts would further enhance the applicability and robustness of findings.