Introduction
Performance crucial for governance since it is a result of a person’s or a group of people’s ability and responsibility to carry out an organization’s mission in a morally and ethically upright manner, both legally and illegally. Enterprise risk management (ERM) is a technological strategy designed to help management use an integrated approach to handle uncertainty and address current difficulties (1). The Risk Management Committee (RMC), a separate executive council, is in charge of managing the risk associated with the corporation’s international operations and overseeing the implementation of the organization’s overall risk management strategy (2).
The committee is advantageous in carrying out its regulatory responsibilities with regard to the company’s risk exposure, methods for risk assessment and regulation, and the management structure that regulates it. RMC is a company asset that helps it to achieve its corporate goals, raise the level of financial statements as a guardian of the corporation’s reputation, and eventually boost the efficiency of the business (3).
A more proactive strategy to manage and decrease business risk has been demanded by shareholders, top executives, and corporate boards (4). Most firms in Nigeria dislike the concept of business risk control, which makes risk detection and prevention disliked as well. This activity will always have an impact on performance if not properly managed (5).
Additionally, the majority of Nigerian businesses do not use integrated risk management practices due to their lack of popularity among businesses operating in Nigeria. As a result, they face the problem of not having an integrated risk management unit within the business (6).
In industrialized nations, the insurance sector significantly contributes to the gross domestic product (GDP). For instance, the insurance industry in China contributes 4.2% of the country’s GDP, and the insurance sector in Japan contributes 4.4% of the country’s GDP. Insurance has a 3% impact on GDP in the United Kingdom, while insurance costs account for 3.1% of GDP in the United States.
In emerging nations, particularly Nigeria, the insurance sector contributed 0.4% to the GDP as its financial viability has lagged behind that of other industrialized nations (7). This is far less than what takes place in industrialized nations. This might be the result of a variety of causes, including the lack of confidence international investors have in the Nigerian insurance business, bad management, weak industry performance, inadequate risk management, and others.
In order to enhance a company’s sustainability and production, money is crucial to its existence and vitality. Furthermore, profit is a sign of effective working resource management, and liquidity is not guaranteed if assets cannot be converted into cash quickly.
An organization may have debts that might negatively impact its long-term performance, causing the business to abruptly discontinue its usual activities and, if necessary, be unable to fund its obligations. The concept of ERM has gained importance globally as a result of global economic disasters, high-profile corporate scandals, and business failures. ERM is a tool that can assist businesses in achieving their objectives.
To reduce and manage business risk, there is increasing pressure from clients, senior executives, and corporate boards. The majority of Nigerian businesses continue to hold strong opposition to the issue of risk recognition and prevention. If handled poorly, shareholder value eventually suffers (8).
As a result of this company’s struggles with the lack of an integrated risk management unit within the organization and the lack of trained staff willing to manage it. Most corporate organizations in Nigeria do not practice integrated risk management, as they rely on a more traditional approach to risk management (5).
Study objective
The main objective of this study is to ascertain the effect of ERM on the corporate performance of insurance firms in Nigeria. Specific objectives are to:
1. Determine the impact of ERM on earnings per share (EPS)
2. Ascertain the impact of ERM on return on asset (ROA); and
3. Examine the impact of ERM on return on equity (ROE).
Hypotheses
H01: ERM have a negligible impact on the EPS of Nigerian listed insurance companies.
H02: ERM has an insignificant effect on the ROA of listed insurance companies in Nigeria.
H03: ERM has a negligible impact on the ROE of Nigerian listed insurance companies, which was accepted.
The study investigates how ERM affects the corporate performance of Nigeria’s listed insurance businesses. The main goal of the study is to ascertain how ERM affects the performance of Nigeria’s listed insurance businesses.
In this paper, the second major section reviews the published literature; the third major section outlines the research’s methodology; the fourth major section displays the findings and its discussion; and the last section offers the study’s conclusion.
Literature review
Enterprise risk management
Enterprise risk management is the process of arranging, directing, and coordinating organizational operations in order to mitigate the impact of risk on an organization’s capital and productivity (9). This signifies that ERM also focuses on the company’s financial strategy in addition to its operations.
The scope of ERM is extensive. Businesses are frequently asked to conduct a risk assessment at the start of the year in order to identify the risks they will face during the year after establishing their objectives. This is done so that they can identify the risks that will have an impact on their performance and create solutions that will effectively reduce the risks.
Modern organizational procedures and daily company operations depend on ERM since it helps companies manage their internal systems. One essential component of a company’s competitiveness is risk management. It enables a company to create a distinctive strategy to reduce possible losses and create a pathway for the exploitation of new possibilities.
ERM enables senior management to successfully manage many forms of risk (10). Effective ERM procedures aid in responding to unforeseen risks, ensuring flexibility, and seizing opportunities, all of which help businesses acquire a competitive advantage (11).
Organizations with risk-related strategies are thought to be able to smooth out their revenue volatility and lessen the effects of financial crises to improve their performance (12). Top management is required to possess the necessary financial expertise to ensure seamless operations in competitive marketplaces (13). ERM techniques are necessary to achieve high profitability and a sustainable competitive position in the present environment.
Performance
Performance is crucial in determining if a company’s model will endure. It is thought to be the primary goal of businesses with a profit motive. A successful company is frequently one that uses its resources effectively and efficiently to ensure its long-term prosperity (i.e., one that reasonably follows its standards and judiciously utilizes its resources toward achieving high performance).
Since it has a long-term impact on their corporate setups, managers of corporate entities are very concerned about how to achieve high financial performance. This includes management efficiency (making the best use of scarce resources), investor goals (maximizing wealth), and lender-driven (debt repayment and interest charges) (14). Financial success is a sign of how well a company uses the capital at its disposal to generate sales.
Most of the time, it offers advice on future activities that may be undertaken in terms of business expansion, executive control, and property purchase. Focusing on the financial lessons management has learned over time is also beneficial. The comparison of similar firms may also be done using these achievements.
Additionally, financial performance is a way to evaluate business processes objectively in monetary terms. It serves to show how well shareholders are doing relative to the start of the accounting period. Simple industry data or financial ratios produced from financial statements may be examined to effectively realize this (1).
If a company wants to continue operating to the satisfaction of its stakeholders, profitability is a crucial factor that must be taken into consideration. Profitability is the most often used measure of organizational growth, improvement of performance, and competitiveness (15). According to Grace et al. (16), the profit capacity of an industry, business, or organization’s commercial operations may be referred to as “profitability.”
It demonstrates how simple it is for management to profit from all available market money. Yang et al. (17) argued against conflating profitability with performance based on an investment’s potential to generate returns. Instead, profitability serves as an indicator of performance to guide management toward increased effectiveness.
Since one of the goals of financial management is to maximize the owners’ wealth, profitability is a highly significant driver of performance. The study by Yang et al. concluded that profitability is one of the most important objectives of financial management. A firm that is not lucrative cannot prosper. However, a very profitable business may reward its owners with enormous earnings.
Research methodology
Research design
Ex post facto research design was used to collect data from individual company annual reports. The explanation is that publicly available financial statements provide all the data required for the inquiry.
Data collection methods
The audited financial statements included in the annual reports of the sample firms served as the study’s instrument for data gathering since these firms’ financial statements already had all the pertinent information required for the research. Ratio analysis and content analysis–two significant analyses–were performed.
In order to complete this research project, important data from the financial statements of the firms that are included in their annual reports were extracted using content analysis. A quantitative link between two variables is expressed using ratio analysis. The appropriate ratios were calculated using the methods stated in the model after data were collected from financial statements.
Model specification and variable measurement
Regression Equation:
Where
μi = disturbance term. β0 = intercept, β1–β4 = coefficient of the independent variables.
The test above is carried out at 5% test statistics.
Analysis
Computations and interpretation of used data
With the significant level of 0.05 (Table 1), the Hausman test result indicated a probability value of 0.6720, which is more than the 0.05 significant level; as a consequence, the study’s null hypothesis cannot be rejected. The Breusch–Pagan Lagrangian multiplier test result is 0.0000 (which is less than the significance level of 5%). This indicates that pooled ordinary least squares regression (OLS) is a good estimator of the model. The study rejects the null hypothesis and suggests that the pooled OLS effect is the most suitable model estimator.
Table 1. Regression analysis for model one: Random effect.
Since the residuals of the model are constant over time, the study accepts the null hypothesis, but it is crucial to estimate panel data models while checking for cross-sectional dependency. There is no evidence of cross-sectional dependency in the data according to the results of Pesaran’s test of cross-sectional independence, which had a p-value of 0.7577 and was larger than the study’s chosen threshold of significance of 5%. There is therefore sufficient data to conclude that the model does not exhibit cross-sectional dependency.
Utilizing the Breusch–Godfrey Serial Correlation, LM test was carried out. The analysis shows that there is a serial correlation issue in the model if the test has a p-value of 0.0050 (which is less than the 5% significant level). As a result, the research does not support the null hypothesis.
In conclusion, the outcome of the business risk management regression analysis reveals that the constant 2.710 exhibits a positive beta coefficient. The regression model’s findings in Table 1 show that the size of the risk committee (RCS) has a negative and insignificant impact on EPS (α = − 2.354, p = 0.253) indicating an increase in RCS would result in a decrease in EPS of −2.354. The impact of the risk committee meeting (RCM), however, has a positive and significant impact on EPS (α = 1.96, p = 0.423). Risk committee independence (RCI) has a positive but insignificant effect on EPS (α = 20.99, p = 0.2049) indicating a one-unit increase in RCI would result in a 20.99304 rise in EPS. A one-unit increase in RCM would result in a 1.959850-unit increase in EPS. This outcome is in line with the underlying hypothesis, which predicted that the ERM proxies (RCM and RCI) would have a positive impact on EPS, whereas RCS would have a negative one. According to the value of the individual t-statistic, ERM has negligible influence on profits per share at the accepted threshold of significance for this study of 5%.
Within the framework of the model, changes in EPS are only affected by ERM to the extent of 1%, with the other 99% being explained by other variables that have the potential to affect the dependent variable. As a result, the primary model has low explanatory power, as indicated by the coefficient of determination. The likelihood of the F-statistic showing that this model is not statistically significant emphasizes this even more.
The F-statistic is 1.377662 at the 0.05 level of significance, with a p-value of 0.254277, which is higher than the 0.05 level of significance used in this investigation. The first null hypothesis, according to which ERM has no appreciable impact on the EPS of Nigeria’s listed insurance companies, was accepted. Therefore, according to regression estimates, ERM, as defined by the size, meetings, and independence of the risk committee, has no discernible impact on the EPS of listed insurance firms in Nigeria.
In Table 2, the Hausman test result indicated a probability value of 0.8943, which is more than the 5% level of significance, indicating that the study’s null hypothesis cannot be rejected. The results of the Breusch–Pagan Lagrangian multiplier test for both models (a value of 0.0139), which are below the significance level of 5%, show that pooled OLS is a good estimator of the model.
Table 2. Model two regression analysis: Random effect.
The result of the Breusch–Pagan test for heteroskedasticity indicated that there was no heteroskedasticity, with a p-value of 0.0799. Since the residuals of the model are constant over time, the study accepts the null hypothesis. As a result, it is crucial to estimate panel data models while checking for cross-sectional dependency. There is no evidence of cross-sectional dependency in the data, according to the results of Pesaran’s test of cross-sectional independence, which had a p-value of 0.7108 and was larger than the study’s chosen threshold of significance of 5%. There is therefore sufficient data to conclude that the model does not exhibit cross-sectional dependency.
The Breusch–Godfrey Serial Correlation LM test was used for the serial association test, and a p-value of 0.0507, higher than the significant level of 5%, indicates that the model is free of serial correlation issues. As a result, the research does not reject the null hypothesis. In conclusion, the diagnostic tests showed that the model does not have serial correlation issues or heteroskedasticity issues. Random effects were employed to estimate the impact because fixed effects and pooled OLS effects would not be suitable estimators for the model.
The outcome of the ERM regression analysis demonstrates that the constant, 0.288288, has a positive beta coefficient. Risk committee meetings (RCM) have a negative, insignificant effect on ROA (α = −0.501, p = 0.504), so increasing RCM by one unit results in a −0.501 decrease in ROA. Furthermore, risk committee independence (RCI) has a positive but insignificant effect on ROA (α = 0.187075, p = 0.766), implying that a unit increase in RCS results in a 0.1871 increase in ROA.
This conclusion is consistent with a priori expectations since it was anticipated that the ERM proxy (RCI) would have good impacts on ROA, whereas RCS and RCM were anticipated to have negative effects on ROA and positive effects on ROA, respectively. According to the value of each t-statistic, ERM had no discernible impact on return on assets at the accepted 5% level of significance for this study.
Adjusted R2, the model’s explanatory power, has a coefficient of determination of −0.025554, suggesting that within the framework of the model, ERM’s independence accounts for 0% of changes in ROA, with the remaining 100% being explained by other variables that may have an influence on the dependent variable.
Since the primary model has no ability to explain anything, the coefficient of determination confirms this. The likelihood of the F-statistic showing that this model is not statistically significant emphasizes this even more. The F-statistic is 0.178 at the 0.05 level of significance, with a p-value of 0.911, which is higher than the 0.05 level of significance used in this investigation.
The second null hypothesis, which states that ERM has little or no impact on listed insurance businesses in Nigeria’s ROA, was accepted. Therefore, according to the regression estimates, ERM, as assessed by the size, meeting frequencies, and independence of the risk committee, has no discernible impact on the ROA of listed insurance companies in Nigeria.
In Table 3, the Hausman test result indicated a probability value of 0.2200, which is more than the 5% level of significance, affirming that the study cannot reject the null hypothesis of the Hausman specification test. In the random effect estimation used to estimate the model, the Breusch–Pagan Lagrangian multiplier test results for both models show that random effect is a good estimator of the model (a value of 0.6412), which is greater than the significance level of 5%. The study does accept the null hypothesis, implying that the random effect is the most appropriate model estimator.
Table 3. Model three regression analysis: Random effect.
The Breusch–Pagan test for heteroskedasticity used shows that there is no heteroskedasticity with a p-value of 0.9864, which is greater than the 5% significant level chosen for the study. Since the model residuals are constant over time, the study accepts the null hypothesis. As a result, it is crucial to estimate panel data models while checking for cross-sectional dependency. There is no evidence of cross-sectional dependency in the data, according to the results of the Pesaran test of cross-sectional independence, which had a p-value of 0.1821 and was larger than the study’s chosen threshold of significance of 5%.
There is therefore sufficient data to conclude that the model does not exhibit cross-sectional dependency. According to the test’s null hypothesis, there is no serial association using the Breusch–Godfrey Serial Correlation LM Test with a p-value of 0.3103, which is higher than the significant threshold of 5%. As a result, the research does not reject the null hypothesis.
In conclusion, the diagnostic tests showed that the model does not have serial correlation issues or heteroskedasticity issues. Random effects were employed to estimate the impact because fixed effects and pooled OLS effects would not be suitable estimators for the model. Regression analysis for ERM results in a positive beta coefficient for the constant of 35.74.
RCM has a negative, insignificant effect on ROE (α = −4.054787, p = 0.7347 denoting a unit increase in RCM would lead to a −4.054787 decrease in ROE), whereas risk committee independence (RCI) has a positive, insignificant effect on ROE (α = −1.894841, p = 0.8511 denoting a unit increase in RCS would result in a value of −1.8948).
This conclusion is consistent with a priori expectations since it was anticipated that the ERM proxy (RCI) would have good impacts on ROE, while RCS and RCM were anticipated to have negative effects on ROE and positive effects on ROA, respectively. According to the value of each t-statistic, ERM had no discernible impact on ROE at the accepted 5% level of significance for this study.
Adjusted R2, the model’s explanatory power, has a coefficient of determination of −0.0289, suggesting that in the context of the model, ERM’s independence accounts for 0% of changes in ROE. The remaining 99% is being explained by other variables that could potentially have an influence on the dependent variable since the primary model has no ability to explain anything.
The likelihood of the F-statistic showing that this model is not statistically significant emphasizes this even more. The F-statistic is 0.072 at the 0.05 level of significance with a p-value of 0.975, which is higher than the 0.05 level of significance used in this investigation.
The third null hypothesis, according to which ERM has no appreciable impact on listed insurance businesses in Nigeria’s ROE, was accepted. Therefore, ERM, as defined by the size, independence, and meetings of the risk committee, does not have an appreciable impact on the ROE of listed Nigerian insurance firms.
Discussion of findings
The first model demonstrated that the size of the risk committee has a negligible negative impact on EPS, while risk committee meetings have a positive but insignificant impact, and risk committee independence has a positive but insignificant impact.
The independent variables’ explanatory capacities show that the joint fluctuations in the independent variables only account for 1% of the variance in EPS, with other factors accounting for the other 99% of the variation in EPS. ERM, as determined by the size, independence, and meetings of the risk committee, does not have an appreciable impact on the EPS of listed insurance firms in Nigeria. According to the second model, RCI has a positive but insignificant influence on ROA, whereas risk committee size has a positive but insignificant effect. Risk committee meetings have a negative, insignificant effect. The independent variables’ explanatory capacities show that their combined fluctuations explain 0% of the variance in ROA, while the remaining 100% of the variation in ROA is due to external factors that are not included in this model. ERM, as determined by the size, independence, and frequency of meetings of the risk committee, had no appreciable impact on the ROA of listed insurance firms in Nigeria.
The third model showed that the size of the risk committee has a negative negligible impact on ROE and the frequency of its meetings, but the independence of the risk committee has a positive minor impact. According to the independent variables’ explanatory capabilities, the combined variations in the independent variables only account for 0% of the variance in ROE, as the remaining 100% of the variation in ROE is due to external factors that this model does not account for, making this model not able to explain anything. ERM, as determined by the size, independence, and frequency of meetings of the risk committee, had no appreciable impact on the ROE of listed insurance firms in Nigeria.
Conclusion and recommendations
Conclusion
The management consequence is that members of the risk committee should be proficient in managing risk-related issues and make sure the committee is granted its independence to operate as it has a favorable influence on performance. The committee meeting should be held regularly for performance improvement as well as to give the committee the opportunity to meet and debate crucial ERM and other risk-related issues that will advance the firm and shield it from any expensive risks. The study would help the authorities, such as the Securities and Exchange Commission (SEC) and the Central Bank of Nigeria, understand how ERM influences listed insurance businesses’ financial performance.
In order to improve reporting by providing rules that enable investors to make rational judgments, this research will assist them in ensuring that both voluntary and mandated disclosures of ERM plans are disclosed in the annual report. The research’s conclusions may be used by the government and financial regulatory authorities to improve their knowledge of how ERM methods and strategies affect business performance in Nigeria.
To the financiers, the implication for investors is that it will assist them in making investment decisions and provide them with a thorough understanding of how the ERM policies and procedures put in place by the board of directors and management will impact the operations of their business and add value. Additionally, the study has implications for future researchers since the results will advance knowledge, provide a way for them to conduct research by utilizing additional variables/aspects that impact financial performance, and promote ERM practices in both developed and developing nations.
The study came to the conclusion that the performance of listed insurance companies in Nigeria as measured by EPS, ROE, and ROA is not significantly impacted by ERM as measured by risk committee size, meeting frequency, and independence. Organizations have rare and precious resources. A business that possesses distinctive resources (physical and intangible) performs better than other firms that lack resources and capabilities. The study’s conclusion is that ERM should be integrated into an organization’s internal resources in order to guarantee profitability.
Recommendations
The study’s findings led to the following recommendations:
Risk committee meetings should be held frequently to boost performance and give the committee the chance to meet and discuss significant issues relating to ERM, while risk committee members should be effective in managing risk-related matters and ensure that the committee is given its full independence to function properly since it shows a positive performance relationship.
In order to improve reporting by providing standards that enable investors to make rational judgments, regulators should guarantee that both optional and required disclosures of ERM plans are disclosed in the annual report.
Additionally, management should make sure that the size of the risk committee’s members is balanced in line with the recommended amount in order to improve performance and boost returns to investors and shareholders.
Some organizations’ resources help them gain a competitive edge and/or improve long-term performance. Organizational resources should be managed effectively and used efficiently for optimal performance.
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