The Impact of Enterprise Risk Management on Firm Competitiveness: The Mediating Role of Competitive Advantage in the Omani Insurance Industry

Abstract

In today’s complex economy, firms face various risks. The increasing risks and exposures hinder top performance and impede investments in new project circles. This study examines how Enterprise Risk Management (ERM) practices affect the non-financial performance of Omani insurance companies and investigates the partial mediating role of Competitive Advantage (CA). Using 439 survey responses analysed with PLS-SEM, the results reveal that ERM practices have a positive and significant effect on non-financial performance, and that CA mediates the effects of Internal Environment, Event Identification, and Risk Assessment. This reinforces the strategic dimension of embedding competitive advantage into risk management frameworks. This study offers evidence of how integrating ERM practices can impact organisational performance. It provides a foundation for ongoing research in sectors and areas not previously examined, particularly in developing countries where organisational resilience is imperative. Our study demonstrates how ERM enhances non-financial performance within insurance companies while supporting the view that ERM is a long-term strategic element, not merely limited to risk management. The research contributes evidence for broader application by demonstrating competitive advantage as a mediator. The model facilitates the investigation of ERM impacts across various sectors and regions, especially in developing countries where organisational resilience is crucial.

1. Introduction

Firm performance is one of the most vital indicators of long-term success, and reflects the efficiency, effectiveness, and competitiveness of an organisation. This includes crucial financial and non-financial information used to make strategic and sustainable decisions (Aifuwa 2020; Pang and Lu 2018). In addition, while financial figures offer a snapshot of a company’s short-term economic performance, non-financial performance (NFP) metrics and indicators are now acknowledged as crucial determinants of an organisation’s longer-term viability and strength. NFP offers greater insight as to how well-positioned it is for future growth. Often, these attributes empower competitive advantage (CA), enhance brand reputation, and harbour a resilient and flexible company culture. NFP is a methodology that focuses on one of the most important aspects for companies today: how well firms do on non-financial ‘metrics’ such as sustainability, social responsibility, and stakeholder relationships. Thus, in an era of strong corporate social responsibility and stakeholder governance, embedding NFP into holistic Enterprise Risk Management (ERM) is crucial for achieving a balanced, long-lasting organisation (Vasile et al. 2022). Enterprise Risk Management (ERM) is a holistic framework that integrates risk identification, assessment, and response across all organisational levels (COSO 2004). For insurance companies—whose business model is inherently centred on risk—ERM provides strategic tools to enhance non-financial performance such as customer satisfaction, employee engagement, and service quality.

Insurance companies are classified as financial institutions. However, they also play an essential role in economic and social development through the revenues generated, known as insurance premiums, which individuals and corporations pay for insurance coverage. These premiums are then invested in various industries to generate a return. In the Omani context, the Capital Market Authority (CMA), now known as the Financial Services Authority (FSA), regulates the insurance sector and requires each insurance company to report its information and financial figures quarterly to the regulator. The 2021–2022 Insurance Market Index, which was published in 2023, indicates that there are 19 insurance companies regulated in Oman, with 10 of them regarded as national companies and thus categorised under listed public shareholding companies. The insurance sector includes 30 officially licensed insurance brokers and 161 insurance agents. The insurance market comprises gross written premiums (GWP) of OMR 541.326 million, equivalent to approximately USD 1.4 billion.

An ERM system is typically seen as equipping organisations with the frameworks and structures needed for resilience and competence and to address the aftermath of crises that have led to the collapse of major companies across sectors across the globe (Jalilvand and Moorthy 2023). Several studies claim that adopting ERM practices improves firm performance (COSO 2017; Malik et al. 2020; Paape and Speklé 2012; Phan et al. 2020). Usable ERM practices can provide a Competitive Advantage and help companies grow (Blanco-Mesa et al. 2019). A study conducted by Anton and Nucu (2020) using a large sample set from 2008 to 2019 indicated that firm performance is the most studied topic in ERM.

However, Simon et al. (2015) indicated that several key Non-Financial Performance (NFP) indicators are influenced by effective ERM, such as customer satisfaction and retention, employee engagement and satisfaction, operational efficiency, innovation and adaptability, the quality of services, and stakeholder trust and relationships. In recent years, linking these key Non-Financial Performance (NFP) outcomes to comprehensive ERM programs has emerged as a significant determinant of the success and longevity of organisations.

Despite the abundance of earlier research on the effects of ERM in small- and medium-sized businesses, there have been comparatively few studies on ERM deployment in insurance companies, particularly in developing countries (Anton and Nucu 2020). Given the increasing growth of ERM issues, additional studies should be conducted to evaluate the impact of ERM procedures on organisational performance to identify fresh evidence (Alajmi 2019). Most Omani organisations lack awareness and focus on ERM and its principles. In addition, their adoption is still in its early stages (Al-Farsi 2020).

With the increasing complexity of firm processes, ERM has become an essential management function (Quang et al. 2024). The various crises around the world, pandemics, terrorism, the emergence of new technology, and major geopolitical upheavals are just a few of the many external threats companies face. Technological disruption, human error, constantly changing client needs, and uncertain resources (human, financial, and material) are problems in risk management. Most of these factors were present several decades ago. We are witnessing the emergence of a complex infrastructure and communication environment that represents a novel evolution of systems unprecedented in human experience. ERM practices should keep up with these changes by implementing new and more flexible methodologies (Bakos and Dumitrașcu 2021).

The relationship between ERM practices and NFP is not a simple, direct relationship. Instead, it is important to understand the dynamics of the Competitive Advantage as a mediator between ERM and NFP. Competitive Advantage is a powerful enabler of ever-increasing business differentiation, boosting the ability of ERM to convert to real-world NFP value. While a mature ERM framework, by its very nature, reduces risk and strengthens operational resilience, the applied leverage of Competitive Advantage strategically ensures that these inherent positives have strong explanatory power in accounting for this multifaceted phenomenon. The concept of Competitive Advantage within the context of the ERM-NFP process enables organisations to elucidate better how ERM practices can lead to ongoing organisational success. This is consistent with the Resource-Based View (RBV) theory, which argues that firms can achieve a Competitive Advantage by harnessing unique and valuable resources within the firm that cannot be easily replicated or copied. Bridging Competitive Advantage into ERM processes enables organisations to transform risk management from a reactive mechanism to a proactive, performance-enhancing mechanism, further solidifying its position in the long-term sustainability framework. Understanding this threefold connection helps enrich theoretical discussions. It provides a practical basis for decisions related to management, which is particularly useful for aligning risk management issues with the organisation’s broader objectives.

In this aspect, Competitive Advantage acts as a strategic enabler of ERM practices, not just a risk-mitigating process; ERM practices also act as organisational competitors and NFP-driving mechanisms. Incorporating Competitive Advantage considerations directly into ERM strategies enables firms to realign their focus from a reactive posture where risk management only serves to mitigate harm, towards proactively exploiting opportunities linked to risk within the organisational structure, providing a powerful lever for firms to leverage their risk management efforts more effectively, reinforce their market position, and safeguard their long-term viability in a complex and dynamic marketplace.

In light of this consideration, this study contributes to the literature by providing new evidence on the associations between ERM and NFP, particularly with regard to the relatively new development of ERM in the Omani insurance sector. While prior studies have mostly focused on financial outcomes, this study introduces Competitive Advantage as a mediator, reinforcing the ERM-NFP relationship. The empirical results from the PLS-SEM analysis of 439 responses indicated that ERM had a positive influence on NFP, with Competitive Advantage partially mediating this relationship. This study extends resource-based view (RBV) theory by showing that Competitive Advantage translates into greater risk management benefits, advising firms seeking long-term resilience. On an applied these results to provide guidance for insurers and policymakers on strategically approaching ERM as a means of improving competitiveness and sustainability. This study addresses a significant gap in the literature by highlighting the non-financial outcomes of ERM and establishing a pathway for future ERM research in developing economies and across various industries.

With the increasing complexity of firm processes, ERM has become an incredibly important, if immature, management function (Quang et al. 2024). Various crises, technological disruptions, and uncertain resources have challenged firms. ERM practices should adapt to these changes with flexible methodologies (Bakos and Dumitrașcu 2021). Establishing an ERM system enhances resilience, enabling firms to manage crises (Jalilvand and Moorthy 2023) effectively. Several studies have highlighted the role of ERM in enhancing firm performance and Competitive Advantage (Malik et al. 2020; Phan et al. 2020).

Despite growing global interest in Enterprise Risk Management, empirical evidence from emerging economies and from the insurance sector remains limited (Anton and Nucu 2020; Eastman et al. 2024). Existing studies have concentrated primarily on financial outcomes, leaving the effects of ERM on non-financial performance indicators—such as customer satisfaction, service quality, and employee engagement—largely underexplored. Moreover, the potential mediating role of Competitive Advantage (CA) in translating ERM practices into performance gains has received minimal empirical attention, especially within the context of Gulf Cooperation Council (GCC) countries and Oman in particular. This study addresses these gaps by investigating how ERM practices affect non-financial performance in Omani insurance companies and by testing CA as a mediating mechanism. To our knowledge, this is among the first studies to provide firm-level evidence on these relationships within the Omani insurance industry, thereby offering both theoretical and practical contributions. Many Omani companies are not widely aware of or adopting ERM (Al-Farsi 2020). Non-Financial Performance (NFP) is becoming crucial for assessing firms in addition to financial figures (Vasile et al. 2022). Poor ERM negatively affects customer satisfaction, employee engagement, and innovation (Simon et al. 2015). The positive impact of ERM-NFP on Competitive Advantage occurs both directly and indirectly (Saeidi et al. 2020; Quang et al. 2024). The ERM-CA-NFP relationships enlighten firms on how to become resilient and strategically competitive.

2. Literature Review and Hypotheses Development

2.1. Enterprise Risk Management

Enterprise Risk Management (ERM) is a relatively recent framework that integrates the identification, assessment, and response to risks across all business units, aligning risk management with a firm’s strategic objectives (COSO 2004; Anton and Nucu 2020). Unlike traditional silo-based approaches, ERM promotes cross-functional coordination, board-level oversight, and value creation, transforming risk management from a compliance exercise into a source of competitive advantage. This holistic perspective enables firms—particularly insurers whose core business is risk—to anticipate interactions among diverse risk types and allocate resources more effectively (Hoyt and Liebenberg 2011; Pagach and Warr 2010). Despite growing research, a comprehensive understanding of the drivers and consequences of ERM adoption remains incomplete. Empirical studies have largely examined small and medium-sized enterprises, while evidence from insurance companies—especially in developing economies such as Oman—remains limited (Anton and Nucu 2020). As global financial crises and other shocks underscore the need for flexible, organisation-wide risk management, ERM continues to evolve beyond its insurance origins to address rapidly changing business environments (Bensaada and Taghezout 2019; Al-Farsi 2020; Bakos and Dumitrașcu 2021).

The relationship between ERM and firm performance has yielded inconsistent results because of various ERM proxy measures and limited information on ERM implementation processes. The expanded perspective on ERM’s impact of ERM beyond financial metrics to encompass a broader range of organisational performance dimensions represents a crucial area of research. This study enhances our understanding of ERM practices and their effectiveness in addressing new, unpredictable, and emerging risks, thereby providing valuable insights into implementing strategies for managing complex risk scenarios (González et al. 2020).

In today’s dynamic environment, organisations face increased vulnerability to unforeseen circumstances, emphasising the importance of effective risk assessment strategies. Risk assessment enables businesses to recognise, rank, and address potential threats, informing critical decisions regarding service providers, security measures, and service structures. This study highlights risk assessment as a crucial component of ERM, underscoring the need for further investigation in this area (Sobel and Reding 2013; Oladoyinbo et al. 2023; Florio and Leoni 2017).

Research findings on the impact of risk assessment on organisational performance and Competitive Advantage are mixed. Several studies have demonstrated a positive relationship between risk assessment and organisational performance, CA, and company performance. Jaber (2020) found a significant positive impact of risk assessment on organisational performance, while Saeidi et al. (2019) discovered a substantial positive correlation between risk assessments and CA. Shahrin and Ibrahim (2021) identified a notable influence of risk assessment on company performance, while Suttipun et al. (2019) indicated a significant positive relationship between risk assessment and organisational performance.

Conversely, other studies have reported negative or insignificant relationships between risk assessment and organisational performance. Nyagah (2014) revealed the negative impact of risk assessment on company performance, and Alawattegama (2018) observed that risk assessment negatively affects organisational performance. Yakob et al. (2019) found no significant relationship between risk assessment and company performance, whereas Shad et al. (2019) reported a negative but insignificant effect of risk assessment on organisational performance.

These conflicting findings underscore the complexity of ERM implementation and its impact on organisational outcomes, highlighting the need for further research to elucidate the factors that influence the effectiveness of risk assessment strategies in diverse organisational contexts. As organisations navigate increasingly complex and uncertain business environments, understanding the nuanced relationships among ERM practices, risk assessment, and organisational performance remains critical for academic inquiry and practical application.

2.1.1. Internal Environment

The changing business environment has made evident the constraints of traditional risk-management systems. That has led organisations to adopt a holistic risk identification and management approach, such as ERM practices. Nonetheless, book-based approaches have been used in prior studies on ERM disclosures, and it has been shown that such methods fail to yield honest and accurate data (Otekunrin et al. 2021). The internal environment reflects the organisational tone, including risk appetite, ethical values, and communication processes that guide risk management (COSO 2004). Recent evidence suggests that robust ERM practices can also enhance tax planning by improving internal coordination and cross-functional communication, thereby strengthening firm value (Eastman et al. 2024). This finding reinforces the view that the internal environment not only supports risk awareness but also creates tangible financial benefits through more effective strategic decision-making.

Several studies have explored the effects of the internal environment on organisational outcomes. Li et al. (2025) conclude that ERM implementation expands the manager’s ability to evaluate uncertainty in the internal environment, a process that enhances forecast behaviour through ERM. Nyagah (2014), Indris and Primiana (2015), Alshura and Assuli (2017), Shad et al. (2019), Wiagustini and Wistawan (2021), and Ndungi (2022) highlighted that a positive correlation exists between the internal environment and company performance, while Saeidi et al. (2019), Cheraghalizadeh et al. (2021), and Afshar Jahanshahi et al. (2023) proved that the internal environment significantly enhances competitive advantage. On the other hand, Yakob et al. (2019) and Alawattegama (2018) observed no significant impact of the internal environment on firm performance. Moreover, Karaca and Şenol (2017) and Suttipun et al. (2019) reported no detectable effect.

2.1.2. Event Identification

There are observable streams to incorporate enterprise-level risk, primarily from various management science domains, such as ERM, corporate risk management, integrated risk management, and business risk management. Although there are some differences, like the terms used to describe them, industry application, and geographical application, the common ground of all these methodologies is identifying the risks that compose one of the key elements of the ERM. COSO recommends conducting risk identification at the corporate and business unit levels and categorising risks into main categories and subcategories.

The literature examining the influence of event identification on competitive advantage and organisational performance reveals inconclusive findings. Neel and Xu (2025) suggest that firms employing Enterprise Risk Management (ERM) are more adept at discerning undisclosed misconduct among peers, consequently reducing their vulnerability to the repercussions of financial restatements and associated downside risks. Saeidi et al. (2019) and Yuwono and Ellitan (2025) assert that there exists a significantly positive influence on competitive advantage, while Jaber (2020) underscores a notable positive impact on firm performance. In contrast, Yakob et al. (2019) and Lagat and Tenai (2017) report an insignificant effect on firm performance. Additionally, Nyagah (2014) and Suttipun et al. (2019) observe detrimental effects on firm performance. Alawattegama (2018) reports no significant effect, while Shad et al. (2019) document a negative, albeit insignificant, impact on firm performance. One of the key management theories is the resource-based view (RBV) theory (Wernerfelt 1984). Thus, it highlights the critical role that different internal resources play in enabling a firm to outperform others. This theory has been presented in numerous influential papers and continues to be discussed by many people. This study is grounded in the Resource-Based View (RBV) theory, which posits that a firm’s resources, management techniques—specifically, Enterprise Risk Management (ERM)—and performance development are critical assets that contribute to competitive advantage (CA) and overall firm performance. However, these resources and techniques must be heterogeneous, highlighting that not all ERM practices yield equivalent effectiveness. Research examining the relationships between management techniques such as ERM, competitive advantage, and firm performance has produced varied and inconsistent findings.

2.1.3. Risk Assessment

Once risks are identified, assessing their likelihood and impact is critical for prioritising management actions. Berry-Stölzle and Xu (2018) show that ERM enables firms to uncover “gap” threats—risks that fall between traditional categories and might otherwise be overlooked—by promoting a holistic, cross-class evaluation. This evidence highlights the importance of comprehensive risk assessment in enhancing resilience and supports our expectation that practical risk assessment has a positive impact on non-financial performance. Standard methodologies in this context include quantitative and qualitative analyses, risk scoring, risk matrices, and risk heat maps, all aimed at assessing and prioritising risks (Eshima and Anderson 2017). Several studies have reported varying findings regarding the impact of risk assessment on firm performance. Recent evidence from Ricardianto et al. (2023) indicates that a holistic risk assessment under ERM significantly enhances firm performance by bolstering competitive advantage. Their research suggests that risk assessment improves strategic responsiveness by allowing firms to prioritise threats concerning their long-term objectives and facilitating effective risk mitigation. Additionally, Jaber (2020) affirmed that risk assessment has a statistically significant and positive correlation with firm performance. In contrast, Alawattegama (2018) found that risk assessment has a negative impact on firm performance, while Shad et al. (2019) concluded that risk assessment has a non-significant effect on firm performance. Consequently, a limited body of literature addresses the association between risk assessment and firm performance, highlighting the need for further investigation.

2.1.4. Risk Response

The organisations select their response to risk (avoidance, acceptance, mitigation or sharing) and compose action plans to ensure alignment of risks with the entity’s risk appetite and orientation (COSO 2017). It is essential to identify and assess the potential risks that could impact the achievement of strategic and business objectives. The risks were prioritised according to the severity of their impact. The organisation then selects risk responses and broadly views its chosen risks. This process yields important information, which is then delivered to stakeholders affected by technological risk (Borghesi and Gaudenzi 2012).

Research findings on the impact of risk responses on organisational outcomes reveal a nuanced landscape. Quang et al. (2024) assert that ERM can enhance organisational adaptive and operational efficiencies through increased supply chain resilience, thus illustrating how risk responses can influence non-financial performance metrics. In addition, Saeidi et al. (2019) demonstrate a robust positive correlation between risk response and competitive advantage, along with a significant positive relationship with overall firm performance (Nyagah 2014; Alawattegama 2018). Conversely, Yakob et al. (2019) report that risk response does not significantly affect firm performance. Furthermore, Shad et al. (2019) as well as Suttipun et al. (2019) document that risk responses may yield negative and non-significant impacts on firm performance.

2.2. Competitive Advantage

In the contemporary economic landscape, the concept of CA holds significant importance for organisations, as it facilitates enhanced performance outcomes (Batista et al. 2016). Porter (1980) posited that organisations employing effective differentiation strategies can position themselves to achieve a monopolistic advantage over rival firms. He further asserted that a robust CA is instrumental in fostering superior business performance while underscoring that an inadequate CA can impede organisational success until firms ultimately experience failure in the competitive environment (Kakati and Dhar 2002).

To elaborate on CA within organisations, the Resource-Based View (RBV) serves as a significant strategic management framework that underscores the importance of an organisation’s internal resources and capabilities as pivotal factors in achieving CA and optimal performance. According to the RBV, firms can secure a sustained competitive advantage by acquiring valuable, rare, inimitable, and non-substitutable resources—attributes that make them difficult for competitors to replicate (Barney 1991). These resources may include tangible assets (e.g., physical infrastructure, technological innovations) and intangible assets (e.g., human capital, organisational culture, and brand equity). The distinctiveness provided by the resource differentiation model, along with the specific combinations of resources within an organisation, empowers firms to generate unique value propositions that distinguish them from competitors, thereby enabling superior long-term performance (Wernerfelt 1984).

The mediating role of CA in the relationship between ERM and firm performance can be explained as a sequential order of interconnected effects. ERM enhances an organisation’s ability to identify, assess, and mitigate risks, thereby strengthening its operational and strategic capacity (COSO 2004). These augmented capacities help an organisation distinguish itself from competitors, achieve cost efficiencies, and innovate to create a competitive advantage (Hoyt and Liebenberg 2011). Once the organisation is able to create a competitive advantage, it develops improved financial and operational performance. The organisation has positioned itself to take advantage of market opportunities and withstand environmental obstacles (Barney 1991). There are empirical studies in the literature in support of this mediating role.

For instance, studies have determined that competitive advantage and NFP are positively and significantly correlated (Lechner and Gudmundsson 2014; Bapat and Mazumdar 2015; Saeidi et al. 2015), and they report significant NFP improvements associated with competitive advantage. Yang et al. (2018) also elucidated some focus on this relationship by showing that ERM practices significantly impacted the competitive advantage-performance relationship. Ricardianto et al. (2023) compiled these results, suggesting that firms can increase NFP through the synergistic application of ERM practices and the accumulation of competitive advantage, which mediates the ERM-performance relationship.

2.3. Non-Financial Firm Performance

The concept of firm performance encompasses an organisation’s ability to achieve its strategic, operational, and financial objectives. Performance is a multidimensional construct that encompasses various areas of consideration, including financial performance, operational performance, market performance, stakeholder satisfaction, and Non-Financial Performance (Richard et al. 2009). Firm performance is a measure of success for an organisation, reflecting the effectiveness of management practices, resource allocation decisions, and strategic decisions.

Financial performance is one of the most widely used indicators of a firm’s performance. It encompasses several key metrics related to financial performance, including profitability, revenue growth, return on assets (ROA), and return on equity (ROE). Recent evidence from Jordanian commercial banks indicates that the adoption of financial technology (FinTech) has a significant positive impact on profitability, underscoring the importance of technology-driven strategies in achieving a competitive advantage (Alshehadeh et al. 2022). These attributes help to understand the firm’s capability to generate profits, thereby creating value for its shareholders (Venkatraman and Ramanujam 1986). An example of this relationship is a study conducted by Hoyt and Liebenberg (2011) where companies with advanced ERM had better profitability and financial stability, demonstrating financial performance due to risk management practices.

While financial figures provide a snapshot of a company’s short-term economic performance, the significance of non-financial performance (NFP) metrics and indicators is increasingly recognised as a crucial determinant of an organisation’s long-term viability and strength. Recent research indicates that non-financial outcomes, such as customer satisfaction and brand reputation, serve as vital indicators of organisational resilience and sustainability (Chao et al. 2022). Furthermore, Liu et al. (2022) contend that non-financial indicators are essential determinants of a firm’s long-term competitiveness, success, and adaptability to changing environments. Additionally, NFP offers profound insights into how well-positioned a company is for future growth. Frequently, these attributes empower competitive advantage (CA), enhance brand reputation, and foster a resilient and adaptable corporate culture. NFP represents a methodology that emphasises one of the most critical aspects for contemporary companies: their performance on non-financial metrics, such as sustainability, social responsibility, and stakeholder relationships. Therefore, in an era characterised by robust corporate social responsibility and stakeholder governance, integrating NFP into a holistic Enterprise Risk Management (ERM) framework is essential for achieving a balanced, sustainable organisation (Vasile et al. 2022).

Historically, a firm’s performance was measured through financial indicators such as return on assets, profitability ratios, and others. However, for long-term success, non-financial indicators such as customer satisfaction, employee engagement, and others have been considered equally critical as financial metrics (Kaplan and Norton 1992; Ittner and Larcker 2003). These dimensions reflect the underlying sustainability and health of the firm (Simon et al. 2015).

2.4. Hypothesis Development

Despite the abundance of earlier research on the effects of ERM in SMEs, there have been comparatively few studies on ERM deployment in insurance companies, particularly in developing countries (Anton and Nucu 2020). Many Omani companies are not widely aware of or adopting ERM (Al-Farsi 2020). Non-financial performance (NFP) is becoming increasingly crucial for assessing firms, in addition to financial figures (Vasile et al. 2022). Poor ERM negatively affects customer satisfaction, employee engagement, and innovation (Simon et al. 2015). The positive impact of ERM-NFP on Competitive Advantage occurs both directly and indirectly (Saeidi et al. 2020; Quang et al. 2024). The ERM-CA-NFP relationships enlighten firms on how to become resilient and strategically competitive.

This study argues that the value potential of ERM can only be released through an ongoing, dynamic, organisation-wide process of risk identification, assessment, and management (Hamzah et al. 2022; Amar et al. 2022). Tools like ERM, which entail recognising, evaluating, and monitoring risks, are increasingly important to impact a firm’s non-financial performance. ERM can enhance operational efficacy, maintain stakeholder confidence, ensure regulatory compliance, create safe and healthy working environments, promote environmental sustainability, and foster innovation and strategic vision by systematically identifying and assessing risks across multiple operational domains. Effective risk management enables organisations to survive and thrive in these uncertain times, shielding their brand equity, reputation, and other elements of nonfinancial performance.

Moreover, a study focusing on Jordanian companies conducted by Abdaljabar et al. (2025) found adherence to the COSO ERM’s principles, such as internal environment, allowed for a greater sense of risk-based decision-making, resulting in enhanced coordination of processes as well as accountability of the employees. These aspects directly contributed to increased supply chain resilience and organisational agility capabilities, and each of these was identified as a non-financial performance indicator. Saeidi et al. (2020) stated that event identification as part of ERM practices provides unique opportunities to deploy better strategic decision-making as risk awareness aligns with organisational objectives to improve non-financial performance. Yuwono and Ellitan (2025) highlighted that combining risk management with organisational culture is critical in achieving, maintaining, and enhancing competitive advantage. However, this study considered the cigarette industry in Kalimantan, Indonesia, highlighting the need for future research in other industries and various regions. Prior empirical studies have illustrated the importance of exploring mediating variables in financial and performance-related frameworks. For example, Al Mamoori et al. (2025) validated the mediating role of dividends in the relationship between cash flows and stock returns in Iraqi banks, demonstrating how intermediary constructs significantly shape financial outcomes in emerging markets. In line with this logic, our study examines Competitive Advantage as a mediating mechanism linking ERM practices to Non-Financial Performance, with particular relevance to the under-explored insurance sector in Oman.

Based on this synthesis of empirical evidence and theoretical grounding, the relationship between ERM practices, CA, and NFP has emerged as a fertile area for further research. This variation in results between studies highlights the need for additional investigations that can provide greater sophistication in examining the mediating variables and contextual factors involved. Future studies should focus on identifying the mechanisms through which ERM practices directly translate into improved CA and consequently better NFP, which will be the key to developing context-specific and effective risk management policies. With these insights, we defined the following hypotheses:

H1. 

Internal Environment has a significantly positive influence on firms’ Non-Financial Performance (NFP).

H2. 

Event identification has a significant positive influence on non-financial firms (NFP).

H3. 

Risk assessment significantly positively influences firms’ Non-Financial Performance (NFP).

H4. 

Risk Response has a significantly positive influence on firms’ Non-Financial Performance (NFP).

H5. 

Competitive Advantage (CA) mediates the relationship between the internal environment and firms’ non-financial performance (NFP).

H6. 

Competitive Advantage (CA) mediates the event identification-firms’ Non-Financial Performance (NFP) relationship.

H7. 

Competitive Advantage (CA) mediates the risk assessment-firms’ Non-Financial Performance (NFP) relationship.

H8. 

Competitive Advantage (CA) mediates the risk response -firms’ Non-Financial Performance (NFP) relationship.

This conceptual framework examines the direct influence of Enterprise Risk Management (ERM) practices—specifically Risk Assessment, Internal Environment, Risk Response, and Risk Identification—on the performance of non-financial firms (NFP). Additionally, it explores the mediating effect of Competitive Advantage (CA) within this relationship, as shown in Figure 1.

3. Research Methodology

This quantitative, correlational, and objective survey-based study explores the relationships between independent, mediating, and dependent variables. The reasoning behind such methodology relates to Ponto (2015), who declares that this kind of methodology allows researchers to provide quantity and connections between variables. In contrast, they can use different ways of questioning. Sekaran and Bougie (2016) recommend including all logical decision-making options in the research design.

Determining an appropriate sample size is crucial, as it is often impractical to collect data from the entirety of a population due to constraints related to time, financial resources, and the human effort required for data collection. Consequently, Sekaran and Bougie (2016) and Zikmund et al. (2013) advocate using sampling methods rather than including the entire population in research endeavours. Additionally, utilising a representative sample enhances the credibility of the study’s findings, with a generally acceptable sample size ranging from 30 to 500, contingent on the type of research conducted (Sekaran and Bougie 2016).

The target population of this study consists of the employees of insurance companies in Oman, totalling 2167 individuals across top, middle, and operational management positions. A total of 600 questionnaires were distributed to employees across all 19 licensed insurance companies (10 national and 9 foreign branches) as listed by the Financial Services Authority (FSA 2023). Of these, 439 usable responses were returned, resulting in a response rate of 73.2%. This nationwide approach ensured representation of the full diversity of the Omani insurance market rather than a single firm. The survey was administered electronically through Google Forms and distributed to all firms with prior approval from the National Centre for Statistics & Information. This population encompasses all employment levels, including top, middle, and operational management positions, as defined by the 2021–2022 Insurance Market Index, published in 2023, regarding premium volumes.

Top management includes employees in C-level positions such as Chief Executive Officer (CEO), Chief Financial Officer (CFO), Chief Operating Officer (COO), and Chief Risk Officer (CRO). These positions are critical for decision-making, policy development, and stakeholder management. Middle management is represented by department managers, branch managers, and customer service managers, who serve as a vital link between executives and staff. They are responsible for translating strategic goals into operational objectives and directing teams towards their achievement.

Operational management refers to clerks, customer service personnel, and front office staff who handle the day-to-day operational functions of the organisation (Mintzberg 1979). According to Krejcie and Morgan (1970), the appropriate sample size for this population is calculated to be 327 respondents; however, the actual data collection yielded responses from 439 individuals. Saunders et al. (2019) affirmed that a larger sample size of 439 enhances the robustness of statistical analyses, particularly when considering potential outliers (Saunders et al. 2019).

The questionnaire was prepared for insurance companies in Oman. The Capital Market Authority (CMA), now called the Financial Services Authority (FSA), is the supervisor and regulator of these companies, responsible for ensuring compliance with the law, protecting policyholders, and maintaining stability in the insurance market by setting standards through policies and guidelines. In addition to limited audits of insurance companies to guarantee their adherence to legal frameworks, and issuing circulars on a regular basis, the FSA monitors compliance with a variety of companies. The questionnaire was developed with carefully written items and operational definitions for each variable to ensure respondents understood their meaning. The questionnaire was translated into Arabic and English via professional legal translation firms, with the back-translation process ensuring accuracy (as shown in Table S1 in Supplementary Materials).

The electronic questionnaire, developed using Google Forms, incorporates an introductory video that outlines the research objectives prior to respondents engaging with the survey. This link has received prior approval from the National Centre for Statistics & Information in Oman, as it pertains to data collection within the region for a duration of six months. The Financial Services Authority (FSA) has disseminated the survey link to insurance companies in Oman, requesting that they circulate it among their employees within a one-week timeline. Engagement with employers addressed their inquiries concerning the study, successfully conveying the necessity and significance of this research. No biases were identified against respondents, as each employer directly communicated with their employees through mass emails sent to all staff. This study employed a convenience sampling method, facilitating relative ease in accessing respondents whom their respective employers have systematically coordinated. This methodology was deemed appropriate given that the researcher exerted neither influence nor control over employees’ willingness to participate in the study.

3.1. Data Collection, Population, and Respondents

This study employs a customised questionnaire based on the established literature on ERM practices, CA, and organisational performance. Population: Omani insurance company participants. A 5-point Likert scale is used to measure the study variables, ranging from 1 (strongly disagree) to 5 (strongly agree). This scale was chosen because of its flexibility in capturing multiple nuanced responses to specific questions.

This study complied with all applicable ethical standards. Prior to data collection, ethical clearance was obtained from the National Centre for Statistics & Information (NCSI) in Oman (Approval No. 12/2024). All respondents were informed about the purpose of the research and assured that participation was entirely voluntary. Informed consent was obtained electronically before the survey began, and participants were guaranteed anonymity and confidentiality of their responses. No identifying personal data were collected, and all data were used solely for academic purposes.

3.2. Measurement of the Study’s Variables

This study uses non-financial metrics, measuring firms’ NFP in two dimensions: employee engagement and customer satisfaction. This is consistent with earlier studies (Alagarsamy et al. 2023; Al-Dmour et al. 2019; Chandni and Rahman 2020; Judge et al. 2003; Rane et al. 2023; Subramaniam and Youndt 2005; Ershova et al. 2018; Szłapka et al. 2017). Similarly, this research assessed ERM practices according to the COSO (2004) framework. The Competitive Advantage was evaluated through Corporate Image and Quality of Service, in line with previous studies (Chang 2011; Dunk 2007; Flynn et al. 1995; Saeidi et al. 2019; Singh et al. 2019).

3.3. Multiple Regression Analysis

The study utilised multiple regression analysis (a multivariate statistical technique) to evaluate the hypothesised relationships between dependent variables and several independent and predictive variables. This analysis method is particularly suitable for studies with a dependent variable expected to serve as the subject of the independent, mediating, and moderating variables. Established as a method of description, multiple regression summarises and describes the linear relationships between independent mediators or mediating and moderating variables on a dependent variable and was used in this study to clarify how the study’s variables relate to and with each other cumulatively (Hair et al. 2010). While this study applies PLS-SEM to model latent constructs and examine mediation effects, we acknowledge the limitation of this approach in establishing causality. Future studies could benefit from adopting econometric techniques such as instrumental variable regressions to mitigate endogeneity and strengthen causal claims (Dionne et al. 2018).

4. Findings

4.1. Data Analysis and Findings

Partial Least Squares Structural Equation Modelling (PLS-SEM) is a robust analysis tool that offers effective epidemic and path modelling estimation, as well as non-linear relation performance. In datasets with a large number of indicators and non-normal distributions, the latter method has advantages for theory testing (Hair et al. 2021; Kock 2015). Data cleaning and preprocessing were performed using SPSS (ver. 23.0), where common method bias and linearity checks were conducted to ensure high data quality. Hypothesis testing was conducted using SmartPLS 4, one of the most advanced software packages used for testing complex relationships to validate the proposed theoretical model. Maintaining a strict analysis and enhancing the validity of the outputs.

4.2. Common Method Bias (CMB)

The study rigorously assessed common method bias (CMB) using both Heterotrait–Monotrait Ratio (HTMT) and inner Variance Inflation Factor (VIF) values to ensure the validity of the findings. As per Nitzl (2016), CMB may be indicated when the correlations between primary constructions exceed 0.90. In this study, all HTMT correlation values were below this threshold, with the highest value recorded at 0.846 (refer to the HTMT table), confirming that CMB was not a concern. Additionally, inner VIF values were analysed to detect any potential multicollinearity issues, where values exceeding 3.30 suggest model contamination. This study’s highest inner VIF value was 2.569 (refer to the structural model assessment table), well below the recommended threshold outlined by Kock (2015). These findings collectively confirm that CMB did not pose a significant issue in this research.

4.3. Intercorrelations of the Study Variables

Table 1. Descriptive statistics and intercorrelations of study variables.

Variables	IE	EI	RA	RR	CA	NFP	Mean	SD
IE	1						2.484	1.248
EI	0.912 **	1					2.464	1.139
RA	0.925 **	0.904 **	1				2.485	1.159
RR	0.918 **	0.916 **	0.932 **	1			2.433	1.107
CA	0.919 **	0.910 **	0.924 **	0.907 **	1		2.444	1.162
NFP	0.918 **	0.919 **	0.930 **	0.918 **	0.933 **	1	2.479	1.150

Note: N = 439. IE = Internal Environment; EI = Event Identification; RA = Risk Assessment; RR = Risk Response; CA = Competitive Advantage; NFP = Non-Financial Performance. Means and standard deviations (SD) are based on a 5-point Likert scale (1 = strongly disagree to 5 = strongly agree). Values below the diagonal are Pearson correlation coefficients. ** p < 0.01 (two-tailed).

shows the study variables’ means, standard deviations (SD), and intercorrelations. The Internal Environment (IE) had a mean score of 2.484 (SD = 1.248), indicating that participants perceived internal environment practices within their organisations as neutral to somewhat positive. Risk Assessment (RA) had the highest mean value (2.485, SD = 1.159), indicating that respondents showed relatively stronger agreement than the other variables, while Risk Response (RR) had the lowest mean value (2.433, SD = 1.107). Given that the study has utilised a 5-point Likert scale (1 = strongly disagree; 5 = strongly agree), a mean score around 2.5 can be interpreted as indicating a neutral to slightly positive perception, emphasising that participants neither strongly disagreed nor strongly agreed with the survey statements. This interpretation is supported by previous studies, which have defined Likert mean scores from 2.0 to 3.0 as representing neutral to moderate agreement levels (Boone and Boone 2012). However, all variables had statistically significant positive relationships with Non-Financial Firm Performance (NFP) at the p < 0.01 level, which strongly suggests the existence of positive relationships between ERM practices, competitive advantage, and non-financial performance. The Means, SD, and Correlations of the study variables can be seen in Table 1.

4.4. Demographic Profile of the Respondents

The demographic profile revealed a diverse sample, with Arabic speakers (64.94%) and males (58.77%) forming the majority. Job positions were well distributed across operational (47.15%), middle (36.67%), and top management (16.17%). Experience levels varied, with 34.62% having over 15 years of experience. Although gender, age, education, job position, and experience data were collected and are reported here to characterise the sample, these demographic factors were not entered as control variables in the structural equation model because the primary objective was to test the relationships among ERM, CA, and NFP.

Most respondents worked in established companies (51.94% older than 15 years) with large workforces (43.96% having more than 200 employees). The educational qualifications were primarily degrees (37.81%) and diplomas (32.80%). This diversity strengthens the validity and generalizability of the study, offering a comprehensive view of the target population.

The respondent profile revealed a diverse sample, featuring varied language preferences, gender distribution, job positions, experience levels, company characteristics, and educational qualifications. This demographic diversity strengthens the validity and generalizability of the study, offering a comprehensive representation of the target population.

4.5. Evaluation of Measurement Model (Outer Model)

To ensure the reliability and validity of our study constructs, we conducted rigorous internal consistency tests using two widely accepted measurement models: calculated composite reliability (CR) and Cronbach’s alpha (CA). As shown in

Table 2. Constructs validity and reliability.

Constructs	Items	FL	CA	CR	AVE
CA	CA1	0.920	0.967	0.968	0.860
	CA2	0.921
	CA3	0.953
	CA4	0.909
	CA5	0.939
	CA6	0.922
EI	EI1	0.918	0.956	0.957	0.884
	EI2	0.949
	EI3	0.948
	EI4	0.945
IE	IE1	0.942	0.963	0.964	0.900
	IE2	0.942
	IE3	0.959
	IE4	0.951
NFP	NFP1	0.914	0.972	0.972	0.835
	NFP2	0.894
	NFP3	0.921
	NFP4	0.916
	NFP5	0.916
	NFP6	0.903
	NFP7	0.924
	NFP8	0.922
RA	RA1	0.952	0.963	0.963	0.900
	RA2	0.933
	RA3	0.964
	RA4	0.945
RR	RR1	0.891	0.944	0.945	0.857
	RR2	0.950
	RR3	0.932
	RR4	0.929

Notes: CR: Composite Reliability; AVE: Average Variance Extracted; CA: Cronbach’s Alpha.

, the internal consistency of all constructs was excellent, with Composite Reliability (CR) > 0.7 and Cronbach’s alpha (CA) > 0.7. These results affirm the strong reliability of the constructs (Hair et al. 2021). Furthermore, all items had factor loadings (FLs) above the 0.70 margin, establishing a robust correlation with the latent variables. Chin (1998) and Hair et al. (2021). These results unequivocally confirm the validity of the measurement model.

Having met the relevant threshold criteria (i.e., factor loadings, Average Variance Extracted (AVE), Cronbach’s alpha (CA), and composite reliability (CR)), we tested the constructs for discriminant validity using two established methods: the Fornell-Larcker criterion and the Heterotrait–Monotrait Ratio (HTMT). The diagonal cells in

Table 3. Discriminant validity- HTMT and Fornell-Larcker.

Constructs	CA	EI	IE	NFP	RA	RP
CA	0.927
EI	0.811	0.940
IE	0.719	0.813	0.949
NFP	0.633	0.920	0.519	0.914
RA	0.724	0.905	0.525	0.331	0.949
RR	0.707	0.914	0.617	0.719	0.633	0.926

Notes: Diagonal values show the square root of the Average Variance Extracted (AVE); off-diagonal values represent inter-construct correlations. HTMT values below 0.90 indicate acceptable discriminant validity.

are the square roots of the AVE values, and the correlations are below. Based on the Fornell–Larcker criterion (Fornell and Larcker 1981), we compared the square root of AVE values with the appropriate correlations, as shown in Figure 2. The diagonal represents the square root of AVE, and the diagonal values are always more significant than the correlations below them. These findings provide compelling evidence that discriminant validity has been achieved according to the parameters set forth by the Fornell–Larcker criterion, reinforcing the robustness of our study.

The off-diagonal values are the correlations between latent variables, and the diagonal is the square root of AVE.

The discriminant validity of the constructs was also evaluated using the heterotrait–monotrait (HTMT) criterion, as suggested by Henseler et al. (2015). Our analysis demonstrated that all correlation values among the constructs were below 0.9, indicating satisfactory levels of discriminant validity. This outcome aligns with the threshold recommended by Henseler et al. (2015). Specifically, in

Table 4. Discriminant validity- HTMT.

Constructs	CA	EI	IE	NFP	RA	RP
CA
EI	0.846
IE	0.651	0.775
NFP	0.562	0.754	0.649
RA	0.657	0.442	0.760	0.562
RR	0.749	0.803	0.762	0.759	0.678

Notes: Diagonal values show the square root of the Average Variance Extracted (AVE); off-diagonal values represent inter-construct correlations. HTMT values below 0.90 indicate acceptable discriminant validity.

, the highest observed HTMT value is 0.846, confirming that no significant concerns regarding discriminant validity were identified. Consequently, the measurement model demonstrated acceptable discriminant validity to confirm that each of the constructs is empirically distinct.

4.6. Assessment of Structural (Inner) Model

After thoroughly evaluating the measurement model, we assessed collinearity, the coefficient of determination (R²), and effect size within the structural model. This involved analysing R² values, f² values, and the inner Variance Inflation Factor (VIF), as illustrated in

Table 5. Assessment of the structural model.

Endogenous Variables	R Square	Adjusted R2	Predictive Accuracy
CA	0.795	0.795	Substantial
NFP	0.818	0.817	Substantial

Note: Predictive accuracy (R²): Substantial ≥ 0.26; Moderate ≥ 0.13; Weak ≥ 0.02 (Cohen 1989).

and

Table 6. Effect Size and Collinearity Assessment of Predictors in the Structural Model.

Predictors	Effect Size (f2) on CA	Effect Size (f2) on NFP	Magnitude	VIF (CA)	VIF (NFP)	Collinearity Status
CA		0.221	Medium		2.569	Acceptable
EI	0.083	0.064	Weak	1.945	2.608	Acceptable
IE	0.074	0.024	Weak	2.382	3.081	Acceptable
RA	0.214	0.058	Weak-Medium	2.419	2.603	Acceptable
RR	0.008	0.028	Weak	1.355	1.434	Acceptable

Note: Effect size (f²): Substantial ≥ 0.35; Medium ≥ 0.15; Weak ≥ 0.02 (Cohen 1989). Collinearity (VIF): Acceptable if ≤5.0 (Hair et al. 2017).

, respectively. The results met the recommended thresholds for R², f², and the inner VIF, thus confirming the model’s robustness and reliability. Using these criteria, we examined the outcomes of the hypothesis testing.

The R² values for Competitive Advantage (CA) of 0.795 and for Non-Financial Performance (NFP) of 0.818 indicate that the model explains a significant percentage of the variance in these constructs. The significant R² also exceeds the Cohen (1989) benchmark of 0.26, which suggests adequate explanatory power of the model. The following is a summary of the effect sizes (f²) and examines potential collinearity concerns through VIF values.

4.7. Hypotheses Testing Results

Figure 3 and

Table 7. Hypotheses Testing Results: Direct and Mediated Effects.

Hypotheses	OS/Beta	SD	95% Bias Corrected Confidence Interval		t	p	Decision	Mediation
			LL	UL
H1: IE → NFP	0.109	0.051	0.009	0.212	2.121	0.034	Supported
H2: EI → NFP	0.213	0.052	0.107	0.310	4.118	0.000	Supported
H3: RA → NFP	0.236	0.045	0.137	0.316	5.184	0.000	Supported
H4: RR → NFP	0.125	0.051	0.039	0.240	2.451	0.015	Supported
H5: IE → CA → NFP	0.083	0.023	0.048	0.138	3.664	0.000	Supported	Partial
H6: EI → CA → NFP	0.081	0.030	0.038	0.153	2.741	0.006	Supported	Partial
H7: RA → CA → NFP	0.108	0.026	0.066	0.166	4.134	0.000	Supported	Partial
H8: RR → CA → NFP	0.028	0.021	−0.010	0.074	1.355	0.176	Not Supported

Notes: N = 439. OS/Beta = standardised path coefficient estimated using Partial Least Squares Structural Equation Modelling (PLS-SEM); SD = standard error of the bootstrapped estimate; LL and UL = lower and upper bounds of the 95% bias-corrected confidence interval; t = bootstrapped t-statistic based on 10,000 resamples; p = two-tailed significance level. Mediation indicates whether Competitive Advantage (CA) partially mediates the relationships between Enterprise Risk Management (ERM) practices and Internal Environment (IE), Event Identification (EI), Risk Assessment (RA), Risk Response (RR) and Non-Financial Performance (NFP). Significance is denoted at p < 0.05. Paths with confidence intervals that do not include zero are interpreted as significant.

present the results for the proposed hypotheses derived from bootstrapping with 10,000 resampling iterations. Hypothesis 1 (H1), which examined the direct relationship between the Internal Environment (IE) and Non-Financial Performance (NFP), yielded statistically significant results (p = 0.034), confirming a significant relationship. The t-value of 2.121 exceeded the critical threshold of 1.96, and the positive beta value (0.109) indicated a significant positive effect of the Internal Environment (IE) on Non-Financial Performance (NFP).

Hypothesis 2 (H2), investigating the direct relationship between Event Identification (EI) and Non-Financial Performance (NFP), also showed significant results (p = 0.000, t = 4.118, beta = 0.213), demonstrating a positive effect of Event Identification (EI) on Non-Financial Performance (NFP).

Hypothesis 3 (H3), assessing the direct relationship between Risk Assessment (RA) and Non-Financial Performance (NFP), produced significant findings (p = 0.000, t = 5.184, beta = 0.236), highlighting the positive effect of Risk Assessment (RA) on Non-Financial Performance (NFP).

Hypothesis 4 (H4), exploring the direct relationship between Risk Response (RR) and Non-Financial Performance (NFP), revealed statistically significant results (p = 0.015, t = 2.451, beta = 0.125), indicating a positive effect of Risk Response (RR) on Non-Financial Performance (NFP).

Hypothesis 5 examined the mediating role of Competitive Advantage in the relationship between IE and NFP, with findings supporting this hypothesis (p = 0.000, t = 3.664). The confidence intervals for the indirect effects (LL = 0.048, UL = 0.138) excluded zero, confirming significant partial mediation by CA.

Hypothesis 6 tested the mediating effect of Competitive Advantage on the relationship between EI and NFP, and the results supported this hypothesis (p = 0.006, t = 2.741). The confidence intervals (LL = 0.038, UL = 0.153) excluded zero, affirming a significant partial mediation by CA.

Hypothesis 7, which evaluated the mediating effect of Competitive Advantage on the relationship between Risk Assessment (RA) and NFP, also found support (p = 0.000, t = 4.134), with confidence intervals (LL = 0.066, UL = 0.166) excluding zero, confirming significant partial mediation.

However, Hypothesis 8, examining the mediating effect of Competitive Advantage in the relationship between Risk Response (RR) and NFP, was not supported. The p-value (0.176) exceeded 0.05, the t-value (1.355) fell below 1.96, and the confidence intervals (LL = −0.010, UL = 0.074) included zero, indicating no significant mediation.

The results of the PLS-SEM analysis reveal that all four ERM dimensions—Internal Environment, Event Identification, Risk Assessment, and Risk Response—have positive and statistically significant effects on Non-Financial Performance (NFP). Among these, Risk Assessment exhibits the strongest positive path coefficient, indicating that a systematic evaluation of potential threats enhances service quality and customer satisfaction more than other ERM components. Furthermore, Competitive Advantage (CA) partially mediates the positive relationships of Internal Environment, Event Identification, and Risk Assessment with NFP, confirming that ERM improves organisational capabilities, which in turn drive superior non-financial outcomes. In contrast, the indirect effect of Risk Response through CA is positive but not significant, suggesting that this dimension influences NFP primarily through its direct pathway (as shown in Figure 3).

4.8. Discussion of Findings

This study provides significant empirical support for all eight hypotheses examining the relationship between ERM and NFP in the Omani insurance industry. The results demonstrate that the internal environment, event identification, risk assessment, risk response, and Competitive Advantage are significantly and positively associated with NFP. These findings contribute to the growing evidence on ERM and its organisational relevance, particularly in emerging economies.

The positive impact of the internal environment on NFPs aligns with the study’s findings of Nyagah’s (2014). However, it contrasts with Yakob et al. (2019) and Alawattegama (2018), highlighting the importance of context-specific ERM implementation. This discrepancy underscores the need for greater awareness of the effectiveness of ERM to vary across organisational and contextual lenses. The impact of event identification on NFP is consistent with the findings of both Jaber (2020) and Saeidi et al. (2019), underscoring the importance of systematic risk consciousness for organisational performance improvement.

The benefits of risk assessment based on the NFP are consistent with those of Suttipun et al. (2019) and Shahrin and Ibrahim (2021), who stress its critical importance in strategic decision-making. This differs from Nyagah (2014), who found a negative relationship, likely due to differences in organisational uptake of risk assessment into their processes. The positive effect of risk response on NFP is aligned with Saeidi et al. (2019). However, it is remarkably different from Yakob et al. (2019), reflecting the specific culture of risk response that underpins the Omani insurance industry.

This study’s significant contribution is that it shows the competitive advantage to be a significant mediator variable between these ERM practices and the NFP. This finding aligns with the research conducted by Ricardianto et al. (2023) and Saeidi et al. (2020), which found that ERM is more effective in the presence of CA, especially in developing economies. This study, which examines the potential role of mediation, whereas Florio and Leoni (2017) did not, offers a more fine-grained understanding of how ERM impacts organisational performance.

Pagach and Warr (2010) found mixed support for the impacts of ERM; however, the current study provides strong evidence of such on non-financial measures in a developing setting. These findings affirm Muslih (2019) that ERM has a positive transformative potential in emerging markets. This study incorporates the Resource-Based View (Wernerfelt 1984) to emphasise the empirical relationship between the strategic alignment of ERM with organisational resources to achieve NFP.

This study advances understanding of the multidimensional benefits of ERM by presenting a comprehensive framework that aligns ERM practices with competitive strategies. The findings provide practical guidance for insurance practitioners and policymakers, particularly in developing economies. As ERM continues to evolve, future research could extend these insights through longitudinal designs, cross-industry comparisons, and mixed-methods approaches to capture its long-term effects. The mediation analysis further reveals that Competitive Advantage (CA) positively and significantly mediates the relationships of Internal Environment, Event Identification, and Risk Assessment with Non-Financial Performance (NFP), indicating that ERM strengthens organisational capabilities—such as resource coordination and strategic flexibility—that drive superior non-financial outcomes. However, the mediation pathway for Risk Response is not significant, suggesting that its influence on NFP occurs primarily through direct effects rather than via CA.

In conclusion, this study significantly enhances the body of knowledge on ERM by investigating the impact of ERM on NFP and establishing the mediating role of the CA. These findings underscore the strategic importance of ERM implementation in enhancing organisational resilience and performance, especially in dynamic and evolving business environments. The findings contribute to theory building and, at a practical level, provide insights for practitioners seeking to enhance their organisations’ competitive advantage through implementing ERM systems. Consequently, the results offer strong empirical support for the proposed model and demonstrate the importance of ERM practices to improve competitive advantage and non-financial performance.

5. Conclusions

5.1. Summary and Conclusions

This study confirms that ERM practices contribute to better non-financial performance among Omani insurers and that Competitive Advantage plays a partial mediating role in this relationship. Specifically, CA transmits the positive effects of Internal Environment, Event Identification, and Risk Assessment to NFP, underscoring the Resource-Based View proposition that unique organisational resources transform risk management capabilities into measurable performance gains.

The findings indicate that Competitive Advantage (CA) acts as a significant mediator linking Enterprise Risk Management (ERM) practices to Non-Financial Performance (NFP). PLS-SEM results show that CA partially mediates the effects of Internal Environment, Event Identification, and Risk Assessment on NFP (p < 0.01). In contrast, the mediation path from Risk Response to NFP through CA is not significant (p = 0.176). CA is strongly correlated with NFP (r = 0.933) and demonstrates a meaningful effect size (f² = 0.221), suggesting that ERM-driven competitive advantage enhances a firm’s resilience and market position through its corporate image, service quality, and stakeholder trust. Although PLS-SEM is appropriate for predictive modelling with complex latent constructs, the nesting of respondents within 19 insurance firms raises the possibility of firm-level clustering effects. Because PLS-SEM does not readily accommodate multilevel structures, formal adjustments for intra-class correlation were not feasible. Future studies should apply multilevel SEM or hierarchical modelling to account for firm-level variance and validate these relationships further (Hair et al. 2017). These results align with prior research (Nyagah 2014; Jaber 2020; Saeidi et al. 2019) while highlighting contextual differences noted by Yakob et al. (2019) and Alawattegama (2018). By integrating the Resource-Based View (Wernerfelt 1984), the study underscores the strategic alignment of ERM with organisational resources to drive non-financial outcomes, supporting evidence from Ricardianto et al. (2023), Saeidi et al. (2020), and Muslih (2019) on ERM’s transformative potential in emerging markets.

5.2. Policy Recommendations

The findings offer several actionable insights for Omani insurers, industry regulators, and policymakers. First, the positive and significant relationships between ERM practices and non-financial performance suggest that regulators such as the Financial Services Authority (FSA) should encourage or mandate the adoption of integrated ERM frameworks, providing technical guidelines and training to ensure consistent implementation across the industry. Second, insurers should integrate ERM into their strategic planning and decision-making processes, moving beyond compliance to leverage ERM as a source of competitive advantage. This includes cross-departmental risk committees, scenario planning, and continuous staff training on risk awareness and communication. Third, industry associations could develop benchmarking tools and an ERM maturity index to allow firms to monitor progress and share best practices. These measures would not only strengthen risk governance within individual companies but also enhance the overall stability and reputation of the Omani insurance sector, supporting the broader goals of Oman Vision 2040.

5.3. Limitations

Despite its important contributions, this study also has some limitations that should be addressed in future studies. Limited generalizability focuses on a single sector within a specific culture. On the other hand, self-reported data may be biased, which could call into question the credibility of the results. In order to address all of these shortcomings and elaborate further on the contributions of this study, future (empirical) research should, among others, consider studying ERM practices across various sectors in order to increase the generalizability of the results, study the interplay between the emerging technologies and ERM practices, study ERM practices cross-nationally within developed and developing economies, conduct longitudinal studies that investigate whether ERM practices can be positively sustained over time, and finally focus on the human capital aspect of ERM to provide insights into long-term pay-offs for the company and optimisation of competitive positioning within its sector.

While demographic data such as gender, age, education, and job position were collected, these variables were not incorporated as control factors in the PLS-SEM analysis. Future studies may benefit from including these demographics as control or moderating variables to account for potential heterogeneity among respondents and to validate the robustness of the relationships observed in this study.

5.4. Future Research Directions

Future research should investigate the development of a comprehensive ERM index that encompasses all four dimensions of ERM to evaluate the overall maturity of risk management practices. An index of this nature could enhance comparisons across firms and industries, support benchmarking of ERM implementation, and provide greater insights into the impact of integrated risk management on organisational performance. Future research may focus on creating a composite ERM index to evaluate overall maturity, investigate potential moderators like organisational culture, and broaden the analysis to include other GCC countries for cross-market comparisons.