Abstract

This review investigates the claims that better enterprise risk management (ERM) enhances organisational value. It examined 212 papers, covering financial and non-financial firms across many jurisdictions and cultures. Of the 285 findings, 194 (68%) showed a positive relationship between ERM and firm performance, with only 13 findings (4.5%), from 11 papers, showing a negative relationship.

Most of the findings are based on correlations. The small number of papers that address causation indicate that ERM adoption leads to better short-term performance, sometimes with a lag.

The review concludes that:

• There is generally a strong positive relationship between ERM implementation and firm value, across a wide range of contexts.
• There are many confounding factors that affect the exact form and strength of the relationship between ERM and value.
• There are limited but consistent indications that the relationship is causal, with sound ERM implementation leading to enhanced firm value.

More research is needed to demonstrate the value of ERM for non-corporate organizations.

1 Introduction

1.1 Risk and enterprise risk management

The first comprehensive risk management standard in English was published as AS/NZS 4360 in 1995 (Standards Australia and Standards New Zealand, 1995), although several earlier standards had addressed aspects of risk analysis (for example: NS 5814, Standard Norge, 1991; CAN/CSA Q634-M91, Canadian Standards Association, 1991). AS/NZS 4360 was revised twice (Standards Australia and Standards New Zealand, 1999 and 2004), and other national standards bodies soon introduced comparable standards (for example: CAN/CSA-Q850-97, Canadian Standards Association, 1997; ONR 49002-1, Österreichisches Normungsinstitut, 2004).

AS/NZS 4360 was intended to be generic, applying to risks and their management in a wide range of contexts. It spawned numerous handbooks about specific techniques and how to apply risk management across different sectors, management activities and processes.

AS/NZS 4360:2004 was used as the initial draft for what became the first international standard on risk management, ISO 31000:2009, later revised as ISO 31000:2018 (International Organization for Standardization, 2009 and 2018).

AS/NZS 4360 had introduced the idea that risk was associated with objectives, and that the impacts of risk were not just negative but could be beneficial or detrimental for those objectives (Table 1). In addition, while AS/NZS 4360 outlined how to establish effective risk management, ISO 31000 included more detailed sections on principles and an organizational framework for risk management. These factors allowed risk management to be incorporated into organizational and management processes in a systematic way that was recognized by formal national and international standards.

Meanwhile the Committee of Sponsoring Organizations of the Treadway Commission (COSO) published its integrated framework for enterprise risk management (ERM), which it defined this way:

Enterprise risk management is a process, effected by an entity’s board of directors, management and other personnel, applied in strategy setting and across the enterprise, designed to identify potential events that may affect the entity, and manage risk to be within its risk appetite, to provide reasonable assurance regarding the achievement of entity objectives (COSO, 2004).

Bromily et al. (2015, Tables 1 and 2) collated 23 broadly similar definitions and descriptions of ERM.

As well as laying the foundation for the adoption of risk management in organizations, ISO 31000 and COSO coincided with the development of corporate governance codes in several jurisdictions. These influenced many of the debates about the balance between performance and conformance.

For example, Mervyn King SC led the committee that developed a series of reports on corporate governance in South Africa, referred to as King I through to King V (Institute of Directors in Southern Africa, 1994, 2002, 2009, 2016 and 2025). King noted that ‘entrepreneurship and enterprise are among the important factors that drive business. Emerging economies have been driven by entrepreneurs who take business risks and initiatives’ (King I), and ‘Enterprise is the disposition to engage in undertakings of risk. Business is the undertaking of risk for reward’ (King II). King III was more explicit about the requirement to take risks: ‘It is the duty of the board of a trading enterprise to undertake a measure of risk for reward and to try to improve the economic value of a company’. This aligns with COSO (2004):

The underlying premise of enterprise risk management is that every entity exists to provide value for its stakeholders. All entities face uncertainty, and the challenge for management is to determine how much uncertainty to accept as it strives to grow stakeholder value. Uncertainty presents both risk and opportunity, with the potential to erode or enhance value.

1.2 Rationale for this paper

An underlying assumption in corporate governance codes is that ERM is worthwhile, and both ISO 31000 and COSO claim that ERM has benefits.

The purpose of risk management is the creation and protection of value. It improves performance, encourages innovation and supports the achievement of objectives. (ISO 31000, 2018, emphasis added)

… integrating enterprise risk management practices throughout an entity helps to accelerate growth and enhance performance. (COSO, 2017, emphasis added)

Nocco and Stulz (2006) offer a general argument for the benefits of ERM, and Bohnert et al. (2019) review more specific confirmation, but many executives are still not convinced. In a survey of 623 executives across four regions, Beasley and Branson (2024) found that only 16% thought that ERM provided a unique competitive advantage (rated as ‘Mostly’ or ‘Extensively’ on a 5-point scale). Lack of perceived value and additional bureaucracy were perceived as major barriers to ERM effectiveness (Figure 1).

This paper sets out to explore and justify the claims made for the benefits of ERM in enhancing organizational performance.

1.3 Structure of the paper

Section 2 summarises previous work on the relationships between ERM and organisational performance and the factors that influence them. It describes the approach adopted in this paper, the papers included in this review and those that are excluded.

Section 3 discusses ways of measuring ERM effectiveness and organisational performance, and how they are classified in this paper.

Section 4 provides the main evidence, analysis and findings from the review, both overall and for companies with particular characteristics. It discusses possible reasons for no or inconclusive findings, and for negative relationships observed between ERM and performance. Chapter 5 extends the analysis to discuss correlation and causation and the limited number of papers that address this explicitly.

Further discussion and conclusions are provided in Section 6.

Section 7 contains references for the papers cited here, as well as for the papers included in the analysis but not cited explicitly in the text.

2 Background

Previous work

Many authors have examined aspects of ERM and the relationship between ERM and firm performance. The focus has been primarily on listed companies, where performance information is readily available, although some researchers have accessed information for non-listed organisations from annual reports. Papers have examined a wide range of companies, sectors, locations, economies and ownership structures.

Table 2 lists selected survey papers covering aspects of ERM and performance published since 2010.

Examining ERM and performance

Analyses of the relationship between ERM and firm performance are not always straightforward. Many company characteristics are linked closely to ERM implementation and performance, particularly company size. Figure 2 illustrates the approach of most authors, and indicates some of the confounding factors that complicate the analysis.

The statistical analyses are generally based on correlations between measures of firm performance as dependent variables and measures of ERM effectiveness as independent variables, with the confounding company characteristics included to a greater or lesser degree. However, correlation does not imply causation, an important topic discussed in more detail later.

Paper selection and coverage

This review includes references that use definitions of ERM aligned broadly with the ISO 31000 (International Organization for Standardization, 2018) or COSO (2004). It focuses on companies, rather than countries or sectors (e.g. see Podpiera, 2006, for a sector-level analysis of international banking). It excludes papers that focus only on specific financial risk management practices, such as hedging and the use of derivatives, some of which are surveyed by Krause and Tse (2015).

The papers here are not confined to internationally recognized peer-reviewed journals, nor to highly cited papers. Instead, a wider set of sources has been accessed, to increase the range of geographies, sectors and companies examined.

Some papers have been written by authors for whom English is not a first language, or seemingly published with little or no peer or editorial review, which has required close reading and interpretation. While having possible imperfections, these papers provide insights and coverage that is valuable and should not be ignored. In particular, they extend early research into ERM and performance, which was confined largely to the North American finance sector where data were readily available, to less developed countries and to smaller companies. Indeed, papers relating to companies in Asia dominate the sample (Figure 3, Table 3), with Indonesia and Malaysia contributing over two thirds of them. (Note that this coverage differs significantly from some of the reviews in Table 2, which are often restricted to papers published in high-ranking academic journals.)

The majority of papers were published in the past ten years (Figure 4). Many papers generated findings about several measures of performance that are recorded individually here.

The papers relate to the finance sector, to non-financial firms alone, and to a mix of financial and non-financial firms (Figure 5). Much of the early research concerned insurers, where there are strong regulatory and reporting requirements, with an emphasis on risk management, and good data are available, but recent papers cover a wider range of sectors. The majority of papers examine companies listed on securities exchanges. About 13% examine companies with specific characteristics: Islamic banks, sharia-compliant companies, small and medium enterprises (SMEs), family firms and state-owned enterprises (SOEs).

3 Measuring ERM and performance

3.1 ERM measures

Overview

Several measures of an organization’s ERM effectiveness are used in the literature, and several kinds of data sources (Table 4, Figure 6). Linke and Florio (2019) provide a survey.

Content analyses usually follow a multi-step process:

1. Search financial reports, financial media and company web sites for ERM-related phrases such as mentions of ERM, CRO appointment, risk committees, Board discussions about risk, or specific components of ‘good ERM’

2. Manually review the ‘hits’ to confirm their context and relevance

3. Identify the earliest evidence to set an indicator variable if the analysis spans several years.

Self-assessments use online or paper questionnaire surveys, or interviews. They assess the presence of components of ERM, or their degree of implementation, often on 5- or 7-point Likert scales.

Maturity assessments use a range of data sources.

ERM adoption and ERM indices

Measures of ERM adoption are often based on a single criterion (e.g. appointment of a CRO), expressed as a binary assessment (0 or 1). Measures like this can indicate causation by comparing firm performance before and after ERM adoption; causation is discussed explicitly later.

Instead of a single criterion as a proxy for ERM adoption, ERM indices combine multiple components into a single measure. The components might be based on the COSO (2004) framework, ISO 31000 (International Organization for Standardization, 2018), corporate governance principles, or mandatory reporting requirements. The number of components varies from two or three up to 108 (e.g. Desender and Lafuente, 2009). The index measure is usually the percentage of components present. Most ERM indices give equal weights to the index components, although some authors used experts to generate specific weights (e.g. Lundqvist, 2014a; Meskovic and Zaimovic, 2021).

With some caveats, binary observations may be useful proxies for ERM adoption. For example, in a sample of 134 North American risk specialists, Mensah and Gottwald (2016) found significant positive correlations of ERM implementation (self-assessed on a 5-point scale) with CRO appointment, the presence of an audit committee and top management support.

Florio and Leoni (2017} reduced six ERM components to a single binary measure, equal to 1 if at least 4 of 6 components of ‘good ERM’ are present: a CRO; a Risk Committee; regular board reporting; frequent risk assessments; assessments below company level; and the use of both qualitative and quantitative methods.

ERM maturity

Many professional organisations have developed processes for assessing what they call ERM maturity, but what are really assessments of ERM effectiveness. They evaluate, score and combine a range of components of ‘good ERM’, similar to ERM indices. Only three maturity models were used in the papers reviewed here, from Standard and Poor’s (2015), Aon (2017) and Ernst & Young (2013).

Most ERM maturity assessments rank firms on an ordinal scale. For example, Standard and Poor’s (2015) assesses five factors – risk management culture, risk controls, emerging risk management, risk models, and strategic risk management – to develop a 5-point ERM score for insurers, summarised in Table 5. Aon (2017) reports maturity on a 9-point scale, based on 10 characteristics with 40 specific components, each scored from 1 (basic) to 5 (advanced). Ernst & Young (2013) assesses companies in the top 20%, middle 60% and lowest 20% of its maturity scale, but does not describe the scale further.

Distinguishing maturity models from ERM indices is useful because they generally follow a defined protocol and they are often implemented independently. This can provide greater comparability of assessments in some circumstances. (For a general description and examples of maturity measures, see Antonucci, 2016.)

‘Circular’ or common-cause ERM index measures

There can be a great deal of circularity in the comparison of an ERM index with a firm’s value-linked performance measure if ERM index components are based on market or accounting factors (Figure 7). The main focus of this paper is on ERM as an organisational process, so we have treated indicators of ERM performance that use financial performance indicators and finance-related measures of risk as circular and excluded them.

For example, Gordon, Loeb and Tseng (2009) used components from COSO (2004) to develop an ERM index, and several authors followed this lead (e.g. Malek, Zaman and Buckby, 2020; Ramlee and Ahmed, 2015). They use two indicators for each component (Table 6), but several of the indicators are linked to financial outcomes and so are themselves performance measures. It is unsurprising that Gordon, Loeb and Tseng found a positive relationship between ERM measured this way and firm performance expressed as excess stock market returns, a measure that includes both earnings and firm beta.

Similarly, composite indicators of risk used for banking supervision, such as CAMELS (with components of Capital adequacy, Asset quality, Management, Earnings, Liquidity and Sensitivity to market risk; see Kagan, 2023) are treated as circular and excluded. In the context of insurance firms, Kiptoo, Kariuki and Ocharo (2021) examined the relationship between four finance-related ERM effectiveness indicators – credit risk (non-performing receivables / total receivables), market risk (investment income / average investments), operational risk (net earned premiums / total assets) and liquidity risk (current assets / current liabilities) – and return on assets. Papers like this are omitted too.

Quality of ERM measures

The findings reported here are summarised with an informal quality rating (Table 7) based on the notions that content analysis is more reliable than self-assessment by managers, and more assessment factors are better than fewer. Third-party maturity assessment is separated, as there are several different forms of maturity rating and the detailed processes are not always described in detail; they are likely to include various sources of company information, including surveys and content analyses; and they may also include financial measures that can lead to circular reasoning as noted above.

3.2 Performance measures

Overview

Many measures of firm performance have been used (Table 8), but value- and earnings-related measures are the most common.

The papers reviewed here all relate to companies, whether public, private or state-owned, apart from one (Nurhayati, 2019) that examines financial outcomes for universities. Fletcher and Abbas (2018) discuss how value might be measured in public sector agencies, using a ‘public value account’ to quantify the outcomes of risk scenarios, with the US Transport Safety Administration as a case study, but no measures of this kind are included here.

Quality of performance measures

Market-related measures like Tobin’s Q are the most widely used measures of firm value. Tobin’s Q, defined as the ratio of the total market value of the firm to its total asset value, shows the under-valuation (Q < 1) or over-valuation (Q > 1) of a firm. Measures like this are favoured because:

• They are market based and so data are available readily

• They reflect longer-term market expectations about the future than short-term indicators related to historical earnings

• They are less subject to management manipulation than measures of performance based on company accounts.

Table 9 shows how performance measures are classified in this paper, with market-based measures preferred.

3.3 Classifying outcomes

The findings are classified into three groups (Table 10). These are generally aligned with the authors’ original analyses, although a small number have been downgraded from positive to inconclusive where the descriptions in the papers were unclear or appeared contradictory. No attempt has been made to review the statistical analyses in the papers in detail.

4 Evidence and analysis

4.1 Overview

Overall, 68% of the findings show a positive relationship between ERM and firm performance at the 5% significance level or better (Figure 8), across a range of measures of ERM (Table 4) and performance (Table 8). Just under 5% show a significant negative relationship, and 27% provide outcomes that are inconclusive or not statistically significant.

4.2 Findings and measures

Findings are more likely to be positive if they are based on larger sample sizes (Figure 9, where the percentages indicate the portion of positive findings in each group).

Figure 10 and Figure 11 summarise the findings according to the way ERM effectiveness is classified and the source of information for assessing performance. Perhaps unsurprisingly, there are no negative findings associated with self-assessments.

Figure 12 summarises the findings according to the kinds of performance measures examined. Note the similarities between Figure 11 and Figure 12: value measures are generally derived from market information, and earnings measures are generally derived from company accounts. ‘Other’ measures were noted in Table 8.

4.3 Sector analysis

The overall findings do not show significant sector differences (Figure 13).

Just under 13% of the papers and a little over 10% of the findings relate to firms with specific characteristics, summarised in Table 11 and Table 12, noting that some family firms may be SMEs. Of the findings, 18 were positive, none was negative and 11 were inconclusive. There were fewer positive and more inconclusive findings for these firms compared to the overall findings for all firms, probably reflecting the quality of the data, but the sample size is small so few inferences can be drawn.

4.4 Findings of ‘None’ or ‘Inconclusive’

Of the findings reviewed, 27% were classified as ‘None’ or ‘Inconclusive’. There are many possible reasons why clear relationships may not have been identified: there are no relationships to be found; or the study did not find them, for some of the reasons noted in Table 13. Elshandidy et al (2018) provide further discussion.

Table 14 provides a small sample of examples of confounding factors that influence how ERM and value are estimated. These can complicate statistical analyses and lead to inconclusive outcomes if not addressed appropriately. Beasley, Clune and Hermanson (2005) analyse factors that influence the extent of ERM implementation, and Sekerci and Pagach (2020) discuss the effect or ownership, but neither link their factors to performance.

4.5 Negative relationships

Only 13 findings, from 11 papers, showed a negative relationship between ERM and firm performance. Table 15 summarises the negative findings and possible reasons for them.

5 Correlation and causation

Most of the findings discussed in this paper reflect the outcomes of correlation analyses. However, correlation is not the same as causation. There are several ways in which correlation can arise (Figure 14).

1. ERM might improve governance, management decisions and organisational processes, leading to better performance.
2. Good organisational performance may generate resources that can be used to improve organisational processes, including ERM.
3. There are underlying organisational factors, like the characteristics noted in Figure 2, as well as more general features that might be called ‘good management’ or ‘sound governance’, that are themselves correlated with both ERM effectiveness and performance outcomes and drive them in the same direction.

There is an implicit assumption in the conclusions of many papers that effective ERM drives improved performance, path A in Figure 14, but few examine causation explicitly. Figure 15 shows potential operational links between ERM components, based on positive correlations observed by Gates, Nicholas and Walker (2012, Figure 1). Bohnert et al. (2019, Table 1) summarise some of the broader organisational discussions and findings that support this.

As a specific example, Berry-Stölzle and Xu (2018) showed that US listed insurers with an ERM program have a 1-3% lower cost of capital than firms without, and ERM adoption leads to a lower cost of capital by 43 to 60 basis points after one year. They also showed that causality in the other direction is unlikely. Figure 16 shows possible causal links for this, in a diagram adapted from Farrell and Gallagher (2018, Figure 1).

Causation can be inferred by comparing firm performance before and after a change in ERM. For example, by comparing performance before and after ERM adoption, Ai, Chen and Zhao (2014) showed a significant and positive effect of ERM on firm value measured by Tobin’s Q:

• ERM adoption increased value by 4%, for 1,506 Chinese listed non-financial firms (6,782 observations)

• ERM adoption increased value by 6%, for 254 Chinese non-financial State Owned Enterprises (1,317 observations).

Before-and-after analyses have also been used to examine the effect of duration of ERM implementation (years since ERM introduced): e.g. Ariff and Rosmaini (2017); Marc, Sprčiċ and Žagar (2018); Shadaei (2021).

Other authors have addressed path B in Figure 14. For example, in their discussion about the appointment of a Chief Risk Officer, Liebenberg and Hoyt (2003, p 43) note that ‘we expect that firms with higher earnings volatility will value ERM more than other firms and are thus more likely to appoint a CRO.’

Anton and Nucu (2020, Table 5) list a range of determinants of ERM implementation, many of which are value measures of the kind discussed in Table 8, including book to market ratio, return on assets and earnings volatility that reflect path B. Many of the confounding factors in Table 14 can also be common drivers of both ERM implementation and organisational performance.

Based on their analysis of Italian non-financial companies listed on the Milan Stock Exchange between 2011 and 2013, Florio and Leoni (2017) warn that causation may change in the medium term:

The level of ERM sophistication is not influenced by operating profitability in the short term …

However, there is some evidence that in the medium term different levels of accounting performance make available more or less resources to be invested … in designing and performing the ERM system …

A persistently higher operating performance facilitates the implementation of more advanced ERM practices.

Table 16 summarises some of the papers that discuss causation.

6 Discussion and conclusions

Scope

The purpose of this paper was to investigate the claims for the benefits of ERM for improving organisational value. It has differed from previous analyses in several ways.

Much of the early analysis of ERM and performance focussed on financial firms, and particularly insurers based in North America or Europe, where reliable performance measures and information about risk management implementation were readily available. The finance sector is tightly regulated, so standardised information is prepared and audited for supervising bodies. This is often available publicly from regulators themselves, or from the submissions of listed companies to securities exchanges. More recent research included here expands the scope of analysis considerably.

This review examined 212 papers, with 285 findings (Figure 4), extending the analysis to a wide range of non-financial sectors. While financial companies continue to be the focus a substantial portion of the papers in this review (Figure 5, Figure 13), 40% of the papers deal exclusively with non-financial firms and a further 30% examine unspecified mixes of financial and non-financial firms. Where the activities of non-financial firms are specified, they cover a wide range of business activities across some 13 individual sectors.

This review also extended the geographic scope. A large proportion of papers were from Asia, and particularly Indonesia and Malaysia (Figure 3).

As well as a wider geographic scope, analyses address organisations that have different cultures from 'traditional' listed companies in North American or European environments (Table 11, Table 12). These include: Islamic banks and Sharia-compliant firms; small and medium enterprises (SMEs) and family firms; and state-owned enterprises (SOEs).

Measures and outcomes

ERM is a complex activity that extends across many parts of an organisation. There are many factors that influence whether and how it is implemented and to what extent, and how it might be related to firm value (Table 14). It is likely that the way these influencing factors have been addressed, or not addressed, has contributed to the number of outcomes rated as None or Inconclusive (Table 13).

Some of the metrics used for ERM implementation are not clearly defined, again contributing to the number of None or Inconclusive outcomes. Binary indicators of ERM adoption seem particularly vague, and their predictive value is difficult to assess (Figure 10).

Of the 285 findings recorded, 194 (68%) showed a positive relationship between ERM and firm performance (Figure 8). Only 13 findings (4.5%), from 11 papers, showed a negative relationship between ERM and firm performance (Table 15). Most of the findings in this review are based on correlations. The small number of papers that address causation indicate that ERM adoption leads to better short-term performance, sometimes with a lag (Table 16).

It seems reasonable to conclude that:

• There is generally a strong positive relationship between ERM implementation and firm value, across a wide range of contexts.

• There are many confounding factors that affect the exact form and strength of the relationship between ERM and value.

• There are limited but consistent indications that the relationship is causal, with sound ERM implementation leading to enhanced firm value.

Extensions and further research

The findings here relate to companies, and most of the performance measures are derived from market or accounting data (Table 8). The companies are primarily listed, with a scattering of small, medium and family firms.

Few authors discuss whether these findings apply to organisations in the public and not-for-profit sectors. The operational links between ERM components and perceived performance illustrated in Figure 15 relate to general management activities, irrespective of the corporate or non-corporate context, suggesting they might, but there is little reported evidence.

Some of the performance measures in Table 8 that are not specifically related to price or profit might also apply to organizations that are not structured as companies. Ariff et al (2014) propose a framework for evaluating risk management and performance in higher education, suggesting non-financial measures might relate to such factors as international ranking, reputation, academic performance, good governance, talent management and operational continuity, but they did not explore the effects of ERM. Nurhayati (2019) explored higher education in Indonesia, but she confined her analysis to ERM and financial performance, finding a significantly effect mediated through the role of governance. Fletcher and Abbas (2018) lament the absence of a comprehensive value measure for public-sector decision making, and discuss the development of a ‘public value account’ to quantify the outcomes of risk scenarios.

Clearly more work is needed to demonstrate the value of ERM for non-corporate organizations in a rigorous way.

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