Explanatory Factors of Materiality Disclosure in the Non-Financial Reporting of European Listed Companies

Abstract

This study analyses disclosures on materiality in non-financial information (NFI) reporting by examining their likely explanatory factors, including entities’ financial or structural characteristics, governance features, and contextual factors, grounded in a set of relevant theories. Based on archival research and content analysis, this study uses consolidated NFI reports from 2021 of entities listed in the main Euronext indices. The descriptive analysis reveals that while 71% of companies present a materiality matrix, only about half (50%) meet all eight criteria of materiality disclosure, with double materiality being addressed by just 16%. The regression results show that the level of materiality disclosure is significantly and positively associated only with the size of the board of directors, whereas other expected relationships, such as those with firm size, profitability, or debt, were not statistically significant, challenging traditional assumptions from stakeholders, agency, and positive accounting theories. These findings suggest that governance structures may play a more decisive role in transparency regarding materiality than the entities’ financial or structural characteristics. This research contributes to both the academic literature and practice by identifying explanatory factors and empirical patterns in materiality disclosure in NFI reporting, which may be relevant for standard-setting bodies, regulators, auditors, and stakeholders.

1. Introduction

Materiality is a fundamental concept in the preparation and evaluation of both financial and non-financial information (NFI), enabling the distinction between relevant and non-relevant data [1]. One of the main challenges surrounding materiality is its subjective nature, with multiple definitions and the need for judgement [2].

In the context of sustainability reporting, materiality encompasses two complementary dimensions. Financial materiality refers to information that influences the entity’s enterprise value and is relevant to investors and creditors. Impact materiality, on the other hand, concerns the significant effects—positive or negative—that an entity’s activities have on the environment and society, regardless of their immediate financial implications. The integration of these two perspectives gives rise to the concept of double materiality, which recognises that sustainability issues can be material both from a financial standpoint and from the broader impact they have on stakeholders and ecosystems.

Historically, the first definitions of materiality were developed by organisations aiming to standardise financial reporting and auditing, namely, the International Accounting Standards Board (IASB) and the International Auditing and Assurance Standards Board (IAASB) [3]. Due to ongoing difficulties in operationalising the concept, both boards have recently revised standards and issued guidance to clarify its meaning and application. Materiality is inherently flexible and dynamic [4], evolving in response to shifting priorities and emerging challenges. Nevertheless, it has traditionally been addressed more extensively in financial reporting [5].

Meanwhile, NFI has gained prominence due to its role in performance evaluation, value creation, and sustainability across social, environmental, governance, ethical, and economic dimensions [6]. With growing awareness of environmental, social, and governance (ESG) issues, entities must adapt their strategies and practices to meet stakeholder expectations [7].

The growing urgency of global challenges such as climate change, social inequality, and human rights issues has intensified demands for transparency beyond financial performance. Investors, regulators, and civil society increasingly expect organisations to disclose how their operations affect, and are affected by, environmental and social systems. Non-financial reporting thus serves not only as a communication tool but also as a mechanism for accountability and sustainable value creation. In this context, the disclosure of material information helps stakeholders assess an entity’s alignment with the United Nations Sustainable Development Goals (SDGs), its resilience to climate-related risks, and its contribution to social justice. Consequently, NFI reporting plays a pivotal role in bridging corporate performance with global sustainability priorities and in fostering trust between companies and their broader stakeholder networks.

This growing relevance has led to regulatory developments in the European Union (EU) and other international bodies, such as the IFRS Foundation [8,9]. Notably, the EU issued Directive 2014/95 (Non-Financial Reporting Directive, NFRD) and proposed the Corporate Sustainability Reporting Directive (CSRD) in 2021, emphasising transparency in ESG disclosures [10,11]. Specifically, the double-materiality approach was endorsed by the European Union through the CSRD and the European Sustainability Reporting Standards (ESRS), which seek to align corporate transparency with sustainable development goals.

As a result, the definition and framing of materiality in NFI reporting have become increasingly important. Initially, ESG factors were considered material primarily from a financial standpoint [12]. However, stakeholders, including NGOs and regulators, have argued that this narrow view must be expanded to encompass social and environmental impacts to achieve broader sustainability goals [12]. ESG issues encompass a wide range of topics [13].

Despite regulatory progress, concerns about greenwashing—the practice of misrepresenting sustainability performance to gain reputational or financial advantages—remain central to the debate on non-financial disclosure. Insufficient or selective disclosure can obscure the true environmental and social impact of corporate activities, undermining stakeholder trust and market integrity. The International Capital Market Association (ICMA) highlights these risks, warning that inconsistent sustainability claims may distort capital allocation and weaken confidence in green financial instruments [14]. Similarly, recent research provides empirical evidence that some issuers of thematic bonds fail to meet their state sustainability commitments, illustrating how inadequate transparency facilitates greenwashing [15]. These findings reinforce the importance of robust materiality disclosure frameworks to ensure that reported information is both relevant and reliable.

This study evaluates the materiality disclosures in NFI reporting through eight key questions that cover relevant aspects to gauge their quality. It stands out for its broad scope, covering multiple countries, sectors, and explanatory factors. The subjective nature of materiality and the lack of a standardised NFI reporting structure hinder consistent implementation [16]. Therefore, this research is timely and relevant, especially for standard-setting bodies, regulators, auditors, and stakeholders involved in NFI reporting.

Despite the increasing attention to NFI and the concept of double materiality, prior studies have mainly focused on specific industries, individual countries, or narrow sets of explanatory variables. There remains a lack of cross-country evidence that jointly considers organisational, governance, and contextual determinants of materiality disclosure in the European setting. Furthermore, while the literature offers extensive theoretical discussions on the determinants of NFI disclosure, empirical studies addressing how governance characteristics, such as board composition, interact with contextual factors to shape materiality reporting are scarce.

This study fills that gap by providing a multi-country analysis of European listed companies, integrating diverse explanatory factors grounded in the relevant theories found in the literature. By linking board characteristics, financial indicators, and country- and sector-level dimensions to the materiality disclosures, this research contributes to a more comprehensive understanding of what drives transparency in NFI reporting under evolving EU regulations.

The paper is structured into five sections: this introduction, a literature review, materials and methods, presentation and discussion of findings, and finally, conclusions, limitations, and suggestions for future research.

2. Theoretical Framework

This paper primarily draws on stakeholders, agency and signalling theory to explain the determinants of materiality disclosure in NFI reporting.

According to stakeholder theory, organisations are expected to respond to diverse stakeholder demands by enhancing transparency and reducing information asymmetry through voluntary disclosure. Firms that operate in environments with greater stakeholder pressure tend to disclose more non-financial information to maintain legitimacy, meet expectations, and build trust [17,18,19,20]. Stakeholder theory, therefore, provides a comprehensive lens through which to understand the motivations for materiality disclosure in sustainability and NFI reporting [19,20].

Agency theory complements this view by positing that governance mechanisms—such as the size and composition of the board of directors—serve to align managers’ actions with stakeholder and shareholder interests. Through effective oversight, boards can reduce agency costs and information asymmetry, promoting higher-quality disclosures [21,22,23]. From this perspective, entities with stronger governance structures are expected to exhibit more comprehensive materiality reporting [24,25,26].

In turn, signalling theory suggests that firms disclose NFI to convey positive signals to investors and other market participants, thereby differentiating themselves and reducing perceived risk. Empirical evidence supports this mechanism in sustainability-related contexts. For instance, research shows that firms issuing green bonds use them as a signal of environmental commitment, obtaining reputational and financial benefits [27]. Similarly, there is evidence from the European Central Bank that green finance is associated with lower funding rates for banks, reinforcing the notion that credible sustainability signals are valued by the market [28]. Although not a central theoretical framework in this study, these findings contextualise the strategic motivations that may influence voluntary disclosure decisions regarding materiality in NFI reporting.

Therefore, stakeholders, agency and signalling theories emerge as overarching frameworks and are the most directly related to the entities’ (financial or structural) characteristics, governance and accountability dimensions that underpin materiality disclosure practices. In common, stakeholders and signalling theories explain how external demands shape disclosure practices, while agency theory explains how internal governance mechanisms influence them.

Nonetheless, other theories, such as positive accounting theory, are acknowledged as providing useful contextual insights into disclosure behaviour. In addition, the upper echelon and resource dependence theory are specifically addressed to support gender matters regarding the women’s integration into the board of directors, while the legitimacy and institutional theories specifically concern cross-country or sectoral differences.

The linkage between theories and the following seven hypotheses is summarised as follows:

• Entities’ (financial or structural) characteristics: it relates to three hypotheses, namely H1 (Size), H2 (Profitability) e H3 (Debt). Stakeholders, agency and signalling theories comprise the main theories, which can also be supported by the positive accounting theory. They might jointly explain how external pressures and internal incentives drive disclosure based on economic size, performance, and leverage.
• Governance features: it relates to two hypotheses, namely H4 (Board Size) e H5 (Gender Diversity). Stakeholders and agency theories can provide strong support for the first, while the upper echelon and resource dependence emerge as relevant for the latter. They might explain how meeting stakeholders’ interests, structuring the board and including diversity can shape disclosure quality.
• Contextual factors: it relates to two hypotheses, namely H6 (Sector) e H7 (Country of Origin). Legitimacy, stakeholder theories arise as relevant, being complemented by the institutional theory. They might explain how social legitimacy, stakeholders’ expectations and institutional environments influence disclosure variation.

According to the literature, entity size is a key factor in explaining the NFI disclosures [29]. Stakeholder theory suggests a positive association between entity size and stakeholder pressure, as larger entities face greater demands to meet stakeholder expectations, reduce information asymmetries, and attract future investors [17,20]. Agency theory also supports the notion that larger entities are more likely to disclose NFI [30]. These entities are better equipped to absorb agency costs and use NFI disclosure to reduce asymmetries between principals and agents, thereby improving decision-making [19]. Finally, the positive accounting theory posits that larger entities, being more politically visible, are subject to greater public scrutiny and therefore more leaned to disclose NFI [22]. Based on these theories and supporting research [17,20,25,30,31,32,33], the following hypothesis is proposed:

H1: 

The materiality disclosures in NFI reports are positively associated with entity size.

Profitability is widely recognised as essential for business continuity [20]. More profitable entities have greater resources to invest in creating and disseminating sustainability information [24]. Their directors may disclose tailored information to gain competitive advantages and enhance investor confidence [34]. Agency and signalling theories are commonly used to explain the link between profitability and NFI disclosure. From an agency perspective, profitable entities can afford the costs of preparing and disseminating NFI, thereby reducing information asymmetries and justifying performance to shareholders [18,19,31,35]. Signalling theory suggests, in turn, that profitable entities might disclose more NFI to differentiate themselves and send positive signals to potential investors [29,31,36]. Despite some contradictory findings [18,35], the literature generally supports a positive association between profitability and NFI disclosure [32], leading to the following hypothesis:

H2: 

The materiality disclosures in NFI reports are positively associated with entity profitability.

Agency and positive accounting theories also suggest a positive relationship between debt and NFI disclosure. Highly indebted entities may increase disclosure to mitigate agency and capital costs and avoid conflicts between creditors and shareholders [21,30]. Positive accounting theory supports this view, arguing that indebted entities use NFI disclosure to attract investors and reduce debt costs [33]. Besides profitability, signalling theory posits that firms disclose NFI to send positive signals to investors and other market participants, helping them stand out and mitigate perceived risk [27,28]. Although some studies report a negative association [29,33], the following hypothesis is formulated based on prevailing evidence [32]:

H3: 

The materiality disclosures in NFI reports are positively associated with entity debt.

Stakeholders and agency theories both support the idea that board size positively influences NFI disclosure. A larger board can better balance stakeholder interests and address a broader range of issues, enhancing effectiveness and reducing opportunistic behaviour [19,20]. Agency theory adds that a larger board improves managerial oversight, reduces agency costs, and enhances disclosure quality through diverse knowledge and experience [23]. Supported by empirical studies [19,20,23,26], the following hypothesis is proposed:

H4: 

The materiality disclosures in NFI reports are positively associated with the number of board members.

Gender diversity on boards enhances transparency and sensitivity in disclosures, reflecting gender-specific traits identified in the literature [30]. Diverse boards bring new perspectives and experiences, improving NFI dissemination [37,38]. Women’s distinct communication styles foster better stakeholder engagement and decision-making [39,40]. Upper echelons theory additionally posits that strategic outcomes are shaped by managers’ cognitive traits, such as gender, which influence NFI disclosure [31,41,42]. Resource dependence theory suggests that women are more attuned to social and environmental issues, which promotes NFI disclosure [43,44]. Based on these theories and supporting studies [31,42,45,46], the following hypothesis is proposed:

H5: 

The materiality disclosures in NFI reports are positively associated with gender diversity on the board.

Legitimacy theory suggests that sector influences NFI disclosure, as entities in environmentally sensitive industries disclose more to maintain societal legitimacy [47,48,49]. Institutional theory posits that entities in similar sectors behave similarly due to shared goals, pressures, and regulations [19,50,51]. Based on these theories and supporting literature [19,20,47,52,53], the following hypothesis is proposed:

H6: 

The materiality disclosures in NFI reports are influenced by the entity’s sector.

The entities’ country of origin may also affect NFI disclosure. Stakeholder theory suggests that entities adapt their disclosures to meet local stakeholder expectations [54,55]. At the country level, institutional theory posits that disclosure practices are shaped by formal and informal institutions, such as regulatory regimes, political systems, and cultural contexts on disclosure practices [19,52,56,57]. Based on these perspectives and supporting studies [23,52,54], the following hypothesis is then proposed:

H7: 

The materiality disclosures in NFI reports are influenced by the entity’s country of origin.

The next section provides the materials and methods used to assess the findings.

3. Materials and Methods

This study aims to assess the materiality disclosures in NFI reports and their explanatory factors. To achieve this, archival research was adopted as the methodological approach, and content analysis was employed as the investigative technique. For this purpose, the NFI reports were sourced from pieces of information reported by the sampled entities, which provide that content but frequently use, however, distinct denominations, namely NFI inserted as an additional piece into traditional annual reports, sustainability reports, and integrated reports for the year 2021.

Six of the seven countries represented in the main Euronext indices were selected. Italy was excluded due to the absence of a corresponding index in Milan. Entities were identified through the Euronext website, and in cases where companies were listed in multiple indices, the location of their headquarters was used as the selection criterion. This process resulted in an initial sample of 144 entities.

A second selection criterion was applied to identify entities that disclosed materiality in their NFI reports. This involved searching for terms such as “material” and/or “materiality” in the report’s original language. As a result, 33 entities that did not address materiality were excluded, yielding a final sample of 111 entities across the six Euronext indices.

To ensure geographic diversity and comparability across European markets, the study selected entities listed in the main Euronext indices. These indices were chosen due to their high market capitalization and representativeness of national economies within the European Union and EEA. Euronext is one of the largest pan-European stock exchange groups, with a combined market capitalization exceeding €6 trillion as of 2021 [58], making it a relevant and influential platform for assessing non-financial disclosure practices across sectors and countries.

Therefore, the sample was restricted to entities listed in the six main Euronext indices to ensure comparability under a common regulatory and reporting framework. While this excludes smaller European markets, the selected countries represent the main countries’ indices of reference from Euronext other than Italy, thus capturing the larger listed NFI reporters. Furthermore, entities that did not address materiality were excluded to ensure conceptual consistency with the study’s focus.

Table 1. Entity selection criteria.

Index	Initial Sample: Entities in Their Euronext Indices	Exclusions: Entities That Do Not Report Materiality in the Disclosure of NFI	Final Sample
AEX—The Netherlands	25	6	19
BEL-20—Belgium	20	2	18
ISEQ-20—Ireland	20	6	14
PSI-20—Portugal	15	4	11
OBX GR—Norway	25	6	19
CAC-40—France	39	9	30
Total	144	33	111

Source: Authors.

summarises the selection criteria and sample composition.

To assess materiality disclosures, eight key questions were developed based on prior studies [59,60,61,62] and technical standards, such as Global Reporting Initiative (GRI) [13]. This disclosure ratio intends to gauge the quality of materiality disclosure through a diversity of relevant matters related to this topic. These key questions are listed in

Table 2. Key questions about materiality in NFI reporting.

Key Questions
Q1: Does the entity have a materiality matrix?
Q2: Does the entity identify stakeholders involved in defining the materiality matrix?
Q3: Does the entity refer to the concept of double materiality?
Q4: Does the entity explain the stakeholder engagement techniques used?
Q5: Does the entity describe the process for defining material topics?
Q6: Does the entity indicate stakeholder relevance for each material item?
Q7: Does the entity specify the standard used for NFI disclosure?
Q8: Does the entity include an independent audit report on the NFI?

Source: Authors.

.

Although limited assurance of NFI will only become mandatory under the CSRD from 2025 onwards, this criterion was retained because several entities voluntarily included independent assurance in their 2021 reports. This voluntary practice reflects early adoption trends and is consistent with the GRI and previous literature [63], which identify assurance as a key quality enhancer of sustainability disclosures. Therefore, the inclusion of this question captures firms’ proactive transparency behaviour before regulatory enforcement.

Each question was scored as “1” for a positive response and “0” otherwise. These scores were used to construct a Disclosure Index (DI), calculated as follows:

$$DI={\displaystyle \frac{{\sum }_{i=1}^{m}d}{{\sum }_{i=1}^{n}d}}$$

(1)

where

d = 1 if the item is disclosed, 0 if not

m = number of disclosed items

n = total number of items

The DI serves as the dependent variable, allowing for a general assessment of materiality disclosures across entities.

Table 3. Independent variables used as proxies for explanatory factors.

Explanatory Factors	Independent Variables	Related Studies
Size	Total assets (Assets or asset_Ln)	[6,8,17,18,19,20,25,29,30,31,33,64,65]
Profitability	Net income for the period/Assets (ROA)	[6,8,18,19,29,30,31,33,35,36,45,64]
Debt	Liabilities/Equity (Debt)	[20,30,31,45]
Size of the board of directors	Total number of members of the board of directors (Board size)	[19,20,29,30,45,65]
Gender diversity on the board	The percentage of women on the board of directors (gender)	[19,20,29,30,31,42,45,46]
Sector	Main sector (sector)	[19,20,31,47,53,60]
Country	Index reference country or where the entities are headquartered, if included in two indices (country)	[19,20,52,54]

Source: Authors.

presents the independent variables used as proxies for the explanatory factors, along with supporting literature.

Financial data (size, profitability, debt) were obtained from consolidated financial statements. Board composition and gender diversity were sourced from management reports. Asset values were converted to euros and log-transformed for consistency. For entities reporting in currencies other than the U.S. dollar, conversion was based on the exchange rate as of the 2021 reporting date.

Sectors were grouped into six categories based on the Industry Classification Benchmark (ICB), as shown in

Table 4. Entities by sector.

ICB Classification	Sector Code	Number of Entities	% of Entities
Telecommunications (10) and Technologies (15)	S1	12	10.81%
Health (20)	S2	4	3.6%
Finance (30) and Real Estate (35)	S3	24	21.62%
Consumer Goods (40)	S4	25	22.52%
Industry (50) and basic materials (55)	S5	33	29.72%
Energy (60) and utilities (65)	S6	13	11.71%
Total	-	111	100%

Source: Authors.

.

Countries were also coded from “1” to “6” as shown in

Table 5. Entities by country.

Country (Index)	Country Code	Number of Entities	% of Entities
Netherlands (AEX)	C1	19	17.12%
Belgium (BEL-20)	C2	18	16.22%
Ireland (ISEQ-20)	C3	14	12.61%
Portugal (PSI-20)	C4	11	9.91%
Norway (OBX GR)	C5	19	17.12%
France (CAC-40)	C6	30	27.03%
Total	-	111	100%

Source: Authors.

.

To analyse the explanatory factors influencing materiality disclosure, an Ordinary Least Squares (OLS) regression model was developed. Equation (2) presents the model:

$$\begin{array}{ll}DIX={\beta }_0+{\beta }_1& {asset}_{Ln}+{\beta }_{2}ROA+{\beta }_{3}Debt+{\beta }_{4}Board\_size\\ & +{\beta }_5\%gender+{\beta }_6\mathrm{s}\mathrm{e}\mathrm{c}\mathrm{t}\mathrm{o}\mathrm{r}1+{\beta }_7\mathrm{S}2+{\beta }_8\mathrm{S}3\\ & +{\beta }_9\mathrm{S}4+{\beta }_{10}\mathrm{S}6+{\beta }_{11}\mathrm{C}1+{\beta }_{12}\mathrm{C}2+{\beta }_{13}\mathrm{C}3\\ & +{\beta }_{14}\mathrm{C}5+{\beta }_{15}\mathrm{C}6+\mathrm{Ɛ}\end{array}$$

(2)

For categorical variables (sector and country), reference categories were excluded from the model. S5 (Industry & Basic Materials) was excluded from the regression model as a reference category due to its traditionally high environmental impact, which has been widely associated with increased non-financial disclosure. Prior literature suggests that companies in environmentally sensitive sectors tend to disclose more extensively to maintain legitimacy and respond to stakeholder pressure (e.g., [18,19]). By excluding this sector, the model uses it as a baseline to compare disclosure practices across other sectors, allowing for clearer interpretation of sectoral effects relative to the most disclosure-intensive category.

C4 (Portugal) was excluded from the regression equation to serve as the baseline category in the country-level analysis. In regression models involving categorical variables, one category must be omitted to avoid multicollinearity—this omitted category becomes the reference point against which the effects of other categories (countries) are measured. Portugal was selected as the baseline due to its moderate representation in the sample and, like others, its regulatory alignment with EU sustainability directives, making it a neutral comparator for cross-country variation.

The next section presents and discusses the findings.

4. Presentation and Discussion of the Findings

This section is divided into two parts: the first presents and analyses the findings, while the second discusses the results.

4.1. Presentation and Analysis of Findings

Table 6. Key questions and average DI (as a percentage).

Key Questions	In Percentage
Q1: Does the entity have a materiality matrix?	71
Q2: Does the entity identify stakeholders involved in defining the materiality matrix?	64
Q3: Does the entity refer to the concept of double materiality?	16
Q4: Does the entity explain the stakeholder engagement techniques used?	49
Q5: Does the entity describe the process for defining material topics?	48
Q6: Does the entity indicate stakeholder relevance for each material item?	14
Q7: Does the entity specify the standard used for NFI disclosure?	84
Q8: Does the entity include an independent audit report on the NFI?	52
Average DI	50

Source: Authors.

presents the materiality disclosures in NFI reports, based on the eight key questions (Q1 to Q8), along with the average Disclosure Index (DI).

The global average DI is 50%. The most frequently disclosed items are Q1 (materiality matrix, 71%) and Q7 (reporting standard, 84%). In contrast, Q3 (double materiality, 16%) and Q6 (stakeholder relevance per item, 14%) show the lowest index of materiality disclosures. Notably, the GRI standard, either alone or combined with the Sustainability Accounting Standards Board (SASB), is the most used framework among entities disclosing under Q7, accounting for approximately 84% of cases.

Before conducting the OLS regression analysis,

Table 7. Average of independent variables by country and sector.

	Assets (€M)	ROA	Debt	Board Size	Gender
Total	113,686	0.05	3.80	12	38
By country
C1: Netherlands (AEX)	135,599	0.09	4.55	12	37
C2: Belgium (BEL-20)	44,371	0.06	2.18	12	33
C3: Ireland (ISEQ-20)	28,867	0.02	3.20	12	32
C4: Portugal (PSI-20)	16,040	0.04	4.42	14	32
C5: Norway (OBX GR)	32,544	0.05	3.90	9	38
C6: France (CAC-40)	268,171	0.04	4.28	14	45
By Industry
S1: Telecommunications (10) and Technologies (15)	19,062	0.12	1.65	12	31
S2: Health (20)	14,924	0.07	1.00	11	40
S3: Finance (30) and Real Estate (35)	406,349	0.02	9.94	14	37
S4: Consumer Goods (40)	38,625	0.06	1.38	12	40
S5: Industry (50) and basic materials (55)	25,102	0.06	2.32	12	39
S6: Energy (60) and utilities (65)	60,328	0.01	3.70	12	38

Source: Authors.

presents the average values of the independent variables by country and sector.

Based on the data presented in Table 7, the country-level analysis reveals no significant differences across the general independent variables, except for assets, which vary notably among countries. In contrast, sector-level analysis shows that S3 (Finance and Real Estate) exhibits marked differences in both assets and debt levels compared to other sectors, indicating potential structural distinctions in financial profiles.

To ensure the robustness of the OLS regression model, preliminary collinearity diagnostics were conducted.

Table 8. Correlation table.

	C1	C2	C3	C4	C5	C6	S1	S2	S3	S4	S5	S6	Assets	ROA	Debt	Board Size	Gender
DI	0.140	−0.109	−0.038	0.034	−0.004	0.026	0.183	−0.222 *	−0.085	0.016	0.087	−0.083	0.071	0.096	0.053	0.300 **	−0.034
C1	1	−0.200 *	−0.173	−0.151	−0.207 *	−0.277 **	0.227 *	0.040	0.110	−0.016	−0.139	−0.166	0.180	0.295 **	0.064	−0.037	−0.051
C2		1	−0.167	−0.146	−0.200 *	−0.268 **	0.083	0.177	0.185	−0.120	−0.126	−0.084	−0.100	0.069	−0.133	0.004	−0.192 *
C3			1	−0.126	−0.173	−0.231 *	−0.132	−0.073	0.064	0.250 **	−0.069	−0.138	−0.189 *	−0.169	−0.043	−0.066	−0.199 *
C4				1	−0.151	−0.202 *	−0.018	−0.064	−0.101	−0.179	0.180	0.160	−0.240 *	−0.085	0.039	0.190 *	−0.187 *
C5					1	−0.277 **	−0.004	−0.088	−0.122	−0.073	0.018	0.281 **	−0.216 *	−0.034	0.008	−0.402 **	0.014
C6						1	−0.147	−0.009	−0.123	0.109	0.137	−0.032	0.417 **	−0.095	0.055	0.290 **	0.466 **
S1							1	−0.067	−0.183	−0.188 *	−0.226 *	−0.127	−0.221 *	0.376 **	−0.140	−0.049	−0.221 *
S2								1	−0.102	−0.104	−0.126	−0.070	−0.041	0.060	−0.102	−0.084	0.038
S3									1	−0.283 **	−0.342 **	−0.191 *	0.469 **	−0.281 **	0.605 **	0.170	−0.042
S4										1	−0.351 **	−0.196 *	−0.071	0.091	−0.245 **	−0.081	0.104
S5											1	−0.237 *	−0.186	0.056	−0.180	−0.014	0.076
S6												1	−0.006	−0.236 *	−0.007	0.003	0.003
Assets													1	−0.278 **	0.582 **	0.501 **	0.242 *
ROA														1	−0.360 **	−0.292 **	0.005
Debt															1	0.333 **	0.090
Board Size																1	−0.075

Note: * The correlation is significant at the 5% level; ** The correlation is significant at the 1% level. All correlation coefficients below 0.5 suggest no multicollinearity. Source: Authors.

presents the correlation matrix for the independent variables, confirming that there are no significant collinearity issues. All correlation coefficients are below the threshold of 0.5, suggesting that multicollinearity is not a concern in this analysis.

Based on the absence of collinearity issues, the multivariate OLS regression model was applied to assess the relationship between the materiality disclosures in NFI reports, with the DI as the dependent variable, and the proposed explanatory factors (the set of independent variables). The results are presented in

Table 9. Regression Model Summary.

Model	$\mathit{R}$	${\mathit{R}}^2$	$\mathbf{A}\mathbf{d}\mathbf{j}\mathbf{u}\mathbf{s}\mathbf{t}\mathbf{e}\mathbf{d}{\mathit{R}}^2$	Standard Estimation Error	Durbin-Watson
1	0.507	0.257	0.140	0.216	2.307

Source: Authors.

.

Diagnostic tests confirmed no issues of heteroskedasticity or multicollinearity. The model’s overall fit (R² = 0.216; adjusted R² = 0.140; F = 2.175, p = 0.012) indicates moderate explanatory power and statistical significance. Therefore, it means that the model explains approximately 14% of the total variance in the materiality disclosures, as indicated by the adjusted R² value, which refines R² by accounting for the number of predictors, offering a more accurate measure of model fit. Additionally, the Durbin-Watson statistic confirms the reliability of the model, as the result falls within the acceptable range for assessing the independence of residuals.

The overall validity of the model was further tested through an analysis of variance (ANOVA), as presented in

Table 10. ANOVA analysis of variance model.

Model		Sum of Squares	df	Mean Squares	Z	Sig
1	Regression	1.670	15	0.111	2.175	0.012
	Residuals	4.861	95	0.051
	Total	6.531	110

Source: Authors.

. This test confirms that the model is statistically significant and can be used to draw inferences, supporting its applicability in explaining variations in the materiality disclosures.

Finally,

Table 11. Multivariate OLS regression model results for explanatory factors of materiality disclosures.

Model		Non-Standard Coefficients		Standardised Coefficient	T	Sig	Collinearity Statistics
		B	Standard Deviation	Beta			Tolerance	VIF
1	Constant	0.035	0.201		0.177	0.860
	C1: Netherlands (AEX)	0.100	0.109	0.156	0.923	0.359	0.275	3.631
	C2: Belgium (BEL-20)	0.009	0.100	0.014	0.093	0.926	0.339	2.954
	C3: Ireland (ISEQ-20)	0.052	0.102	0.071	0.505	0.614	0.400	2.501
	C5: Norway (OBX GR)	0.136	0.099	0.211	1.375	0.172	0.334	2.996
	C6: France (CAC-40)	−0.044	0.104	−0.081	−0.423	0.673	0.215	4.661
	S1: Telecommunications (10) and Technologies (15)	0.058	0.085	0.074	0.676	0.500	0.655	1.528
	S2: Health (20)	−0.265	0.125	−0.204	−2.128	0.036	0.853	1.172
	S3: Finance (30) and Real Estate (35)	−0.117	0.085	−0.199	−1.373	0.173	0.373	2.679
	S4: Consumer Goods (40)	−0.021	0.063	−0.037	−0.335	0.739	0.658	1.520
	S6: Energy (60) and utilities (65)	−0.102	0.080	−0.135	−1.276	0.205	0.697	1.434
	Asset_ln	−0.001	0.021	−0.005	−0.033	0.974	0.289	3.458
	ROA	0.415	0.450	0.105	0.923	0.358	0.603	1.659
	Debt	0.001	0.006	0.030	0.216	0.829	0.415	2.412
	Board size	0.031	0.008	0.466	3.704	0.000	0.495	2.021
	Gender	0.002	0.003	0.078	0.720	0.473	0.663	1.509

Source: Authors.

presents the detailed results of the regression model, including the statistical significance of each explanatory factor and its respective impact on the materiality disclosures in NFI reports.

Table 11 indicates that only the independent variable board size and Sector 2 (Health) show statistically significant results, with p-values below the 10% threshold. This suggests that these two factors may have a meaningful influence on the materiality disclosures in NFI reports.

Regarding the Variance Inflation Factor (VIF), all independent variables exhibit acceptable values—below or close to 3—indicating that multicollinearity is not a concern and that the regression estimates are reliable.

4.2. Discussion of the Findings

Considering the proposed hypotheses, the findings reveal that a single hypothesis was confirmed—H4, related to the number of members on the board of directors. Consequently, the assumptions regarding the other explanatory factors—namely, size, profitability, debt, gender diversity on the board, sector, and country—were not supported, suggesting that governance structures may play a more decisive role in transparency regarding this topic than the entities’ financial or structural characteristics.

More specifically, the results for H4 confirm a positive association between the materiality disclosures in NFI reports and board size, consistent with previous studies [19,20,23,26]. These findings validate stakeholder theory, as a larger board enhances the connection between the entity and its stakeholders [19,20]. They also corroborate agency theory, since a larger board improves managerial oversight, thereby promoting the quality and transparency of disclosed information [23].

Conversely, the hypothesis related to entity size (H1) contradicts prior research [17,25,30,31,32,33], and challenges the theoretical expectations of stakeholder, agency, and positive accounting theories. A possible explanation is that entities do not disclose more NFI solely due to their size, but rather because of intrinsic factors such as their strategic interest in meeting stakeholder needs [20].

Regarding profitability (H2), the results do not confirm a positive association with materiality disclosure. This contradicts studies grounded in agency, signalling, and positive accounting theories. However, the literature on this topic is mixed, with evidence of positive associations [32], negative associations [18,35], and inconclusive findings [29,31,36], probably because disclosures (transparency) may reflect managerial interests [34]. In this context, managers of highly profitable entities may perceive limited strategic value in investing in NFI disclosure, deeming it insufficient to yield competitive advantages [18].

As for debt (H3), the results do not support a positive association with NFI disclosure. Prior studies have reported positive [32], negative [29,33], and null associations [30,31], thus failing to confirm agency and positive accounting theories. Entities with high debt levels may be subject to restrictive financial arrangements that limit their ability to allocate resources to NFI disclosure, especially when such expenditures negatively affect net results [33].

Concerning gender diversity on the board (H5), the findings do not confirm a positive association with NFI disclosure. This contradicts research that supports such a relationship [42,45,46], and therefore does not validate upper echelons theory or resource dependence theory.

Regarding the sector (H6), the results do not support its influence on the materiality disclosures in NFI reports. This finding diverges from previous literature [19,47,52,53] and fails to confirm legitimacy and institutional theories. The underrepresentation of certain sectors, such as Sector 2 (Health), may have contributed to this outcome.

Finally, the hypothesis that country of origin influences materiality disclosure (H7) was not confirmed. While some studies support this influence [23,52,54], others do not [19]. Based on institutional theory, the lack of variation may be attributed to the fact that all countries analysed are EU members, subject to similar regulations and development levels. As a result, they face comparable stakeholder pressures and adopt relatively uniform disclosure practices [19].

The next section summarises the conclusions.

5. Conclusions

This study focused on the analysis of materiality-related information within NFI reporting, with a particular focus on the explanatory factors influencing the materiality disclosures. The descriptive analysis reveals that while 71% of companies present a materiality matrix, only about half (50%) meet all eight criteria of materiality disclosure, with double materiality being addressed by just 16%. The findings also identified a positive influence of board size on the level of disclosure, thereby supporting both stakeholder and agency theories and suggesting a more relevant role played by the governance structures in transparency regarding materiality than the entities’ financial or structural characteristics.

In addition, it is worthwhile to mention that the adjusted R² value of 14% for the regression model performed reinforces that the explanatory variables included in the model account for only a modest portion of the variation in materiality disclosure practices. This low explanatory power highlights the complexity and multidimensional nature of non-financial reporting, where disclosure decisions may be influenced by factors beyond structural and governance characteristics. For instance, qualitative elements such as corporate culture, leadership commitment to sustainability, stakeholder engagement intensity, and ESG-specific expertise within the organisation may play a significant role but are not captured by the current model. Additionally, external pressures—such as media scrutiny, investor activism, or evolving regulatory expectations—could also shape disclosure behaviour in ways that are difficult to quantify. These findings suggest that future research should consider integrating qualitative data, longitudinal designs, or hybrid models to better capture the nuanced drivers of materiality disclosure in NFI reporting.

Following, this study revealed fluctuations concerning the disclosure of materiality across the key questions analysed, which is also a relevant finding to be considered since inefficiencies regarding the disclosure of material aspects can lead to a decrease in the quality of sustainable reporting [66]. Higher frequencies of disclosures were particularly observed in relation to the reporting standard used—where the GRI framework predominates—and in the presentation of the materiality matrix for each material item, as well as references to the concept of double materiality.

This research offers valuable contributions to both academic and professional domains, including regulators, supervisory bodies, auditors, and various stakeholders involved in NFI reporting. It highlights the diverse approaches to materiality disclosure and provides a framework for analysing disclosure practices across different organisational characteristics. Furthermore, it enables the identification of potential standards and patterns in materiality disclosure based on distinctive entity attributes.

However, this study is subject to certain limitations. A key constraint affecting both research objectives is the sample size, which limited the depth of analysis for specific subgroups, particularly those related to country and sector. Moreover, the inherent subjectivity of the data collection process, based on content analysis and archival research, may introduce bias. This stems from the varied terminology used by entities to describe material topics and the differing formats in which materiality matrices are presented, both of which require interpretive judgement by the researcher.

Particularly, content analysis relies on the subjective interpretation of textual disclosures, which—even when guided by structured coding schemes—can introduce researcher bias. The quality of disclosure is inferred from the presence of keywords or statements, which may not fully capture the depth or intent of the information reported. Moreover, variations in terminology and reporting formats across countries and languages can lead to inconsistencies in scoring.

Archival research, while suitable for comparability and objectivity, is limited by the availability and completeness of publicly disclosed reports. Companies may selectively publish non-financial information, and the archival nature of the data restricts causal inference. Therefore, the results should be interpreted as associative rather than causal relationships.

To address these limitations, future research could expand the sample size and adopt methodologies that reduce researcher subjectivity, such as leveraging artificial intelligence and machine learning tools for automated content analysis. Similar research, including non-European or non-listed European entities, could be of relevance to compare the findings and identify the possible different patterns from them. Future research could also complement this approach with interviews, surveys, or mixed methods designs to validate and deepen the understanding of disclosure practices.

In addition, further studies may also explore whether adverse events—such as the COVID-19 pandemic or the war in Ukraine—have influenced materiality disclosure practices, both in terms of content and frequency. Lastly, future research could examine the implications of omitting the materiality matrix in NFI reporting under the revised GRI standards [67], particularly regarding stakeholder perceptions of transparency and the usefulness of disclosed information.