Unveiling ESG Controversy Risks: A Multi-Criteria Evaluation of Whistleblowing Performance in European Financial Institutions

Abstract

Financial institutions face increased reputational, regulatory, and ethical risks as the frequency and complexity of Environmental, Social, and Governance (ESG) controversies increase. Whistleblowing mechanisms are essential in the context of institutional resilience and the mitigation of internal governance failures. This study quantifies the exposure of 364 European financial institutions to a variety of ESG controversies to assess the effectiveness of whistleblowing during the fiscal year 2024. A whistleblowing performance index that captures the relative influence of ESG-related risk factors—such as corruption allegations, environmental violations, and executive misconduct—is constructed using a hybrid Multi-Criteria Decision-Making (MCDM) framework that is based on Entropy Weighting and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). The results emphasize that the perceived efficacy of whistleblower systems is substantially influenced by the frequency of media-reported controversies and the presence of robust anti-bribery policies. The study provides a data-driven, replicable paradigm for assessing internal governance capabilities in the face of ESG risk pressure. Our findings offer actionable insights for regulators, compliance officers, and ESG analysts who are interested in evaluating and enhancing ethical accountability systems within the financial sector by connecting the domains of financial risk management, corporate ethics, and sustainability governance.

1. Introduction

Whistleblowing is crucial for the improvement of corporate governance, the promotion of transparency, and the guarantee of organizational accountability. Whistleblower systems serve as an indispensable intermediary between internal operations and external expectations as stakeholders demand ethical business practices. This significance has increased in tandem with the increasing frequency of Environmental, Social, and Governance (ESG) controversies, such as allegations of corporate malfeasance, labor rights violations, and greenwashing, which pose a threat to stakeholder trust and institutional legitimacy (Hennequin 2020; Motarjemi 2023; Schiemann and Tietmeyer 2022). Financial institutions are subject to increased scrutiny when such controversies arise because of their systemic significance and public visibility.

Whistleblower protection frameworks have made substantial progress; however, their efficacy during ESG controversies is still unexplored. Most of the current research concentrates on the psychological and organizational factors that influence reporting, or it treats whistleblowing in isolation from broader governance systems. Concurrently, whistleblowing has been largely disregarded as a mechanism of accountability in ESG scholarship. A critical vacuum is created by this lack of integration, as there is a scarcity of empirical evidence regarding the impact of ESG-related controversies on whistleblowing performance at the institutional level, particularly in large-scale financial entities. Systematic, quantitative instruments that can accurately represent the multidimensional complexity of governance under ESG duress are necessary to address this imbalance (Ling et al. 2023).

This study addresses this requirement by employing a hybrid Multi-Criteria Decision-Making (MCDM) methodology that integrates entropy weighting and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) to assess the effectiveness of whistleblowing programs at 364 European financial institutions in 2024. The research makes three contributions. Initially, it introduces a methodological innovation by extending a robust MCDM framework, which is extensively employed in technical disciplines, to the realm of corporate governance and ethics. Second, it provides extensive empirical evidence regarding the impact of ESG controversies, including corruption scandals and environmental infractions, and the capacity of institutions to make whistleblowing disclosures. Third, it offers actionable insights for financial institutions, regulators, and ESG evaluators who are interested in enhancing the resilience of governance frameworks, fostering a “speak-up” culture, and preventing reputational risks.

2. Understanding ESG Practices in the Context of Whistleblowing

Traditionally, the literature on whistleblowing has prioritized the psychological, ethical, and organizational antecedents of reporting behavior, with an emphasis on individual-level drivers such as moral courage, perceived retaliation risk, or organizational justice (Provost 2023; Akhtar et al. 2024). Nevertheless, subsequent research has underscored the significance of governance structures and institutional design in determining whistleblowing outcomes (Vandekerckhove and Phillips 2019).

Nevertheless, a critical deficit persists in the comprehension of the operation of whistleblowing systems within the broader context of ESG-related organizational risk, despite this progress. Studies have only recently begun to establish a correlation between ESG performance and internal ethical reporting structures, and even fewer have quantitatively evaluated the impact of ESG controversies on institutional whistleblowing capacity (Schiemann and Tietmeyer 2022; Ghafoor and Gull 2024). Moreover, whistleblowing is frequently discussed in isolation from systemic reputation and compliance mechanisms, which restricts our capacity to assess it as a dynamic governance tool.

The absence of integration between whistleblowing research and the rapidly expanding corpus of ESG literature further complicates this divide. Although ESG frameworks are having an increasing impact on corporate reporting and risk management, their intersection with internal governance mechanisms such as whistleblowing is still lacking in theory. ESG controversies may not only impact external reputational risk but also alter internal employee perceptions of trust, ethical leadership, and psychological safety—factors that have been demonstrated to influence the likelihood of disclosure. Nevertheless, this feedback mechanism has not yet been adequately addressed in empirical literature.

Methodologically, the existing scholarship is also restricted, as it primarily relies on qualitative case studies, interview data, or regression-based survey research (Bjørkelo 2013). Although beneficial, these methods may not adequately convey the institutional complexity and multidimensionality that are associated with whistleblowing performance in large-scale financial entities that are subject to ESG scrutiny. Additionally, there is a scarcity of comparative studies that assess whistleblowing performance across institutions using consistent, scalable indicators.

In order to address three specific gaps in the literature, our study contributes: (1) it conceptualizes whistleblowing effectiveness as an ESG issue, rather than a behavioral or ethical dilemma; (2) it introduces a Multi-Criteria Decision-Making (MCDM) framework to evaluate whistleblowing efficacy, providing methodological innovation in a field that is still dominated by qualitative approaches; and (3) it operationalizes whistleblowing resilience across a large dataset of European financial institutions using transparent, reproducible metrics. In doing so, the study addresses recent requests for empirical tools that incorporate organizational transparency, risk, and ethics into the assessment of ESG performance. The objective of our research is to enhance comprehension of the structural effects of ESG controversies on internal ethical accountability mechanisms by utilizing a systems-level perspective that considers institutional complexity and regulatory variability.

3. Materials and Methods

3.1. Data

The objective of this study is to evaluate the impact of ESG conflicts on the whistleblower performance metrics of 364 publicly traded financial institutions in Europe (

Table 1. Criteria evaluated in TOPSIS analysis.

Criteria	Description	Measurement Scale	Source
C1. Policy Bribery and Corruption Score	Does the firm state in its code of conduct that it aims to prevent bribery and corruption in all its operations? -Implement a policy within the code of conduct to combat bribery and corruption in operations. Take into account information from the code of conduct section in all reports. -Legal compliance data is excluded. Encompasses inappropriate or improper payments, special favors, extortion, or kickbacks.	0 to 1	Refinitiv Eikon
C2. Bribery, Corruption and Fraud Controversies Score	Is the company under the spotlight of the media because of a controversy linked to bribery and corruption, political contributions, improper lobbying, money laundering, parallel imports or any tax fraud?	0 to 1	Refinitiv Eikon
C3. ESG Controversies Score	The ESG controversies category score assesses a company’s vulnerability to environmental, social, and governance disputes and adverse occurrences reported in worldwide media.	0 to 1	Refinitiv Eikon
C4. Environmental Controversies Score	Is the corporation now receiving media attention due to an issue related to the environmental consequences of its activities on natural resources or local communities?	0 to 1	Refinitiv Eikon
C5. Wages Working Condition Controversies Score	Is the firm now receiving media attention owing to controversies involving its workers, contractors, or suppliers related to pay disputes, layoffs, or working conditions?	0 to 1	Refinitiv Eikon
C6. Anti-competition Controversies Score	Is the corporation now under public scrutiny due to a problem related to anti-competitive practices, such as antitrust violations, monopolistic behavior, price-fixing, or kickbacks?	0 to 1	Refinitiv Eikon
C7. Responsible Marketing Controversies Score	Is the corporation under public scrutiny due to an issue related to its marketing methods, namely the aggressive promotion of unhealthy food to susceptible consumers?	0 to 1	Refinitiv Eikon
C8. Executive Compensation Controversies Score	Is the corporation receiving public attention due to an issue about elevated executive or board compensation?	0 to 1	Refinitiv Eikon
C9. Insider Dealings Controversies Score	Is the firm now under public scrutiny due to an issue involving insider trading and other share price manipulations?	0 to 1	Refinitiv Eikon
C10. Accounting Controversies Score	Is the corporation now under public scrutiny due to a scandal associated with aggressive or opaque accounting practices?	0 to 1	Refinitiv Eikon

). The focus of this sample is determined by contextual relevance and the availability of data. Europe’s sophisticated ESG disclosure frameworks, such as the EU Taxonomy, the Sustainable Finance Disclosure Regulation (SFDR), and the Corporate Sustainability Reporting Directive (CSRD), make it an appropriate setting. These regulations guarantee a high level of transparency and comparability in ESG reporting, rendering European institutions particularly well-suited for systematic evaluation. In addition, the dataset was obtained from Refinitiv Eikon, which provides comprehensive coverage of ESG controversy and governance indicators for European institutions in 2024. The potential to expand the sample to other regions within the scope of this study is limited by the lack of comparable and consistent coverage of institutions outside of Europe.

The study guarantees relevant to a policy environment at the vanguard of sustainable finance, consistency in reporting standards, and methodological rigor by focusing on European financial institutions. However, we recognize that the results are most directly generalized to the European context. This framework could be expanded to encompass non-European institutions and various categories of financial organizations in future research, thereby facilitating comparative insights across a variety of regulatory environments and governance traditions.

To achieve the research objective, we implemented the TOPSIS decision-making methodology and entropy weight to analyze this relationship. The data were obtained from the Refinitiv Eikon database, which is managed by Thomson Reuters, and pertain to Fiscal Year 2024. The year 2024 was selected intentionally, as it is the most recent year for which comprehensive and validated ESG controversy and governance data were available across all criteria in Refinitiv Eikon. Missing or inconsistent records from previous years would have compromised the comparability of institutions. Therefore, the use of 2024 as the focus date guarantees data consistency and offers a comprehensive cross-sectional analysis of the efficacy of whistleblowing in the context of current ESG pressures.

Although entropy weight and TOPSIS are typically employed in engineering, supply chain management, and operational research, their application has expanded to encompass organizational behavior and corporate governance. Recent research has demonstrated that MCDM methodologies are adept at simulating complex decision-making scenarios that involve a variety of stakeholder criteria, particularly in the areas of ESG performance assessment and ethical governance (Nguyen et al. 2023). Considering the numerous facets of whistleblower performance—including the convergence of institutional frameworks, ESG risk exposure, and reputational constraints—MCDM methodologies offer a systematic, reproducible method for comparing different businesses.

This study’s methodology (entropy weight and TOPSIS) facilitates the comparative classification and prioritization of institutions based on multiple weighted factors. Nevertheless, the findings indicate associations rather than causal relationships between the effectiveness of whistleblowing and ESG issues. To experimentally verify causal pathways, it is advised that future research utilize regression analysis, structural equation modeling, or longitudinal designs. The criteria evaluated in the TOPSIS analysis are presented in Table 1, which concludes this section.

3.2. Shannon Entropy Weight Method

The TOPSIS methodology is a systematic method that improves decision-making by using a specified framework and diverse criteria. The TOPSIS model with entropy weight is a hybrid methodology that integrates the TOPSIS approach with the entropy technique. The main aim of this system is to determine the weight of each evaluation criteria with the Shannon entropy weight method. Subsequently, it employs a way to ascertain the most efficacious strategy for rating evaluation items. In the alternative framework, the major objective of the entropy weight TOPSIS approach is to identify the best solution, ensuring that all attribute values attain their maximum (or minimum) potential (Ragazou et al. 2024; Abdullah et al. 2023). An evaluation object is deemed optimum if it is nearest to the best solution and furthest from the worst solution, as determined by the relative distances between each evaluation object and these solutions. Evaluation items that do not meet these criteria are deemed unacceptable. Entropy is a notion that enhances the TOPSIS technique by efficiently using information from the original dataset, irrespective of sample size limitations. Furthermore, it has the advantages of adaptable functioning and no information loss (Nascimento et al. 2023; Chen 2019).

Prior to presenting the approaches, we make the initial assumption that there are $\mathcal{n}$ alternatives $A=\left\{A_1{,A}_2{,A}_3,\dots ,A_{\mathcal{n}}\}\right.$, and m criteria $M=\left\{M_1{,M}_2{,M}_3,\dots ,M_m\}\right.$, where $i\in A,j\in M,i=\left\{1,2,3,\dots ,n\right\},j=\left\{1,2,3,\dots ,m\right\}$.

The matrix $X^{\prime }={(x^{″}}_{ij})$_n×m in Equation (1) is a decision matrix of $n\times m$. The weights of criteria M can be represented by weight vector $W=\left\{w_1,w_2,w_3,\dots ,w_m\right\}$, which satisfy $\sum _{j=1}^m{w}_j=1.$

$$X^{\prime }=\left[\begin{array}{ccc}{x^{\prime }}_{11}& {x^{\prime }}_{12}& {x^{\prime }}_{1m}\\ {x^{\prime }}_{21}& \dots & {x^{\prime }}_{2m}\\ {x^{\prime }}_{n1}& {x^{\prime }}_{n2}& {x^{\prime }}_{nm}\end{array}\right]$$

(1)

The weight may be ascertained using the Shannon entropy weight method, which depends on the extent of data dispersion. Initially, the Min–Max method was employed to normalize the original choice matrix, which has dimensions. No offset is applied in accordance with the standard convention; probabilities that are equal to zero are regarded as $0·\ln 0=0.$

$$x_{ij}={\displaystyle \frac{{x^{\prime }}_{ij}-\min \left({x^{\prime }}_j\right)}{\max \left({x^{\prime }}_j\right)-\min \left({x^{\prime }}_j\right)}}+0.001,\mathit{where}i=1,2,3,\dots ,n,\mathit{and}j=1,2,3,\dots ,m.$$

(2)

The entropy value, represented as eje_jej, was calculated using Equation (4). Entropy quantifies the extent of data dispersion. The entropy value decreases as the data becomes more scattered, indicating that the data encompasses a higher quantity of information. Conversely, the entropy value escalates as the data becomes more concentrated, signifying a decrease in the amount of information present in the data.

In the entropy step, the original decision matrix $X=\left(x_{ij}\right)$ is first column-normalized to form a probability distribution:

$$p_{ij}={\displaystyle \frac{x_{ij}}{\sum _{i=1}^n{x}_{ij}}}, i=1,\dots .,n;j=1,\dots ..,m$$

(3)

By construction, $\sum _{i=1}^n{p}_{ij}=1$ for each criterion $j$. With n = 364 institutions, typical non-zero entries in

Table A1. Vector normalization (probability matrix).

C1	C2	C3	C4	C5	C6	C7	C8	C9	C10
0.00335	0.00308	0.00299	0.00284	0.00283	0.00285	0.00287	0.00276	0.00276	0.00276
0.00338	0.00308	0.00299	0.00284	0.00283	0.00285	0.00287	0.00276	0.00276	0.00276
0.00335	0.00000	0.00149	0.00284	0.00283	0.00285	0.00287	0.00276	0.00276	0.00276
0.00335	0.00000	0.00067	0.00284	0.00283	0.00000	0.00287	0.00000	0.00276	0.00276
0.00335	0.00308	0.00299	0.00284	0.00283	0.00285	0.00287	0.00276	0.00276	0.00276
0.00347	0.00308	0.00299	0.00284	0.00283	0.00285	0.00287	0.00276	0.00276	0.00276
0.00335	0.00308	0.00299	0.00284	0.00283	0.00285	0.00287	0.00276	0.00276	0.00276
0.00347	0.00308	0.00299	0.00284	0.00283	0.00285	0.00287	0.00276	0.00276	0.00276
0.00335	0.00308	0.00299	0.00284	0.00283	0.00285	0.00287	0.00276	0.00276	0.00276
0.00335	0.00308	0.00299	0.00284	0.00283	0.00285	0.00287	0.00276	0.00276	0.00276
0.00000	0.00308	0.00299	0.00284	0.00283	0.00285	0.00287	0.00276	0.00276	0.00276
0.00347	0.00308	0.00299	0.00284	0.00283	0.00285	0.00287	0.00276	0.00276	0.00276
0.00347	0.00308	0.00299	0.00284	0.00283	0.00285	0.00287	0.00276	0.00276	0.00276
0.00000	0.00308	0.00299	0.00284	0.00283	0.00285	0.00287	0.00276	0.00276	0.00276
0.00358	0.00308	0.00299	0.00284	0.00283	0.00285	0.00287	0.00276	0.00276	0.00276
0.00348	0.00296	0.00299	0.00278	0.00275	0.00279	0.00279	0.00275	0.00274	0.00272
0.00335	0.00000	0.00060	0.00284	0.00000	0.00285	0.00287	0.00276	0.00276	0.00276
0.00335	0.00000	0.00012	0.00000	0.00000	0.00000	0.00000	0.00276	0.00276	0.00000
0.00358	0.00308	0.00299	0.00284	0.00283	0.00285	0.00287	0.00276	0.00276	0.00276
0.00356	0.00296	0.00299	0.00278	0.00275	0.00279	0.00279	0.00275	0.00274	0.00272
0.00338	0.00308	0.00299	0.00284	0.00283	0.00285	0.00287	0.00276	0.00276	0.00276
0.00000	0.00307	0.00299	0.00277	0.00272	0.00287	0.00277	0.00277	0.00271	0.00277
0.00000	0.00308	0.00299	0.00284	0.00283	0.00285	0.00287	0.00276	0.00276	0.00276
0.00335	0.00000	0.00149	0.00284	0.00000	0.00285	0.00287	0.00276	0.00276	0.00276
0.00335	0.00308	0.00299	0.00284	0.00283	0.00285	0.00287	0.00276	0.00276	0.00276
0.00338	0.00308	0.00299	0.00284	0.00283	0.00285	0.00287	0.00276	0.00276	0.00276
0.00338	0.00308	0.00137	0.00284	0.00283	0.00285	0.00287	0.00276	0.00276	0.00276
0.00338	0.00308	0.00089	0.00284	0.00000	0.00285	0.00287	0.00276	0.00276	0.00276
0.00358	0.00308	0.00299	0.00284	0.00283	0.00285	0.00287	0.00276	0.00276	0.00276
0.00335	0.00000	0.00114	0.00284	0.00283	0.00000	0.00287	0.00276	0.00276	0.00276
0.00347	0.00308	0.00299	0.00284	0.00283	0.00285	0.00287	0.00276	0.00276	0.00276
0.00000	0.00308	0.00299	0.00284	0.00283	0.00285	0.00287	0.00276	0.00276	0.00276
0.00335	0.00000	0.00114	0.00284	0.00283	0.00285	0.00000	0.00276	0.00276	0.00276
0.00000	0.00296	0.00299	0.00278	0.00275	0.00279	0.00279	0.00275	0.00274	0.00272
0.00000	0.00309	0.00299	0.00285	0.00284	0.00284	0.00288	0.00276	0.00275	0.00276
0.00432	0.00308	0.00299	0.00284	0.00283	0.00285	0.00287	0.00276	0.00276	0.00276
0.00335	0.00308	0.00235	0.00284	0.00283	0.00285	0.00287	0.00276	0.00276	0.00276
0.00335	0.00000	0.00003	0.00000	0.00000	0.00000	0.00000	0.00276	0.00276	0.00276
0.00338	0.00308	0.00299	0.00284	0.00283	0.00285	0.00287	0.00276	0.00276	0.00276
0.00000	0.00309	0.00299	0.00285	0.00284	0.00284	0.00288	0.00276	0.00275	0.00276
0.00432	0.00308	0.00299	0.00284	0.00283	0.00285	0.00287	0.00276	0.00276	0.00276
0.00428	0.00309	0.00299	0.00285	0.00284	0.00284	0.00288	0.00276	0.00275	0.00276
0.00333	0.00309	0.00104	0.00285	0.00284	0.00284	0.00000	0.00276	0.00275	0.00276
0.00348	0.00000	0.00101	0.00278	0.00275	0.00279	0.00279	0.00275	0.00274	0.00272
0.00432	0.00308	0.00299	0.00284	0.00283	0.00285	0.00287	0.00276	0.00276	0.00276
0.00432	0.00308	0.00299	0.00284	0.00283	0.00285	0.00287	0.00276	0.00276	0.00276
0.00000	0.00309	0.00299	0.00285	0.00284	0.00284	0.00288	0.00276	0.00275	0.00276
0.00000	0.00308	0.00299	0.00284	0.00283	0.00285	0.00287	0.00276	0.00276	0.00276
0.00000	0.00308	0.00299	0.00284	0.00283	0.00285	0.00287	0.00276	0.00276	0.00276
0.00000	0.00308	0.00299	0.00284	0.00283	0.00285	0.00287	0.00276	0.00276	0.00276
0.00432	0.00308	0.00299	0.00284	0.00283	0.00285	0.00287	0.00276	0.00276	0.00276
0.00432	0.00308	0.00299	0.00284	0.00283	0.00285	0.00287	0.00276	0.00276	0.00276
0.00428	0.00309	0.00299	0.00285	0.00284	0.00284	0.00288	0.00276	0.00275	0.00276
0.00358	0.00308	0.00299	0.00284	0.00283	0.00285	0.00287	0.00276	0.00276	0.00276
0.00428	0.00309	0.00299	0.00285	0.00284	0.00284	0.00288	0.00276	0.00275	0.00276
0.00000	0.00308	0.00299	0.00284	0.00283	0.00285	0.00287	0.00276	0.00276	0.00276
0.00432	0.00308	0.00299	0.00284	0.00283	0.00285	0.00287	0.00276	0.00276	0.00276
0.00335	0.00308	0.00299	0.00284	0.00283	0.00285	0.00287	0.00276	0.00276	0.00276
0.00348	0.00296	0.00299	0.00278	0.00275	0.00279	0.00279	0.00275	0.00274	0.00272
0.00338	0.00308	0.00299	0.00284	0.00283	0.00285	0.00287	0.00276	0.00276	0.00276
0.00000	0.00308	0.00299	0.00284	0.00283	0.00285	0.00287	0.00276	0.00276	0.00276
0.00000	0.00308	0.00299	0.00284	0.00283	0.00285	0.00287	0.00276	0.00276	0.00276
0.00335	0.00000	0.00045	0.00000	0.00283	0.00000	0.00287	0.00276	0.00276	0.00276
0.00372	0.00296	0.00220	0.00278	0.00275	0.00279	0.00279	0.00275	0.00000	0.00272
0.00432	0.00308	0.00299	0.00284	0.00283	0.00285	0.00287	0.00276	0.00276	0.00276
0.00000	0.00309	0.00299	0.00285	0.00284	0.00284	0.00288	0.00276	0.00275	0.00276
0.00335	0.00308	0.00299	0.00284	0.00283	0.00285	0.00287	0.00276	0.00276	0.00276
0.00000	0.00308	0.00299	0.00284	0.00283	0.00285	0.00287	0.00276	0.00276	0.00276
0.00335	0.00000	0.00053	0.00284	0.00283	0.00000	0.00000	0.00276	0.00276	0.00276
0.00000	0.00308	0.00299	0.00284	0.00283	0.00285	0.00287	0.00276	0.00276	0.00276
0.00000	0.00308	0.00299	0.00284	0.00283	0.00285	0.00287	0.00276	0.00276	0.00276
0.00000	0.00296	0.00299	0.00278	0.00275	0.00279	0.00279	0.00275	0.00274	0.00272
0.00000	0.00309	0.00299	0.00285	0.00284	0.00284	0.00288	0.00276	0.00275	0.00276
0.00432	0.00308	0.00299	0.00284	0.00283	0.00285	0.00287	0.00276	0.00276	0.00276
0.00335	0.00000	0.00031	0.00000	0.00000	0.00285	0.00000	0.00276	0.00276	0.00276
0.00000	0.00308	0.00299	0.00284	0.00283	0.00285	0.00287	0.00276	0.00276	0.00276
0.00358	0.00308	0.00299	0.00284	0.00283	0.00285	0.00287	0.00276	0.00276	0.00276
0.00000	0.00296	0.00299	0.00278	0.00275	0.00279	0.00279	0.00275	0.00274	0.00272
0.00358	0.00308	0.00299	0.00284	0.00283	0.00285	0.00287	0.00276	0.00276	0.00276
0.00335	0.00308	0.00299	0.00284	0.00283	0.00285	0.00287	0.00276	0.00276	0.00276
0.00335	0.00308	0.00299	0.00284	0.00283	0.00285	0.00287	0.00276	0.00276	0.00276
0.00358	0.00308	0.00299	0.00284	0.00283	0.00285	0.00287	0.00276	0.00276	0.00276
0.00000	0.00308	0.00299	0.00284	0.00283	0.00285	0.00287	0.00276	0.00276	0.00276
0.00335	0.00308	0.00299	0.00284	0.00283	0.00285	0.00287	0.00276	0.00276	0.00276
0.00335	0.00308	0.00299	0.00284	0.00283	0.00285	0.00287	0.00276	0.00276	0.00276
0.00000	0.00308	0.00299	0.00284	0.00283	0.00285	0.00287	0.00276	0.00276	0.00276
0.00348	0.00000	0.00043	0.00000	0.00275	0.00279	0.00000	0.00275	0.00274	0.00272
0.00335	0.00000	0.00149	0.00284	0.00283	0.00285	0.00287	0.00276	0.00276	0.00276
0.00335	0.00308	0.00299	0.00284	0.00283	0.00285	0.00287	0.00276	0.00276	0.00276
0.00335	0.00000	0.00053	0.00000	0.00000	0.00285	0.00287	0.00276	0.00276	0.00276
0.00335	0.00308	0.00299	0.00284	0.00283	0.00285	0.00287	0.00276	0.00276	0.00276
0.00358	0.00308	0.00299	0.00284	0.00283	0.00285	0.00287	0.00276	0.00276	0.00276
0.00000	0.00308	0.00299	0.00284	0.00283	0.00285	0.00287	0.00276	0.00276	0.00276
0.00358	0.00308	0.00299	0.00284	0.00283	0.00285	0.00287	0.00276	0.00276	0.00276
0.00338	0.00308	0.00299	0.00284	0.00283	0.00285	0.00287	0.00276	0.00276	0.00276
0.00000	0.00308	0.00299	0.00284	0.00283	0.00285	0.00287	0.00276	0.00276	0.00276
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0.00338	0.00308	0.00299	0.00284	0.00283	0.00285	0.00287	0.00276	0.00276	0.00276
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0.00335	0.00308	0.00235	0.00000	0.00283	0.00285	0.00287	0.00276	0.00276	0.00276
0.00338	0.00308	0.00299	0.00284	0.00283	0.00285	0.00287	0.00276	0.00276	0.00276
0.00000	0.00308	0.00299	0.00284	0.00283	0.00285	0.00287	0.00276	0.00276	0.00276
0.00000	0.00296	0.00299	0.00278	0.00275	0.00279	0.00279	0.00275	0.00274	0.00272
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0.00338	0.00308	0.00299	0.00284	0.00283	0.00285	0.00287	0.00276	0.00276	0.00276
0.00338	0.00000	0.00019	0.00284	0.00283	0.00285	0.00000	0.00276	0.00276	0.00276
0.00335	0.00308	0.00299	0.00284	0.00283	0.00285	0.00287	0.00276	0.00276	0.00276
0.00358	0.00308	0.00299	0.00284	0.00283	0.00285	0.00287	0.00276	0.00276	0.00276
0.00000	0.00308	0.00299	0.00284	0.00283	0.00285	0.00287	0.00276	0.00276	0.00276
0.00000	0.00309	0.00299	0.00285	0.00284	0.00284	0.00288	0.00276	0.00275	0.00276
0.00335	0.00308	0.00299	0.00284	0.00283	0.00285	0.00287	0.00276	0.00276	0.00276
0.00338	0.00000	0.00233	0.00284	0.00283	0.00285	0.00287	0.00276	0.00276	0.00276
0.00000	0.00308	0.00299	0.00284	0.00283	0.00285	0.00287	0.00276	0.00276	0.00276
0.00432	0.00308	0.00299	0.00284	0.00283	0.00285	0.00287	0.00276	0.00276	0.00276
0.00338	0.00308	0.00299	0.00284	0.00283	0.00285	0.00287	0.00276	0.00276	0.00276
0.00335	0.00308	0.00299	0.00284	0.00283	0.00285	0.00287	0.00276	0.00276	0.00276
0.00358	0.00308	0.00299	0.00284	0.00283	0.00285	0.00287	0.00276	0.00276	0.00276
0.00335	0.00308	0.00299	0.00284	0.00283	0.00285	0.00287	0.00276	0.00276	0.00276
0.00348	0.00296	0.00299	0.00278	0.00275	0.00279	0.00279	0.00275	0.00274	0.00272
0.00000	0.00296	0.00299	0.00278	0.00275	0.00279	0.00279	0.00275	0.00274	0.00272
0.00358	0.00308	0.00299	0.00284	0.00283	0.00285	0.00287	0.00276	0.00276	0.00276
0.00347	0.00308	0.00299	0.00284	0.00283	0.00285	0.00287	0.00276	0.00276	0.00276
0.00428	0.00309	0.00299	0.00285	0.00284	0.00284	0.00288	0.00276	0.00275	0.00276
0.00000	0.00308	0.00299	0.00284	0.00283	0.00285	0.00287	0.00276	0.00276	0.00276
0.00358	0.00308	0.00299	0.00284	0.00283	0.00285	0.00287	0.00276	0.00276	0.00276
0.00335	0.00308	0.00299	0.00284	0.00283	0.00285	0.00287	0.00276	0.00276	0.00276
0.00335	0.00308	0.00299	0.00284	0.00283	0.00285	0.00287	0.00276	0.00276	0.00276
0.00335	0.00308	0.00299	0.00284	0.00283	0.00285	0.00287	0.00276	0.00276	0.00276
0.00348	0.00296	0.00299	0.00278	0.00275	0.00279	0.00279	0.00275	0.00274	0.00272
0.00347	0.00308	0.00299	0.00284	0.00283	0.00285	0.00287	0.00276	0.00276	0.00276
0.00000	0.00308	0.00299	0.00284	0.00283	0.00285	0.00287	0.00276	0.00276	0.00276
0.00000	0.00308	0.00299	0.00284	0.00283	0.00285	0.00287	0.00276	0.00276	0.00276
0.00358	0.00308	0.00299	0.00284	0.00283	0.00285	0.00287	0.00276	0.00276	0.00276
0.00000	0.00308	0.00299	0.00284	0.00283	0.00285	0.00287	0.00276	0.00276	0.00276
0.00000	0.00297	0.00299	0.00277	0.00267	0.00282	0.00273	0.00275	0.00272	0.00273
0.00335	0.00308	0.00235	0.00284	0.00283	0.00285	0.00287	0.00276	0.00276	0.00276
0.00000	0.00308	0.00299	0.00284	0.00283	0.00285	0.00287	0.00276	0.00276	0.00276
0.00335	0.00000	0.00005	0.00000	0.00000	0.00000	0.00000	0.00000	0.00276	0.00276
0.00335	0.00000	0.00077	0.00284	0.00283	0.00285	0.00000	0.00276	0.00276	0.00276
0.00335	0.00308	0.00299	0.00284	0.00283	0.00285	0.00287	0.00276	0.00276	0.00276
0.00338	0.00308	0.00299	0.00284	0.00283	0.00285	0.00287	0.00276	0.00276	0.00276
0.00000	0.00308	0.00299	0.00284	0.00283	0.00285	0.00287	0.00276	0.00276	0.00276
0.00358	0.00308	0.00299	0.00284	0.00283	0.00285	0.00287	0.00276	0.00276	0.00276
0.00000	0.00308	0.00299	0.00284	0.00283	0.00285	0.00287	0.00276	0.00276	0.00276
0.00000	0.00308	0.00299	0.00284	0.00283	0.00285	0.00287	0.00276	0.00276	0.00276
0.00347	0.00308	0.00299	0.00284	0.00283	0.00285	0.00287	0.00276	0.00276	0.00276
0.00335	0.00308	0.00299	0.00284	0.00283	0.00285	0.00287	0.00276	0.00276	0.00276
0.00000	0.00308	0.00299	0.00284	0.00283	0.00285	0.00287	0.00276	0.00276	0.00276
0.00440	0.00296	0.00299	0.00278	0.00275	0.00279	0.00279	0.00275	0.00274	0.00272
0.00354	0.00309	0.00299	0.00285	0.00284	0.00284	0.00288	0.00276	0.00275	0.00276
0.00348	0.00296	0.00299	0.00278	0.00275	0.00279	0.00279	0.00275	0.00274	0.00272
0.00335	0.00308	0.00299	0.00284	0.00283	0.00285	0.00287	0.00276	0.00276	0.00276
0.00358	0.00308	0.00299	0.00284	0.00283	0.00285	0.00287	0.00276	0.00276	0.00276
0.00335	0.00308	0.00299	0.00284	0.00283	0.00285	0.00287	0.00276	0.00276	0.00276
0.00335	0.00308	0.00299	0.00284	0.00283	0.00285	0.00287	0.00276	0.00276	0.00276
0.00428	0.00309	0.00299	0.00285	0.00284	0.00284	0.00288	0.00276	0.00275	0.00276
0.00357	0.00000	0.00117	0.00277	0.00267	0.00282	0.00000	0.00275	0.00272	0.00273
0.00335	0.00308	0.00299	0.00284	0.00283	0.00285	0.00287	0.00276	0.00276	0.00276
0.00000	0.00308	0.00299	0.00284	0.00283	0.00285	0.00287	0.00276	0.00276	0.00276
0.00335	0.00000	0.00092	0.00284	0.00283	0.00285	0.00287	0.00276	0.00276	0.00276
0.00335	0.00000	0.00039	0.00000	0.00283	0.00000	0.00287	0.00276	0.00276	0.00276
0.00338	0.00308	0.00299	0.00284	0.00283	0.00285	0.00287	0.00276	0.00276	0.00276
0.00454	0.00297	0.00299	0.00277	0.00267	0.00282	0.00273	0.00275	0.00272	0.00273
0.00335	0.00000	0.00235	0.00284	0.00283	0.00285	0.00287	0.00276	0.00276	0.00276
0.00335	0.00308	0.00299	0.00284	0.00283	0.00285	0.00287	0.00276	0.00276	0.00276
0.00335	0.00308	0.00299	0.00284	0.00283	0.00285	0.00287	0.00276	0.00276	0.00276
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0.00354	0.00309	0.00299	0.00285	0.00284	0.00284	0.00288	0.00276	0.00275	0.00276
0.00000	0.00308	0.00299	0.00284	0.00283	0.00285	0.00287	0.00276	0.00276	0.00276
0.00358	0.00308	0.00299	0.00284	0.00283	0.00285	0.00287	0.00276	0.00276	0.00276
0.00335	0.00308	0.00299	0.00284	0.00283	0.00285	0.00287	0.00276	0.00276	0.00276
0.00338	0.00308	0.00299	0.00284	0.00283	0.00285	0.00287	0.00276	0.00276	0.00276
0.00000	0.00308	0.00299	0.00284	0.00283	0.00285	0.00287	0.00276	0.00276	0.00276
0.00335	0.00308	0.00299	0.00284	0.00283	0.00285	0.00287	0.00276	0.00276	0.00276
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0.00335	0.00308	0.00299	0.00284	0.00283	0.00285	0.00287	0.00276	0.00276	0.00276
0.00335	0.00308	0.00299	0.00284	0.00283	0.00285	0.00287	0.00276	0.00276	0.00276
0.00475	0.00301	0.00299	0.00279	0.00268	0.00284	0.00274	0.00274	0.00272	0.00275
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0.00000	0.00308	0.00299	0.00284	0.00283	0.00285	0.00287	0.00276	0.00276	0.00276
0.00358	0.00308	0.00299	0.00284	0.00283	0.00285	0.00287	0.00276	0.00276	0.00276
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0.00335	0.00308	0.00092	0.00284	0.00283	0.00285	0.00000	0.00276	0.00276	0.00276
0.00358	0.00308	0.00127	0.00284	0.00283	0.00285	0.00287	0.00276	0.00276	0.00276
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0.00000	0.00308	0.00299	0.00284	0.00283	0.00285	0.00287	0.00276	0.00276	0.00276
0.00335	0.00308	0.00299	0.00284	0.00283	0.00285	0.00287	0.00276	0.00276	0.00276
0.00432	0.00308	0.00299	0.00284	0.00283	0.00285	0.00287	0.00276	0.00276	0.00276
0.00335	0.00000	0.00235	0.00284	0.00283	0.00285	0.00287	0.00276	0.00276	0.00276
0.00358	0.00308	0.00299	0.00284	0.00283	0.00285	0.00287	0.00276	0.00276	0.00276
0.00358	0.00308	0.00299	0.00284	0.00283	0.00285	0.00287	0.00276	0.00276	0.00276
0.00338	0.00308	0.00299	0.00284	0.00283	0.00285	0.00287	0.00276	0.00276	0.00276
0.00358	0.00308	0.00299	0.00284	0.00283	0.00285	0.00287	0.00276	0.00276	0.00276
0.00354	0.00309	0.00299	0.00285	0.00284	0.00284	0.00288	0.00276	0.00275	0.00276
0.00358	0.00308	0.00299	0.00284	0.00283	0.00285	0.00287	0.00276	0.00276	0.00276
0.00335	0.00000	0.00114	0.00284	0.00283	0.00285	0.00287	0.00276	0.00276	0.00276
0.00348	0.00296	0.00299	0.00278	0.00275	0.00279	0.00279	0.00275	0.00274	0.00272
0.00000	0.00308	0.00299	0.00284	0.00283	0.00285	0.00287	0.00276	0.00276	0.00276
0.00338	0.00308	0.00299	0.00284	0.00283	0.00285	0.00287	0.00276	0.00276	0.00276
0.00000	0.00308	0.00299	0.00284	0.00283	0.00285	0.00287	0.00276	0.00276	0.00276
0.00338	0.00308	0.00299	0.00284	0.00283	0.00285	0.00287	0.00276	0.00276	0.00276
0.00358	0.00308	0.00299	0.00284	0.00283	0.00285	0.00287	0.00276	0.00276	0.00276
0.00000	0.00308	0.00299	0.00284	0.00283	0.00285	0.00287	0.00276	0.00276	0.00276
0.00358	0.00308	0.00299	0.00284	0.00283	0.00285	0.00287	0.00276	0.00276	0.00276
0.00000	0.00308	0.00299	0.00284	0.00283	0.00285	0.00287	0.00276	0.00276	0.00276
0.00335	0.00308	0.00299	0.00284	0.00283	0.00285	0.00287	0.00276	0.00276	0.00276
0.00338	0.00308	0.00299	0.00284	0.00283	0.00285	0.00287	0.00276	0.00276	0.00276
0.00338	0.00308	0.00299	0.00284	0.00283	0.00285	0.00287	0.00276	0.00276	0.00276
0.00338	0.00308	0.00137	0.00284	0.00283	0.00285	0.00287	0.00276	0.00276	0.00276
0.00362	0.00301	0.00299	0.00279	0.00268	0.00284	0.00274	0.00274	0.00272	0.00275
0.00358	0.00308	0.00299	0.00284	0.00283	0.00285	0.00287	0.00276	0.00276	0.00276
0.00335	0.00308	0.00299	0.00284	0.00283	0.00285	0.00287	0.00276	0.00276	0.00276
0.00000	0.00308	0.00299	0.00284	0.00283	0.00285	0.00287	0.00276	0.00276	0.00276
0.00000	0.00308	0.00299	0.00284	0.00283	0.00285	0.00287	0.00276	0.00276	0.00276
0.00335	0.00308	0.00299	0.00284	0.00283	0.00285	0.00287	0.00276	0.00276	0.00276
0.00338	0.00308	0.00233	0.00284	0.00283	0.00285	0.00287	0.00276	0.00276	0.00276
0.00391	0.00297	0.00299	0.00277	0.00267	0.00282	0.00273	0.00275	0.00272	0.00273
0.00337	0.00309	0.00299	0.00285	0.00284	0.00284	0.00288	0.00276	0.00275	0.00276
0.00000	0.00308	0.00299	0.00284	0.00283	0.00285	0.00287	0.00276	0.00276	0.00276
0.00428	0.00309	0.00299	0.00285	0.00284	0.00284	0.00288	0.00276	0.00275	0.00276
0.00432	0.00308	0.00299	0.00284	0.00283	0.00285	0.00287	0.00276	0.00276	0.00276
0.00335	0.00308	0.00235	0.00000	0.00283	0.00285	0.00287	0.00276	0.00276	0.00276
0.00000	0.00297	0.00299	0.00277	0.00267	0.00282	0.00273	0.00275	0.00272	0.00273
0.00335	0.00308	0.00299	0.00284	0.00283	0.00285	0.00287	0.00276	0.00276	0.00276
0.00358	0.00308	0.00299	0.00284	0.00283	0.00285	0.00287	0.00276	0.00276	0.00276
0.00348	0.00296	0.00299	0.00278	0.00275	0.00279	0.00279	0.00275	0.00274	0.00272
0.00338	0.00308	0.00299	0.00284	0.00283	0.00285	0.00287	0.00276	0.00276	0.00276
0.00333	0.00309	0.00235	0.00285	0.00284	0.00284	0.00000	0.00276	0.00275	0.00276
0.00432	0.00308	0.00299	0.00284	0.00283	0.00285	0.00287	0.00276	0.00276	0.00276
0.00000	0.00308	0.00299	0.00284	0.00283	0.00285	0.00287	0.00276	0.00276	0.00276
0.00358	0.00308	0.00299	0.00284	0.00283	0.00285	0.00287	0.00276	0.00276	0.00276
0.00335	0.00308	0.00299	0.00284	0.00283	0.00285	0.00287	0.00276	0.00276	0.00276
0.00338	0.00308	0.00299	0.00284	0.00283	0.00285	0.00287	0.00276	0.00276	0.00276
0.00335	0.00308	0.00299	0.00284	0.00283	0.00285	0.00287	0.00276	0.00276	0.00276
0.00358	0.00308	0.00227	0.00284	0.00000	0.00285	0.00287	0.00276	0.00276	0.00276
0.00338	0.00308	0.00299	0.00284	0.00283	0.00285	0.00287	0.00276	0.00276	0.00276
0.00338	0.00308	0.00299	0.00284	0.00283	0.00285	0.00287	0.00276	0.00276	0.00276
0.00000	0.00307	0.00299	0.00277	0.00272	0.00287	0.00277	0.00277	0.00271	0.00277
0.00000	0.00308	0.00299	0.00284	0.00283	0.00285	0.00287	0.00276	0.00276	0.00276
0.00335	0.00308	0.00299	0.00284	0.00283	0.00285	0.00287	0.00276	0.00276	0.00276
0.00358	0.00308	0.00299	0.00284	0.00283	0.00285	0.00287	0.00276	0.00276	0.00276
0.00335	0.00308	0.00299	0.00284	0.00283	0.00285	0.00287	0.00276	0.00276	0.00276
0.00358	0.00000	0.00027	0.00284	0.00283	0.00000	0.00287	0.00276	0.00276	0.00276
0.00335	0.00308	0.00235	0.00284	0.00283	0.00285	0.00287	0.00276	0.00276	0.00276
0.00000	0.00308	0.00299	0.00284	0.00283	0.00285	0.00287	0.00276	0.00276	0.00276
0.00000	0.00308	0.00299	0.00284	0.00283	0.00285	0.00287	0.00276	0.00276	0.00276
0.00428	0.00309	0.00299	0.00285	0.00284	0.00284	0.00288	0.00276	0.00275	0.00276
0.00335	0.00000	0.00235	0.00284	0.00283	0.00285	0.00287	0.00276	0.00276	0.00276
0.00432	0.00308	0.00299	0.00284	0.00283	0.00285	0.00287	0.00276	0.00276	0.00276
0.00358	0.00308	0.00299	0.00284	0.00283	0.00285	0.00287	0.00276	0.00276	0.00276
0.00335	0.00308	0.00299	0.00284	0.00283	0.00285	0.00287	0.00276	0.00276	0.00276
0.00335	0.00308	0.00299	0.00284	0.00283	0.00285	0.00287	0.00276	0.00276	0.00276
0.00348	0.00000	0.00123	0.00278	0.00275	0.00279	0.00279	0.00275	0.00274	0.00272
0.00000	0.00296	0.00299	0.00278	0.00275	0.00279	0.00279	0.00275	0.00274	0.00272
0.00000	0.00308	0.00299	0.00284	0.00283	0.00285	0.00287	0.00276	0.00276	0.00276
0.00000	0.00296	0.00299	0.00278	0.00275	0.00279	0.00279	0.00275	0.00274	0.00272
0.00348	0.00296	0.00299	0.00278	0.00275	0.00279	0.00279	0.00275	0.00274	0.00272
0.00335	0.00308	0.00299	0.00284	0.00283	0.00285	0.00287	0.00276	0.00276	0.00276
0.00335	0.00308	0.00299	0.00284	0.00283	0.00285	0.00287	0.00276	0.00276	0.00276
0.00335	0.00308	0.00299	0.00284	0.00283	0.00285	0.00287	0.00276	0.00276	0.00276
0.00335	0.00308	0.00299	0.00284	0.00283	0.00285	0.00287	0.00276	0.00276	0.00276
0.00335	0.00308	0.00299	0.00284	0.00283	0.00285	0.00287	0.00276	0.00276	0.00276
0.00358	0.00308	0.00299	0.00284	0.00283	0.00285	0.00287	0.00276	0.00276	0.00276
0.00358	0.00308	0.00299	0.00284	0.00283	0.00285	0.00287	0.00276	0.00276	0.00276
0.00354	0.00309	0.00299	0.00285	0.00284	0.00284	0.00288	0.00276	0.00275	0.00276
0.00335	0.00308	0.00299	0.00284	0.00283	0.00285	0.00287	0.00276	0.00276	0.00276
0.00335	0.00308	0.00299	0.00284	0.00283	0.00285	0.00287	0.00276	0.00276	0.00276
0.00358	0.00308	0.00299	0.00284	0.00283	0.00285	0.00287	0.00276	0.00276	0.00276
0.00000	0.00297	0.00299	0.00277	0.00267	0.00282	0.00273	0.00275	0.00272	0.00273
0.00338	0.00308	0.00299	0.00284	0.00283	0.00285	0.00287	0.00276	0.00276	0.00276
0.00000	0.00308	0.00299	0.00284	0.00283	0.00285	0.00287	0.00276	0.00276	0.00276
0.00358	0.00308	0.00299	0.00284	0.00283	0.00285	0.00287	0.00276	0.00276	0.00276
0.00358	0.00308	0.00299	0.00284	0.00283	0.00285	0.00287	0.00276	0.00276	0.00276
0.00358	0.00308	0.00227	0.00284	0.00283	0.00285	0.00000	0.00276	0.00276	0.00276
0.00000	0.00308	0.00299	0.00284	0.00283	0.00285	0.00287	0.00276	0.00276	0.00276
0.00358	0.00308	0.00299	0.00284	0.00283	0.00285	0.00287	0.00276	0.00276	0.00276
0.00432	0.00308	0.00299	0.00284	0.00283	0.00285	0.00287	0.00276	0.00276	0.00276
0.00000	0.00308	0.00299	0.00284	0.00283	0.00285	0.00287	0.00276	0.00276	0.00276
0.00358	0.00308	0.00299	0.00284	0.00283	0.00285	0.00287	0.00276	0.00276	0.00276
0.00335	0.00000	0.00235	0.00284	0.00283	0.00285	0.00287	0.00276	0.00276	0.00276
0.00335	0.00308	0.00299	0.00284	0.00283	0.00285	0.00287	0.00276	0.00276	0.00276
0.00335	0.00308	0.00299	0.00284	0.00283	0.00285	0.00287	0.00276	0.00276	0.00276
0.00432	0.00308	0.00299	0.00284	0.00283	0.00285	0.00287	0.00276	0.00276	0.00276
0.00358	0.00308	0.00299	0.00284	0.00283	0.00285	0.00287	0.00276	0.00276	0.00276
0.00440	0.00296	0.00299	0.00278	0.00275	0.00279	0.00279	0.00275	0.00274	0.00272
0.00335	0.00308	0.00299	0.00284	0.00283	0.00285	0.00287	0.00276	0.00276	0.00276
0.00000	0.00309	0.00299	0.00285	0.00284	0.00284	0.00288	0.00276	0.00275	0.00276
0.00335	0.00000	0.00235	0.00284	0.00283	0.00285	0.00287	0.00276	0.00276	0.00276
0.00358	0.00308	0.00299	0.00284	0.00283	0.00285	0.00287	0.00276	0.00276	0.00276
0.00338	0.00308	0.00299	0.00284	0.00283	0.00285	0.00287	0.00276	0.00276	0.00276
0.00000	0.00308	0.00299	0.00284	0.00283	0.00285	0.00287	0.00276	0.00276	0.00276
0.00354	0.00309	0.00299	0.00285	0.00284	0.00284	0.00288	0.00276	0.00275	0.00276
0.00347	0.00308	0.00299	0.00284	0.00283	0.00285	0.00287	0.00276	0.00276	0.00276
0.00347	0.00308	0.00299	0.00284	0.00283	0.00285	0.00287	0.00276	0.00276	0.00276
0.00335	0.00308	0.00299	0.00284	0.00283	0.00285	0.00287	0.00276	0.00276	0.00276
0.00354	0.00309	0.00299	0.00285	0.00284	0.00284	0.00288	0.00276	0.00275	0.00276
0.00358	0.00308	0.00299	0.00284	0.00283	0.00285	0.00287	0.00276	0.00276	0.00276
0.00335	0.00000	0.00235	0.00284	0.00283	0.00285	0.00287	0.00276	0.00276	0.00276
0.00358	0.00308	0.00299	0.00284	0.00283	0.00285	0.00287	0.00276	0.00276	0.00276
0.00000	0.00308	0.00299	0.00284	0.00283	0.00285	0.00287	0.00276	0.00276	0.00276
0.00000	0.00308	0.00299	0.00284	0.00283	0.00285	0.00287	0.00276	0.00276	0.00276
0.00000	0.00308	0.00299	0.00284	0.00283	0.00285	0.00287	0.00276	0.00276	0.00276
0.00356	0.00297	0.00299	0.00277	0.00267	0.00282	0.00273	0.00275	0.00272	0.00273

1 are near 1/n ≈ 0.0027. Identical values and zeros occur when institutions share the same raw score, including zero controversies. Following standard convention, $0·\ln 0=0$ was adopted whenever $p=0$, with no offset applied.

The entropy of criterion $j$ is then computed as follows:

$$e_j={\displaystyle \frac{1}{ln \mathcal{n}}}\sum _{i=1}^n{p}_{ij}\ln p_{ij},$$

(4)

With diversification d_j = 1 − e_j and normalized weight

$$w_j={\displaystyle \frac{d_j}{\sum _{k=1}^m{d}_k}}$$

(5)

The final weight vector is reported in

Table 2. Weight vector of the criteria for the FY2024.

Criterion	C1	C2	C3	C4	C5	C6	C7	C8	C9	C10
ej	0.9561	0.9813	0.9931	0.9948	0.9957	0.9943	0.9933	0.9991	0.9995	0.9995
d = 1 − ej	0.0439	0.0187	0.0069	0.0052	0.0043	0.0057	0.0067	0.0009	0.0005	0.0005
Wj	0.4711	0.2007	0.0739	0.0559	0.0458	0.0611	0.0715	0.0100	0.0050	0.0050

, while the probability-normalized matrix is depicted in Table A1 (vector normalization). To improve transparency, we incorporated an illustrative calculation that is predicated on Criterion 1 (C1). Using Equation (5),

$$w_1={\displaystyle \frac{1-e_1}{{\sum }_{1=1}^7\left(1-e_1\right)}}={\displaystyle \frac{0.0439}{0.0933}}=0.4711$$

The suitability of hybrid MCDM methods for governance and ESG-related evaluations has been recently demonstrated in numerous studies. For example, implemented an entropy–TOPSIS methodology to evaluate the sustainability performance of corporate supply chains, while Nguyen et al. (2024) implemented MCDM models to evaluate organizational resilience and ethical accountability (Nguyen et al. 2024). In addition, Ragazou et al. (2024) applied hybrid MCDM frameworks to financial decision-making and corporate sustainability contexts, while Chu et al. (2020) further demonstrated the ability of entropy-based weighting to address complex governance risks (Biswas et al. 2025; Uyar et al. 2023). This study of whistleblowing effectiveness under ESG controversies is further supported by the versatility of the entropy–TOPSIS methodology in capturing multivariate governance challenges, as evidenced by these applications.

Implementation details. Algorithm A1 offers a comprehensive, step-by-step procedure that encompasses the following: (i) a min–max transformation with a benefit/cost orientation, (ii) column normalization to probabilities $p_{ij}$ (iii) the computation of Shannon entropies $e_{j }$ and diversities $d_{j }$, (iv) normalized weights $w_j$, (v) TOPSIS vector normalization and weighting, and (vi) the computation of PIS/NIS distances and ${RC}_{i }$. All intermediate values are replicated in a completely functional Example A1 (3 institutions × 3 criteria).

Table A3. Refinitiv Eikon ESG Screener fields used.

Criterion	Refinitiv Eikon Field Name (Display)	Notes
C1	Policy: Bribery & Corruption	Governance policy item (benefit).
C2	Controversies: Bribery, Corruption & Fraud	Controversy score (cost).
C3	ESG Controversies—Overall	Aggregate controversies (cost).
C4	Environmental Controversies	(cost).
C5	Wages & Working Conditions Controversies	(cost).
C6	Anti-competition Controversies	(cost).
C7	Responsible Marketing Controversies	(cost).
C8	Executive Compensation Controversies	(cost).
C9	Insider Dealings Controversies	(cost).
C10	Accounting Controversies	(cost).

Note: The field titles above are consistent with the ESG Screener labels in Refinitiv Eikon (Governance policy item and Controversies category items). In the event that your journal favors vendor mnemonics, we can also incorporate them into the final proof in Table A3 (second column).

contains the precise Refinitiv Eikon field names that were employed for each criterion.

3.3. TOPSIS Model

Hwang and Yoon established the TOPSIS model to assess the closeness of alternatives to optimum solutions (Hwang and Yoon 2025). To assess the proximity, we calculate the Euclidean distance between the ideal and anti-ideal solutions and each objective selection. The anti-optimal solution is characterized by the lowest value for each assessment criteria, while the optimum solution is defined by the highest value for each evaluation criterion. The optimal choice is the most appropriate answer, as it closely resembles the ideal solution and is significantly distinct from the anti-ideal solution. Initially, the positive and negative criteria in Equation (6) must be normalized to eradicate any dimensional discrepancies. Equation (6) employs the Min–Max method, which is crucial for the execution of standardization across several dimensions. The Shannon entropy weight method facilitates the alignment of gains and losses throughout its development. The method for determining order preference according to correspondence with optimal resolution is outlined (Zournatzidou 2024; Zournatzidou and Floros 2023; Zournatzidou et al. 2024).

$$positive:x_{ij}^{+}={\displaystyle \frac{{x^{\prime }}_{ij}-\min \left({x^{\prime }}_j\right)}{\max \left({x^{\prime }}_j\right)-\min \left({x^{\prime }}_j\right)}}$$

(6)

$$negative:x_{ij}^{-}={\displaystyle \frac{\max \left({x^{\prime }}_j\right)-{x^{\prime }}_{ij}}{\max \left({x^{\prime }}_j\right)-\min in\left({x^{\prime }}_j\right)}}$$

(7)

$$\min \left({x^{\prime }}_j\right)=\{\underset{i}{\min }{x^{\prime }}_{ij}|1<i<n,1<j<m\}$$

$$\max \left({x^{\prime }}_j\right)=\{\underset{i}{\max }{x^{\prime }}_{ij}|1<i<n,1<j<m\}$$

The dimensionless standardized decision matrix $xij$ is constructed utilizing normalized positive and negative criteria to formulate the initial choice matrix in Equation (6), as shown in Equation (7).

$$X^{\prime }=\left[\begin{array}{cccc}{x}_{11}& x_{12}& \dots & x_{1m}\\ x_{21}& \dots & \dots & x_{2m}\\ ⋮& \dots & \dots & ⋮\\ x_{n1}& x_{n2}& \dots & x_{nm}\end{array}\right],\mathit{where}i=1,2,3,\dots ,n\mathit{and}j=1,2,3,\dots ,m.$$

(8)

In addition, the decision matrix in Equation (8) is obtained by multiplying each element ${\mathcal{v}}_{ij}=w_j\times x_{ij},$ where $w_j=(w_1,w_2,w_3,\dots ,w_m)$ is obtained from Equation (5) and meets the condition $\sum _{j=1}^m{w}_j=1$ and $x_{ij}$ is generated using Equation (7).

$$V=\left[\begin{array}{cccc}{\mathcal{v}}_{11}& {\mathcal{v}}_{12}& \dots & {\mathcal{v}}_{1m}\\ {\mathcal{v}}_{21}& \dots & \dots & {\mathcal{v}}_{2m}\\ ⋮& \dots & \dots & ⋮\\ {\mathcal{v}}_{n1}& {\mathcal{v}}_{n2}& \dots & {\mathcal{v}}_{nm}\end{array}\right]=\left[\begin{array}{cccc}{w}_1{x}_{11}& w_2{x}_{12}& \dots & w_m{x}_{1m}\\ w_1{x}_{21}& w_2{x}_{22}& \dots & w_m{x}_{2m}\\ ⋮& \dots & \dots & ⋮\\ w_1{x}_{n1}& w_2{x}_{n2}& \dots & w_m{x}_{nm}\end{array}\right]$$

(9)

Equation (9) delineates the positive ideal solution (PIS) as the maximum value and the negative ideal solution (NIS) as the lowest value for each criterion. Equations (10) and (11) are used to determine the distance between each option and the PIS and NIS.

$$PIS:P^{+}=\{({\mathcal{v}}_1^{+},{\mathcal{v}}_2^{+},{\mathcal{v}}_3^{+},\dots ,{\mathcal{v}}_m^{+})\}=\{(\underset{i}{\max }{\mathcal{v}}_{ij}|j\in M)\}$$

(10)

$$NIS:P^{-}=\{({\mathcal{v}}_1^{-},{\mathcal{v}}_2^{-},{\mathcal{v}}_3^{-},\dots ,{\mathcal{v}}_m^{-})\}=\{(\underset{i}{\min }{\mathcal{v}}_{ij}|j\in M)\}$$

(11)

$$d_i^{+}=\sqrt{\sum _{j=1}^m({\mathcal{v}}_{ij}-{\mathcal{v}}_j^{+}}{)}^2,i=1,2,3,\dots ,n,\mathit{and}j=1,2,3,\dots ,m.$$

(12)

$$d_i^{-}=\sqrt{\sum _{j=1}^m({\mathcal{v}}_{ij}-{\mathcal{v}}_j^{-}}{)}^2,i=1,2,3,\dots ,n,\mathit{and}j=1,2,3,\dots ,m.$$

(13)

When employing the TOPSIS procedure, it is imperative to specify the orientation of each criterion. The policy bribery and corruption score (C1) is regarded as a benefit criterion in this study, as higher values represent more robust anti-bribery frameworks and thereby enhance the effectiveness of disclosures. Contrastingly, all other indicators (C2–C10), which encompass a variety of ESG controversies, are regarded as cost criteria. This is since higher values indicate a higher likelihood of undesirable events, including corruption, fraud, environmental violations, or labor disputes.

All criteria were normalized through vector normalization. The benefit–cost orientation was consistently applied to the definition of the PIS and NIS after weighting with the entropy-derived weights. For benefit criteria, the ideal best corresponds to the maximum value, while the ideal worst corresponds to the minimum. For cost criteria, the ideal best corresponds to the minimum value, and the ideal worst the maximum. This process guarantees that the weighted normalized matrix is oriented consistently and that the distance measures that result in TOPSIS accurately reflect the desirability of each criterion.

Ultimately, calculate the coefficient of relative closeness (RC).

$$R{C}_i={\displaystyle \frac{d_i^{-}}{d_i^{-}+d_i^{+}}},i=1,2,3,\dots ,n$$

(14)

4. Results

The entropy–TOPSIS analysis provided a weighted ranking of whistleblowing effectiveness across 364 European financial institutions for FY 2024. Table 2 presents the entropy values, divergence measures, and normalized weights for the ten criteria. The findings reveal three major dimensions shaping whistleblowing performance: (1) policy and governance frameworks, (2) media exposure, and (3) ESG controversies.

4.1. Policy and Governance Determinants

The policy bribery and corruption score (C1) is the most influential determinant, with a weight of 0.4711. This underscores the critical role of formal anti-bribery provisions in the reinforcement of institutional whistleblowing systems. In addition to serving as compliance measures, governance-oriented policies also establish a foundation for employee trust, indicating an organization’s dedication to ethical accountability. Institutions that establish accessible advisory and reporting channels within their anti-bribery frameworks increase the credibility of their organization and the propensity of their employees to report misconduct.

The second most significant factor is the bribery, corruption, and fraud controversies score (C2), which is weighted at 0.2007. This criterion illustrates the process by which governance credibility is evaluated when institutions are investigated for fraud or corruption. In these situations, the efficacy of whistleblowing is contingent upon the degree to which internal mechanisms are perceived as protective and responsive. Robust frameworks promote opportune disclosure and reinforce governance legitimacy, while weak frameworks risk disengagement.

The primary output of the TOPSIS analysis is the classification of institutions based on their closeness coefficients (RC_i), in addition to the relative weights of the criteria. The ten institutions that performed the best and the worst are summarized in

Table 3. TOPSIS RC_i scores for the top-ten and bottom-ten institutions (FY2024).

Rank	Institution ID	RCi
1	132	0.7419
2	348	0.7405
3	146	0.7371
4	142	0.7367
5	147	0.7338
6	144	0.7337
7	345	0.7325
8	133	0.7321
9	349	0.7297
10	141	0.7296
…	…	…
355	280	0.0496
356	295	0.0490
357	275	0.0465
358	302	0.0463
359	278	0.0438
360	301	0.0434
361	293	0.0429
362	277	0.0421
363	297	0.0414
364	276	0.0009

, which contains these results. The table demonstrates that the top-ranked institutions have RC_i values exceeding 0.73, which is indicative of a relatively low incidence of ESG controversies and robust anti-bribery policies. Conversely, the institutions that are ranked lowest exhibit RC_i values that are less than 0.05, which suggests that they are persistently exposed to controversies related to governance, fraud, or corruption.

Table A4. Full ranking (all 364 institutions).

Institution	RCi	Rank
157	0.741903	1
304	0.741405	2
181	0.73975	3
244	0.739564	4
87	0.736679	5
194	0.732506	6
163	0.731416	7
138	0.728694	8
38	0.728464	9
18	0.728405	10
346	0.722453	11
149	0.722453	11
75	0.718426	13
44	0.714976	14
69	0.714292	15
314	0.714144	16
106	0.711207	17
310	0.710289	18
52	0.710289	18
51	0.710289	18
74	0.710289	18
344	0.710289	18
41	0.710289	18
46	0.710289	18
57	0.710289	18
36	0.710289	18
45	0.710289	18
65	0.710289	18
282	0.710289	18
114	0.710289	18
197	0.710289	18
338	0.710289	18
201	0.710289	18
243	0.710289	18
249	0.710289	18
290	0.710289	18
213	0.710289	18
161	0.709665	38
63	0.709531	39
55	0.70668	40
123	0.70668	40
156	0.70668	40
53	0.70668	40
308	0.70668	40
281	0.70668	40
42	0.70668	40
242	0.70668	40
90	0.705428	48
139	0.704664	49
33	0.703415	50
4	0.701864	51
30	0.700121	52
186	0.699462	53
17	0.698047	54
24	0.694957	55
199	0.69418	56
202	0.69418	56
218	0.692434	58
160	0.692434	58
257	0.691687	60
205	0.69098	61
112	0.69098	61
3	0.69031	63
88	0.69031	63
359	0.685981	65
349	0.685981	65
164	0.685981	65
309	0.685981	65
341	0.685981	65
250	0.685981	65
278	0.678249	71
238	0.678249	71
64	0.660197	73
226	0.656301	74
232	0.656301	74
335	0.645598	76
271	0.642165	77
189	0.64039	78
245	0.64039	78
296	0.640316	80
212	0.636774	81
20	0.636032	82
216	0.636023	83
364	0.636023	83
54	0.633524	85
125	0.633524	85
223	0.633524	85
292	0.633524	85
334	0.633524	85
133	0.633524	85
286	0.633524	85
337	0.633524	85
143	0.633524	85
340	0.633524	85
219	0.633524	85
153	0.633524	85
272	0.633524	85
358	0.633524	85
345	0.633524	85
350	0.633524	85
170	0.633524	85
265	0.633524	85
263	0.633524	85
256	0.633524	85
29	0.633524	85
254	0.633524	85
252	0.633524	85
185	0.633524	85
251	0.633524	85
191	0.633524	85
193	0.633524	85
19	0.633524	85
210	0.633524	85
15	0.633524	85
333	0.633524	85
121	0.633524	85
209	0.633524	85
92	0.633524	85
77	0.633524	85
325	0.633524	85
241	0.633524	85
117	0.633524	85
108	0.633524	85
82	0.633524	85
360	0.633524	85
240	0.633524	85
79	0.633524	85
311	0.633524	85
302	0.633524	85
227	0.633524	85
234	0.633524	85
329	0.633524	85
235	0.633524	85
324	0.633524	85
94	0.633524	85
150	0.627949	136
231	0.627949	136
168	0.627949	136
204	0.627949	136
217	0.627949	136
225	0.627949	136
255	0.627949	136
353	0.627949	136
357	0.627949	136
326	0.627949	136
175	0.625347	146
167	0.625347	146
258	0.625347	146
318	0.625347	146
119	0.625347	146
221	0.625347	146
129	0.625347	146
16	0.625347	146
287	0.625347	146
59	0.625347	146
151	0.625347	146
6	0.619089	157
130	0.619089	157
355	0.619089	157
146	0.619089	157
122	0.619089	157
8	0.619089	157
12	0.619089	157
13	0.619089	157
31	0.619089	157
354	0.619089	157
188	0.618716	167
28	0.617871	168
43	0.615151	169
270	0.612987	170
27	0.612987	170
289	0.610628	172
100	0.610429	173
283	0.610429	173
277	0.609587	175
233	0.606806	176
211	0.606806	176
351	0.606806	176
200	0.606806	176
21	0.606806	176
206	0.606806	176
195	0.606806	176
26	0.606806	176
196	0.606806	176
331	0.606806	176
253	0.606806	176
224	0.606806	176
297	0.606806	176
115	0.606806	176
294	0.606806	176
298	0.606806	176
288	0.606806	176
60	0.606806	176
141	0.606806	176
220	0.606806	176
2	0.606806	176
105	0.606806	176
39	0.606806	176
162	0.606806	176
269	0.606806	176
268	0.606806	176
98	0.606806	176
172	0.606806	176
262	0.606806	176
260	0.606806	176
95	0.606806	176
101	0.606806	176
214	0.606806	176
222	0.605292	209
37	0.605292	209
305	0.605292	209
136	0.605292	209
239	0.605292	209
279	0.605042	214
248	0.602622	215
319	0.602622	215
322	0.602622	215
323	0.602622	215
313	0.602622	215
312	0.602622	215
327	0.602622	215
303	0.602622	215
301	0.602622	215
328	0.602622	215
295	0.602622	215
293	0.602622	215
285	0.602622	215
342	0.602622	215
276	0.602622	215
273	0.602622	215
343	0.602622	215
321	0.602622	215
347	0.602622	215
356	0.602622	215
267	0.602622	215
246	0.602622	215
320	0.602622	215
1	0.602622	215
236	0.602622	215
158	0.602622	215
155	0.602622	215
154	0.602622	215
152	0.602622	215
147	0.602622	215
140	0.602622	215
128	0.602622	215
127	0.602622	215
126	0.602622	215
118	0.602622	215
116	0.602622	215
107	0.602622	215
165	0.602622	215
104	0.602622	215
89	0.602622	215
85	0.602622	215
84	0.602622	215
81	0.602622	215
80	0.602622	215
67	0.602622	215
58	0.602622	215
25	0.602622	215
10	0.602622	215
9	0.602622	215
7	0.602622	215
5	0.602622	215
91	0.602622	215
166	0.602622	215
111	0.602622	215
192	0.602622	215
187	0.602622	215
171	0.602622	215
198	0.602622	215
230	0.602622	215
215	0.602622	215
174	0.602622	215
207	0.602622	215
176	0.602622	215
203	0.602622	215
180	0.602622	215
179	0.602622	215
178	0.602622	215
97	0.599441	282
99	0.012715	283
330	0.012715	283
135	0.012715	283
182	0.012715	283
284	0.012715	283
34	0.012596	288
317	0.012596	288
315	0.012596	288
78	0.012596	288
72	0.012596	288
103	0.012596	288
120	0.012596	288
190	0.00944	295
299	0.005284	296
22	0.005284	296
83	0.001178	298
362	0.001178	298
48	0.001178	298
361	0.001178	298
49	0.001178	298
247	0.001178	298
50	0.001178	298
76	0.001178	298
229	0.001178	298
11	0.001178	298
56	0.001178	298
208	0.001178	298
71	0.001178	298
14	0.001178	298
70	0.001178	298
32	0.001178	298
68	0.001178	298
336	0.001178	298
62	0.001178	298
61	0.001178	298
23	0.001178	298
352	0.001178	298
339	0.001178	298
332	0.001178	298
266	0.001178	298
169	0.001178	298
86	0.001178	298
132	0.001178	298
134	0.001178	298
137	0.001178	298
142	0.001178	298
144	0.001178	298
280	0.001178	298
145	0.001178	298
261	0.001178	298
148	0.001178	298
275	0.001178	298
274	0.001178	298
173	0.001178	298
264	0.001178	298
159	0.001178	298
259	0.001178	298
291	0.001178	298
131	0.001178	298
124	0.001178	298
184	0.001178	298
93	0.001178	298
96	0.001178	298
102	0.001178	298
307	0.001178	298
306	0.001178	298
183	0.001178	298
177	0.001178	298
109	0.001178	298
363	0.001178	298
237	0.001178	298
300	0.001178	298
113	0.001178	298
316	0.001178	298
66	0.000864	357
348	0.000864	357
35	0.000864	357
40	0.000864	357
228	0.000864	357
73	0.000864	357
47	0.000864	357
110	0.000864	357

(Appendix A) contains the comprehensive classification of all 364 institutions to guarantee transparency. This Table enables readers to closely monitor the position of any particular institution and to analyze the broader distribution of rankings in greater detail. Overall, the RC_i scores exhibit a significant degree of heterogeneity in the efficacy of whistleblowing across European financial institutions, with a mean of 0.497 and a standard deviation of 0.269, ranging from 0.0009 to 0.7419. The distribution of RC_i values is depicted in Figure 1, which exhibits a moderately skewed pattern. In this pattern, a smaller subset of institutions is notably strong or weak, while the majority of institutions cluster around the average. The unequal adoption and enforcement of whistleblowing and anti-bribery mechanisms in the sector are underscored by this spread.

Robust governance safeguards, particularly in the context of anti-bribery frameworks (C1), and a reduced number of reported controversies in their ESG records are the distinguishing characteristics of the top-performing institutions listed in Table 3. In contrast, the weakest performers are those who are frequently linked to recurrent controversy incidents (C2–C10) and have limited evidence of robust internal governance structures. These results offer critical insights into the areas that regulators, investors, and institutions should prioritize when assessing or fortifying whistleblowing mechanisms.

4.2. Media Exposure and Stakeholder Pressures

Additionally, the analysis indicates that whistleblowing dynamics are significantly impacted by media-reported controversies (C2 and C3). The incentives and hesitations of potential whistleblowers are influenced by the increased reputational risks that institutions encounter when scandals are extensively covered by the press.

While media pressure compels organizations to enhance governance responses, it may also discourage internal reporting if employees are concerned about retaliation or reputational damage (Berlinger et al. 2025). Whistleblowing mechanisms must be incorporated into organizational culture with transparent remedial actions and explicit protections, according to the results. By doing so, organizations can reduce the adverse consequences of media scrutiny and transform external attention into a motivator for accountability.

4.3. ESG Controversy Effects

In addition to corruption and media exposure, whistleblowing performance is also influenced by ESG controversies, such as environmental infractions (C4, weight 0.0559), labor and working condition disputes (C5, 0.0458), anti-competitive practices (C6, 0.0611), and executive compensation controversies (C8, 0.0100). Even though these criteria have reduced individual weights, they collectively illustrate that the resilience of governance structures is substantially tested by ESG challenges.

Whistleblowers who report ESG violations play a critical role in governance by safeguarding investors, guaranteeing adherence to environmental and labor regulations, and promoting ethical leadership in accordance with the EU’s escalating sustainability disclosure standards (von Rosing et al. 2024). Institutions that neglect to incorporate whistleblowing into their ESG frameworks are at risk of creating “ethical mirages”: systems that are nominally compliant but lack credibility and effectiveness.

In conclusion, the findings underscore three interrelated dimensions of whistleblowing effectiveness in European financial institutions: the burden of ESG-related controversies, the influence of media exposure, and the efficacy of policy and governance frameworks. The design, implementation, and maintenance of ethical accountability systems by institutions are influenced by each of these determinants. The subsequent section delves into the implications of these discoveries for corporate governance, with a particular emphasis on the collective influence of anti-bribery regulations, stakeholder scrutiny, and ESG challenges on the credibility and resilience of whistleblowing mechanisms.

4.4. Robustness and Sensitivity Analysis

To evaluate the stability of the TOPSIS results in relation to the entropy-derived weights, we conducted a weight-sensitivity analysis. As per the study’s criterion directives, the policy score on bribery and corruption (C1) was classified as a benefit criterion, while the remaining controversy-related criteria (C2–C10) were classified as cost criterion. Starting from the baseline entropy weights (Table 2), we (i) perturbed each weight one-at-a-time (±10%) and renormalized the vector to sum to one, and (ii) simultaneously drew 100 random perturbations within ±10% for all weights (with renormalization). The ideal best/worst, the TOPSIS closeness coefficients and rankings, and the weighted-normalized decision matrix were recomputed for each perturbed vector.

The findings (

Table 4. Robustness summary.

Scenario	Spearman ρ	Top-10 Overlap
OAT +10% (min)	0.998	0.70
OAT +10% (mean)	1.000	0.96
OAT +10% (max)	1.000	1.00
OAT −10% (min)	0.998	0.80
OAT −10% (mean)	1.000	0.97
OAT −10% (max)	1.000	1.00
Random ±10% (5th pct)	0.995	0.70
Random ±10% (mean)	0.999	0.88
Random ±10% (95th pct)	1.000	1.00

) suggest a high level of stability. 0.998–1.000 for the +10% one-at-a-time scenarios (mean 0.9997) and 0.998–1.000 for the −10% scenarios (mean 0.9997) were the Spearman rank correlations between the baseline and perturbed rankings. 0.995, 0.999, and 1.000 were the 5th, mean, and 95th percentiles of the correlation under 100 random ±10% perturbations, respectively. The top 10 institutions maintained a high degree of overlap under perturbed weights (one-at-a-time scenarios: 70–100%, mean ≈ 96–97%; random perturbations: 5th/mean/95th percentiles 70%/88%/100%). In addition, we calculated a benefit-adjusted Simple Additive Weighting (SAW) index using the same criterion directions as a straightforward cross-method assessment. The rank correlation between SAW and TOPSIS was ρ = 0.973, suggesting that the overall ordering was consistent across the methods. Taken together, these diagnostics indicate that the primary findings are resilient to plausible variations in the weighting scheme.

4.5. Exploratory Associative Analysis

We conducted additional exploratory analyses to investigate the relationships between the closeness coefficients (RC_i) and the fundamental criteria, as the interpretation of the findings is associative rather than causal. The initial step was to calculate Spearman rank correlations between RC_i and all ten criteria (C1–C10). Institutions with more robust anti-bribery frameworks tend to achieve higher whistleblowing efficacy, as indicated in

Table A5. Spearman correlations between RC_i and criteria (C1–C10).

	Spearman_rho	p_Value
C1	0.83725	0.00000
C2	−0.33155	0.00000
C3	−0.44135	0.00000
C4	−0.14797	0.00467
C5	−0.14641	0.00513
C6	−0.23793	0.00000
C7	−0.16462	0.00162
C8	−0.16075	0.00210
C9	−0.08758	0.09525
C10	−0.14733	0.00485

(Appendix A). RC_i is strongly and positively associated with the anti-bribery policy score (C1). Conversely, most controversy-related indicators (C2–C10) are negatively correlated with RC_i, indicating that whistleblowing outcomes are systematically undermined by frequent exposure to ESG controversies. The most powerful negative associations are observed in corruption and fraud controversies, while the relationship with C9 is not statistically significant.

To further elucidate this pattern, we contrasted institutions that were grouped based on the strength of their anti-bribery policy (above vs. below the sample median). The Mann–Whitney U test results confirmed that institutions with stronger anti-bribery safeguards attained significantly higher RC_i scores (mean = 0.647) than those with weaker safeguards (mean = 0.356), with a p-value of less than 0.001 (

Table A6. MannWhitney_GroupTest.

Median_C1	RCi_Mean_Strong	RCi_Mean_Weak	MannWhitney_U	p_Value
0.00335	0.646674	0.355968	28,935	0.00000

, Appendix A).

Collectively, these associative analyses serve to bolster the theoretical connections between the efficacy of whistleblowing and governance structures. More effective whistleblowing environments are empirically associated with strong anti-bribery policies, whereas greater exposure to ESG controversies is consistently associated with weaker outcomes. These results serve as an additional empirical link between the theoretical predictions delineated in Section 2 and the outcomes of the entropy–TOPSIS model.

5. Discussion

5.1. The Influence of Policy and Governance

The results suggest that the most significant determinant of whistleblowing effectiveness is the bribery and corruption policy score (C1 = 0.4711). This emphasizes the critical role of anti-bribery frameworks in evaluating the legitimacy of disclosure systems. Institutions that integrate comprehensive anti-corruption regulations into their governance frameworks not only comply with external regulations but also exhibit a genuine commitment to ethical accountability. These policies are essential for fostering organizational trust, which is essential for encouraging employees to report misconduct internally. Conversely, employees are inclined to perceive whistleblowing channels as ineffective when such policies are inadequately enforced or symbolic in nature, which subsequently reduces their propensity to report malfeasance.

The governance credibility is most vulnerable to reputational stress, as demonstrated by the significant weight of the bribery, corruption, and fraud controversies score (C2 = 0.2007). Institutions that are susceptible to corruption controversies must establish robust whistleblowing policies to maintain their credibility. Employees may elect to remain silent or disengage if internal reporting mechanisms are perceived as inadequate or retaliatory. However, whistleblowing systems can be transformed from symbolic compliance tools to dynamic governance instruments that elevate legitimacy during crises when institutions exhibit enforceability and responsiveness in such circumstances.

5.2. Stakeholder Perceptions and Media Exposure

A further critical dimension is also revealed by the analysis: the impact of media exposure, specifically through C2 and C3 (0.0739). The behavior of whistleblowers is substantially affected by media-driven controversies, which both present opportunities and challenges for governance. One the one hand, public crises require institutions to act promptly, thereby strengthening their governance procedures to protect stakeholder confidence and reputation. In contrast, employees may be apprehensive about disclosing internal issues for fear that their disclosures will be mishandled, ignored, or escalated in a way that will jeopardize their careers because of excessive or antagonistic media scrutiny.

The media’s dual function stresses the importance of maintaining a balance between transparency and protection in whistleblowing frameworks. Companies must guarantee anonymity, prevent retaliation, and explicitly communicate remedial measures by ensuring that employees perceive internal reporting as a secure and credible alternative to external exposure. By achieving this, institutions can convert external scrutiny into a governance resource that enhances accountability rather than undermining it.

5.3. The Dynamics of ESG Controversy

The findings emphasize the impact of media pressures, malfeasance, and ESG-related controversies. Even though criteria such as environmental infractions (C4, weight 0.0559), labor disputes (C5, 0.0458), anti-competitive practices (C6, 0.0611), and executive compensation controversies (C8, 0.0100) have lower individual weights, their combined impact demonstrates that ESG controversies function as systemic governance stress trials. Institutions that have robust ethical cultures and integrated ESG accountability frameworks are more likely to withstand such controversies, while those with superficial compliance structures are at risk of what may be referred to as “ethical mirages,” in which the appearance of robust systems conceals weak ethical infrastructures (Xiao and Xu 2025; Dicuonzo et al. 2024; Lu and Cheng 2024).

This supports the theoretical perspective that ESG controversies are not incidental to governance, but rather essential to the assessment of institutional legitimacy. Organizational resilience in the face of sustainability challenges, regulatory compliance, and investor protection can be improved by whistleblowing mechanisms that are effectively incorporated into ESG systems.

5.4. Consequences for Governance on a Broader Scale

Collectively, these findings contribute to corporate governance theory by introducing whistleblowing as a dynamic governance instrument that adjusts to institutional design, media scrutiny, and ESG pressures. In an era of escalating sustainability demands, disclosure systems emerge as critical instruments for institutional resilience, in contrast to static compliance mechanisms. They reinforce legitimacy and accountability by adapting to external expectations and organizational vulnerabilities.

The findings further underscore the necessity for financial institutions to transcend the formal establishment of disclosure channels in order to guarantee their practical effectiveness. This encompasses the implementation of credible anti-bribery frameworks, the safeguarding of whistleblowers in the event of media exposure, and the integration of disclosure systems into ESG accountability structures. The implication for regulators and ESG evaluators is that assessments should prioritize the credibility, enforcement, and integration of whistleblowing policies into ESG reporting standards, in addition to their existence.

Additionally, this study underscores the methodological advantages of employing a hybrid entropy–TOPSIS model to assess governance in the context of ESG controversies (DasGupta 2022; Cicchiello et al. 2023). The approach establishes a transparent and replicable framework for both scholarly inquiry and institutional decision-making in ethical governance by systematically weighting and ranking multidimensional determinants. The study’s overall contributions to theory, methodology, and practice are delineated below, building on these insights.

This study makes three primary contributions to literature and practice. It broadens the application of multi-criteria decision-making (MCDM) to the intersection of ESG controversies and whistleblowing, a domain in which such quantitative approaches are still restricted, at the theoretical level. The study provides empirical evidence that complements and expands upon current research on corporate governance and ethical behavior by demonstrating that the effectiveness of whistleblowing is systematically influenced by anti-bribery safeguards and controversy exposures. Secondly, the study introduces a hybrid entropy–TOPSIS framework, a transparent and reproducible instrument for assessing the performance of institutional governance. Robustness tests and associative analyses at the methodological level provide support for this framework. Third, the study’s ultimate product is a ranked assessment of the efficacy of whistleblowing in 364 European financial institutions under ESG controversy conditions. This assessment is pragmatic in nature. This ranking, in conjunction with the methodological framework, is directly applicable to financial institutions themselves as a benchmarking and improvement tool, for investors and ESG evaluators seeking to integrate governance quality into decision-making, and for regulators seeking to monitor compliance.

5.5. Practical Consequences and Constraints

There are numerous practical implications of our findings. By utilizing these discoveries, regulators may enhance supervisory and disclosure frameworks by benchmarking corporate exposure to ESG controversies. In order to validate or stress-test existing assessments, ESG analysts and rating agencies may implement the entropy–TOPSIS approach as a complementary instrument. Proactive governance interventions can mitigate reputational and financial risks in specific domains, including bribery, fraud, and environmental controversies, as the ranking outcomes emphasize for corporate compliance officers. Nevertheless, it is imperative to acknowledge a number of constraints. First and foremost, the analysis is predicated on Refinitiv’s commercial ESG scores, which may contain methodological biases. In the second place, causality is not implied by the associative statistical interpretation. Third, the investigation is restricted to disclosures from FY2024, serving as a one-year assessment rather than a longitudinal perspective. Finally, controversy data may be influenced by media coverage biases that influence the prominence of specific incidents. Therefore, future research should triangulate multiple ESG data providers, expand the temporal scope, and investigate causal mechanisms that connect controversies with financial and reputational performance.

6. Conclusions

This study utilized a hybrid entropy–TOPSIS framework to investigate the correlation between disclosure effectiveness and ESG controversies in European financial institutions, building upon these contributions. The central role of anti-bribery policies, the dual influence of media exposure, and the disruptive impact of ESG-related controversies were identified as three interrelated determinants of whistleblowing performance as a result of the analysis of ten governance- and ESG-related criteria across 364 institutions. Collectively, these findings indicate that whistleblowing is not a static compliance mechanism, but rather a dynamic governance instrument that is influenced by sustainability challenges, reputational pressures, and institutional design.

Methodologically, the study contributes by demonstrating the applicability of a hybrid entropy–TOPSIS model to governance research, which is further supported by robustness and associative analyses. This method enables the transparent and systematic weighting of multivariate factors, resulting in a data-driven and replicable instrument that can assist institutional decision-making in intricate ethical and sustainability contexts.

The results also have substantial practical implications for a variety of stakeholder groups. The findings underscore the necessity of bolstering anti-bribery frameworks, ensuring anonymity and protection for whistleblowers, and incorporating reporting systems into more comprehensive ESG accountability strategies for financial institutions. The evidence emphasizes the necessity of evaluating the efficacy of whistleblowing mechanisms, rather than merely their existence, when evaluating the quality of governance for regulators. Lastly, the study underscores the importance of integrating whistleblowing performance into accountability and disclosure standards to promote substantive governance practices, rather than symbolic ones, for ESG evaluators and rating agencies. The study’s cross-sectional focus on the year 2024 is a limitation, as it depicts the most recent conditions in the evolving ESG regulatory landscape but does not enable an examination of temporal dynamics. To assess the stability and evolution of whistleblowing efficacy under varying ESG pressures, future research should expand the analysis to multiple years and across sectors.

In conclusion, this investigation emphasizes the importance of whistleblowing as a fundamental component of ethical governance and institutional accountability in the ESG era. This framework provides a robust foundation for the promotion of responsible financial practices, the enhancement of transparency, and the fortification of governance resilience across European institutions by combining empirical evidence with methodological innovation, which is beneficial to both academics and practitioners.