Evaluating Executives and Non-Executives’ Impact toward ESG Performance in Banking Sector: An Entropy Weight and TOPSIS Method

Abstract

Financial institutions should prioritize the adoption of comprehensive Environmental, Social, and Corporate Governance (ESG) disclosure policies to improve their market reputation and decrease capital expenditures. The current study’s research objective is to investigate the impact of both inside and outside executives on the successive adoption of ESG strategies, based on the sustainable leadership theoretical framework and the bottom-up corporate governance theory. Data for the current study were obtained from the Refinitiv Eikon database and analyzed through using the entropy weight and TOPSIS techniques. The research suggests that including fully autonomous board members has the potential to improve the transparency of firms’ ESG criteria. This result was derived from an analysis of data pertaining to the behavior of CEOs and non-executives at the company level in Fiscal Year (FY) 2023. The verification of the soundness and dependability of this finding has been carried out by scrutinizing the problem of endogeneity and diverse techniques of data representation. Furthermore, our study has disproven the idea that having CEOs on the board of directors may significantly improve the ESG performance of financial institutions. Consequently, the research proposes that adopting a strict policy of board independence has the capacity to alleviate the environmental, social, and governance repercussions that arise from the control of internal executives, namely CEOs.

1. Introduction

If the financial sector is to contribute to the attainment of net zero, then banks must serve as the key player of any Environmental, Social and Corporate Governance (ESG) strategy. As the primary financiers of high-emitting industries, they serve as the primary entry point for corporations to access global capital markets. However, every employee in a company in the financial sector has a responsibility to contribute to ESG; from the C-suite to the apprentice, each position can guarantee that a bank’s ESG performance aligns with its objectives. For instance, a Chief Financial Officer may be interested in ESG from a risk perspective and may seek to influence policies to mitigate climate-related or ESG-related risks (Ling et al. 2023; Ragazou et al. 2024). In the interim, a Chief Marketing Officer may evaluate sustainability from the perspective of brand reputation and implement and communicate policies that they believe will have the greatest influence on the purchasing decisions of potential consumers. Given that each position has the potential to contribute to and benefit from a company’s ESG initiatives and sustainable practices, it is worth exploring the ways in which a specific role within an organization will be influenced by ESG in the years ahead (Wu and Qin 2024; Benuzzi et al. 2024).

Also, the obligations of ESG extend far beyond the confines of the executive board room. Managers at all levels have the duty to effectively implement ESG practices and convey policies and plans to staff. Managers play a crucial role as intermediaries in providing feedback to executives with the aim of enhancing policy. Sustainability experts are tasked with implementing ESG strategies; human resources are crucial in promoting social fairness and sustainable culture; and legal roles are responsible for ensuring compliance and the integration of ESG into contracts (Kartal et al. 2024; Wu and Qin 2024; Oh et al. 2024; Jiang et al. 2024; Chen et al. 2024; Bang et al. 2023). Implementing a role-based approach to ESG inside an organization guarantees that policies and culture are in synchronization, capitalizes on the skills and knowledge of people within the business, and allows the company to execute a more customized plan by utilizing the unique requirements and talents of the team members. While the designated ESG leader has the ultimate authority, each job has a crucial and distinct responsibility in maintaining the efficacy of a company’s ESG program and will also receive advantages in their role from a robust ESG performance (Agnese et al. 2023; Bang et al. 2023).

Furthermore, the disclosure of ESG information is significantly influenced by non-executive directors to focus on the concerns of stakeholders. ESG investment decisions are significantly influenced by non-executive directors, who are frequently referred to as independent directors (Liu et al. 2024; Kenneth David et al. 2024). This position involves the protection of stakeholders’ interests, the promotion of transparency, and the fulfillment of legal obligations. The primary research objective of this study is to reassess the influence of both internal and external executives on the ESG score in the European banking sector. The researcher employed the entropy weight and TOPSIS methodologies to assess the influence of executives and non-executives on the ESG performance of the European banking sector to achieve the study’s objective.

The findings underscore the significance of board members who satisfy strict specific independence criteria. The following are the requirements: not being currently employed by the company, not having served on the board for a period exceeding ten years, not being a major shareholder with a stake of more than 5%, not being a member of multiple boards, not having any immediate family ties to the corporation, and not receiving any form of compensation other than remuneration for board service. Consequently, the findings of this study challenge the prevailing stakeholder beliefs that assert that only high-ranking administrators possess the requisite skills to improve ESG performance. Also, the current study has the potential to confirm the facilitating effect of external executives on the improvement of corporate ESG performance using European data. The primary objective of this document is to offer European financial institutions with suggestions for enhancing their ESG performance. In addition, the findings of this investigation provide a theoretical framework for European financial institutions to improve their ESG performance by implementing reasonable management compensation incentives for external executives. During the process of establishing corporate compensation, this value is a critical reference point.

The structure of the paper is as follows: Section 2 presents the literature review related to the importance of the ESG performance of the European financial institutions and the crucial role of executives and non-executives toward it. Section 3 describes the materials and methods used to approach the research aim and Section 4 illustrates the results of the study. Finally, Section 5 discusses the findings, the implications at both the theoretical and practical levels, and the limitations of the current research, while Section 6 concludes the paper.

2. Literature Review

2.1. Sustainable Banking Practices in the EU: Shaping the Future of Finance

Europe has emerged as a global champion in sustainable financing in recent years. The European Union (EU), which is at the vanguard of the European financial sector, is integrating sustainability into its investments, operations, and regulatory frameworks. Environmental, social, and governance (ESG) factors are increasingly being prioritized. The European Union recognizes that sustainable finance is indispensable for the attainment of its internal policy objectives. This is accomplished by allocating investments toward the transition to a sustainable, resource-efficient, and low-carbon economy, as detailed in the European Green Deal and the EU’s global sustainability commitments (Yin and Yang 2024).

The European Green Deal was unveiled by the European Commission in December 2019. This initiative is a comprehensive strategy that seeks to convert the European economy into a sustainable and climate-neutral model by 2050. The initiative is committed to the simultaneous promotion of economic development, the guarantee of an equitable transition for all EU regions and citizens, and the mitigation of climate change, biodiversity loss, and pollution (Jang et al. 2022; Kostyuchenko et al. 2024; Vara Prasad et al. 2023). The European Green Deal has received EUR 1 trillion in support from the EU Commission, with the objective of transforming the European economy into a more environmentally responsible one. The preponderance of this funding will be sourced from the 2021–2027 Multiannual Financial Framework (MFF) and the NextGenerationEU fund. Nevertheless, the Green Deal’s ambitious objectives will not be effectively achieved solely through the support of the general populace. A significant portion of the remaining sum is funded by the private sector. It is essential to establish appropriate legislative framework requirements and incentives to encourage future ESG investments. Consequently, the EU is increasingly advocating for regulatory modifications and actions to encourage these types of investments. The European Green Deal strategy has initiated a plethora of initiatives this year that are consistent with its core objectives. Following an accord with the Parliament in February, the Council authorized the Commission’s proposal for the European Green Bond Regulation on 24 October 2023 (Omo-Okoro et al. 2023; Goyal and Llop 2024). The European Green Bond Regulation (EUGBS) would establish a rigorous standard for green bonds.

Moreover, EU institutions and executives have made a concerted effort to incorporate ESG factors; however, this process is still in its infancy. It is imperative to accelerate the integration of effective ESG into the risk management, business strategies, investment policies, and prudential supervision of institutions (Bruch et al. 2024; Horn 2024; Nagel et al. 2023). Core sustainability teams have been established by banks in recent years to improve their governance structures. These teams are responsible for the integration of ESG practices throughout the entire organization. The incorporation of ESG variables into bank risk management strategies is still impeded by a substantial obstacle: the absence of a uniform, exhaustive definition of ESG risks that is applicable to all banks.

To better understand the progress of the ESG performance of the European financial institutions during the Fiscal Years (FYs) 2019 to 2023 per European country, we have developed with the use of the ArcGIS Pro 2.8 software maps (Figure 1a–c) that depict the ESG performance. It is noteworthy that among the FYs, Greek financial institutions seem to have achieved very good progress as regards their ESG score. Except from Greece, financial institutions in the Scandinavia region also present a high integration rate of ESG strategies. In particular, ESG practices can be characterized as a natural fit for the Nordic banks, as the Nordic region has a long and proud history in social sustainability and the related areas around this concept (Susan et al. 2024; Alola et al. 2023; Khatri and Kjærland 2023; Mitchell et al. 2024). Moreover, it is worth to refer to the UK financial sector, which can be characterized as a longstanding proponent of climate responsibility. However, the ESG performance score of the banks in the UK highlight that the rules of sustainability and adoption of ESG should be rewritten. However, one unexplored factor that can further explain the ESG performance of financial institutions in the European area is that of the role of the outside executives. The following subsection discusses the above factor as well.

2.2. The Power of Independent Executives: The Bottom-Up Corporate Governance Approach

Non-executive directors are frequently perceived by the public as a means of overseeing negligent CEOs and regulating corporate misconduct. Nevertheless, the precise nature of their contribution to organizations remains ambiguous. Independent CEOs have been shown to be capable of accurately predicting profitability, performance, and value creation, as evidenced by academic research (Hempel and Fay 1994; Chee et al. 2022). The corporate governance discourse must prioritize the internal supervision of the CEO’s direct subordinates. Independence is essential in a variety of corporate governance scenarios. Internal auditors must maintain independence from the colleagues they audit; external auditors must maintain independence from their customers; and non-executive directors must maintain a certain level of independence from their executive counterparts on a board. In terms of a concept, what is the definition of “independence”?

Individuals may exhibit independence as a critical element of professional conduct and professionalism. It involves the removal of any restrictions that may obstruct the pursuance of the appropriate course of action, as well as the prevention of undue influence from entrenched interests (Jang et al. 2022; Huang and Li 2019; Zhang et al. 2023). By being able to “stand apart” from improper influences and free from management constraints, it is possible to make precise and unadulterated decisions on a specific subject. An auditor who has maintained a long-standing relationship with a client may lack the requisite impartiality. The auditor’s capacity to effectively represent the interests of shareholders may be jeopardized by their relationship with the client, as they are obligated to prioritize the interests of the shareholders over those of the client. The auditor may not demonstrate the necessary level of meticulousness or may unjustifiably grant the client the benefit of the doubt. Non-executive directors (NEDs) are subject to the same principle. Numerous nations designate NEDs as independent directors to underscore this specific characteristic. Non-Executive Directors (NEDs) are appointed by shareholders to represent their interests on corporate boards. The fundamental fiduciary obligation of non-executive directors is to the company’s shareholders. It is essential that the interests of other firm stakeholders, including executive directors, trade unions, and intermediate management, remain unencumbered and unaffected (Yan et al. 2024).

The assurance of independence is a common concern in a variety of organizational contexts, as its absence may lead to an ethical dilemma. Individuals forge alliances and networks over protracted periods of time, and the intensity of their interactions varies. Audit engagement partners may develop a thorough comprehension of their consumers over the course of numerous years. Furthermore, the establishment of connections between executive board members and NEDs can be facilitated by board collaboration. Consequently, the level of independence varies. What are the distinctive benefits of NED independence when we conduct a thorough examination of their functions? As previously mentioned, the primary fiduciary obligation of a non-executive director is to the shareholders of the company. To enhance the independence of NEDs, certain shareholders advocate for the appointment of new directors from sectors that are not competitive with the company’s industry. As employees transition between contending firms and occasionally partake in collaborative industry organizations, the emergence of informal networks poses a threat to independence within an industry (Hempel and Fay 1994).

The advantages and disadvantages of appointing NEDs from outside the relevant sector or those with some industry knowledge are presently the subject of debate. Prior industry experience offers a network of connections, a more profound comprehension of the technical aspects of critical industry issues, and a more comprehensive understanding of the strategic challenges that the sector encounters. While past industry involvement may enhance a NED’s contributions, it may also detract from the NED’s objectivity and independence by introducing biases from previously held perspectives. Consequently, the demonstration of independence may be improved by the nomination of non-executive directors from outside the sector. In addition to the benefits of the NED effect, which entails the examination of a problem from a novel perspective, the absence of prior material business relationships generally suggests that a NED lacks prior affiliations or biases that could compromise their independence (Hyun et al. 2024; Q. Wang et al. 2024).

The efficacy of a non-executive board is frequently attributed to the combination of talents and knowledge that it employs by a multitude of firms. Although certain NEDs may provide technical expertise, others may provide a more comprehensive political or regulatory perspective. In prominent and highly visible corporations, non-executive directors who provide a social or political perspective during board discussions may be of strategic significance. To offer valuable insights, they may elect retired senior government officials or former leaders of prominent corporations to their boards. It is crucial to underscore that these individuals have not engaged in any significant economic transactions with the corporation, which ensures their material independence (Hempel and Fay 1994; Chan et al. 2023).

3. Materials and Methods

3.1. Data

The objective of the study is to investigate the role of executives and non-executives’ impact toward ESG performance in the European banking sector. Specifically, research was conducted on 84 listed financial institutions for the Fiscal Year (FY) 2023 that are headquartered in Europe (31 countries) and have an ‘A’ score, which indicates excellent relative ESG performance and a high degree of transparency in reporting material ESG data publicly. The main reason for selecting only the financial institutions with the highest ESG score rating is that these companies are usually those which can be characterized as global market leaders ESG. Thus, it is important to investigate the behavior of their executive and non-executive members toward their ESG performance. Furthermore, this research utilized data and the ESG rating of the selected companies from the Refinitiv Eikon database.

Table 1. Description of the selected criteria. Source: Own elaboration.

Criteria	Description	Measurement
C1. Executive Members Gender Diversity, Percent Score	Represents the proportion of women in leadership positions (Zhang et al. 2023; Yan et al. 2024).	Percent
C2. Board Attendance Score	Specifies whether the financial institution discloses information on the presence of individual board members at board meetings (Ghafoor and Gull 2024).	Percent
C3. Board Tenure Score	Displays the mean duration of tenure for each member of the board (Bennouri et al. 2024; Wu et al. 2024).	Percent
C4. Non-Executive Board Members Score	Provides the proportion of non-executive board members.	Percent
C5. Independent Board Members Score	Displays the proportion of board members who are independent, as indicated by each of the chosen financial institutions (Wu et al. 2024; Ji et al. 2024).	Percent
C6. Strictly Independent Board Members Score	The percentage of board members who meet specific independence criteria, including not being employed by the company, not having served on the board for more than ten years, not being a major shareholder with more than 5% of holdings, not having membership on multiple boards, not having immediate family ties to the corporation, and not receiving any compensation other than board service remuneration, is indicated (Bhardwaj and Seijts 2021).	Percent

presents the description of the variables that were considered as the criteria for the employment of the entropy weight and TOPSIS methods.

3.2. Entropy Weight Method

The TOPSIS method is currently being implemented in the current research as a multicriteria approach to decision-making, which involves the evaluation of multiple elements within a well-defined framework in relation to the data analysis method. Entropy weighing is the assignment of weights to a diverse array of variables or factors based on the degree of uncertainty or unpredictability, as determined by entropy. The TOPSIS model, a composite methodology that integrates entropy and TOPSIS approaches, was introduced by Kaur et al. (2023). The entropy weight approach is the primary objective of this system, which is to assess the significance of each assessment indicator. It then utilizes the method of selecting the most optimal solution to rank assessment items (Tzitiridou-Chatzopoulou et al. 2024; Zournatzidou et al. 2024; Zournatzidou and Floros 2023).

According to Dwivedi et al. (2018), the fundamental principle of the entropy weight TOPSIS approach is to determine the optimal solution in which all attribute values have reached their highest or lowest value in comparison to other alternatives. The relative proximity of an assessment object to the best and worst solutions is used to evaluate its optimality. Assessment items are considered optimal when they are situated in close proximity to the most effective solution and the furthest from the least desirable option. Conversely, it is considered suboptimal if it fails to meet these criteria. The TOPSIS technique is significantly influenced by entropy, as it incorporates information from the original data without imposing any restrictions on the sample size. It ensures a versatile functionality and minimal information loss (Atenidegbe and Mogaji 2023; Aras et al. 2017; Noei et al. 2017).

Before 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 $i\in A,j\in M,i=\left\{\mathrm{1,2},3,\dots ,n\right\},j=\left\{\mathrm{1,2},3,\dots ,m\right\}$. The matrix $X^{\prime }={({x^{\prime }}^{\prime }}_{ij})$ in Equation (1) is a decision matrix of $n\times m$. The weights of criteria M can be represented by the 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 is determined by the Shannon entropy weight technique, which evaluates the degree of data dispersion (Nascimento et al. 2023). To begin, we employ the Min–Max method to normalize the n original choice. Subsequently, we adjust the normalized formula to the right by 0.001 units to facilitate the subsequent logarithmic computations.

$$x_{ij}={\displaystyle \frac{{x^{\prime }}_{ij}-\min \left({x^{\prime }}_j\right)}{\max \left({x^{\prime }}_j\right)-\min \left({x^{\prime }}_j\right)}}+0.001, \mathrm{w}\mathrm{h}\mathrm{e}\mathrm{r}\mathrm{e} i=\mathrm{1,2},3,\dots ,n, \mathrm{a}\mathrm{n}\mathrm{d} j=\mathrm{1,2},3,\dots ,m.$$

(2)

The entropy value, denoted as e_j, has been determined using Equation (4). Entropy quantifies the extent of data dispersion, indicating that the data contain a greater quantity of information; the entropy value decreases as the data exhibit greater variation. The entropy value increases as the data become more concentrated, indicating a decrease in the quantity of information present in the data.

$$r_{ij}={\displaystyle \frac{x_{ij}}{{\sum }_{i=1}^n{x}_{ij}}}, i=\mathrm{1,2},3,\dots ,n, \mathrm{a}\mathrm{n}\mathrm{d} j=\mathrm{1,2},3,\dots ,m.$$

(3)

$$e_j=-{\displaystyle \frac{1}{\ln n}}\sum _{i=1}^n{r}_{ij}, i=\mathrm{1,2},3,\dots ,n \mathrm{a}\mathrm{n}\mathrm{d} j=\mathrm{1,2},3,\dots ,m.$$

(4)

The weight $w_j$ is determined by Equation (5).

$$w_j={\displaystyle \frac{1-e_j}{{\sum }_{j=1}^m\left(1-e_j\right)}}$$

(5)

3.3. TOPSIS Method

The TOPSIS approach, created by Hwang and Yoon in 1981, assesses the alternatives by measuring their closeness to the ideal solutions. To assess proximity, we compute the Euclidean distance between each target option and the ideal as well as anti-ideal solutions. The optimum solution is determined by identifying the most advantageous value for each evaluation criteria, whereas the suboptimal solution is described by the least beneficial value for each assessment criterion. We ultimately choose the most favorable option, which closely aligns with the ideal solution and significantly deviates from the unfavorable solution, as our favorite choice. First, we individually standardize the positive and negative elements of the choice matrix in Equation (1) to reduce any inconsistencies in dimensions across the various criteria, highlighting the need of implementing uniformity across several aspects utilizing the Min-Max method. This approach streamlines the process of assessing the pros and downsides involved in building the entropy weight TOPSIS.

$$\begin{gathered} positive:x_{ij}^{+}={\displaystyle \frac{{x^{\prime }}_{ij}-\min \left({x^{\prime }}_j\right)}{\max \left({x^{\prime }}_j\right)-\min \left({x^{\prime }}_j\right)}} \\ negative:x_{ij}^{-}={\displaystyle \frac{\max \left({x^{\prime }}_j\right)-{x^{\prime }}_{ij}}{\max \left({x^{\prime }}_j\right)-\min in\left({x^{\prime }}_j\right)}} \\ \min \left({x^{\prime }}_j\right)=\{\underset{i}{\min }{x^{\prime }}_{ij}\left|1<i<n,1<j<m\right\} \\ \max \left({x^{\prime }}_j\right)=\{\underset{i}{\max }{x^{\prime }}_{ij}\left|1<i<n,1<j<m\right\} \end{gathered}$$

(6)

The dimensionless normalized decision matrix x_ij is generated by normalizing the positive and negative criteria to construct the initial choice matrix in Equation (6), as illustrated in Equation (7).

$$X^{\prime }=\left[\begin{array}{cccc}{x}_{11}& x_{12}& \cdots & x_{1m}\\ x_{21}& \cdots & \cdots & x_{2m}\\ ⋮& \cdots & \cdots & ⋮\\ x_{n1}& x_{n2}& \cdots & x_{nm}\end{array} \right], \mathrm{w}\mathrm{h}\mathrm{e}\mathrm{r}\mathrm{e} i=\mathrm{1,2},3,\dots ,n \mathrm{a}\mathrm{n}\mathrm{d} j=\mathrm{1,2},3,\dots ,m.$$

(7)

Furthermore, the decision matrix in Equation (8) is derived 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).

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

(8)

The positive ideal solution (PIS) is defined as the utmost value and the negative ideal solution (NIS) as the minimum value for each criterion in Equation (9). The distance of each alternative from the Positive Ideal Solution (PIS) and Negative Ideal Solution (NIS) is determined using Equations (10) and (11).

$$\begin{gathered} \mathrm{P}\mathrm{I}\mathrm{S}:P^{+}=\left\{{\mathcal{v}}_1^{+},{\mathcal{v}}_2^{+},{\mathcal{v}}_3^{+},\dots , {\mathcal{v}}_m^{+}\right\}=\{(\underset{i}{\max }{\mathcal{v}}_{ij}|j\in M)\} \\ \mathrm{N}\mathrm{I}\mathrm{S}:P^{-}=\left\{{\mathcal{v}}_1^{-},{\mathcal{v}}_2^{-},{\mathcal{v}}_3^{-},\dots , {\mathcal{v}}_m^{-}\right\}=\{(\underset{i}{\min }{\mathcal{v}}_{ij}|j\in M)\} \end{gathered}$$

(9)

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

(10)

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

(11)

Below is the computation for the coefficient of relative proximity (RC).

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

(12)

4. Results

According to Section 3.1, the ESG performance of European financial institutions was evaluated using six criteria specified in Table 1, which were applied to the impact of CEOs and non-executives. Thomson Reuters operates the Refinitiv Eikon database, from which specific variables and data were obtained.

In addition, the study was analyzed using the choice matrix that was generated, which can be categorized into three primary domains. Several epochs and their associated phases are outlined in the subsequent sections. First and foremost, the decision matrix’s data are normalized using the $N1$ approach. The purpose of this method is to establish a standardized structure for the data, thereby enabling their comparability. After the completion of Step 1, a normalized matrix was generated for each $Ni$. The normalized matrices are illustrated in

Table A1. Normalization of the matrix.

C1	C2	C3	C4	C5	C6
0.150	0.000	0.035	0.027	0.127	0.000
0.170	0.000	0.035	0.136	0.093	0.000
0.000	0.114	0.077	0.154	0.146	0.000
0.022	0.125	0.143	0.125	0.089	0.000
0.000	0.124	0.157	0.155	0.146	0.156
0.123	0.000	0.081	0.124	0.136	0.000
0.126	0.000	0.135	0.121	0.067	0.000
0.169	0.132	0.052	0.010	0.070	0.000
0.161	0.158	0.010	0.100	0.097	0.171
0.000	0.114	0.031	0.154	0.146	0.181
0.122	0.144	0.058	0.119	0.128	0.000
0.045	0.000	0.121	0.027	0.026	0.002
0.133	0.000	0.140	0.136	0.133	0.000
0.020	0.000	0.159	0.014	0.108	0.000
0.133	0.114	0.091	0.105	0.048	0.112
0.103	0.142	0.004	0.150	0.146	0.174
0.031	0.124	0.004	0.052	0.090	0.145
0.000	0.114	0.137	0.154	0.104	0.118
0.167	0.000	0.187	0.063	0.105	0.077
0.141	0.159	0.115	0.124	0.096	0.093
0.066	0.124	0.116	0.155	0.146	0.169
0.095	0.000	0.175	0.008	0.069	0.074
0.000	0.114	0.184	0.154	0.146	0.173
0.052	0.000	0.094	0.004	0.068	0.000
0.000	0.000	0.092	0.154	0.146	0.156
0.111	0.000	0.160	0.148	0.085	0.000
0.000	0.114	0.029	0.154	0.115	0.000
0.020	0.162	0.162	0.041	0.044	0.000
0.045	0.159	0.049	0.124	0.136	0.181
0.146	0.124	0.036	0.131	0.133	0.173
0.045	0.155	0.155	0.041	0.038	0.002
0.173	0.000	0.107	0.014	0.026	0.074
0.013	0.000	0.171	0.011	0.077	0.029
0.065	0.114	0.116	0.058	0.036	0.093
0.056	0.000	0.160	0.090	0.017	0.000
0.119	0.124	0.134	0.131	0.133	0.173
0.042	0.142	0.117	0.150	0.135	0.000
0.044	0.124	0.073	0.121	0.087	0.134
0.163	0.114	0.066	0.088	0.115	0.163
0.126	0.000	0.090	0.010	0.024	0.000
0.000	0.124	0.000	0.155	0.146	0.000
0.172	0.119	0.147	0.120	0.109	0.127
0.000	0.114	0.096	0.154	0.146	0.156
0.052	0.132	0.119	0.089	0.051	0.000
0.089	0.000	0.103	0.128	0.119	0.154
0.051	0.122	0.127	0.019	0.129	0.112
0.050	0.175	0.126	0.077	0.124	0.000
0.165	0.124	0.015	0.021	0.023	0.069
0.000	0.124	0.014	0.155	0.146	0.169
0.156	0.158	0.049	0.077	0.035	0.000
0.031	0.000	0.192	0.038	0.074	0.000
0.179	0.103	0.132	0.154	0.132	0.173
0.134	0.124	0.109	0.155	0.146	0.175
0.121	0.155	0.104	0.106	0.095	0.000
0.173	0.114	0.024	0.088	0.115	0.000
0.133	0.114	0.088	0.088	0.115	0.163
0.056	0.168	0.065	0.112	0.029	0.034
0.000	0.124	0.062	0.155	0.146	0.181
0.039	0.111	0.141	0.063	0.139	0.000
0.019	0.152	0.043	0.157	0.142	0.132
0.137	0.122	0.104	0.121	0.139	0.151
0.090	0.147	0.133	0.012	0.076	0.040
0.055	0.162	0.049	0.078	0.131	0.102
0.120	0.122	0.176	0.136	0.147	0.156
0.169	0.000	0.180	0.128	0.006	0.000
0.117	0.124	0.148	0.134	0.069	0.131
0.061	0.114	0.005	0.004	0.107	0.118
0.161	0.132	0.099	0.089	0.125	0.000
0.158	0.000	0.010	0.124	0.136	0.181
0.134	0.124	0.034	0.079	0.080	0.137
0.152	0.114	0.008	0.076	0.079	0.109
0.175	0.142	0.032	0.150	0.152	0.000
0.000	0.114	0.176	0.154	0.146	0.173
0.000	0.000	0.063	0.112	0.119	0.181
0.106	0.124	0.056	0.030	0.002	0.000
0.158	0.114	0.128	0.137	0.126	0.160
0.133	0.114	0.096	0.135	0.135	0.175
0.052	0.132	0.022	0.089	0.141	0.000
0.165	0.000	0.023	0.072	0.137	0.178
0.125	0.000	0.054	0.040	0.076	0.118
0.030	0.114	0.159	0.056	0.067	0.107
0.092	0.000	0.047	0.075	0.104	0.000
0.175	0.000	0.127	0.091	0.089	0.000
0.173	0.114	0.175	0.115	0.036	0.059

and

Table A2. Calculation of the Euclidean distance between PIS and NIS. Source: Own elaboration.

Si+	Si−	Si+ Si−	Si−/(Si+ Si−)
0.0747	0.0283	0.1031	0.2749
0.0196	0.0336	0.0531	0.6321
0.0433	0.0306	0.0739	0.4142
0.0395	0.0339	0.0734	0.4621
0.0675	0.0637	0.1312	0.4852
0.0187	0.0278	0.0465	0.5972
0.0128	0.0299	0.0428	0.6996
0.0355	0.0419	0.0774	0.5410
0.0703	0.0732	0.1436	0.5101
0.0758	0.0676	0.1434	0.4712
0.0368	0.0403	0.0771	0.5232
0.0283	0.0165	0.0448	0.3691
0.0132	0.0325	0.0458	0.7106
0.0323	0.0202	0.0525	0.3845
0.0475	0.0528	0.1003	0.5265
0.0713	0.0703	0.1416	0.4963
0.0662	0.0562	0.1224	0.4588
0.0571	0.0514	0.1085	0.4738
0.0283	0.0461	0.0744	0.6198
0.0479	0.0556	0.1035	0.5376
0.0671	0.0674	0.1344	0.5011
0.0325	0.0367	0.0693	0.5302
0.0713	0.0686	0.1399	0.4907
0.0295	0.0150	0.0445	0.3369
0.0633	0.0561	0.1194	0.4700
0.0138	0.0310	0.0447	0.6925
0.0450	0.0289	0.0739	0.3910
0.0460	0.0396	0.0856	0.4626
0.0760	0.0716	0.1476	0.4850
0.0672	0.0705	0.1377	0.5121
0.0423	0.0386	0.0809	0.4770
0.0300	0.0418	0.0718	0.5819
0.0344	0.0229	0.0574	0.3995
0.0464	0.0438	0.0902	0.4854
0.0232	0.0229	0.0461	0.4962
0.0656	0.0705	0.1361	0.5181
0.0407	0.0375	0.0782	0.4792
0.0596	0.0549	0.1145	0.4795
0.0624	0.0678	0.1302	0.5209
0.0206	0.0249	0.0455	0.5476
0.0480	0.0310	0.0791	0.3925
0.0504	0.0622	0.1126	0.5523
0.0676	0.0612	0.1288	0.4749
0.0377	0.0337	0.0715	0.4720
0.0559	0.0572	0.1131	0.5061
0.0539	0.0497	0.1036	0.4797
0.0455	0.0421	0.0876	0.4806
0.0427	0.0458	0.0885	0.5178
0.0740	0.0649	0.1389	0.4671
0.0384	0.0445	0.0829	0.5368
0.0291	0.0239	0.0530	0.4518
0.0631	0.0735	0.1366	0.5380
0.0662	0.0719	0.1381	0.5204
0.0367	0.0426	0.0793	0.5370
0.0327	0.0408	0.0734	0.5553
0.0623	0.0660	0.1283	0.5143
0.0461	0.0407	0.0868	0.4692
0.0757	0.0687	0.1443	0.4757
0.0370	0.0313	0.0683	0.4584
0.0649	0.0576	0.1225	0.4703
0.0592	0.0648	0.1240	0.5224
0.0408	0.0410	0.0818	0.5015
0.0570	0.0510	0.1080	0.4722
0.0602	0.0674	0.1276	0.5283
0.0036	0.0387	0.0423	0.9159
0.0530	0.0595	0.1125	0.5288
0.0577	0.0481	0.1058	0.4548
0.0318	0.0432	0.0751	0.5762
0.0652	0.0685	0.1337	0.5124
0.0575	0.0590	0.1165	0.5066
0.0500	0.0523	0.1023	0.5111
0.0368	0.0466	0.0834	0.5593
0.0713	0.0683	0.1396	0.4895
0.0709	0.0625	0.1333	0.4684
0.0354	0.0331	0.0685	0.4828
0.0600	0.0686	0.1287	0.5335
0.0657	0.0704	0.1361	0.5172
0.0426	0.0318	0.0744	0.4277
0.0642	0.0677	0.1319	0.5130
0.0455	0.0463	0.0918	0.5045
0.0520	0.0479	0.0998	0.4795
0.0252	0.0196	0.0448	0.4372
0.0113	0.0361	0.0473	0.7617
0.0317	0.0498	0.0814	0.6112

of Appendix A. The next stage of the analysis involved the assignment of weights to each criterion, denoted as w_ij, and the construction of a weighted normalized matrix, following the application of the method outlined in Section 3 of this research. The following methodologies were implemented in this investigation: entropy weight and TOPSIS. A detailed analysis of the weights ascribed to each selected criterion is provided in

Table 2. Entropy weight TOPSIS approach determines criteria weight. Source: Own elaboration.

Criterion	C1	C2	C3	C4	C5	C6
$e_j$	0.93089	0.918287	0.954517	0.963958	0.976212	0.870133
D = 1 − $e_j$	0.06911	0.081713	0.045483	0.036042	0.023788	0.129867
$W_j$	0.17904	0.211691	0.117829	0.093372	0.061628	0.33644

.

A subsequent determination was made of the Euclidean distance between the positive ideal solution (PIS) and the negative ideal solution (NIS). For both the positive and negative solutions of each choice, the normalized Euclidean distance is determined by Equations (10) and (11). The results are illustrated in

Table A3. Ranking the total score using the TOPSIS algorithm.

	Countries	Ranking ESG Score by Refinitiv Eikon	Ranking ESG Score by TOPSIS Method
1	Czech Republic	0.9432	0.2749
2	Iceland	0.9293	0.6321
3	United Kingdom	0.9195	0.4142
4	Belgium	0.9171	0.4621
5	United Kingdom	0.9163	0.4852
6	Switzerland	0.9117	0.5972
7	Norway	0.8968	0.6996
8	Germany	0.8960	0.5410
9	Ireland	0.8939	0.5101
10	United Kingdom	0.8925	0.4712
11	Netherlands	0.8787	0.5232
12	Switzerland	0.8782	0.3691
13	Spain	0.8713	0.7106
14	France	0.8563	0.3845
15	United Kingdom	0.8524	0.5265
16	Italy	0.8454	0.4963
17	United Kingdom	0.8451	0.4588
18	United Kingdom	0.8411	0.4738
19	Italy	0.8408	0.6198
20	Switzerland	0.8384	0.5376
21	United Kingdom	0.8335	0.5011
22	Switzerland	0.8274	0.5302
23	United Kingdom	0.8245	0.4907
24	Germany	0.8241	0.3369
25	United Kingdom	0.8226	0.4700
26	France	0.8180	0.6925
27	United Kingdom	0.8172	0.3910
28	France	0.8163	0.4626
29	Switzerland	0.8150	0.4850
30	United Kingdom	0.8126	0.5121
31	Liechtenstein	0.8095	0.4770
32	United Kingdom	0.7991	0.5819
33	Sweden	0.7979	0.3995
34	United Kingdom	0.7902	0.4854
35	Germany	0.7871	0.4962
36	United Kingdom	0.7852	0.5181
37	Italy	0.7848	0.4792
38	Switzerland	0.7820	0.4795
39	United Kingdom	0.7818	0.5209
40	Norway	0.7810	0.5476
41	United Kingdom	0.7800	0.3925
42	Norway	0.7758	0.5523
43	United Kingdom	0.7718	0.4749
44	Germany	0.7713	0.4720
45	United Kingdom	0.7670	0.5061
46	Sweden	0.7646	0.4797
47	Switzerland	0.7640	0.4806
48	United Kingdom	0.7633	0.5178
49	United Kingdom	0.7627	0.4671
50	Ireland	0.7604	0.5368
51	United Kingdom	0.7593	0.4518
52	United Kingdom	0.7589	0.5380
53	United Kingdom	0.7533	0.5204
54	Italy	0.7494	0.5370
55	United Kingdom	0.7489	0.5553
56	United Kingdom	0.7478	0.5143
57	Romania	0.7460	0.4692
58	United Kingdom	0.7436	0.4757
59	Poland	0.7430	0.4584
60	Russia	0.7384	0.4703
61	Greece	0.7369	0.5224
62	Malta	0.7356	0.5015
63	France	0.7326	0.4722
64	Greece	0.7324	0.5283
65	Denmark	0.7324	0.9159
66	United Kingdom	0.7320	0.5288
67	France	0.7316	0.4548
68	Germany	0.7260	0.5762
69	Switzerland	0.7254	0.5124
70	United Kingdom	0.7240	0.5066
71	United Kingdom	0.7233	0.5111
72	Italy	0.7214	0.5593
73	United Kingdom	0.7184	0.4895
74	Guernsey	0.7168	0.4684
75	United Kingdom	0.7148	0.4828
76	United Kingdom	0.7112	0.5335
77	United Kingdom	0.7112	0.5172
78	Germany	0.7094	0.4277
79	Switzerland	0.7092	0.5130
80	Norway	0.7075	0.5045
81	United Kingdom	0.7053	0.4795
82	Sweden	0.7048	0.4372
83	Ireland	0.7005	0.7617
84	United Kingdom	0.7004	0.6112

(see Appendix A).

By using the entropy weight and TOPSIS methods, it was highlighted that Criterion 6 is the one with the highest importance related to the investigation of the impact of the executives and non-executives toward the ESG performance of the European financial institutions. Specifically, this criterion expresses the proportion of board members who meet specific criteria for independence, including the following: (i) not being employed by the company, (ii) not having served on the board for more than ten years, (iii) not being a major shareholder with more than 5% of holdings, (iv) not having membership on multiple boards, (v) not having immediate family ties to the corporation, and (vi) not receiving any compensation other than board service remuneration.

The inside directors of the financial institutions, such as CEOs and executive board members, often possess a profound understanding of the company’s environmental requirements, including both internal aspects and external influences. This enables them to advocate for ecologically friendly corporate practices. Furthermore, merging the roles of CEO and chairman offers the potential to accelerate and accomplish environmental objectives, as opposed to having them as distinct positions (Sklavos et al. 2024). Conversely, a strong CEO may incentivize corporations to adopt environmentally friendly management practices. Nevertheless, an overabundance of executive power may lead to suboptimal corporate green governance and an increased degree of environmental risk. Multiple studies indicate that CEOs who possess substantial authority inside their organizations may neglect environmental management, the control of environmental hazards, and other operational concerns. The board’s capacity for independent operation and autonomous decision-making may be compromised by the merging of the CEO and board chairman responsibilities (CEO duality), potentially resulting in a reduced emphasis on environmental governance and green mergers and acquisitions (Kiohos and Sariannidis 2010; Mallidis et al. 2024).

However, the level of independence in businesses is a critical attribute of a board of directors and can be determined by the proportion of “outside” (i.e., external or independent) board members. In contrast to inside directors, the stakeholder theory posits that outside directors exhibit more resistance to influence from shareholders and management, and their activities are not constrained by organizational limits (Qiu 2017; Abdelkader et al. 2024; Agnese et al. 2024). Hence, including board independence as a pivotal attribute of the company’s board significantly bolsters its efficacy. Furthermore, the company’s external directors are entrusted with a greater degree of social accountability and are subjected to greater potential harm to their reputation, which allows them to provide more extensive disclosures. Thus, the findings of the current research brought down those previous theories, which support that inside executives can be act as a catalyst for enhanced ESG disclosure in the financial sector (Ji et al. 2024; Chee et al. 2022; Chu 2024).

Independent non-executive directors who possess strategic vision and external expertise may offer a broader perspective, enhanced neutrality, diverse opinions, and attributes that complement those of their executive director counterparts. The synergy between executive and independent non-executive directors may yield extraordinary results when directors understand each other’s contributions and encourage involvement, frank discussion, trust, and mutual respect. A diverse array of cognitive styles, perspectives, experiences, and techniques can encourage more in-depth inquiry and challenge, which in turn leads to more nuanced, thorough, and exploratory debates that improve creativity, innovation, and informed decision-making. As a result, this approach may be beneficial to other European financial institutions in their efforts to implement ESG initiatives. Additionally, discrepancies may arise between independent and executive directors during the process of determining strategic direction, devising policies, producing and/or ratifying strategy, making decisions, and maintaining accountability through compliance or performance reporting. These may be related to priority, focus, concern, or viewpoints. For example, executive directors may harbor concerns regarding the organization’s ability to achieve ambitious objectives when independent directors who are well-intentioned but excessively optimistic advocate for them. Divisiveness may arise over time if discussions fail to produce consensus.

5. Discussion

The fundamental tenets of ESG have been around for millennia, mostly in the domains of social and governance. Throughout history, there has been a strong emphasis on companies prioritizing social responsibility by ensuring safe and empathetic working conditions and promoting gender equality in the workplace. Investors have increasingly embraced the concept of ESG due to growing apprehensions around climate change and environmental deterioration (Agnese et al. 2024).

ESG disclosure is a method of communicating the extent to which a firm is transparent about its ESG operations. Several aspects explain why a larger cohort of investors is increasingly using ESG disclosures in investing strategies: ESG information disclosure primarily serves as a helpful indicator for evaluating an enterprise’s operational performance. ESG disclosure mitigates the information gap between companies and stakeholders by providing data on environmental, social, and governance dimensions. As a result, it reduces the expenses and uncertainties associated with investing in assets for organizations (Dhaliwal et al. 2012). Ryou et al. (2022) argues that a company’s disclosure of its ESG practices may attract investors by highlighting its commitment to sustainable development and its potential for future success (Ryou et al. 2022). Van de Geer and Bühlmann (2009) contended that corporations may bolster their competitiveness via the successful implementation of CSR (Van de Geer and Bühlmann 2009). ESG disclosure may improve a company’s financial performance by facilitating the adoption of ESG policies. Chen et al. (2018) provide proof that when enterprises choose to communicate their ESG practices, it indicates their confidence in their own achievements in ESG issues (Ragazou et al. 2024; Ling et al. 2023).

The increasing importance and prominence of ESG disclosure in the current business environment are apparent, as previously mentioned. What are the determinants that impact a company’s ESG disclosure? Previous studies have shown a significant link between the ESG performance of companies and the expertise and credentials of their CEO and board members. These characteristics include several elements, such as the CEOs’ professional background, their gender, and their physical attributes. However, the role of outside executives, who maintain strict independence from the businesses in the sector, remains an unexplored factor that could potentially influence the ESG performance of European financial institutions (Sun et al. 2024; Ruan et al. 2024).

The engagement of independent directors in ESG activities has significantly impacted the corporate governance environment. ESG criteria, including a company’s environmental effect, social responsibility, and governance procedures, have become essential for organizations dedicated to long-term sustainability and ethical operations. Independent directors, who are non-executive members of a company’s board, have a distinct advantage in promoting the integration of ESG considerations. Consultants provide firms with impartial viewpoints and a range of specialized knowledge, therefore guaranteeing a harmonious equilibrium between ethical conduct and financial success. Their ever-changing function involves several crucial elements (Caldeira dos Santos and Pereira 2022).

At first, independent directors improve board oversight by actively engaging in conversations about ESG issues. They ensure openness and ethical conduct by rigorously evaluating ESG initiatives, thus holding firms accountable. In addition, they emphasize the need for adopting sustainable performance indicators, which guarantee that corporate goals are in line with wider social and environmental goals. In addition, these directors help to reduce risk by identifying possible ESG dangers and opportunities (Wu et al. 2024; M. Wang et al. 2024). They promote comprehensive decision-making that considers the interests of workers, communities, and the environment, giving priority to stakeholder interests that go beyond those of shareholders. In addition, independent directors have a role in facilitating communication between the organization and its many stakeholders, acting as channels for successful stakeholder engagement. It is crucial for firms that are trying to address complex ESG concerns, since it fosters trust and open communication (Ferriani 2023; Rojo-Suárez and Alonso-Conde 2024; Geng et al. 2024).

The expanding role of independent directors in ESG matters is a crucial aspect of contemporary corporate governance. By willingly assuming these obligations, they assist firms in achieving sustainable development, implementing ethical standards, and enhancing value generation for all stakeholders. The business sphere has seen a significant shift in the involvement of independent directors in ESG matters (Mishra et al. 2024; Seifert et al. 2024). Enhancing board oversight is a crucial aspect of their growth obligations. This article examines the essential role of independent directors in improving board oversight to successfully integrate ESG considerations. Independent directors enhance company governance by broadening their focus to include ESG considerations. Their role involves supervising ESG initiatives to ensure that the organization’s business activities align with societal values and ethical principles. Enhanced ESG oversight enables sustainable decision-making that creates long-lasting value for both the firm and its stakeholders (He et al. 2023; Huang et al. 2023).

Finally, independent directors have a crucial responsibility in guaranteeing the accurate and truthful reporting of ESG issues. This, in turn, enhances investor confidence and trust. They reduce the likelihood of financial, reputational, and operational disruptions by efficiently recognizing and addressing ESG-related risks through effective oversight. Moreover, independent directors cultivate a culture of principled leadership by advocating for the integration of ESG considerations into the company’s strategic framework (Tian et al. 2024). ESG standards assist boards in making well-informed choices that strike a balance between long-term sustainable growth and immediate financial advantages. In essence, external executives, who often have a wide range of experience, provide invaluable and diverse viewpoints that are essential for the thorough supervision of ESG issues. They closely collaborate with management to guarantee the effective creation, implementation, and oversight of ESG initiatives. Future research proposals should be focused on the investigation of the impact of outside executive’s board members to the financial institutions with an ESG score below 70 rating. Moreover, in terms of the limitations of the current study, one of them was the limitation on the variables from the database of Refinitiv Eikon regarding the variables of inside and outside executives in the financial institutions. Also, further analysis by using PROMETHEE II will contribute to the deep investigation of the issue under investigation.

6. Conclusions

Organizations face a multitude of issues continuously. The intricacies of strategic planning and financial management, together with the difficulties of maintaining growth, may provide formidable obstacles. Companies must also confront the problem of integrating ESG principles to guarantee sustainable continuance. The role of a non-executive director (NED) is essential in addressing these problems, offering strategic counsel and experience to drive enterprises toward sustained success. This essay analyzes the pivotal function of a non-executive director or external executive, highlighting its capacity to substantially improve ESG performance.

This study indicates that a financial institution’s board must have at least one purely non-executive member, since they provide an unbiased viewpoint on governance and decision-making related to the execution of ESG policies. Non-executive directors (NEDs) provide strategic counsel on the execution of ESG principles, using their substantial experience and knowledge, unlike internal executive directors who engage in daily operations. The distinction is in their capacity to detach from operational issues, enabling them to provide impartial strategic counsel. Sustainability is a collective obligation, with every employee dedicated to the organization’s ethical governance, social responsibility, and environmental stewardship. A more diversified NED board is essential to provide oversight and direction on the many difficulties confronting the organization. This will enhance investment possibilities and ensure that the ESG agenda is thorough and aligned with its intended goals.

This investigation’s conclusions indicate that European financial institutions must intensify their efforts to achieve systemic and revolutionary change, assuring complete sustainability in all activities. The NED board’s insufficient acknowledgment and involvement in sustainability is puzzling, considering the critical importance of sustainability integration and the advanced skills it necessitates from leadership. We must ensure the accountability of the European financial industry for its environmental and social ramifications, especially among C-Suite executives. Given the potential ramifications, it is essential for all corporate committees to address this issue thoroughly. NED boards may hinder the formation of a more diverse directorate because of their resistance to change and their misunderstanding of sustainability as a conventional NED responsibility.

The present study advances the bottom-up corporate governance paradigm by emphasizing a critical element that enables financial institutions to implement ESG policies. Initiating from the grassroots level is often reactive and centered on reporting, devoid of a lucid comprehension of the intended objectives. Bottom-up ESG is a short-term perspective that is almost certain to fail in generating long-term value. When ESG executives focus only on attaining net zero, quantifying and disclosing carbon metrics, or adhering to the reporting mandates of new laws, it exemplifies a bottom-up approach to ESG. Bottom-up ESG emphasizes a clear, often legislative-driven goal. Consequently, a top-down strategy incorporates (bottom-up) actions into a whole program and viewpoint. This may contribute both conceptually and practically to the examined field. Moreover, the results of the present research might enhance and augment the theoretical framework of sustainable leadership.