ESG Scores and Shareholder Value: Exploring the Dichotomy between EVA and MVA in Emerging Markets Firms

ESG Scores and Shareholder Value: Exploring the Dichotomy between EVA and MVA in Emerging Markets Firms | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article ESG Scores and Shareholder Value: Exploring the Dichotomy between EVA and MVA in Emerging Markets Firms Medha Doshi, Dipasha Sharma, Deepraj Mukherjee, Satish Kumar This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6766431/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 30 Apr, 2026 Read the published version in Discover Sustainability → Version 1 posted 13 You are reading this latest preprint version Abstract This study examines the relationship between Environmental, Social, and Governance (ESG) scores and shareholder value creation measured through Economic Value Added (EVA) and Market Value Added (MVA). Using panel data from 220 Indian non-financial firms listed on the National Stock Exchange (NSE) 500 between 2015 and 2020, we explore whether ESG disclosures enhance or detract from shareholder value. The results reveal a negative relationship between ESG scores and EVA driven by high expenditures on ESG activities without immediate financial gains. Conversely, ESG scores were positively associated with MVA, suggesting long-term market benefits. The findings underscore the dichotomy between the short-term operational impact and the long-term market perception of ESG initiatives. This study has implications for managers and policymakers aiming to balance sustainability goals with shareholder expectations. ESG non-finance sector shareholder value creation value-added measures EVA MVA India Introduction The global shift towards sustainability, ethics, and stakeholder demands is pushing companies to prioritize long-term value creation over short-term profitability (Zumente & Bistrova, 2021 ). Sustainability and financial performance assessments are increasingly being conducted using Environmental, Social, and Governance (ESG) scores, which have evolved from the Corporate Social Responsibility (CSR) framework to become key tools for stakeholder value creation (Aboud & Diab, 2019 ). This shift indicates that positive financial outcomes and sustained gains can be expected, benefiting all stakeholders (Zumente & Bistrova, 2021 ). Researchers have increasingly focused on ESG, which initially stemmed from CSR. ESG parameters assess the impact on corporate financial performance and the value generated by social initiatives led by management. ESG has since become essential to Socially Responsible Investing (SRI), serving as a quantitative tool for evaluating the financial objectives crucial for investment decisions (La Torre et al., 2021 ). The adoption of ESG principles is increasing in developing countries, particularly Asia, where these principles were previously more common in developed nations. The growing interest in ESG research in Asia indicates a global shift towards sustainability. Public companies adopt sustainability measures and disclose ESG information to gain stakeholder support, with stakeholders emphasizing the importance of ESG considerations. In some Southeast Asian countries, regulators have mandated sustainability disclosures to enhance transparency. This regulatory push reflects global commitment to sustainable practices and corporate governance. India's rapid growth is fueled by a favorable business environment, investor confidence, regulatory frameworks, political stability, and government sustainability initiatives. ESG reporting in India started in 2009 with voluntary corporate social responsibility guidelines from the Ministry of Corporate Affairs, leading to Business Responsibility Reports (BRR) by the top 100 companies through market capitalization. In 2012, BRR compliance became mandatory for the top 500 companies and expanded to the top 1000 companies by 2015. From the financial year to 2022–2023, the top 1000 companies must prepare the Business Reporting and Sustainability Report (BRSR), enabling stakeholders to assess firm performance and future positioning. The impact of ESG scores on value creation can be understood positively, by maintaining good stakeholder relationships leading to competitive advantage and long-term positive value creation, or negatively, if high ESG activity expenditures reduce profits and value creation (Preston and O'Bannon, 1997). Market Value Added (MVA) has gained importance over Economic Value Added (EVA) by considering both market and accounting value, with positive MVA indicating value creation and negative MVA indicating value destruction (Carini, 2017). Few studies have investigated the impact of ESG scores on stakeholder value creation using EVA and MVA approaches (Signori et al., 2020; Thomsen, 2020 ), particularly in Asian countries such as India, highlighting the need for similar research in the Asian subcontinent. Companies engage in ESG activities to satisfy shareholder demands and expectations (Beurden & Gossling, 2008 ). Shareholders seek more information about sustainability. Atan et al. ( 2016 ) found no relationship between the ESG scores and EVA. Stakeholders recognize the role of ESG information in enhancing company returns. Communicating ESG data reflects management's efficiency in reaching sustainability targets, thus contributing to company growth and success (Amran & Ooi, 2014 ). Integrating ESG into strategy helps achieve good financial performance and creates value, as indicated by EVA, a crucial metric for stakeholders, particularly investors. EVA generates both long- and short-term shareholder values. MVA is a better indicator than EVA because it considers market capitalization. Lim ( 2022 ) found no association between ESG scores and MVA, whereas Yanagi ( 2018 ) and Naeem et al. ( 2022 ) observed a positive relationship in emerging countries. The ESG scores are generally positively correlated with firm performance and market value. Possebon et al. (2024) reported that higher ESG scores are associated with a reduced cost of capital and enhanced operating performance, potentially contributing to increased EVA (Possebon et al., 2024). Similarly, Tampakoudis and Anagnostopoulou (2020) suggested that acquiring firms with superior ESG performance can augment the acquirer's market value, indicating a potential positive effect on MVA. Notably, some contradictions and nuances have emerged in these findings. Adeneye et al. (2022) demonstrate that while ESG scores are positively associated with book leverage, they are negatively associated with market leverage. This finding suggests that the relationship between ESG performance and firm value may be complex and contingent on the specific financial metrics employed. In conclusion, although the direct impact on EVA and MVA is not explicitly addressed, the overall evidence suggests that higher ESG scores generally exert a positive influence on various aspects of financial performance and market value. However, this relationship may vary across industries, contexts, and ESG components (Ahmad et al. 2023; Tamasiga et al. 2024). Further research focusing specifically on the EVA and MVA in relation to ESG scores is necessary to draw definitive conclusions. Therefore, this study investigates the dual impact of ESG scores on shareholder value in an emerging market context, distinguishing between short-(EVA) and long-term (MVA) effects. Literature Review and Hypotheses Formation Theoratical Framework Stakeholder value creation is widely discussed in corporate finance. Wealth can be created if the top management takes measures concerning the optimum utilization of resources. Some theories that apply to this study are neo-classical theory and stakeholder theory. Neoclassical theory aims to maximize profits to create shareholder value (Panigrahi, 2017 ). Its application in assessing EVA and MVA seems very simple. The problem concerns theory, assuming that investors have adequate investment knowledge. However, the economic benefits derived are not captured by this theory. (Tisdell & Wilson, 2012 ) The stakeholder theory postulates the importance of stakeholders in the creation of values. (Zhang et al., 2020 ). Managers should prioritize all company stakeholders and not select a few. The company needs to meet the demands of stakeholders who insist on disclosing more ESG information and become aware of the regulatory requirements of the company to engage in ESG practices (Arayssi et al.,2020). The goal of stakeholder theory is to meet the increased demands of stakeholders by providing benefits to the company if it is involved in sustainability initiatives, which will result in continuous support from all stakeholders and lead to improved profitability (Behl et al. 2021; Khojastehpour and Johns 2014 ; Kaur & Singh,2021). Barnett and Salomon,2006 postulated that companies that take care of their stakeholders create goodwill and reputation, leading to strong support from them during trying times and helping to maintain their competitive position in the market (Gholami, et al., 2022 ). Better ESG performance improves reputation and leads to better relations between the ESG initiative and the stakeholder's values (Gholami, et al., 2022 ; Godfrey, et al. , 2006, Muller & Kraussl, 2011). Hence, companies are motivated to improve their reputation and fulfill their responsibilities towards stakeholders, which results in value creation for the company. ESG scores demonstrate the effective utilization of a company’s resources. Instrumental stakeholder theory postulates that by satisfying the expectations of stakeholders, the company benefits by receiving strong support from stakeholders. Literature Review Earlier, importance was primarily given to being ethical with all stakeholders, especially at the managerial and organizational levels, rather than focusing on valuation and accounting perspectives (Signori et al., 2021 ). Studies related to stakeholder value creation that support stakeholder theory have recently gained importance among researchers recently (Signori et al., 2021 , Bapuji, et al. ,2018 ). Freeman’s stakeholder theory states that stakeholders are essential for joint value creation. (Signori et al., 2021 ) It is imperative to understand the type of value that is being created in the collaboration. Earlier, corporations intended for shareholder value creation had many limitations towards financial performance through value-creation methods. Thus, there is a need for a stakeholder performance evaluation system (Chakravarthy,1986). By maximizing the interests of other stakeholders, shareholder wealth is also maximized. (Signori et al., 2021 , Jensen, 2002) There are two ways to understand the impact of ESG scores on value creation (Preston and O'Bannon, 1997). One of the results would be positive if the company could maintain a good relationship with the stakeholders, resulting in a competitive advantage gained, thus resulting in positive value creation over a more extended period. A negative result will occur if a lot of expenditure is incurred for ESG activities, reducing profits and, hence, less value creation. Owing to the narrow approach of EVA, MVA has gained importance as it considers both market value and accounting value. This is the difference between a firm's current market value and the capital employed by shareholders, as given in the balance sheet ( Carini , 2017). The value is created for a company with a positive MVA and the value gets destroyed with a negative MVA ( Carini , 2017). Few studies have been undertaken to understand the relationship between ESG scores and EVA and MVA in Asian countries, especially India. Many earlier studies on value maximization used Tobin's Q and ROA analyses to understand the relationship between ESG scores and value creation for stakeholders. Few studies have examined the impact of ESG scores on stakeholders' value creation using EVA and MVA approaches (Signori et al., 2020 , Thomsen, 2020 ). However, the research was primarily conducted in developed countries, so there was a need to undertake similar research on the Asian subcontinent. A company generally engages in ESG activities to meet the demands and expectations of all shareholders (Beurden & Gossling, 2008 ). Shareholders demand more information on sustainability concerning their operations. A study by Atan et al.. al. (2016) revealed no relationship between ESG scores and EVA. The stakeholders understand the importance of ESG information in improving the returns from the company. The communication of ESG information reflects the efficiency of management in attaining sustainability targets, which will contribute towards the growth and success of companies (Amran & Ooi, 2014 ) The inclusion of ESG as part of a strategy would help companies achieve good financial performance and create value, which can be indicated by EVA. EVA is an important indicator for stakeholders, especially investors who have invested money in the company. EVA leads to long-term and short-term values for shareholders, which leads to the following hypothesis: H1: A positive relationship exists between ESG scores and EVA. The shareholders take proactive steps towards tackling the environmental issues of the company and undertake different initiatives to reduce environmental hazards, reduce carbon emissions, manage energy and water usage, have eco-friendly packaging, and increase R&D expenditure to positively affect the company's performance in the subsequent years (Matos, 2020 ; Goncalves et al., 2023). One study indicated that there is a positive relationship between environmental disclosure scores and EVA (Alsayegh et al., 2020 ). Another study conducted in Australia by Sila and Cek in 2017 showed that environmental performance had a statistically significant and positive impact on EVA. Also, a study conducted in 2023, by (Gonçalves et al., 2023 ; and Bueno-García et al., 2022 ) indicated that the environmental score has a positive impact on EVA and is statistically significant as well. Some studies have shown that environmental scores have a positive and statistically significant effect on EVA (Gonçalves et al., 2023 ). However, a study by Yawika and Handayani (2019) in Indonesia showed that investors and firms do not consider environmental performance. Therefore, to reaffirm the results of the earlier study, the following hypothesis was tested: H1a: A positive relationship exists between Environmental scores and EVA. Social scores include training, diversity, inclusion, health and safety of employees, spending money on society, and responsibility toward customers (Matos, 2020 ). In their study of US markets, Cek and Eyupolo (2020) reflected that the company's efforts towards social initiatives translated into providing economic benefits to the company. A study by Sila and Cek (2017), conducted in Australia, showed similar results. Similar results were reflected in the research conducted in Malaysia and Singapore by Tarmuji et al. ( 2016 ). In contrast, the results were reflected in a study by Yawika and Handayani (2019) in Indonesia, where stakeholders did not consider social performance. A study conducted in the Czech Republic and Estonia in 2015 by Strouhal et al. ( 2015 ) assessed the impact of a social score on economic performance, but did not support this relationship using the value addition approach (Gonçalves et al., 2023 ). The inconclusive results from different studies led to the formulation of the following hypotheses: H1 b: A positive relationship exists between Social scores and EVA. The governance pillar of ESG includes board-room factors, such as diversity, ownership, audit committees, higher management remuneration and compensation, fraud, and bribery (Matos, 2020 ). In a study conducted by Ionescu et al. ( 2019 ), the corporate governance pillar of ESG impacted EVA. Besides the study conducted by Tarmuji et al., 2016 in Malaysia and Singapore and by Cek and Eyupoglu, 2020 reflected similar results. However, Yawika and Handayani (2019) reflected otherwise. In addition, the study by Goncalves (2023) reflected that the relationship between governance score and EVA is insignificant. Since the results are inconclusive, the following hypothesis is formulated: H1 c: A positive relationship exists between Governance scores and EVA. MVA is a better indicator than EVA because it considers market capitalization figures. Research conducted by Lim in 2022 indicated that there was no association between ESG scores and MVA. The results of the research by Yanagi, 2018 reflected a positive relationship between ESG and MVA. Similar results were observed in a study conducted by Naeem et al. ( 2022 ) in emerging countries. To confirm these results, the following hypothesis was formulated: H2 A positive relationship exists between ESG scores and MVA. Johnson et al. ( 2019 ) reflected the negative impact of environmental and social scores on MVA. A study conducted by Lim ( 2022 ) indicated a positive relationship between environmental score and MVA. Lee and Kwon ( 2019 ) found a positive association between environmental greening and market value-added. Similar results were observed in another study by Naeem et al. ( 2022 ), which indicated that companies prioritizing environmental concerns as part of their company policies are likely to see a positive impact on MVA. The results of a study by Mayyi (2022) showed that there is no relationship between environmental score and MVA. H2a: A positive relationship exists between Environmental scores and MVA. The governance score of MVA (Johnson et al., 2019 ). In a study by Naeem et al. ( 2022 ), social scores had a positive and significant impact on MVA, which reflects that the companies involved in social initiatives would have higher market value and, hence, would be a lucrative option for future investors for investments. The results of a study by Mayyi (2022) showed that there was no relationship between social scores and MVA. Since the results are inconclusive, the following hypothesis is proposed. H2b: A positive relationship exists between Social scores and MVA. There is a positive and significant relationship between governance scores and MVA (Naeem et al., 2022 , Johnson et al.,2019). The results of a study by Mayyi (2022) showed that there is no relationship between governance score and MVA; hence, the hypothesis is constructed. H2c: A positive relationship exists between Governance scores and MVA. Research Methodology Data Collection and Sample The data were collected from 500 non-finance companies on the National Stock Exchange to test the hypotheses. The ESG scores, individual E, S, and G, and financial data have been gathered from the Bloomberg database, which provides scores that range from 0.1 to 100 for 11,500 companies (Bloomberg Disclosure Score, 2023). This study examines the impact of ESG scores on shareholder value creation using measures such as EVA and MVA from the years 2015–2020. This period is relevant considering that BRR became mandatory for the top 500 companies as per market capitalization in India. The six-year time period was suitable for conducting the longitudinal study. Panel data is used as it captures the variation that could occur due to time and cross-section effects. Banks and financial services have been excluded, as governance norms are robust because of the stringent requirements of the RBI relating to capital adequacy norms. In addition, banks follow different accounting schemes compared with other sectors (Kim, Park, and Lee, 2018). A sample of 220 non-finance firms from a population of 500 was selected to study the relationship between ESG scores and shareholder-value creation. Data and Variables The variables used for the study are listed in Table 1 . Table 1 List of variables taken for the study Variable Names Definition Source Dependent Variables EVA spread Return on Invested Capital (ROIC%) -Weighted Average Cost of Capital (WACC%) Bloomberg Log of MVA Natural logarithm of Market value of Equity-Book value of Equity Bloomberg Independent Variables ESG Composite scores based on E,S,G scores Bloomberg E Environmental performance based on environmental parameters Bloomberg S Social performance based on social parameters Bloomberg G Governance performance based on the governance parameter Bloomberg Control Variables ROA Net Income/Total Assets Bloomberg Size The lognormal of the assets are taken for the study Bloomberg Financial Leverage Total debt to Total assets Bloomberg Capital Expenditure Total capital expenditure by Total assets Bloomberg R&D Intensity R&D Expenditure to Total Sales CMIE and Bloomberg Price to Book Ratio Market price of the company to Book Value of the company Bloomberg The following models were considered based on multivariate regression to understand the relationship between ESG and shareholder-value creation: EVA it = α + β 1 ESG it + β 2 ROA it + β 3 LNASST it* β 4 FnLg it + β 5 CAPITAL_EXPENDITURE it + β 6 R_D__INTENSITY it + β 7 PRICETO BOOK it + ε it MVA it = α + β 1 ESG it * β 2 ROA it + β 3 LNASST it* β 4 FnLg it + β 5 CAPITAL_EXPENDITURE it * β 6 *R_D__INTENSITY it + β 7 PRICETO BOOK it + ε it where i denotes each firm and t is the corresponding year. Shareholder value creation is captured primarily by EVA Spread and MVA. The EVA spread is used in this research, as it is widely used in many studies (Gonçalves et al., 2021; La Torre, M et al., 2021 ; La Torre et al., 2023 ; Strekalina et al.,2023). The ESG component is replaced by its individual pillars–Environmental, Social, and Governance–to understand their relationship with shareholder value creation. Table 1 shows the definitions of the variables used in the study. The control variables were taken from the existing literature. The following control variables have been used in the study: Return on Assets: A well-efficient company managing its assets effectively will result in higher profits, thereby increasing EVA, resulting in a positive correlation with EVA (Kangarloei, et al, 2012). Size: The logarithmic normal of the company's assets was taken based on previous studies (Gonçalves, et al., 2023 ; Chowdhury et al., 2019 ; Johnson et al., 2019 ; Lee & Kown, 2019; Amahalu, 2018 ). Companies with larger assets would reap the benefits of economies of scope and better relationships with stakeholders (Taliento et al. 2019 , Gonçalves, et al., 2023 ). Financial Leverage: The ratio of total debt to total assets is used for financial leverage in this study. Highly leveraged firms are susceptible to higher costs of distress and affect shareholders’ value creation process (Gonçalves, et al., 2023 ; Johnson et al., 2019 ; Chowdhury et al., 2019 ). R&D Intensity: The level of expenditure incurred by the company in R&D to total sales. It affects the growth of a company in the medium and long term (Gonçalves, et al., 2023 ; Carini, et al. 2017 ) Capital Expenditure: The Capital expenditure is total capital expenditure by total assets. It is used as a proxy for investments, which leads to better performance and, ultimately, shareholder value creation (Gonçalves, et al., 2023 ; Chowdhury et al., 2019 ). Price to Book Ratio: The price-to-book ratio creates a good reputation among shareholders, who would be ready for lower returns, resulting in higher EVA. Results and Discussion Table 2 Descriptive statistics: Variables Mean Std. Dev. Min Max ESG 27.46 12.91 0.83 70.21 EScr 16.70 14.56 0.42 67.44 SScr 31.30 13.73 3.51 89.06 GScr 51.14 10.92 3.57 99.31 DTA 20.08 18.29 -0.04 124.64 ROA 8.99 8.41 -49.10 76.77 LnAsst 11.20 1.70 1.45 16.27 FnLg 3.05 10.21 0.37 185.61 EVASpread 1.01 11.33 -31.29 119.49 PricetoBook 5.36 7.04 0.10 87.81 CAPEX 0.58 2.35 0.00 37.44 RDIntensity 0.01 0.02 0.00 0.22 LnMVA 11.33 1.51 5.95 15.70 From Table 2 , it can be seen that among ESG, EScore, SScore, and GScore, GScore has the maximum mean of 51.138, while EScore has the lowest mean of 16.69. Diagnostic Tests: There are several statistical limitations to panel data, which can lead to misleading outcomes. There are many companies compared with the number of years; therefore, diagnostic tests are conducted for the same number of years. There are multiple tests to check for the same, which are described below. Multicollinearity: Regression models show the relationship between explanatory and dependent variables. However, the results could be distorted if there is a correlation between the explanatory variables. Correlation testing was performed using Pearson's Correlation Matrix (PCM). PCM is superior to the Variance Inflation Factor (VIF) as it drops variables or leads to the collection of more data until the issue of multicollinearity is sorted, which makes the model impractical to perform (Mukeredzi, 2019 ). Table 3 Correlation matrix: ESG EScr SScr GScr FnLg EVA Spread Price to Book RD Intensity LnMVA CAPEX ROA LnAsst ESG 1.00 EScr 0.92 1.00 SScr 0.69 0.65 1.00 GScr 0.72 0.58 0.26 1.00 FnLg -0.04 -0.03 -0.05 -0.02 1.00 EVASpread -0.03 -0.04 -0.05 -0.02 -0.06 1.00 PricetoBook -0.06 -0.08 -0.09 -0.02 -0.03 0.58 1.00 RDIntensity -0.04 -0.02 -0.08 -0.03 -0.03 -0.03 -0.05 1.00 LnMVA 0.42 0.37 0.28 0.31 -0.04 0.32 0.37 0.03 1.00 CAPEX 0.13 0.13 0.13 0.07 0.01 -0.06 -0.08 -0.05 0.01 1.00 ROA -0.04 -0.03 -0.05 -0.02 -0.19 0.70 0.36 0.01 0.32 0.04 1.00 LnAsst 0.47 0.44 0.34 0.35 0.09 -0.09 -0.14 0.00 0.42 -0.02 -0.27 1.00 Note: EScr, Environmental Score; SScr-Social Score; GScr-Governance Score; FnLg, Financial Leverage; RDIntensity, R&D Intensity; CAPEX, Capital Expenditure; ROA-Return on Assets; LnMVA, Lognormal of MVA Table 3 lists the correlation matrices. Coefficients of correlation of more than 0.8 indicates multicollinearity (Gujarati et al., 2012). In the table, the correlation values were less than 0.8, ruling out multi-collinearity issues. The initial results reflect a negative relationship between the EVA Spread and the independent variables ESG, E, S, and G, and control variables, namely Financial Leverage, R&D Intensity, Capital Expenditure, and Lognormal of Assets representing the size. The exceptions are ROA and price-to-book value. MVA has a positive relationship with the independent variables and control variables, except for Financial Leverage. Furthermore, to test for multicollinearity, the tolerance and variance inflation test (VIF) was conducted. The values lie between 0.2 and 10; hence, multicollinearity is ruled out. Empirical Results: On running the ordinary least squares regression, it was found that ESG scores and EVA have a negative and significant relationship, as seen in Table 4 , as seen in the earlier study conducted by Mittal et al. ( 2008 ). These results are similar to those of a study conducted by Torre et al. (2020) on banks. There is also a negative relationship between Financial Leverage, Capital expenditures, and R&D Intensity. In addition, the p-value for R&D Intensity is greater than 0.05, indicating that there is no insignificant relationship with EVA. The adjusted R-squared value was 66.08. There is a negative relationship between the individual pillars E, S, and G and their relationship with EVA. The negative relationship between E, S, and G, and EVA is not in line with previous studies that showed a positive relationship between them. It is also observed that social scores have an insignificant impact on EVA, thus rejecting H1 b . Additionally, R&D Intensity has a statistically insignificant relationship with EVA. Table 4 Relationship between ESG scores and EVA EVASpread Coefficient P Value Coefficient P Value Coefficient P Value Coefficient P Value ESG -0.04 0.01 E Score -0.03 0.05 S Score -0.01 0.40 G Score -0.05 0.01 ROA 0.85 0.00 0.85 0.00 0.84 0.00 0.85 0.00 LnAsst 0.86 0.00 0.82 0.00 0.74 0.00 0.82 0.00 FnLg -0.58 0.03 -0.55 0.04 -0.55 0.04 -0.58 0.03 c.LnAsst#c.FnLg 0.05 0.03 0.05 0.04 0.05 0.04 0.05 0.03 CAPEX -0.23 0.00 -0.24 0.00 -0.26 0.00 -0.25 0.00 RDIntensity -10.76 0.24 -10.07 0.27 -10.38 0.26 -10.35 0.26 PricetoBookRatio 0.61 0.00 0.60 0.00 0.61 0.00 0.61 0.00 R-square 66.29 66.22 66.14 66.31 Adjusted R-square 66.08 66.01 65.93 66.10 The positive and significant relationship between ESG scores and MVA, as seen in Table 5 , is in line with a previous study conducted by Naeem et al.. al, 2022. The adjusted R value is66.10. Considering the individual pillars E, S, and G, the results in Table 5 indicate that there is a positive and significant impact of all three pillars on MVA. In observing the control variables, financial leverage has a negative relationship with MVA. Capital expenditure has a negative impact on MVA when the Environmental Score is considered an independent variable. Both leverage and capital expenditure have an insignificant relationship with MVA at the 5% significance level. Table 5 Relationship between ESG scores and MVA MVA Coefficient P value Coefficient P Value Coefficient P Value Coefficient P Value ESG 0.03 0.00 E Score 0.02 0.00 S Score 0.02 0.00 G Score 0.02 0.00 ROA 0.06 0.00 0.06 0.00 0.06 0.00 0.06 0.00 LnAsst 0.36 0.00 0.37 0.00 0.41 0.00 0.42 0.00 FnLg -0.10 0.19 -0.15 0.05 -0.12 0.11 -0.12 0.10 c.LnAsst#c.FnLg 0.01 0.25 0.01 0.08 0.01 0.17 0.01 0.16 CAPEX -0.01 0.71 0.00 0.83 0.00 0.92 0.00 0.87 RDIntensity 4.10 0.01 3.60 0.02 4.33 0.01 3.42 0.03 PricetoBookRatio 0.07 0.00 0.07 0.00 0.07 0.00 0.07 0.00 R-square 66.31 52.25 50.87 49.64 Adjusted R-square 66.10 51.90 50.51 49.27 Some variables change over time; therefore, the panel data regression remains the same. Differences in cross-section and time can be determined either with a random variable or constant, which leads to the evolution of the fixed effects model and random effects model. The fixed effects model helps to understand the effects of variables that change over time. However, it does not allow the estimation of unchanging variables, which are estimated using a random-effect regression model. To decide whether a fixed-effect or random-effect model should be used, a Hausman test was used. The Hausman test was used to determine the usage of fixed and random effects. To determine the generalized least squares in static models with pooled cross-sectional time-series data (Chouaibi et al., 2020). A large and significant Hausman result implies that the fixed-effects model should be applied (Chelawat & Trivedi, 2016 ). On running the test, the results showed that the p-value was significant at the 5% level. Therefore, the fixed effects regression model for both EVA Spread and MVA with ESG and its dimensions, E, S, and G, is appropriate for all models. In cross-sectional data, there is an issue of heteroscedasticity, which means that there is an inconsistent standard deviation and that the variances in the error are not constant. The Breusch-Pagan (BP) test was used to test for heteroscedasticity. The results of the BP test with EVA spread and MVA as dependent variables have a significantly high p-value, indicating that the null hypothesis of homoscedasticity is rejected, which shows that there is no constancy in the error terms. Concerning the usage of control variables in the study, the results reflected that all the control variables except R&D intensity were significant in explaining EVA and MVA. Reverse Causality Test The Dumiterscu-Hurlin Panel Granger Causality test was used to understand the causality between the reference series. The results indicate that there is no causal relationship between the lag of the variables and the other variables. Robustness: To control for the endogeneity of the individual explanatory variables and heteroskedasticity due to certain specific features of the firms over time, 2SLS and the generalized method of moments are widely used. The 2SLS is the most widely used test in the regression of instrumental variables, but it is not an ultimate test when there is heteroscedasticity or autocorrelation in the random disturbance term in different equations (Zheng et al., 2022 ). The loopholes of 2SLS are mitigated by the Generalized Method of Moments (GMM), as it has the benefit of using independent variables to create instrumental variables (Zheng et. Al, 2022 ). The study involves the use of panel data; hence, dynamic panel models using a system generalized method of moments (GMM) approach to understand the relationship between ESG and shareholder value creation using the EVA spread approach and MVA would be appropriate. The system GMM was applied in this study to evaluate whether the results were impacted by reverse causality and endogeneity. The system GMM approach propounded by Roodman (2009) uses orthogonal deviation to lessen the adverse consequences of data loss and improve consistency in the estimation of coefficients (Goncalves et al., 2023). The stability of the system GMM depends on the assumption that instrumental variables are used and that there is no autocorrelation between error terms, making it more adaptable for a panel of limited companies as part of research (Chiaramonte et al., 2022 ). For GMM estimates to be valid, there should be no second-order serial autocorrelation in the residuals and in the validity of the instruments analyzed. For this purpose, first-order (AR1) and second-order (AR2) tests were used for serial correlation. Additionally, the study also reports the Sargan and Hansen test for over-identifying restrictions that confirm the validity of the selected instruments. Endogeneity issues and goodness of fit can be tested using 2SLS, system AR tests, and Sargan and Hansen tests. AR (1) and AR (2) are the serial correlation tests on residuals used to measure whether endogeneity exists in the data. Sargan and Hansen's tests are tests of over-identifying restrictions under the null hypothesis to check the validity of the instruments. Stata software was used for GMM estimation. The AR (1), AR (2), and Hansen and Sargan tests were used to check whether the correct instruments were used for the study and to confirm the appropriateness of the dynamic GMM modeling. This study examines the relationship between ESG and its scores with the value creation measures EVA and MVA for National Stock Exchange (NSE) 500 companies from 2015 to 2020. There are few papers in the Indian context trying to explain the relationship between ESG and its components, E, S, and G, with EVA and MVA. The results in this study reflect the negative relationship between EVA and ESG and its components, which is in contrast to the previous research conducted by Atan et. al, 2016 , G Arian, 2022, Gholami et al., 2022 . This negative relationship indicates that the ESG information disclosed by companies does not result in value creation for shareholders. In addition, the variables ROA and price-to-book ratio were used in the study as control variables, which were earlier not part of any study. Furthermore, the results indicate that there is a positive relationship between MVA and ESG and its dimensions E, S, and G. However, there is an insignificant impact of social score on MVA. The results of the study are in line with those of Naeem et al. ( 2022 ) and Barauskaite and Streimikiene ( 2021 ). The results of this study reflect that firms disclosing ESG information result in long-term and better value creation for shareholders. To overcome the limitations of the OLS method, the data were tested using the Hausman test to decide on a fixed-effect regression model or random-effect model. Based on these results, it was concluded that a fixed fixed effects regression model should be used. The Hausman test was used to check for endogeneity issues. So, to tackle it the system GMM model was used. To check for heteroscedasticity, the Breusch–Pagan test was used, and it could be concluded from the results that heteroskedasticity was present. This study addresses the gap in understanding the relationship between ESG with EVA and MVA using the variables ESG, E, S, and G, and its impact on EVA and MVA in India. Implications The study has managerial and regulatory implications that are as follows: Managerial Implications: The improvement in value creation for shareholders creates a win-win situation for shareholders as well as the company. Adopting different ESG initiatives leads to an improved image among investors. Awareness will be created among managers about the importance of ESG and the need for companies to have ESG disclosures; hence, the motivation to not only disclose mandatory information but also voluntary information (Kao,2024). It also provides feedback to managers to develop effective corporate policies related to the disclosure of sustainability information to improve financial performance and corporate value. Besides, there will be reaffirmation that if the company is more transparent in its disclosures and makes the anti-corruption part of the day-to-day operations, it will lead to less information asymmetry and a reduction in the firm's risk. This could also result in investments from both domestic and international players. This study contributes to the fields of corporate finance and sustainable finance. The study on ESG is in its nascent stages; hence, more research is needed in this field to be applicable in reality. It will also help researchers use ESG to understand the value creation process in companies. This study will help future researchers gain a holistic perspective on the relationship between ESG and stakeholder value creation. The results to date have been mixed, and the study sheds some light on additional channels and variables that could be considered for future research. The study would help researchers to be consultants and provide logical solutions to the government, which will eventually be passed on to companies. Regulatory Implications: Regulators should motivate companies to initiate many sustainability initiatives and disclose them same (Joudi, et al., 2023 ). Much awareness needs to be created among non-listed companies to disclose ESG information. The regulator should also appreciate the companies adopting regulations relating to ESG, the initiatives taken, and their responsible behavior (Gholami et al., 2022 ). It will give feedback to the regulator to focus on creating a conducive atmosphere for firms, providing different incentives based on the investments made in different ESG dimensions to help firms create value for their stakeholders. It will provide insight into filling the loopholes in the existing legal framework to minimize evasion. Conclusions and Limitations of the study The results of this study reflect the impact of sustainable initiatives reflected through ESG disclosures on value creation for non-financial firms in the Nifty 500 index. By considering the sustainable initiatives taken by non-finance companies and evaluating the value creation process using EVA and MVA, this study provides thought-provoking insights into the impact of ESG disclosure score and economic profit, reflecting the consideration of EVA and MVA as a measure for evaluating stakeholders’ value creation. The study highlights the fact that investments in sustainability initiatives have an impact on economic performance and, ultimately, on the value creation of stakeholders. The scope could be expanded to include financial service companies for five years. This study can also be undertaken to understand the different ways of evaluating value creation and which methods explain the best relationship between ESG and value creation. A cross-country comparison could also be undertaken to understand the similarities or differences in the results reflected. The different independent and control variables could be included in the studies to improve our findings. The analysis can also be conducted by bifurcating companies based on market capitalization and understanding the results. Declarations Funding Statement: None of the Authors have received any funding to conduct any part of this study. Ethical Compliance: There is no human participation or data related to Human behaviour involved. Ethics, Consent to Participate, and Consent to Publish declarations: Not Applicable. Data Access Statement: Research data supporting this publication has been accessed from the paid licence terminal of Bloomberg database available with the authors’ University. As the data set has been drawn from paid terminal, data can not be shared openly and can be maed accessible to Editors on demand. Conflict of Interest declaration : The authors declare that they have no affiliations with or involvement in any organization or entity with any financial interest in the subject matter or materials discussed in this manuscript. Author Contributions : MD and DS conceived the original, contributed to the design and implementation of the research, to the analysis of the results and to the writing of the manuscript. DM and SK supervised the project and reviewed all drafts. References Aboud A, Diab A. (2019). The financial and market consequences of environmental, social, and governance ratings. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6766431","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":471273825,"identity":"854c21ef-6ea7-4dd4-b9bb-7776dd584604","order_by":0,"name":"Medha Doshi","email":"","orcid":"","institution":"Symbiosis International University","correspondingAuthor":false,"prefix":"","firstName":"Medha","middleName":"","lastName":"Doshi","suffix":""},{"id":471273826,"identity":"a3d4ebab-89ff-4494-81ed-bd4e683bf193","order_by":1,"name":"Dipasha Sharma","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABEUlEQVRIiWNgGAWjYDCCw0DM2MDAYADmVUjIgagDD4jXcsbGGKwlAZ+WA8haGNvSEhtADHxa+I7zHvz4c4edvTn72acbfrAdTp8fdvgh0BY7Od0G7FokD/MlS/OeSU7c2ZNudrOH53DuxttpBkAtycZmB7BrMTjMYyDN2MacYHAgje0GjwRQy+wEkJYDidtwazH++bOt3t7g/DO2m38MDqcbzk7/QEiLmQRv22HGDTfS2G7zJKQlyEvn4LcF6Jc0a96244kbbjxjuy1zwMZwg3ROwYEEA9x+4Tt/9vDNn23VQIelsd18+09CXn52+uYPHyrs5HBpYWDgQXcqWKUBLuXYtMg34FM9CkbBKBgFIxEAAKzVaUsulzs+AAAAAElFTkSuQmCC","orcid":"","institution":"Symbiosis International University","correspondingAuthor":true,"prefix":"","firstName":"Dipasha","middleName":"","lastName":"Sharma","suffix":""},{"id":471273827,"identity":"eb715c87-9c04-4d21-931c-feca5cf2d62b","order_by":2,"name":"Deepraj Mukherjee","email":"","orcid":"","institution":"Kent State University","correspondingAuthor":false,"prefix":"","firstName":"Deepraj","middleName":"","lastName":"Mukherjee","suffix":""},{"id":471273828,"identity":"b4963b9d-d453-4d03-b6bf-57b3d84c062b","order_by":3,"name":"Satish Kumar","email":"","orcid":"","institution":"Indian Institute of Management Nagpur","correspondingAuthor":false,"prefix":"","firstName":"Satish","middleName":"","lastName":"Kumar","suffix":""}],"badges":[],"createdAt":"2025-05-28 09:23:18","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6766431/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6766431/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s43621-026-03095-y","type":"published","date":"2026-04-30T15:58:25+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":108437710,"identity":"eab54874-5d0a-4217-9cae-a0f2837e2c46","added_by":"auto","created_at":"2026-05-04 16:02:46","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":587285,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6766431/v1/a7737b3a-53e5-43de-90fd-6fb5da009622.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"ESG Scores and Shareholder Value: Exploring the Dichotomy between EVA and MVA in Emerging Markets Firms ","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe global shift towards sustainability, ethics, and stakeholder demands is pushing companies to prioritize long-term value creation over short-term profitability (Zumente \u0026amp; Bistrova, \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Sustainability and financial performance assessments are increasingly being conducted using Environmental, Social, and Governance (ESG) scores, which have evolved from the Corporate Social Responsibility (CSR) framework to become key tools for stakeholder value creation (Aboud \u0026amp; Diab, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). This shift indicates that positive financial outcomes and sustained gains can be expected, benefiting all stakeholders (Zumente \u0026amp; Bistrova, \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Researchers have increasingly focused on ESG, which initially stemmed from CSR. ESG parameters assess the impact on corporate financial performance and the value generated by social initiatives led by management. ESG has since become essential to Socially Responsible Investing (SRI), serving as a quantitative tool for evaluating the financial objectives crucial for investment decisions (La Torre et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe adoption of ESG principles is increasing in developing countries, particularly Asia, where these principles were previously more common in developed nations. The growing interest in ESG research in Asia indicates a global shift towards sustainability. Public companies adopt sustainability measures and disclose ESG information to gain stakeholder support, with stakeholders emphasizing the importance of ESG considerations. In some Southeast Asian countries, regulators have mandated sustainability disclosures to enhance transparency. This regulatory push reflects global commitment to sustainable practices and corporate governance.\u003c/p\u003e \u003cp\u003eIndia's rapid growth is fueled by a favorable business environment, investor confidence, regulatory frameworks, political stability, and government sustainability initiatives. ESG reporting in India started in 2009 with voluntary corporate social responsibility guidelines from the Ministry of Corporate Affairs, leading to Business Responsibility Reports (BRR) by the top 100 companies through market capitalization. In 2012, BRR compliance became mandatory for the top 500 companies and expanded to the top 1000 companies by 2015. From the financial year to 2022\u0026ndash;2023, the top 1000 companies must prepare the Business Reporting and Sustainability Report (BRSR), enabling stakeholders to assess firm performance and future positioning.\u003c/p\u003e \u003cp\u003eThe impact of ESG scores on value creation can be understood positively, by maintaining good stakeholder relationships leading to competitive advantage and long-term positive value creation, or negatively, if high ESG activity expenditures reduce profits and value creation (Preston and O'Bannon, 1997). Market Value Added (MVA) has gained importance over Economic Value Added (EVA) by considering both market and accounting value, with positive MVA indicating value creation and negative MVA indicating value destruction (Carini, 2017). Few studies have investigated the impact of ESG scores on stakeholder value creation using EVA and MVA approaches (Signori et al., 2020; Thomsen, \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), particularly in Asian countries such as India, highlighting the need for similar research in the Asian subcontinent.\u003c/p\u003e \u003cp\u003eCompanies engage in ESG activities to satisfy shareholder demands and expectations (Beurden \u0026amp; Gossling, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). Shareholders seek more information about sustainability. Atan et al. (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) found no relationship between the ESG scores and EVA. Stakeholders recognize the role of ESG information in enhancing company returns. Communicating ESG data reflects management's efficiency in reaching sustainability targets, thus contributing to company growth and success (Amran \u0026amp; Ooi, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Integrating ESG into strategy helps achieve good financial performance and creates value, as indicated by EVA, a crucial metric for stakeholders, particularly investors. EVA generates both long- and short-term shareholder values. MVA is a better indicator than EVA because it considers market capitalization. Lim (\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) found no association between ESG scores and MVA, whereas Yanagi (\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) and Naeem et al. (\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) observed a positive relationship in emerging countries.\u003c/p\u003e \u003cp\u003eThe ESG scores are generally positively correlated with firm performance and market value. Possebon et al. (2024) reported that higher ESG scores are associated with a reduced cost of capital and enhanced operating performance, potentially contributing to increased EVA (Possebon et al., 2024). Similarly, Tampakoudis and Anagnostopoulou (2020) suggested that acquiring firms with superior ESG performance can augment the acquirer's market value, indicating a potential positive effect on MVA. Notably, some contradictions and nuances have emerged in these findings. Adeneye et al. (2022) demonstrate that while ESG scores are positively associated with book leverage, they are negatively associated with market leverage. This finding suggests that the relationship between ESG performance and firm value may be complex and contingent on the specific financial metrics employed. In conclusion, although the direct impact on EVA and MVA is not explicitly addressed, the overall evidence suggests that higher ESG scores generally exert a positive influence on various aspects of financial performance and market value. However, this relationship may vary across industries, contexts, and ESG components (Ahmad et al. 2023; Tamasiga et al. 2024). Further research focusing specifically on the EVA and MVA in relation to ESG scores is necessary to draw definitive conclusions.\u003c/p\u003e \u003cp\u003eTherefore, this study investigates the dual impact of ESG scores on shareholder value in an emerging market context, distinguishing between short-(EVA) and long-term (MVA) effects.\u003c/p\u003e"},{"header":"Literature Review and Hypotheses Formation","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eTheoratical Framework\u003c/h2\u003e \u003cp\u003eStakeholder value creation is widely discussed in corporate finance. Wealth can be created if the top management takes measures concerning the optimum utilization of resources. Some theories that apply to this study are neo-classical theory and stakeholder theory.\u003c/p\u003e \u003cp\u003eNeoclassical theory aims to maximize profits to create shareholder value (Panigrahi, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Its application in assessing EVA and MVA seems very simple. The problem concerns theory, assuming that investors have adequate investment knowledge. However, the economic benefits derived are not captured by this theory. (Tisdell \u0026amp; Wilson, \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2012\u003c/span\u003e)\u003c/p\u003e \u003cp\u003eThe stakeholder theory postulates the importance of stakeholders in the creation of values. (Zhang et al., \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Managers should prioritize all company stakeholders and not select a few. The company needs to meet the demands of stakeholders who insist on disclosing more ESG information and become aware of the regulatory requirements of the company to engage in ESG practices (Arayssi et al.,2020). The goal of stakeholder theory is to meet the increased demands of stakeholders by providing benefits to the company if it is involved in sustainability initiatives, which will result in continuous support from all stakeholders and lead to improved profitability (Behl \u003cem\u003eet al.\u003c/em\u003e 2021; Khojastehpour and Johns \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Kaur \u0026amp; Singh,2021). Barnett and Salomon,2006 postulated that companies that take care of their stakeholders create goodwill and reputation, leading to strong support from them during trying times and helping to maintain their competitive position in the market (Gholami, et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Better ESG performance improves reputation and leads to better relations between the ESG initiative and the stakeholder's values (Gholami, et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Godfrey, \u003cem\u003eet al.\u003c/em\u003e, 2006, Muller \u0026amp; Kraussl, 2011). Hence, companies are motivated to improve their reputation and fulfill their responsibilities towards stakeholders, which results in value creation for the company.\u003c/p\u003e \u003cp\u003eESG scores demonstrate the effective utilization of a company\u0026rsquo;s resources. Instrumental stakeholder theory postulates that by satisfying the expectations of stakeholders, the company benefits by receiving strong support from stakeholders.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eLiterature Review\u003c/h3\u003e\n\u003cp\u003eEarlier, importance was primarily given to being ethical with all stakeholders, especially at the managerial and organizational levels, rather than focusing on valuation and accounting perspectives (Signori et al., \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Studies related to stakeholder value creation that support stakeholder theory have recently gained importance among researchers recently (Signori et al., \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2021\u003c/span\u003e, Bapuji, \u003cem\u003eet al.\u003c/em\u003e,2018\u003cem\u003e).\u003c/em\u003e Freeman\u0026rsquo;s stakeholder theory states that stakeholders are essential for joint value creation. (Signori et al., \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) It is imperative to understand the type of value that is being created in the collaboration. Earlier, corporations intended for shareholder value creation had many limitations towards financial performance through value-creation methods. Thus, there is a need for a stakeholder performance evaluation system (Chakravarthy,1986). By maximizing the interests of other stakeholders, shareholder wealth is also maximized. (Signori et al., \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2021\u003c/span\u003e, Jensen, 2002)\u003c/p\u003e \u003cp\u003eThere are two ways to understand the impact of ESG scores on value creation (Preston and O'Bannon, 1997). One of the results would be positive if the company could maintain a good relationship with the stakeholders, resulting in a competitive advantage gained, thus resulting in positive value creation over a more extended period. A negative result will occur if a lot of expenditure is incurred for ESG activities, reducing profits and, hence, less value creation.\u003c/p\u003e \u003cp\u003eOwing to the narrow approach of EVA, MVA has gained importance as it considers both market value and accounting value. This is the difference between a firm's current market value and the capital employed by shareholders, as given in the balance sheet (\u003cem\u003eCarini\u003c/em\u003e, 2017). The value is created for a company with a positive MVA and the value gets destroyed with a negative MVA (\u003cem\u003eCarini\u003c/em\u003e, 2017).\u003c/p\u003e \u003cp\u003eFew studies have been undertaken to understand the relationship between ESG scores and EVA and MVA in Asian countries, especially India. Many earlier studies on value maximization used Tobin's Q and ROA analyses to understand the relationship between ESG scores and value creation for stakeholders. Few studies have examined the impact of ESG scores on stakeholders' value creation using EVA and MVA approaches (Signori \u003cem\u003eet al., 2020\u003c/em\u003e, Thomsen, \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). However, the research was primarily conducted in developed countries, so there was a need to undertake similar research on the Asian subcontinent.\u003c/p\u003e \u003cp\u003eA company generally engages in ESG activities to meet the demands and expectations of all shareholders (Beurden \u0026amp; Gossling, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). Shareholders demand more information on sustainability concerning their operations.\u003c/p\u003e \u003cp\u003eA study by Atan et al.. al. (2016) revealed no relationship between ESG scores and EVA. The stakeholders understand the importance of ESG information in improving the returns from the company. The communication of ESG information reflects the efficiency of management in attaining sustainability targets, which will contribute towards the growth and success of companies (Amran \u0026amp; Ooi, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2014\u003c/span\u003e)\u003c/p\u003e \u003cp\u003eThe inclusion of ESG as part of a strategy would help companies achieve good financial performance and create value, which can be indicated by EVA. EVA is an important indicator for stakeholders, especially investors who have invested money in the company. EVA leads to long-term and short-term values for shareholders, which leads to the following hypothesis:\u003c/p\u003e \u003cp\u003e \u003cem\u003eH1: A positive relationship exists between ESG scores and EVA.\u003c/em\u003e \u003c/p\u003e \u003cp\u003eThe shareholders take proactive steps towards tackling the environmental issues of the company and undertake different initiatives to reduce environmental hazards, reduce carbon emissions, manage energy and water usage, have eco-friendly packaging, and increase R\u0026amp;D expenditure to positively affect the company's performance in the subsequent years (Matos, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Goncalves et al., 2023). One study indicated that there is a positive relationship between environmental disclosure scores and EVA (Alsayegh et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Another study conducted in Australia by Sila and Cek in 2017 showed that environmental performance had a statistically significant and positive impact on EVA. Also, a study conducted in 2023, by (Gon\u0026ccedil;alves et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; and Bueno-Garc\u0026iacute;a et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) indicated that the environmental score has a positive impact on EVA and is statistically significant as well.\u003c/p\u003e \u003cp\u003eSome studies have shown that environmental scores have a positive and statistically significant effect on EVA (Gon\u0026ccedil;alves et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). However, a study by Yawika and Handayani (2019) in Indonesia showed that investors and firms do not consider environmental performance. Therefore, to reaffirm the results of the earlier study, the following hypothesis was tested:\u003c/p\u003e \u003cp\u003e \u003cem\u003eH1a: A positive relationship exists between Environmental scores and EVA.\u003c/em\u003e \u003c/p\u003e \u003cp\u003eSocial scores include training, diversity, inclusion, health and safety of employees, spending money on society, and responsibility toward customers (Matos, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). In their study of US markets, Cek and Eyupolo (2020) reflected that the company's efforts towards social initiatives translated into providing economic benefits to the company. A study by Sila and Cek (2017), conducted in Australia, showed similar results. Similar results were reflected in the research conducted in Malaysia and Singapore by Tarmuji et al. (\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). In contrast, the results were reflected in a study by Yawika and Handayani (2019) in Indonesia, where stakeholders did not consider social performance. A study conducted in the Czech Republic and Estonia in 2015 by Strouhal et al. (\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) assessed the impact of a social score on economic performance, but did not support this relationship using the value addition approach (Gon\u0026ccedil;alves et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The inconclusive results from different studies led to the formulation of the following hypotheses:\u003c/p\u003e \u003cp\u003e \u003cem\u003eH1 b: A positive relationship exists between Social scores and EVA.\u003c/em\u003e \u003c/p\u003e \u003cp\u003eThe governance pillar of ESG includes board-room factors, such as diversity, ownership, audit committees, higher management remuneration and compensation, fraud, and bribery (Matos, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). In a study conducted by Ionescu et al. (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), the corporate governance pillar of ESG impacted EVA. Besides the study conducted by Tarmuji et al., \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2016\u003c/span\u003e in Malaysia and Singapore and by Cek and Eyupoglu, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2020\u003c/span\u003e reflected similar results. However, Yawika and Handayani (2019) reflected otherwise. In addition, the study by Goncalves (2023) reflected that the relationship between governance score and EVA is insignificant. Since the results are inconclusive, the following hypothesis is formulated:\u003c/p\u003e \u003cp\u003e \u003cem\u003eH1 c: A positive relationship exists between Governance scores and EVA.\u003c/em\u003e \u003c/p\u003e \u003cp\u003eMVA is a better indicator than EVA because it considers market capitalization figures. Research conducted by Lim in 2022 indicated that there was no association between ESG scores and MVA. The results of the research by Yanagi, \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2018\u003c/span\u003e reflected a positive relationship between ESG and MVA. Similar results were observed in a study conducted by Naeem et al. (\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) in emerging countries. To confirm these results, the following hypothesis was formulated:\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eH2\u003c/strong\u003e \u003cp\u003eA positive \u003cem\u003erelationship exists between ESG scores and MVA.\u003c/em\u003e\u003c/p\u003e \u003c/p\u003e \u003cp\u003eJohnson et al. (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) reflected the negative impact of environmental and social scores on MVA. A study conducted by Lim (\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) indicated a positive relationship between environmental score and MVA. Lee and Kwon (\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) found a positive association between environmental greening and market value-added. Similar results were observed in another study by Naeem et al. (\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), which indicated that companies prioritizing environmental concerns as part of their company policies are likely to see a positive impact on MVA. The results of a study by Mayyi (2022) showed that there is no relationship between environmental score and MVA.\u003c/p\u003e \u003cp\u003e \u003cem\u003eH2a: A positive relationship exists between Environmental scores and MVA.\u003c/em\u003e \u003c/p\u003e \u003cp\u003eThe governance score of MVA (Johnson et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). In a study by Naeem et al. (\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), social scores had a positive and significant impact on MVA, which reflects that the companies involved in social initiatives would have higher market value and, hence, would be a lucrative option for future investors for investments. The results of a study by Mayyi (2022) showed that there was no relationship between social scores and MVA. Since the results are inconclusive, the following hypothesis is proposed.\u003c/p\u003e \u003cp\u003e \u003cem\u003eH2b: A positive relationship exists between Social scores and MVA.\u003c/em\u003e \u003c/p\u003e \u003cp\u003eThere is a positive and significant relationship between governance scores and MVA (Naeem et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2022\u003c/span\u003e, Johnson et al.,2019). The results of a study by Mayyi (2022) showed that there is no relationship between governance score and MVA; hence, the hypothesis is constructed.\u003c/p\u003e \u003cp\u003e \u003cem\u003eH2c: A positive relationship exists between Governance scores and MVA.\u003c/em\u003e \u003c/p\u003e"},{"header":"Research Methodology","content":"\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eData Collection and Sample\u003c/h2\u003e \u003cp\u003eThe data were collected from 500 non-finance companies on the National Stock Exchange to test the hypotheses. The ESG scores, individual E, S, and G, and financial data have been gathered from the Bloomberg database, which provides scores that range from 0.1 to 100 for 11,500 companies (Bloomberg Disclosure Score, 2023).\u003c/p\u003e \u003cp\u003eThis study examines the impact of ESG scores on shareholder value creation using measures such as EVA and MVA from the years 2015\u0026ndash;2020. This period is relevant considering that BRR became mandatory for the top 500 companies as per market capitalization in India. The six-year time period was suitable for conducting the longitudinal study. Panel data is used as it captures the variation that could occur due to time and cross-section effects. Banks and financial services have been excluded, as governance norms are robust because of the stringent requirements of the RBI relating to capital adequacy norms. In addition, banks follow different accounting schemes compared with other sectors (Kim, Park, and Lee, 2018). A sample of 220 non-finance firms from a population of 500 was selected to study the relationship between ESG scores and shareholder-value creation.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eData and Variables\u003c/h3\u003e\n\u003cp\u003eThe variables used for the study are listed in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eList of variables taken for the study\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable Names\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDefinition\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSource\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDependent Variables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEVA spread\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReturn on Invested Capital (ROIC%) -Weighted Average Cost of Capital (WACC%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBloomberg\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLog of MVA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNatural logarithm of Market value of Equity-Book value of Equity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBloomberg\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eIndependent Variables\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eESG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eComposite scores based on E,S,G scores\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBloomberg\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEnvironmental performance based on environmental parameters\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBloomberg\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSocial performance based on social parameters\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBloomberg\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGovernance performance based on the governance parameter\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBloomberg\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eControl Variables\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eROA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNet Income/Total Assets\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBloomberg\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSize\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eThe lognormal of the assets are taken for the study\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBloomberg\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFinancial Leverage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal debt to Total assets\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBloomberg\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCapital Expenditure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal capital expenditure by Total assets\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBloomberg\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR\u0026amp;D Intensity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eR\u0026amp;D Expenditure to Total Sales\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCMIE and Bloomberg\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrice to Book Ratio\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMarket price of the company to Book Value of the company\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBloomberg\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe following models were considered based on multivariate regression to understand the relationship between ESG and shareholder-value creation:\u003c/p\u003e \u003cp\u003eEVA\u003csub\u003eit\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;α\u0026thinsp;+\u0026thinsp;β\u003csub\u003e1\u003c/sub\u003eESG\u003csub\u003eit +\u003c/sub\u003e β\u003csub\u003e2\u003c/sub\u003eROA\u003csub\u003eit\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;β\u003csub\u003e3\u003c/sub\u003eLNASST\u003csub\u003eit*\u003c/sub\u003e β\u003csub\u003e4\u003c/sub\u003eFnLg\u003csub\u003eit\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;β\u003csub\u003e5\u003c/sub\u003e CAPITAL_EXPENDITURE\u003csub\u003eit\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;β\u003csub\u003e6\u003c/sub\u003eR_D__INTENSITY\u003csub\u003eit\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;β\u003csub\u003e7\u003c/sub\u003e PRICETO BOOK\u003csub\u003eit\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;ε\u003csub\u003eit\u003c/sub\u003e\u003c/p\u003e \u003cp\u003eMVA\u003csub\u003eit\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;α\u0026thinsp;+\u0026thinsp;β\u003csub\u003e1\u003c/sub\u003eESG\u003csub\u003eit *\u003c/sub\u003e β\u003csub\u003e2\u003c/sub\u003eROA\u003csub\u003eit\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;β\u003csub\u003e3\u003c/sub\u003eLNASST\u003csub\u003eit*\u003c/sub\u003e β\u003csub\u003e4\u003c/sub\u003eFnLg\u003csub\u003eit\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;β\u003csub\u003e5\u003c/sub\u003e CAPITAL_EXPENDITURE\u003csub\u003eit\u003c/sub\u003e * β\u003csub\u003e6\u003c/sub\u003e*R_D__INTENSITY\u003csub\u003eit\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;β\u003csub\u003e7\u003c/sub\u003e PRICETO BOOK\u003csub\u003eit\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;ε\u003csub\u003eit\u003c/sub\u003e\u003c/p\u003e \u003cp\u003ewhere i denotes each firm and t is the corresponding year. Shareholder value creation is captured primarily by EVA Spread and MVA.\u003c/p\u003e \u003cp\u003eThe EVA spread is used in this research, as it is widely used in many studies (Gon\u0026ccedil;alves et al., 2021; La Torre, M et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; La Torre et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Strekalina et al.,2023). The ESG component is replaced by its individual pillars\u0026ndash;Environmental, Social, and Governance\u0026ndash;to understand their relationship with shareholder value creation. Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows the definitions of the variables used in the study. The control variables were taken from the existing literature.\u003c/p\u003e \u003cp\u003eThe following control variables have been used in the study:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eReturn on Assets: A well-efficient company managing its assets effectively will result in higher profits, thereby increasing EVA, resulting in a positive correlation with EVA (Kangarloei, et al, 2012).\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eSize: The logarithmic normal of the company's assets was taken based on previous studies (Gon\u0026ccedil;alves, et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Chowdhury et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Johnson et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Lee \u0026amp; Kown, 2019; Amahalu, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Companies with larger assets would reap the benefits of economies of scope and better relationships with stakeholders (Taliento et al. \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2019\u003c/span\u003e, Gon\u0026ccedil;alves, et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eFinancial Leverage: The ratio of total debt to total assets is used for financial leverage in this study. Highly leveraged firms are susceptible to higher costs of distress and affect shareholders\u0026rsquo; value creation process (Gon\u0026ccedil;alves, et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Johnson et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Chowdhury et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2019\u003c/span\u003e ).\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eR\u0026amp;D Intensity: The level of expenditure incurred by the company in R\u0026amp;D to total sales. It affects the growth of a company in the medium and long term (Gon\u0026ccedil;alves, et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Carini, et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2017\u003c/span\u003e)\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eCapital Expenditure: The Capital expenditure is total capital expenditure by total assets. It is used as a proxy for investments, which leads to better performance and, ultimately, shareholder value creation (Gon\u0026ccedil;alves, et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Chowdhury et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003ePrice to Book Ratio: The price-to-book ratio creates a good reputation among shareholders, who would be ready for lower returns, resulting in higher EVA.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e"},{"header":"Results and Discussion","content":"\u003cp\u003e \u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDescriptive statistics:\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eStd. Dev.\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMin\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMax\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eESG\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e27.46\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12.91\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.83\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e70.21\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEScr\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e16.70\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e14.56\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.42\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e67.44\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSScr\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e31.30\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e13.73\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.51\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e89.06\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGScr\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e51.14\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10.92\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.57\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e99.31\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDTA\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e20.08\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e18.29\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.04\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e124.64\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eROA\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8.99\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8.41\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-49.10\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e76.77\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLnAsst\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e11.20\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.70\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.45\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e16.27\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFnLg\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.05\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10.21\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.37\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e185.61\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEVASpread\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.01\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11.33\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-31.29\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e119.49\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePricetoBook\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5.36\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7.04\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.10\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e87.81\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCAPEX\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.58\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.35\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e37.44\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRDIntensity\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.22\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLnMVA\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e11.33\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.51\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.95\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e15.70\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e \u003cp\u003e\u003c/p\u003e \u003cp\u003eFrom Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, it can be seen that among ESG, EScore, SScore, and GScore, GScore has the maximum mean of 51.138, while EScore has the lowest mean of 16.69.\u003c/p\u003e\n\u003ch3\u003eDiagnostic Tests:\u003c/h3\u003e\n\u003cp\u003eThere are several statistical limitations to panel data, which can lead to misleading outcomes. There are many companies compared with the number of years; therefore, diagnostic tests are conducted for the same number of years. There are multiple tests to check for the same, which are described below.\u003c/p\u003e\n\u003ch3\u003eMulticollinearity:\u003c/h3\u003e\n\u003cp\u003eRegression models show the relationship between explanatory and dependent variables. However, the results could be distorted if there is a correlation between the explanatory variables. Correlation testing was performed using Pearson's Correlation Matrix (PCM). PCM is superior to the Variance Inflation Factor (VIF) as it drops variables or leads to the collection of more data until the issue of multicollinearity is sorted, which makes the model impractical to perform (Mukeredzi, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCorrelation matrix:\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e\u003ccolgroup cols=\"13\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eESG\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEScr\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSScr\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eGScr\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eFnLg\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eEVA\u003c/p\u003e \u003cp\u003eSpread\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003ePrice\u003c/p\u003e \u003cp\u003eto\u003c/p\u003e \u003cp\u003eBook\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eRD\u003c/p\u003e \u003cp\u003eIntensity\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eLnMVA\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003eCAPEX\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c12\"\u003e \u003cp\u003eROA\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c13\"\u003e \u003cp\u003eLnAsst\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eESG\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEScr\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.92\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSScr\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.69\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.65\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGScr\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.72\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.58\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.26\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFnLg\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.04\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.03\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.05\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.02\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEVASpread\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.03\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.04\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.05\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.02\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.06\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePricetoBook\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.06\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.08\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.09\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.02\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.03\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.58\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRDIntensity\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.04\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.02\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.08\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.03\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.03\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.03\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.05\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLnMVA\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.42\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.37\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.28\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.31\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.04\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.32\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.37\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCAPEX\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.13\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.13\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.13\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.06\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.08\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e-0.05\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eROA\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.04\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.03\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.05\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.02\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.19\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.70\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.36\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.32\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLnAsst\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.47\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.44\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.34\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.35\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.09\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.14\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.42\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e-0.02\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e-0.27\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"13\"\u003eNote: EScr, Environmental Score; SScr-Social Score; GScr-Governance Score; FnLg, Financial Leverage; RDIntensity, R\u0026amp;D Intensity; CAPEX, Capital Expenditure; ROA-Return on Assets; LnMVA, Lognormal of MVA\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e \u003cp\u003e\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e lists the correlation matrices. Coefficients of correlation of more than 0.8 indicates multicollinearity (Gujarati et al., 2012). In the table, the correlation values were less than 0.8, ruling out multi-collinearity issues. The initial results reflect a negative relationship between the EVA Spread and the independent variables ESG, E, S, and G, and control variables, namely Financial Leverage, R\u0026amp;D Intensity, Capital Expenditure, and Lognormal of Assets representing the size. The exceptions are ROA and price-to-book value. MVA has a positive relationship with the independent variables and control variables, except for Financial Leverage.\u003c/p\u003e \u003cp\u003eFurthermore, to test for multicollinearity, the tolerance and variance inflation test (VIF) was conducted. The values lie between 0.2 and 10; hence, multicollinearity is ruled out.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eEmpirical Results:\u003c/h2\u003e \u003cp\u003eOn running the ordinary least squares regression, it was found that ESG scores and EVA have a negative and significant relationship, as seen in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, as seen in the earlier study conducted by Mittal et al. (\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). These results are similar to those of a study conducted by Torre et al. (2020) on banks. There is also a negative relationship between Financial Leverage, Capital expenditures, and R\u0026amp;D Intensity. In addition, the p-value for R\u0026amp;D Intensity is greater than 0.05, indicating that there is no insignificant relationship with EVA. The adjusted R-squared value was 66.08.\u003c/p\u003e \u003cp\u003eThere is a negative relationship between the individual pillars E, S, and G and their relationship with EVA. The negative relationship between E, S, and G, and EVA is not in line with previous studies that showed a positive relationship between them. It is also observed that social scores have an insignificant impact on EVA, thus rejecting \u003cem\u003eH1\u003c/em\u003e\u003csub\u003e\u003cem\u003eb\u003c/em\u003e\u003c/sub\u003e. Additionally, R\u0026amp;D Intensity has a statistically insignificant relationship with EVA.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eRelationship between ESG scores and EVA\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e\u003ccolgroup cols=\"9\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEVASpread\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCoefficient\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP Value\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCoefficient\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP Value\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCoefficient\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eP Value\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eCoefficient\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eP Value\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eESG\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.04\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eE Score\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.03\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eS Score\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.01\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.40\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG Score\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.05\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eROA\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.85\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.85\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.84\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.85\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLnAsst\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.86\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.82\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.74\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.82\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFnLg\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.58\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.55\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.55\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.58\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ec.LnAsst#c.FnLg\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCAPEX\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.23\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.24\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.26\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.25\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRDIntensity\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-10.76\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.24\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-10.07\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.27\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-10.38\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.26\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-10.35\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.26\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePricetoBookRatio\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.61\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.60\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.61\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.61\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR-square\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e66.29\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e66.22\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e66.14\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e66.31\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdjusted R-square\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e66.08\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e66.01\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e65.93\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e66.10\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e \u003cp\u003e\u003c/p\u003e \u003cp\u003eThe positive and significant relationship between ESG scores and MVA, as seen in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, is in line with a previous study conducted by Naeem et al.. al, 2022. The adjusted R value is66.10.\u003c/p\u003e \u003cp\u003eConsidering the individual pillars E, S, and G, the results in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e indicate that there is a positive and significant impact of all three pillars on MVA. In observing the control variables, financial leverage has a negative relationship with MVA. Capital expenditure has a negative impact on MVA when the Environmental Score is considered an independent variable. Both leverage and capital expenditure have an insignificant relationship with MVA at the 5% significance level.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eRelationship between ESG scores and MVA\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e\u003ccolgroup cols=\"9\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMVA\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCoefficient\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCoefficient\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP Value\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCoefficient\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eP Value\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eCoefficient\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eP Value\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eESG\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eE Score\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eS Score\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG Score\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eROA\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLnAsst\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.36\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.37\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.41\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.42\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFnLg\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.10\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.19\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.15\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.12\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.12\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.10\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ec.LnAsst#c.FnLg\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.25\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.17\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.16\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCAPEX\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.01\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.71\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.83\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.92\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.87\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRDIntensity\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.10\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.60\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e4.33\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e3.42\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePricetoBookRatio\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR-square\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e66.31\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e52.25\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e50.87\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e49.64\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdjusted R-square\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e66.10\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e51.90\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e50.51\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e49.27\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e \u003cp\u003e\u003c/p\u003e \u003cp\u003eSome variables change over time; therefore, the panel data regression remains the same. Differences in cross-section and time can be determined either with a random variable or constant, which leads to the evolution of the fixed effects model and random effects model. The fixed effects model helps to understand the effects of variables that change over time. However, it does not allow the estimation of unchanging variables, which are estimated using a random-effect regression model. To decide whether a fixed-effect or random-effect model should be used, a Hausman test was used. The Hausman test was used to determine the usage of fixed and random effects. To determine the generalized least squares in static models with pooled cross-sectional time-series data (Chouaibi et al., 2020). A large and significant Hausman result implies that the fixed-effects model should be applied (Chelawat \u0026amp; Trivedi, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). On running the test, the results showed that the p-value was significant at the 5% level. Therefore, the fixed effects regression model for both EVA Spread and MVA with ESG and its dimensions, E, S, and G, is appropriate for all models.\u003c/p\u003e \u003cp\u003eIn cross-sectional data, there is an issue of heteroscedasticity, which means that there is an inconsistent standard deviation and that the variances in the error are not constant. The Breusch-Pagan (BP) test was used to test for heteroscedasticity. The results of the BP test with EVA spread and MVA as dependent variables have a significantly high p-value, indicating that the null hypothesis of homoscedasticity is rejected, which shows that there is no constancy in the error terms.\u003c/p\u003e \u003cp\u003eConcerning the usage of control variables in the study, the results reflected that all the control variables except R\u0026amp;D intensity were significant in explaining EVA and MVA.\u003c/p\u003e \u003cp\u003eReverse Causality Test\u003c/p\u003e \u003cp\u003eThe Dumiterscu-Hurlin Panel Granger Causality test was used to understand the causality between the reference series. The results indicate that there is no causal relationship between the lag of the variables and the other variables.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eRobustness:\u003c/h2\u003e \u003cp\u003eTo control for the endogeneity of the individual explanatory variables and heteroskedasticity due to certain specific features of the firms over time, 2SLS and the generalized method of moments are widely used. The 2SLS is the most widely used test in the regression of instrumental variables, but it is not an ultimate test when there is heteroscedasticity or autocorrelation in the random disturbance term in different equations (Zheng et al., \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The loopholes of 2SLS are mitigated by the Generalized Method of Moments (GMM), as it has the benefit of using independent variables to create instrumental variables (Zheng et. Al, \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The study involves the use of panel data; hence, dynamic panel models using a system generalized method of moments (GMM) approach to understand the relationship between ESG and shareholder value creation using the EVA spread approach and MVA would be appropriate. The system GMM was applied in this study to evaluate whether the results were impacted by reverse causality and endogeneity. The system GMM approach propounded by Roodman (2009) uses orthogonal deviation to lessen the adverse consequences of data loss and improve consistency in the estimation of coefficients (Goncalves et al., 2023). The stability of the system GMM depends on the assumption that instrumental variables are used and that there is no autocorrelation between error terms, making it more adaptable for a panel of limited companies as part of research (Chiaramonte et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). For GMM estimates to be valid, there should be no second-order serial autocorrelation in the residuals and in the validity of the instruments analyzed. For this purpose, first-order (AR1) and second-order (AR2) tests were used for serial correlation. Additionally, the study also reports the Sargan and Hansen test for over-identifying restrictions that confirm the validity of the selected instruments.\u003c/p\u003e \u003cp\u003eEndogeneity issues and goodness of fit can be tested using 2SLS, system AR tests, and Sargan and Hansen tests. AR (1) and AR (2) are the serial correlation tests on residuals used to measure whether endogeneity exists in the data. Sargan and Hansen's tests are tests of over-identifying restrictions under the null hypothesis to check the validity of the instruments. Stata software was used for GMM estimation. The AR (1), AR (2), and Hansen and Sargan tests were used to check whether the correct instruments were used for the study and to confirm the appropriateness of the dynamic GMM modeling.\u003c/p\u003e \u003cp\u003eThis study examines the relationship between ESG and its scores with the value creation measures EVA and MVA for National Stock Exchange (NSE) 500 companies from 2015 to 2020. There are few papers in the Indian context trying to explain the relationship between ESG and its components, E, S, and G, with EVA and MVA. The results in this study reflect the negative relationship between EVA and ESG and its components, which is in contrast to the previous research conducted by Atan et. al, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2016\u003c/span\u003e, G Arian, 2022, Gholami et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2022\u003c/span\u003e. This negative relationship indicates that the ESG information disclosed by companies does not result in value creation for shareholders. In addition, the variables ROA and price-to-book ratio were used in the study as control variables, which were earlier not part of any study.\u003c/p\u003e \u003cp\u003eFurthermore, the results indicate that there is a positive relationship between MVA and ESG and its dimensions E, S, and G. However, there is an insignificant impact of social score on MVA. The results of the study are in line with those of Naeem et al. (\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) and Barauskaite and Streimikiene (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The results of this study reflect that firms disclosing ESG information result in long-term and better value creation for shareholders.\u003c/p\u003e \u003cp\u003eTo overcome the limitations of the OLS method, the data were tested using the Hausman test to decide on a fixed-effect regression model or random-effect model. Based on these results, it was concluded that a fixed fixed effects regression model should be used. The Hausman test was used to check for endogeneity issues. So, to tackle it the system GMM model was used.\u003c/p\u003e \u003cp\u003eTo check for heteroscedasticity, the Breusch–Pagan test was used, and it could be concluded from the results that heteroskedasticity was present.\u003c/p\u003e \u003cp\u003eThis study addresses the gap in understanding the relationship between ESG with EVA and MVA using the variables ESG, E, S, and G, and its impact on EVA and MVA in India.\u003c/p\u003e \u003cp\u003eImplications\u003c/p\u003e \u003cp\u003eThe study has managerial and regulatory implications that are as follows:\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eManagerial Implications:\u003c/h2\u003e \u003cp\u003eThe improvement in value creation for shareholders creates a win-win situation for shareholders as well as the company. Adopting different ESG initiatives leads to an improved image among investors. Awareness will be created among managers about the importance of ESG and the need for companies to have ESG disclosures; hence, the motivation to not only disclose mandatory information but also voluntary information (Kao,2024). It also provides feedback to managers to develop effective corporate policies related to the disclosure of sustainability information to improve financial performance and corporate value. Besides, there will be reaffirmation that if the company is more transparent in its disclosures and makes the anti-corruption part of the day-to-day operations, it will lead to less information asymmetry and a reduction in the firm's risk. This could also result in investments from both domestic and international players.\u003c/p\u003e \u003cp\u003eThis study contributes to the fields of corporate finance and sustainable finance. The study on ESG is in its nascent stages; hence, more research is needed in this field to be applicable in reality. It will also help researchers use ESG to understand the value creation process in companies. This study will help future researchers gain a holistic perspective on the relationship between ESG and stakeholder value creation. The results to date have been mixed, and the study sheds some light on additional channels and variables that could be considered for future research. The study would help researchers to be consultants and provide logical solutions to the government, which will eventually be passed on to companies.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eRegulatory Implications:\u003c/h2\u003e \u003cp\u003eRegulators should motivate companies to initiate many sustainability initiatives and disclose them same (Joudi, et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Much awareness needs to be created among non-listed companies to disclose ESG information. The regulator should also appreciate the companies adopting regulations relating to ESG, the initiatives taken, and their responsible behavior (Gholami et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). It will give feedback to the regulator to focus on creating a conducive atmosphere for firms, providing different incentives based on the investments made in different ESG dimensions to help firms create value for their stakeholders. It will provide insight into filling the loopholes in the existing legal framework to minimize evasion.\u003c/p\u003e \u003c/div\u003e "},{"header":"Conclusions and Limitations of the study","content":"\u003cp\u003eThe results of this study reflect the impact of sustainable initiatives reflected through ESG disclosures on value creation for non-financial firms in the Nifty 500 index. By considering the sustainable initiatives taken by non-finance companies and evaluating the value creation process using EVA and MVA, this study provides thought-provoking insights into the impact of ESG disclosure score and economic profit, reflecting the consideration of EVA and MVA as a measure for evaluating stakeholders\u0026rsquo; value creation. The study highlights the fact that investments in sustainability initiatives have an impact on economic performance and, ultimately, on the value creation of stakeholders. The scope could be expanded to include financial service companies for five years. This study can also be undertaken to understand the different ways of evaluating value creation and which methods explain the best relationship between ESG and value creation. A cross-country comparison could also be undertaken to understand the similarities or differences in the results reflected. The different independent and control variables could be included in the studies to improve our findings. The analysis can also be conducted by bifurcating companies based on market capitalization and understanding the results.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding Statement:\u003c/strong\u003e None of the Authors have received any funding to conduct any part of this study.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical Compliance:\u003c/strong\u003e There is no human participation or data related to Human behaviour involved.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics, Consent to Participate, and Consent to Publish declarations:\u0026nbsp;\u003c/strong\u003eNot Applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Access Statement:\u003c/strong\u003e Research data supporting this publication has been accessed from the paid licence terminal of Bloomberg database available with the authors’ University. As the data set has been drawn from paid terminal, data can not be shared openly and can be maed accessible to Editors on demand.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of Interest declaration\u003c/strong\u003e: The authors declare that they have no affiliations with or involvement in any organization or entity with any financial interest in the subject matter or materials discussed in this manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e: MD and DS conceived the original, contributed to the design and implementation of the research, to the analysis of the results and to the writing of the manuscript. DM and SK supervised the project and reviewed all drafts.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAboud A, Diab A. (2019). The financial and market consequences of environmental, social, and governance ratings. 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Sustainability. 2022;14(14):8677.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"discover-sustainability","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"disu","sideBox":"Learn more about [Discover Sustainability](https://www.springer.com/43621)","snPcode":"","submissionUrl":"","title":"Discover Sustainability","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"ESG, non-finance sector, shareholder value creation, value-added measures, EVA, MVA, India","lastPublishedDoi":"10.21203/rs.3.rs-6766431/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6766431/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study examines the relationship between Environmental, Social, and Governance (ESG) scores and shareholder value creation measured through Economic Value Added (EVA) and Market Value Added (MVA). Using panel data from 220 Indian non-financial firms listed on the National Stock Exchange (NSE) 500 between 2015 and 2020, we explore whether ESG disclosures enhance or detract from shareholder value. The results reveal a negative relationship between ESG scores and EVA driven by high expenditures on ESG activities without immediate financial gains. Conversely, ESG scores were positively associated with MVA, suggesting long-term market benefits. The findings underscore the dichotomy between the short-term operational impact and the long-term market perception of ESG initiatives. This study has implications for managers and policymakers aiming to balance sustainability goals with shareholder expectations.\u003c/p\u003e","manuscriptTitle":"ESG Scores and Shareholder Value: Exploring the Dichotomy between EVA and MVA in Emerging Markets Firms","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-16 09:46:49","doi":"10.21203/rs.3.rs-6766431/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-06-21T10:37:37+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-06-20T09:11:44+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-06-16T06:32:23+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-06-14T13:21:55+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"189237901214730362440854507879725921277","date":"2025-06-12T16:21:50+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"56897243511194371169626344140460837611","date":"2025-06-10T10:19:01+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"29140196142213195745494109625265216848","date":"2025-06-10T02:32:31+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"218707273338469021673920011370652408572","date":"2025-06-10T00:12:11+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-06-09T23:47:48+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-06-08T18:36:55+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-06-02T11:55:22+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-06-02T11:54:37+00:00","index":"","fulltext":""},{"type":"submitted","content":"Discover Sustainability","date":"2025-05-28T09:13:51+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"discover-sustainability","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"disu","sideBox":"Learn more about [Discover Sustainability](https://www.springer.com/43621)","snPcode":"","submissionUrl":"","title":"Discover Sustainability","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"a8d09255-822e-456b-a11f-a836c8b0337e","owner":[],"postedDate":"June 16th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-05-04T16:01:38+00:00","versionOfRecord":{"articleIdentity":"rs-6766431","link":"https://doi.org/10.1007/s43621-026-03095-y","journal":{"identity":"discover-sustainability","isVorOnly":false,"title":"Discover Sustainability"},"publishedOn":"2026-04-30 15:58:25","publishedOnDateReadable":"April 30th, 2026"},"versionCreatedAt":"2025-06-16 09:46:49","video":"","vorDoi":"10.1007/s43621-026-03095-y","vorDoiUrl":"https://doi.org/10.1007/s43621-026-03095-y","workflowStages":[]},"version":"v1","identity":"rs-6766431","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6766431","identity":"rs-6766431","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}