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This paper is intended as an original contribution to researching ESG from a PR perspective, as well as offering a case study of the use of letters as a mode of corporate communication.

The methodology is interdisciplinary but is centred on a critical discourse analysis of the organizational rhetoric on ESG in the annual letters of BlackRock CEO Larry Fink from 2018 to 2023. The article also considers the content of the BlackRock letters alongside the campaign rhetoric deployed by opponents to ESG over the same period.

The analysis of the letters showed up a difference in tempo and tone between the courteous, collaborative and somewhat “corporate” style of text in the Fink letters and the more urgent and confrontational tone of opponents that adopted a populist line of argumentation against ESG in general and Larry Fink and BlackRock in particular.

While advantages can accrue to CEOs and corporations for speaking out on issues, there are also perils awaiting in the contemporary environment for opinion. The findings suggest it is also important to gauge the intensity of cultural and political division in society when speaking out on contentious issues and make a judgement on whether to proceed based on that analysis. Moreover, in countries where the middle ground of public opinion has eroded, ideology and cultural affiliations can prevail instead of openness to argument and counter-argument on topics such as climate change.

The paper presents a fresh case study of a CEO who has been prominent in shaping the discourse on ESG, which has itself become is a matter of contemporary relevance to public relations. The findings offer original insights that are additive to existing guidance and criteria for CEOs deciding to speak out on issues on behalf of their organizations.

Introduction: The global financial crisis has affected the financial markets and has had social consequences in addition to economic ones. The concept of ‘sustainability’ concerns firms reaching their main corporate goals. So, to maximise corporate financial performance (FINP), firms pay attention to non-financial data, such as elements of governance, social, and environmental concerns (henceforth referred to as ESG). Therefore, non-financial information provided by EGS factors measured by the ESG score has a crucial role in incorporating strategy and firm performance.

Purpose: This chapter looks at how ESG scores affect the performance of firms. The term ‘ESG’ describes how corporate operations include ESG principles. The ESG score is a novel way to gauge a company’s sustainability.

Methodology: ESG practices are a current phenomenon that has taken the attention of researchers in the last decades. Besides the amount of research conducted, researchers still need consensus regarding its impact. This chapter implements a systematic literature review to compile the research on ESG performance (ESGP) and how it affects business performance.

Findings: Businesses can incorporate sustainability practices into their operations with the help of ESG reports. ESG reports and scores provide non-financial information, which is crucial for businesses to achieve sustainability in their activities and attract more investors. The chapter contributes to the literature by creating value through a comprehensive and theoretical literature review.

Grounded in the stakeholder theory and signaling theory, this study aims to broaden the research agenda on environmental, social and governance (ESG) investing by uncovering public sentiments and key themes using Twitter data spanning from 2009 to 2022.

Using various machine learning models for text tonality analysis and topic modeling, this research scrutinizes 1,842,985 Twitter texts to extract prevalent ESG investing trends and gauge their sentiment.

Gibbs Sampling Dirichlet Multinomial Mixture emerges as the optimal topic modeling method, unveiling significant topics such as “Physical risk of climate change,” “Employee Health, Safety and well-being” and “Water management and Scarcity.” RoBERTa, an attention-based model, outperforms other machine learning models in sentiment analysis, revealing a predominantly positive shift in public sentiment toward ESG investing over the past five years.

This study establishes a framework for sentiment analysis and topic modeling on alternative data, offering a foundation for future research. Prospective studies can enhance insights by incorporating data from additional social media platforms like LinkedIn and Facebook.

Leveraging unstructured data on ESG from platforms like Twitter provides a novel avenue to capture company-related information, supplementing traditional self-reported sustainability disclosures. This approach opens new possibilities for understanding a company’s ESG standing.

By shedding light on public perceptions of ESG investing, this research uncovers influential factors that often elude traditional corporate reporting. The findings empower both investors and the general public, aiding managers in refining ESG and management strategies.

This study marks a groundbreaking contribution to scholarly exploration, to the best of the authors’ knowledge, by being the first to analyze unstructured Twitter data in the context of ESG investing, offering unique insights and advancing the understanding of this emerging field.

This study mainly explores how ESG performance (ESG stands for Environment, Social, and Governance) affects corporate downside risk through innovation input and innovation output, thereby promoting sustainable development of enterprises.

Using Chinese A-share listed companies from 2014 to 2022 as research samples, a stepwise regression method is used to empirically test the impact of ESG performance on corporate innovation and downside risk by constructing multiple multivariate primary regression models.

ESG performance is beneficial for obtaining external resources and alleviating principal-agent problems. It can promote enterprises to increase innovation input and improve innovation output, thereby enhancing their core competitiveness, and suppressing their downside risk. This inhibitory effect is more significant in non-state-owned enterprises, non-high-tech enterprises, and enterprises where the chairman and the general manager are not combined in one. Further additional analysis has found that equity concentration weakens the inhibitory effect of ESG performance on corporate downside risk, equity balance strengthens the inhibitory effect of ESG performance on corporate downside risk, indicating that a mutually restrictive equity structure is conducive to promoting enterprises to actively fulfill ESG responsibility, thereby improving corporate innovation level and resolving their downside risk.

Enterprise managers, policy makers, and other practitioners can clearly see the benefits of implementing ESG measures, further strengthen their confidence in sustainable development, actively apply ESG concepts to the entire production and operation process of enterprises, increase attention and implementation of ESG elements, and promote the healthy and vigorous development of enterprises and macroeconomics.

The research conclusions reveal the inherent mechanism by which ESG performance empowers enterprises to improve their innovation level and reverse their performance decline, effectively expanding the theoretical achievements of ESG performance in enterprise innovation and risk management.

The study examines the impact of behavioral biases, such as herd behavior, overconfidence and reactions to ESG News, on Socially Responsible Investing (SRI) decisions in the Indian context. Additionally, it explores gender differences in SRI decisions, thereby deepening the understanding of the factors shaping SRI choices and their implications for sustainable finance and gender-inclusive investment strategies.

The study employs Bayesian linear regression to analyze the impact of behavioral biases on SRI decisions among Indian investors since it accommodates uncertainties and integrates prior knowledge into the analysis. Posterior distributions are determined using the Markov chain Monte Carlo technique, ensuring robust and reliable results.

The presence of behavioral biases presents challenges and opportunities in the financial sector, hindering investors’ SRI engagement but offering valuable opportunities for targeted interventions. Peer advice and hot stocks strongly predict SRI engagement, indicating external influences. Investors reacting to extreme ESG events increasingly integrate sustainability into investment decisions. Gender differences reveal a greater inclination of women towards SRI in India.

The sample size was relatively small and restricted to a specific geographic region, which may limit the generalizability of the findings to other areas. While efforts were made to select a diverse sample, the results may represent something different than the broader population. The research focused solely on individual investors and did not consider the perspectives of institutional investors or other stakeholders in the SRI industry.

The study's practical implications are twofold. First, knowing how behavioral biases, such as herd behavior, overconfidence, and reactions to ESG news, affect SRI decisions can help investors and managers make better and more sustainable investment decisions. To reduce biases and encourage responsible investing, strategies might be created. In addition, the discovery of gender differences in SRI decisions, with women showing a stronger propensity, emphasizes the need for targeted marketing and communication strategies to promote more engagement in sustainable finance. These implications provide valuable insights for investors, managers, and policymakers seeking to advance sustainable investment practices.

The study has important social implications. It offers insights into the factors influencing individuals' SRI decisions, contributing to greater awareness and responsible investment practices. The gender disparities found in the study serve as a reminder of the importance of inclusivity in sustainable finance to promote balanced and equitable participation. Addressing these disparities can empower individuals of both genders to contribute to positive social and environmental change. Overall, the study encourages responsible investing and has a beneficial social impact by working towards a more sustainable and socially conscious financial system.

This study addresses a significant research gap by employing Bayesian linear regression method to examine the impact of behavioral biases on SRI decisions thereby offering more meaningful results compared to conventional frequentist estimation. Furthermore, the integration of behavioral finance with sustainable finance offers novel perspectives, contributing to the understanding of investors, investment managers, and policymakers, therefore, catalyzing responsible capital allocation. The study's exploration of gender dynamics adds a new dimension to the existing research on SRI and behavioral finance.

The aim of this research paper is to analyze the connection between ESG performance and financial performance within the real estate sector. By focusing on ESG ratings and pillar scores as proxies for ESG performance, the study investigates how these factors impact both profitability and market indicators.

With data sourced from over 680 publicly listed real estate companies, the research employs a fixed effects regression model to analyze the findings. By utilizing this method, the study can assess the impact of governance, environmental and social factors on both the accounting and market performance of real estate companies.

The outcomes of this study underscore a link between sustainability, particularly environmental aspects and financial performance. However, the study also reveals a contrasting result: governance factors are associated with adverse financial outcomes. Nevertheless, it is important to highlight the limitations as the results present a mixed picture with limited significant findings.

Companies should prioritize improvements in environment to boost profitability, while they should carefully consider the costs and benefits associated with enhancing their governance structure.

By focusing on this industry and adopting a global perspective, the study addresses a gap in the literature. The research’s innovative approach to utilizing ESG ratings and pillar scores as proxies for ESG performance enhances its originality. Furthermore, the research’s identification of the differing impacts of environmental and governance factors on financial outcomes add novel perspectives to the discourse.

Based on stakeholder, agency and institutional theory, this study aims to examine the role of institutional ownership in the relationship between environmental, social and governance practices and CEO compensation.

Utilizing a fixed-effect panel regression analysis, this research utilized a panel data approach, analyzing data spanning from 2014 to 2021, focusing on US companies listed on the S&P500 stock market index. The dataset encompassed 219 companies, leading to a total of 1,533 observations.

The analysis identified that environmental scores significantly impact CEO equity-linked compensation, unlike social and governance scores. Additionally, it was found that institutional ownership acts as a moderating factor in the relationship between the environmental score and CEO equity-linked compensation, as well as the association between the social score and CEO equity-linked compensation. Interestingly, the direction of these moderating effects varied between the two relationships, suggesting a nuanced role of institutional ownership.

This research makes a unique contribution to the field of corporate governance by exploring the relatively understudied area of institutional ownership's influence on the ESG practices–CEO compensation nexus.

The study aims to analyse the race towards green development and United Nations sustainable development goals (SDGs) in the cases of Huawei and Shell. Both companies are the leaders in their respective industries. Huawei is an example case study representing the information and communications technology (ICT) industry whereas Shell is an example case study representing the oil and gas industry. The research analyses of the races in achieving UN SDGs were undertaken based on the innovation diffusion framework with the use of machine learning algorithms trained to extract data on sustainability activities and initiatives.

The research analyses the two case studies of Huawei and Shell. The research was undertaken through the steps of training machine learning algorithms, industry benchmarking and evaluating the performance of the race. The analyses regarding the activities and initiatives of Huawei and Shell in contributing towards SDGs are based on the data in the past 10 years (Years 2010–2019) using machine learning to extract data on sustainability activities and initiatives. In the case of Huawei, 313 sustainability reports were fed to the unsupervised machine learning algorithms revealing 15,101 sustainability actions and initiatives related to UN SDGs in the ICT industry. In the case of Shell, 2,015 sustainability reports were fed to the unsupervised machine learning algorithms revealing 47,365 sustainability actions and initiatives related to UN SDGs in the oil and gas industry.

The analyses of findings revealed that Huawei and Shell performed very well in progressing towards the UN SDGs. Huawei had strong performance in the ICT industry with regard to SDGs No. 3, 4, 7, 8, 11, 12 and 16 while Shell had strong performance in the oil and gas industry with regard to SDGs No. 3, 4, 6, 7, 8, 12 and 16. Both companies had placed a focus on achieving SDG 12 responsible consumption and production, SDG 7 affordable and clean energy and SDG 4 quality education. The synthesised business model innovations of Huawei and Shell had shown their environmental, social and governance strategies – Huawei’s 2030 vision for green development and Shell’s 2050 vision for net zero emissions.

The five pillars of people, planet, prosperity, peace and partnership according to the UN 2030 agenda for sustainable development have shown the way a company operates to promote sustainable eco-systems. The extent to which both Huawei and Shell link corporate strategies to the UN SDGs has reflected their implementation progress. Furthermore, the business model innovations of Huawei and Shell provides a useful framework which can be applied to encourage other companies/organisations in various industries to undertake ESG activities in practice.

The main contribution of this research is the application of machine learning algorithms and the innovation diffusion model in analysing the SDGs performance. The study applies the innovation diffusion framework to explore strategic actions and initiatives of Huawei and Shell in transitioning towards sustainability. The use of machine learning algorithms has identified their sustainability approach in achieving the UN SDGs.

As financial firms incorporate impact strategies more extensively into their operations, they are asked to sustain their impact claims and thus face increased risks of regulatory scrutiny and lawsuits from private and public parties. The lack of reliable frameworks to measure impact gives rise to phenomena like impact washing, leading to litigations. This article aims to explore the main factors contributing to the impact litigation risk and the mechanisms employed by practitioners in the impact investing field to navigate and address this challenge.

We conducted semi-structured interviews involving three impact investors and three impact lawyers with specific knowledge of ESG and impact controversies, adopting the Gioia Methodology for the analysis. We triangulated such information with the analysis of secondary data.

The “great noise” around the impact investing world and the rise of impact washing, the lack of shared standards for measuring impacts and the misalignment of interests among actors involved in the initiatives constitute a potential “litigation bomb”. Such a scenario is detrimental to an investment strategy, which has the potential to tackle societal issues.

This study represents an initial effort to connect the academic debate on impact litigation with the expert’s active “on-field” standpoints. The identified and validated drivers of impact litigations provide valuable insight to enhance the governance and accountability of impact investing. Implementing Impact Measurement and Management (IMM) tools, participatory governance models, clear impact-focused contracts and a proactive approach could serve as prospective solutions to mitigate the risk of disputes.

This study explores the moderating effect of board gender diversity (BGD) between a firm's Environmental, Social, and Governance (ESG) performance and Economic value added (EVA) using NSE-listed 331 companies' data from 2015 to 2020, forming 1986 firm-year observations.

Our study is based on panel data; hence, we use a system GMM panel regression model to confirm whether the BGD moderates ESG and EVA. We also address the endogeneity issues.

Overall, our study reported a positive moderating effect of BGD between ESG and EVA. Similar results were observed across the chemical and financial services industries. However, in the case of the healthcare and consumer goods industries, we did not find support for the moderating effect.

The implications of our results are considerable and relevant for regulators, governing bodies, and corporate managers. It helps them understand how BGD plays a vital role in influencing the effect of ESG on a firm's EVA.

No existing research has explored the moderating effect of BGD between ESG and EVA, to the authors' best knowledge. Therefore, our study extends the existing literature and further supports resource dependency, agency, and stakeholder theories of corporate governance.