Faculty Publications
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Item Decoding mood of the Twitterverse on ESG investing: opinion mining and key themes using machine learning(Management Research Review, 2024-07) Jaiswal, Rachana; Gupta, Shashank; Tiwari, Aviral KumarPurpose 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. Design/methodology/approach 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. Findings 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. Research limitations/implications 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. Practical implications 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. Social implications 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. Originality/value 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.Item Environmental, social and governance-type investing: a multi-stakeholder machine learning analysis(Management Decision, 2025-03-26) Jaiswal, Rachana; Gupta, Shashank; Tiwari, Aviral KumarPurpose This research delves into the determinants influencing the adoption of environmental, social and governance (ESG) investing through an analysis of social media dialogs using the uses and gratification theory. Design/methodology/approach This study employs a mixed-methods approach, integrating sentiment analysis, topic modeling, clustering, causal loop analysis and ethnography to examine ESG-related content on social media. Analyzing social media data, study identified key themes and derived ten propositions about ESG investing. Industry professionals, financial advisors and investors further validated these findings through expert interviews. Combining data-driven analysis and qualitative insights provides a comprehensive understanding of how social media shapes investor preferences and decision-making in the ESG domain. Findings Environmental aspects, such as conservation, preservation of natural resources, renewable and clean energy, biodiversity, restoration and eco-friendly products and technologies, shape attitudes toward ESG investing. Social considerations, including inclusivity, diversity, social justice, human rights, stakeholder engagement, transparency, community development and philanthropy, significantly influence ESG investing sentiments. Governance elements such as transparency, accountability, ethical governance, compliance, risk management, regulatory compliance and responsible leadership also play a pivotal role in shaping ESG investing opinions. Practical implications This study presents actionable insights for policymakers and organizations by identifying key constructs in ESG investing and proposing an integrated framework that includes mediating factors like resource efficiency and stakeholder engagement alongside moderating elements such as regulatory environment and investor preferences. Policymakers should establish standardized ESG reporting frameworks, incentivize sustainable practices and use social media data for regulatory purposes. For businesses, integrating social media insights into decision-making can enhance ESG communication strategies and accountability. These measures will foster greater transparency, strengthen investor relations and contribute to a more sustainable and inclusive global economy. Originality/value To the authors' best knowledge, this is the first study to investigate improving ESG investing preferences based on big data mined from social media platforms.