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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 aims to examine member attachment in hybrid identity organizations (HIOs), assessing the distinct effects of identification with respect to two elements (normative and utilitarian) of a hybrid’s identity. Specifically, the author explored how such dual identifications influence commitment and exit/voice/loyalty.

To distinguish the effects of the two identities, the author used the mechanism of identity congruence – the gap between identity perceptions and expectations – as an analog of identification. The models of identity gap, commitment and exit/voice/loyalty were examined via a survey of agricultural cooperative members.

Both the social and economic forms of identity gap were significantly related to commitment and exit/voice/loyalty. In addition, commitment mediated the relationship between identity gap and exit/voice/loyalty.

The results demonstrate the distinctive effects of the dual identities and reinforce the importance of delineating such differences when examining identification in hybrid organizations.

Managers should recognize the duality inherent in hybrid organizational identification and understand the potential for different outcomes stemming from the separate identities.

This study represents the first quantitative examination of an integrated model of dual identification and commitment in HIOs. It is also unique in exploring the exit/voice/loyalty framework as a consequent of identification.