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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.

Amidst the turbulent tides of geopolitical uncertainty and pandemic-induced economic disruptions, the information technology industry grapples with alarming attrition and aggravating talent gaps, spurring a surge in demand for specialized digital proficiencies. Leveraging this imperative, firms seek to attract and retain top-tier talent through generous compensation packages. This study introduces a holistic, integrated theoretical framework integrating machine learning models to develop a compensation model, interrogating the multifaceted factors that shape pay determination.

Drawing upon a stratified sample of 2488 observations, this study determines whether compensation can be accurately predicted via constructs derived from the integrated theoretical framework, employing various cutting-edge machine learning models. This study culminates in discovering a random forest model, exhibiting 99.6% accuracy and 0.08° mean absolute error, following a series of comprehensive robustness checks.

The empirical findings of this study have revealed critical determinants of compensation, including but not limited to experience level, educational background, and specialized skill-set. The research also elucidates that gender does not play a role in pay disparity, while company size and type hold no consequential sway over individual compensation determination.

The research underscores the importance of equitable compensation to foster technological innovation and encourage the retention of top talent, emphasizing the significance of human capital. Furthermore, the model presented in this study empowers individuals to negotiate their compensation more effectively and supports enterprises in crafting targeted compensation strategies, thereby facilitating sustainable economic growth and helping to attain various Sustainable Development Goals.

The cardinal contribution of this research lies in the inception of an inclusive theoretical framework that persuasively explicates the intricacies of a machine learning-driven remuneration model, ennobled by the synthesis of diverse management theories to capture the complexity of compensation determination. However, the generalizability of the findings to other sectors is constrained as this study is exclusively limited to the IT sector.

The purpose of the study was to develop a powder shampoo with antioxidant attributes.

Dry shampoo compositions were formulated containing alpha olefin sulfonate (AOS), sodium cocoyl isethionate (SCI), microcrystalline cellulose, mannitol, carboxymethyl cellulose, maltodextrin and sodium benzoate with or without extract of Cinnamomum zeylanicum bark. Cinnamon extract was chosen for this study owing to its ubiquitously known antioxidant attributes. The formulations were tested for detergency action and antioxidant potential in vitro.

Cinnamomum zeylanicum extract exhibited noticeable antioxidant activity in vitro. The authors observed that addition of the bark extract to the shampoo formulation was associated with remarkable increase in total phenolic content, total antioxidant activity and radical scavenging activity without any effect on detergency action.

This preliminary study provides a powder shampoo formulation which exhibits antioxidant attributes as a result of incorporation of cinnamon bark extract. Clinical efficacy of the formulation remains to be tested.

Owing to the powder format of the shampoo, the formulation can be manufactured with ease and economically. Functionalizing the formulation with enhancement of antioxidant activity by incorporation of cinnamon bark extract may be associated with beneficial clinical outcomes, which remains to be tested.

The proposed formulation may be stored and sold in eco-friendly packing material, thus could pave the way for reducing the burden of plastic consumption by the shampoo industry.

The present work demonstrates that incorporation of cinnamon bark extract to a powder shampoo formulation, containing AOS and SCI as principle surfactants, significantly enhances its antioxidant attributes.

This paper aims to explore the nature and degree to which marketing affects consumption behavior of bottom of the pyramid (BOP) population. The objective of the study is to examine, identify and explain aspects of consumption behavior that evidences the influence of marketing practices on the BOP consumers.

The study uses a long interview-based approach for an in-depth qualitative investigation of consumption behaviors of BOP consumers.

Key findings that emerged from the research are: widespread usage of international brands and expenditure on products outside of the core bundle of consumption, susceptibility to sales promotions, need to look and feel good and use “fairness” creams, susceptibility to advertising and celebrity endorsements and influence of store personnel.

For managers, this research suggests a careful examination of the likely consequences of their marketing actions. A set of guidelines are provided to them for doing business in a responsible manner at the BOP markets.

Recommendations for public policymakers are offered that stress on the need for ethical marketing exchanges to address the concern over possible exploitation of this vulnerable population.

Extant literature on BOP has largely been conceptual in nature, relying on various case studies. This study empirically examines the nature and influence of marketing in the purchase behavior of BOP consumers. This is perhaps the first study providing empirical support to the argument that the poor consumers divert their scarce financial resources from fulfilling basic needs to purchasing non-essential discretionary products under the influence of BOP marketing.