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The purpose of this paper is to determine the attractiveness or unattractiveness of cyberloafing in the workplace using Q methodology and the Kano model.

The perception of employees towards cyberloafing was investigated based on Q methodology, and then they were prioritized using Kano model. Ten IT companies were selected for the case study. In this study, a mixed method was used. First, 30 participants were interviewed. Next, after extracting the comments, Q-matrix was presented to 30 participants and they completed the matrix cells. Finally, Kano questionnaire was designed using the items obtained from Q methodology and distributed among 30 participants.

Q methodology led to nine perceptions, and the priorities of Kano model were proponents of increasing employees' dependence on the internet, economic thinkers, the indifferent, dissatisfied, proponents of receiving information, self-control proponents, the profit-minded, mind destroyer and satisfaction-oriented. Cyberloafing is considered unattractiveness with adverse effects. The combination of Q methodology and Kano model can improve the analysis of the results.

This study is one of the few studies in which Q methodology is improved by Kano model. In the past, Q methodology alone examined people’s perception, but by combining these two methods, it is determined which perception is more satisfying and which one is more important, and then a general result can be reached.

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.