Exploring the challenges of remote work on Twitter users' sentiments: From digital technology development to a post-pandemic era

Jose Ramon Saura, Domingo Ribeiro-Soriano and Pablo Zegarra Saldaña

Journal of Business Research, 2022, vol. 142, issue C, 242-254

Abstract: The boost in the use and development of technology, spurred by COVID-19 pandemic and its consequences, has sped up the adoption of new technologies and digital platforms in companies. Specifically, companies have been forced to change their organizational and work structures. In this context, the present study aims to identify the main opportunities and challenges for remote work through the use of digital technologies and platforms based on the analysis of user-generated content (UGC) in Twitter. Using computer-aided text analysis (CATA) and natural language processing (NLP), in this study, we conduct a sentiment analysis developed with Textblob, which works with machine learning. We then apply a mathematical algorithm for topic modeling known as Latent Dirichlet allocation (LDA) model. Based on the results obtained from these data-mining techniques, we identify 11 topics, of which 3 are negative (Virtual Health, Privacy Concerns and Stress), 4 positive (Work-life balance, Less stress, Future and Engagement), and 3 neutral (New Technologies, Sustainability, and Technology Issues). In addition, we also identify and discussed 6 opportunities and 5 challenges in relation to the use and adoption of digital technologies and platforms for teleworking. Finally, theoretical and practical implications of the study are presented for companies that develop strategies based on teleworking and the adoption of new technologies in which stress management is configured as one of the most relevant indicators for further research on remote work. From the applied perspective, executives and policymakers can use the results of the present study to re-evaluate the benefits of remote work for employees.

Keywords: Remote working; Sentiment analysis; Computer-aided text analysis; Topic modeling; Twitter, UGC (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (20)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:jbrese:v:142:y:2022:i:c:p:242-254

DOI: 10.1016/j.jbusres.2021.12.052

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