Work-from-home (WFH) during COVID-19 pandemic – A netnographic investigation using Twitter data

This paper aims to create a better understanding of the challenges posed by work from home (WFH) during the ongoing COVID-19 pandemic, to investigate the public sentiment toward this transition, and to develop a conceptual model incorporating the relationships among the factors that influence the effectiveness of WFH.

This paper uses netnography method to collect data from the Twitter platform and uses Python programming language, Natural Language Processing techniques and IBM SPSS 26 to conduct sentiment analysis and directed content analysis on the data. The findings are combined with an extensive review of the remote work literature to develop a conceptual model.

Results show the majority of tweets about WFH during the pandemic are positive and objective with technology and cyber security as the most repeated topics in the tweets. New challenges to WFH during pandemic include future uncertainty, health concerns, home workspaces, self-isolation, lack of recreational activities and support mechanisms. In addition, exhaustion and technostress mediate the relationship between the antecedents and outcomes of WFH during the ongoing COVID-19 pandemic. Finally, the fear of pandemic and coping strategies moderates these relationships.

This paper is one of the first efforts to comprehensively investigate the challenges of WFH during a crisis and to extend the remote work literature by developing a conceptual model incorporating the moderating effects of fear of pandemic and coping strategies. Moreover, it is the first paper to investigate the tweeting behavior of different user types on Twitter who shared posts about WFH during the ongoing pandemic.