Forced innovation: leveraging text data to analyse firms response to COVID-19

Purpose: The purpose of this study is to explore and present a clear overview of innovation topics during the first year of the COVID-19 pandemic, and then organise these topics into various analyses. Design/methodology/approach: The authors use multiple language analysis methods, such as text mining and latent Dirichlet allocation topic modelling, to address the research questions. A total of 440 news articles are analysed using Python and Google Colaboratory tools. Findings: The analysis identified 20 innovation topics, highlighted sector-specific analyses and proposed phases of innovation. The authors suggest that each sector develops unique patterns and forms of innovation for long-term benefits and further research. This study expands upon existing literature on innovation and crisis at a theoretical level by incorporating an actor as the agency. Research limitations/implications: Based on the findings, the authors conclude that the COVID-19 pandemic has prompted businesses to adopt dynamic capabilities. Furthermore, the authors provide several strategic recommendations for addressing the pandemic in the developing context. The study discusses the roles of policymakers, business practitioners and academia in this context as well. Originality/value: Very few studies specifically explore and identify forced innovation topics in emerging countries during the pandemic. There has been no review of forced innovations implemented in Indonesia using news media as a source. Additionally, this study presents the trajectory of innovation during the time of crises. © 2024 Elsevier B.V., All rights reserved.