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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.

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.

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.

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.

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.

This study aims to evaluate patterns, trends and knowledge networks within social security research. By using bibliometric analysis, the research seeks to provide a comprehensive perspective on the evolution of global social security research. The purpose extends to identifying significant contributors, collaborative clusters and multifaceted issues addressed in the field.

This study uses bibliometric analysis to assess social security research trends and knowledge networks from 2015 to 2023. Using the Web of Science database, 6,152 relevant articles are analyzed. Quantitative techniques such as coauthorship network analysis, institutional productivity rankings and keyword clustering are applied for a comprehensive understanding.

The findings indicate a rising trajectory of publications in social security research, with the USA, China and Europe emerging as top contributors. Coauthorship patterns reveal collaborative clusters with focused research interests. Prominent authors emphasize key aspects like public policy, economics, health and labor dynamics related to social security. Keyword clustering identifies nine thematic clusters, ranging from inequality and poverty to retirement and disability reforms. A thematic map visualizes overarching categories, including motor themes, basic themes, niche themes and emerging themes.

This bibliometric study offers original insights into global social security research, providing a comprehensive understanding of its evolution, significant contributors and diverse thematic issues addressed. The originality lies in the application of quantitative techniques, including coauthorship network analysis and keyword clustering, to reveal collaborative patterns and thematic clusters. The study’s value extends to facilitating evidence-based decision-making for advancing the critical domain of social security through international collaboration and impactful research aligned with societal needs.