Resilience through Innovation: How crises reshape science and technology

Crises alter the trajectory of science and technology by forcing urgent, concrete objectives into view. The Fukushima disaster catalyzed breakthroughs in nuclear safety and seismology; Ebola accelerated viral genomics and vaccine development; COVID-19 propelled mRNA technologies and digital epidemiology. Such events abruptly shift the landscape, opening entirely new lines of enquiry while disrupting established hierarchies. This project examines how innovators respond to these shifts and how quickly different types of innovators adapt. This project aims to leverage recent advances in big data infrastructure and artificial intelligence to link crises with the innovations they spark. By combining large-scale bibliometric data with natural experiments around disasters, it will deliver reproducible indicators, datasets, and causal evidence on how crises create or reconfigure opportunities in science and innovation.

We have combed through over 26,000 emergency events maintained by EM-Dat (Delforge et al., 2023), a comprehensive emergency events database founded by WHO, and linked it to OpenAlex (Priem, Piwowar, & Orr, 2022), a recently released fully open index of over 245 million scholarly works. Using natural language processing algorithms, we identify scientists whose topics witnessed an increase in salience after specific disasters (e.g. Nano Bioremediation after the Deepwater Horizon Oil Spill of 2010). Further, by specifying our fine-grained topic structure into a high dimensional vector space, we have constructed various measures for interdisciplinarity (e.g. Rao Sterling Diversity index) and changes in trajectory (e.g. cosine distance) to quantify the extent to which different scientists change their research in response to new demands. By linking scientific articles to patents through patent citations to science (Lin et al., 2023), the project extends analysis from shifts in science to their downstream impact on technological development.

The project pursues three objectives:
(1) Empirically connect specific major natural, technological and epidemiological disasters to the research domains they affect using state-of-the-art natural language processing.
(2) Assess how these events reconfigure the innovation landscape, with particular attention to the role of less established actors.
(3) Equip policymakers and institutions with tools to anticipate and harness crisis-driven opportunities.

Science and the study of science have long been a source of resilience (Bush, 1945). By systematically examining scientists' responses to disasters, this project will contribute to efforts aimed at using science to create a more disaster-resilient society. More broadly, it will offer a clearer understanding of the flexibility of science, which will help shape a future-oriented vision that deepens our understanding of the interplay between social demands and innovative scientific solutions. The findings will help decision-makers direct resources and support not only today's key research areas and scientists but also to emerging fields and adaptable scientists who may prove vital in the future. With its broad scope, this project will be relevant to researchers across disciplines and to policymakers across levels and geographies. As with other scholars engaged in the burgeoning area of the “science of science” (Fortunato et al., 2018), we aim to publish our research results in both science and management journals and share its practical implications through management and policy articles, and case studies.

Work Plan:
To date, the project has completed the literature review, established the data lake, and built crosswalks between disaster databases and science and technology datasets. The theoretical framework and core measures of disaster-driven shifts have been finalized, and data analysis and econometric modeling tasks have been initiated.

Months 1–3 (2025)
•Literature review and data lake set up
•Disambiguate and create data crosswalks between disaster databases and science & technology datasets
•Develop theory and construct measures on disaster-driven shifts in science and technology

Months 4–6 (2025)
•Validate data crosswalks between disaster databases and science & technology datasets
•Develop theory and construct measures on disaster-driven shifts in science and technology

Months 7–9 (2025)
•Refine theoretical framework and finetune measures on disaster-driven shifts in science and technology
•Initiate data analysis & generate descriptive statistics

Months 10–12 (2025)
•Perform data analysis & econometric modeling
•Conduct outreach & engagement activities

Months 13–15 (2026)
•Finalize econometric models and recover causal estimates
•Conduct outreach & engagement activities
•Write early stage working papers

Months 16–18 (2026)
•Conduct outreach & engagement activities
•Present working papers at seminars and leading academic conferences (Academy of Management, Strategic Management Society)

Months 19–21 (2026)
•Prepare academic publications for leading management journals (e.g. Administrative Science Quarterly, Organization Science)
•Conduct outreach & engagement activities

Months 21–24 (2026)
•Continue outreach and engagement activities, including roundtables with scientists and policymakers
•Write practitioner-focused articles (e.g. MIT Technology Review, Harvard Business Review)
•Submit final report

Team:
The project team combines internationally recognized expertise in the science of science, innovation management, and organizational transformation, ensuring rigorous analysis of how crises reshape scientific and technological trajectories. By bringing together the experience of established scholars and the fresh perspectives of an early-career researcher, the team integrates complementary disciplinary backgrounds, from strategy and management to mathematics and data science, while also reflecting gender parity and strong female leadership.

(PI) Prof. Ivanka Visnjic is Professor of Innovation at Esade Business School. She is a leading scholar on innovation and business model transformation, with particular expertise in the shift to services, digital, and sustainability. She earned her PhD at KU Leuven and has held visiting positions at the University of Cambridge, the University of California, Berkeley, and London Business School, contributing to initiatives such as the Cambridge Service Alliance, a business-led innovation partnership. Her research has been published in top outlets including Harvard Business Review, MIT Sloan Management Review, Organization Science, and the Journal of Operations Management. Her contributions have been recognized with multiple awards, including the Abbie Griffin High Impact Award and the Thomas P. Hustad Best Paper Award from the Journal of Product Innovation Management, as well as the Ambassador Award from the Journal of Operations Management.

(Collaborator) Prof. Michaël Bikard is an Associate Professor of Strategy at INSEAD. He is a globally recognized expert in the science of science and innovation, with a PhD from MIT Sloan and publications in leading academic journals including Management Science, Administrative Science Quarterly and Organization Science, where he serves as senior editor. Professor Bikard has won first place at the MIT Sloan Doctoral Thesis Prize, and multiple best paper nominations. His research on scientific attention, institutional influence, and knowledge flows sits at the core of this project.

(Researcher) Dr. Kaushik Bagchi is a postdoctoral researcher with a PhD in Innovation Management from Esade Business School, studying the creation and commercialization of science. He has a background in mathematics with a bachelor’s degree from The Ohio State University and an MBA from an Indian Institute of Management, and has extensive expertise in analyzing large datasets, working with APIs, and applying Python, R, and SQL for data analysis.