Collaborative Research: EAGER: International Type II: Assessing the Role of Social Innovation for Resilience in Global Collaborative Research

Part 1.

A paramount scientific challenge is to learn from the COVID-19 pandemic crisis given the uncertainty of the timing and permanent effects on international research. Even in the unlikely event of pandemic eradication, new work patterns may replace pre-pandemic norms and practices of international collaboration. While technological solutions matter for facilitating adaptation to the pandemic, social innovations at the individual and team levels are equally or more important for generating resilience within scientific teams during the protracted crisis.

The future of international collaborative work can benefit from an understanding of how scientists have shown team resilience during this time, strengthening teams, and meaningfully transferring knowledge. In an increasingly globalized scientific landscape the grand policy question is how to internationalize scientific teams while, at the same time, responding to local demands.

Our goal is to characterize different forms of social innovation in international collaborative work during the pandemic, while taking into account local contexts, opportunities, and institutional factors. Part 2.

This project addresses the topic of global social innovation in science capacity in the face of the COVID-19 pandemic by examining three intertwining features of the social dynamics of international collaborative teams: Social innovation, Adaptation and Resilience, and Learning and Transferability. Social innovation refers to new and different ways of modifying individual and group behavior within the context of team science.

The research design builds on interdisciplinary knowledge about individual conduct and group dynamics within the context of teams. The project involves a series of case studies focused around distinct internationally collaborative teams across four countries: Austria, Latvia, Spain and the United States. Data are drawn from bibliometric data sources, semi-structured interviews, focus groups, and a survey of researchers.

Teams include new emergent collaborations that establish norms for interaction during the pandemic, or adaptive collaborations that adjust to the barriers and constraints of the pandemic. A novel methodological approach to identifying teams for case study is employed by implementing advanced computing techniques in a new and robust bibliometric dataset, complemented by other snowball sampling techniques.

The project will conclude with an international workshop to share and disseminate findings that further international collaboration.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.