Complexity leadership in action: a team science case study

Gemma Jiang; Diane Boghrat; Jenny Grabmeier; Jennifer E Cross

doi:10.3389/frma.2023.1211554

Complexity leadership in action: a team science case study

Front Res Metr Anal. 2023 Jul 27:8:1211554. doi: 10.3389/frma.2023.1211554. eCollection 2023.

Authors

Gemma Jiang¹, Diane Boghrat², Jenny Grabmeier³, Jennifer E Cross¹

Affiliations

¹ Institute for Research in the Social Sciences, Colorado State University, Fort Collins, CO, United States.
² Imageomics Institute, The Ohio State University, Columbus, OH, United States.
³ Translational Data Analytics Institute, The Ohio State University, Columbus, OH, United States.

Abstract

Introduction: This team science case study explores one cross-disciplinary science institute's change process for redesigning a weekly research coordination meeting. The narrative arc follows four stages of the adaptive process in complex adaptive systems: disequilibrium, amplification, emergence, and new order.

Methods: This case study takes an interpretative, participatory approach, where the objective is to understand the phenomena within the social context and deepen understanding of how the process unfolds over time and in context. Multiple data sources were collected and analyzed.

Results: A new adaptive order for the weekly research coordination meeting was established. The mechanism for the success of the change initiative was best explained by complexity leadership theory.

Discussion: Implications for team science practice include generating momentum for change, re-examining power dynamics, defining critical teaming professional roles, building multiple pathways towards team capacity development, and holding adaptive spaces. Promising areas for further exploration are also presented.

Keywords: adaptive process; complexity leadership theory; effective meetings; emergence; enable cross-disciplinary research; team capacity development; team science; teaming professionals.

Grants and funding

Funding for this research is provided by the U.S. National Science Foundation Award ID: #2118240 HDR Institute: Imageomics: A New Frontier of Biological Information Powered by Knowledge-Guided Machine Learning.