Research teams as complex systems: implications for knowledge management

Abstract

The recent increase in research collaboration creates the need to better understand the interaction between individual researchers and the collaborative team. The paper elaborates the conceptualisation of research teams as complex systems which emerge out of the local interactions of individual members operating in their local research groups, and which exhibit different dynamics: the local, the global dynamics, and the contextual dynamics. A model of research teams as complex systems is also introduced. This conceptualisation provides unique insights on management of distributed research teams: (a) the internal operations of some teams are more sensitive to external events than others; (b) conflicts emerge as a mismatch of management structures at the different levels in which a team operates; and (c) teams of high complexity have additional coordination needs, which can be fulfilled by the use of information and communication technologies. Recommendations are drawn for the use of a complex adaptive systems model in the field of knowledge management.

Keywords:

• systems thinking
• small teams
• knowledge teams
• complex systems

This article is developed from ‘Research teams as complex systems and implications for research governance’, by Eleftheria Vasileiadou, which appeared in Melkers, J., Monroe-White T. and Cozzens S. (eds.), 2011 Atlanta Conference on Science and Innovation Policy Proceedings, Institute of Electrical and Electronics Engineers. © 2011 IEEE.

One can imagine variable x at the individual level influencing variable y at the team level in an anticipatory mode. That would be described as x(t+1)=ay(t)+b. Path dependence would be formally described as x(t−1)=ay(t)+b.

This article is developed from ‘Research teams as complex systems and implications for research governance’, by Eleftheria Vasileiadou, which appeared in Melkers, J., Monroe-White T. and Cozzens S. (eds.), 2011 Atlanta Conference on Science and Innovation Policy Proceedings, Institute of Electrical and Electronics Engineers. © 2011 IEEE.

One can imagine variable x at the individual level influencing variable y at the team level in an anticipatory mode. That would be described as x(t+1)=ay(t)+b. Path dependence would be formally described as x(t−1)=ay(t)+b.

Acknowledgements

The paper has greatly benefitted from comments by Rafael Gonzalez, Gaston Heimeriks, Diana Lucio-Arias, Karolina Safarzyńska (in alphabetic order), and two anonymous referees.

Additional information

Notes on contributors

Eleftheria Vasileiadou

About the author

Eleftheria Vasileiadou is an Assistant Professor in Science, Technology and Public Policy at the VU University Amsterdam. She studied communication science at the Aristotle University Thessaloniki and the University of Amsterdam, and obtained her doctoral degree on the use of Information and Communication Technologies in research collaboration. Since 2001, she has been conducting research on issues of science production and communication; and complexity theory and methodology.