TY - JOUR
T1 - Revealing the Relational Mechanisms of Research for Development Through Social Network Analysis
AU - Apgar, Marina
AU - Fournie, Guillaume
AU - Haesler, Barbara
AU - Higdon, Grace Lyn
AU - Kenny, Leah
AU - Oppel, Annalena
AU - Pauls, Evelyn
AU - Smith, Matthew
AU - Snijder, Mieke
AU - Vink, Daan
AU - Hossain, Mazeda
N1 - Funding Information:
Funding was provided by Natural Environment Research Council.
Publisher Copyright:
© 2023, The Author(s).
PY - 2023/4
Y1 - 2023/4
N2 - Achieving impact through research for development programmes (R4D) requires engagement with diverse stakeholders across the research, development and policy divides. Understanding how such programmes support the emergence of outcomes, therefore, requires a focus on the relational aspects of engagement and collaboration. Increasingly, evaluation of large research collaborations is employing social network analysis (SNA), making use of its relational view of causation. In this paper, we use three applications of SNA within similar large R4D programmes, through our work within evaluation of three Interidsiplinary Hubs of the Global Challenges Research Fund, to explore its potential as an evaluation method. Our comparative analysis shows that SNA can uncover the structural dimensions of interactions within R4D programmes and enable learning about how networks evolve through time. We reflect on common challenges across the cases including navigating different forms of bias that result from incomplete network data, multiple interpretations across scales, and the challenges of making causal inference and related ethical dilemmas. We conclude with lessons on the methodological and operational dimensions of using SNA within monitoring, evaluation and learning (MEL) systems that aim to support both learning and accountability.
AB - Achieving impact through research for development programmes (R4D) requires engagement with diverse stakeholders across the research, development and policy divides. Understanding how such programmes support the emergence of outcomes, therefore, requires a focus on the relational aspects of engagement and collaboration. Increasingly, evaluation of large research collaborations is employing social network analysis (SNA), making use of its relational view of causation. In this paper, we use three applications of SNA within similar large R4D programmes, through our work within evaluation of three Interidsiplinary Hubs of the Global Challenges Research Fund, to explore its potential as an evaluation method. Our comparative analysis shows that SNA can uncover the structural dimensions of interactions within R4D programmes and enable learning about how networks evolve through time. We reflect on common challenges across the cases including navigating different forms of bias that result from incomplete network data, multiple interpretations across scales, and the challenges of making causal inference and related ethical dilemmas. We conclude with lessons on the methodological and operational dimensions of using SNA within monitoring, evaluation and learning (MEL) systems that aim to support both learning and accountability.
KW - Collaboration
KW - Evaluation
KW - Learning
KW - Relational
KW - Research for Development
KW - Social network analysis
UR - http://www.scopus.com/inward/record.url?scp=85146773563&partnerID=8YFLogxK
U2 - 10.1057/s41287-023-00576-y
DO - 10.1057/s41287-023-00576-y
M3 - Article
C2 - 36714538
AN - SCOPUS:85146773563
SN - 0957-8811
VL - 35
SP - 323
EP - 350
JO - European Journal of Development Research
JF - European Journal of Development Research
IS - 2
ER -