Apgar Marina, Fournie Guillaume, Haesler Barbara, Higdon Grace Lyn, Kenny Leah, Oppel Annalena, Pauls Evelyn, Smith Matthew, Snijder Mieke, Vink Daan, Hossain Mazeda
Institute of Development Studies, University of Sussex, Library Road, Falmer, Brighton, BN1 9RE East Sussex UK.
Royal Veterinary College, 4 Royal College St, London, NW1 0TU UK.
Eur J Dev Res. 2023;35(2):323-350. doi: 10.1057/s41287-023-00576-y. Epub 2023 Jan 25.
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.
The online version contains supplementary material available at 10.1057/s41287-023-00576-y.
通过研究促进发展计划（R4D）实现影响力需要与研究、发展和政策领域的不同利益相关者进行互动。因此，要理解此类计划如何支持成果的产生，就需要关注互动和合作的关系层面。越来越多的大型研究合作评估采用社会网络分析（SNA），利用其因果关系的关系视角。在本文中，我们通过在全球挑战研究基金的三个跨学科中心的评估工作，在类似的大型R4D计划中使用SNA的三种应用，以探索其作为一种评估方法的潜力。我们的比较分析表明，SNA可以揭示R4D计划内互动的结构维度，并有助于了解网络如何随时间演变。我们反思了这些案例中的常见挑战，包括应对因网络数据不完整导致的不同形式的偏差、跨尺度的多种解释，以及进行因果推断和相关伦理困境的挑战。我们最后总结了在旨在支持学习和问责的监测、评估和学习（MEL）系统中使用SNA的方法和操作层面的经验教训。
在线版本包含可在10.1057/s41287-023-00576-y获取的补充材料。
