Bridging Learning and Action: How Did CLARISSA’s Participatory Adaptive Management Approach Foster Innovation, Effectiveness, and Stakeholder Empowerment? - Prieto Martin, Pedro; Apgar, Marina; Afroze, Jiniya; Arulanantham, Amit; Hicks, Jacqueline; Karki, Shanta; Mareschal, Sophie; Paul, Sukanta; Snijder, Mieke; Uddin, Forhad; Veitch, Helen (CLARISSA Research and Evidence Paper, 2024)
Child Labour: Action-Research-Innovation in South and South-Eastern Asia (CLARISSA) is an evidence and innovation-generation programme funded by the United Kingdom’s Foreign, Commonwealth & Development Office (FCDO), responding to the challenge of the worst forms of child labour (WFCL) in Bangladesh and Nepal. It is a challenge characterised by a poor understanding of its drivers and a lack of evidence on what works to combat it. To handle such fundamental uncertainty, the programme adopts a child-centric and participatory action research approach, which is supported by an adaptive management model to respond better to challenges and opportunities. From its inception, the programme needed to navigate shocks and challenges, such as Covid-19 lockdowns, political upheaval, and sustained budget cuts, which put its capacity to learn and evolve to the test. This paper shares insights emerging from evaluating CLARISSA’s participatory adaptive management (PAM) practices, connecting them with current discussions on adaptive management. It provides an in-depth evaluation of CLARISSA’s PAM approach, exploring how adaptive strategies were implemented and evolved throughout the programme’s life cycle. Multiple cases of adaptation and misadaptation were selected and analysed through a series of in-depth interviews and review of programme documentation, allowing us to assess whether and how the adaptive management practices have been operationalised, the degree to which they led to enhanced decision-making and effectiveness, and their empowering effect on children and other programme stakeholders. Through conceptual analysis and real-world examples, the paper examines the key stages and critical dimensions of PAM, conveying the complexities and dynamism of adaptive management in development work, while highlighting both the successes and challenges encountered in operationalising PAM within CLARISSA.

What have we learned about learning? Unpacking the relationship between knowledge and organisational change in development agencies - Yanguas, Pablo (German Development Institute / Deutsches Institut für Entwicklungspolitik (DIE), 2021)
Development cooperation has spent decades wrangling over the merits, evidence, and implications of what we may term “the learning hypothesis”: the idea that increased knowledge by development organisations must logically lead to increased effectiveness in the performance of their development activities. Organisations of all stripes have built research and monitoring and evaluation (M&E) departments, adopted a multitude of knowledge management systems and tools, and tinkered with different ways to structure their organograms to stimulate knowledge sharing and learning. The topic of organisational learning is particularly significant as the global development community grapples with increasingly complex problems and the aspiration of evidence-based policymaking. This paper presents an analytical framework for interrogating “the learning hypothesis”, breaking it down into causal steps: knowledge causes learning, learning causes organisational change, change causes effectiveness. The framework focuses on the first two sub-hypotheses, mapping out the conceptual space around them by outlining potential relationships between different types of knowledge – tacit and explicit, internal and external – and between different types of learning – operational and strategic. This map provides a foundation for three key research questions: What impact has the rising knowledge agenda had on development organisations? Which factors appear to enable or inhibit organisational learning? What is the relationship between operational and strategic learning and organisational change? A review of available evaluations and studies, including two cases from former UK Department for International Development (DFID) and the World Bank, reveals that there is insufficient evidence to support the causal claim that knowledge leads to learning and thereby to organisational change in development agencies. Sources point to tacit learning prevailing while explicit knowledge management systems flounder, and external advocacy agendas appear more compelling than internal research and evaluation products. It is not entirely clear how, or indeed, whether operational and strategic learning intersect, with delivery-level lessons hardly aggregating into structural or policy shifts. Organisational change – even that aimed at enhancing learning – is rarely based on lessons learned from practice. More research is necessary to fully unpack the learning hypothesis, but what limited evidence is available disproves rather than confirms its central claim. This has significant implications for the future of learning in development agencies as advocated by thought leaders, researchers, and reformers. In particular, the latter should consider an evidence-based reassessment of the function and value-for-money of research and M&E in development practice, and a more critical examination of the politics of external advocacy efforts around innovative aid approaches like thinking and working politically, adaptive management, or results-based management.

Knowledge Retention and Transfer (KRT) - Model overview presentation - USAID (USAID, 2023)
What problem is the KRT model trying to solve? For workforces that experience continuous staff turnover, the lack of systematic knowledge transfer can often lead to: - Loss of programmatic momentum, - Duplication of efforts and frustration, and - Wasted time and resources. The Knowledge Retention and Transfer (KRT) model provides tools, processes, and practices to individuals, teams, offices, and organizations to improve knowledge handover, which in turn improves efficiency and programmatic and operational learning.