Agricultural innovation systems (AIS) are increasingly recognized as complex adaptive systems in which interventions cannot be expected to create predictable, linear impacts. Nevertheless, the logic models and theory of change (ToC) used by standard-setting international agricultural research agencies and donors assume that agricultural research will create impact through a predictable linear adoption pathway which largely ignores the complexity dynamics of AIS, and which misses important alternate pathways through which agricultural research can improve system performance and generate sustainable development impact. Despite a growing body of literature calling for more dynamic, flexible and “complexity-aware” approaches to monitoring and evaluation, few concrete examples exist of ToC that takes complexity dynamics within AIS into account, or provide guidance on how such theories could be developed. This paper addresses this gap by presenting an example of how an empirically-grounded, complexity-aware ToC can be developed and what such a model might look like in the context of a particular type of program intervention. Two detailed case studies are presented from an agricultural research program which was explicitly seeking to work in a “complexity-aware” way within aquatic agricultural systems in Zambia and the Philippines. Through an analysis of the outcomes of these interventions, the pathways through which they began to produce impacts, and the causal factors at play, we derive a “complexity-aware” ToC to model how the cases worked. This middle-range model, as well as an overarching model that we derive from it, offer an alternate narrative of how development change can be produced in agricultural systems, one which aligns with insights from complexity science and which, we argue, more closely represents the ways in which many research for development interventions work in practice. The nested ToC offers a starting point for asking a different set of evaluation and research questions which may be more relevant to participatory research efforts working from within a complexity-aware, agricultural innovation systems perspective.

Despite a growing body of literature calling for more dynamic, flexible and “complexity-aware” approaches to monitoring and evaluation, few concrete examples exist of ToCs that take complexity dynamics within agriculture into account.

Towards a complexity-aware theory of change for participatory research programs working within agricultural innovation systems

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Systems change requires complex interventions. Cross-sector partnerships (CSPs) face the daunting task of addressing complex societal problems by aligning different backgrounds, values, ideas and resources. A major challenge for CSPs is how to link the type of partnership to the intervention needed to drive change. Intervention strategies are thereby increasingly based on Theories of Change (ToCs). Applying ToCs is often a donor requirement, but it also reflects the ambition of a partnership to enhance its transformative potential. The current use of ToCs in partnering efforts varies greatly. There is a tendency for a linear and relatively simple use of ToCs that does limited justice to the complexity of the problems partnerships aim to address. Since partnership dynamics are already complex and challenging themselves, confusion and disagreement over the appropriate application of ToCs is likely to hamper rather than enhance the transformative potential of partnerships. We develop a complexity alignment framework and a diagnostic tool that enables partnerships to better appreciate the complexity of the context in which they operate, allowing them to adjust their learning strategy. This paper applies recent insights into how to deal with complexity from both the evaluation and theory of change fields to studies investigating the transformative capacity of partnerships. This can (1) serve as a check to define the challenges of partnering projects and (2) can help delineate the societal sources and layers of complexity that cross-sector partnerships deal with such as failure, insufficient responsibility taking and collective action problems at four phases of partnering.

The authors develop a framework to ensure alignment between the ToC of cross-sector partnerships and the complexity of the problems they aim to address.

Capturing Collaborative Challenges: Designing Complexity-Sensitive Theories of Change for Cross-Sector Partnerships

Models for theories of change vary widely as do how they are used. What constitutes a good or robust theory of change has not been discussed much. This article sets out and discusses criteria for robust theories of change. As well, it discusses how these criteria can be used to undertake a vigorous assessment of a theory of change. A solid analysis of a theory of change can be extremely useful, both for designing or assessing the designs of an intervention as well as for the design of monitoring regimes and evaluations. The article concludes with a discussion about carrying out a theory of change analysis and an example.

This article discusses criteria for robust theories of change and how these can be used to undertake a vigorous assessment of a Theory of Change.