This thesis explores the application of qualitative complexity-aware evaluation methods that enable evaluators and policy makers to better understand and improve sustainable tourism interventions. In order to capture lessons from interventions, it is vital to move beyond evaluation as a box-ticking exercise and spending reports for funders. Based on the premise that people, organisations, and destinations do not behave in a rational, linear way, we need complexity-aware approaches that embed learning and evaluation for more effective tourism policy.

This thesis aims to enable policy makers to learn through purposeful evaluation by showcasing three complementary methods, namely, Participatory Systems Mapping, Realist Appreciative Inquiry, and Process Tracing. They are all applied in a participatory manner to the evaluation of the Sustainability Commitment intervention in Barcelona, Spain. Three research questions drove the research, focusing on (i) how change was expected to unfold, (ii) how learning among beneficiaries occurred, and (iii) what type of learning was delivered by the intervention.

A key takeaway from this research is that participatory, qualitative, complexity-aware evaluation methods can transform evaluation from an activity to fear into a positive, co-created, learning-oriented process for all stakeholders involved. This is significant because such shifts in mindset can be a catalyst for reflexivity and learning at multiple levels. Another key theme is that we need to consider the methods selected to conduct monitoring and evaluation, as well as who should be involved, and who should take ownership of the ongoing evaluation process. These all drive outcomes. This thesis highlights the importance of more comprehensive, suitable evaluation methods that strengthen internal evaluation capacity building. In line with a growing call for more holistic evaluation methods in the field of sustainable tourism interventions, this thesis offers a novel methodological framework to help researchers and practitioners better evaluate, understand and improve ongoing and future interventions.