Mapping (mis)alignment within a collaborative network using homophily metrics
Collaborative approaches can overcome fragmentation by fostering consensus and connecting stakeholders who prioritize similar activities. This makes them a promising approach for complex, systemic problems such as lack of reliable, safe water, sanitation, and hygiene (WASH) services in low-income countries. Despite the touted ability of collaborative approaches to align priorities, there remains no comprehensive way to measure and map alignment within a network of actors. Methodological limitations have led to inconsistent guidance on if, and how much, alignment is needed around a commonvision(e.g., universal, reliable access to WASH) and/or around an agreed set ofactivities(e.g. passing a bill to promote water scheme maintenance models). In this work, we first define alignment as the extent to which actors work with others who share priorities. We then develop and test a method that uses social network analysis and qualitative interview data to quantify and visualize alignment within a network. By investigating how alignment of two strong, well-functioning WASH collaborative approaches evolved over three years, we showed that while alignment on a commonvisionmay be a defining aspect of collaborative approaches, some alignment around specificactivitiesis also required. Collaborative approaches that had sub-groups of members that all prioritized the same activities and worked together were able to make significant progress on those activities, such as drafting and passing a county-wide water bill or constructing a controversial fecal sludge disposal site. Despite strong sub-group formation, networks still had an overall tendency for actors to work with actors with different prioritized activities. While this reinforces some existing knowledge about collaborative work, it also clarifies inconsistencies in theory on collaborative approaches, calls into question key aspects of network literature, and expands methodological capabilities.
Pugel, K., Javernick-Will, A., Nyaga, C., Dimtse, D., Mussa, M., Henry, L., and K. Linden. (2022). “Mapping (mis)alignment within a collaborative network using homophily metrics”. PLOS Water.