Collaborative networks are horizontal settings of public governance that enhance interactions between a diversity of actors (for example, civil servants, companies or citizens). They can help cross-cutting public policies (for example, climate policies) to gain coherence and become more innovative. To do so, collective learning, defined as the broadened and mutual understanding of public issues arising out of repeated social interactions, is critical but not spontaneous. In particular, the diversity of participants creates learning opportunities that do not necessarily transform into concrete learning. So, how does diversity lead to collective learning in collaborative networks? To address this research question, this article researched two collaborative networks within the city administration of Schaerbeek (Belgium). Based on semistructured interviews, mental models were used to assess collective learning, and social network analysis was performed to understand the structure of interactions between diverse members. The findings show that the influence of diversity on collective learning was contingent on the collaborative network, but fostered by social interactions, with noticeable links between formal and informal interactions. From these findings, the article makes three scholarly contributions. First, it deepens our understanding of collective learning, with a focus on the development of shared understandings as a condition of consensus formation. Second, it builds on psychology and resource management research to assess collective learning through mental models, and provides a new approach to the measurement of policy learning. Third, it contributes to the debate on the implications of different inclusion levels and conditions for the results of collaborative governance and their transformation in policy innovations.
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Oct 1, 2024
Nicole K Jeffrey + 2
Open Access