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PurposeThis study explores how district leaders shape the absorptive capacity of research–practice partnerships (RPPs) when addressing local problems of practice. Design/methodology/approachDrawing from Farrellet al.(2019), we focus on three core partnership domains shaping absorptive capacity: (a) prior knowledge, (b) communication pathways and (c) strategic knowledge leadership. We then employ a mixed-methods, multiple-case study design (Yin, 2013), highlighting two districts participating in a five-year National Science Foundation grant (2019–2024) to improve math instruction and learning. Data collected include semi-structured interviews (N = 180), the Visions of High-Quality Mathematics Instruction Rubric (VHQMI), social network surveys, meeting observations (N = 80) and the effective teams rubric (ETR). FindingsData suggest that district leaders variably shape an RPP’s absorptive capacity to engage in joint-partnership work. Notably, these differences are related to the degree to which district leaders promote or hinder the assimilation of new knowledge among other stakeholders within the RPP network. Originality/valueIndeed, we do not yet know how district leaders moderate stakeholder involvement within a given RPP network and how such involvement might influence the partnership’s absorptive capacity to address local problems of practice. 

Abstract This article provides a systematic review of the network formation literature in the public sector. In particular, we code and categorize the theoretical mechanisms used in empirical network research to motivate collaboration and tie formation. Based on a review of the 107 articles on network formation found in 40 journals of public administration and policy from 1998 to 2019, we identify 15 distinct theoretical categories. For each category, we describe the theory, highlight its use in the literature, and identify limitations and concerns with current applications. Overall, we find that most studies rely on a similar set of general theories of network formation. More importantly, we find that most theoretical mechanisms are not well specified, and empirical tests are often unable to directly assess the specific underlying mechanism. The results of our review highlight the need for our field to embrace experimental designs, develop panel network datasets, and engage in more network-level research.