A learning motivation? Public service motivation and policy learning within eight Belgian collaborative networks

Collaborative networks are particular governance structures in which a diversity of actors interact regularly to solve problems that they cannot solve alone (Agranoff 2006). Learning in collaborative networks is crucial to coping with the complex and uncertain problems that characterize today’s world. By exchanging information with each other, participants acquire knowledge that helps generate new ideas and implement joint action, fostering innovative public programs and services to address environmental issues (Heikkila & Gerlak, 2013; Koebele 2019; Leach et al. 2014; Newig et al. 2019). While public service motivation (PSM) or “an individual’s orientation to delivering services to people with a purpose to do good for others and society” (Kim et al., 2013, p. 80; Perry, 1996) has been related to various dimensions of policymaking and public service delivery (Ritz et al., 2016), PSM-policy learning relations have not been investigated so far (with few exceptions: e.g., Ki, 2021), especially in collaborative governance structures. How does PSM model learning in collaborative networks? To address this research question, we rely on logistic network autocorrelation models of learning in eight Belgian collaborative networks that focus on public-sector innovation about environmental and other public policies and services, based on data from a web survey of 92 (77%) of their participants and semi-structured interviews with 78 (65%) of them. We look at the different effects of the various dimensions of PSM on various forms of learning. Furthermore, we examine how collaborative networks model PSM and PSM-learning relations. We conclude with implications related to the organization of collaborative networks as well as with a research agenda.