Collective coproduction of emergency services: Exploring key determinants using machine learning
使用机器学习方法，研究了影响公民参与应急服务集体合作生产意愿的关键因素，发现感知效能是最重要的决定因素，且可改变因素比人口特征影响更大。
The potential benefits of coproduction are widely recognized in public administration literature, particularly when these efforts are undertaken collectively rather than individually. Despite the importance of collective coproduction, there has been limited scholarly investigation into the factors that influence citizens’ willingness to participate in such initiatives, with only a few notable exceptions. To promote citizen participation in the collective coproduction of public services, it is crucial to thoroughly understand the determinants that promote involvement in these processes. Therefore, in this article, we employ machine learning techniques to examine the relative importance of factors influencing citizens’ willingness to participate in collective coproduction initiatives, particularly in the context of emergency services. Our findings indicate that individuals’ perceived efficacy (external efficacy) is the most critical determinant of their willingness to participate in collective coproduction efforts. Furthermore, mutable factors - such as the perceived importance of emergency services and confidence in local government performance - exert a more substantial influence on citizens’ willingness to collectively coproduce emergency services compared to other immutable factors like demographic characteristics.