Policy Innovation Diffusion via Network Embeddedness: A Study of Big Data Policing Infrastructure Adoptions among Local Police Departments in Taiwan

This paper examines how network embeddedness influences the diffusion of big data policing infrastructure among local police departments in Taiwan. Using the case of the electronic patrol signature system (EPSS), which replaces paper-based patrol logs with digital records to collect, store, and manage day-to-day patrol data (location, time, and person), a theoretical framework that links three policy diffusion mechanisms (learning, imitation, and competition) with network features and theories is proposed to improve identification. Through dyadic analysis of intrinsic characteristics and pairwise network relations of 22 local police departments, the results suggest that competitive pressure between structurally equivalent departments is the dominant factor that drives the rapid diffusion of EPSS, more than internal budgetary constraints or citizen-to-police ratio, while geographic proximity and larger centrality difference to adopters also facilitate policy learning and imitation. The paper contributes to the literature by providing a comprehensive identification strategy of policy diffusion mechanisms via network embeddedness, and by shedding light on the motivations and conditions for adopting big data policing technology in police organizations.