Collaborative data innovation: developing a theoretical and empirical understanding of drivers, barriers and outcomes

Purpose Collaborative data innovation (CDI) is the process through which multi-actor collaborations collect and share data for responses to public issues. The aim of this conceptual and empirical paper is to provide a theoretical and empirical understanding of this relatively new phenomenon. Building upon theories of collaborative innovation and data collaboratives, a heuristic model of the barriers, drivers and outcomes of CDI is developed. Design/methodology/approach The heuristic model of CDI is explored in an empirical case study of citizen-sensing to generate information about air quality in the province of Utrecht in the Netherlands. This case study employs a mixed-methods design, using both qualitative and quantitative methods for data collection. Findings The case study, the Sniffer Bicycle in the Netherlands, generates insights into drivers, barriers and outcomes of CDI. The findings suggest that the later stages of a CDI that focus on the institutionalization require a different set of drivers than the earlier experimental phase. Research limitations/implications This explorative work into CDI can form the basis for more systematic empirical work into this form of collaboration in sectors as diverse as noise around airports, biodiversity, water quality, traffic safety, resilience of ecosystems and many other topics. More research specifically needs to focus on ways to deal with later-stage barriers related to ownership and accountability in order for CDIs to reach their full potential in providing information for responses to complex societal problems. Practical implications The study highlights that organizations need to apply different strategies in the earlier and later phases of CDIs. The earlier phases require well-known drivers such as space and budget to work informally and flexibly, leadership of collaboration and ability to mobilize, setting up experiment and coordinating actors. The later phases of CDI require drivers such as agreements and standards to ensure equal accountability and ownership in the later stage of institutionalization. Social implications In many countries, citizens are increasingly interested in forming data collaboratives to advance issues they find important in society. Air quality can clearly be one of these issues. The study highlights how citizen engagement can actually generate relevant information about this topic. The research shows that, to impact government policies, data collaboratives need to be well linked to the institutional dimensions of government. Originality/value The research provides three contributions: a conceptual understanding of CDI, the empirical identification of a clear distinction between early-phase barriers related to experimentation and the later-phase barriers related to institutionalization and a critical analysis of the role given to citizens in data collaboratives.