Open data is being increasingly looked at as a major driver of public service innovation. Open access to datasets and advanced analytical tools are believed to generate valuable new knowledge that can be turned into data-driven services. At the same time, open data is also believed to spur open governance and enable the engagement of various stakeholders in the co-creation of services. Despite this appealing vision of open data-driven co-creation of public services, we are far from understanding how it can be realized in practice. We turned to 63 experts and practitioners in a survey covering six European countries and found a multitude of barriers that need to be overcome first. Luckily we also found some drivers. This paper provides some first insights into these drivers and barriers and proposes policy recommendations to foster a data-driven transformation of public service creation.

Innovation is essential for our ability to overcome global issues such as climate change, natural resource depletion, and inequality. A central aspect of innovation is the scaling process. While an abundance of studies on innovation scaling exist in many different disciplines, there is a lack of shared understanding of what scaling means and how it can be successfully achieved. This systematic literature review addresses both these issues by reviewing 147 articles on “innovation scaling” making several contributions to research on innovations and innovation scaling. First, in outlining the ontological differences between “diffusion” and “scaling”, clear conceptual boundaries are established, which provide clarity and support cross-disciplinary consilience. Second, based on the analysis of articles, eleven common modal contextual factors that influence the outcomes of innovation scaling across contexts and disciplines are presented. Third, an initial theoretical framework of the innovation scaling process is developed, outlining four theoretical propositions. As a fourth contribution, the article establishes a research agenda for the future development of innovation scaling research across many research domains.

Data for Policy (dataforpolicy.org), a global community, focuses on policy–data interactions by exploring how data can be used for policy in an ethical, responsible, and efficient manner. Within its journal, six focus areas, including Data for Policy Area 1: Digital & Data-driven Transformations in Governance, were established to delineate the evolving research landscape from the Data for Policy Conference series. This review addresses the absence of a formal conceptualization of digital and data-driven transformations in governance within this focus area. The paper achieves this by providing a working definition, mapping current research trends, and proposing a future research agenda centered on three core transformations: (1) public participation and collective intelligence; (2) relationships and organizations; and (3) open data and government. The paper outlines research questions and connects these transformations to related areas such as artificial intelligence (AI), sustainable smart cities, digital divide, data governance, co-production, and service quality. This contribution forms the foundational development of a research agenda for academics and practitioners engaged in or impacted by digital and data-driven transformations in policy and governance.