Conference Agenda

The increasing adoption of digital technologies, including algorithms and artificial intelligence (AI), has profoundly reshaped local governance by transforming decision-making processes, working routines and stakeholder interactions (Busuioc 2021; Gong, Yang, and Shi 2020; Meijer & Grimmelikhuijsen, 2020; Sousa et al.,

2019). This digital transformation promises improved service quality and innovative solutions to complex challenges (Cath 2018; Koulu 2020; Meijer 2018; Mergel, Rethemeyer, and Isett 2016).

However, it has also given rise to critical concerns on privacy, lack of interoperability, reliance on third parties, and insufficient staff capacities (Hacker & Neyer, 2023; Kempeneer & Heylen, 2023; Kitchin & McArdle, 2016; Klievink et al., 2017). In addition, governments frequently encounter difficulties in implementing data-driven solutions in an efficient, transparent, fair, and democratic manner.

As local governments increasingly rely on data, they must also navigate the risks associated with datafication, algorithmic bias, and unintended consequences of algorithmic decision-making (Sadowski, 2021; Ziosi et al., 2022). Therefore, this paper explores the conditions and circumstances that shape the (un)successful digital transformation in local governance. To do so, we focus on health data governance. As shown during the COVID-19 crisis, public health policy is a key area where data and digital technologies hold significant potential in local governance (Petrova & Tairov, 2022; Sweeney, 2020; Williams et al., 2022). The pandemic also introduced new health data registries and accompanying policies (Kist, 2022).

Drawing on local experiences during and after the pandemic, this paper identifies the capacities required at both the meso and micro levels to establish the necessary soft and hard infrastructure for responsible health data governance.

A qualitative research strategy was employed to answer this question, consisting of 10 semi-structured interviews with key stakeholders within five local governments. These interviews were followed by validation through a focus group with regional and national experts in health data governance. Purposive sampling ensured in-depth insights from policymakers and administrative professionals with direct experience in managing and (re)using health data.

The findings contribute to the broader debate on balancing technological potential and opportunities with ethical and democratic concerns, emphasizing the need to ensure that digital innovations serve the public interest while safeguarding citizens' rights and trust in government.

The digital transformation of public administration increasingly depends on the ability to coordinate collaborative data innovation (CDI) among various institutional actors. Platform ecosystems have the potential to promote transparency, civic engagement, economic growth, and improve service government delivery. There is a need for public management to revisit the transformative theories developed to date to allow for appropriate management of emerging intelligent technologies and the platformization of governments. (Kim et al., 2022).

In this perspective, two research questions arise: How do we address the infrastructural disconnect between organizations that provide and those that consume data? What happens when information flows transcend organizational boundaries, and what are the enabling factors favour the completion of this antithesis?

A case study was conducted to answer these research questions. The Italian National Data Platform (PDND) promotes interoperability, transparency, and public value creation across the Italian public sector. Drawing on the theoretical framework of CDI, which integrates insights from collaborative innovation and data collaborative literature (Meijer & Ettlinger, 2025), this research explores the PDND as both a technical infrastructure and governance innovation.

This case study situates the PDND within the European context of digital government reform, highlighting how it operationalizes the principles of interoperability and the once-only policy while also addressing the cultural, legal, and technological barriers identified in CDI literature (Meijer & Ettlinger, 2025; Widlak & Peeters, 2025). The analysis reveals that the PDND's success is supported by strong institutional leadership, the establishment of common data standards, and the gradual institutionalization of collaborative practices—key drivers (Crosby et al., 2017).

PDND has achieved significant milestones: more than 6,000 municipalities and numerous central agencies have integrated with the platform, facilitating the co-production of services. The research argues that the PDND exemplifies the transition from experimental, ad hoc data collaborations to an institutionalized model of CDI, where public value is generated through efficiency gains and enhanced citizen empowerment (Hartley et al., 2013).

The implications are twofold: For research, the PDND provides a rich empirical setting to refine theories of collaborative data innovation in complex, multi-actor public systems. For practice and policy, it offers actionable insights into designing digital infrastructures that balance standardization with flexibility and institutional control with participatory openness.

References

Crosby, B. C., ‘T Hart, P., & Torfing, J. (2017). Public value creation through collaborative innovation. Public Management Review, 19(5), 655–669. https://doi.org/10.1080/14719037.2016.1192165

Hartley, J., Sørensen, E., & Torfing, J. (2013). Collaborative Innovation: A Viable Alternative to Market Competition and Organizational Entrepreneurship. Public Administration Review, 73(6), 821–830. https://doi.org/10.1111/puar.12136

Kim, S., Andersen, K. N., & Lee, J. (2022). Platform government in the era of smart technology. Public Administration Review, 82(2), 362-368.

Meijer, A., & Ettlinger, K. (2025). Collaborative data innovation: Developing a theoretical and empirical understanding of drivers, barriers and outcomes. International Journal of Public Sector Management. https://doi.org/10.1108/IJPSM-08-2024-0284

Widlak, A. C., & Peeters, R. (2025). A theory of the infrastructure-level bureaucracy: Understanding the consequences of data-exchange for procedural justice, organizational decision-making, and data itself. Government Information Quarterly, 42(2), 102021. https://doi.org/10.1016/j.giq.2025.102021