Conference Agenda

Despite national mandates and the widespread availability of e‑procurement platforms, the digitalization of public procurement procedures in Italy remains uneven. This paper investigates what drives that variation by focusing on regional contracting authorities from ordinary‑statute regions between 2019 and 2023. What drives this variation? Drawing on a sample of 30,450 regional procedures from a larger dataset (n≈2,000,000), the study employs descriptive statistics, logistic regressions and cluster analysis to answer this question. Results show that competitive and standardized procedures—such as open and restricted calls—are more likely to be digitalized, whereas discretionary and exceptional mechanisms (e.g., direct awards) exhibit markedly lower uptake. Contextual factors also matter: regional digital maturity (Digital Economy and Society Index - DESI) and participation in the National Recovery and Resilience Plan (NRRP) both boost adoption, while the effect of digital training becomes negligible once NRRP funding is considered. Cluster analysis reveals two distinct regional profiles—Digital diversified and Selective digital‑catalog only—highlighting divergent implementation trajectories. Taken together, the findings suggest that effective digitalization requires aligning policy design with procedural diversity, targeted capacity‑building, and structural incentives.

To address complex societal challenges, governments increasingly need to make evidence-based decisions and require the best available data as input. As much of relevant data is now in the hands of the private sector, governments increasingly resort to purchasing data from private sources. There is, however, scant empirical evidence and complete lack of understanding of the experiences of government organizations with this practice. This is problematic because data purchasing can differ substantially from general procurement or procurement in specific sectors. For instance, data purchasing can be highly intertwined with other parts of the purchase. Previous research established that data can be purchased in the form of datasets, data analyses, or data-based services. Other differences relate to the pricing model, ownership, control, privacy, and the accessibility of the data. Therefore, it is important to better understand how governments increasingly interact with data providers. This study asks the following questions: what necessitates governments to purchase private sector data, how is data purchased, and what are the outcomes of such purchasing? By taking the Netherlands as an example and drawing on the framework of public procurement process, we conducted an exploratory multiple case study based on interviews with seven government organizations at national, regional, and local level. These organizations have been identified in our previous research as buyers of data. For the case study, we identified nine use cases within these seven organizations in the mobility or spatial domains where there is an active business-to-government data market. Per each case, we interviewed respondents from the government organizations in various roles (procurement officer, data/digital innovation officer, and wherever possible contract manager or main user). The interviews amounted to sixteen and were conducted in April-May 2024. We asked the respondents questions concerning: (1) the preparation phase and what necessitated purchasing of the data from the private sector; (2) purchasing phase and how the data was purchased; and (3) the performance evaluation phase and what the outcomes of the purchases were. In our results, we report on these three elements of the analysis, drawing on the comparative analysis of the cases, and discuss, among others, the purpose of the purchases and to what extent alternatives and joint procurement were considered, the purchasing procedures and respective market characteristics, and the governments’ assessment and satisfaction with the purchases. We conclude with the discussion of the challenges and opportunities and reflect on facilitating and impeding policies in this domain. Our research presents the lessons from the Dutch case, but they are transferrable to other EU countries as the topic of data purchasing has received so little attention so far.

Governance principles and regulation are implemented in activities where the public interest is strong. These are organizations that are simultaneously linked by strong regulation that defines public tasks, interdependencies in the performance of key tasks, and a competitive situation. These include, for example, airports that compete in the markets of knowledge, competence and economy. Simultaneous dependencies cause an interdependencies problem in which it is not possible to solve an individual problem by a single action. This paper is based on data collected at 15 million passangers airport testbed in 2024. The stuctural data consist passenger data from safety control to the gate and is completed by empirical data collected using LiDar technology. The structural and empirical data will combine to form an image of the linear and non-linear behavior of people flow and in particular queue management systems and analytic predictions based on holistic data analysis. We will discuss the opportunities and constraints of confronting the public interest in a highly regulated market of knowledge, economy and competence.

The paper is focusing on two scholarly discussions. First, we will discuss the airport functions in the smart buildings framework, where public interest of smart cities is a key driver for management of infrastructures from diverse empirical data collection and databases. The systems theory is an angle to bring the opportunity to study airports empirically, allowing the organization of organizations, people's behavior and response to situations. We will formalize the choices and preferences of the passangers and thus systematically implement interaction by mapping the controversies (Rodriguez-Valencia et al., 2024; Schouten 2014). We will analyse the data based on the holistic approach of structural and empiric data and problems with its application (Yanagisawa et al., 2013). On a holistic basis, we refer to the defined complex system which is not predicated from examining the behavior of its separate parts. This systemic premise is a global way of looking at airport operations and system involving humans at airports. Finally, we will draw conclusions on functional improvements based on extensive and complex data (de Neufville 2020; Suikat et al., 2020).