Despite an increasing interest in the strategies to promote open data use in recent years, there has been a substantial lack of empirical and theoretical analysis of the governance modes that favored different types of open data initiatives. To address this gap, this study asks: How do governance modes support open data sharing in open government data platforms? To answer this question, we assess the coherence of the open data governance contexts of France and Ireland when sharing data on open government data platforms during the Covid-19 crisis. The study uses a multi-method approach involving both interviews with experts, identified through purposive sampling, and secondary sources for triangulation purposes. Overall, the governance context supported open data sharing in France and Ireland. Both cases are characterized by a strong central coordination with a solid trust relationship and clear legal frameworks. France, more than Ireland, relied on a market governance mode, and Ireland scored higher in networked governance due to the creation of social capital. The results provide new insights on how to combine governance modes that support open government data initiatives through coordination, collaboration with the private sector, and involvement of different actors. Practitioners can use our insights as examples of governance strategies that are fit for events that need a timely open data response.

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Open government and open (government) data are seen as tools to create new opportunities, eliminate or at least reduce information inequalities and improve public services. More than a decade of these efforts has provided much experience, practices, and perspectives to learn how to better deal with them. This paper focuses on benchmarking of open data initiatives over the years and attempts to identify patterns observed among European countries that could lead to disparities in the development, growth, and sustainability of open data ecosystems. To do this, we studied benchmarks and indices published over the last years (57 editions of 8 artifacts) and conducted a comparative case study of eight European countries, identifying patterns among them considering different potentially relevant contexts such as e-government, open government data, open data indices and rankings, and others relevant for the country under consideration. Using a Delphi method, we reached a consensus within a panel of experts and validated a final list of 94 patterns, including their frequency of occurrence among studied countries and their effects on the respective countries. Finally, we took a closer look at the developments in identified contexts over the years and defined 21 recommendations for more resilient and sustainable open government data initiatives and ecosystems and future steps in this area.@en

There is a lack of understanding of the elements that constitute different types of value-adding public data ecosystems and how these elements form and shape the development of these ecosystems over time, which can lead to misguided efforts to develop future public data ecosystems. The aim of the study is twofold: (1) to explore how public data ecosystems have developed over time and (2) to identify the value-adding elements and formative characteristics of public data ecosystems. Using an exploratory retrospective analysis and a deductive approach, we systematically review 148 studies published between 1994 and 2023. Based on the results, this study presents a typology of public data ecosystems and develops a conceptual model of elements and formative characteristics that contribute most to value-adding public data ecosystems. Moreover, this study develops a conceptual model of the evolutionary generation of public data ecosystems represented by six generations that differ in terms of (a) components and relationships, (b) stakeholders, (c) actors and their roles, (d) data types, (e) processes and activities, and (f) data lifecycle phases. Finally, three avenues for a future research agenda are proposed. This study is relevant for practitioners suggesting what elements of public data ecosystems have the most potential to generate value and should thus be part of public data ecosystems. As a scientific contribution, this study integrates conceptual knowledge about the elements of public data ecosystems, the evolution of these ecosystems, defines a future research agenda, and thereby moves towards defining public data ecosystems of the new generation.@en

Purpose: The purpose of this paper is to highlight the drivers, barriers, benefits and risks affecting the integration of Internet of Things (IoT) into the e-government and to provide a future research agenda. Design/methodology/approach: Existing literature examining the relationships between e-government and IoT is scanned and evaluated by conceptualizing the IoT concept in the e-government perspective. Findings: The study shows that there are drivers to integrate IoT in e-government, such as ensuring the economy, efficiency and effectiveness of government operations, which would largely establish a relationship between the government and the citizens. Furthermore, there are barriers to such integration, given the lack of political will, the appropriate information technology infrastructure, the training of the stakeholders with a focus on the employee and the like. Originality/value: The integration of IoT in e-government is a novel and weakly explored concept, particularly in the light of new advances such as blockchain in the e-government, which requires further exploration and conceptualization, thereby achieving a shared/common vision and body of knowledge for its further successful and sustainable adoption – to the best of the authors’ knowledge, the current study is one of these initial attempts.@en

The future is likely to see an increase in the use of automated decision-making systems in the public sector, which employ Artificial Intelligence and, in particular, machine learning techniques, to enable more proactive and personalised delivery of public services. Proactive delivery can reduce the administrative burdens on citizens and government staff. While there is a small but growing body of literature that highlights the benefits of proactive public services, the implementation of such services is data intensive and can harm citizens beyond privacy concerns. Proactive service delivery requires high degrees of automated data processing using various data sources and algorithms that reduce the level of human control that both citizens and public officials have in verifying or correcting system errors. The purpose of this workshop is to initiate a discussion about proactive and personalised public services, discussing them and learning from the EGOV community about the practice of applying proactive and personalised services in different countries. This includes presenting an initial version of the developed framework for proactive and personalised public services, which is expected to provide further research directions.@en

Open Government Data (OGD) is a fundamental source for sustainability-oriented and data-driven innovation by citizens, companies, and other actors. However, many government agencies are reluctant to openly share their data with the public. While the resistance of public organizations to openly share government data has been investigated in previous research, most of these studies are focused on the reuse of open government data by companies and citizens. There is a paucity of research applying theoretical models to study the provision of OGD, and more specifically, the resistance of public organizations to make government data publicly available. We argue that Innovation Resistance Theory (IRT), which considers both functional and psychological factors, can be used to study OGD barriers, where OGD is seen as a source of innovation. This study aims to develop an OGD-adapted IRT model to empirically identify predictors affecting public agencies' resistance to openly sharing government data. Based on a review of the literature on both IRT research and barriers associated with open data sharing by public agencies, we develop an initial version of the model. In our future research, we plan to conduct exploratory interviews in multiple countries to refine the model. Ultimately, we will validate the refined model to study the resistance of public authorities to openly sharing government data in a quantitative study. @en