The complex societal problems that we face today require unprecedented collaboration and evidence-based decisions. These collaboration processes are further propelled by the datafication of virtually all spheres of public life. To benefit from this, the data needs to be made available to allow for data analytics. Thus, data sharing becomes a crucial aspect of cross-sector collaborations that aim to create and capture value from information. Compared to collaborations where data sharing is not the main goal, data sharing partnerships face a number of novel challenges, such as mitigating data risks, complying with data protection legislation, and ensuring responsible data use. Navigating these waters and achieving data sharing can be challenging for both governments and businesses, as well as other actors. How do organizations from different sectors manage to achieve data sharing for addressing societal challenges? To address this research question, we apply a framework of three models of cross sector social partnerships developed in the field of organization studies to structure the analysis of six cases. Our analysis suggests that to a certain extent the partnership model determines the types of drivers and challenges to sharing data in a partnership. Leveraging the drivers and anticipating these challenges can help organizations be more aware of key terms of the collaboration and the mechanisms that can be used to succeed in their partnership goals.@en

It has been advocated that sharing business data can generate public value. Still this information sharing often needs to be done on voluntary basis and that often poses major challenges. The main research question addressed in this paper is: How is voluntary information sharing to create public value achieved and what are the drivers and mechanisms to achieve that? While voluntary information sharing to achieve public value is recognized in the eGovernment literature, this literature is limited to understand how such information sharing can be achieved. To address the research question, we borrow a framework of platforms for cross sector social partnerships from organization studies and use it as a conceptual lens to structure the analysis of three case studies where voluntary information sharing was achieved in different domains. Building on the framework and our case analysis, we distinguish three types of information sharing collaborations, namely Resource-dependence platform, Social Issue platform, and Societal Sector platform which allow to distinguish the motivations why parties enter into voluntary information sharing collaborations. Our analysis suggests that while the higher goal of the voluntary information sharing may be the same (i.e. to create public value), parties are driven by different motivations of why they enter into the information sharing collaborations. Furthermore, in each of these different types of collaborations the mechanisms of how the information sharing was achieved, as well as the role the government can play, differ.

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For decades good governance scholarship has focused attention on the importance of government openness [26], [34]. Since the 1960s, Freedom of Information (FOI) legislation has formed the backbone of institutional support for opening information and documents [76] and participatory processes [4], [79]. However, FOI represents a passive approach to releasing information. Persons or organizations must still request the information they want, referred to casually, as freedom of information requests. Since the 1990s, publishing documents on websites or using communication technologies to engage citizens in participation processes has signaled a more proactive approach to releasing government information and political engagement. Since 2003, governments have re-envisioned their passive and proactive approaches to include an open data agenda, [18], [62], where publishing documents and data in open formats, [35], [63] is the preferred way. Collectively, these developments have forged the basis for what has been commonly referred to as the open government and data movements [30]. Open data practices and policies are praised for their potential to generate public value, particularly through innovation, economic growth, and transparency, [5], [9], [18], [21], [81]. The nature and character of open data has been hailed for its innovative capacity and transformative power [19], [35], [40], [45], [80]. Various studies have confirmed that proactively releasing public and private data in open formats creates considerable benefits for citizens, researchers, companies and other stakeholders, such as business creation or having the ability to understand public or private problems in new ways through advanced data analytics, [5], [9], [18], [21], [81]. Only a handful of articles examine both the unintended consequences and negative side effects of opening data, [33] and the underlying causal mechanisms that actually lead to the desired open data benefits [5]. Open data research is still in its infancy, and as a result, the extant literature uses limited application and development of theory toward understanding the open data phenomenon. While scholars acknowledge diverse perspectives, it is not clear which theories are most relevant, nor whether a single or integrated theory is needed. This special issue is part of a series of two special issues about open data. This issue focuses on the relationship between innovation and open data, while the second special issue emphasizes research on open data related to transparency and open data policies. To realize the practical benefits of this transformative practice and to develop theory, more research needs to focus on understanding how innovation occurs through open data activities. The papers in this special issue begin to address this gap. The introductory article discusses the state-of-the-art with respect to understanding the context of open data innovation, developments, challenges and barriers, presents an overview of open data research and outlines emerging research directions.@en