Purpose: In “data collaboratives”, private and public organizations coordinate their activities to leverage data to address a societal challenge. This paper aims to focus on analyzing challenges and coordination mechanisms of data collaboratives. Design/methodology/approach: This study uses coordination theory to identify and discuss the coordination problems and coordination mechanisms associated with data collaboratives. The authors also use a taxonomy of data collaborative forms from a previous empirical study to discuss how different forms of data collaboratives may require different coordination mechanisms. Findings: The study analyzed data collaboratives from the perspective of organizational and task levels. At the organizational level, the authors argue that data collaboratives present an example of the bazaar form of coordination. At the task level, the authors identified five coordination problems and discussed potential coordination mechanisms to address them, such as coordination by negotiation, by third party, by standardization, to name a few. Research limitations/implications: This study is one of the first few to systematically analyze the phenomenon of “data collaboratives”. Practical implications: This study can help practitioners better understand the coordination challenges they may face when initiating a data collaborative and to develop successful data collaboratives by using coordination mechanisms to mitigate these challenges. Originality/value: Data collaboratives are a novel form of data-driven initiatives which have seen rapid experimentation lately. This study draws attention to this concept in the academic literature and highlights some of the complexities of organizing data collaboratives in practice.@en

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

There is a lack of research concerning the factors influencing the success or failure of open data initiatives. Based on the results of two workshops, we provide a list of 47 success factors for open data publication and 18 success factors for open data use. We further use three case studies (ENGAGE, Open NY, and Open Vienna) to examine how the criticality of factors varies depending on the geographical level and other characteristics of the open data initiative. The cases, representing open data initiatives at city, regional, and transnational levels, point at different categories of critical success factors. Our key conclusions are that 1) the criticality of the factors depends considerably on the context of the open data initiative; 2) a number of success factors appear to be more universally applicable than others; 3) the factors that are critical to all three cases are derived from many different success factor categories, which suggests that open data initiatives should adopt an interdisciplinary approach, and 4) further work is needed to detail the success factors for open data publication and use in other contexts.@en

An increasing number of initiatives have emerged around the world to help facilitate data sharing and collaborations to leverage different sources of data to address societal problems. They are called “data collaboratives”. Data collaboratives are seen as a novel way to match real life problems with relevant expertise and data from across the sectors. Despite its significance and growing experimentation by practitioners, there has been limited research in this field. In this article, the authors report on the outcomes of a panel discussing critical issues facing data collaboratives and develop a research and development agenda. The panel included participants from the government, academics, and practitioners and was held in June 2017 during the 18 th International Conference on Digital Government Research at City University of New York (Staten Island, New York, USA). The article begins by discussing the concept of data collaboratives. Then the authors formulate research questions and topics for the research roadmap based on the panel discussions. The research roadmap poses questions across nine different topics: conceptualizing data collaboratives, value of data, matching data to problems, impact analysis, incentives, capabilities, governance, data management, and interoperability. Finally, the authors discuss how digital government research can contribute to answering some of the identified research questions. @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.@en

Data collaboratives present a new form of cross-sector and public-private partnership to leverage (often corporate) data for addressing a societal challenge. They can be seen as the latest attempt to make data accessible to solve public problems. Although an increasing number of initiatives can be found, there is hardly any analysis of these emerging practices. This paper seeks to develop a taxonomy of forms of data collaboratives. The taxonomy consists of six dimensions related to data sharing and eight dimensions related to data use. Our analysis shows that data collaboratives exist in a variety of models. The taxonomy can help organizations to find a suitable form when shaping their efforts to create public value from corporate and other data. The use of data is not only dependent on the organizational arrangement, but also on aspects like the type of policy problem, incentives for use, and the expected outcome of data collaborative. @en