Collaboration in Open Government Data Ecosystems: Open Cross-sector Sharing and Co-development of Data and Software

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Abstract

Background: Open innovation highlights the potential benefits of external collaboration and knowledge-sharing, often exemplified through Open Source Software (OSS). The public sector has thus far mainly focused on the sharing of Open Government Data (OGD), often with a supply-driven approach with limited feedback-loops. We hypothesize that public sector organizations can extend the open innovation benefits by also creating platforms, where OGD, related OSS, and open standards are collaboratively developed and shared. Objective: The objective of this study is to explore how public sector organizations in the role of platform providers facilitate such collaboration in the form of OGD ecosystems and how the ecosystem’s governance may be structured to support the collaboration. Method: We conduct an exploratory multiple-case study of two such ecosystems, focused on OGD related to the Swedish labor market and public transport sector, respectively. Data is gathered through interviews, document studies, and prolonged engagement at one of the platform providers. Results: The study presents governance structure and collaboration practices of the two ecosystems and discusses how these contribute to the platform providers’ goals. The case studies highlight the need for platform providers to take an active and multi-functional role in enabling the sharing of data and software from and between the members of the ecosystem. Conclusions: We conclude that OGD ecosystems offer public sector organizations a possibility to catalyze the potential innovation output of OGD, but that it requires investment and adoption of an open and collaborative mindset.

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APA

Linåker, J., & Runeson, P. (2020). Collaboration in Open Government Data Ecosystems: Open Cross-sector Sharing and Co-development of Data and Software. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 12219 LNCS, pp. 290–303). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-57599-1_22

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