Open Data Ecosystems — An empirical investigation into an emerging industry collaboration concept
2021 (English)In: Journal of Systems and Software, ISSN 0164-1212, E-ISSN 1873-1228, Vol. 182, article id 111088Article in journal (Refereed) Published
Abstract [en]
Software systems are increasingly depending on data, particularly with the rising use of machine learning, and developers are looking for new sources of data. Open Data Ecosystems (ODE) is an emerging concept for data sharing under public licenses in software ecosystems, similar to Open Source Software (OSS). It has certain similarities to Open Government Data (OGD), where public agencies share data for innovation and transparency. We aimed to explore open data ecosystems involving commercial actors. Thus, we organized five focus groups with 27 practitioners from 22 companies, public organizations, and research institutes. Based on the outcomes, we surveyed three cases of emerging ODE practice to further understand the concepts and to validate the initial findings. The main outcome is an initial conceptual model of ODEs’ value, intrinsics, governance, and evolution, and propositions for practice and further research. We found that ODE must be value driven. Regarding the intrinsics of data, we found their type, meta-data, and legal frameworks influential for their openness. We also found the characteristics of ecosystem initiation, organization, data acquisition and openness be differentiating, which we advise research and practice to take into consideration. © 2021 The Author(s)
Place, publisher, year, edition, pages
Elsevier Inc. , 2021. Vol. 182, article id 111088
Keywords [en]
Empirical study, Open data, Open data ecosystem, Open innovation, Data acquisition, Ecosystems, Open source software, Open systems, Ordinary differential equations, Data Sharing, Empirical investigation, Empirical studies, Industry collaboration, New sources, Open datum, Software-systems
National Category
Software Engineering
Identifiers
URN: urn:nbn:se:ri:diva-56911DOI: 10.1016/j.jss.2021.111088Scopus ID: 2-s2.0-85115889980OAI: oai:DiVA.org:ri-56911DiVA, id: diva2:1613755
Note
Funding details: VINNOVA, 2018-04341, 2019-05150, 2020-00025; Funding text 1: We thank our collaborator Sofie Westerdahl of Mobile Heights for co-organizing the workshops. Thanks to the participants in the focus groups for their contributions. Thanks also to Dr. Markus Borg, RISE, and the anonymous reviewers of this journal for reviewing an earlier version of this paper. This work was funded by the Swedish National Innovation Agency, VINNOVA , under grant 2018-04341 for groundbreaking ideas in industrial development, grant 2020-00025 for ESS Data Lab, grant 2019-05150 for Road Data Lab, and by the Swedish Public Employment Service .
2021-11-232021-11-232024-06-13Bibliographically approved