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Knowing What to Share: Selective Revealing in Open Data

Enders, Tobias; Wolff, Clemens; Satzger, Gerhard

Abstract:
For a long time, organizations have operated within their own microcosm. However, with the world getting more connected and with constantly increasing competition, companies have started to allow their organizational boundaries to become permeable. A recent phenomenon instantiating the in- and outflow of knowledge in private sector organizations is their engagement in open data initiatives. While revealing data creates new opportunities such as co-created innovation, it does not come without risk. Prematurely exposing data may harm the data provider, creating a need for guidance in assessing whether or not to share a given dataset. Based on a structured literature review, we conduct a qualitative content analysis to derive general concepts impacting the decision-making process. We identify twelve concepts split into five decision criteria, six dataset metrics and a macro-level factor, which we combine into an early-stage conceptual decision framework. Furthermore, we conduct a cross-impact analysis based on expert feedback and derive initial insights on the relationship between datset metrics and decision criteria. By this, we contribute to the understanding of selectively revealing data in the context of open data and provide an initial direction to practitioners on balancing knowledge protection and knowledge sharing.



Zugehörige Institution(en) am KIT Karlsruhe Service Research Institute (KSRI)
Institut für Wirtschaftsinformatik und Marketing (IISM)
Publikationstyp Proceedingsbeitrag
Publikationsdatum 15.06.2020
Sprache Englisch
Identifikator KITopen-ID: 1000119390
Erschienen in European Conference on Information Systems (ECIS), Marrakesch, Marokko, June 15 - 17 2020
Veranstaltung 28th European Conference on Information Systems (ECIS 2020), Marrakesch, Marokko, 15.06.2020 – 17.06.2020
Bemerkung zur Veröffentlichung virtual conference
Schlagwörter Open Data, Selective Revealing, Data Sharing
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