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.
Enders, Tobias; Wolff, Clemens; and Satzger, Gerhard, "Knowing What to Share: Selective Revealing in Open Data" (2020). ECIS 2020 Research-in-Progress Papers. 11.
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