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Opening Pandora’s Box? Guiding Organizations Through Selective Open Data Revealing [in press]

Enders, Tobias; Wolff, Clemens; Kienzle, Laura


Concealing knowledge is critical for companies to maintain their competitive advantage in the long-term. Globalization and digitalization, however, force companies to rethink their approach to knowledge sharing to combat increasing competition. In recent years, private sector organizations have started to engage in open data initiatives, thereby allowing the in- and outflow of knowledge. While open data may foster innovation and increase transparency, it does not come without risk. Incautiously revealing data to the public may harm the data provider itself, calling for guidance on the decision-making process. Based on a design science research (DSR) approach, we address this research problem and thereby make the following contributions: First, we derive universal design requirements for artifacts on selective knowledge revealing. Second, we design and evaluate a method for the application case of open data revealing. For practitioners, we provide concrete guidance for the decision-making process in form of a workshop concept.

Zugehörige Institution(en) am KIT Institut für Wirtschaftsinformatik und Marketing (IISM)
Karlsruhe Service Research Institute (KSRI)
Publikationstyp Proceedingsbeitrag
Publikationsjahr 2021
Sprache Englisch
Identifikator KITopen-ID: 1000133281
Erschienen in Proceedings of 28th Pacific Asia Conference on Information Systems
Veranstaltung 28th Pacific Asia Conference on Information Systems (PACIS 2021), Online, 12.07.2021 – 14.07.2021
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