A Network-based Tool for Identifying Knowledge Risks in Data-Driven Business Models

Date
2021-01-05
Authors
Fruhwirth, Michael
Pammer-Schindler, Viktoria
Thalmann, Stefan
Journal Title
Journal ISSN
Volume Title
Publisher
Volume
Number/Issue
Starting Page
5218
Ending Page
Alternative Title
Abstract
Data-driven technologies enable organizations to innovate new services and business models and thus hold the potential for new sources of revenue and business growth. However, such new data-driven business models impose new ways for unwanted knowledge spillovers. Current research on data-driven business models and knowledge risks provides little help to identify and discuss such novel risks within the innovation process. We have developed a network-based representation of data-driven business models within one case organization, where it helped to identify knowledge risks in the design process of data-driven business models. In this paper, we further evaluated the artifact through 17 interviews with experts from the domain of business models, data analytics and knowledge management. We found that the network-based representation is suitable to visualize, discuss and create awareness for knowledge risks and see types of data-related value objects and quantification of risks as two recommendations for further research.
Description
Keywords
Securing Knowledge Systems and Managing Knowledge Risks, data-driven business model, design science research, evaluation, knowledge risks, tool
Citation
Extent
10 pages
Format
Geographic Location
Time Period
Related To
Proceedings of the 54th Hawaii International Conference on System Sciences
Rights
Attribution-NonCommercial-NoDerivatives 4.0 International
Rights Holder
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