Abstract
The goal of this paper is to demonstrate the feasibility of combining visualization and reasoning for business model design by combining the machine-interpretability of ontologies with a further development of the widely accepted business modeling tool, the Business Model Canvas (BMC). Since ontologies are a machine-interpretable representation of enterprise knowledge and thus, not very adequate for human interpretation, we present a tool that combines the graphical and human interpretable representation of BMC with a business model ontology. The tool connects a business model with reusable data and interoperability to other intelligent business information systems so that additional functionalities are made possible, such as a comparison between business models. This research follows the design science strategy with a qualitative approach by applying literature research, expert interviews, and desk research. The developed AOAME4BMC tool consists of the frontend, a graphical web-based representation of an enhanced BMC, a web service for the data exchange with the backend, and a specific ontology for the machine-interpretable representation of a business model. The results suggest that the developed tool AOAME4BMC supports the suitability of an ontology-based representation for business model design.
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Peter, M., Montecchiari, D., Hinkelmann, K., Gatziu Grivas, S. (2020). Ontology-Based Visualization for Business Model Design. In: Grabis, J., Bork, D. (eds) The Practice of Enterprise Modeling. PoEM 2020. Lecture Notes in Business Information Processing, vol 400. Springer, Cham. https://doi.org/10.1007/978-3-030-63479-7_17
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