On Deriving Business Models from Process Models: An Empirical Study
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2023-01
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At least two business requirement perspectives of the digital ecosystem should be revisited in case such an ecosystem changes significantly: (1) the business process perspective (e.g. represented by BPMN 2.0 model), and (2) the business value perspective (e.g. depicted by an e3 value model). Although both perspectives differ largely and address different stakeholder concerns, there is also overlap between the two points of view. Moreover, often there is already an explicit understanding of how the parties in the ecosystem execute processes. However, the business value model is, in many cases, left implicit, whereas most disruptive technologies result in significant changes in the business value model. These are important to understand and analyze using a model-based approach such as the e3 value. To speed up the elicitation process, it would be more efficient to elicit an e3 value model using an already existing process model. We test a series of guidelines to derive ane3valuemodel from a given BPMN model. We conducted a controlled experiment through which we analyze the quality of a conceptual model – e3 value business value modeling (e3 value) – derived from another conceptual model – Business Process Modeling Notation v.2.0(BPMN). We measure model quality via validity and completeness with respect to a normative standard solution and an expert solution. The subjects are separated into two groups: the treatment group, which uses the guidelines to derive one model (e3 value) from the other (BPMN), and, the control group, which does not use the guidelines. Furthermore, we analyze and evaluate the implications of the experiment's results to understand the limitations and to improve them in future research.
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On Deriving Business Models from Process Models: An Empirical Study. Isaac da Silva Torres, Francisco Javier Pérez Blanco, Marcelo Fantinato, Juan Manuel Vara Mesa, Jaap Gordijn. Proceedings of the 56th Annual Hawaii International Conference on System Sciences (HICSS 56), 2023. pp. 5789-5798
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