Projecting the future for design science research: An action-case based analysis

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Abstract

Design science research should be relevant, valuable, purposeful and prescriptive. Its value as a relevant source of prescriptions implies the practical usefulness of its results beyond a single expository instantiation. But propagation of such design science products as design principles and theories appears to be a key challenge. In this paper we commence with a DESRIST paper from 2012 that instantiated design principles in an artifact for a bank. That paper included plans and techniques for future use of its principles (propagation), including prescriptions for a five-phase adoption process. In this paper we discuss the propagation issues around generalizing design science research across multiple contexts and propose alternative propagation concepts of projectability and entrenchment. The existing concepts around generalizability have issues that make them less suitable for design science research: context (local/possible worlds) and theoretical statements based on functional explanations. A projection is any relevant instance that supports a theory. Projectability involves defining the relationship between a base case or evidence and a projection. Entrenchment occurs when design principles or theories have stimulated many actual projections. We demonstrate these concepts in a case study of propagation: a chemical manufacturer and service provider that adopted the design principles arising from that 2012 DESRIST banking-based design science research. We conclude that generalizability is too well-oriented to descriptive research and argue that a more appropriate framing for design science research is projectability and entrenchment. The paper includes recommendations to increase the projectability of design science research.

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APA

Baskerville, R., & Pries-Heje, J. (2015). Projecting the future for design science research: An action-case based analysis. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 9073, pp. 280–291). Springer Verlag. https://doi.org/10.1007/978-3-319-18714-3_18

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