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FEDS: a Framework for Evaluation in Design Science Research

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  • John Venable
  • Jan Pries-Heje
  • Richard Baskerville

Abstract

Evaluation of design artefacts and design theories is a key activity in Design Science Research (DSR), as it provides feedback for further development and (if done correctly) assures the rigour of the research. However, the extant DSR literature provides insufficient guidance on evaluation to enable Design Science Researchers to effectively design and incorporate evaluation activities into a DSR project that can achieve DSR goals and objectives. To address this research gap, this research paper develops, explicates, and provides evidence for the utility of a Framework for Evaluation in Design Science (FEDS) together with a process to guide design science researchers in developing a strategy for evaluating the artefacts they develop within a DSR project. A FEDS strategy considers why, when, how, and what to evaluate. FEDS includes a two-dimensional characterisation of DSR evaluation episodes (particular evaluations), with one dimension being the functional purpose of the evaluation (formative or summative) and the other dimension being the paradigm of the evaluation (artificial or naturalistic). The FEDS evaluation design process is comprised of four steps: (1) explicate the goals of the evaluation, (2) choose the evaluation strategy or strategies, (3) determine the properties to evaluate, and (4) design the individual evaluation episode(s). The paper illustrates the framework with two examples and provides evidence of its utility via a naturalistic, summative evaluation through its use on an actual DSR project.

Suggested Citation

  • John Venable & Jan Pries-Heje & Richard Baskerville, 2016. "FEDS: a Framework for Evaluation in Design Science Research," European Journal of Information Systems, Taylor & Francis Journals, vol. 25(1), pages 77-89, January.
    • Handle: RePEc:taf:tjisxx:v:25:y:2016:i:1:p:77-89
    DOI: 10.1057/ejis.2014.36
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    Citations

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    Cited by:

    1. João Barata & Paulo Rupino Cunha & António Dias Figueiredo, 2023. "Self-reporting Limitations in Information Systems Design Science Research," Business & Information Systems Engineering: The International Journal of WIRTSCHAFTSINFORMATIK, Springer;Gesellschaft für Informatik e.V. (GI), vol. 65(2), pages 143-160, April.
    2. Julia Brasse & Hanna Rebecca Broder & Maximilian Förster & Mathias Klier & Irina Sigler, 2023. "Explainable artificial intelligence in information systems: A review of the status quo and future research directions," Electronic Markets, Springer;IIM University of St. Gallen, vol. 33(1), pages 1-30, December.
    3. Raffaele Fabio Ciriello & Alexandra Cecilie Gjøl Torbensen & Magnus Rotvit Perlt Hansen & Christoph Müller-Bloch, 2023. "Blockchain-based digital rights management systems: Design principles for the music industry," Electronic Markets, Springer;IIM University of St. Gallen, vol. 33(1), pages 1-21, December.
    4. Albanese, Massimo, 2023. "Reviewing literature through multidimensional representations," International Journal of Accounting Information Systems, Elsevier, vol. 49(C).
    5. Joshua Holstein & Max Schemmer & Johannes Jakubik & Michael Vössing & Gerhard Satzger, 2023. "Sanitizing data for analysis: Designing systems for data understanding," Electronic Markets, Springer;IIM University of St. Gallen, vol. 33(1), pages 1-18, December.
    6. Laleh Rafati & Geert Poels, 2017. "Value-Driven Strategic Sourcing Based on Service-Dominant Logic," Service Science, INFORMS, vol. 9(4), pages 275-287, December.
    7. Quick, Reiner & Münch, M. & Mayer, J. H., 2023. "Lessons Learned from a Case Study: a Diamond Model for Implementing and Scaling Process Mining," Publications of Darmstadt Technical University, Institute for Business Studies (BWL) 142479, Darmstadt Technical University, Department of Business Administration, Economics and Law, Institute for Business Studies (BWL).
    8. Dedi I. Inan & Ghassan Beydoun & Biswajeet Pradhan, 2022. "Disaster Management Knowledge Analysis Framework Validated," Information Systems Frontiers, Springer, vol. 24(6), pages 2077-2097, December.
    9. Lukas-Valentin Herm & Christian Janiesch & Alexander Helm & Florian Imgrund & Adrian Hofmann & Axel Winkelmann, 2023. "A framework for implementing robotic process automation projects," Information Systems and e-Business Management, Springer, vol. 21(1), pages 1-35, March.
    10. Julius Peter Landwehr & Niklas Kühl & Jannis Walk & Mario Gnädig, 2022. "Design Knowledge for Deep-Learning-Enabled Image-Based Decision Support Systems," Business & Information Systems Engineering: The International Journal of WIRTSCHAFTSINFORMATIK, Springer;Gesellschaft für Informatik e.V. (GI), vol. 64(6), pages 707-728, December.
    11. Tom Lewandowski & Emir Kučević & Stephan Leible & Mathis Poser & Tilo Böhmann, 2023. "Enhancing conversational agents for successful operation: A multi-perspective evaluation approach for continuous improvement," Electronic Markets, Springer;IIM University of St. Gallen, vol. 33(1), pages 1-20, December.
    12. Hendrik Haße & Hendrik Valk & Frederik Möller & Boris Otto, 2022. "Design Principles for Shared Digital Twins in Distributed Systems," Business & Information Systems Engineering: The International Journal of WIRTSCHAFTSINFORMATIK, Springer;Gesellschaft für Informatik e.V. (GI), vol. 64(6), pages 751-772, December.
    13. Christian Meske & Enrico Bunde, 2023. "Design Principles for User Interfaces in AI-Based Decision Support Systems: The Case of Explainable Hate Speech Detection," Information Systems Frontiers, Springer, vol. 25(2), pages 743-773, April.

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