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Design principles for a hybrid intelligence decision support system for business model validation

Author

Listed:
  • Dominik Dellermann

    (University of Kassel
    University of St. Gallen)

  • Nikolaus Lipusch

    (University of Kassel
    University of St. Gallen)

  • Philipp Ebel

    (University of Kassel
    University of St. Gallen)

  • Jan Marco Leimeister

    (University of Kassel
    University of St. Gallen)

Abstract

One of the most critical tasks for startups is to validate their business model. Therefore, entrepreneurs try to collect information such as feedback from other actors to assess the validity of their assumptions and make decisions. However, previous work on decisional guidance for business model validation provides no solution for the highly uncertain and complex context of early-stage startups. The purpose of this paper is, thus, to develop design principles for a Hybrid Intelligence decision support system (HI-DSS) that combines the complementary capabilities of human and machine intelligence. We follow a design science research approach to design a prototype artifact and a set of design principles. Our study provides prescriptive knowledge for HI-DSS and contributes to previous work on decision support for business models, the applications of complementary strengths of humans and machines for making decisions, and support systems for extremely uncertain decision-making problems.

Suggested Citation

  • Dominik Dellermann & Nikolaus Lipusch & Philipp Ebel & Jan Marco Leimeister, 2019. "Design principles for a hybrid intelligence decision support system for business model validation," Electronic Markets, Springer;IIM University of St. Gallen, vol. 29(3), pages 423-441, September.
    • Handle: RePEc:spr:elmark:v:29:y:2019:i:3:d:10.1007_s12525-018-0309-2
    DOI: 10.1007/s12525-018-0309-2
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    Cited by:

    1. Basma Hamrouni & Abdelhabib Bourouis & Ahmed Korichi & Mohsen Brahmi, 2021. "Explainable Ontology-Based Intelligent Decision Support System for Business Model Design and Sustainability," Sustainability, MDPI, vol. 13(17), pages 1-28, September.
    2. Thorsten Schoormann & Maren Stadtländer & Ralf Knackstedt, 2022. "Designing business model development tools for sustainability—a design science study," Electronic Markets, Springer;IIM University of St. Gallen, vol. 32(2), pages 645-667, June.
    3. Niklas Kühl & Max Schemmer & Marc Goutier & Gerhard Satzger, 2022. "Artificial intelligence and machine learning," Electronic Markets, Springer;IIM University of St. Gallen, vol. 32(4), pages 2235-2244, December.
    4. Philipp Ebel & Matthias Söllner & Jan Marco Leimeister & Kevin Crowston & Gert-Jan Vreede, 2021. "Hybrid intelligence in business networks," Electronic Markets, Springer;IIM University of St. Gallen, vol. 31(2), pages 313-318, June.
    5. Jan Brocke & Alexander Maedche, 2019. "The DSR grid: six core dimensions for effectively planning and communicating design science research projects," Electronic Markets, Springer;IIM University of St. Gallen, vol. 29(3), pages 379-385, September.
    6. Milad Mirbabaie & Felix Brünker & Nicholas R. J. Möllmann Frick & Stefan Stieglitz, 2022. "The rise of artificial intelligence – understanding the AI identity threat at the workplace," Electronic Markets, Springer;IIM University of St. Gallen, vol. 32(1), pages 73-99, March.
    7. Stefan Stieglitz & Milad Mirbabaie & Nicholas R. J. Möllmann & Jannik Rzyski, 2022. "Collaborating with Virtual Assistants in Organizations: Analyzing Social Loafing Tendencies and Responsibility Attribution," Information Systems Frontiers, Springer, vol. 24(3), pages 745-770, June.

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    More about this item

    Keywords

    Collective intelligence; Machine learning; Decision support system; Hybrid intelligence; Business model; Decision making;
    All these keywords.

    JEL classification:

    • D81 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Criteria for Decision-Making under Risk and Uncertainty

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