Design: Decision-Support Model

Egbert Steyn (), Merwe Oberholzer (), Matthew Mullarkey () and Pieter Buys ()
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Egbert Steyn: Ascendis Health
Merwe Oberholzer: North-West University
Matthew Mullarkey: University of South Florida
Pieter Buys: North-West University

Chapter Chapter 7 in Programmable Decisions for Business Organizations, 2025, pp 125-135 from Springer

Abstract: Abstract This chapter describes the elaborated action design research (eADR) design iteration, elucidating the design of the envisaged artificial intelligence (AI) decision-support model. This iteration integrates the earlier developmental diagnostic iterations’ _DecisionArtifact, _SocialArtifact, and _DataSetArtifact into the _DesignModelArtifact, guided by a literature-based, stage-driven approach. This artifact encompasses two operational environments: technical and social. In the context of the actor-network theory (ANT), no new actors emerged during this stage, and the actor-network transitioned from interessement to the enrollment phase of transition. In the technical environment, the _DataSetArtifact serves as the knowledge base. User queries trigger specific codes that impact key performance indicators (KPIs), with the knowledge base assisting the inference engine in evaluating and providing feedback through the user interface. The social environment focuses on user adoption, with the _SocialArtifact serving as the foundation for the human experience. It emphasizes the technology acceptance model (TAM), particularly perceived ease of use (PEoU) and perceived usefulness (PU), as well as the value-based adoption model (VAM), which highlights benefits and sacrifices.

Keywords: Actor-network theory; Artificial intelligence; Elaborated action design research; Key performance indicators; Technology acceptance model; Value-based adoption model (search for similar items in EconPapers)
JEL-codes: M15 (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/978-981-95-0724-5_7

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