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How Artificial Intelligence Challenges Tailorable Technology Design

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dc.contributor.authorFechner, Pascal
dc.contributor.authorKönig, Fabian
dc.contributor.authorLockl, Jannik
dc.contributor.authorRöglinger, Maximilian
dc.date2024-06-01
dc.date.accessioned2024-10-30T15:27:30Z
dc.date.available2024-10-30T15:27:30Z
dc.date.issued2024
dc.description.abstractArtificial intelligence (AI) has significantly advanced healthcare and created unprecedented opportunities to enhance patient-centeredness and empowerment. This progress promotes individualized medicine, where treatment and care are tailored to each patient’s unique needs and characteristics. The Theory of Tailorable Technology Design has considerable potential to contribute to individualized medicine as it focuses on information systems (IS) that users can modify and redesign in the context of use. While the theory accounts for both the designer and user perspectives in the lifecycle of an IS, it does not reflect the inductive learning and autonomy of AI throughout the tailoring process. Therefore, this study posits the conjecture that current knowledge about tailorable technology design does not effectively account for IS that incorporate AI. To investigate this conjecture and challenge the Theory of Tailorable Technology Design, a revelatory design study of an AI-enabled individual IS in the domain of bladder monitoring is conducted. Based on the empirical evidence from the design study, the primary contribution of this work lies in three propositions for the design of tailorable technology, culminating in a Revised Theory of Tailorable Technology Design. As the outcome of the design study, the secondary contribution of this work is concrete design knowledge for AI-enabled individualized bladder monitoring systems that empower patients with neurogenic lower urinary tract dysfunction (NLUTD). Overall, this study highlights the value of AI for patient-centeredness in IS design.de
dc.identifier.doi10.1007/s12599-024-00872-9
dc.identifier.issn1867-0202
dc.identifier.urihttp://dx.doi.org/10.1007/s12599-024-00872-9
dc.identifier.urihttps://dl.gi.de/handle/20.500.12116/45330
dc.publisherSpringer
dc.relation.ispartofBusiness & Information Systems Engineering: Vol. 66, No. 3
dc.relation.ispartofseriesBusiness & Information Systems Engineering
dc.subjectBladder monitoring
dc.subjectDeep transfer learning
dc.subjectIndividualization
dc.subjectNeurogenic lower urinary tract dysfunction
dc.subjectSmart wearables
dc.subjectTheory of tailorable technology design
dc.titleHow Artificial Intelligence Challenges Tailorable Technology Designde
dc.typeText/Journal Article
mci.reference.pages357-376

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BISE 66(3) - Juni 2024