Paving the Way to a Green Future With Artificial Intelligence – Exploring Organizational Adoption Factors

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dc.contributor.authorFecho, Mariska
dc.contributor.authorWahl, Nihal
dc.contributor.authorVon Ahsen, Anette
dc.contributor.authorVetter, Oliver
dc.date.accessioned2023-12-26T18:36:27Z
dc.date.available2023-12-26T18:36:27Z
dc.date.issued2024-01-03
dc.identifier.doi10.24251/HICSS.2024.100
dc.identifier.isbn978-0-9981331-7-1
dc.identifier.other732fa889-9ddc-4be4-8b86-9baf3a1f6515
dc.identifier.urihttps://hdl.handle.net/10125/106477
dc.language.isoeng
dc.relation.ispartofProceedings of the 57th Hawaii International Conference on System Sciences
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectAnalytics and Decision Support for Green IS and Sustainability Applications
dc.subjectadoption
dc.subjectartificial intelligence
dc.subjectenvironmental sustainability
dc.subjectinstitutional theory
dc.subjecttoe framework
dc.titlePaving the Way to a Green Future With Artificial Intelligence – Exploring Organizational Adoption Factors
dc.typeConference Paper
dc.type.dcmiText
dcterms.abstractArtificial intelligence (AI) is gaining importance across various industries, enabling resource-efficient production and the sustainable management of business processes. With its capacity to analyze vast, interconnected databases, the adoption of green AI with in organizations can support the development of specific actions aimed at preserving the environment. In order to examine relevant influencing factors, we conducted a qualitative study using the established technology-organization-environment framework and institutional theory as analytical lenses. Through interviews with 21 experts representing diverse industries, we identified 17 factors that play a pivotal role in determining the adoption of green AI. Our key findings show that the adoption of green AI is mainly driven by its performance coupled with coercive pressure. Furthermore, the results reveal eight propositions regarding the effects of the identified factors and provide a basis for further research and guidance for practitioners.
dcterms.extent10 pages
prism.startingpage821

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Analytics and Decision Support for Green IS and Sustainability Applications