Application of the Technology Acceptance Model to an Intelligent Cost Estimation System: An Empirical Study in the Automotive Industry

dc.contributor.author Bodendorf, Frank
dc.contributor.author Franke, Joerg
dc.date.accessioned 2021-12-24T17:26:58Z
dc.date.available 2021-12-24T17:26:58Z
dc.date.issued 2022-01-04
dc.description.abstract Cost estimation methods are crucial to support inter- and intraorganizational cost management. Despite intense research on machine learning and deep learning for the prediction of costs, the acceptance of such models in practice remains unclear. The aim of this study is to evaluate the acceptance of an implemented deep learning-based cost estimation system. In an empirical study at a large Bavarian automotive manufacturer we use surveys to collect opinions and concerns from experts who regularly use the system. The evaluation is framed by the basic theories of the Technology Acceptance Model. The results from 50 questionnaires and qualitative participant observations show further development potentials of intelligent cost estimation systems in terms of perceived usefulness and user-friendliness. Building on our empirical findings we provide implications for both research and practice.
dc.format.extent 10 pages
dc.identifier.doi 10.24251/HICSS.2022.144
dc.identifier.isbn 978-0-9981331-5-7
dc.identifier.uri http://hdl.handle.net/10125/79476
dc.language.iso eng
dc.relation.ispartof Proceedings of the 55th Hawaii International Conference on System Sciences
dc.rights Attribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.uri https://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subject Big Data and Analytics: Pathways to Maturity
dc.subject technology acceptance model
dc.subject intelligent cost estimation
dc.subject emprical study
dc.subject automotive industry
dc.title Application of the Technology Acceptance Model to an Intelligent Cost Estimation System: An Empirical Study in the Automotive Industry
dc.type.dcmi text
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