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

Date
2022-01-04
Authors
Bodendorf, Frank
Franke, Joerg
Contributor
Advisor
Department
Instructor
Depositor
Speaker
Researcher
Consultant
Interviewer
Journal Title
Journal ISSN
Volume Title
Publisher
Volume
Number/Issue
Starting Page
Ending Page
Alternative Title
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.
Description
Keywords
Big Data and Analytics: Pathways to Maturity, technology acceptance model, intelligent cost estimation, emprical study, automotive industry
Citation
Extent
10 pages
Format
Geographic Location
Time Period
Related To
Proceedings of the 55th Hawaii International Conference on System Sciences
Rights
Attribution-NonCommercial-NoDerivatives 4.0 International
Rights Holder
Email libraryada-l@lists.hawaii.edu if you need this content in ADA-compliant format.