Application of the Technology Acceptance Model to an Intelligent Cost Estimation System: An Empirical Study in the Automotive Industry
Location
Online
Event Website
https://hicss.hawaii.edu/
Start Date
3-1-2022 12:00 AM
End Date
7-1-2022 12:00 AM
Description
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.
Application of the Technology Acceptance Model to an Intelligent Cost Estimation System: An Empirical Study in the Automotive Industry
Online
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.
https://aisel.aisnet.org/hicss-55/da/big_data_and_analytics/3