Doctoral Dissertations
Date of Award
8-2018
Degree Type
Dissertation
Degree Name
Doctor of Philosophy
Major
Industrial Engineering
Major Professor
Rapinder S. Sawhney
Committee Members
David L. Greene, John E. Kobza, Andrew Yu
Abstract
Resistance against new innovative technologies by customers has been studied in many publications to improve prediction of behavior. Econometrics models, the Technology Acceptance Model by Fred D. Davis (1989), and market research models are the most widely used modeling techniques to predict and understand customer behaviors. The proposed methodology in this paper advances current models by relaxing many of their assumptions and increasing prediction accuracy. A case study in predicting hybrid car buyer behaviors is performed to illustrate and validate the suggested modeling method named as the Energy Efficiency Technology Acceptance Model.
Recommended Citation
Asudegi, Mohammad Ali, "A Cluster Based Model to Enhance Acceptance of New Energy Driven Technologies. " PhD diss., University of Tennessee, 2018.
https://trace.tennessee.edu/utk_graddiss/5010