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Simulating resistances in innovation diffusion over multiple generations: an agent-based approach for fuel-cell vehicles.

Authors :
Zsifkovits, Martin
Günther, Markus
Source :
Central European Journal of Operations Research; Jun2015, Vol. 23 Issue 2, p501-522, 22p
Publication Year :
2015

Abstract

Innovation resistances play a major role in innovation diffusion, as they do not only hinder the adoption, but might also change a decision maker's evaluation. Although these influences are widely accepted, previous models on the diffusion of new technologies and products have either reduced these multiple dimensions of uncertainties to only one parameter, or have completely neglected them altogether. Both might lead to a pro-innovation bias. Therefore we present an agent-based approach that takes several different innovation resistances in a multi-generation environment into account. Hydrogen vehicles and the necessity of setting up a corresponding infrastructure were chosen for a sample application as they incorporate a band of various dimensions of innovation resistance. Examples are the uncertain infrastructure situation, the uncertainty arising from new and improved features, the uncertainty about the technologies' real ecological benefit, the unknown maintenance cycles and costs, or the ambiguous technical parameters such as vehicle range. These various uncertainties are even more distinctive if multiple technology generations are considered. Our results indicate that a short-term decrease in the adoption rate can be observed although the technological parameters of a later product generation might be more beneficial for the consumers. As we show, this effect can be eased through timing variation of the communication measures. Therefore we conclude that considering multiple innovation resistance factors in innovation diffusion might reduce the pro-innovation bias. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
1435246X
Volume :
23
Issue :
2
Database :
Complementary Index
Journal :
Central European Journal of Operations Research
Publication Type :
Academic Journal
Accession number :
102481790
Full Text :
https://doi.org/10.1007/s10100-015-0391-x