Huang, X., Lin, Y., Lim, M. K. , Tseng, M.-L. and Zhou, F. (2021) The influence of knowledge management on adoption intention of electric vehicles: perspective on technological knowledge. Industrial Management and Data Systems, 121(7), pp. 1481-1495. (doi: 10.1108/IMDS-07-2020-0411)
Full text not currently available from Enlighten.
Abstract
Purpose: Technological innovation is one of the remarkable characteristics of electric vehicles (EVs). This study aims to analyze how consumers' technological knowledge affects their intention to adopt EVs. Design/methodology/approach: Original data were collected via a survey of 443 participants in China. An extended technology acceptance model was constructed to identify the factors influencing consumers' intention to adopt EVs and related technological knowledge pathways. Findings: The results show that consumer technological knowledge is positively and significantly related to EVs' perceived usefulness, perceived ease of use, perceived fun to use and consumers' intention to adopt EVs. In addition, no direct and significant relationship is found between perceived fun to use and willingness to adopt EVs, from the technical knowledge dimension. Practical implications: Imparting consumers with EV technological knowledge and usefulness may be an effective way to enhance their awareness and willingness to use EVs. Moreover, the role of females in the decision to adopt EVs should not be ignored, especially in decisions to purchase a family car. Originality/value: Prior studies lack a technological knowledge-based view, and few studies have discussed how to explore the effects of consumer technological knowledge about EVs on their adoption intention. This study fills the research gap.
| Item Type: | Articles |
|---|---|
| Additional Information: | This study is supported by the Chinese National Funding of Social Science (Grant No. 18BJY066), Chongqing Science and Technology Commission (Grant No. cstc2018jszx-cyzd0711) and the Fundamental Research Funds for the Central Universities (Grant No. 2019CDCGGK318). |
| Status: | Published |
| Refereed: | Yes |
| Glasgow Author(s) Enlighten ID: | Lim, Professor Ming |
| Authors: | Huang, X., Lin, Y., Lim, M. K., Tseng, M.-L., and Zhou, F. |
| College/School: | College of Social Sciences > Adam Smith Business School > Management |
| Journal Name: | Industrial Management and Data Systems |
| Publisher: | Emerald |
| ISSN: | 0263-5577 |
| Published Online: | 30 April 2021 |
University Staff: Request a correction | Enlighten Editors: Update this record
Altmetric
Altmetric