Author
Listed:
- Bireswar Dutta
(English Taught Program in Smart Service Management, Department of Information Technology and Management, Shih Chien University, Taipei 104, Taiwan)
Abstract
The current study explores the factors influencing Taiwanese consumers’ Electric Vehicle (EV) purchase intentions. An integrated study framework, combining the Norm Activation Model (NAM) and the Theory of Planned Behavior (TPB), was employed to provide a holistic understanding of pro-environmental behavior, addressing the limitations of each theory when used independently. A total of 421 responses were examined using a two-phase Structural Equation Modeling (SEM) and Artificial Neural Network (ANN) methodology. SEM identified significant associations, while ANN ranked the relative impact of predictors. The results showed that attitude, perceived behavioral control, and personal norms were positively linked to purchase intention. Problem awareness significantly affected personal norms, attitude, and ascription of responsibility. Sensitivity analysis revealed that ascription of responsibility was the foremost predictor of personal norms, and personal norms had the most substantial impact on attitude. The ANN results largely supported the SEM findings, demonstrating high prediction accuracy (RMSE 0.115–0.122). The study’s originality lies in its hybrid SEM-ANN approach to synthesizing NAM and TPB, providing a nuanced understanding of consumer EV adoption intentions. The findings highlight the need for public awareness campaigns, fostering personal responsibility, and reinforcing positive attitudes toward EVs to promote sustainable mobility. The empirical findings not only enrich the theoretical understanding of how altruistic and rational considerations converge to predict pro-environmental technological adoption but also offer clear targets for policymakers and marketers to influence consumer decision-making.
Suggested Citation
Download full text from publisher
Corrections
All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:17:y:2025:i:19:p:8632-:d:1758294. See general information about how to correct material in RePEc.
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
We have no bibliographic references for this item. You can help adding them by using this form .
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .
Please note that corrections may take a couple of weeks to filter through
the various RePEc services.