Motivating Factors to Use Electric Vehicles Based on Behavioral Intention Model in South Korea
Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 32856
Motivating Factors to Use Electric Vehicles Based on Behavioral Intention Model in South Korea

Authors: Seyedsamad Tahani, Samira Ghorbanpour, Sekyung Han

Abstract:

The global warming crisis forced humans to consider their place in the world and the earth's future. In this regard, Electric Vehicles (EVs) are a significant step towards protecting the environment. By identifying factors that influence people's behavior intentions toward using EVs, we proposed a theoretical model by extending the Technology Acceptance Model (TAM), including three more concepts, Subjective Norm (SN), Self-Efficacy (SE), and Perceived Behavior Control (PBC). The study was conducted in South Korea, and a random sample was taken at a specific time. In order to collect data, a questionnaire was created in a Google Form and sent via Kakao Talk, a popular social media application used in Korea. There were about 220 participants in this survey. However, 201 surveys were completely done. The findings revealed that all factors in the TAM model and the other added concepts such as SNs, SE and PBC significantly affect the behavioral intention of using EVs.

Keywords: Electric vehicles, behavioral intention, subjective norm, self-efficacy, perceived behavior control.

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 496

References:


[1] Hao, Y., et al., What influences personal purchases of new energy vehicles in China? An empirical study based on a survey of Chinese citizens. Journal of Renewable and Sustainable Energy, 2016. 8(6): p. 065904.
[2] Ju, Y., et al., Study of site selection of electric vehicle charging station based on extended GRP method under picture fuzzy environment. Computers & Industrial Engineering, 2019. 135: p. 1271-1285.
[3] Asadi, S., et al., Investigating influence of green innovation on sustainability performance: A case on Malaysian hotel industry. Journal of cleaner production, 2020. 258: p. 120860.
[4] Xu, Y., et al., A SEM–neural network approach to predict customers’ intention to purchase battery electric vehicles in China’s Zhejiang Province. Sustainability, 2019. 11(11): p. 3164.
[5] Chandra, A., S. Gulati, and M. Kandlikar, Green drivers or free riders? An analysis of tax rebates for hybrid vehicles. Journal of Environmental Economics and management, 2010. 60(2): p. 78-93.
[6] Huang, X. and J. Ge, Electric vehicle development in Beijing: An analysis of consumer purchase intention. Journal of cleaner production, 2019. 216: p. 361-372.
[7] Han, S., et al., China’s energy transition in the power and transport sectors from a substitution perspective. Energies, 2017. 10(5): p. 600.
[8] Tu, J.-C. and C. Yang, Key factors influencing consumers’ purchase of electric vehicles. Sustainability, 2019. 11(14): p. 3863.
[9] Liu, Y., Z. Ouyang, and P. Cheng, Predicting consumers’ adoption of electric vehicles during the city smog crisis: An application of the protective action decision model. Journal of environmental psychology, 2019. 64: p. 30-38.
[10] Wu, J., et al., The role of environmental concern in the public acceptance of autonomous electric vehicles: A survey from China. Transportation Research Part F: Traffic Psychology and Behaviour, 2019. 60: p. 37-46.
[11] Yang, J., Jung, J., Ghorbanpour, S., & Han, S. (2022). Data–Driven Fault Diagnosis and Cause Analysis of Battery Pack with Real Data. Energies, 15(5), 1647.
[12] Ghorbanpour, S., T. Pamulapati, and R. Mallipeddi, Swarm and evolutionary algorithms for energy disaggregation: challenges and prospects. International Journal of Bio-Inspired Computation, 2021. 17(4): p. 215-226.
[13] Davis, F.D., Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS quarterly, 1989: p. 319-340.
[14] Tahani, S. and Y.-K. Kim, The Role of Celebrity Identification on Humanitarian Aid.
[15] Ajzen, I., The theory of planned behavior. Organizational behavior and human decision processes, 1991. 50(2): p. 179-211.
[16] Bandura, A., Self-efficacy mechanism in human agency. American psychologist, 1982. 37(2): p. 122.
[17] Parasuraman, R. and V. Riley, Humans and automation: Use, misuse, disuse, abuse. Human factors, 1997. 39(2): p. 230-253.
[18] Sarstedt, M., et al., Partial least squares structural equation modeling (PLS-SEM): A useful tool for family business researchers. Journal of family business strategy, 2014. 5(1): p. 105-115.