What Drives Electric Vehicle Choices? Nepal-Based Mixed-Method Investigation

The study's primary purpose is to analyze consumer purchase decisions for electric vehicles in Nepal. The study employs descriptive and causal design with explanatory sequential mixed method. It collected quantitative data from 400 electric vehicle owners through a structured questionnaire and qualitative data from 15 respondents through interviews. The study adopted SMART PLS-SEM to analyze the direct and indirect effects of independent variables (pre-purchase behavior, financial, environmental, infrastructural, technological, policy, and dealer facilities) on purchase decisions. Research shows that consumer decision was associated with pre-purchase behavior, finance, environment, infrastructure, technology, policy, and dealer services. The purchase decision was influenced by pre-purchase behavior, infrastructure, and technology directly. The purchase decision was affected by financial and environmental factors and dealership services indirectly. However, when attitude dimensions were adjusted, the effects of pre-purchase behavior, infrastructure, and dealership services differed entirely on purchase decisions. Policy mechanisms do not directly or indirectly affect purchase decisions. In conclusion, the government needs to allocate more resources to promote electric vehicles by expanding charging centers collaboratively with dealers to assure customers of social acceptance of electric vehicles in Nepal. Car dealerships need to share charging centers with each other and provide spare parts, warranties, and other services on time as per requirements to boost consumer confidence.

Authors

Publication Details

Journal
Journal of Information Systems Engineering & Management
Published
2024-12-30
DOI
https://doi.org/10.52783/jisem.v9i4s.12025
Primary Topic
Electric Vehicles and Infrastructure
Type
article
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article

What Drives Electric Vehicle Choices? Nepal-Based Mixed-Method Investigation

Raju Bhai Manandhar
Journal of Information Systems Engineering & Management
Electric Vehicles and Infrastructure
article

What Drives Electric Vehicle Choices? Nepal-Based Mixed-Method Investigation

Raju Bhai Manandhar
article en

Abstract

The study's primary purpose is to analyze consumer purchase decisions for electric vehicles in Nepal. The study employs descriptive and causal design with explanatory sequential mixed method. It collected quantitative data from 400 electric vehicle owners through a structured questionnaire and qualitative data from 15 respondents through interviews. The study adopted SMART PLS-SEM to analyze the direct and indirect effects of independent variables (pre-purchase behavior, financial, environmental, infrastructural, technological, policy, and dealer facilities) on purchase decisions. Research shows that consumer decision was associated with pre-purchase behavior, finance, environment, infrastructure, technology, policy, and dealer services. The purchase decision was influenced by pre-purchase behavior, infrastructure, and technology directly. The purchase decision was affected by financial and environmental factors and dealership services indirectly. However, when attitude dimensions were adjusted, the effects of pre-purchase behavior, infrastructure, and dealership services differed entirely on purchase decisions. Policy mechanisms do not directly or indirectly affect purchase decisions. In conclusion, the government needs to allocate more resources to promote electric vehicles by expanding charging centers collaboratively with dealers to assure customers of social acceptance of electric vehicles in Nepal. Car dealerships need to share charging centers with each other and provide spare parts, warranties, and other services on time as per requirements to boost consumer confidence.

Journal of Information Systems Engineering & ManagementVol. 9(4s)
Openalex Percentile: Top 72%
Electric Vehicles and Infrastructure
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What Drives Electric Vehicle Choices? Nepal-Based Mixed-Method Investigation — Raju Bhai Manandhar · Journal of Information Systems Engineering & Management (2024) | TGRS Research Map | TGRS