Interpretive Structural Modeling of Influential Factors Affecting Electric Vehicle Adoption in Saudi Arabia

Meshal Almoshaogeh, Arshad Jamal (), Irfan Ullah, Fawaz Alharbi, Sadaquat Ali, Md Niamot Alahi, Majed Alinizzi and Husnain Haider
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Meshal Almoshaogeh: Department of Civil Engineering, College of Engineering, Qassim University, Buraydah 51452, Saudi Arabia
Arshad Jamal: Department of Civil Engineering, College of Engineering, Qassim University, Buraydah 51452, Saudi Arabia
Irfan Ullah: College of Transportation, Tongji University, Shanghai 201804, China
Fawaz Alharbi: Department of Civil Engineering, College of Engineering, Qassim University, Buraydah 51452, Saudi Arabia
Sadaquat Ali: Department of Civil Engineering, College of Engineering, Qassim University, Buraydah 51452, Saudi Arabia
Md Niamot Alahi: Department of Civil Engineering, College of Engineering, Qassim University, Buraydah 51452, Saudi Arabia
Majed Alinizzi: Department of Civil Engineering, College of Engineering, Qassim University, Buraydah 51452, Saudi Arabia
Husnain Haider: Department of Civil Engineering, College of Engineering, Qassim University, Buraydah 51452, Saudi Arabia

Energies, 2025, vol. 18, issue 19, 1-30

Abstract: Electric vehicle (EV) adoption is a critical step toward achieving sustainable transportation and reducing carbon emissions, especially in regions like Saudi Arabia that are undergoing rapid urban development and energy diversification. However, the widespread adoption of EVs is hindered by a variety of interrelated economic, infrastructural, and policy-related factors. This study aims to systematically identify and structure these influencing factors using Interpretive Structural Modeling (ISM) and Cross-Impact Matrix Multiplication Applied to Classification (MICMAC) analysis. Based on a thorough literature review and expert consultation, 17 key factors affecting EV adoption in Saudi Arabia were identified. The ISM results reveal that purchase price, long-term savings, resale value, urban planning, and accessibility are among the most influential drivers of adoption. The MICMAC analysis complements these insights by categorizing the variables based on their driving and dependence power. The developed hierarchical model provides insights into the complex interdependencies among these factors and offers a strategic framework to support policymakers and stakeholders in accelerating EV uptake. The study contributes to a deeper understanding of the dynamics influencing EV adoption in emerging markets.

Keywords: electric vehicles (EVs); Interpretive Structural Modeling (ISM); MICMAC analysis; EV adoption; policy implications (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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