Examining the Various Drivers Using the ADO Model for Electric Vehicle Adoption in India
Vinay Chauhan & Shiv Kumar
What the paper says
The primary aim of this research article is to identify and analyse the key factors influencing the actual adoption of electric vehicles (EVs) in India, with a specific focus on their role in mitigating climate change and reducing fossil fuel dependency. A systematic literature review (SLR) was mainly conducted using the antecedents–decisions–outcomes (ADO) framework. The academic literature indexed in the Scopus database served as the basis of the analysis. The review specifically focused on studies relevant to the Indian context and the evolving EV ecosystem. The review highlights several antecedent factors, including psychological factors, socio-economic conditions, infrastructural readiness, government incentives, and pricing. These particular antecedents primarily shape consumer decisions, such as attitudes towards EVs, perceived barriers, awareness levels, and the purchasing process. The resulting outcomes include purchase intention, behavioural acceptance, and the market potential of EVs in India. Despite growing environmental awareness and supportive government policies, EV adoption in India is constrained by the psychological, technical, and socio-economic challenges. These barriers must be addressed through targeted, localized policies and public awareness campaigns to accelerate adoption and also to strengthen the sustainable transportation sector. By consolidating fragmented research on EV adoption in India, this study offers a clear understanding of consumer behaviour, market barriers, and policy gaps. The insights contribute to the formulation of effective strategies for enhancing EV adoption, thereby supporting India’s broader climate and sustainability goals.
Evidence weight
Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40
| F · citation impact | 0.50 × 0.4 = 0.20 |
| M · momentum | 0.50 × 0.15 = 0.07 |
| V · venue signal | 0.50 × 0.05 = 0.03 |
| R · text relevance † | 0.50 × 0.4 = 0.20 |
† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.