Interrelationships among EV adoption factors: An ISM-MICMAC approach
Bibliographic record
Abstract
• Identifies key factors driving EV adoption in India using ISM-MICMAC methodology. • Maps interdependencies among EV adoption factors to inform policy and industry. • Provides actionable insights for overcoming barriers to accelerate EV adoption. Transitioning to electric vehicles (EVs) is central to sustainable transport, yet adoption depends on interdependent technical, economic, and infrastructural factors. This study aims to map those interrelationships and identify the most influential drivers of EV uptake. Interpretive Structural Modeling (ISM) and MICMAC (Cross-Impact Matrix Multiplication Applied to Classification) are applied, combining a structured literature synthesis with expert judgments from 12 specialists gathered via semi-structured interviews and a brainstorming session. ISM yields a hierarchical structure of factors while MICMAC classifies them by driving and dependence power to validate the hierarchy. Results show battery range anxiety as the foundational driver, with fast-charging technology and advancements in vehicle technology as successive high-influence factors. Integration with existing infrastructure and home-charging solutions form the next layer of influence, and total cost of ownership (with battery swapping as a complementary option) exerts mid-tier effects, whereas other factors play comparatively minor roles. These findings suggest policy and managerial priorities: reduce range anxiety (technology, information, and network density), accelerate fast-charging rollout, support integration of home and destination charging, and address total cost of ownership through incentives and design-for-cost. Unlike prior ISM-MICMAC studies that modeled only barriers to EV adoption, this study extends the analysis to capture both enabling and constraining interrelationships within a broader, technology-led ecosystem. The model reflects India’s evolving post-policy transition stage, emphasizing technological and infrastructural readiness rather than purely regulatory influence.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".