Interrelationships among EV adoption factors: An ISM-MICMAC approach
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it