A Review of Literature on the Antecedents of Electric Vehicles Promotion: Lessons for Value Chains in Developing Countries
Bibliographic record
Abstract
Electric vehicles (EVs) are a part of the solution to the growing challenges of greenhouse gas emissions and air pollution. The adoption of EV is more pronounced in developed countries than in developing countries. Research on EV adoption is also primarily focused on developed countries. Many of the antecedents of EV promotion noted in the literature do not apply in developing countries because of weak market structures, infrastructure networks, and economies. Moreover, no one study provides a comprehensive understanding of these antecedents. This article identifies the antecedents of EV promotion and explores their utility in developing countries. The literature is searched using the Web of Science database; 198 relevant papers were reviewed using an inductive–deductive approach. The inductive approach is meant to explore the antecedents of EV adoption, while the deductive approach focuses on unraveling how these antecedents unspool in developed countries and can be employed in developing countries. The recursive use of the inductive–deductive approach leads to the development of a taxonomy that further categorizes the antecedents as micro-, macro-, and meso-level antecedents. The taxonomy of antecedents can be used to orchestrate structured and coherent efforts toward promoting EV in developing countries. The article also highlights the need for contextualizing antecedents to the unique infrastructural-, economic-, and market-needs of developing countries. The article provides a foundational understanding for future research focused on the empirical examination of the antecedents of EV.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.017 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".