The customer knowledge gap and electric vehicle adoption: a meta-Analytic Review and future Research agenda
Bibliographic record
Abstract
This is the first meta-analysis to systematically examine how various consumer information sources influence electric vehicle (EV) adoption, synthesizing 393 estimates from 52 studies conducted between 2010 and 2024. Using meta-regression techniques, the study evaluates the direction and strength of this relationship across regions and knowledge sources to clarify inconsistencies in the existing literature. Despite extensive research, a consensus on this relationship remains elusive. This study systematically assesses the impact of consumer knowledge on EV adoption by synthesizing findings from multiple information sources – including media exposure, driving experience, infrastructure awareness, social influence, and policy communication – using meta-analytic techniques. It further explores regional variation in these effects and proposes a structured research agenda to guide future inquiry. Regional subgroup comparisons reveal a positive but modest overall effect of information on EV adoption, with the strongest impacts observed in regions outside Asia, Europe, and North America, and the weakest in North America. Information provided by dealerships and government agencies exerts the most substantial influence, followed by social influence. The study finds no evidence of publication selection bias. Based on these findings, policy and research recommendations are proposed to support targeted, context-sensitive information strategies aimed at accelerating EV adoption.
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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 | 0.047 | 0.115 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.021 |
| Bibliometrics | 0.011 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".