Factors Affecting Consumer’s Intention to Use Electric Vehicles: Mediating Role of Awareness and Knowledge
Bibliographic record
Abstract
This paper explores the role of environmental consequences, perceived barriers, policy interventions, public opinions, and knowledge and awareness in using electric vehicles (EVs). We collected 506 responses about their intention to use the EVs to develop our hypothesis. This study uses knowledge and awareness of the EVs as mediating variables towards adopting the EVs and consumers’ residences as moderating variables. It also introduces several control variables in this proposed research model to measure the effect on the intention to use the EVs. In this research study, we test the importance‐performance map analysis and check Cohen’s f2 to identify a better output. The measurement and structural equation modelling results show that the environmental consequences are a stronger predictor of intention and policy interventions. The findings suggest that government policies can also have an attractive position in the EV segment. In addition, knowledge and awareness mediate the adoption of the EVs. Perceived barriers do not influence consumers to use an EV in India. We test the moderating role of residence with our construct and find a partial moderation role with policy intervention and public opinion. We introduce three self‐created constructs, i.e., intention to use, knowledge, and public opinion. Public opinion for the EVs supports consumers’ intention to use, and knowledge also plays a significant role in using EVs. These newly added constructs will be essential to manufacturers and policymakers while promoting EVs. Also, environmental consequences and policy interventions emerge as significant factors of user behaviour. The paper highlights the critical predictors of consumers’ intention to use the EVs. Thus, it helps society, policymakers, and managers formulate and implement schemes to boost EV purchasing.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".