Development in Electric Vehicle Intention and Adoption: Integrating the Extended Unified Theory of Acceptance and Use of Technology (UTAUT) and Religiosity
Bibliographic record
Abstract
Environmental sustainability has become an urgent problem that cannot be avoided and ignored. The transportation sector is the main cause of greenhouse gas emissions. Many suggest electric vehicles (EVs) as a mechanism for reducing environmental degradation since EVs release less greenhouse gas as compared to combustion cars. However, the sales of EVs in Malaysia are still low compared to the achievements recorded by the neighboring countries. Despite being the earliest to promote electric vehicles, in the first quarter of 2023, Malaysia has only represented 2.4% of the EV market in Southeast Asia. It seems that the success rate in promoting environmental conservation through the adoption of EVs is still minimal. For this reason, the existing study seeks to explore factors related to EV behavioral intention and adoption by using the extended UTAUT (performance expectancy, effort expectancy, social influence, facilitating condition, perceived value/cost, hedonic motive, and habit). The proposed study believes that environmental protection and sustainability must be related to individuals' values and principles which could have been shaped based on religiosity. Therefore, in addition to the extended UTAUT that views EV intention and adoption from the technical and personal perspective, this study proposes religiosity as a predecessor in determining EV intention and adoption. The use of extended UTAUT and religiosity in the model will clarify the extent to which the influence of technical factors, personal factors and religiosity is determining the intention and adoption of EVs.
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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 | 0.006 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".