Travelers’ Adoption Behavior towards Electric Vehicles in Lahore, Pakistan: An Extension of Norm Activation Model (NAM) Theory
Bibliographic record
Abstract
This study aims to identify the travelers’ adoption behavior towards electric vehicles (EVs) using the theoretical background of the Norm Activation Model (NAM) theory. A questionnaire was designed and conducted in Lahore, Pakistan. A total of 402 usable samples were obtained. The collected data were analyzed using factor analysis and Structural Equation Modeling methods. The factor analysis confirmed the hypothesis of the statements designed according to the NAM theory, that is, awareness of consequences (AC), ascription of responsibility (AR), and personal norm (PN). Other factor analyses resulted in the following reliable factors: social and economic values (SEV), personal preferences (PP), willingness to buy (Buy), and willingness to use (Use) of an EV. The results of SEM revealed that the AC, AR, and SEV are significant predictors of PN, whereas the PN and PP are also positive predictors of travelers’ willingness to buy and use. The young travelers (≤30 years), motorcycle users, employees, and trip distance (>10 km) have significant and positive correlations with the PN. The car ownership status of travelers has a positive correlation with the ownership and usage of EVs. Suitable behavioral intervention techniques were derived to promote the ownership and usage of EVs in the context of developing regions.
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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 | 0.001 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".