Innovation Acceptance and Usage Behavior of Smart Electric Vehicle Applications
Bibliographic record
Abstract
Technology Acceptance Model (TAM) was used to look at the factors that affect people's willingness to use new technologies. The study focused on how TAM can be used in smart electric vehicle applications. The key variables examined comprised perceived performance, interface usability, and user awareness. The sample consisted of 249 owners of electric vehicles in Thailand. The results confirmed that the perceived usability of the application—which includes features such as real time charging status monitoring and the convenience of locating charging stations—positively influenced users' attitudes. Moreover, a user centric interface enhanced customer satisfaction and acceptance, thus affecting their intention to persist in using the application. It was found that user experience is very important for making new technologies work well with existing ones. The study also suggested ways to make apps that work better with users' tastes in the future. Henceforth, developers should prioritize intuitive design principles and incorporate user feedback throughout the development process to ensure that applications not only satisfy functional requirements but also elevate overall user engagement. Through this approach, they can develop methods that enhance lasting allegiance and stimulate greater uptake rates within the market.
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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 | 0.004 | 0.018 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".