Evaluating Acceptance of Novel Vehicle-Mounted Perfume Automatic Dispersal Device for Fatigued Drivers
Bibliographic record
Abstract
This paper evaluates the influence of different variables on drivers’ willingness to accept and use a vehicle-mounted perfume automatic dispersal device (VP-ADD) connected to the vehicle’s electronic map. Based on the technical acceptance model, we clarify and condense the explanation of the model used to evaluate the impact of user behavior attitudes and device characteristics on six factors, perceived usefulness, perceived ease of use, attitude towards use, intention to use, perceived playfulness, and perceived risk, proposing eight hypotheses. Then, we assessed the responses of 562 drivers in China using SPSS for reliability and validity and AMOS for structural equation modeling to test our hypotheses. The findings reveal that the perceived usefulness, ease of use, playfulness, and risk significantly affected the willingness to accept and use the VP-ADD. Furthermore, the perceived risk has a negative influence, while the perceived usefulness, perceived ease of use, perceived playfulness, and attitude towards use have a positive influence. This research is significant for further development and application of the VP-ADD. It is essential to alleviate driver fatigue, ensure traffic safety, and provide theoretical and empirical support for designing more popular driving assistance devices. Furthermore, it offers valuable insights for developing fatigue driving warning policies, in-vehicle device guidelines, and traffic safety regulations.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".