Familiar Road Loyalty Modeling Considering the Effect of Truckers’ Emotional Value
Bibliographic record
Abstract
This paper investigates the internal mechanism of the trucker’s willingness to use familiar roads by constructing a structural equation model of road loyalty, in which the influence of the trucker’s “emotional value” is additionally considered. The proposed method can be used to understand the trucker’s psychological needs to improve the level of road service. Based on questionnaire data, AMOS software was used to analyze the correlations and corresponding parameters among 10 latent variables and 30 explicit variables. The model results show that, in addition to the value of roads as a commodity, the emotional dependence of truckers in the process of using them also has an impact on the perceived value of road services. The results of model index scores show that truck drivers’ loyalty to the road does not represent their satisfaction, and familiarity with the road is still “forced” to be the first choice of most truckers due to the cost and trust in the reliability of unfamiliar roads when the road service quality and experience do not meet truckers’ expectations. The latent variable scores indicate that improvements in “road safety” and “costs” are the key points to improve truckers’ overall satisfaction with road freight access.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 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".