Public Transit Loyalty Modeling Considering the Effect of Passengers’ Emotional Value: A Case Study in Xiamen, China
Bibliographic record
Abstract
To better sustain passengers’ loyalty towards bus service, this paper addressed the modeling of the public transit loyalty by the use of structural equation model. As a novel hypothesis, the emotional value was considered to have effects on the perceived value of bus services in this study, which reflected the degree of passengers’ emotional dependence on the public transit. Specifically, in order to better assess the loyalty, seven unobserved variables were measured to construct the structural model, namely, “service guarantee,” “operational services and efficiency,” “emotional value,” “perceived value,” “expectation,” “satisfaction,” and “loyalty.” The goodness-of-fit of the model was estimated and evaluated by using the survey data harvested from Xiamen, China. Besides, the index score of variables was also computed to help determine targeted approaches to better improve the level of bus service. The results indicated that the time cost and the monetary cost actually had no effects on the perceived value of users in the case study. At the same time, however, it also proved that passengers’ emotional value towards the public transit indeed affected passengers’ perception of the service value. In addition, whether users’ perceived value was as expected determined how much passengers satisfied with the service. Regarding the index score of variables, it indicated a great dissatisfaction of passengers towards the current bus service. Unexpectedly, the score of loyalty even still retained a relatively high level, which reflected continue-to-use willingness of passengers. It implied that being subject to economic conditions and other factors, passengers were captive and had to continue relying on the public transit, in spite of their dissatisfaction. As for the improvement direction of bus services, targeted approaches should be determined to improve the quality of bus service, regarding the aspects of “condition of facilities in the bus,” “driving stability and comfort,” “vehicle speed,” and “safety.”
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".