Behavioral Intentions of Urban Rail Transit Passengers during the COVID-19 Pandemic in Tianjin, China: A Model Integrating the Theory of Planned Behavior and Customer Satisfaction Theory
Bibliographic record
Abstract
Understanding the behavioral intentions of public transit passengers during the COVID-19 pandemic is important for transmission control interventions oriented towards public transport system travel behavior. This paper studies the relationship between passengers’ intentions to use public transport, a set of psychological variables, and the influence of transport management policies (POLs) under COVID-19. Specifically, this study presents a framework integrating the theory of planned behavior (TPB) and customer satisfaction (CS) theory and uses partial least squares structural equation modeling (PLS-SEM) applied to the survey responses of 983 residents of Tianjin, China. The empirical results support the validity of this integrated model of public transit use intentions by confirming several hypothesized relationships among the psychological variables studied. Moreover, POLs under COVID-19 are shown to enhance commuters’ intentions primarily via subjective norms (SNs), perceived behavioral control (PBC), perceived service quality (PSQ), and CS. These findings reveal the psychological mechanism through which passengers adjust their public transport travel intentions during the COVID-19 period. Based on the results, some feasible suggestions are proposed to help restore confidence in public transport after the pandemic.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".