Loyalty in Transit: An Analysis of Bus and Rail Users in Two Canadian Cities
Bibliographic record
Abstract
The relationship between customer satisfaction and loyalty has recently received international attention as transit agencies aim to identify ways to increase ridership. Improvements in perceived service quality increase the attractiveness of transit, and therefore lead to growing patronage. The present paper examines how transit users’ perceptions of service quality and user satisfaction influence loyalty. Using information from five years of customer satisfaction questionnaires collected by two Canadian transit providers, this study attempts to better understand the complexities of several factors influencing passenger satisfaction and behavioral intentions. It uses a Structural Equation Modelling approach to develop a series of models that reflects the different groups using transit; captive riders (users who are dependent on transit), choice riders (car owners who choose to take transit), and captive-by-choice riders (users who are dependent on transit but could own a car) are accounted for. The findings from this study are used to define areas where transit agencies can develop specific strategies in order to benchmark user satisfaction with the aim of growing patronage among the different groups. Insight into the perceptions of passengers provides useful information that can help transit agencies understand what inspires customers’ perceptions of satisfaction and loyalty in general.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 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".