Customer Loyalty Differences between Captive and Choice Transit Riders
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Traditionally, efforts to increase the customer base of public transportation agencies have focused primarily on attracting first-time users. Customer retention, however, has many benefits not often realized. Loyal customers provide recommendations to others, increase and diversify their use of the service, and do not require acquisition costs associated with new customers. An earlier study identified key drivers of customer loyalty, with the Chicago Transit Authority (CTA) in Illinois as a case study. A customer loyalty model was created with service value, service quality, customer satisfaction, problem experience, and perception of CTA as constructs. The present study examined customer loyalty differences of captive and choice riders. Captive riders had no viable travel alternatives and might have continued to use transit even if unhappy with service. Choice riders chose to use transit after they compared travel options and might have switched to an alternative if service degraded. Captive riders reported experiencing more problems and were more sensitive to problems; each additional problem brought significant drops in service quality ratings. Captive riders tolerated problems and continued to use transit but showed discontent through their ratings of service quality. Service value was insignificant in captive riders’ loyalty decisions because cost–benefit analysis defined service value as irrelevant to them. The relationship between perceptions of CTA and of service quality was stronger for choice riders. If they began the service with high opinions of the transit agency, they were much more likely to have high ratings of service quality than were captive riders.
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.
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 | 0.006 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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 it