Loyalty and Positive Word-of-Mouth
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
The ability to attract and retain loyal customers depends on the successful implementation of a customer-centric strategy. Customer loyalty is an attitude about an organization and its' services that is manifested by intentions and behaviors of re-patronization and recommendation. In the context of many medical services, loyalty through repeat patronization is not pertinent, whereas loyalty through positive word-of mouth (WOM) recommendation can be a powerful marketing tool. The Shouldice Hospital, a well-known institution for the surgical correction of hernias, instituted a marketing plan to develop a stable base of patients by creating positive WOM advocacy. This study focused on the consequences of both hernia patient overall satisfaction (and overall service quality) and hospital personnel satisfaction on the level of positive WOM advocacy. Using a commitment ladder of positive WOM advocacy, respondents were divided into three categories described as passive supporters, active advocates and ambassador advocates. Patient assessments of overall satisfaction and service quality were significantly related to these progressive levels of WOM for recommending the hospital to potential patients. Similarly, the satisfaction of the hospital employees was also significantly related to these progressive levels of positive WOM about recommending the hospital to potential patients and to potential employees. High levels of satisfaction are required to create true ambassadors of a service organization.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 it