Loyalty and Positive Word-of-Mouth
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.
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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 | 0.002 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".