The Benefit of Becoming Friends: Complaining after Service Failures Leads Customers with Strong Ties to Increase Loyalty
Bibliographic record
Abstract
Service firms spend considerable resources soliciting complaints to initiate recovery efforts and improve their offerings. However, managers may be overlooking the fact that complaints serve an equally important role in engendering loyalty. The authors demonstrate that the strength of social ties between customers and service providers influences the degree to which complaining drives loyalty. Paradoxically, while strongly tied customers fear that complaining threatens their ties with the provider, when they are encouraged to complain, their loyalty increases because offering feedback serves as an effective way to preserve social ties. Conversely, for weakly tied customers, complaining has no effect on loyalty. Furthermore, complaints are more effective in driving loyalty for strongly tied customers when the feedback is directed toward the provider who failed, rather than to an entity external to the failure. Finally, when providers signal an authentic openness to feedback, strongly tied customers are more loyal after complaining, whereas authenticity does little to engender loyalty for weakly tied customers who complain. The value of complaints in driving loyalty is promising both for customers who perceive a strong tie to a particular provider within the firm and, more generally, in service industries wherein strong ties naturally occur.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".