Stories of Service Slip‐Ups: Judgments of Pre‐Service Deservingness Shape Reactions to Service Failures
Bibliographic record
Abstract
ABSTRACT Consumers frequently share service failures, often leading to brand avoidance. Existing research focuses on service‐related factors that emerge during or after the encounter, leaving a critical gap in understanding how pre‐service factors influence reactions. This study reveals that consumer actions before the service—despite being entirely unrelated to the failure—significantly influence third‐party responses. Across four studies, including a consequential choice‐based experiment, it is shown that consumers perceived as undeserving of the service do not trigger typical negative reactions when they complain. Interestingly, these deservingness perceptions, formed before the service encounter and based on factors unrelated to it, drive a shift in third‐party reactions. Moreover, third parties shift the blame onto the complainant and actively support the brand due to justice‐restorative motives instead of just dismissing the complaint. Theoretically, this study introduces pre‐service deservingness as a previously unexplored factor, demonstrating that fairness heuristics shape marketplace behavior even when events are unrelated and temporally distinct. Additionally, it uncovers a shift from skepticism to active brand advocacy, where third parties intervene to restore justice rather than merely discount complaints. From a managerial perspective, brands can strategically navigate service failures by leveraging third‐party justice perceptions to influence consumer advocacy and brand defense.
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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 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".