A Systematic Review of Brand Transgression, Service Failure Recovery and Product-Harm Crisis: Integration and Guiding Insights
Bibliographic record
Abstract
Research studies on brand transgression (BT), service failure and recovery (SFR), and product-harm crisis (PHC) appear to have a common focus, yet the three streams developed surprisingly independently and with limited reference to one another. This situation is unfortunate because all three fields study a similar phenomenon by using complementary conceptualizations, theories, and methods; we argue that this development in silos represents an unnecessary obstacle to the development of a common discipline. In response, this review synthesizes the growing BT, SFR, and PHC literatures by systematically reviewing 236 articles across 21 years using an integrative conceptual framework. In doing so, we showcase how the mature field of SFR in concert with the younger but prolific BT and PHC fields can enrich one another while jointly advancing a broad and unified discipline of negative events in marketing. Through this process, we provide and explicate seven overarching insights across three major themes (theory, dynamic aspects, and method) to encourage researchers to contribute to the interface between these three important fields. The review concludes with academic contributions and practical implications.
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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 | 0.012 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.022 | 0.020 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".