Boundary-breaking opportunities in service failure and recovery
Bibliographic record
Abstract
Purpose The service failure and recovery (SFR) research field has reached its maturity stage and is now at a critical juncture. There are growing calls for fresh perspectives and innovative approaches in SFR research to ensure its continued relevance and growth. The purpose of this paper is to identify boundary-breaking opportunities in SFR research by fundamentally challenging some of the central assumptions of the field. Design/methodology/approach This paper employs a unique “review of reviews” methodology to synthesise findings from 19 prior SFR reviews, complemented by an in-depth analysis of 116 primary articles published in the past five years. Findings This paper makes several contributions. First, it identifies and critically evaluates the central underlying assumptions of SFR, highlighting their inherent limitations in light of emerging conceptual and substantive developments. Second, it offers alternative perspectives that reframe these assumptions and open up new avenues for research. Third, within each alternative perspective, we propose specific research ideas that can benefit from further exploration. To develop the ideas, we build on recent conflicts and negative events in the marketplace. Our review of reviews approach also enables us to track how frequently such ideas have been proposed in prior reviews. Finally, the paper briefly discusses some methodological considerations for conducting more impactful research. Originality/value This paper leverages insights from prior SFR literature reviews and recent research and steeps into real-world marketing issues to challenge the central assumptions of the field and recommend future research avenues.
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.
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 | 0.051 | 0.094 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.005 | 0.024 |
| Scholarly communication | 0.017 | 0.020 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.006 | 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".