Service Failure and Recovery at the Crossroads: Recommendations to Revitalize the Field and its Influence
Bibliographic record
Abstract
In this editorial, we offer a critical assessment of the service failure and recovery (SFR) literature and suggest that the field is at a crossroads in terms of growth and relevance. Specifically, we address two key questions: (1) What is the current state of the field? (2) What avenues should SFR researchers pursue to promote a new stage of success? To answer the first question, we tracked the evolution of SFR articles over the last 15 years by using Web of Science. Our analysis suggests that the recent growth of SFR research is mainly attributable to articles published in specialized journals; the number of articles published in leading journals remains stable and relatively low for the last 10 years. This situation reflects the poor integration of two core SFR domains: Behavioral-subjective research tends to be published in specialized journals, whereas quantitative-objective articles have been in high demand in leading journals. To answer the second question, we propose a dozen research avenues to help the integration of the two domains, so that the whole field can regain prominence. These research avenues are organized in four categories: (1) expanding the static “customer-firm” dyad, (2) studying new contexts that challenge the assumption of recovery, (3) collecting better data and using stronger analytics, and (4) building on the synthetic knowledge base already created. By making such changes, the SFR domain will reclaim its rightful place as an important subfield of service science.
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 | 0.047 | 0.138 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.010 | 0.007 |
| Science and technology studies | 0.006 | 0.014 |
| Scholarly communication | 0.020 | 0.033 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.021 | 0.026 |
| Insufficient payload (model declined to judge) | 0.012 | 0.006 |
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".