Service recovery as an organizational capability
Bibliographic record
Abstract
Purpose This paper aims to explore and conceptualize service recovery as an organizational capability. It proposes a new construct labeled knowledge-enabled recovery effectiveness (KERE). Design/methodology/approach Measures capturing the KERE construct were developed through domain identification, item pool generation using focus group interviews with managers involved in complaint management and content expert validation. Findings A first pool of 73 items was generated and then reduced to 37 items. Focus group interviews confirm the theoretical relevance of the KERE construct. Recovery culture, recovery process and internal recovery resources are the different components of a firm’s knowledge that serve as inputs, or as a source of a firm’s service recovery capabilities. Research limitations/implications A quantitative study is needed in future research to assess the KERE’s construct structure and validity. Practical implications Managers may use the proposed scale to foster effective and relevant marketing strategies by setting clear policies that consider service recovery as a knowledge-based activity rather than a control targeted activity. Originality/value This research demonstrates the mutual dialogue between service recovery and knowledge-based capabilities. Also, it proposes a new concept labeled KERE and a raw scale to further understand firms’ aptitude in service recovery.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| 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".