Retaining customers after service failure recoveries: a contingency model
Bibliographic record
Abstract
Purpose – The purpose of this paper is to propose and empirically test a customer retention contingency model in service failure settings. Specifically, this research investigates how service recovery satisfaction (SRS) influences the relationship quality (RQ)-behavior chain. It also examines the moderating role of RQ and switching cost (SC) in the proposed model. Design/methodology/approach – A two-part survey study was performed and 303 valid responses from banking services users were obtained. The structural equation modeling was used in order to test the research hypotheses. Findings – The results of this study show that SRS influences purchase intentions and behavior via RQ. In addition, SC moderate the effect of RQ on purchase intentions whereas RQ moderates the effect of purchase intentions on purchase behavior. Practical implications – From a managerial standpoint, this research provides implications for service recovery management. In particular, the findings indicate the importance of RQ. When a service failure occurs, RQ not only mediates the effect of SRS on purchase intentions, but also facilitates transforming behavioral intentions into actual behavior. Originality/value – This research fills a void in the service recovery literature by linking service recovery performance to the RQ-behavior chain.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 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".