Retaining customers after service failure recoveries: a contingency model
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.002 |
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 it