I Can Forgive You, But I Can’t Forgive the Firm: An Examination of Service Failures in the Sharing Economy
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
Despite rapid growth of the sharing economy, little is known about consumers’ reactions when sharing services fail. Drawing on attribution theory, in three studies we show that consumers forgive such service failures varyingly, depending on the controllability and the locus of attribution of the failures. Specifically, when a failure has low controllability, consumers are more forgiving when it is attributed to an individual service provider than when it is attributed to a service enabling organization. Empathy toward the service provider explains the increased forgiveness. However, no difference in forgiveness is observed in the case of highly controllable failures, irrespective of the source of attribution. Furthermore, the effect of two recovery strategies – compensation and apology – also varies depending on these conditions. Theoretical and managerial implications are discussed.
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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 | 0.070 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it