Attributions on dissatisfying service encounters
Bibliographic record
Abstract
Based on the proposition that deprivation of control is a key instigator of attribution thoughts, this study explores cross‐national variations in consumers' formation and consequences of attributions on dissatisfying service encounters. We hypothesize that variations in the stage of economic development and the cultural dimension of long‐term versus short‐term orientation affect consumers' perceived level of control in and attributions of dissatisfying service encounters, and the relative effects of various attribution dimensions (including locus, controllable‐by‐organization, and stability) on consumers' switching intentions. Results obtained from a cross‐national survey show that compared to PRC consumers, Canadian consumers experience more deprivation of control in dissatisfying service encounters and exhibit stronger self‐serving biases in forming attributions about their dissatisfying service experiences. Moreover, the controllable‐by‐organization dimension (i.e. whether the problems of the service encounter could be controlled by the service firm) is found to have a stronger effect on the switching intentions of Canadian consumers than that of PRC consumers, while the opposite is found for the stability dimension (i.e. whether the same problem would recur in experiences with the service firm). Managerial implications for multinational service firms, particularly in terms of service recovery strategy for Chinese and Western consumers, are discussed.
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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 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".