How Culture Moderates the Effects of Justice in Service Recovery
Bibliographic record
Abstract
Abstract This article strives to clarify the importance and the effects of cultural differences on consumer satisfaction after a service failure in an individualistic society (Canada) vs. a collectivistic society (Japan). We focus on young, educated consumers to analyze if the contrasts shown in the extant literature between these two cultures are still relevant in the case of young consumers of both cultures when confronted with a service failure and service recovery. We used 150 questionnaires from Japan and Canada, the design of which reflects our theoretical model. Respondents were asked to recall one of their own negative service experience and the service recovery that may have followed. Our statistical analysis is based on Hayes’s PROCESS that allows to test complex moderated and mediated relations. We find that Anger mediates the relation between failure severity and consumer behavioral responses (EXIT and NWOM) similarly for both Japanese and Canadian consumers. Importantly, compensation, involving procedural and distributive justice (i. e. time – speedy service recovery and money) both reduced consumer anger, more so for individualistic consumers. Surprisingly, interactional justice (e. g. courteousness, politeness, and signs of respect) had no impact on neither individualistic nor collectivistic consumers. Our findings suggest that service providers should first fix the negative emotional reactions as a result of service failure. They may reduce this emotional reaction offering monetary compensation to both Individualistic and Collectivistic consumers. Younger collectivistic consumers are not more sensitive to signs of respect and politeness than individualistic consumers, which may show that the younger generation of Japanese consumers is getting closer to the individualistic culture.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".