A cross-cultural perspective on consumer perceptions of service failures’ severity: a pilot study
Bibliographic record
Abstract
Purpose – This paper aims to investigate the cultural variability in assessing the severity of a service failure. Design/methodology/approach – Two separate studies were conducted. The first investigates differences in the perception of service failures across two cultural pools of subjects (allocentrics versus idiocentrics) and within a same country. The second contrasts two levels of comparisons: a cross-cultural values’ level and a cross-country level, to assess differences in the perception service failures’ severity. Findings – Results showed that cultural values differences, when investigated at the individual level (i.e. idiocentrism versus allocentrism) are more significant to understand the influence of culture on the perception of severity, that is, allocentrics perceive more severity in the service failure than idiocentrics. However, a cross-country comparison (i.e. USA versus Puerto Rico) does not show significant differences. Research limitations/implications – Customers may assess, with different sensitivities, the severity of a service failure. These differences are mainly explained by differences in cultural values’ orientations but not differences across countries. Even originating from a same country, customers could perceive with different degrees the seriousness of a same service failure as they may cling to different cultural values. Hence, it is increasingly important to examine the cultural differences at the individual-level rather than a country level. Practical implications – Firms serving international markets as well as multiethnic ones would have advantage to understand cultural differences in the perception of the severity at the individual level rather than at the societal or country level. This is more helpful to direct appropriate service recovery strategies to customers who may have higher sensitivity to the service failure. Originality/value – Little is known about the effect of culture on the severity evaluation, although investigating cross-cultural differences in the assessment of severity is relevant to understand whether offenses are perceived more seriously in one culture than another and then if these offenses will potentially arise confrontational behaviors or not.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 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".