A Cross-cultural Study Of The Effects Of Perceived Belongingness And Firm’s Receptivity On Consumers Dissatisfaction
Bibliographic record
Abstract
As globalization grows, small business firms are faced with the challenge to manage dissatisfied customers with different cultural backgrounds. This challenge becomes more complex especially in multicultural contexts as well multiethnic markets where culture interacts with firm’s group membership and firm’s receptivity. In this research we investigate the moderator effects of the Individualism-Collectivism dimension of culture on the cognition-Affect-behavior process of dissatisfaction, and we discuss how such effects are contingent with the perceived belongingness (exogroup versus endogroup) and the firm’s receptivity. We tested two rival processes explaining differences in behavioral responses of dissatisfied consumers. The first process posits that collectivist consumers, although they may blame the service firm as much as individualist ones do, they feel less negative emotions and engage less in complaining behaviors and voicing to a third party. However, the second process assumes that collectivist consumers, even if they perceive the employees’ firm as belonging to their group (endogroup), they will expect more receptivity, and will be more inclined to voice their complaining. Findings supports that, in case of collectivist consumers, firm’s receptivity fails to weigh against private and third party complaining.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.002 | 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".