Consumer Responses to High Service Attentiveness: A Cross-Cultural Examination
Bibliographic record
Abstract
Although the literature generally indicates that service attentiveness can increase consumer satisfaction, providing extra care and attention in service encounters may backfire and lead to negative consumer outcomes. In addition, because of cross-cultural differences, the effects of high service attentiveness may vary across international markets. The authors conduct a qualitative study, a field experiment, and two laboratory experiments in three countries (Canada, the United States, and China) across various service contexts (hairdressing, telecommunications, and computer repair) to examine cross-cultural consumer responses toward high service attentiveness. Consumers’ negative responses toward high service attentiveness are mediated by their suspicion of ulterior motive, which varies according to their self-construal. Specifically, consumers with an interdependent self-construal (either chronic or primed) tend to have greater suspicion of and negative responses toward high service attentiveness. Furthermore, the effect of interdependent self-construal fostering greater suspicion is attributed to a sharper in-group (vs. out-group) distinction, which is mitigated when the service employee is perceived to be an in-group member. The authors conclude by discussing the theoretical and managerial implications and suggesting future research directions.
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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 | 0.005 | 0.007 |
| 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 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".