Accommodating ethnic minority consumers during service encounters: the fine line
Bibliographic record
Abstract
Purpose This paper aims to examine the effect of intercultural accommodation efforts on service quality perceptions among ethnic minority consumers. Specifically, the paper postulates that during an intercultural service encounter, the impact of the service provider’s language and ethnicity on the consumer’s service quality perceptions is moderated by the level of service involvement, consumer acculturation and perceived discrimination, which, in turn, influence purchase intent. Design/methodology/approach A 2 × 2 between-subjects experimental design with an online nationwide consumer panel of Hispanic consumers was conducted where 377 participants were randomly assigned to a series of service encounter scenarios in the banking service context to manipulate accommodation efforts (yes vs no) and the level of involvement with the service (high vs low). Findings When such language and ethnicity accommodations were offered, highly acculturated minority consumers regarded the service encounter less favorably than low acculturated minority consumers. Moreover, during low-involvement service encounters, intercultural accommodations positively impacted consumer’s service quality perceptions compared to situations involving high-involvement services. Also, minority consumers with perceptions of past discrimination had less favorable evaluations of the service quality than when such perceptions were nonexistent when intercultural accommodation efforts were made by the service provider. Research limitations/implications The findings add to the sparse literature that examines the effectiveness of intercultural accommodation and focuses on the combined use of service provider’s language and ethnicity as a means to enhance service quality. Practical implications The study delivers cautions for service firms not to generalize the receptivity of intercultural accommodation efforts. Given the increasingly sizable segments of minority customers, this study offers insights for service providers to develop suitable recruitment strategies and training programs when devising effective ethnic targeting strategies. Originality/value This research is among the first to explain why the effect of target marketing is not homogenous by expanding the research on intercultural accommodations toward a new context considering service involvement levels among varied minority consumer groups.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".