The effect of service employees’ accent on customer reactions
Bibliographic record
Abstract
Purpose – The primary objective of this article is to investigate customer reactions to service employees with accents that differ from a non-native accent taking into account customer emotions. Design/methodology/approach – This article reports on a study with a 2 (accent of service employee: Australian or Indian) × 2 (service employee’s competency: competent or incompetent) × 2 (customer’s affective state: positive or negative) between-subject experimental design to uncover the effects of service employees’ accent on customers’ reactions. Findings – The findings revealed that hearing a service employee with a foreign accent was not enough on its own to influence customer responses. However, when the service employee is incompetent or the customer was in a negative affective state, a foreign accent appeared to exacerbate the situation. Research limitations/implications – While the findings indicate that accents are used a cue for customers to evaluate service employees, further research should also take service types, service outcomes, customer-service employee relationships, customers’ ethnic affiliation and ethnocentrism into consideration when examining the effect of accents. Practical implications – Service managers need to be aware that accents will exacerbate perceptions of already difficult service situations. Providing competent service will help breakdown stereotypes and improve the acceptance of diversity at the customer–employee interface. Originality/value – This article contributes to the service literature about service attributes and is particularly relevant to economies such as the USA, Canada, the UK, New Zealand and Australia where immigrants are a large part of the service work force.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.007 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".