The effect of accent of service employee on customer service evaluation
Bibliographic record
Abstract
Purpose The primary objective of this paper is to investigate the attitudes, feelings and perceptions of Australian consumers towards service frontline employees with accents that differ from Standard Australian English, taking into consideration service‐country image and customer emotions. Design/methodology/approach This paper reports on a qualitative study designed to uncover the attitudes and perceptions of Australians towards service personnel with foreign accents. Findings The findings revealed that hearing a service provider with a foreign accent, particularly in services encounters without face‐to‐face contacts, often evokes a negative predisposition to certain accents, reduces the customers' level of tolerance and increases the perception of the service provider's lack of understanding. This negative stereotype bias seems to be moderated by the accent (a proxy of ethnicity) and service‐country image and influenced by customer emotions in the service interaction. Research limitations/implications Future studies could also use a controlled experimental design where accent could be used as a sensory cue to further test the validity and reliability of the current findings while controlling for factors such as ethnic background, employment, education and age. Further research should also take service types and service outcomes into consideration in examining the effect of accents on customer service evaluation. Practical implications Accent as a service employee attribute influences customers' evaluation of the service encounter because of the stereotype customers have. Training in language skills, cross‐cultural interpersonal skills and authority to deviate from the script should be given to minimise the negative effect of service employee accent. Service firms also need to develop strategies to manage customer emotions and reactions. Originality/value This paper contributes to the service literature about service employee 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 workforce.
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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 | 0.015 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".