Consumer language preferences in service encounters: a cross‐cultural perspective
Bibliographic record
Abstract
Purpose The importance of the mutual interaction between consumers and the company in service encounters is widely recognised, but researchers have usually presumed that both parties are able to interact with each other. That is not always the case. If they do not share a common language, it may have consequences for the service encounter. This paper aims to analyze consumer language preferences across four language groups. Design/methodology/approach Quantitative and qualitative studies are conducted among bilingual speakers of four languages (English/French and Finnish/Swedish) in two countries (Canada and Finland). Study 1 is a quantitative analysis of the degree of importance that respondents in the various language groups attach to the use of their first language in a variety of service encounters. Study 2 is a qualitative examination of the factors that determine the preferences expressed in Study 1. Findings Use of first language in service encounters is preferred by consumers in all four language groups. However, the reasons for preferring first‐language use differ between countries. Language is shown to have emotional connotations for consumers that go beyond mere communication. Originality/value This is the first study of the role of language in service encounters among consumers from different countries.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 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".