Bibliographic record
Abstract
Purpose To examine the impact of culture on customer service expectations, specifically, how individualists and collectivists use internal and external sources of information to formulate their service expectations. Design/methodology/approach The context was the airline industry and the subject pool consisted of experienced consumers. A survey was employed to measure individualism/collectivism, various internal/external information sources, and the functional and technical dimensions of “should” and “will” service expectations. Hypothesized relationships were tested using a structural equations modeling approach. Findings Both individualists and collectivists relied more on external information sources in formulating their service expectations, gave variable weight to the functional and technical components, and used more realistic “will” expectations to judge service offerings. Internal (external) information sources were relatively more important in forming expectations for collectivists (individualists) than for individualists (collectivists), and “will” (“should”) expectations were more diagnostic for collectivists (individualists) than for individualists (collectivists). Research limitations/implications Generalizability of the findings is limited due to the specific industry under study (airlines), the sample (two geographically‐proximate sub‐cultures), and the scope of the cultural variables considered (individualism/collectivism). Practical implications Whether managers should leverage the functional and/or technical components of services depends in part on the cultural orientation of their customers. Managers should also recognize that customers’ usage of various information sources in forming service expectations is also, in part, culturally determined. Originality/value In this era of globalization, researchers and managers alike need to consider the subtle influences of culture on marketing theories and the formulation of service expectations respectively.
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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 | 0.006 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".