Comparative cross‐cultural service quality: an assessment of research methodology
Bibliographic record
Abstract
Purpose The purpose of this paper is to examine the methodological approaches adopted in cross‐cultural service quality (CCSQ) research and the extent to which these approaches have adhered to the general principles of established cross‐cultural research methodology. Design/methodology/approach A search was conducted to identify CCSQ papers published between 1995 and 2009. The authors searched four well‐known online databases: ABI Inform (Proquest Direct), Emerald Library, ScienceDirect, and EBSCOhost. This search identified 40 studies, which were examined according to three broad groups of methodological issues: research design, instrumentation and data collection, and data analysis and measurement. Findings Despite the acknowledged contributions that these selected studies have made to the services‐marketing field, it is evident from this review that researchers have frequently overlooked many important aspects of cross‐cultural research methodology. These methodological deficiencies are discussed and various remedies are suggested. Originality/value There has been a growing research interest in comparative cross‐cultural service‐quality in recent decades. As this relatively new branch of service‐quality research becomes more prominent, it seems opportune to examine the methodological approaches adopted in these studies and the extent to which these approaches have adhered to the general principles of established cross‐cultural research methodology. This is the first work to examine such a large number of CCSQ studies.
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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 | 0.532 | 0.582 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.042 | 0.045 |
| Science and technology studies | 0.005 | 0.009 |
| Scholarly communication | 0.016 | 0.013 |
| Open science | 0.005 | 0.017 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".