Dimensions of service quality in developed and developing economies: multi‐country cross‐cultural comparisons
Bibliographic record
Abstract
Purpose Despite the rapid growth and internationalization of services, marketers of services realize that to successfully leverage service quality as a global competitive tool, they first need to correctly identify the antecedents of what the international consumer perceives as service “quality.” This paper aims to examine the differences in perception of service quality dimensions between developed and developing economies. Design/methodology/approach Parasuraman et al. proposed a framework consisting of ten determinants or dimensions of service quality: reliability, access, understanding of the customer, responsiveness, competence, courtesy, communication, credibility, security, and tangible considerations. The authors propose 14 hypotheses emphasizing differences in the perception of these dimensions between developed and developing economies by linking these with economic and socio‐cultural factors. Extensive survey data are collected in the context of banking services from three countries: USA, India, and the Philippines and statistically tested using multivariate analysis of variance. Findings Of the 14 hypotheses, 13 were supported (five partially) in that the results for the USA were systematically and significantly different from those for India and the Philippines in the predicted direction. Research limitations/implications While almost all of the hypotheses are supported, future research should look at multiple service sectors and include alternative service quality models to further validate this study. Practical implications Despite limitations, current results have significant implications for international marketing in service strategy formulation, service development, pricing, communications, and service delivery. Originality/value International service managers need to understand the value of environmental differences between countries in terms of economic development and cultural value system and accordingly emphasize the various dimensions of service quality differentially.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".