A Cross-National and Cross-Cultural Approach to Global Market Segmentation: An Application Using Consumers’ Perceived Service Quality
Bibliographic record
Abstract
The spread of global culture is being facilitated by the proliferation of transnational corporations, the rise of global capitalism, widespread aspiration for material possessions, and the homogenization of global consumption. The extent of convergence of cultural values across nations has been debated by international marketing researchers. However, from a practical standpoint, transnational firms require a cross-national, cross-cultural approach to market segmentation that can be used to guide the development of global marketing strategies. In this study, the authors investigate the application of cross-national versus cross-cultural approaches to market segmentation through a rigorous empirical investigation in the context of banking services. Although services constitute the fastest growing sector of the world economy, few studies have examined global market segmentation strategies for them. The authors develop theory-based cross-national hypotheses and test them by estimating a structural model of consumers’ perceived service quality using survey data from two countries: the United States and India. They test cross-cultural hypotheses by estimating the same model on culture-based clusters. They demonstrate that there are distinctive differences between cross-national and cross-cultural models of perceived service quality and highlight the growing relevance of cross-cultural research approaches. More generally, the cross-national, cross-cultural approach to market segmentation can guide the development of global marketing strategies for services and improve business performance.
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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 | 0.007 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".