The Need for Uniqueness among Gulf Cooperation Council Countries’ Consumers: A Cross-Culture Study
Bibliographic record
Abstract
Understanding differences among consumers across varying cultures is of great importance to the success of international retailers. Ignoring the influence of culture and centralized marketing has led to a decline in profits for some international companies. Studying the culture of Middle East countries, particularly the Gulf Cooperation Council Countries (GCCC), Saudi Arabia, United Arab Emirates, Kuwait, Bahrain, Qatar, and Oman, is essential before marketing in these countries. Additionally, the GCCC is one of the top 10 luxury markets in the world. Hofstede model of national culture is crucial for GCCC due to the fact culture norms regarding dress and appearance are nationally adopted. A sample of 170 participants from the GCCC was collected using an online questionnaire of 45 items measuring national culture dimensions and need for uniqueness when shopping for luxury goods. It was found that power distance in all GCCC countries was a significant predictor of having a need for uniqueness, as well as indulgence. Power distance had a positive relationship with the need for uniqueness while indulgence had a negative relationship with the need for uniqueness. For other dimensions, findings indicated that long term vs short term orientation, masculinity, uncertainty avoidance, and individualism were not significant predictors leading to uniqueness. Additionally, the important construct for uniqueness among GCCC consumers is unpopular choice followed by avoiding similarity. Creative choice is less important among the three constructs of uniqueness for GCCC participants.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".