Bibliometric analysis of consumer ethnocentrism and consumer racism with Islamic elements
Bibliographic record
Abstract
This study examines consumer ethnocentrism and consumer racismliterature with Islamic elements, employing bibliometric analysisthrough the Bibliometrix software to analyse current research trendsand identify emerging themes. Retrieving 101 publications with1,277 overall citations from the Scopus database, this study exploresthe structure and evolution of consumer ethnocentrism andconsumer racism. Three bibliometric analyses were conducted,including citation analysis, co-citation analysis, and keyword co-occurrence analysis, to reveal past, present, and future researchtrajectories. The study identifies two significant research streams:the interplay between consumer ethnocentrism and cultural identity,and the impact of consumer racism on purchasing behaviour inIslamic markets. Additionally, the findings highlight the increasingglobal interest in consumer ethnocentrism and consumer racism withIslamic elements, with contributions from 35 countries includingChina, Canada, India, Turkey, and Indonesia, and underscore theneed for high-quality research that addresses the complexities ofconsumer behaviour in diverse cultural contexts. This studycontributes to the theoretical understanding of consumerethnocentrism and consumer racism with Islamic elements,emphasizing the importance of quality over quantity in academicpublications. It also suggests directions for future research to furtherexplore the intersectionality of these concepts with broader socialissues. As one of the first studies to apply bibliometric analysis tothe fields of consumer ethnocentrism and consumer racism withIslamic elements, this research provides valuable insights intosignificant issues and emerging trends, paving the way for futurescholarly inquiry.
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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 | 0.011 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.156 | 0.212 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.000 |
| 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".