Indigenous tourism in Asia and Malaysia: the bibliometric analysis
Bibliographic record
Abstract
Indigenous tourism has gained popularity among tourists seeking diverse cultural experiences. This study provides a comprehensive bibliometric analysis of Indigenous tourism research in Asia, especially in Malaysia, covering publications from 1993 to 2024. Using the Scopus database, 143 documents were analysed with VOSViewer, Microsoft Excel, and Harzing’s Publish or Perish to map co-authorship networks, assess citation metrics, and track thematic trends. The findings indicate a significant rise in research output after 2000, peaking in 2016 (14 articles) and 2017 (13 articles), with 2010 and 2016 registering the highest average number of citations. Most publications were journal articles (102) written in English (138), with Australia leading in research output (47 documents), followed by the United Kingdom (18), Canada (15), and Malaysia (7). Keyword analysis revealed a strong emphasis on ‘Indigenous Tourism’ (74 occurrences), followed by ‘Indigenous Population’ (35), ‘Tourism Development’ (29), and ‘Ecotourism’ (21), which highlights sustainability, heritage, and empowerment as central research themes. The most active institution was the University of Queensland (17 publications), and prominent authors included L. Ruhanen (13 articles) and M. Whitford (9 articles). The field recorded 2,193 total citations, an h-index of 27, and a g-index of 39, which signals robust scholarly engagement. The study also observed increased collaboration among researchers, institutions, and countries, with a growing trend in interdisciplinary studies. It is recommended that future research promote multilingual dissemination, increase the participation of Indigenous communities, and expand beyond the dominant Western focus to include underrepresented regions such as Southeast Asia. These steps are vital for creating more inclusive, equitable, and culturally respectful Indigenous tourism frameworks that align with the goals of sustainable development and the preservation of heritage.
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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 | 0.006 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.130 | 0.175 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".