30 Years of Research in Tourism: Insights from a Probabilistic Topic-Modeling Literature Analysis
Bibliographic record
Abstract
This study aims to explore the topical growth, patterns, and trends of importance in tourism research over the past three decades (1990–2019). By leveraging a large corpus of 18,725 abstracts from 20 leading tourism journals, we employ latent Dirichlet allocation (LDA) to identify, quantify, and semantically interpret the predominant research themes and their evolution within the tourism discipline. The analysis reveals topic distributions across journals, highlighting focused areas as well as diverse topic coverage. Temporal analyses uncover the changes in the popularity of different topics, shedding light on emerging areas –topics that have gained increasing scholarly attention over recent years, indicating their growing significance and influence in the field of tourism research. The study reveals that while certain topics, such as consumer experience, digital innovation, risk behaviors, sustainability, and social media, have drawn more attention recently, others, such as marketing communication and e-tourism, have cooled down over time. Journal-level insights suggest that Visitor Studies and Event Management focus on themes related to visitor engagement and event tourism, whereas journals such as Tourism Economics and Tourism Management continue to emphasize economic growth and demand forecasting. The study provides valuable insights into the research landscape and offers implications for scholars, journal editors, and practitioners. It highlights the importance of fostering collaboration to address future challenges in the multidisciplinary tourism field.
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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 | 0.010 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.022 | 0.027 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".