Advancing Sustainable Hospitality Education: A Systematic Literature Review
Bibliographic record
Abstract
Global challenges such as climate change and social inequality have heightened the emphasis on sustainability across various sectors, with higher education institutions (HEIs) playing a pivotal role in fostering sustainable practices. This systematic literature review assesses the evolution of sustainable hospitality education (SHE) research over the past two decades, highlighting trends and identifying gaps to synthesize the current scholarly discourse. Using a comprehensive search strategy across multiple databases, we identified 39 key articles through a structured screening process. Our findings reveal that SHE is a dynamic and inclusive field, characterized by a diverse array of research methods, educational levels, and sample sizes. The analysis of delivery modes and subject domains underscores the importance of cross-domain issues in SHE research. The review concludes by proposing future directions for SHE, including the development of localized curricula, faculty development programs, and interdisciplinary research initiatives. It also emphasizes the need for longitudinal studies to assess the long-term impact of educational strategies on students' sustainability knowledge and practices. Implementing these recommendations will enable SHE to contribute significantly to the sustainable transformation of the hospitality industry and inspire educational innovation, advancing a greener and more equitable future.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.016 | 0.064 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.026 | 0.025 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| 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".