The intention-behaviour gap in sustainable hospitality: a critical literature review
Bibliographic record
Abstract
Purpose The intention of consumers to behave sustainably is not a reliable predictor of sustainable hospitality choices. This intention-behaviour gap represents one of the biggest challenges for marketers and environment-friendly businesses. To address this issue, this study aims to draw upon the intention-behaviour gap. The authors revise the sustainable hospitality literature to identify the limitations, to evaluate the extent to which the intention-behaviour gap is embedded in the hospitality literature and to provide practical guidance on how to move research forward in the sustainable hospitality field. Design/methodology/approach The authors adopted a five-step process to review and analyse 71 scientific papers published in 14 Hospitality Journals. The authors developed a descriptive overview of the literature showing the publications in this field over the years, the sustainability practices implemented by companies and consumers and the setting of the studies. Finally, the authors conducted a critical analysis of research in sustainable hospitality adopting the intention-behaviour gap lens. Findings Leveraging the descriptive overview and critical analysis, the authors offer four directions for future research to address the existing literature limitations. The authors encourage scholars to expand the scope of the research setting, investigate diverse sustainability practices, integrate existing knowledge on the intention-behaviour gap into sustainable hospitality research and combine traditional research methods with emerging technologies. Practical implications This study exposes the theoretical challenge of applying conventional behaviour theories to sustainable hospitality, prompting a call for framework re-evaluation. It offers practical insights, empowering researchers, marketers and policymakers to navigate and mitigate the intention-behaviour gap in sustainable hospitality. Originality/value The originality of this paper is underscored by its distinctive focus on the unique intention-behaviour gap within sustainable hospitality, coupled with a compelling call to re-evaluate traditional behavioural frameworks. It provides a roadmap for future research in sustainable hospitality, benefiting researchers, policymakers and marketers in promoting sustainable initiatives.
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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 | 0.067 | 0.187 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.035 | 0.018 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.010 | 0.012 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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".