Sustainable consumer choices – critical reflection on hospitality and tourism
Bibliographic record
Abstract
Purpose Strategies to promote more sustainable consumer choices have been gaining interest among tourism and hospitality scholars. In particular, behavioral economic theories of decision-making have gained popularity in the past decade, led by behavioral interventions (BIs) such as the nudge movement. This paper aims to present a critical reflection on this recent trend, with a specific focus on whether these BI approaches are an adequate tool to contribute to long-term behavioral changes, one crucial aim of the promotion of sustainable consumption. Design/methodology/approach Based on a critical review of recent significant academic works in the field, this paper reflects on how nudge principles are applied in the hospitality and tourism sectors, as well as the usual justifications given for their use. This paper then discusses the potential limitations, both theoretical and practical, of using these short-term focused approaches to decisions that intend to have long-term outcomes and aims. Findings BIs in hospitality and tourism have the potential to create long-term sustainable changes through a more comprehensive view of behavioral factors influencing decisions; however, such approaches would need to be strongly embedded in theoretical arguments that question “how” and “why” behavior change could be sustainable in the long term. This paper proposes a conceptual framework to address these concerns for future research. Research limitations/implications This critical reflection proposes a comprehensive framework that will help guide stronger theoretically motivated identification, design and empirical testing of BIs and nudges. Industry can eventually benefit from theoretically stronger interventions that provide a balance between the short-term and long-term influence of BIs to attain customer loyalty and eventually greater value for business stakeholders. Originality/value This reflection paper critically reviews the basis of BIs and recommends a framework to strengthen their theoretical arguments. This reflection focuses on the theoretical critique of BIs and nudges to ensure long-term behavior changes are sustainable. The paper also proposes a comprehensive framework that incorporates well-founded theoretical models to enhance BIs and nudge literature.
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 | 0.033 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.010 | 0.058 |
| Scholarly communication | 0.012 | 0.015 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.006 | 0.016 |
| 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".