MétaCan
Menu
Back to cohort
Record W3035786575 · doi:10.3390/su13084343

The Corporate Responsibility Paradox: A Multi-National Investigation of Business Traveller Attitudes and Their Sustainable Travel Behaviour

2021· article· en· W3035786575 on OpenAlexaffabout
Philip R. Walsh, Rachel Dodds, Julianna Priskin, Jonathon Day, Oxana Belozerova

Bibliographic record

VenueSustainability · 2021
Typearticle
Languageen
FieldEnvironmental Science
TopicEnvironmental Education and Sustainability
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsTourismSustainabilityDemographicsBusiness travelMarketingBusinessSustainable businessSupply sideSustainable developmentSustainable tourismPublic economicsEconomicsPolitical scienceSociology

Abstract

fetched live from OpenAlex

The implementation of sustainability practices in the tourism system requires the participation of a variety of actors. While much research has focused on supply-side issues associated with sustainable tourism, there has been less focus on supply-side issues associated with consumer behaviour and business-related travel. This paper addresses the behaviours of this significant market segment. As behavioural change is seen as a key mechanism for achieving emission reduction, this paper focuses on behaviours of business travels from four countries: Canada, Switzerland, Russia and the U.S., using values-attitudes-behaviour (VAB) theory. We employ Principal Components Analysis to reduce the variables down to four factors and related factor scores. Stepwise multiple linear regression was then used to measure causal associations. The findings show how national cultures, demographics and values influence (although at different levels) the sustainable attitudes and behaviour of business travellers. These results have implications for future corporate travel policy. The recent impact of the COVID-19 global pandemic is also addressed.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.063
Threshold uncertainty score0.836

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.002
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.022
GPT teacher head0.266
Teacher spread0.244 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations18
Published2021
Admission routes2
Has abstractyes

Explore more

Same venueSustainabilitySame topicEnvironmental Education and SustainabilityFrench-language works237,207