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Record W4322491874 · doi:10.32920/22183741.v1

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

2023· preprint· en· W4322491874 on OpenAlexaffabout
Philip R. Walsh, Rachel Dodds, Julianna Priskin, Jonathon Day, Oxana Belozerova

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldBusiness, Management and Accounting
TopicCorporate Social Responsibility Reporting
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsTourismSustainabilityDemographicsSupply sideBusinessBusiness travelMarketingSustainable developmentSustainable businessSustainable tourismPublic economicsPolitical scienceEconomicsSociology

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 behaviourandbusiness-relatedtravel. Thispaperaddressesthebehavioursofthissignificantmarket 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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.036
Threshold uncertainty score0.071

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0020.002
Scholarly communication0.0020.003
Open science0.0000.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.109
GPT teacher head0.297
Teacher spread0.187 · 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 source (direct Gemma or distilled Codex), 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

Citations0
Published2023
Admission routes2
Has abstractyes

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