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Record W4390343033 · doi:10.1080/07053436.2023.2274186

Impact of leisure travel desire on tourist’s motivation for shared accommodations: Moderating role of COVID-19 risk and perceived sanitization

2023· article· en· W4390343033 on OpenAlexvenueno aff
Santanu Mandal, Sujit Kumar Patra

Bibliographic record

VenueLoisir et Société / Society and Leisure · 2023
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicSharing Economy and Platforms
Canadian institutionsnot available
Fundersnot available
KeywordsAccommodationTourismPsychologyRisk perceptionSocial psychologyAffect (linguistics)ContingencyControl (management)Coronavirus disease 2019 (COVID-19)Theory of planned behaviorPerceived controlNorm (philosophy)MarketingBusinessPerceptionEconomicsGeographyPolitical science

Abstract

fetched live from OpenAlex

Our study explored the role of leisure travel desire on attitude, subjective norm, and perceived behavioural control for shared accommodation using a theory of planned behaviour lens. Furthermore, the consequent effects on the intention to stay and pay a premium for sanitization practices are also explored. Lastly, the study evaluates the contingency effects of COVID-19 risk and perceived sanitization practices on the above relationships. The study used partial least squares in SmartPLS 3.3.9 to validate the direct and contingent relationships, by analyzing complete responses from West (n=186) and East (n=234) parts of India. Findings suggest that leisure travel desires in the new normal do positively affect their attitude, subjective norms, and perceived behavioural control towards shared accommodation, which in turn shapes their intention to stay and pay a premium for sanitization practices. Furthermore, while COVID-19 risk reduces the direct effects, perceived sanitization practices enhance them.

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)0.001
metaresearch head score (Gemma)0.000
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 score0.098
Threshold uncertainty score0.651

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.054
GPT teacher head0.308
Teacher spread0.254 · 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

Citations2
Published2023
Admission routes1
Has abstractyes

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