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Record W4416207191 · doi:10.1002/jtr.70146

Why Tourists Avoid Peer‐to‐Peer Accommodations? Insights Into Risk, Trust, and Service Dynamics

2025· article· en· W4416207191 on OpenAlexaff
Ho Young Lee, Pei Zhang, Hwansuk Chris Choi

Bibliographic record

VenueInternational Journal of Tourism Research · 2025
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicSharing Economy and Platforms
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsTourismRisk perceptionService (business)Key (lock)Consumer behaviourAffect (linguistics)

Abstract

fetched live from OpenAlex

ABSTRACT This study examines the factors influencing tourist avoidance of peer‐to‐peer (P2P) accommodation, defined as the deliberate exclusion of such options from tourists' lodging choices. Psychological risk emerged as the strongest predictor of overall perceived risk, followed by performance and physical risks, highlighting the importance of emotional and practical concerns in shaping avoidance behavior. The overall perceived risk and the lack of cost‐saving features directly influenced tourists' decisions to avoid P2P accommodation, underscoring the significant role of economic considerations. Additionally, the lack of trust, whether directed at individual hosts or the P2P platform itself, was a key driver of avoidance. Trust in the platform indirectly affected avoidance behavior through its influence on perceived risk, illustrating its critical mediating role. These findings underscore the importance of P2P platforms in addressing safety, trust, and economic concerns, thereby reducing perceived risks and promoting the broader adoption of their services, particularly among risk‐averse travelers.

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)0.002
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.148
Threshold uncertainty score0.987

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0000.001
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 head0.032
GPT teacher head0.336
Teacher spread0.304 · 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 designNot applicable
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
Published2025
Admission routes1
Has abstractyes

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