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Record W4214554459 · doi:10.1080/13683500.2022.2041564

Does Airbnb offer hedonic or utilitarian products? An experimental analysis of motivations to use Airbnb

2022· article· en· W4214554459 on OpenAlexaff
Jonas Volz, Michael Volgger

Bibliographic record

VenueCurrent Issues in Tourism · 2022
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicSharing Economy and Platforms
Canadian institutionsInnovation Cluster (Canada)
Fundersnot available
KeywordsAccommodationMarketingProduct (mathematics)AdvertisingRelevance (law)Experiential learningPsychologyPosition (finance)Consumption (sociology)BusinessSociologyPolitical science

Abstract

fetched live from OpenAlex

There has been an ongoing debate regarding motivations to book Airbnb accommodations. One position highlights the role of experiences and an alternative position emphasizes the equal relevance of costs and practical amenities. This experimental research indirectly examines these motivations by analysing the impact of affective and cognitive advertising on attitude and behavioural intentions to book with Airbnb. It also compares it with the advertising effectiveness in a strongly hedonic accommodation product such as boutique hotels. A randomized online experiment assigned 525 participants to one out of four text ads and analysed their responses using 2-way ANOVA. Study findings show no significant differences in effectiveness between emotional and rational ads in the case of Airbnb. This contrasts with the control group of boutique hotels where results confirm emotional appeals to be more effective. These findings indicate that Airbnb is chosen for a hybrid mix of utilitarian and hedonic consumption motivations disconfirming purely experiential interpretations.

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)0.002
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.025

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0070.000

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 head0.057
GPT teacher head0.307
Teacher spread0.250 · 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 designBench or experimental
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

Citations27
Published2022
Admission routes1
Has abstractyes

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