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Record W4281662284 · doi:10.1111/ijcs.12840

Consumer intentions to use collaborative economy platforms: A meta‐analysis

2022· article· en· W4281662284 on OpenAlex
Myriam Ertz, Emine Sarigöllü

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueInternational Journal of Consumer Studies · 2022
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicSharing Economy and Platforms
Canadian institutionsMcGill UniversityUniversité du Québec à Chicoutimi
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsSharing economyHedonismWillingness to payPsychosocialFlexibility (engineering)EconomicsPsychologyMarketingSocial psychologyMicroeconomicsBusiness

Abstract

fetched live from OpenAlex

Abstract Collaborative economy platforms (CEP) have been investigated from various disciplines, theoretical frameworks and methodological approaches. Subsequently, numerous models emerged to explain the cognitive process underlying intentions to use CEP. Yet, their findings are fragmented and diverse, impeding thereby theory development and management practice. This article addresses this deficiency by a meta‐analysis of psychosocial determinants of collaborative economy platforms (CEP) use intentions. Based on information from a total of 27 independent samples, we find support for the relation between psychosocial determinants and CEP use intentions, as well as willingness to pay a premium price for CEP. The findings show that (1) emotional and flexibility utility exert the strongest influence on use intentions; (2) functional and social utility exert more influence on willingness to pay a premium price; (3) CEP are primarily used for enjoyment and practical purposes; and (4) hedonism does not strongly lead to an increased willingness to pay.

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.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.881
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.002
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.102
GPT teacher head0.317
Teacher spread0.215 · 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