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Record W4226289476 · doi:10.4018/ijthi.299076

The Impact of Information and Communication Technology Factors on the User Intention to Participate in the Sharing Economy

2022· article· en· W4226289476 on OpenAlex
Pinghao Ye, Liqiong Liu, Joseph Tan

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.

Bibliographic record

VenueInternational Journal of Technology and Human Interaction · 2022
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicSharing Economy and Platforms
Canadian institutionsMcMaster University
Fundersnot available
KeywordsSharing economyUsabilityStructural equation modelingInformation and Communications TechnologyBusinessQuality (philosophy)Work (physics)Information sharingPerspective (graphical)Knowledge managementMarketingPsychologyComputer scienceEngineeringHuman–computer interactionWorld Wide Web

Abstract

fetched live from OpenAlex

The purpose of this paper is to examine the effects of system and interaction quality, security factors, trust and perceived ease to use on the user intention to participate in the sharing economy. Information and communications technology (ICT) promotes the development of a sharing economy. Such influential factors include system and interaction quality, security factors, and ease of use of ICT. In this work, a questionnaire survey was administered with 318 sharing economy users with multiple hypotheses investigated via a structural equation model (SEM). Results show that system and interaction qualities have a significant positive impact on the perceived ease of use (PEU). Safety factors and group psychology also have significant positive effects on perceived trust (PT). Altogether, PEU and PT have significant effects on the users’ adoption of a sharing economy. The paper contributes to the sharing economy and consumer behaviour literature in a comprehensive perspective.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.190
Threshold uncertainty score0.241

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.031
GPT teacher head0.299
Teacher spread0.268 · 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