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Record W2803553185 · doi:10.1142/s0219877019500044

Technology Adoption and Diffusion: A New Application of the UTAUT Model

2018· article· en· W2803553185 on OpenAlexaffabout
Fatima Zahra Barrane, Gahima Egide Karuranga, Diane Poulin

Bibliographic record

VenueInternational Journal of Innovation and Technology Management · 2018
Typearticle
Languageen
FieldDecision Sciences
TopicTechnology Adoption and User Behaviour
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsUnified theory of acceptance and use of technologyKnowledge managementInformation and Communications TechnologyConceptual modelSocial influenceKey (lock)Survey data collectionInformation technologyBusinessConceptual frameworkComputer scienceExpectancy theoryMarketingManagementSociologyEconomicsWorld Wide Web

Abstract

fetched live from OpenAlex

Diffusion of innovation is a key challenge for organizations; it brings social change that alters a system's structure and how it operates. Most of the studies in this area have focused on the information and communication technologies sector (ICT). In this paper, we have sought to understand the acceptance and use of wood-based technology in the non-residential construction sector. For this purpose, we conducted a web survey of 28 engineers in Quebec's construction industry. Upon examining the survey results using the Unified Theory of Acceptance and Use of Technology (UTAUT) theory, we have proposed a conceptual framework specific to the use of wood in non-residential construction and identified the main similarities and differences according to the basic UTAUT model. We have also identified some constraints regarding the use of wood-based technology.

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)0.007
metaresearch head score (Gemma)0.019
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.025
Threshold uncertainty score0.051

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.019
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.004
Science and technology studies0.0010.005
Scholarly communication0.0050.007
Open science0.0020.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0040.001

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 head0.043
GPT teacher head0.357
Teacher spread0.314 · 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 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

Citations38
Published2018
Admission routes2
Has abstractyes

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