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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 OpenAlex

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.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

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.

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: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.346
Threshold uncertainty score0.274

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.002
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.001
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 head0.043
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