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Record W1672459056

Linking a Web-Based Instructional System Usage: the Application of the Technology Acceptance Model

2004· article· en· W1672459056 on OpenAlexaff
Bouchaïb Bahli, Raafat George Saadé

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldDecision Sciences
TopicTechnology Adoption and User Behaviour
Canadian institutionsConcordia University
Fundersnot available
KeywordsTechnology acceptance modelUsabilityComputer scienceProcess (computing)Information systemInformation technologyWeb applicationKnowledge managementWorld Wide WebEngineeringHuman–computer interaction
DOInot available

Abstract

fetched live from OpenAlex

Design and implementation of information systems is costly and involves risk. During the past 30 years, major efforts have been made to understand better this process and its outcomes. A significant result to these efforts is the grouping of factors into a model to facilitate the analysis of information systems use. The technology acceptance model has received a significant attention in MIS research. The model suggests that perceived usefulness and ease of use of a particular technology or a system predicts the attitude towards that technology and, thus, the intention to use it. Using the technology acceptance model, this paper reports the results of an empirical study that investigates the acceptance of Web-based Instructional Systems. One hundred and two participants were surveyed to validate the model. A second generation data analysis method was used, (Partial Least Squares). The findings suggest that the model is appropriate for examining Webbased Instructional Systems acceptance. Some practical and research implications are provided.

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)0.006
metaresearch head score (Gemma)0.031
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: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.033

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.031
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0000.002
Scholarly communication0.0030.004
Open science0.0010.002
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.052
GPT teacher head0.337
Teacher spread0.285 · 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

Citations2
Published2004
Admission routes1
Has abstractyes

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