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Dimensions of Perceived Usefulness: Toward Enhanced Assessment

2007· article· en· W2135099384 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.

Bibliographic record

VenueDecision Sciences Journal of Innovative Education · 2007
Typearticle
Languageen
FieldDecision Sciences
TopicTechnology Adoption and User Behaviour
Canadian institutionsConcordia University
Fundersnot available
KeywordsUsabilityTechnology acceptance modelPerceptionContext (archaeology)Outcome (game theory)PsychologyIntrinsic motivationKnowledge managementComputer scienceApplied psychologySocial psychologyHuman–computer interaction

Abstract

fetched live from OpenAlex

ABSTRACT Students' perceptions about the use of online learning tools have been shown to vary among studies. Their perceptions may have a profound impact on performance in the course and subsequent behavior toward continued use. This article presents a theoretical framework to identify three dimensions of perceived usefulness, namely, performance‐related outcome expectations, personal‐related outcome expectations, and intrinsic motivation. Based on the technology acceptance model (TAM), a new expanded model is proposed to capture more details about students' perceptions of an online learning tool. I also examine the relationships of these three dimensions with perceived ease of use, attitudes, and behavioral intentions to use in the context of online technologies used as an integral component of the course requirements. My findings demonstrate the utility of the expanded TAM to distinguish between the influences of the three proposed dimensions. Results also show that, within the context of this study setup, intrinsic motivation had the most influence on intentions and perceived ease of use of the learning tool had relatively little importance. Limitations and implications are offered.

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)0.016
metaresearch head score (Gemma)0.008
Version: codex-gemma-dda1882f352aValidation 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 score0.670
Threshold uncertainty score0.951

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0160.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.008
Science and technology studies0.0000.001
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 head0.159
GPT teacher head0.486
Teacher spread0.327 · 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