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Record W2739445786 · doi:10.4018/ijissc.2017100104

Understanding Parents' Intention to Use a Learning Community Management System in K-12 Schools

2017· article· en· W2739445786 on OpenAlexaff
Dawit Demissie, Abebe Rorissa, Anteneh Ayanso

Bibliographic record

VenueInternational Journal of Information Systems and Social Change · 2017
Typearticle
Languageen
FieldDecision Sciences
TopicTechnology Adoption and User Behaviour
Canadian institutionsBrock University
Fundersnot available
KeywordsUnified theory of acceptance and use of technologyExpectancy theoryContext (archaeology)PsychologyLearning ManagementKnowledge managementApplied psychologySocial psychologyComputer scienceMathematics educationGeography

Abstract

fetched live from OpenAlex

Information systems that facilitate communication among faculty, staff, and parents are increasingly being adopted by K–12 schools. Despite their promise, schools are resisting these systems. The Unified Theory of Acceptance and Use of Technology (UTAUT) has been rarely tested to explain behavioral intention, acceptance, and sustained use of technology in the context of developing nations. We apply the UTAUT model to examine behavioral intention to use a learning community management system at a K-12 school in the Bahamas. Data were collected from 162 parents through a survey questionnaire. Results showed that facilitating conditions (FC), performance expectancy (PE), and effort expectancy (EE) are significantly related to behavioral intention. In addition, Age has a moderating role in PE and FC with respect to their effects on behavioral intention. Our findings extend the validation of the UTAUT model in different environments and may help educators, administrators, and policymakers implement meaningful ICT policies in line with their community's educational aspirations.

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 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)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.136
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0020.003
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 head0.438
GPT teacher head0.418
Teacher spread0.019 · 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 teacher head, not a consensus.

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

Citations1
Published2017
Admission routes1
Has abstractyes

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