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Record W2604550836 · doi:10.34105/j.kmel.2017.09.005

Examining the antecedents of Facebook acceptance via structural equation modeling: A case of CEGEP students

2017· article· en· W2604550836 on OpenAlex
Tenzin Doleck, Paul Bazelais, David John Lemay

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

VenueKnowledge Management & E-Learning An International Journal · 2017
Typearticle
Languageen
FieldDecision Sciences
TopicTechnology Adoption and User Behaviour
Canadian institutionsMcGill University
Fundersnot available
KeywordsStructural equation modelingTechnology acceptance modelPsychologySocial psychologySocial mediaUsabilityWorld Wide WebComputer science

Abstract

fetched live from OpenAlex

Although the last decade has witnessed social networking sites of varied flavors, Facebook’s user growth continues to balloon, and relatedly, Facebook remains popular among the college populace. While there has been a growing body of work on ascertaining antecedents of Facebook use among college students, Collège d'enseignement général et professionnel (CEGEP) students’ acceptance of Facebook remains underexplored. The purpose of this study was to analyze CEGEP students’ acceptance of Facebook using the technology acceptance model (TAM). Structural equation modeling was conducted on data from a survey of 214 CEGEP students. We find that Facebook use is motivated by the core TAM constructs as well as the added factors of peer influence, perceived enjoyment, perceived self-efficacy, relative advantage, risk, and trust.

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)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.367
Threshold uncertainty score0.729

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0040.001
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 head0.213
GPT teacher head0.462
Teacher spread0.249 · 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