The Influence of the Social, Cognitive, and Instructional Dimensions on Technology Acceptance Decisions among College-Level Students
Bibliographic record
Abstract
Technology acceptance models are primarily focused on the cognitive dimension of user beliefs. However, researchers have identified a range of situational and contextual factors that influence user attitudes and behavioral intention towards a given technology. We advance a situated model of e-learning acceptance among college students combining factors from the community of inquiry (COI) framework and the technology acceptance model (TAM), specifying core relationships within, and theoretically informed path relationships between the two frameworks. Using a sample of 121 respondents, we test a structural model using generalized structured component analysis. Collectively the situated model helped explain 63.7% variance in Behavioral Intention and 25% of the variance in Use suggesting that our model has strong explanatory power. Policymakers can leverage this information to boost acceptance of e-learning and platforms among their academic communities by promoting e-learning environments with strong Teacher, Social, and Cognitive Presence.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
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".