Often asserted, never confirmed: the role of attitude in the acceptance of mandatory technology use, let’s settle this question statistically for LMS use in the educational context
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.
Bibliographic record
Abstract
The objective of this research is to evaluate statistically the role of the attitude variable as a mediator in the unified theory of acceptance and use of technology (UTAUT) model in the context of mandatory use of technology. We chose this objective to address two research gaps: the prevailing divide in opinions about the role of attitude in technology acceptance models and the contradictory results obtained by acceptance models in the context of mandatory use of technology. Achieving this objective will enable us to address the perennial question of the role of attitude in acceptance literature and to assess the boundaries of UTAUT, thus contributing to its continued development and providing guidance for its evolution. Data were collected from 475 students enrolled in online courses at a Canadian university that required the mandatory use of a learning management system (LMS). Results show that attitude exerts full mediation of the relationships between the independent variables performance expectancy and effort expectancy and the dependent variable behavioural intention. This mediation becomes partial for facilitating conditions and fades out for social influence. We call into question the relevance of behavioural intention as a measure of technology acceptance and present theoretical contributions and practical recommendations.
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 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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it