Bibliographic record
Abstract
In this study, we investigate the influence of two external influences i.e., Ease of finding and Computer anxiety on the technology acceptance model (TAM) and the continuance intention of using a popular course management system (CMS): WebCT. The study used a sample of 72 students that have experience using the software. The students came from four local higher education institutions. In order to study nature of the relationships among the constructs, eight (8) hypotheses were formulated and tested using a structural equation modeling technique: Partial Least Squares (PLS). The predictive power of the model was adequate and the study found support for seven of eight hypotheses. Regarding the impact of the antecedents on continuance intention in the use of technology, the results offer the following insights: when computer anxiety is low, students are able to use the system without much difficulty, and are likely to continue to use CMS in the future. Similarly, students will continue the tool as long as they find it easy to navigate. Perhaps due to contextual factors, the data did not support the relationship between Perceived usefulness and Usage. This particular finding is at variance with the TAM’s results and viewpoint. The study’s implications for research and practice are succinctly outlined.
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 machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".