The effect of teaching methods on university students’ intention to use online learning: Technology Acceptance Model (TAM) validation and testing
Bibliographic record
Abstract
Web 2.0 has changed the way consumers access information. This study aims to investigate the relationship between social media (Watsons’ Facebook page) content and consumers repurchase intention. In addition, it determined whether E-WOM and interactivity can act as the mediating variables between the social media content and repurchase decision. The data were collected through online and offline questionnaires. A total of 146 valid questionnaires were obtained and analyzed using Partial Least Square Structural Equation Modeling (PLS-SEM) through the SMART-PLS 3.3.9 software. The findings support the direct effect of social media content on E-WOM, interactivity, and repurchase intention. Moreover, the results confirmed the mediating role of interactivity between social media content and repurchase intention, however, E-WOM does not mediate between social media content and repurchase intention. The present study suggests some managerial implications for beauty brand retailers and provides fundamental strategies related to their social media.
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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 | 0.008 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".