The Mediating Effect of Intention to Use on the Relationship between Mobile Learning Application and Knowledge and Skill Usage
Bibliographic record
Abstract
Mobile learning (m-learning) has evolved as an alternative way of training delivery in a variety of businesses and sectors. Mobile device technology is continuously developing and improving, resulting in more mobile device use. In a corporate setting, the usage of mobile devices as learning aids has become a new delivery technique. Telekom Malaysia (TM) has also adopted this learning tool for staff training. This research has been conducted to determine the mediating effect of intention to use on the relationship between mobile learning applications and knowledge and skill usage. There are five objectives for this research. Hypotheses have been generated to be tested according to the Technology Acceptance Model (TAM) and Kirkpatrick Evaluation Model. The questionnaire was used for data collection. SmartPLS version 3.2.8 and IBM SPSS Statistics version 26 statistical software were used in the analysis. The finding revealed that there was an effect of perceived ease of use (PEOU) and perceived usefulness (PU) on TM employee knowledge and skill usage. In addition, the study also found there was a mediating effect of Intention to Use (ITU) on the relationship between PEOU and PU with TM employee knowledge and skill usage.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".