The role of motivation of unified theory acceptance, use of technology model and innovation dif-fusion theory on e-learning intention of SMEs employee
Bibliographic record
Abstract
In the digital era and the era of the industrial revolution 4.0, the quality of human resources needs to be improved in order to have competencies that are in accordance with the needs of the organization. Therefore, an effective and efficient method is needed to improve employee competence by using e-learning technology. This study analyzes the Diffusion of Innovation Theory through the Unified Theory of Acceptance and Use of Technology (UTAUT) model. The study uses quantitative data obtained by surveying 400 SME employees in Indonesia using an e-learning system. Dissemination of data is by using online questionnaires through social media. Data analysis uses structural equation modeling (SEM) modeling. The results of data analysis show that the theory of diffusion of innovations mediated by intrinsic and extrinsic motivation in the UTAUT model had a significant effect. The aspects that make up the theory of diffusion of innovation significantly and directly affect the intention of SMEs employees to use the e-learning system. The findings show that the diffusion of innovation theory has various indicators for the application of learning systems and further e-learning development so that they can have a real and significant impact on improving organizational performance and competitiveness.
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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 | 0.011 |
| 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 | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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; 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".