Analysis of Individual Factors in Improving Knowledge Sharing: Case Study of Accounting Education Students
Bibliographic record
Abstract
Education is a long-term investment in human resources for the survival of human civilisation in the world. Advances in technology can be used as a supporting tool in the learning process. However, the technology used can be influenced by individual and organisational factors in its use. This study aims to determine individual, organisational and technological factors in the student knowledge sharing process. This study uses a quantitative approach with a descriptive survey design method. Respondents in this study were students of the Accounting Education, Universitas Sebelas Maret class 2018-2020, with 149 students. The indicators used in the measurement are individual factors (self-efficacy, willingness to share, and reciprocal rules), organisational factors (lecturer support and competitiveness), and technological factors (availability of technology and use of technology). The data analysis method uses the SEM model. The study results show that individual and technology factors affect the knowledge-sharing process, and the organisation does not affect the knowledge-sharing process.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".