The impact of digital technology training on developing academics’ digital competence in higher education context
Bibliographic record
Abstract
Purpose A new normal regarding teaching and learning has been established after COVID-19. The present study aims to examine the effectiveness of digital technology training on developing academics’ digital competence in higher education context. A conceptual model was developed using stimulus–organism–response (SOR) theory. Additionally, this study investigates the mediating effect of transfer of learning and the moderating effect of innovative climate in the relation between trainer capability and academics’ digital competence. Design/methodology/approach In total, 24 digital technology training sessions were organized. Data were collected from the 24 digital technology training sessions with 384 participants and analyzed using SPSS PROCESS macro. Findings The results indicated that digital technology training content and trainer capability were positively associated with academics’ digital competence. Mediation analysis indicated that transfer of learning mediated the relation between trainer capability and digital competence. Moderated mediated analysis revealed that the relationship between trainer capability and transfer of learning is stronger under a higher innovative climate. Originality/value This study contributes to the literature by applying the SOR theory in the context of digital technology training, providing a novel theoretical perspective on how digital training influences academics’ digital competencies. The study offers empirical evidence on the underlying process regarding the effect of digital technology training on academics’ digital competence. The findings revealed that transfer of learning as well as innovative climate play important intervening roles in enhancing academics’ digital competence. Higher education institutions can implement policies to promote the transfer of learning and innovative climate, allowing academics to learn innovative digital technology.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".