Investigating the role of digital transformation and digital innovation on school performance
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This research aims to analyze the relationship between digital transformation and school performance and the relationship between digital innovation and school performance. The research method is quantitative through surveys, research data was obtained by distributing online questionnaires to 489 high school teachers throughout Indonesia who were selected using a simple random method. Data analysis used covariance-based structural equation modelling (CB-SEM) with SmartPLS 4.0 software to analyze research data. The independent variables are digital transformation and digital innovation, and the dependent variable is school performance. The stages of data analysis are validity testing, reliability testing, model fit testing and significance testing of hypothesis testing. The results of this research are that digital transformation has a positive and significant relationship with performance. Moreover, digital innovation has a positive and significant relationship to performance. The findings of this research support and prove the results of previous research that digital transformation has a positive effect on organizational performance and innovation and confirms the direct influence of innovation on organizational performance. The contribution of this research is aimed at various literature related to the role of digital transformation and innovation on organizational performance. Meanwhile, for practical implications, digital transformation and innovation can improve organizational performance, especially in schools.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.004 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it