Factors Influencing the Digital Transformation Toward High-Performance Education Organizations
Bibliographic record
Abstract
This study investigates in-depth information about the factors influencing the digital transformation of an educational establishment to becoming a high-performance education organization through the dimensions of digital enterprise architecture, digital transformation, and high-performance education organization using structural equation modeling (SEM) as a tool to verify the model. A sample of 520 staff members, selected using a multi-stage random sampling method from 22 departments under the Office of the Basic Education Commission (Head Office), Ministry of Education, Thailand, answered an online questionnaire. The results revealed that the model was valid and fit with the empirical data. The results also showed that business architecture, data architecture, application architecture, technology architecture, security architecture, human capital architecture, and infrastructure architecture had a direct and indirect influence on the context of digital transformation and high-performance education organizations. There was technology architecture and human capital architecture that had an indirect influence on high-performance education organization; other than that, there was none. All hypotheses (H1–H10) were supported by statistical criteria. These results indicate that digital enterprise architectures are essential development tools influencing an organization toward becoming a high-performance education organization.
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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 | 0.001 | 0.006 |
| 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 | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".