The Development of Agile Enterprise Architecture for Digital Transformation in Higher Education Institutions
Bibliographic record
Abstract
The rapid evolution of digital technologies has led to significant transformations in various industries, including higher education. This study explores the role of Agile Enterprise Architecture (AEA) in supporting digital transformation initiatives within Higher Education Institutions (HEIs). AEA provides a flexible, adaptive, and iterative framework to manage the complex and dynamic nature of digital transformation. We conducted a qualitative study using a multiple case-study approach, investigating four HEIs that implemented AEA in their digital transformation initiatives. Data were collected through semi-structured interviews, document analysis, and participant observation. We employed thematic analysis to identify the key factors contributing to the successful implementation of AEA in these institutions. Our findings revealed that AEA plays a crucial role in facilitating digital transformation by providing a holistic, systematic, and adaptive framework. The AEA approach enables HEIs to effectively manage the complexities of digital transformation, enhance their agility, and respond to changing stakeholder needs. Key success factors include strong leadership, effective communication, a skilled workforce, and a culture of collaboration and continuous improvement. The study contributes to the understanding of AEA's role in promoting digital transformation in higher education and offers practical implications for HEIs looking to leverage digital technologies for improved performance and stakeholder satisfaction. Further research is needed to explore the generalizability of these findings to other contexts and industries.
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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 | 0.017 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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".