Building an Integrated Digital Transformation System Framework: A Design Science Research, the Case of FedUni
Bibliographic record
Abstract
The purpose of this paper is to propose an integrated digital transformation system framework (IDTSF) to help support business leaders and teams in making their products, services, and operations more streamlined and competitive. The framework will help organizations to best meet user/customer needs with minimum waste and time and enables businesses to achieve efficiency compared with island and traditional sequential approaches. The proposed framework can also provide insights to help organizations to avoid common failures when deploying digital transformation initiatives. The paper follows the design science research (DSR) and the information systems design science research (ISDSR) methodologies to develop the IDTSF model and a practical design artifact. The main problems were the initiation, execution, and governance challenges associated with digital transformation. After identifying the problems and the objectives, a relevant IDTSF model was synthesized and tested as a design artifact. The results of the test of the proposed artifact showed its effectiveness and efficiency in facilitating the components of the model in creating a cohesive framework.
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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 | 0.011 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.007 | 0.009 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| 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".