Managing Change in the Digital Age: A Comparative Study of Change Management and Digital Transformation Models
Bibliographic record
Abstract
Today’s business market demand firms to innovate to remain competitive, and the biggest challenge of innovation is managing the change process. This paper highlights the importance of digital transformation for organizations to remain competitive and generate a competitive advantage in the hyper-competitive business market. Adopting new technology is complex, requiring dealing with the technical and human side of change. Change management is critical to ensure successful transformations and tackle the resistance to change, which is one of the main challenges any change initiative faces. This paper compares six change management models against eight digital transformation models to identify similarities and differences, which can be seen as strengths and weaknesses. The analysis of the models enabled the identification of critical activities that organizations embarking on change initiatives must follow to ensure the success of the implementation and sustainability of the change. The activities were categorized into four main stages to facilitate the comparison of the digital transformation and change management frameworks. The paper concludes that there is a need for a more robust model for both change management and digital transformation, capable of reflecting the current situation that organizations face where the only constant is change.
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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 | 0.007 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".