Digital transformation as a multi-phase process: a longitudinal study of corporate strategy and business unit adaptation
Bibliographic record
Abstract
This study investigates how digital transformation unfolds over time within a multi-business manufacturing firm. Drawing on a longitudinal case study of SweX—a global industrial firm—we trace the dynamics of digital transformation across three empirically derived phases: experimentation, consolidation, and acceleration. Five interrelated patterns shape the process: (1) digital transformation unfolds recursively rather than linearly; (2) tensions arise between corporate strategy and business unit adaptation; (3) monetizing digital innovation remains challenging; (4) structural adjustments are needed to balance stability and change; and (5) temporal asymmetry—misalignments between technology deployment and customer readiness—can hinder digital transformation. We organize these insights around three overarching themes—organizational tension, structural adjustment, and organizational adaptation—developed through iterative analysis across corporate and business unit levels. The study advances process-oriented perspectives on strategy by showing how recursive patterns of tension, structural change, and organizational adaptation drive digital transformation in complex, multi-level firms.
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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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".