Pinpointing the Driving Forces Propelling Digital Business Transformation
Bibliographic record
Abstract
Comprehending the motivating factors that drive Digital Business Transformation (DBT) is crucial for cultivating success in DBT initiatives. The objective of the research outlined in this paper was to pinpoint and categorize the factors that inspire companies to embark on the DBT journey. Through qualitative analysis, employing expert interviews as the method, the authors extracted the necessary information to address three key research questions: (i) What are the external drivers of DBT in the plastic extrusion machine industry? (ii) Which internal factors are driving DBT in these companies? (iii) Is there anything else significantly impacting the DBT initiatives? The identified driving forces propelling DBT in German businesses within this industry include external factors: skill shortage, social impact, COVID-19, supply bottlenecks, competitiveness, and customer requirements; internal factors: cost reduction, process acceleration, efficiency increases, and time savings; and mixed factors: attitude of young people, basic education, and work–life balance. The insights derived from this research enhance the understanding of the circumstances and dynamics of traditional companies across other Western European countries. Our findings enrich the existing theory by presenting a distinctive threefold categorization of the drivers behind DBT, providing unique insights into the factors propelling the advancement of DBT initiatives.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".