Digital transformation as a business development strategy
Bibliographic record
Abstract
Purpose This study investigates the strategic dimensions of digital transformation in business, focusing on the development and implementation of a digital strategy. It explores the evolution of digital services, highlights key steps in digital transformation, and introduces the Hoshin Kanri model as a tool for structuring digital transformation strategies. The aim is to equip organizations with frameworks to enhance competitiveness and adapt to technological advancements. Design/methodology/approach The study employs bibliometric analysis using Scopus and VOSviewer to examine global research trends in digital transformation strategy. The Hoshin Kanri methodology is adapted to design a strategic framework for digital business transformation. A combination of systemic, synergistic, and critical evaluative approaches underpins the analysis, alongside a review of literature spanning business management, digital ecosystems, and strategic planning. Findings Key findings include the identification of five steps in digital transformation strategy development, ranging from vision formation to digital acceleration. The Hoshin Kanri model is presented as a novel adaptation for digital strategy, integrating technology, human resources, customer orientation, processes, and financial planning. The analysis underscores the role of integrated approaches in mitigating risks and enhancing the efficacy of digital transformations. Originality/value This study is the first to adapt the Hoshin Kanri model specifically for digital business transformation. It provides a comprehensive strategy roadmap that aligns digital transformation with corporate objectives, organizational culture, and stakeholder needs, offering practical insights for businesses navigating the Web 4.0 era.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.020 | 0.012 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".