From digital culture to digital transformation: Examining the roles of digital leadership and technological supports
Bibliographic record
Abstract
This research explores the complicated relationship between digital culture and digital transformation, specifically examining the mediating role of digital leadership and the moderating influence of technological support. A robust methodological approach was employed; the sample size of 385 participants was calculated according to Cochran’s formula, yielding 361 validated questionnaires for comprehensive statistical analysis. In the research, an interconnected set of questions along with a 5-point Likert scale was used where the measure scale was specialized in digital culture, transformation, digital leadership, and technological support. Along with an expert panel and an initial field test, construct validity and content validity were ensured. The scale was developed in English, after which it was systematically translated to Arabic, the meaning of which was kept the same as the original English version. The findings show how important digital culture is to digital transformation, as well as its overriding role through digital leadership. Also, while direct influence on digital transformation is strong, technological infrastructure does not have a moderating influence. These results help build a new framework to support organizations in adopting new digital practices. This research provides organizations with actionable insights in regards to improving transformational initiatives in digital business by highlighting the importance of digital leadership and culture readiness. This research enriches the discourse on digital transformation by empirically examining the mediating role played by digital leadership and the moderating influence exercised by technological support within the broader construct of digital culture. The analysis is underpinned by a rigorously validated instrument that has been culturally adapted to ensure contextual relevance.
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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 | 0.009 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".