The Missing Link in Digital Transformation Leadership: Unpacking the Role of Knowledge
Bibliographic record
Abstract
ABSTRACT Leading digital transformation (DT) is challenging due to the unforeseen hurdles that arise through the novelty of digital technologies and the broad scope of organisational change. Even those with a wealth of experience and skills may struggle to respond adequately to inherently novel situations. While skills and experience are necessary for leading DT, continuously acquiring technology and business knowledge is equally important for navigating unfamiliar situations that DT often presents. As such, knowledge represents the missing link that warrants equal attention in driving successful DT. We examined the different knowledge types that digital leaders require to effectively navigate DT. Drawing on the IT innovation and DT literatures, we developed the DT knowledge framework with six knowledge types. We analysed these knowledge types in 138 interview excerpts of chief technology officers (CTOs), chief information officers (CIOs) and chief digital officers (CDOs) leading DT taken from 128 industry articles. We find that technology know‐what —that is, knowing what technologies are available and their capabilities —and business know‐how —that is, knowing how to execute organisational change needed for DT, are the two most important knowledge types. We further unpacked the dimensions of each of these knowledge types and offered recommendations for practitioners through our novel knowledge perspective on DT leadership. We also discuss the implications of our knowledge perspective for advancing DT scholarship.
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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 | 0.010 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.007 | 0.027 |
| Scholarly communication | 0.011 | 0.018 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".