Navigating the Digital Frontier: The Art of Ambidextrous Leadership Definition of Digital Leadership
Bibliographic record
Abstract
When it comes to leaders who have been recognized for successfully navigating their organizations through digital transformation, names such as Satya Nadella, Jeff Bezos, Bob Igor, and Ajay Banga come top of mind. Nadella, CEO of Microsoft, shifted the company’s focus from traditional software to cloud services and embraced a culture of innovation to introduce products, such as Azure and Office 365. Bezos, founder and former CEO of Amazon, was relentless in sustaining focus on customer experience, data-driven decision-making, and investments in technologies like cloud computing and artificial intelligence. Igor, CEO of The Walt Disney Company, recognized the need to expand their digital footprint to remain competitive in the entertainment industry and launched the Disney+ streaming service. Finally, Banga, former CEO of Mastercard, played an important role in the digital transformation of the financial industry by re-imagining digital payment solutions to include contactless payments, mobile payments, and digital security innovations. From a strategy perspective, it is not surprising that each of these transformational success stories is unique in the way technology was leveraged to develop a competitive advantage. However, when it comes to leadership, there is much debate about whether notable changes in response to technological advancements have changed the qualities, styles, and approaches taken by today’s CEOs and other organizational leaders…
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.015 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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".