Reflecting Emerging Digital Technologies in Leadership Models
Bibliographic record
Abstract
In this chapter, Smith and Cockburn reaffirm their claim in a previous book that today's global business contexts are volatile, uncertain, complex, and ambiguous (VUCA), and leaders must focus more on complex thinking skills and mindsets than developing behavioral competencies. In so doing, leaders must be familiar with the benefits and drawbacks of emerging digital technologies and use these technologies appropriately. In the previous book, the authors defined flexible and dynamic leadership models that assure success in the above contexts and described learning related processes essential to mastering the ability to adapt at rates consistent with the business complexity leaders now face. In this chapter, they extend their previous research and review newly emerging factors contributing to global business complexity in the era of the Fourth Industrial Revolution (IR4.0) and explain how these elements may be applied by leaders, including CEOs and Boards of Directors, to augment the power of their recommended leadership models.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.009 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".