Strategists on the Board in a Digital Era
Bibliographic record
Abstract
Considering the complexities and dynamics that firms are facing in a digital era, it is no exaggeration to argue that the way boards of directors contribute to strategy needs some new perspectives. In this article, we reconsider some of the commonly used notions and assumptions of board strategizing. We conceptualize a framework for board strategizing by revisiting and providing new elements to the work introduced by McNulty and Pettigrew in 1999 (Strategists on the board. Organization Studies, 20(1): 47-74. http://doi.org/10.1177/0170840699201003). Our framework highlights a number of timely board practices that have the potential to improve the way boards strategize under conditions of increasing digitalization. Further, the findings suggest that valuable strategic actions and priorities can be made by boards that use and develop dynamic capabilities as they strategize. Implications for theory and practice as well as future research directions are discussed.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".