Digitalization and Boards of Directors: A New Era of Corporate Governance?
Bibliographic record
Abstract
The ongoing megatrend of digitalization is significantly affecting societies and organizations. How organizations deal with the impact of digitalization may determine whether or not they will be competitive in the future. The board of directors may hereby play a key role for the organization to adapt to the changing strategic context. But based on various corporate failures, boards’ work and their effectiveness have recently been questioned. Are todays’ boards equipped to create values for organizations in the future? We address this important research question by introducing a framework where digitalization is predicted to influence boards in two areas. First, we argue that boards in the future consist of virtual networks of people where needs to monitor management diminish and shared leadership approaches are emphasized. Second, we suggest that boards work according to a dynamic board agenda based on organizational threats and opportunities. The agenda is built around learning and knowledge management and is reflected in the committee structure. Using dynamic capabilities arguments, we propose a framework with the ambition to contribute to the understanding of what makes boards fit future organizational needs. With such an approach, this is the first study that contributes to knowledge on boards by examining how boards need to adapt to meet the challenges in a digital world. The implications for theory and practice call for changed perspectives on what boards do and how they look like.
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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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.020 |
| Scholarly communication | 0.011 | 0.015 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| 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".