Sustainability (Is Not) in the Boardroom: Evidence and Implications of Attentional Voids
Bibliographic record
Abstract
Strategic leadership and corporate governance scholars have long been interested in how boards of directors make decisions pertaining to important strategic issues that can have a material impact on their organizations. To date, however, research on board decision-making, especially as it relates to issues of corporate social responsibility (CSR), environmental management, or sustainability, has concentrated almost exclusively on structural, demographic, or ownership factors of boards and their impact on various aspects of corporate social or environmental performance. Even still, many reputable corporations with exemplary corporate governance structures continue to make questionable strategic decisions with regards to environmental sustainability. As such, this research seeks to look into the “black box” of corporate governance to understand exactly how boards of directors are dealing (or not) with issues related to environmental sustainability. To do so, we conducted a series of qualitative interviews with directors and were surprised to find that social and environmental sustainability was simply not debated in the boardroom. Using an attention-based view of the firms (ABV), we present a process-based model that explains this phenomenon and introduce the new construct of attentional voids so as to contribute to our understanding of governing for social and environmental sustainability.
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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 | 0.023 | 0.133 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.013 |
| Scholarly communication | 0.006 | 0.010 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".