A configurational perspective of boards' attention structures
Bibliographic record
Abstract
Abstract Research Question/Issue What combinations of board attributes and contextual factors explain boards' selective distribution of attention between their dual role of resource provisioning and monitoring? At the board level, we consider board structure and breadth of knowledge, while the context in which boards operate is captured by the degree of external scrutiny, operational complexity, performance, and ownership structure. Research Findings/Insights Our study demonstrates that there are multiple ways board attributes bundle and combine with important elements of the context to promote similar board attention structures. Our findings provide evidence of the causal complexity underlying this phenomenon and corroborate the notions of equifinality and asymmetric causality among board‐, firm‐, and institution‐level conditions conducive to boards allocating more attention to either their resource provisioning or monitoring roles. Theoretical/Academic Implications Our findings support the attention‐based view (ABV), suggesting that boards' selective distribution of attention is regulated by the combination of skills and knowledge directors bring to the firm and the stimuli provided by contextual factors. In doing so, we underscore the need for an extended theory on board effectiveness, as resource dependence‐ and agency‐based prescriptions about boards' behavior may be incomplete, since there is limited consideration by these theories of the bounded rationality of directors and the complex relationships between the factors that can frame boards' selective distribution of attention. Practitioner/Policy Implications Our study informs efforts to disentangle the conditions under which different attributes combine and regulate boards' distribution of attention, which has implications for nomination committees and powerful actors who have influence on board appointments. Because our results reveal several causal paths that can promote similar board attention structures, decision makers may wish to recruit directors with specific attributes that will be the best fit for the firm's contextual conditions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 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".