Bibliographic record
Abstract
Purpose –This paper aims to investigate a team dynamics based approach to assess board effectiveness, namely the interplay between boardroom decision-making processes and the board members' cognitive mental models. Design/methodology/approach –A socio-cognitive perspective is utilized for analyzing board processes and determining board effectiveness. Utilizing the concepts of team mental models and sensemaking, a theoretically grounded model of board effectiveness is developed, wherein the propositions predict the causality and effect of the socio-cognitive and sensemaking processes on board effectiveness. Findings –The proposed model is able to analyze the relationship among the different decision-making processes and members' cognitive models as determinants of board effectiveness, wherein the board's decision making process mediates the board's cognitive model – effectiveness relationship, while the board's cognitive model moderates the decision process – effectiveness relationship. Research limitations/implications –The conceptual model advances a rationale that might explain the mixed or modest findings in literature on the relationship between board demographics, dynamics and effectiveness. Practical implications –The model allows practitioners and policy makers an alternative mechanism to assess board effectiveness, that is able to not only integrate the demographic, diversity and dynamics related measures, but also enables a clear understanding of the cognitive influences on board decision making and effectiveness. Originality/value –The conceptual model encompasses most of the relevant constructs and findings of previous studies and offers a parsimonious yet holistic understanding of the boardroom mechanisms that might determine board effectiveness.
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 | 0.008 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.001 | 0.009 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".