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Brokering a Stick for a Hammer (or a Rock): Inter-Board-Committee Cooptation

2018· article· en· W2814279871 on OpenAlexaff
Shelby Gai, J. Yo‐Jud Cheng, Andy Wu

Bibliographic record

VenueAcademy of Management Proceedings · 2018
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCorporate Finance and Governance
Canadian institutionsKellogg's (Canada)
Fundersnot available
KeywordsNominationLegitimacyAuditContext (archaeology)Audit committeeCompensation (psychology)Public relationsTest (biology)BusinessAccountingPolitical scienceLawPsychologySocial psychology

Abstract

fetched live from OpenAlex

Environmental threats do not affect all groups equally within an organization. Due to this uneven external pressure, only certain groups engage in problemistic search, limiting the pool of solutions to those that are within the affected group’s control. When an affected group does not have a viable solution, we argue that the group will seek access to another group’s set of solutions. Melding the BTF framework with work on brokers, we posit that tertius iungens brokers who connect the affected and unaffected groups may use their coordinating ability to coopt one group to advantage another. We study this dynamic in the context of board committees following a peer restatement event. Peer restatements constitute a legitimacy threat that directly affects the audit committee of the focal firm. Unable to signal legitimacy themselves, the audit committee may seek to coopt the decisions of the compensation and nomination committees, which control more visible signals of legitimacy: CEO compensation and new director appointments. They accomplish this through multi-committee directors (MCDs), who link the audit committee with one of the other two. Notably, only MCDs with tertius iungens abilities are successful. We test and find evidence for inter-committee cooptation. Audit-compensation cooptation results in a decrease in CEO compensation, while audit-nomination cooptation results in new director appointments that change the composition of the board to the advantage of the audit committee. Importantly, this latter cooptation creates an endogenous feedback loop, facilitating coordination–and further cooptation–should the audit committee encounter a similar threat in the future.

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 imitation

Not 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.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.028
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.016
Threshold uncertainty score0.053

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.028
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0030.003
Scholarly communication0.0050.005
Open science0.0010.004
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0160.002

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.

Opus teacher head0.035
GPT teacher head0.262
Teacher spread0.227 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2018
Admission routes1
Has abstractyes

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