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Record W2998559248 · doi:10.54648/eulr2019040

The Interplay of Board Control with Board Configuration: Evidence from the UK

2019· article· en· W2998559248 on OpenAlexaff
Ioannis Gkliatis, Dimitrios N. Koufopoulos, Aspasia Pastra

Bibliographic record

VenueEuropean Business Law Review · 2019
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCorporate Governance and Law
Canadian institutionsInstitute on Governance
Fundersnot available
KeywordsAccountingControl (management)On boardPrincipal–agent problemBusinessTest (biology)Agency (philosophy)Set (abstract data type)PsychologyCorporate governanceManagementComputer scienceEngineeringSociologyEconomics

Abstract

fetched live from OpenAlex

The study aims to explore the control role that board directors undertake and understand the impact of several board characteristics on these roles. Building on existing literature a model was developed to test the hypothesised relationships – i.e. directors’ control role with board characteristics. The responses were collected from 115 directors in UK organisations. Principal component analysis was conducted to reduce the data and propose a set of directors’ roles and correlation as well as regression analyses are utilised in order to test the hypothesised relationships. The results of the statistical analysis propose some impact of the board characteristics on what directors do, extending the limited empirical evidence found in the literature. However, the theoretical framework needs further examination and research. The study is evidenced by various limitations. Firstly, additional constructs can be added as determinants of the directors’ control role. Secondly, the response rate in the survey is relatively low which is regarded as a limitation, although there are limited studies offering quantitative results from board members. Board of directors, board characteristics, agency theory, control role, board structure, independent directors, frequency of board meetings, board size, CEO duality, board tenure

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.710
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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.

Opus teacher head0.017
GPT teacher head0.221
Teacher spread0.204 · 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 teacher head, not a consensus.

Study designNot applicable
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
Published2019
Admission routes1
Has abstractyes

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