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Record W4306665554 · doi:10.5947/jeod.2022.003

The Influence of Board Diversity and Board Conflict on Performance in Consumer Cooperatives in South Korea

2022· article· en· W4306665554 on OpenAlexaff
SUN-HEE LEE, HYUN-JU KANG, SANG-YOUN LEE

Bibliographic record

VenueJournal of Entrpreneurial and Organizational Diversity · 2022
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCooperative Studies and Economics
Canadian institutionsUniversité du Québec à Montréal
FundersSungKongHoe University
KeywordsDiversity (politics)Corporate governanceValue (mathematics)Gender diversityTask (project management)Functional diversityOn boardBusinessPublic relationsAccountingMarketingPolitical scienceManagementEconomicsComputer scienceEngineeringLaw

Abstract

fetched live from OpenAlex

Board of directors are important elements in the structure and management of cooperatives. This study examines the relationships between cooperative board diversity (i.e., value and functional) and three performance variables (social performance, operational performance, and democratic performance). Additionally, we hypothesize that conflict (task or relational) mediates these relationships. We argue that decision making, policy, and performance depend on the composition of a board. The empirical analysis, using a sample of 423 female board members in 66 local consumer cooperatives in South Korea, finds that board value diversity can have a negative effect on performance, even though their functional background diversity may be positive, while conflicts over performing tasks may mediate functional background diversity and performance. More specifically, the results indicate that board value diversity is positive and significantly related to relational conflicts, while functional background diversity is negative and significantly related to task conflicts. The findings suggest that performance of cooperatives can be improved with more diverse board with functional backgrounds. We hope that this paper could offer a significant contribution to both the board and corporate governance literature and the diversity literature.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.886

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.002
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.013
GPT teacher head0.182
Teacher spread0.169 · 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.

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

Citations3
Published2022
Admission routes1
Has abstractyes

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