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Record W4353089587 · doi:10.1002/smj.3499

Board experiential diversity and corporate radical innovation

2023· article· en· W4353089587 on OpenAlexfundno aff
Aurora Genin, Wenting Ma, Vineet Bhagwat, Gennaro Bernile

Bibliographic record

VenueStrategic Management Journal · 2023
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCorporate Finance and Governance
Canadian institutionsnot available
FundersCanadian Intensive Care FoundationKelley School of Business, Indiana UniversityNational Science Foundation
KeywordsCorporate governanceDiversity (politics)BusinessStakeholderValue (mathematics)ShareholderMarketingExperiential learningPublic relationsManagementEconomicsSociologyPolitical scienceFinance

Abstract

fetched live from OpenAlex

Abstract Research Summary How does board experiential diversity affect corporate radical innovation? We find that the combined diversity of directors' educational, industrial, and organizational experiences spurs the quantity and quality of path‐breaking patents developed at a firm. Instrumental variable analysis leveraging exogenous variation in firm access to the nonlocal supply of directors with diverse experiences indicates causality, which is corroborated by difference‐in‐differences tests. Firm heterogeneity suggests experientially diverse directors spur radical innovation by better serving the firm's advisory needs rather than via improved governance. Our findings enrich theoretical insights into how corporate board leadership may affect innovation and long‐term value creation at the firm. Managerial Summary This study offers practical guidance on director recruitment. Board directors with diverse educational, industrial, and organizational experiences can support the invention of radical technology. This type of innovation can create substantial economic and social value. Noting the benefits of diverse experiences in the boardroom, corporate executives can search beyond the traditional director pedigree (e.g., Ivy League‐educated financiers), where female and minority individuals remain underrepresented. In doing so, the firm can find more qualified candidates to assemble a demographically and intellectually diverse board, thus cultivating an inclusive corporate culture conducive to shareholder and stakeholder value creation.

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: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.247
Threshold uncertainty score0.796

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.001
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 head0.065
GPT teacher head0.235
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 designTheoretical or conceptual
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

Citations66
Published2023
Admission routes1
Has abstractyes

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