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Record W3004747871

Institutional Resistance: All-Male Boards in the 21st Century. Administrative Sciences Association of Canada.

2017· article· en· W3004747871 on OpenAlexaboutno aff
Bjoern C. Mitzinneck, Judith L. Walls, Glenn Dowell

Bibliographic record

VenueAlexandria (UniSG) (University of St.Gallen) · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicGender Diversity and Inequality
Canadian institutionsnot available
Fundersnot available
KeywordsInstitutional theoryBusinessResistance (ecology)LicensePublic relationsInstitutional investorIsomorphism (crystallography)Resource dependence theoryAccountingCorporate governancePolitical scienceManagementEconomicsLawFinance
DOInot available

Abstract

fetched live from OpenAlex

According to institutional theory, isomorphism enables firms to acquire a license-to-operate and vital organizational resources. Yet, organizational fields often include small numbers of organizations that resist prevalent institutionalized norms. We study what sets institutional resistors apart from their conforming peers. We hypothesize that organizations with features which shield them from institutional pressures (reduced susceptibility) or compel them to ignore specific norms (reduced receptivity) are most likely to follow a strategy of institutional resistance. Empirically, we focus on major publicly traded corporations resisting mounting pressures to include women on boards. We find support for our hypotheses in a panel of S&P500 firms from 2003-2012. Specifically, all-male boards are more likely in firms which are comparatively less visible than their peers or primarily engage in business-to-business sales, reducing their susceptibility to institutional pressures. Moreover, all-male boards are more likely in firms with more inside directors, older directors, as well as those head-quartered in more conservative communities, reducing their receptivity for the institutional norm in question. We discuss implications for institutional theory and research on corporate social responsibility.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.809
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0030.001
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.092
GPT teacher head0.288
Teacher spread0.196 · 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 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

Citations0
Published2017
Admission routes1
Has abstractyes

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