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Record W1853988981 · doi:10.1002/cjas.1325

Microfoundations of institutional change: Contrasting institutional sabotage to entrepreneurship

2015· article· en· W1853988981 on OpenAlexvenueno aff
C. Lakshman, Manzoom Akhter

Bibliographic record

VenueCanadian Journal of Administrative Sciences / Revue Canadienne des Sciences de l Administration · 2015
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicManagement and Organizational Studies
Canadian institutionsnot available
Fundersnot available
KeywordsMicrofoundationsEntrepreneurshipOrganizational fieldLegitimacyInstitutional theoryContext (archaeology)PoliticsField (mathematics)Economic systemPolitical scienceBusinessSociologyEconomicsSocial science

Abstract

fetched live from OpenAlex

Abstract This paper addresses little understood microfoundations of institutionally driven organizational change and utilizes an institutional‐conflict‐based approach to examine innovation in organizational forms. Using a two‐case comparative analysis, we longitudinally examine the antecedents, mechanisms, and success/failure of attempts at change by institutional entrepreneurs. We analyze and develop theoretical insights on the interplay between internal political processes and external competitive actions in the creation of innovation in organizational forms and the subsequent legitimacy struggles through which an organizational field evolves in a sports (cricket) business context. We draw implications for institutional actors by observing patterns in organizational and institutional evolution in such contexts. We contribute to institutional entrepreneurship literature by developing a nuanced process model of success and failure in institutional entrepreneurship. Copyright © 2015 ASAC. Published by John Wiley & Sons, Ltd.

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)0.007
metaresearch head score (Gemma)0.038
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
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 score0.007
Threshold uncertainty score0.036

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.038
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0040.003
Science and technology studies0.0010.009
Scholarly communication0.0050.004
Open science0.0010.006
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.187
GPT teacher head0.315
Teacher spread0.128 · 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 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

Citations8
Published2015
Admission routes1
Has abstractyes

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