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Experimental Research in Institutional Theory

2012· article· en· W4252918977 on OpenAlexaffabout
Alex Bitektine, Patrick Haack, Kimberly D. Elsbach

Bibliographic record

VenueAcademy of Management Proceedings · 2012
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicManagement and Organizational Studies
Canadian institutionsHEC Montréal
Fundersnot available
KeywordsInstitutional theoryLegitimacyConversationIdentification (biology)Empirical researchField (mathematics)VignetteExperimental researchPolitical scienceSociologyPositive economicsEpistemologyPsychologySocial psychologySocial scienceLawPolitics

Abstract

fetched live from OpenAlex

Recognizing that institutions are enacted by individuals, this symposium seeks to draw the researchers’ interest to empirical exploration of micro foundations of institutions and institutional processes using experimental methods. Experimental research can be used to confirm (or disconfirm) cause and effect relationships that are often suggested but difficult to isolate in contextually-rich field studies in institutional theory. Although experimental research has been largely overlooked by institutionalists in the last three decades, the few experimental studies that did address institutional processes - the studies by Zucker (1977) and Elsbach (1994) - have had a substantial influence on the development of the institutional theory. The goal of this symposium thus is to help build the case for experimental methods in institutional research. Specifically, we seek (1) to facilitate a conversation on the role of experiments in the development and testing of prevalent constructs in institutional theory, (2) to encourage scholars to utilize mixed-method designs, which allow triangulation of results from “conventional” methods with experimental exploration of causal mechanisms and micro-institutional insights, and (3) to create a community of institutional scholars that pursue or intend to pursue experiments in their research.Institutional Theory and Organizational Decision-Making: From small groups to herdsPresenter: Pamela S. Tolbert; Cornell U.Presenter: Verena Krause; Cornell U.Presenter: Rachel Ruttan; Northwestern U.An Experimental Vignette Approach in Institutional AnalysisPresenter: Aafke Raaijmakers; Tilburg U.Presenter: Patrick Vermeulen; Radboud U. NijmegenPresenter: Marius T.H. Meeus; Tilburg U.Beyond Text Analysis: The unmet promise of experiments in legitimacy researchPresenter: Patrick Haack; U. of ZurichGroup Identification in the Legitimacy Judgment ProcessPresenter: Leigh Plunkett Tost; U. of Washington, SeattlePresenter: Steven Blader; New York U.Presenter: Kimberly A Wade-Benzoni; Duke U.Organizational Legitimacy, Reputation and Status: Development and validation of empirical measuresPresenter: Alex B. Bitektine; HEC MontrealPresenter: Kevin Hill; HEC Montreal

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)0.117
metaresearch head score (Gemma)0.206
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.117
Threshold uncertainty score0.618

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1170.206
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0030.002
Bibliometrics0.0050.006
Science and technology studies0.0050.034
Scholarly communication0.0090.014
Open science0.0040.009
Research integrity0.0050.009
Insufficient payload (model declined to judge)0.0180.001

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 head0.072
GPT teacher head0.321
Teacher spread0.249 · 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 designBench or experimental
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

Citations1
Published2012
Admission routes2
Has abstractyes

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