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Record W1510101766 · doi:10.1002/sej.1168

Top Management Team Shared Leadership and Organizational Ambidexterity: a Moderated Mediation Framework

2013· article· en· W1510101766 on OpenAlexaff
Oli Mihalache, Justin J.P. Jansen, Frans A. J. Van Den Bosch, Henk Volberda

Bibliographic record

VenueStrategic Entrepreneurship Journal · 2013
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicInnovation and Knowledge Management
Canadian institutionsWilfrid Laurier University
Fundersnot available
KeywordsAmbidexterityMediationKnowledge managementBusinessEnablingModerated mediationLeadership styleSample (material)PsychologyManagementSociologyComputer scienceSocial psychologyEconomics

Abstract

fetched live from OpenAlex

This study proposes top management team ( TMT ) shared leadership as an important enabler of organizational ambidexterity. Moreover, we examine both how and when TMT shared leadership enhances organizational ambidexterity by considering two TMT processes as mediators (i.e., cooperative conflict management style and decision‐making comprehensiveness) and two elements of organizational structure (i.e., connectedness and centralization of decision making) as important contingencies. We test our moderated mediation framework using time‐lagged data from a cross‐industry sample of 202 firms. We discuss how our findings extend strategic entrepreneurship, ambidexterity, and leadership research and provide implications for practice. Copyright © 2013 Strategic Management Society.

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)0.009
metaresearch head score (Gemma)0.028
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.009
Threshold uncertainty score0.049

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.028
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0020.004
Scholarly communication0.0040.003
Open science0.0020.006
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0080.000

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 head0.038
GPT teacher head0.233
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 source (direct Gemma or distilled Codex), 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

Citations264
Published2013
Admission routes1
Has abstractyes

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