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Record W4382797188 · doi:10.1111/emre.12585

External information seeking and organizational ambidexterity in SMEs: Does empowerment climate matter?

2023· article· en· W4382797188 on OpenAlexaff
Céline Bérard, L. Martin Cloutier

Bibliographic record

VenueEuropean Management Review · 2023
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicInnovation and Knowledge Management
Canadian institutionsUniversité de MontréalUniversité du Québec à Montréal
FundersRégion Auvergne-Rhône-Alpes
KeywordsAmbidexterityBusinessEmpowermentOrganisation climateKnowledge managementMarketingPsychologySocial psychologyComputer scienceEconomics

Abstract

fetched live from OpenAlex

Abstract Access to external information is considered crucial to achieving organizational ambidexterity (OA) while presenting specific challenges for SMEs due to their limited resources. However, little is known about how SMEs can best benefit from their external information‐seeking activities for OA purposes, given specific organizational practices. Our paper addresses this research gap by analyzing the effects of external information seeking (i.e., environmental scanning and external managerial networking) on OA while considering the moderating role of empowerment climate in SMEs. Based on a survey administered to CEOs of 1439 French manufacturing SMEs, our main results indicate that empowerment climate positively moderates the effect of scanning breadth on OA but negatively moderates the effect of networking depth. This suggests that SMEs should emphasize external information‐seeking activities that are appropriate to their level of empowerment climate so that the positive effects on OA can be fully realized.

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)0.003
metaresearch head score (Gemma)0.008
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 score0.003
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.010
GPT teacher head0.228
Teacher spread0.219 · 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

Citations6
Published2023
Admission routes1
Has abstractyes

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