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Record W2965519329 · doi:10.5465/ambpp.2019.156

Synergistic Impacts of Entrepreneurial and Learning Orientations on Performance: A Meta-Analysis

2019· article· en· W2965519329 on OpenAlex
Kanhaiya Kumar Sinha, Piers Steel, Chad Saunders, James R. Dewald

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueAcademy of Management Proceedings · 2019
Typearticle
Languageen
FieldComputer Science
TopicOrganizational and Employee Performance
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsPsychologyMeta-analysisMedicine

Abstract

fetched live from OpenAlex

Entrepreneurial orientation (EO) and learning orientation (LO) are two key strategic orientations that are thought to individually influence firm performance. However, there is little understanding regarding their mutual relationship – similarities and complementarities – and their combinative effect on performance. To address this, we meta-analyzed 60 samples from 59 studies based on 16762 firms using a random effects model, to find the relationships between EO, LO, and firm performance. We find that the correlation between EO and LO is fairly large (r = 0.44) and is moderated by the country’s entrepreneurship profile, represented by entrepreneurial intention and fear of failure. While EO and LO operate through similar processes, they have independent, additive and synergistic effects on performance. EO and LO explain 17% and 13% of performance respectively while synergistically explaining 21% when combined. Furthermore, the EO-LO intercorrelation moderates the EO-performance and LO-performance relationships, so under conditions of high association, their combined effect can reach 38% of the performance variance.

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.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.600
Threshold uncertainty score0.373

Codex and Gemma teacher scores by category

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

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 head0.022
GPT teacher head0.249
Teacher spread0.228 · 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