MétaCan
Menu
Back to cohort
Record W4225624994 · doi:10.1080/00472778.2022.2051177

Market orientation, failure learning orientation, and financial performance

2022· article· en· W4225624994 on OpenAlex

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

VenueJournal of Small Business Management · 2022
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicInnovation and Socioeconomic Development
Canadian institutionsUniversity of Regina
Fundersnot available
KeywordsMarket orientationOrientation (vector space)BusinessPsychologyMarketingGeometryMathematics

Abstract

fetched live from OpenAlex

Although it is widely acknowledged that market orientation and learning orientation are necessary success factors, the exact dynamics of the relationship between the constructs and firm performance is not perfectly clear. This article offers a new depth of understanding to this research by introducing and exploring the role of failure learning orientation. Specifically, the roles of market orientation, learning orientation, and failure learning orientation are explored with performance. Unlike previous literature, learning orientation did not mediate the market orientation and performance relationship. Instead, support was found for market orientation’s direct effect on performance and its indirect effect via failure learning orientation. The mediating role of failure learning orientation suggests that more specific learning typologies may alter the market orientation, learning orientation, and performance relationship. The study adds to existing market orientation research and the growing failure learning orientation literature. It emphasizes failure learning orientation’s importance in enhancing competitiveness and superior performance.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.397
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.008
GPT teacher head0.187
Teacher spread0.179 · 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