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Record W4381614619 · doi:10.1504/ijesb.2023.131615

Does industry experience positively moderate the quadratic relationship between coopetition and financial performance Evidence from the New Zealand wine sector

2023· article· en· W4381614619 on OpenAlexaff
James M. Crick, Dave Crick, Jessica M. Peixinho

Bibliographic record

VenueInternational Journal of Entrepreneurship and Small Business · 2023
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicBusiness Strategy and Innovation
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsCoopetitionEntrepreneurshipBusinessWineIndustrial organizationEconomicsMarketingFinanceMicroeconomicsFood science

Abstract

fetched live from OpenAlex

Earlier research has established that a positive relationship exists between coopetition (the interplay between cooperation and competition) and financial performance. However, certain studies have investigated this link as being linear and/or without potential moderating factors. Consequently, under resource-based theory (and its association with the relational view), this current study evaluates the nonlinear (quadratic - inverted U-shaped) relationship between coopetition and financial performance under different degrees of industry experience. Survey data collection took place via a sample of 101 wine producers in New Zealand (passing all major assessments of reliability and validity, including common method variance and endogeneity bias). Additionally, 20 semi-structured interviews explored the in-depth meanings behind the statistical results. Specifically, the findings indicated that coopetition exhibited a quadratic relationship with financial performance. Furthermore, industry experience positively moderated this association, as it helps decision-makers to yield mutually beneficial performance outcomes. Collectively, this study contributes to knowledge by evaluating the complexities of coopetition strategies and their impact on financial performance. This investigation ends with some practitioner implications, alongside a series of limitations and avenues for future research.

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 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)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.033
Threshold uncertainty score0.558

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.002
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 head0.061
GPT teacher head0.262
Teacher spread0.200 · 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 teacher head, 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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