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Record W3090568911 · doi:10.1108/ijebr-05-2020-0273

Coopetition and sales performance: evidence from non-mainstream sporting clubs

2020· article· en· W3090568911 on OpenAlexaff
James M. Crick, Dave Crick

Bibliographic record

VenueInternational Journal of Entrepreneurial Behaviour & Research · 2020
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicBusiness Strategy and Innovation
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsCoopetitionCompetitor analysisMarketingBusinessHarmCommon-method varianceMultilevel modelTemptationCompetition (biology)MainstreamVariance (accounting)Industrial organizationEconomicsIncentiveMicroeconomicsPsychology

Abstract

fetched live from OpenAlex

Purpose Small sports clubs are the life-blood of particular communities, even though many are under-resourced and have difficulties in operating under an individualistic business model. Although coopetition (simultaneous cooperation and competition) has been recognised as a positive driver of performance, the complexities of this association remain under-researched. Consequently, grounded in resource-based theory and the relational view, the purpose of this current study is to examine the moderating roles of inter-firm conflict and competitive intensity in the coopetition–sales performance relationship. Design/methodology/approach After undertaking 25 field interviews, survey data were collected from 151 non-mainstream sporting clubs in New Zealand. This setting was ideal, since it hosts high-degrees of cooperativeness and competitiveness. After assessing the statistical data for all major robustness checks (including common method variance and endogeneity bias), the hypothesised and control paths were tested through a hierarchical regression analysis. Findings Coopetition had a positive relationship with sales performance, but inter-firm conflict yielded a negative interaction effect. Surprisingly, this link was positively moderated by competitive intensity. Practical implications Under-resourced entrepreneurs (like those in many small sports clubs) should consider cooperating with their competitors, as these strategies can assist them to improve their sales performance. However, they should be careful when engaging in such activities due to the considerable risk that rival firms could behave opportunistically, which might harm their performance. That being said, owner-managers are advantaged if they operate in sectors where there are lots of competitors because there is increased scope to collaborate with “complementary” and trustworthy rivals that can help them to achieve mutually-beneficial outcomes. Indeed, sporting governing bodies (including those that operate on a non-profit basis) should encourage their members to engage in coopetition due to these positive financial consequences. Originality/value This investigation contributes to the extant literature by evaluating the competitive forces affecting the link between coopetition and sales performance. Specifically, new evidence emerges on the circumstances where coopetition is (and is not) a performance-enhancing entrepreneurial strategy. Further, this investigation provides unique insights regarding coopetition among non-mainstream sporting clubs, adding new knowledge to the sports entrepreneurship literature. Moreover, by infusing resource-based theory with the relational view, stronger arguments feature how owner-managers can navigate the paradoxical forces that drive coopetition activities. This study ends with several 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 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)0.002
metaresearch head score (Gemma)0.010
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 score0.042
Threshold uncertainty score0.083

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.087
GPT teacher head0.335
Teacher spread0.248 · 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

Citations39
Published2020
Admission routes1
Has abstractyes

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