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Record W2740162543 · doi:10.1177/1476127016682973

Maneuvering multimarket competition: The effects of multimarket contact and strategic alliances on performance of single-market firms

2016· article· en· W2740162543 on OpenAlexafffund
You‐Ta Chuang, Kelly Thomson

Bibliographic record

VenueStrategic Organization · 2016
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicBusiness Strategy and Innovation
Canadian institutionsYork University
FundersFu Jen Catholic UniversityYork University
KeywordsCompetition (biology)BusinessMarket shareIndustrial organizationSingle marketMicroeconomicsEconomicsInternational tradeMarketingEuropean union

Abstract

fetched live from OpenAlex

Research on multimarket competition has focused on how multimarket contact shapes competitive behavior of firms that face each other in multiple markets. To date, there has been little attention to how multimarket contact affects single-market firms nor how single-market firms cope with multimarket competition. In this study, we examine the effects of multimarket competition and strategic alliances on single-market firms’ market share. Our analysis shows that the degree of multimarket contact firms had outside of a single-market firm’s market negatively affected the single-market firm’s market share. Yet, the number of strategic alliances a single-market firm had and having alliances with multimarket firms helped the single-market firm to cope with competitive pressure derived from multimarket contact and enhance its market share.

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)0.015
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 score0.006
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.015
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0030.003
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.001

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 head0.015
GPT teacher head0.196
Teacher spread0.181 · 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

Citations11
Published2016
Admission routes2
Has abstractyes

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