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Record W2224113681 · doi:10.1177/0149206315615399

Multimarket Contact, Strategic Alliances, and Firm Performance

2015· article· en· W2224113681 on OpenAlex
You‐Ta Chuang, Kristina Dahlin, Kelly Thomson, Yung-Cheng Lai, Chun‐Chi Yang

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 Management · 2015
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicInnovation and Knowledge Management
Canadian institutionsYork University
FundersNvidia
KeywordsForbearanceIndustrial organizationBusinessCompetition (biology)MicroeconomicsEconomics

Abstract

fetched live from OpenAlex

Research on multimarket contact and firm performance has produced mixed results. To reconcile this discrepancy, we theorize how varying levels of multimarket contact may generate mutual forbearance that influences firm performance. We also examine how strategic alliances moderate the relationship between levels of multimarket contact and firm performance. Our analysis of 233 semiconductor firms across 52 markets reveals that multimarket contact has an inverted U-shaped relationship with a multimarket firm’s market share. The number of strategic alliances that a firm has helps to further extend the positive effect of multimarket contact and mitigate its negative effect on the firm’s market share. Accordingly, our study contributes to the literature on multimarket competition by shedding light on the conditions under which multimarket contact may increase/decrease firm 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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.849
Threshold uncertainty score0.527

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.052
GPT teacher head0.244
Teacher spread0.192 · 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