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Record W4293660584 · doi:10.1287/stsc.2022.0174

See You in Your Backyard: Multipoint Contact, Firm’s Capacity and Capability, and Technological Expansion

2022· article· en· W4293660584 on OpenAlexaff
You‐Ta Chuang, David H. Weng, Chia-Hung Wu, Kelly Thomson

Bibliographic record

VenueStrategy Science · 2022
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicBusiness Strategy and Innovation
Canadian institutionsYork University
Fundersnot available
KeywordsIndustrial organizationCompetition (biology)Product (mathematics)BusinessTechnological changeCompetitive advantageEconomicsMarketing

Abstract

fetched live from OpenAlex

Past studies of multipoint competition have mainly focused on the effect of multipoint contact in product markets on firm competitive behaviors in those markets only. Our study advances this literature by examining how multipoint contact in product markets affects a firm’s expansion into a rival’s technological areas. We further investigate the factors that moderate the proposed effect. Our analyses suggest that the degree of multipoint contact between a firm and a rival in product markets has an inverted U-shaped relationship with the degree of the firm’s expansion into the rival’s technological areas (patent classes). Furthermore, the firm’s capacity (market share) and capability (status in terms of technological development) relative to the rival’s have different moderating effects on the relationship between multipoint contact in product markets and the firm’s expansion into the rival’s technological areas. Our findings contribute to the literatures on multipoint contact, competitive dynamics, and firm technology strategies.

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)0.001
metaresearch head score (Gemma)0.012
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 score0.018
Threshold uncertainty score0.059

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.012
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0030.004
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0180.001

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 head0.043
GPT teacher head0.251
Teacher spread0.208 · 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

Citations5
Published2022
Admission routes1
Has abstractyes

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