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Record W2076886718 · doi:10.1142/s0218495808000028

ACHIEVING SUPERIOR INTERNATIONAL NEW VENTURE (INV) PERFORMANCE: EXPLOITING SHORT-TERM DURATION OF TIES

2008· article· en· W2076886718 on OpenAlex

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 Enterprising Culture · 2008
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicPrivate Equity and Venture Capital
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsDuration (music)ExploitPortfolioTerm (time)BusinessResource (disambiguation)Investment (military)Industrial organizationComputer scienceFinancePolitical scienceComputer security

Abstract

fetched live from OpenAlex

Network ties help international new ventures (INVs) achieve success. However, researchers have paid little attention to the duration of network ties and the impact of duration on performance. I draw on network analysis and the resources-based view to examine this area and propose a conceptual model that depicts the variables and mediating factors for INV performance. The model explains how INVs acquire, manage and exploit ties to achieve superior performance. I argue that resource-constrained INVs can minimize their investment of time and capital, and maximize the economic effect of ties, by using briefer time periods and short-term projects. I also propose that INVs adopt a 'hedging' or portfolio approach to managing ties, by collecting larger number of prospects to reduce uncertainty. The model and propositions contribute to the body of literature in network analysis and INVs. The paper highlights implications for research and practice.

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)0.000
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.225
Threshold uncertainty score0.535

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.003
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 head0.027
GPT teacher head0.241
Teacher spread0.213 · 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