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Record W2953089714 · doi:10.1111/joms.12518

A Transaction Cost Perspective of Alliance Portfolio Diversity

2019· article· en· W2953089714 on OpenAlexaff
Christopher R. Penney, James G. Combs

Bibliographic record

VenueJournal of Management Studies · 2019
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicInnovation and Knowledge Management
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsAllianceTransaction costPortfolioDiversity (politics)BusinessDatabase transactionIndustrial organizationPerspective (graphical)MarketingComputer scienceFinanceDatabaseSociologyPolitical scienceArtificial intelligence

Abstract

fetched live from OpenAlex

Abstract Alliance portfolio diversity (APD) helps firms access diverse capabilities and knowledge. APD can also increase transaction costs, but it is unknown whether and how transaction cost theory’s (TCT’s) insights about hierarchical integration operate at the portfolio level. We adapt TCT to the portfolio level to suggest that the transaction costs from APD encourage integration into alliance partners’ industries, and we introduce the concept of shared‐specific investments to pinpoint one source of transaction costs within portfolios and predict which industries will be integrated. Using data from 1996–2013 on S&P 500 firms, we find evidence in support of our theorising. Juxtaposing results with other theoretical perspectives suggests that TCT offers complementary insights about which activities to perform in the firm versus the alliance portfolio.

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)0.020
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.014
Threshold uncertainty score0.048

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.020
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0030.003
Science and technology studies0.0010.003
Scholarly communication0.0050.008
Open science0.0010.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0140.000

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 head0.036
GPT teacher head0.277
Teacher spread0.241 · 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

Citations30
Published2019
Admission routes1
Has abstractyes

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