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Record W3121467310 · doi:10.1287/msom.2016.0598

Open or Closed? Technology Sharing, Supplier Investment, and Competition

2017· article· en· W3121467310 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

VenueManufacturing & Service Operations Management · 2017
Typearticle
Languageen
FieldDecision Sciences
TopicAuction Theory and Applications
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsCompetition (biology)Industrial organizationBusinessPoolingDilemmaEmerging technologiesInvestment (military)Complementary assetsCommerceEconomicsComputer science

Abstract

fetched live from OpenAlex

Competing technologies in emerging industries create uncertainties that discourage supplier investments. Open technology can induce supplier investments, but may also lead to intensified future competition. In this paper, we study competing manufacturers’ open-technology strategies. We show that despite the risk of intensifying future competition, open technologies by competing manufacturers may constitute an equilibrium and can indeed induce supplier investments. In addition, we identify a technology-risk-pooling benefit; namely, by opening technologies, competing manufacturers can induce supplier investments in both technologies and later adopt the one preferred by the market. However, manufacturers may also exhibit the prisoner’s dilemma and close their technologies despite the risk-pooling benefit. In this case, there is potential for collaborative technology sharing through cross licensing. Finally, we show that manufacturers may sometimes close their technologies to force supplier investments.

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 categoriesScience and technology studies, Scholarly communication, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.556
Threshold uncertainty score1.000

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 studies0.0030.000
Scholarly communication0.0030.001
Open science0.0030.003
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.102
GPT teacher head0.391
Teacher spread0.289 · 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