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Record W1898136411 · doi:10.1002/smj.2371

When do firms change technology‐sourcing vehicles? The role of poor innovative performance and financial slack

2015· article· en· W1898136411 on OpenAlex
Razvan Lungeanu, Ithai Stern, Edward J. Zajac

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

VenueStrategic Management Journal · 2015
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicInnovation and Knowledge Management
Canadian institutionsKellogg's (Canada)
Fundersnot available
KeywordsPortfolioDiversification (marketing strategy)BusinessAllianceIndustrial organizationPopulationPerspective (graphical)MarketingEconomicsFinance

Abstract

fetched live from OpenAlex

This paper examines the adjustments firms make to the composition of their portfolios of technology‐sourcing vehicles (i.e., alliance, acquisition, or go‐it‐alone) in response to poor innovative performance. We advance a behavioral perspective on the make/buy/ally question, suggesting that differences in financial slack will generate different portfolio decisions. Specifically, we posit that firms with greater levels of financial slack are more likely to respond to poor innovative performance by opting for (1) greater vehicle diversification, and (2) new sourcing vehicles, while firms with less financial slack will respond by (1) downscoping their portfolio of sourcing vehicles, and (2) reverting to more familiar vehicles. We find support for our predictions using extensive data from the population of U.S. public pharmaceutical firms from 1992 to 2006 . Copyright © 2015 John Wiley & Sons, Ltd.

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: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.659
Threshold uncertainty score0.654

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
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 head0.036
GPT teacher head0.232
Teacher spread0.196 · 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