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Innovate to Survive: The Effect of Technology Competition on Corporate Bankruptcy

2011· article· en· W2105187902 on OpenAlexfundno aff
Assaf Eisdorfer, Po‐Hsuan Hsu

Bibliographic record

VenueFinancial Management · 2011
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCorporate Finance and Governance
Canadian institutionsnot available
FundersSingapore Management UniversityNational University of SingaporeCanadian Intensive Care FoundationUniversity of Connecticut
KeywordsBankruptcyEarningsCompetition (biology)BusinessStock (firearms)Financial distressBankruptcy predictionIndustrial organizationMonetary economicsAccountingEconomicsFinancial systemFinance

Abstract

fetched live from OpenAlex

This paper establishes a strong relation between technology competition and corporate bankruptcy. Using detailed firm‐level patent data, we show that: 1) the capability of firms to innovate predicts future bankruptcies better than the typical measures such as Z‐score and credit rating, 2) technology‐related bankruptcies are less sensitive to the business cycle and industry success, and 3) firms that go bankrupt as a result of technology competition experience larger declines in earnings and stock prices.

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)0.026
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 score0.006
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.026
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.002
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0060.001

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 head0.019
GPT teacher head0.198
Teacher spread0.180 · 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

Citations75
Published2011
Admission routes1
Has abstractyes

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