MétaCan
Menu
Back to cohort
Record W4381856331 · doi:10.1007/s12232-023-00423-w

Empirical evidence of the parent company's influence on spin-off: from creation to performance

2023· article· en· W4381856331 on OpenAlexaboutno aff
Jorge Figueiredo, António Cardoso, Maria Nascimento Cunha

Bibliographic record

VenueInternational Review of Economics · 2023
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicEntrepreneurship Studies and Influences
Canadian institutionsnot available
Fundersnot available
KeywordsSample (material)Competition (biology)BusinessShareholderShareholder valueSpin (aerodynamics)MaximizationIndustrial organizationMarketingParent companyEmpirical evidenceMicroeconomicsEconomicsFinanceEngineeringCorporate governancePhysicsSubsidiary

Abstract

fetched live from OpenAlex

Abstract Companies to adapt to today's society, characterized by continuous changes, adopt strategies to search for new opportunities, sometimes emerging business models, different from the parent company's business area. If they do not want to diversify their core business, they choose to create a new company that will be independent from the parent company, the spin-off. The aim of this study is to analyze and analyze the parent company's influence on spin-off performance, in terms of motivating factors for creation, transferred resources, relationship type and spin-off performance in the post-spin-off period. To achieve this objective, 31 surveys were analyzed, answered by workers who occupy management positions in spin-off companies in the USA and Canada. The statistical analysis of the data suggests that the concentration of the business area and the maximization of shareholder value are the main reasons for the creation of the spin-off. The most mentioned spin-off challenges are aggressive competition and efficient allocation of efficient resources. One of the characteristics of the spin-off is that it is an independent company, a fact that is not verified in the sample under analysis. However, the spin-off's performance does not change when it becomes independent.

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 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)0.001
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 score0.058
Threshold uncertainty score0.290

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.093
GPT teacher head0.340
Teacher spread0.247 · 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 teacher head, 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

Citations5
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueInternational Review of EconomicsSame topicEntrepreneurship Studies and InfluencesFrench-language works237,207