MétaCan
Menu
Back to cohort
Record W4379911573 · doi:10.1002/cjas.1729

The firm is dead; long live the firm: Classifying firm survival after mergers and acquisitions

2023· article· en· W4379911573 on OpenAlexvenueno aff
Timothy M. Madden, Laura T. Madden, Anne D. Smith, Jason A. Strickling

Bibliographic record

VenueCanadian Journal of Administrative Sciences / Revue Canadienne des Sciences de l Administration · 2023
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicFirm Innovation and Growth
Canadian institutionsnot available
Fundersnot available
KeywordsTypologyMergers and acquisitionsBusinessIndustrial organizationProcess (computing)MarketingFinanceComputer science

Abstract

fetched live from OpenAlex

Abstract Identifying the surviving firms following mergers and acquisitions (M&A) presents challenges in organizational research. The paper offers a solution to this issue by examining prior research on M&A and firm survival to identify three empirically‐relevant criteria—strategic intent, corporate name retention, and top management team composition—and integrate them into a process‐driven typology of firm survival following M&A. To demonstrate the utility of this method, we apply this typology to an industry rich with M&A activity: the United States telecommunications industry.

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)0.004
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Scholarly communication
Consensus categoriesScience and technology studies
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.314
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0040.005
Scholarly communication0.0010.001
Open science0.0010.000
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 head0.136
GPT teacher head0.305
Teacher spread0.169 · 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; both teacher heads agree on what is shown here.

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

Citations1
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueCanadian Journal of Administrative Sciences / Revue Canadienne des Sciences de l AdministrationSame topicFirm Innovation and GrowthFrench-language works237,207