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Do Acquirers Benefit from Target Alliances? Taking a Relational View of Synergy

2016· article· en· W2417157102 on OpenAlexaff
Panos Desyllas, Martin C. Goossen, Corey Phelps

Bibliographic record

VenueAcademy of Management Proceedings · 2016
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicBusiness Strategy and Innovation
Canadian institutionsMcGill University
Fundersnot available
KeywordsAllianceBusinessExtant taxonEconomic rentMergers and acquisitionsValue (mathematics)Value creationIndustrial organizationEvent studySample (material)Stock (firearms)MarketingMicroeconomicsEconomicsFinanceContext (archaeology)

Abstract

fetched live from OpenAlex

Although relational resources and rents derived from interfirm partnerships represent an important source of firm value, extant M&A research has focused on synergies involving resources that target firms own or control. This study builds on the relational view of the firm to examine whether and under what conditions acquirers benefit from resources embedded in target firm alliance relationships. We investigate these questions using an event study methodology and a sample of 344 biopharmaceutical acquisitions in the USA. We find that an acquired firm’s alliance activity has an inverted U-shaped relationship with acquirer abnormal stock returns. We also find that this relationship is positively moderated by the acquirer’s experience with acquiring companies with alliances. These results contribute to the acquisition and organizational learning literatures by showing how relational resources of target firms and acquirer experience in dealing with such resources interact to influence value creation and capture in acquisitions.

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)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.716
Threshold uncertainty score0.909

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.003
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.035
GPT teacher head0.248
Teacher spread0.212 · 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 designTheoretical or conceptual
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

Citations0
Published2016
Admission routes1
Has abstractyes

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