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Record W1974549813 · doi:10.5430/ijba.v5n5p14

International Acquisitions: Spains Actividades de Construcción y Servicios (ACS) Battle for Control of Germanys Hochtief (HT)

2014· article· es· W1974549813 on OpenAlexvenueno aff
H. Sutton, Kirstin Bremser, J. Paul

Bibliographic record

VenueInternational Journal of Business Administration · 2014
Typearticle
Languagees
FieldEconomics, Econometrics and Finance
TopicDefense, Military, and Policy Studies
Canadian institutionsnot available
Fundersnot available
KeywordsBattleControl (management)BusinessCommand and controlMergers and acquisitionsOperations managementMarketingOperations researchManagementComputer scienceFinanceEconomicsTelecommunicationsEngineeringHistory

Abstract

fetched live from OpenAlex

In March 2007 ACS purchased a holding in Hochtief of 25.08%, at this time ACS stated that this was purely a strategic holding and no further involvement was planned. However, in September 2010 ACS announced that it intended to bid for the remaining shares. This started an exhaustive hostile takeover battle in which both parties used all instruments available to achieve their goals. Most literature considering acquisitions address either the question of how to select the most appropriate target or how to integrate the target once acquired (Post Acquisition Integration). This paper considers the key phase between target selection and PAI; that of convincing the current owners to sell their holdings to the acquiring company. This often public battle for control is crucial for the success of the acquisition company´s strategy. This paper documents the battle for Hochtief by ACS in detail. Firstly, the relevant industries in which the companies are active are described briefly. Secondly, the company´s current positions, controlling structures and driving personalities are introduced. Finally, the development of both the acquisition and defence tactics used by combatants is described in detail, which eventually resulted in ACS gaining control of Hochtief in June 2011.

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)0.001
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.633
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.020
GPT teacher head0.276
Teacher spread0.256 · 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.

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

Citations0
Published2014
Admission routes1
Has abstractyes

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