International Acquisitions: Spains Actividades de Construcción y Servicios (ACS) Battle for Control of Germanys Hochtief (HT)
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".