- Relationships between Service Oriented Architectures (SOA) and Mergers and Acquisitions (M&A).
Bibliographic record
Abstract
The increased competition caused by the global economy we are facing has forced companies to develop well established corporate strategies. Merging or acquiring a company has for a long time been used by organizations as one of the main strategic tools for expanding globally or for entering new markets. The problem is that over 50 percent of the Mergers and Acquisitions (M&A) fail by delivering the expected outcome, which tends to be depending on integration problems connected to the integration of the business systems. For many years the integration has been ruled by enterprise resource planning (ERP) systems or other centralized solutions, but today the new buzzword for integrating business systems is Service Oriented Architectures (SOA). SOA is an architecture that depends on loose couplings of services which make it possible to connect any business system. The purpose for this study is to deepen the understanding of the relationship between SOA and M&A, which is done by concluding existing literature about the two sub areas in order to generate relationships between use of SOA and M&A. The relationships are then used as a foundation for the research which focuses on verifying the relationships and are performed by case studies at five companies. The companies in this research are Alfa Laval, Dynapac, KCI Konecranes, Sandvik and Volvo/CE. The research has contributed with several relationships between SOA and M&A being discovered, for example that the use of SOA leads to better communication between business and IS departments, which facilitates the integration of an acquired company, and that the use of SOA increases the chance for receiving the expected benefits from an M&A.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 0.002 |
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".