Facilitating the Merger of Multinational Companies
Bibliographic record
Abstract
This paper reports on case research investigating the challenges presented by a newly formed supply chain after a merger and acquisition (M&A) and the subsequent solution – the enactment of a global virtual enterprise (GVE). Adaptive Structuration Theory (AST) is used as a lens to view and understand the transformational effects that occurred after the merger and the adoption of the GVE. A case study approach was adopted with empirical data collected from corporate web sites, direct participation in the project, and in-depth interviews with the two merged multinational supply networks set in Asia (the sub-ordinates are based in China, Taiwan, Thailand, and Vietnam) and North America (sites in Canada and the U.S.). The major problems encountered in the M&A process in the supply chain included incompatible product codes, redundant business processes, no unified ERP platforms, conflict of interests of supply chain entities, etc. The findings show the GVE approach improved the efficiency and effectiveness of this global acquisition through the re-alignment of organizational structures and personnel. Implications for practice and the further application of AST to the study of global supply chains and M&A are raised.
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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 | 0.009 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".