The Effective Integration of a US Acquisition into French Group: The Role of OD Intervention
Bibliographic record
Abstract
This paper presents a case study of the acquisition of a US subsidiary by a French family firm and the integration of the acquisition into the family corporate group enacted through an OD intervention. We aim to identify the main stages and key success factors of this acquisition and integration process. We show that the integration process, undertaken using an organization development intervention method was progressive and included three stages: 1) integration of the management system, 2) integration of commercial and industrial systems, then 3) integration of the information system. The paper contributes to the organization development literature by showing how a strategic acquisition and its integration into a larger family firm group was facilitated by use of an OD intervention process that used the socio-economic approach to management (SEAM) to identify hidden costs of the integration and to overcome them. We also contribute to the literature by uncovering 3 distinct phases of integration and showing the acquisition and integration process commenced before the formal legal acquisition. We thus extend the timeline of the integration process and show how the OD aspect of the process greatly facilitated the success of the US acquisition and its integration into the large French group.
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 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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
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".