Integration and Synergy Generation in Cross Border Acquisitions: A Case Study of Business Failure and Success ‘Made in Japan’
Bibliographic record
Abstract
This article seeks to advance international business researcher and practitioner insights into processes of cross-border mergers and acquisitions. Specifically, this article highlights the extent to which processes of strategic integration might impact positively and / or negatively on the long term business performance of the newly formed firm during the post-acquisition period – a measure of performance we refer to here as ‘synergy’. In methodological terms, this article develops a mixed-method case study approach, generating, analysing and interpreting empirical data designed to illustrate processes of strategic integration implemented by senior managers at a Japanese firm, Nippon Sheet Glass (NSG), after its acquisition of a UK firm, Pilkington. The case study is longitudinal, drawing on quantitative and qualitative data gathered and analysed between 2006 and 2017. Applying a combination of event study methodology to an iterative analysis of business performance data along with coded analysis of data from in-depth interviews with key stakeholders, this article explores the extent to which strategic integration can both ‘fail’ in terms of achieving synergy and - for a number of generalizable reasons proposed in this article - ultimately ‘succeed’: for example, in cases where the acquiring and acquired firms attempt to integrate their human resource management systems while benefitting from investments of ‘patient’ capital, which (we propose) might be a distinctive feature of cross-border acquisitions sourced in Japan.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.005 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".