Bibliographic record
Abstract
Purpose The purpose of this paper is to show how Sarbanes‐Oxley is motivating corporate boards to bring heightened scrutiny for all aspects of the acquisition process, including valuation. This paper examines how in addition to the high‐tech M&A strategic objective, the decision on the post closing integration strategy should be considered as part of a target's firm valuation. Design/methodology/approach The paper argues that, within the high‐tech sector, the target firm characteristics in relation to the market are not sufficient for the basis of valuation. Planned management interventions, including the acquirer's management integration strategy decision, should also be considered. It further argues that intellectual capital retention, an important element of the target firm's valuation, is directly related to how closely the pace and degree of integration matches the acquirer's strategic objectives and the outcome of the due diligence. Findings Based on the findings from a set of interviews with M&A practitioners in the high‐tech field, due diligence, post closing integration planning and identity are viewed as important factors to the acquisition outcome. They appear not to be considered for the target firm valuation, which may result in an acquirer paying an excessive premium. Practical implications Finally the paper proposes a framework to guide high‐tech firms to select an integration strategy and its valuation impact in relation to the acquisition strategic objective, the due diligence outcome and the integration strategy. Originality/value The outcome of this project is raising the importance of due diligence and post integration strategy in relation to other factors that impact transaction outcome such as intellectual capital retention and valuation.
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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".