Bibliographic record
Abstract
Purpose The purpose of this paper is to determine what the effects of acquisition are on R&D patterns. Design/methodology/approach This paper tests whether the actual post‐acquisition R&D intensity of the combined firm deviated from the predicted R&D intensity, where the predicted amount is an asset‐weighted average of pre‐acquisition values. Findings The results indicate that the combination of technology sourcing and technological relatedness have strong predictive powers for determining changes in post‐acquisition R&D intensity. Technology sourcing acquisition of unrelated technologies results in an increase in post‐acquisition R&D intensity, as predicted. Acquirers in this situation may be using their acquisition as a platform for research expansion. Research limitations/implications The dataset used in this paper was restricted to public acquirers and targets for completeness of financial information. It would be useful to determine the extent to which a technology sourcing acquirer is predicted to enter into an acquisition and also whether technology sourcing can be used as a predictor for the ultimate target company out of a pool of potential targets. Practical implications The results can be used to inform managers on a strategic level when research strategy deviates from what the theory would predict. For example, if a company that did a technology sourcing acquisition of an unrelated product subsequently decreased R&D intensity, then rival pharmaceutical firms can ascertain that the acquired research was ultimately determined to be too risky or unviable. Originality/value The value in this paper is the unique measurement for technology sourcing.
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 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.004 |
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".