Technology sourcing and output of established firms in a regime of encompassing technological change
Bibliographic record
Abstract
Abstract This paper argues that when the technological basis of an industry is changing, the firm's approach to technology sourcing plays a critical role in building the capabilities needed to generate new technical outputs. Using survey and archival data from the U.S. pharmaceutical industry during the period 1981–91, we find that different approaches to technology sourcing (internal R&D and external R&D) are related to different types of biotechnology‐based output at the end of the period. Internal R&D was positively associated with patent output. Acquisition activity was positively related to number of biotechnology‐based products. Greater use of R&D contracts and licenses was associated with stronger reputation for possessing expertise in biotechnology. These findings underscore the importance of taking a multifaceted approach to technology sourcing in order to build the absorptive capacity needed to generate new technical output. Surprisingly, we also found that involvement in joint ventures was negatively related to patent output. This raises interesting questions about the strategic use of joint ventures in a regime of encompassing technological change. Copyright © 2003 John Wiley & Sons, Ltd.
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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 | 0.004 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".