On the heels of giants: Internal network structure and the race to build on prior innovation
Bibliographic record
Abstract
Abstract Research Summary Strategy research has long been concerned with how firms build on the technological knowledge they create, and has focused on legal enforcement, complementary assets, and location decisions. Less attention has been paid to how internal firm structures support the appropriation of future cumulative innovations: what has been termed “generative appropriability.” Drawing on innovation and social network research, we propose that more integrated intrafirm inventor networks, and those with nearly decomposable structures, facilitate generative appropriability by accelerating within‐firm generation of follow‐on innovations, thus outpacing rivals. We find evidence for these effects in patent data from 1,417 large corporations over 26 years. The acceleration effect is strongest in the critical first few years after an initial invention. Managerial Summary Innovation is a cumulative and competitive process, so if firms fail to build on their own innovations quickly, rivals will capture value by doing so. We study how the degree to which a firm's inventors are connected to each other through co‐patenting relationships is related to its ability to build on its prior innovations faster than rivals. We find that firms whose researchers are connected to more of its other researchers are better able to quickly develop follow‐on innovations, but this advantage decays over time. We also find that the pattern of connectedness most associated with this outcome is one in which tight clusters of researchers in the core of the network are linked to each other by “bridging ties.”
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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 | 0.002 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".