Organizational change and the dynamics of innovation: Formal R&D structure and intrafirm inventor networks
Bibliographic record
Abstract
Abstract Research Abstract Prior research has argued and shown that firms with more centralized R&D produce broader innovations, but the organizational mechanisms underlying this relationship are underexplored. This gap limits our understanding of whether and how formal R&D structure can be used as a lever to influence research outcomes. To address this question, we study the relationship between formal R&D structure, internal inventor networks, and innovative behavior and outcomes. We find that centralization of R&D budget authority increases the connectedness of internal inventor networks, which in turn increases the breadth of both innovation impact and technological search. Surprisingly, decentralization does not have the opposite effect. Our results suggest that changes in formal structure influence innovation outcomes through changes in inventor networks, with a lag reflecting organizational inertia. Managerial Abstract Diversified corporations can organize their R&D functions to be more or less centralized. Prior research has shown that this organizational choice is associated with different types of innovative outcomes. But what happens when a corporation changes its level of R&D centralization? This paper suggests that centralization of R&D gradually leads to new patterns of collaboration among inventors, which in turn will be associated with innovations that draw on and influence a wider range of technologies. However, future work is needed to understand why decentralization does not appear to have the opposite effect.
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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 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".