Bibliographic record
Abstract
Scholars have presented conflicting perspectives regarding whether star inventors have a positive or negative effect on firms’ innovation trajectory (i.e., exploratory or exploitative). The objective of this study is to shed light on this important, yet under-theorized, issue in the context of R&D teams. Because stars are at the forefront of knowledge exchange with external parties, we argue that the boundary-spanning ties that stars establish with external parties provide distant learning opportunities conducive to an R&D team’s exploratory innovation. However, the positive effect of these ties on team exploration may be undermined, or even flipped, by firm institutions that revolve around stars and the double-edged role of stars’ high productivity. Applying multilevel modeling to a sample of 88,835 R&D teams in the U.S. high-technology sector, we find support for our hypotheses. This paper contributes to the literatures on stars and innovation by presenting a paradoxical view of stars’ effect on firm innovation and examining the mechanisms underlying firms’ innovation trajectory through a micro-lens.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".