Dual Networking: How Collaborators Network in Their Quest for Innovation
Bibliographic record
Abstract
Organizations typically employ a division of labor between specialist creator roles and generalist business roles in a bid to orchestrate innovation. We seek to determine the extent to which individuals dividing the work across roles can also benefit from dividing their network. We argue that collaborating individuals benefit from connecting to the same groups but different individuals within those groups—an approach we label dual networking—rather than from a pure divide-and-conquer approach. To test this argument, we study a dual career-ladder setting in a large multinational in which R&D managers and technologists partner up in their quest for innovation. We find that collaborators who engage in dual networking attain an innovation performance advantage over those who connect to distinct groups. This advantage stems from the opportunity to engage in the dual interpretation of input the partners receive, as well as from dual influencing that helps them to gain momentum for their proposed innovations, and it leads to more effective elaboration and championing of their ideas. In demonstrating these effects, we advance understanding of how collaborators organize their networking activities to best achieve innovative outcomes.
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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 | 0.009 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.008 | 0.008 |
| Scholarly communication | 0.014 | 0.013 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".