The performance of relational ties: A functional approach in the biotechnology industry
Bibliographic record
Abstract
Understanding the performance of social networks has attracted the attention of contemporary management research. The performance of a firm's strength of social ties has been the subject of considerable debate. On the one hand, strong ties draw on redundant and close partner experiences to increase a firm's specialization. On the other hand, weak ties gain access to non-redundant ideas, resources and opportunities to increase a firm's flexibility to market opportunities. Strong and weak ties have, thus, been depicted as opposing influences to a firm's performance. This study, however, offers an alternative explanation to this strong and weak tie debate. In this study, a theoretical and empirical examination of strong and weak tie performance is conducted in the biotechnology industry. This study finds strong and weak ties exhibit distinct knowledge sharing and commercializing functions that positively impact a biotechnology firm's performance. By incorporating the distinctive functions of strong and weak ties, a firm's tie strength does not exert opposing influences to performance. In addition, due to their distinctive functions, strong and weak ties exhibit diminishing return effects. This suggests a firm can develop a network structure that maximizes its ability to develop its research knowledge and capitalize on commercializing opportunities. The contributions and implications of this study are also discussed.
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 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 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".