Impact of Degree Centrality, Structural Holes, and Network Density of External Corporate Network Ties on Ambidextrous Innovation
Bibliographic record
Abstract
ABSTRACT In the complex and ever‐changing environment of the information age, companies face dual challenges of limited resources and the need for continuous innovation. However, the question of how to effectively leverage external resources to balance ambidextrous innovation remains underexplored in the existing literature. This paper addresses this gap by examining the role of social network characteristics, specifically degree centrality, structural holes (SH), and network density, in facilitating ambidextrous innovation. It further investigates how internal control (IC) quality and CEO duality moderate these relationships. The findings indicate that: (1) degree centrality and SH have a positive effect on ambidextrous innovation, whereas network density exerts a negative effect; (2) CEO duality weakens the relationship between corporate social networks (CSNs) and exploratory innovation (EI); (3) IC positively moderates the relationship between degree centrality and ambidextrous innovation, between SH and exploitative innovation (DI), and also strengthens the negative relationship between network density and EI. By comprehensively evaluating the influence of various CSN characteristics on ambidextrous innovation, this study broadens the scope of social network research. These findings help companies optimize their social network structure, enhance their IC systems, and refine their corporate governance, which enables them to benefit from knowledge sharing and enhance their ambidextrous innovation capabilities.
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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 | 0.027 |
| 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 | 0.002 | 0.002 |
| Open science | 0.000 | 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".