Knowledge spillover in corporate financing networks: embeddedness and the firm's debt performance
Bibliographic record
Abstract
Abstract Building on social embeddedness theory, we examine how the competencies and resources of one corporate actor in a network are transferred to another actor that uses them to enhance transactions with a third actor—a strategic process we dub ‘network transitivity.’ Focusing on the properties of network transitivity in the context of small‐firm corporate finance, we consider how embedded relations between a firm and its banks facilitate the firm's access to distinctive capabilities that enable it to strategically manage its trade‐credit financing relationships. We apply theory and original case‐study fieldwork to explore the types of resources and competencies available through bank–firm relationships and to derive hypotheses about how embedded bank–firm relationships affect the strategy of small‐ to medium‐sized firms. Using a separate large‐scale data set, we then test the generalizability of our hypotheses. Our qualitative analyses show that embedded bank–firm ties provide special governance arrangements that facilitate the firm's access to bank‐centered informational and capital resources, which uniquely enhance the firm's ability to manage trade credit. Consistent with our arguments, our statistical analyses show that small‐ to medium‐sized firms with embedded ties to their bankers were more likely to take lucrative early‐payment trade discounts and avoid costly late‐payment penalties than were similar firms that lacked embedded ties—suggesting that social embeddedness beneficially affects the financial performance of the firm. Copyright © 2002 John Wiley & Sons, Ltd.
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 | 0.001 | 0.013 |
| 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) | 0.007 | 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".