Industry Peer Networks: Constructive Collaboration for Effective Marketing and Management Practices
Bibliographic record
Abstract
With small businesses becoming increasingly important to economic growth and job creation, there must be new ways of structuring to take advantage of collaboration and to be able to compete against large firms. Industry peer networks (IPNs) have emerged to meet this challenge. This study investigates the processes and effectiveness of an IPN whose member small firms are located in the United States, Canada, the United Kingdom, and Australia. The findings suggest that the more socially embedded the IPN members are within their respective peer groups (i.e. organizing IPN-related activities, partnering with business endeavors, discussing and advising each other about business issues, and participating in socializing activities), the higher the perceived level of learning in marketing and management practices. The findings also suggest that the implementation of transformational leadership practices is partially mediated by the perceived level of management learning, but the width of the product portfolio was not mediated by the perceived level of marketing learning.
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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 | 0.012 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.008 |
| Scholarly communication | 0.011 | 0.009 |
| Open science | 0.001 | 0.013 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 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".