The Impact of Brokerage in a Communication Network on Productivity: Evidence from Sensor Data
Bibliographic record
Abstract
Abstract Problem-solving effectiveness is key to organizational performance. To solve problems, gathering information from colleagues is critical, and positioning brokerage in communication networks is beneficial. The communication network for problem-solving is formed depending on the nature of the problem. Thus, the problem-solving network is the relational event network, and the connection of the problem-solving network dynamically changes over time depending on the problem basis. This study investigates the dynamics of brokerage in a problem-solving network and its impact on productivity in a company that provides technical support and troubleshooting for the IT system that its corporate customers use. By exploiting high-frequency data on face-to-face communication among employees collected by wearable sensors, we established the following results. First, the communication partners of each employee change weekly, which is a reasonable time to solve problems in the company. Second, with the change in the communication network, employees who position brokerage also change on a weekly basis. Third, while brokerage in a week has a positive impact on employee performance during the week, it has no impact on employee performance in the following week.
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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 | 0.007 | 0.065 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".