The contingent effect of work roles on brokerage in professional organizations
Bibliographic record
Abstract
Abstract In this paper, we consider whether brokerage in an intra-organizational communication network and type of work role interact to predict individual performance in a professional organization. The independent–interdependent nature of work roles is considered a key factor in structural contingency theory, but is yet to be studied in relation to brokerage. We propose that a brokerage position has a joint effect on performance along with work role in a study of organization-wide communication network in an architectural firm with 65 employees. Our analysis suggests an association between brokerage and role-prescribed performance for individuals in both interdependent and independent types of work roles. Our findings also suggest that interdependent roles requiring broad, organization-wide collaboration, and communication with others, brokerage is positively associated with the performance prescribed by the role, but for independent roles, wherein collaboration and communication are somewhat limited by the formal role, brokerage has far less of an effect. Our findings contribute to brokerage theory by comparing how brokerage affects performance in two distinct work roles by illustrating how the benefits of brokerage seem more restricted to those in interdependent work roles. The contribution of this paper is to suggest the independent–interdependent nature of work role as a boundary condition for brokerage.
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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 | 0.006 | 0.048 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".