Accumulating trust in networks: The interplay between social structure and networking behavior
Bibliographic record
Abstract
The predominant focus of the organizational literature on trust has been the direct interactions between ego and other actors, which has solidified our understanding of the dyadic foundations of trust. We have learned far less, however, about the formation of trust in the context of an extended network of actors whose interactions are more limited, diffuse, and distal. In this paper, we build on the emerging literature on network forms of trust to argue that actors accumulate trust by both leveraging their position in social structure and by engaging in networking behavior. We test our predictions using a sample of data from an online trading platform consisting of 28,000 traders across 48 weeks. Our data allows us to observe the accumulation of trust in the form of “copy trading” whereby traders risk their own financial capital by allocating a portion of their portfolio to be automatically based on the investments of other designated traders. We find that traders who occupy positions of higher status in the network and traders who signal positive sentiments in their communication behaviors accumulate higher levels of trust. Furthermore, the positive effect of networking behavior on accumulation of trust is amplified by network status. In sum, we contribute to the organizational literature on the formation of trust by demonstrating that network structure acts as a prism of social cues and as pipes of signaling behavior about trustworthiness that combine to explain the accumulation of trust.
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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 | 0.003 | 0.034 |
| 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 | 0.003 | 0.005 |
| Open science | 0.000 | 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".