Prismatic Trust: How Structural and Behavioral Signals in Networks Explain Trust Accumulation
Bibliographic record
Abstract
The predominant focus of the organizational literature on trust has been on direct interactions between actors. Whereas this emphasis has solidified our understanding of the dyadic foundations of trust, we know relatively little about the mechanisms of trust creation in network contexts. In this paper, we introduce the network mechanism of prismatic trust to explain why some actors are more trusted than others. Specifically, we posit that networks act as prisms that generate signals of trustworthiness based on not only actors’ positions in the social structure, but also their networking behavior. Moreover, we also theorize that the combination of signals from network structure and behavior amplifies trust accumulation in network actors. We test our predictions using data from an online social trading platform with more than 28,000 traders across 38 weeks. We find that traders who occupy positions of higher status in the network and those who express positive sentiments in the content of their communications (networking behaviors), accumulate more trustors. Furthermore, the positive effects of network status and the expression of positive sentiments on trust accumulation are mutually reinforcing. In sum, we contribute to the organizational literature on trust by proposing the role of a prismatic view in explaining how trust accumulates in network actors as a function of their position in social structure, their networking behavior, and a combination of the two. This paper was accepted by Isabel Fernandez-Mateo, organizations. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2021.02810 .
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".