Building Bridges in the Digital Age: How online platforms foster trust during a crisis
Bibliographic record
Abstract
Trust has long been studied as a key factor in explaining why an actor is willing to risk vulnerability to others amid uncertainty and potential risks. While various forms and antecedents of trust have been explored, its development between strangers during periods of heightened uncertainty remains under-examined. To address this gap, we conducted a qualitative study of a mutual aid platform launched during the Covid-19 pandemic in China. Our process model identifies three core digital affordances—verifiability, targetability, and protectability—that foster institution-based trust among platform users. Additionally, our findings suggest that institution-based trust acts as a critical precursor to emotional trust. This indicates that digital platforms not only activate impersonal, system-based trust as previously theorized, but also enhance interpersonal and emotional trust. Moreover, we show that positive platform interactions during times of crisis can lead users to extend their trust to a broader range of societal members and social activities beyond platform interactions. Lastly, our study highlights that while uncertainty is essential for trust to emerge, trust building on platforms also depends on the platform provider’s established reputation, which aids trust transfer and facilitates the initial exploration of the platform.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| 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".