The Commonplace
Home Papers Evidence Explore Trends Syntheses Digests References Docs 🎲 Workforce Futures
← Papers
Direction, evidence grade, and study type are AI-generated labels (gpt-5-mini), not human-verified. Syntheses are LLM-written. "Tensions" are machine-detected candidates, not confirmed contradictions. A research-acceleration tool, not peer review. How this is built →

Transparency, empathy and shared values—rather than technical claims alone—drive stakeholder trust in complex, risky technologies; that trust in turn predicts acceptance and mediates compliance, suggesting firms and regulators should prioritize clear uncertainty communication, engagement, and visible accountability to accelerate AI adoption.

Communicating Risk in the Age of Misinformation: Empirical Frameworks for Understanding Stakeholder Trust
Matthew S. Weber, Chaeyeong Margo Lee, David S. Kosson · August 21, 2026 · Risk Analysis
openalex review_meta medium evidence 7/10 relevance Summary only summary available; pdf_status=paywall DOI Source PDF

Structured author observations

Linked only from stored provider relations; the raw author line above is never matched by name.

OpenAlex

Latest observation:

  1. Matthew S. Weber provider ID
  2. Chaeyeong Margo Lee provider ID
  3. David S. Kosson provider ID

Semantic Scholar

Latest observation:

  1. Matthew S. Weber provider ID
  2. Chaeyeong Margo Lee provider ID
  3. D. Kosson provider ID
Trust—shaped more by transparency, empathy, and perceived value alignment than by technical expertise alone—acts as an outcome, a predictor of acceptance/lower perceived risk, and a mediating mechanism translating credibility and transparency into compliance and cooperative behaviors in scientifically complex contexts.

Citation observations

Cumulative provider counts captured on specific dates; providers are never combined.

This systematic literature review examines how stakeholder trust is conceptualized and measured in risk and science communication amid increasingly complex information environments shaped by misinformation and disinformation. Following PRISMA procedures, which provide a framework for conducting a systematic review with transparency, replicability, and completeness, we searched major databases and screened studies using explicit inclusion criteria. For instance, trust had to be operationalized and measured in risk or scientifically complex contexts; credibility studies were retained when credibility was a trust-related dimension related to stakeholder communication. Scientifically complex contexts are taken to refer to settings where information is inherently complex and nuanced, and understanding requires advanced knowledge. The final corpus (k = 69) includes quantitative, qualitative, and mixed-methods designs, with a post-2020 increase aligning with pandemic-era scholarship. The findings identify three roles for trust. First, stakeholder trust as an outcome is strengthened more by transparency, empathy, and perceived value similarity than by expertise claims alone. Second, trust as a broad concept often embodies more than just detailed technical understanding; rather, trust predicts acceptance of technologies and policies and lower perceived risk. Third, as a mechanism, stakeholder trust mediates links between credibility, transparency, or value similarity and downstream outcomes such as compliance and cooperation. From organizations to interpersonal interactions, stakeholder trust formation reflects the characteristics of trustors, trustees, and context. We integrate these strands into a communication approach that emphasizes clarity about uncertainty, engagement, and visible accountability as preconditions for trust. Practically, the review underscores the design of transparent messaging, alignment with public values without oversimplification, and participatory approaches that treat stakeholders as partners. Conceptually, we separate judgments about whether information seems credible from trust in the people or institutions behind it. We argue that trust helps connect what people think and feel to what they are willing to do, especially when they must make decisions under uncertainty in today's complex information environment.

Summary

Main Finding

Stakeholder trust in risk and scientifically complex communication plays three linked roles: (1) an outcome that is more strongly built by transparency, empathy, and perceived value similarity than by expertise claims alone; (2) a predictor of acceptance (of technologies/policies) and lower perceived risk even without detailed technical understanding; and (3) a mechanism that mediates how credibility, transparency, or value congruence translate into downstream behaviors such as compliance and cooperation. Effective trust-building requires clarity about uncertainty, engagement, and visible accountability rather than relying solely on technical expertise.

Key Points

  • Three conceptual roles of trust:
    • Outcome: trust is an end-state strengthened by transparency, empathy, and shared values.
    • Predictor: trust influences acceptance and lowers perceived risk independent of technical understanding.
    • Mechanism: trust mediates the effect of credibility/transparency/value alignment on behavior.
  • Credibility and trust are related but distinct: credibility judgments about information are separable from trust in people or institutions.
  • Trust formation is multi-factorial: depends on trustor characteristics, trustee attributes, and contextual features.
  • Practical strategies to build trust:
    • Transparent messaging that clarifies uncertainty.
    • Communication that signals empathy and aligns with public values without oversimplifying.
    • Participatory/engagement approaches treating stakeholders as partners and demonstrating accountability.
  • Research trend: increased scholarship after 2020, reflecting pandemic-era attention to trust and misinformation/disinformation in complex information environments.

Data & Methods

  • Review framework: PRISMA-guided systematic review to ensure transparency, replicability, and completeness.
  • Search & screening: major databases were searched; studies were screened using explicit inclusion criteria.
  • Inclusion criteria highlights:
    • Trust had to be operationalized and measured in risk or scientifically complex contexts.
    • Credibility studies were included when treated as a trust-related dimension in stakeholder communication.
    • Scientifically complex contexts were defined as settings where information is inherently nuanced and requires advanced knowledge.
  • Final corpus: k = 69 studies spanning quantitative, qualitative, and mixed-methods designs.
  • Temporal pattern: notable increase in relevant studies post-2020.
  • Measurement heterogeneity: studies used a variety of operationalizations and metrics for trust, credibility, and downstream outcomes, complicating direct meta-analytic aggregation.

Implications for AI Economics

  • Adoption and diffusion:
    • Trust functions as a key determinant of AI adoption by firms and consumers; transparency, perceived value alignment, and demonstrated accountability will likely increase adoption and reduce perceived deployment risks more than appeals to technical expertise alone.
  • Policy, regulation, and compliance:
    • Trust mediates how regulatory communication and governance mechanisms affect compliance. Policies that foreground uncertainty, stakeholder engagement, and visible enforcement/accountability can raise compliance with AI governance.
  • Market outcomes and competition:
    • Firms that invest in transparent communication, empathetic stakeholder engagement, and value-aligned positioning can gain competitive advantages through higher consumer willingness to adopt AI products and services.
  • Measurement and empirical strategy for AI economics research:
    • Distinguish between credibility of AI outputs (information-level) and institutional/actor trust (trust-level) in survey and experimental designs.
    • Treat trust flexibly as an outcome, predictor, or mediator depending on the causal model; explicitly measure potential mediators (e.g., perceived transparency, value congruence) and downstream outcomes (adoption, compliance, cooperation).
    • Use mixed methods and experimental designs (messaging experiments, lab-in-the-field) to identify causal pathways and to unpack heterogeneity across populations and contexts.
  • Design of AI systems and communication:
    • Algorithmic transparency should emphasize intelligible explanations of uncertainty and limitations, not just technical detail.
    • Participatory design and stakeholder involvement in AI development and deployment can foster perceived value alignment and trust, improving social acceptance and lowering resistance.
  • Information environment and misinformation:
    • In complex, misinformation-prone settings, trust-building must go beyond correcting facts: sustained, empathetic engagement and institutional accountability are central to counteracting disinformation’s behavioral effects.
  • Research gaps to address in AI economics:
    • Standardize trust metrics in AI contexts to improve comparability.
    • Study trustee (firm/institution) characteristics that most effectively convert transparency and accountability into economic outcomes.
    • Quantify heterogeneous effects across demographic and stakeholder groups, and examine long-term dynamics of trust in evolving AI ecosystems.

Assessment

Paper Typereview_meta Evidence Strengthmedium — A systematic PRISMA-guided review of 69 studies provides broad, triangulated evidence that trust plays multiple roles in complex/risk communication, but included studies are heterogeneous (quantitative, qualitative, mixed methods), measurement varies widely, and the review did not report a quantitative meta-analytic estimate or strong causal identification across domains, limiting the strength of causal claims. Methods Rigormedium — The review follows PRISMA, used explicit search and inclusion criteria, and screened major databases, which indicate solid review practices; however, heterogeneity in operationalizations, variable study quality within the corpus, and lack of pooled causal estimates reduce methodological rigor relative to high-quality meta-analyses of homogeneous studies. SampleA systematic review corpus of k = 69 studies (quantitative, qualitative, and mixed-methods) focused on trust and credibility in risk or scientifically complex communication contexts; studies were identified via searches of major databases with explicit inclusion criteria and show a temporal uptick in publications after 2020. Themesadoption governance human_ai_collab GeneralizabilityMeasurement heterogeneity across studies limits comparability and pooling of effects., Corpus likely over-represents pandemic-era (post-2020) public-health and misinformation contexts, which may not generalize to all AI deployment settings., Geographic and sectoral coverage not specified—possible bias toward high-income-country or health/environment domains., Many included studies are observational or single-context experiments, limiting causal generalizability across time and populations., Limited direct empirical evidence tying trust-building interventions to long-run economic outcomes (adoption, productivity, wages) in AI-specific settings.

Claims (10)

ClaimDirectionOutcomeConfidence & EvidenceDetails
Transparency, empathy, and perceived value similarity build stakeholder trust in risk and scientifically complex communication more strongly than expertise claims alone. Ai Safety And Ethics positive Stakeholder trust in communicators, institutions, or information
Reading fidelity high
Study strength medium
n=69
0.24
Stakeholder trust predicts acceptance of technologies and policies and is associated with lower perceived risk even when people lack detailed technical understanding. Adoption Rate positive Technology or policy acceptance and perceived risk
Reading fidelity high
Study strength medium
n=69
0.24
Trust mediates the relationship between credibility, transparency, or value congruence and downstream behaviors such as compliance and cooperation. Regulatory Compliance positive Compliance and cooperation
Reading fidelity high
Study strength medium
n=69
0.24
Credibility judgments about information are distinct from trust in people or institutions, and should be measured separately. Ai Safety And Ethics mixed Perceived information credibility and interpersonal or institutional trust
Reading fidelity high
Study strength medium
n=69
0.24
Trust formation depends on characteristics of the trustor, attributes of the trustee, and contextual features. Ai Safety And Ethics mixed Trust formation
Reading fidelity high
Study strength medium
n=69
0.24
Transparent messaging that clarifies uncertainty, empathetic communication aligned with public values, and participatory engagement with visible accountability are identified as practical trust-building strategies. Ai Safety And Ethics positive Stakeholder trust
Reading fidelity high
Study strength medium
n=69
0.24
The review included 69 studies spanning quantitative, qualitative, and mixed-methods designs. Other other Study corpus and research-design composition
Reading fidelity high
Study strength high
n=69
k = 69 studies
0.4
Relevant scholarship on trust in risk and scientifically complex communication increased notably after 2020. Research Productivity positive Publication volume on the topic
Reading fidelity high
Study strength medium
n=69
0.24
The heterogeneity of trust, credibility, and downstream-outcome measures complicates direct meta-analytic aggregation. Research Productivity negative Comparability and aggregability of study findings
Reading fidelity high
Study strength high
n=69
0.4
The review recommends that research distinguish credibility of AI outputs from institutional or actor trust and model trust flexibly as an outcome, predictor, or mediator. Ai Safety And Ethics positive Validity and interpretability of trust measurement in AI research
Reading fidelity high
Study strength low
n=69
0.12

Notes