Public distrust springs from misinformation, opacity and broken expectations and erodes compliance and legitimacy across sectors; transparency helps but rarely suffices — rebuilding trust requires combined relational, cultural and structural reforms.

The antecedents, consequences and repair mechanisms of public distrust: A systematic literature review

Agni Shanti Mayangsari, Badri Munir Sukoco, Reinhard Bachmann, Juansih, Elisabeth Supriharyanti · September 01, 2026 · International Journal of Management Reviews

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Public distrust is a distinct, multilevel phenomenon driven by misinformation, opacity, unmet expectations and institutional failures, and while transparency is commonly used to repair distrust it is often insufficient without complementary relational, cultural and structural reforms.

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Abstract Public distrust has become a pressing challenge across governments, social enterprises (SEs) and businesses, shaping compliance, participation and legitimacy. Despite the well‐established research on trust, the scholarship on distrust remains fragmented and lacks systematic synthesis. This review addresses this gap by examining the antecedents, consequences and repair mechanisms of public distrust across organizational contexts. We conducted a systematic literature review of 54 peer‐reviewed articles published between 2006 and 2025, following the six‐step framework of Durach. Using the Gioia methodology, we identified first‐order categories such as misinformation, lack of transparency and unmet expectations and mapped them onto the multilevel framework of interpersonal, organizational and inter‐organizational distrust, complemented by sectoral distinctions between public sector organizations, SEs and business enterprises (BEs). The findings indicate that the antecedents of public distrust include policy failures, corruption and communication breakdowns, whereas the consequences range from reduced compliance and reputational decline to systemic instability. Across all organizational types, transparency emerges as the most frequently applied repair mechanism, yet its effectiveness often depends on combining it with relational, cultural and structural approaches that address underlying expectations about organizational accountability and integrity. This review advances conceptual clarity on trust and distrust by consolidating public distrust as a distinct evaluative construct, developing a multilevel, cross‐sectoral framework and clarifying how organizational responses address distrust following institutional failures. It further delineates the conditions under which trust and distrust should be analytically distinguished, particularly when stakeholders evaluate different organizational attributes. The study also identifies gaps in the literature, including the under‐representation of SEs and BEs, and calls for more diverse methodological approaches to deepen understanding of distrust and its repair.

Summary

Main Finding

Public distrust is a distinct, multilevel evaluative construct driven by factors such as misinformation, lack of transparency and unmet expectations. Across public sector organizations, social enterprises (SEs) and business enterprises (BEs), distrust arises from policy failures, corruption and communication breakdowns and leads to consequences ranging from reduced compliance and reputational decline to systemic instability. Transparency is the most commonly used repair mechanism, but it is often insufficient alone and works best when combined with relational, cultural and structural interventions that directly address expectations about accountability and integrity.

Key Points

Scope and contribution
- Systematic review of 54 peer‑reviewed articles (2006–2025) consolidating fragmented literature on public distrust.
- Conceptual advance: positions public distrust as distinct from trust and maps it across interpersonal, organizational and inter‑organizational levels with sectoral distinctions (public sector, SEs, BEs).
Antecedents of public distrust
- Misinformation and communication failures.
- Lack of transparency and unmet expectations about performance or values.
- Policy failures, corruption and institutional breaches of accountability.
Consequences
- Reduced compliance with policies and programs.
- Reputational damage and loss of legitimacy.
- Potential for broader systemic instability and weakened institutional functioning.
Repair mechanisms
- Transparency is the most frequently applied repair strategy.
- Effective distrust repair typically requires combining transparency with: relational measures (stakeholder engagement, dialogue), cultural changes (norms, leadership signaling) and structural reforms (governance, accountability mechanisms).
Analytical clarity
- The review clarifies when trust and distrust should be treated as distinct constructs—particularly when stakeholders evaluate separate attributes (e.g., competence vs. integrity).
Literature gaps
- Under‑representation of research on SEs and BEs.
- Need for more methodological diversity (longitudinal, experimental, mixed methods) and sector‑specific analyses.

Data & Methods

Evidence base: 54 peer‑reviewed articles published 2006–2025.
Review protocol: followed Durach’s six‑step systematic review framework.
Analysis: used the Gioia methodology to extract first‑order categories (e.g., misinformation, lack of transparency, unmet expectations) and derive higher‑order themes.
Thematic mapping: organized findings into a multilevel framework (interpersonal, organizational, inter‑organizational) and across sectors (public sector, SEs, BEs).

Implications for AI Economics

Causes of distrust in AI systems mirror general public‑distrust antecedents
- Misinformation about AI capabilities and impacts, opaque model behavior, and unmet expectations (promises of automation or fairness) can generate strong distrust that reduces adoption and participation.
Economic consequences
- Reduced uptake of AI‑enabled public services and products, lower compliance with algorithmic decisions, slower diffusion of productive AI technologies.
- Reputational losses and market penalties for firms deploying perceived unaccountable AI can alter investment incentives, raise transaction costs and create market fragmentation.
- Systemic effects: widespread distrust in AI governance can hamper regulation, lead to ad‑hoc restrictions, or encourage rent‑seeking behaviors that distort AI markets.
Repair strategies relevant to AI economics
- Transparency alone (e.g., model cards, disclosure) is necessary but not sufficient. Economic actors should combine transparency with:
- Relational measures: participatory design, stakeholder engagement, public consultation to rebuild legitimacy.
- Cultural interventions: organizational norms prioritizing ethics, training and leadership commitment to responsible AI.
- Structural fixes: independent audits, accountability mechanisms, regulatory standards and enforcement to align incentives.
Measurement and policy design
- Policy and empirical work should distinguish trust and distrust along attributes relevant to AI (competence, fairness, integrity, privacy) rather than treating them as opposites.
- Cost–benefit analyses of transparency and audit mechanisms should account for when these measures actually restore behavior (e.g., increased adoption, compliance) versus when they are symbolic.
Research agenda for AI economics
- More studies on how SEs and BEs—which play major roles in deploying AI—experience and repair public distrust.
- Use longitudinal, experimental and field methods to measure causal effects of distrust on adoption, market outcomes and welfare.
- Evaluate which combinations of transparency, relational and structural interventions are most cost‑effective across sectors and population subgroups.
- Investigate distributional implications: how distrust and its repair impact marginalized groups and shape inequality in AI benefits.

If you’d like, I can (a) map specific repair interventions (e.g., model cards, audits, stakeholder boards) to expected economic outcomes, or (b) draft a short research design to estimate how distrust affects AI adoption in public services.

Assessment

Paper Typereview_meta Evidence Strengthmedium — Systematic synthesis of 54 peer‑reviewed articles using an explicit review protocol provides a broad, triangulated evidence base about antecedents, consequences and remedies for public distrust, but the underlying studies are mostly observational/descriptive and heterogeneous with limited causal identification, reducing confidence in strong causal claims. Methods Rigorhigh — The authors followed a recognized six‑step systematic review protocol and applied the Gioia methodology to derive first‑order and higher‑order themes, which increases transparency and analytic traceability; however, conclusions depend on the quality and scope of the included studies, many of which lack experimental or longitudinal designs. SampleSystematic review of 54 peer‑reviewed articles published between 2006 and 2025, covering literature on public distrust across public sector organizations, social enterprises (SEs) and business enterprises (BEs); studies synthesized qualitatively using Gioia methodology and organized into multilevel (interpersonal, organizational, inter‑organizational) and sectoral frameworks. Themesgovernance adoption human_ai_collab org_design inequality GeneralizabilityUnder‑representation of social enterprises and business enterprises limits sectoral generalizability, Geographic and cultural coverage not specified — findings may be biased toward contexts represented in the literature (likely high‑income or English‑language studies), Many included studies are cross‑sectional and descriptive, limiting causal inference and temporal generalizability, Heterogeneity in study designs and measures of (dis)trust complicates transferability of specific interventions across contexts, Potential publication bias toward studies documenting problems and interventions

Claims (12)

Claim	Direction	Outcome	Confidence & Evidence	Details
Public distrust is conceptualized as a distinct, multilevel evaluative construct rather than simply the absence of trust. Governance And Regulation	mixed	Conceptual distinction between trust and distrust	Reading fidelity high Study strength medium	n=54 0.24
Misinformation, communication failures, lack of transparency, and unmet expectations are recurring antecedents of public distrust. Ai Safety And Ethics	negative	Public distrust	Reading fidelity high Study strength medium	n=54 0.24
Policy failures, corruption, and institutional breaches of accountability contribute to public distrust across public-sector, social-enterprise, and business-enterprise contexts. Governance And Regulation	negative	Public distrust	Reading fidelity high Study strength medium	n=54 0.24
Public distrust is associated with reduced compliance with policies and programs. Regulatory Compliance	negative	Compliance with policies and programs	Reading fidelity high Study strength medium	n=54 0.24
Public distrust can produce reputational damage, loss of legitimacy, broader systemic instability, and weakened institutional functioning. Organizational Efficiency	negative	Organizational reputation, legitimacy, and institutional functioning	Reading fidelity high Study strength medium	n=54 0.24
Transparency is the most frequently used repair strategy for public distrust, but transparency alone is often insufficient. Governance And Regulation	mixed	Effectiveness of distrust-repair mechanisms	Reading fidelity high Study strength medium	n=54 0.24
Distrust repair is generally more effective when transparency is combined with relational measures, cultural changes, and structural reforms. Governance And Regulation	positive	Distrust repair and restoration of legitimacy	Reading fidelity high Study strength medium	n=54 0.24
Trust and distrust should be treated as distinct constructs when stakeholders evaluate separate attributes such as competence and integrity. Decision Quality	mixed	Attribute-specific evaluations of competence and integrity	Reading fidelity high Study strength medium	n=54 0.24
The reviewed literature under-represents social enterprises and business enterprises and lacks sufficient longitudinal, experimental, and mixed-methods research. Other	negative	Coverage and methodological diversity of the evidence base	Reading fidelity high Study strength high	n=54 0.4
The review used Durach’s six-step systematic-review framework and Gioia methodology to derive first-order categories and higher-order themes. Other	positive	Systematic organization and thematic analysis of the literature	Reading fidelity high Study strength high	n=54 0.4
In the AI context, misinformation, opaque model behavior, and unmet expectations about automation or fairness can generate distrust that reduces adoption and participation. Adoption Rate	negative	AI adoption and participation	Reading fidelity high Study strength low	not reported 0.12
Transparency measures such as model cards and disclosure are necessary but not sufficient for repairing distrust in AI systems. Ai Safety And Ethics	mixed	AI distrust repair	Reading fidelity high Study strength low	not reported 0.12