A systematic review of 53 studies shows blockchain trust research has been narrowly focused on technical features and acceptance models; the authors propose a three‑dimension sociotechnical trust framework and an agenda of multi-level, mixed-method research to correct this tilt and improve policy and market predictions.

Main Finding

A systematic review of 53 multidisciplinary studies finds that research on consumer trust in blockchain is dominated by technological determinism and acceptance-model theories, producing a narrow, decontextualised understanding. The authors propose a multidimensional, sociotechnical trust framework (three trust dimensions + four-paradigm typology) and a research agenda (multi-level theory integration, innovative methods, contextual expansion) to correct theoretical inertia and better explain how consumers form trust in blockchain-enabled systems.

Key Points

• Review scope and analytic lens:

  • Systematic review of 53 high-quality studies using the ADO-TCM (Antecedents, Decisions, Outcomes, Theory, Context, Methods) framework.
  • Multidisciplinary coverage but reveals concentrated theoretical and contextual patterns.

• Three critical issues identified:

• Technological determinism: equating technical capability (e.g., security, immutability) with consumer trust, marginalising psychological, social and institutional factors.
• Theoretical convergence on acceptance models (e.g., TAM/UTAUT): obscures blockchain-specific trust phenomena and limits explanatory reach.

• Narrow geographic and domain scope: leads to self-reinforcing knowledge hierarchies and limited generalisability across applications and cultures.

• Proposed multidimensional trust framework:

  • Three trust dimensions:
  • Technological architecture trust (trust in the system’s technical properties).
  • Information source trust (trust in the provenance, quality and signaling of data on-chain/off-chain).
  • Social actor trust (trust in people and organisations using or governing the system).
  • Four-paradigm typology:
  • Interpersonal trust
  • Technology trust
  • Institutional trust
  • Sociotechnical trust (integrative perspective emphasizing interactions across users, technology, and institutions)

• Research themes and recommendations:

  • Multi-level theoretical integration: combine micro (psychology), meso (organisational/institutional), and macro (regulatory/cultural) theories.
  • Methodological innovation: more longitudinal, experimental, mixed-methods, cross-cultural, and comparative studies.
  • Contextual expansion: broaden geographic and application-domain coverage to improve external validity and reduce knowledge hierarchies.

• Contribution:

  • Moves beyond acceptance models by conceptualising trust as dynamic, multidimensional, and emergent from sociotechnical interactions; offers an actionable agenda for theory and empirical work.

Data & Methods

• Methodological approach: systematic literature review of 53 studies using the ADO-TCM framework to code and synthesize antecedents, decisions, outcomes, theory, context and methods across the literature.
• Coverage: multidisciplinary sources (information systems, management, possibly computer science, business and social sciences). The review highlights dominant use of acceptance models and technical measures in primary studies, and limited geographic/domain diversity.
• Analytical outcome: identification of conceptual gaps (overemphasis on technical features), theoretical inertia, and methodological concentration; formulation of a novel conceptual framework and typology based on patterns observed across the coded studies.

Implications for AI Economics

• Measurement & modeling of trust:

  • Economic models of blockchain adoption and market behaviour should treat trust as multidimensional (technological, informational, social) and dynamic, not a single, technology-driven variable. Static models that equate technical security with trust risk biased predictions about user uptake and market outcomes.
  • Empirical work should explicitly measure the proposed trust dimensions and allow for interactions (e.g., institutional quality can amplify or substitute for technical assurances).

• Market structure, adoption, and network effects:

  • Trust sources affect adoption thresholds, network externalities and tipping points differently. For example, high institutional trust may accelerate adoption in contexts where technological understanding is low, altering forecasts of platform growth and first-mover advantages.
  • Platform competition and pricing strategies should account for heterogeneity in which trust dimension matters most across user segments and regions.

• Policy, regulation and welfare:

  • Regulatory interventions (certification, dispute-resolution regimes, consumer protections) function as institutional trust levers. Economists evaluating policy impacts should model how regulations interact with technical design to shape utility, transaction costs and welfare.
  • Ignoring sociocultural variation can mis-estimate the distributional effects of blockchain deployments across countries and demographic groups.

• Methodological implications for empirical AI-economics research:

  • Use mixed methods, RCTs, natural experiments and structural estimation to identify causal roles of different trust dimensions on transaction volumes, pricing, and adoption.
  • Agent-based and structural models that incorporate heterogeneous agents with differing trust priors and multi-source trust signals can better capture emergent market dynamics in decentralized platforms.

• Design of AI systems interacting with blockchain:

  • For AI-driven marketplaces, recommendation engines, or automated contracting, designers should incorporate signals from information-source and social-actor trust (e.g., provenance metadata, reputation systems, institutional endorsements), not only cryptographic assurances.
  • Mechanism design and incentive structures for decentralized platforms should be stress-tested under varied trust regimes to avoid fragile equilibria that appear stable under technology-centric assumptions.

• Research agenda actionable items for AI economists:

  • Incorporate the multidimensional trust measures into structural adoption and demand models; estimate elasticities of adoption with respect to each trust dimension.
  • Evaluate how trust interventions (legal, reputational, technical) substitute or complement each other using RCTs or quasi-experimental designs.
  • Study cross-country variation to identify how cultural and institutional contexts shape the returns to investments in technical versus institutional trust-building.
  • Build agent-based simulations to explore market-level outcomes when trust evolves endogenously through interactions between users, AI agents and institutions.

Overall, shifting from technology-centric to sociotechnical trust models can improve the predictive accuracy and policy relevance of AI-economics research on blockchain-enabled markets, platforms and services.