From institutional trust to AI adoption: a trust transfer and risk perception model of AI-enabled public service acceptance in China's digital government context

Governing AI innovation in local government: how trust and risk perceptions shape public support

This study examines how citizens' experiences with public sector AI shape understanding, trust, and risk perceptions, and how these factors influence public support for responsible AI in local government contexts. Survey data from 1,183 respondents across Australia, Hong Kong, and Saudi Arabia were analysed using structural equation modelling. The framework links experience, perceived understanding, trust, perceived risks and benefits, attitudes toward urban AI and surveillance AI, and the prioritisation of responsible AI principles. Experience with local government AI significantly enhances perceived understanding, which in turn strengthens trust. Trust increases perceived benefits and favourable attitudes toward AI-enabled urban services. However, perceived risks remain a central driver of responsible AI prioritisation. Positive attitudes toward routine urban AI applications are also associated with greater openness to more contested surveillance AI applications. Cross-context differences indicate that these pathways vary according to governance setting, sample composition, and exposure to AI-enabled services. The findings are constrained by cross-sectional, self-reported data and differences in sampling strategies across contexts. Future research should use longitudinal designs and extend the model across additional institutional settings. Responsible implementation requires trust-building, public engagement, and proactive risk governance. Local governments can adopt an experience-first approach by beginning with visible urban AI services that build familiarity and demonstrate public value, while extensions to surveillance or other sensitive applications require stronger safeguards, transparency, and public justification. The findings inform the development of transparent, accountable, and citizen-centred AI governance, supporting socially acceptable and equitable urban innovation. The study advances a comparative, local government-centred model of public sector AI innovation by integrating experiential, cognitive, and perceptual dimensions. It offers novel insights into public support mechanisms for responsible local government AI.

Calibrated Trust in Agentic AI Work Systems: A Social Trust Calibration Framework for Responsible Human-AI Adoption

Agentic artificial intelligence (AI) is transforming social and organisational life by moving AI from an assistive tool into a semi-autonomous actor that can plan, recommend, communicate, coordinate workflows and trigger decisions. Existing debates on trustworthy AI often emphasise technical reliability, legal compliance or ethical principles, but social adoption depends on a subtler question: how do people learn when to trust, distrust, verify or refuse AI outputs in everyday work? This conceptual research develops the Social Trust Calibration Framework (STCF) for responsible human-AI adoption in organisations and public institutions. Using an integrative conceptual synthesis of trust theory, automation research, organisational sensemaking, algorithmic management, human-centred AI and contemporary AI governance guidance, the study identifies the mechanisms through which AI trust becomes either calibrated, excessive, deficient or displaced. The resulting framework argues that trust in agentic AI must be calibrated across four mutually dependent layers: capability trust, process trust, institutional trust and identity trust. The paper contributes a formal definition of calibrated AI trust, a social calibration loop, a diagnostic matrix of trust states, a maturity model and seven research propositions for future empirical testing. The analysis shows that appropriate reliance cannot be achieved by accuracy alone. It requires visible evidence, role-specific verification routines, accountable decision rights, psychological safety, transparent escalation paths and protection of human agency. The paper concludes that responsible AI adoption should be treated as a social trust calibration problem rather than a simple technology acceptance problem. This reframing offers practical guidance for leaders, educators and policymakers seeking to scale AI while preserving human judgement, legitimacy and social confidence.

Beyond the machine: risk, fear, optimism and the foundations of public trust in AI

What drives public trust in artificial intelligence (AI)? This study examines the individual and institutional foundations of AI trust across two contrasting democracies: Japan and the United Kingdom. Drawing on original survey data ( N  = 3235), we test a set of hypotheses derived from trust-transfer perspectives and self-efficacy research, covering institutional trust, AI self-efficacy, technological optimism, perceived societal threat, and job displacement anxiety. The results show that trust in AI is shaped by both psychological predispositions and broader beliefs about the trustworthiness of political and scientific institutions. Trust in government, university scientists, and other people consistently predicts AI trust in both countries, even when controlling for demographic and attitudinal variables. While optimism about AI’s benefits increases trust in both contexts, fear of AI plays a stronger negative role in the UK. Unexpectedly, the belief that AI will replace one’s job is positively associated with trust in Japan but unrelated in the UK. These findings highlight how national context shapes public confidence in emerging technologies and point to the importance of governance frameworks that foster informed capability and institutional legitimacy.

Strategic trust transfer in AI driven self-storage: mitigating the liability of newness for SMEs in Taiwan

This study examines how resource-constrained small and medium enterprises (SMEs) in Taiwan’s emerging AI-driven self-storage market mitigate the liability of newness. It conceptualizes trust transfer from tangible service cues to brand trust as an associational cognitive hierarchy, providing a perception-based signaling framework for nascent ventures to cultivate adoption intentions. Grounded in signaling and trust transfer theories, the research identifies six antecedents across entrepreneurial signals and tangible trust cues. Structural equation modeling analyzed survey data collected from 362 Taiwanese consumers utilizing a category-level evaluation protocol. Environmental safety, economic feasibility, and locational convenience drive service trust. While reputation and communication positively influence brand trust, enterprise size shows no statistically significant direct association. A full mediation model confirms that service trust indirectly drives usage intention through brand trust, which acts as the primary direct driver. Rather than attempting to artificially project enterprise scale, SME operators should strategically allocate capital toward AI-specific operational touchpoints (such as smart access and automated safety monitoring). Furthermore, managers must implement transparent communication, data privacy standards, and mandatory human-in-the-loop emergency escalation protocols. This research resolves the human-absent physical touchpoint boundary and reverse cognitive pathway gap by integrating signaling and trust transfer frameworks in an unstaffed physical setting. It demonstrates how technology-enabled SMEs can structure localized service trust into brand-level credibility to drive adoption.

Applying EASTL ethical constructs in AI-driven CRM: the mediating role of sustainable customer trust in enhancing customer retention in private sector banks

Investigating ethical AI practices and its impact on Customer Retention and its influence from the one-sided effect shows that ethical AI is not only a compliance obligation, but a strategic lever for building trust and long-term customer relationships.

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