Human judgment, governance, and well-being in digitally mediated service systems.
- Submission status
- Open
- Submission deadline
Generative artificial intelligence is rapidly becoming embedded in service systems worldwide. People increasingly encounter generative AI when seeking advice, receiving support, making decisions, learning, working, purchasing, and accessing public or professional services. These systems span commercial customer service, advertising, tourism and hospitality, digital platforms, education, health-related information, financial and professional advice, and public services. Although adoption is global, the ways in which generative AI is used and governed vary across cultural norms, institutional capacities, regulatory regimes, service infrastructures, and levels of digital access. These differences create an urgent need for comparative social-science research on how generative AI is experienced, evaluated, and governed in real service settings.
As generative AI moves from experimentation to routine service delivery across regions and sectors, its service-systems lens provides a concrete setting in which people directly encounter, depend on, contest, and are governed by generative AI, while its comparative orientation enables research on how these processes differ across societies. This Collection therefore aims to provide a globally relevant, human-centered understanding of responsible generative AI and to connect research communities that often study human judgment, technology governance, service systems, and well-being.
This Collection is organized around three connected themes: human judgment, governance, and well-being. Human judgment includes trust, reliance, resistance, perceived authenticity and empathy, responsibility attribution, expertise, and risk. Governance includes disclosure, accountability, privacy, fairness, transparency, professional standards, service failures, and the allocation of responsibility between users, organizations, professionals, and AI systems. Well-being includes confidence, anxiety, vulnerability, empowerment, inclusion, social connection, misinformation exposure, and the distribution of benefits and harms across different groups.
The call for papers particularly welcomes cross-national, cross-cultural, and cross-sector research examining generative AI in service communication and decision support; human-AI collaboration among frontline employees and professionals; AI-generated advice, recommendations, reviews, advertising, and educational or health-related information; responsible design and governance of AI-mediated services; inequalities in access and capability; and institutional or regulatory differences in the adoption and use of generative AI. Studies from both developed and emerging economies, including comparative work across regions, are especially encouraged.
The collection is particularly interested in research that addresses the following questions:
- How do users, consumers, employees, professionals, and citizens decide when to trust, rely on, resist, or override generative AI outputs in service systems?
- How do disclosure, transparency, explainability, perceived empathy, authenticity, and AI errors shape user judgment and service trust?
- How does generative AI reshape frontline work, professional expertise, human-AI collaboration, and responsibility attribution in service organizations?
- How should organizations and institutions govern misinformation, privacy, fairness, bias, unequal access, accountability, and service failures involving generative AI?
- How do AI-mediated service encounters affect psychological and social well-being, including confidence, anxiety, vulnerability, empowerment, inclusion, and social connection?
- How is generative AI used and evaluated in high-stakes service settings such as education, health related information, finance, public services, and professional advice?
- How do cultural, institutional, economic, and regulatory differences shape adoption, trust, governance, and outcomes of generative AI in service systems?
- What can cross-national and cross-sector comparisons reveal about responsible generative AI practices across developed and emerging economies?
The Collection welcomes conceptual, empirical, qualitative, quantitative, mixed-methods, review, and policy-oriented submissions from management, psychology, marketing, communication, information systems, sociology, science and technology studies, service research, public policy, education, health communication, digital humanities, and related fields. Cross-disciplinary and policymaking-proximate research is particularly welcome where it illuminates behavioural, organizational, governance, ethical, social, or well-being dimensions of generative AI in service systems.
