Prasongchai Setthasuravich

Generative artificial intelligence (GenAI) is widely considered to hold transformative potential for public administration, but its adoption in local governments remains uneven, weakly institutionalized, and shaped by informal employee experimentation. This study advances the literature by examining GenAI adoption during a rare moment of institutional restructuring in Vietnam, where the 2025 administrative reform abolished the district tier and shifted responsibilities to ward- and commune-level government. To address the unique dynamic of GenAI adoption, this research extended the Unified Theory of Acceptance and Use of Technology (UTAUT) by integrating trust in technology, perceived risk, technological awareness, and perceived organizational support. Survey data were collected from 302 civil servants across eight post-merger ward- and commune-level administrative units in a transitioning Vietnamese province. The study employs a dual-methodological approach, integrating Partial Least Squares Structural Equation Modeling (PLS-SEM) with fuzzy-set Qualitative Comparative Analysis (fsQCA) to evaluate both net effects of individual variables and the underlying causal complexity. The PLS-SEM results show that performance expectancy, social influence, technological awareness, and perceived risk are positively associated with GenAI adoption intention, while effort expectancy and trust in technology operate indirectly through performance expectancy rather than exerting a direct effect. This suggests that, in the context of local government reform, trust matters primarily when it strengthens employees’ belief that GenAI can improve work performance. Perceived organizational support attenuates the positive relationship between perceived risk and adoption intention, suggesting a buffering role in reducing risk-driven experimentation under institutional uncertainty toward more structured use. The fsQCA findings reveal causal asymmetry and equifinality: adoption intention emerges through multiple pathways anchored in perceived usefulness, social influence, and facilitating conditions, whereas non-adoption is associated with low awareness and weak organizational support. The study contributes to public administration, digital governance and technology acceptance research by showing how GenAI adoption is shaped not only by individual perceptions, but also by institutional change.

Thailand’s transport systems face persistent challenges, including severe urban congestion, traffic safety issues, and overdependence on unsustainable modes of transport. Much of this underperformance reflects state policy choices and weak coordination across agencies, which have favoured stand-alone, capital-intensive projects over integrated networks and services. The result has been uneven benefits that often widen social and spatial inequalities. This chapter reviews Thailand’s transport systems and examines how state institutions shape outcomes across modes. It argues for transport governance reform to align budgets, contracts, and data around shared, service-level objectives so that agencies work together and investment translates into safer, fairer, and more resilient mobility.

1. In contemporary public administration, perceived public trust has emerged as a strategic institutional asset. This serves as an essential foundation for legitimacy, policy compliance, and long-t...

Technical and Vocational Education and Training (TVET) is central to Cambodia’s skills-development agenda, but limited evidence explains how students form expectations about public TVET pathways. Grounded in Social Cognitive Career Theory (SCCT), this study examined the factors associated with students’ outcome expectations in public TVET institutions in Phnom Penh, Cambodia. A cross-sectional survey was conducted with 616 bachelor’s-degree students, measuring self-efficacy, perceived social support, perceived government and institutional support, and outcome expectations. Data were analyzed using hierarchical multiple regression and stratified subgroup regressions by sector-based major group. Outcome expectations were positively correlated with all three predictors. Controlling for age and gender, the full model explained 64.9% of the variance in outcome expectations, with perceived government and institutional support showing the strongest association, followed by self-efficacy and perceived social support. In the stratified analyses, self-efficacy and perceived government and institutional support were significant within every sector-based subgroup, whereas perceived social support was significant only in the Engineering/Industrial Technical subgroup. These findings provide associational support for SCCT in a Southeast Asian, lower-middle-income setting and indicate that institutional and government credibility may carry particular weight where a public TVET system is still being established.

Generative Artificial Intelligence (GenAI) is rapidly reshaping public-sector operations, yet its adoption in developing countries remains poorly understood. Existing research focuses largely on traditional AI in developed contexts, leaving unanswered questions about how GenAI interacts with institutional, organizational, and governance constraints in resource-limited settings. This study examines the organizational factors shaping GenAI adoption in Vietnamese local government using 25 semi-structured interviews analyzed through the Technology–Organization–Environment (TOE) framework. Findings reveal three central dynamics: (1) the emergence of informal, voluntary, and bottom-up experimentation with GenAI among civil servants; (2) significant institutional capacity constraints—including absent strategies, limited budgets, weak integration, and inadequate training—that prevent formal adoption; and (3) an “AI accountability vacuum” characterized by data security concerns, regulatory ambiguity, and unclear responsibility for AI-generated errors. Together, these factors create a state of governance paralysis in which GenAI is simultaneously encouraged and discouraged. The study contributes to theory by extending the TOE framework with an environment-specific construct—the AI accountability vacuum—and by reframing resistance as a rational response to structural gaps rather than technophobia. Practical implications highlight the need for capacity-building, regulatory guidance, accountable governance structures, and leadership-driven institutional support to enable safe and effective GenAI adoption in developing-country public sectors.