<![CDATA[Introduction: Urban waste growth in metropolitan areas intensifies sanitation challenges and environmental health risks. Smart waste management offers data-driven waste services; however, its effectiveness depends on public acceptance. Despite increasing implementation of smart waste technologies, limited empirical evidence explains how behavioral and psychosocial determinants shape citizen acceptance in developing urban contexts, particularly in relation to perceived environmental health conditions rather than objectively measured environmental outcomes. This study investigates the determinants of smart waste management acceptance and its association with perceived urban environmental health in Jabodeta, Indonesia, using an extended Technology Acceptance Model that integrates digital literacy, environmental concerns, social norms, and perceived risk Methods: A cross-sectional survey of 120 respondents was conducted and analyzed using partial least squares structural equation modeling with bootstrapping. Measurement quality met recommended thresholds, including outer loadings greater than 0.70, average variance extracted ranging from 0.52 to 0.78, strong reliability, and HTMT values below 0.90. Results: The structural model explained substantial variance in perceived ease of use (R² = 0.81) and perceived usefulness (R² = 0.69), moderate-to-strong variance in acceptance (R² = 0.62), and modest variance in perceived urban environmental health (R² = 0.18). Digital literacy positively predicted perceived ease of use (? = 0.52), while perceived risk negatively predicted perceived ease of use (? = ?0.37). Perceived ease of use strongly predicted perceived usefulness (? = 0.74). Environmental concern (? = 0.26) and social norms (? = 0.15) positively predicted perceived usefulness. Perceived usefulness (? = 0.32) and perceived ease of use (? = 0.19) positively predicted acceptance, and acceptance was positively associated with perceived urban environmental health (? = 0.43). Mediation analysis identified statistically significant indirect effects for selected pathways; however, these effects should be interpreted cautiously due to inconsistencies in directional patterns. Conclusion: The findings suggest that perceived environmental health benefits associated with smart waste management are linked to sustained public acceptance. This acceptance is supported by digital literacy, socially reinforced perceptions of usefulness, and effective risk communication combined with reliable service delivery. These results highlight the importance of behavioral and perceptual factors in shaping citizen engagement with smart environmental technologies, while acknowledging that the study reflects perceived rather than objectively measured environmental outcomes.]]>
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