From waste to data: a strategic foresight study using an integrated UTAUT–TTF model

Purpose This study aims to examine how smart waste technologies can contribute to shaping sustainable urban futures by transforming waste into actionable data. Focusing on the Traffy Waste Application in Thailand, the research integrates the unified theory of acceptance and use of technology with the task-technology fit framework to explore behavioral and technological factors influencing adoption. By situating technology acceptance within a foresight perspective, the study highlights the potential of digital platforms to drive systemic change in urban sustainability. Design/methodology/approach A quantitative survey was conducted with 352 households. Data were analyzed using partial least squares structural equation modeling to assess the predictive power of the integrated model. Findings The model explained 79.5% of the variance in adoption intention. Technology fit and performance expectancy emerged as the strongest predictors, while technology characteristics significantly influenced effort expectancy. These findings underscore the importance of aligning innovation with citizen needs to accelerate sustainable adoption. Originality/value By reframing waste as a source of strategic urban data, this study advances foresight on how digital waste management platforms can inform long-term planning, policy design and the transition toward circular and resilient cities. The findings provide actionable insights for policymakers and practitioners, particularly in developing countries, on integrating smart city technologies to support sustainable urban futures.