Smart Decision-Making in the Future City: A Social Innovation Perspective

Abstract

The evolution of future cities represents a crucial dimension of global sustainable urbanization. Although an increasing amount of study has investigated diverse aspects of future city development, relatively insufficient focus has been placed on analyzing the phenomenon from the perspective of smart decision-making. In this setting, artificial intelligence (AI) acts as a transformational technology force capable of redefining decision-making processes and advancing urban sustainability. This study examines the influence of AI in strengthening smart decision-making and its potential to foster sustainability in future city environments, viewed through the framework of social innovation. A thorough research model was developed to clarify the complex interrelationships among AI, social innovation, and smart decision-making via triple correlations. Within this framework, social innovation acts as a mediating variable, whereas the internal threats linked to the Internet of Things (IoT) operate as a moderating factor. Demographic factors, including gender, age, and education, were incorporated as control variables. An empirical survey was administered to capture citizens’ views on the contribution of AI to decision-making processes within city governance. Employing purposive sampling ensured the collection of data that was both relevant and representative. Data analysis was performed using SPSS and AMOS, with confirmatory factor analysis (CFA) applied to assess the validity and reliability of the measurement constructs. The empirical findings reveal that AI—particularly through the usage of big data derived from sensor networks—plays a central role in facilitating social innovation and data-based decision-making in future urban contexts. The results further demonstrate that social innovation mediates the relationship between AI and decision-making effectiveness, thereby strengthening governance efficiency. Conversely, internal IoT vulnerabilities were found to weaken this positive association, highlighting the need for robust cybersecurity measures. Overall, this study provides significant theoretical and practical implications for policymakers, urban planners, and scholars. It underscores the pivotal role of social innovation in integrating AI into the design of sustainable and resilient cities, while simultaneously emphasizing the importance of addressing security and governance challenges stemming from IoT-related risks. By illuminating these dynamics, the research contributes to the broader discourse on AI-enabled urban sustainability and smart governance in the era of digital transformation.