Smart Cities and Citizen Adoption: Exploring Tourist Digital Maturity for Personalizing Recommendations

Abstract

Due to the irruption of new technologies in cities such as mobile applications, geographic information systems, internet of things (IoT), Big Data, or artificial intelligence (AI), new approaches to citizen management are being developed. The primary goal is to adapt citizen services to this evolving technological environment, thereby enhancing the overall urban experience. These new services can enable city governments and businesses to offer their citizens a truly immersive experience that facilitates their day-to-day lives and ultimately improves their standard of living. In this arena, it is important to emphasize that all investments in infrastructure and technological developments in Smart Cities will be wasted if the citizens for whom they have been created eventually do not use them for whatever reason. To avoid these kinds of problems, the citizens’ level of adaptation to the technologies should be evaluated. However, although much has been studied about new technological developments, studies to validate the actual impact and user acceptance of these technological models are much more limited. This work endeavors to address this deficiency by presenting a new model of personalized recommendations based in the technology acceptance model (TAM). To achieve the goal, this research introduces an assessment system for tourists’ digital maturity level (DMT) that combines a fuzzy 2-tuple linguistic model and the analytic hierarchy process (AHP). This approach aims to prioritize and personalize the connection and communication between tourists and Smart Cities based on the digital maturity level of the tourist. The results have shown a significant correlation between technology usage and the potential for personalized experiences in the context of tourism and Smart Cities.