Abstract
ABSTRACT Despite various definitions of Smart Cities (SCs), it is challenging to characterize what “the best” Smart City would be. This is demonstrated by various frameworks of indicators and rankings, which present different cities as the smartest. This article aims to contribute to the understanding of concepts of SCs, focusing on 21 indicator frameworks, which were studied through text analysis and text mining techniques. The indicator frameworks were researched as units of analysis through an inductive interpretive approach. Frameworks of different regions (Europe, Asia, North and Latin America) were analyzed, and their results indicate that these frameworks are not immune to contextual factors affecting cities. They present categories and indicators that are, on the one hand, very similar, indicating an isomorphic trend, and, on the other, very different from each other due to the contextual influence of the environment and the stakeholders involved. The unprecedented contribution of this article is to show, using statistical analysis, that naming “the” SC would be an ineffectual task.
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