Smart City Puebla: measuring smartness

Stephanie Garcidueñas Nieto, Pablo Collazzo Yelpo, Katya Pérez Guzmán


Objective of the study: this empirical study revisits the meaning and scope of the ‘smart city’ concept, measuring ‘smartness’ in an emerging market setting.

Methodology / approach: a data reduction exercise is conducted through a principal component analysis of 22 smart city variables and a two-step cluster analysis for the 217 municipalities of the State of Puebla (Mexico), so as to identify the defining challenges to ‘smartness’ in a developing economy city.

Originality / Relevance: the prevailing models that measure urban ‘smartness’, notably Giffinger’s and Cities in Motion, arguably miss to capture the socioeconomic challenges of cities in a developing market context.

Main results: two distinctive factors emerge from the data reduction exercise, namely ‘marginalization’, referring to social and economic inequalities, and ‘access to services’, particularly public health and education, to define the challenges emerging market cities would need to address in their path to ‘smartness’.

Theoretical / methodological contributions: we introduce a revised approach to measure city ‘smartness’, claiming that access to public services (education and health) helps reduce social inequality and marginalization, which are core indicators to redefine smart cities in emerging markets.

Social / management contributions: even if the analysis is carried out on data from a single region, our findings could be a meaningful input to a more generalizable model to measure city ‘smartness’ in emerging markets, with implications to multiple stakeholders, particularly policy-makers, suggesting basic inequalities and access to education and health services should be addressed, before attempting to improve traditional smart city indicators. 


Smart city; Principal components analysis; Inequality; Public services; Emerging markets

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