Unfamiliar but desired: citizens’ attitudes toward smart city applications

Abstract Urban administrations increasingly rely on AI and data-driven solutions to address complex societal problems, such as climate change and the distribution of limited resources. Since the 2000s, the term smart city has been used to describe cities that use data and technology to foster efficiency, environmental sustainability, and citizens’ quality of life. Although marketed as a promising way to a digital and data-driven future, numerous examples in recent years show that technology-based solutions may come with unintended and undesired side effects, e.g., excluding certain groups of people from access to resources. This may result in discrimination and increased social inequalities. While computer science and engineering research has been very active in developing smart city technologies, much less is known about the public’s attitudes towards such technologies and whether these attitudes vary across different social groups. To address this gap, we conducted a survey study (N = 2021) on public attitudes towards various smart city applications in May 2023 in a high-quality probability-based online panel in Germany. We presented respondents with eleven smart city technologies across four domains: mobility, social inclusion, public safety, and energy supply. Respondents indicated whether they are familiar with them and how much they would like to see the applications implemented in their neighbourhood. Using latent class analysis, we identified patterns of familiarity and desirability, and examined how these relate to gender, age, education, mobility impairment, migration background, income, and urbanicity. Our analysis reveals distinct attitude profiles towards smart city technologies, with certain socio-demographic characteristics associated with different degrees of familiarity and desirability. The study makes a twofold contribution to the research on citizens’ views on smart city applications: first, it offers a social science perspective that focuses on inequalities in public attitudes. Second, it complements the predominantly qualitative, small-sample literature with a quantitative analysis using a high-quality probability-based sample of the German population.

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Publication Details

Journal
AI & Society
Published
2025-12-23
DOI
https://doi.org/10.1007/s00146-025-02815-8
Primary Topic
Smart Cities and Technologies
Type
article
Field-Weighted Citation Impact
0.00

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article

Unfamiliar but desired: citizens’ attitudes toward smart city applications

Daria Szafran, Ruben L. Bach
AI & Society
Smart Cities and Technologies
article

Unfamiliar but desired: citizens’ attitudes toward smart city applications

Daria Szafran, Ruben L. Bach
article en

Abstract

Abstract Urban administrations increasingly rely on AI and data-driven solutions to address complex societal problems, such as climate change and the distribution of limited resources. Since the 2000s, the term smart city has been used to describe cities that use data and technology to foster efficiency, environmental sustainability, and citizens’ quality of life. Although marketed as a promising way to a digital and data-driven future, numerous examples in recent years show that technology-based solutions may come with unintended and undesired side effects, e.g., excluding certain groups of people from access to resources. This may result in discrimination and increased social inequalities. While computer science and engineering research has been very active in developing smart city technologies, much less is known about the public’s attitudes towards such technologies and whether these attitudes vary across different social groups. To address this gap, we conducted a survey study (N = 2021) on public attitudes towards various smart city applications in May 2023 in a high-quality probability-based online panel in Germany. We presented respondents with eleven smart city technologies across four domains: mobility, social inclusion, public safety, and energy supply. Respondents indicated whether they are familiar with them and how much they would like to see the applications implemented in their neighbourhood. Using latent class analysis, we identified patterns of familiarity and desirability, and examined how these relate to gender, age, education, mobility impairment, migration background, income, and urbanicity. Our analysis reveals distinct attitude profiles towards smart city technologies, with certain socio-demographic characteristics associated with different degrees of familiarity and desirability. The study makes a twofold contribution to the research on citizens’ views on smart city applications: first, it offers a social science perspective that focuses on inequalities in public attitudes. Second, it complements the predominantly qualitative, small-sample literature with a quantitative analysis using a high-quality probability-based sample of the German population.

AI & Society
University of Mannheim (DE), Mannheim Centre for European Social Research (DE)
Volkswagen Foundation
Reduced inequalities
Openalex Percentile: Top 33%
Smart Cities and Technologies
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