Abstract
The term Smart City has gained a lot of attention lately. While there is no unique definition of a Smart City, this chapter focuses on governance issues and in particular, on the use of information and communication technologies to facilitate and boost citizens’ engagement in public life and decision-making. Specifically, the aim is to analyze the factors that shape citizens’ e-Participation in Smart Cities. Based on the civic voluntarism model we explore to what extent individual resources, political engagement and access to networks influence citizens’ decision to e-participate. Our findings suggest that e-Participation is mostly explained by citizens’ political interest and external political efficacy, the intensity of their online activity, the number of daily contacts and the degree of involvement in political/civic associations; as to socioeconomic factors, we find that age, educational attainment and labor situation are statistically significant.
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Notes
- 1.
See Albino et al. (2015) for a review of the different definitions of Smart City.
- 2.
See Meijier and Rodríguez-Bolívar (2016), for a recent review of the concept of Smart Governance.
- 3.
Goodspeed (2015) suggests that many of the problems faced by Smart Cities are wicked problems, in the sense that they “have no definitive description, involve value judgments, and take place in unique contexts that make it difficult to accurately test solutions. As a consequence, any method of addressing them is inherently political” (Goodspeed 2015, p. 85).
- 4.
According to the European Commission (2014), Spain, the United Kingdom (UK) and Italy are the European countries with the highest number of Smart Cities. As to Smart Governance, the leading countries are Spain, France, Germany, the UK, Italy and Sweden.
- 5.
In addition to Barcelona, the European Commission (2014) identified other five successful examples of Smart Cities in the EU-28: Amsterdam (the Netherlands), Copenhagen (Denmark), Helsinki (Finland), Manchester (the UK) and Vienna (Austria).
- 6.
These authors define “outstanding Smart Cities” as “those cities that were finalists or had received any award in the World Smart City Awards during the last 5 years” (Sandulli et al. 2017, p. 619). The Spanish cities included in their case-based study are: Barcelona, Bilbao, La Coruña, Madrid, Málaga and Santander. The other cities are: Vienna (Austria); Mons (Belgium); Rio de Janeiro (Brazil); Medellin (Colombia); Milan, Trento and Genoa (Italy); Manchester, London, Birmingham and Sheffield (the United Kingdom); and Chicago and Boston (the United States of America). Then, in Sandulli et al. (2017)’ sample, Spain is the country with the largest number of Smart Cities (six cities), followed by the United Kingdom with four.
- 7.
In the first call for proposals of the Spanish National Plan for Smart Cities, 49 proposals were presented out of a total of 104 eligible urban areas (Red.es 2014).
- 8.
In order to check whether restricting our sample to Internet users could introduce any sample selection bias, we have estimated Heckman selection models (Wooldridge 2010): the first equation explains whether (or not) an individual uses the Internet; and the second equation considers whether, once online, he/she e-participates. Results indicate that there is no selection bias and thus confirm that e-Participation and Internet use can be analyzed as two independent equations. Moreover, municipality size is not a statistically significant factor to explain the use of the Internet across the Spanish population. See the Annex for the results of the probit regression on the use of the Internet.
- 9.
Cramér’s V is a measure of association between two categorical variables. A value of 0 indicates that there is no association between them. The higher the value, the stronger the relationship between the variables.
- 10.
We drop this variable because it less statistically significant that political interest.
- 11.
We have also checked whether there was a non-linear relationship between age and e-Participation, by including the square of age in the estimations. Result did not provide any statistically significant evidence to support this hypothesis. Results are available upon request.
- 12.
We thank one of the reviewers for raising this point.
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Annex: Probit Regression of the Use of the Internet in Spain
Annex: Probit Regression of the Use of the Internet in Spain
Variables | Use of the internet |
|---|---|
Age | −0.008*** (0.001) |
Woman | −0.038*** (0.015) |
Education: Secondary studies (1st stage) | 0.177*** (0.034) |
Education: Secondary studies (2nd stage) | 0.318*** (0.041) |
Education: Vocational training | 0.305*** (0.040) |
Education: College/university | 0.399*** (0.042) |
Status: Skilled workers | −0.012 (0.025) |
Status: Old middle class | 0.039 (0.028) |
Status: New middle class | 0.102*** (0.026) |
Status: Higher/upper middle class | 0.092*** (0.035) |
Status: DK/DA | −0.002 (0.048) |
Work: Unemployed | −0.029 (0.021) |
Work: Not active | −0.018 (0.020) |
Municipality: 2001–10,000 inhabitants | −0.055* (0.029) |
Municipality: 10,001–50,000 inhabitants | 0.005 (0.027) |
Municipality: 50,001–100,000 inhabitants | −0.015 (0.033) |
Municipality: 100,001–400,000 inhabitants | −0.001 (0.028) |
Municipality: 400,001–1,000,000 inhabitants | 0.012 (0.038) |
Municipality: More than 1,000,000 inhabitants | −0.002 (0.041) |
Regional dummies | Included |
Observations | 1752 |
H0: βiMunicipality = 0 i = 2,…,7 chi2(6) =9.57 Prob> chi2 = 0.1440 |
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Novo Vázquez, A., Vicente, M.R. (2019). Exploring the Determinants of e-Participation in Smart Cities. In: E-Participation in Smart Cities: Technologies and Models of Governance for Citizen Engagement. Public Administration and Information Technology, vol 34. Springer, Cham. https://doi.org/10.1007/978-3-319-89474-4_8
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