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Rabito, D. , Sanches S. L., Carvalho, L. C. & Paiva, I. (2022). Influence of contingency factors on the development of smart cities in Brasil. International Journal of Innovation. 10 (4), 696-728

D. H. Rabito et al.,  "Influence of contingency factors on the development of smart cities in Brasil", in Int. Journal of Innovation, vol. 10, no. 4, pp. 696-728, 2022

@article{rabito2022_1715182781688,
	author = "Rabito, D.  and Sanches S. L. and Carvalho, L. C. and Paiva, I.",
	title = "Influence of contingency factors on the development of smart cities in Brasil",
	journal = "International Journal of Innovation",
	year = "2022",
	volume = "10",
	number = "4",
	doi = "10.5585/iji.10i4.21914",
	pages = "696-728",
	url = "https://periodicos.uninove.br/innovation/article/view/21914"
}

TY  - JOUR
TI  - Influence of contingency factors on the development of smart cities in Brasil
T2  - International Journal of Innovation
VL  - 10
IS  - 4
AU  - Rabito, D. 
AU  - Sanches S. L.
AU  - Carvalho, L. C.
AU  - Paiva, I.
PY  - 2022
SP  - 696-728
SN  - 2318-9975
DO  - 10.5585/iji.10i4.21914
UR  - https://periodicos.uninove.br/innovation/article/view/21914
AB  - Objective of the study: To analyze the influence of contingency factors (environment, structure,
organizational size and organizational culture) on the 100 best-ranked Brazilian municipalities in the
2020 Connected Smart Cities Ranking.
Methodology/approach: Data were collected from: Atlas of Human Development in Brazil (AtlasBR);
Federal Administration Council (CFA); Brazilian Accounting and Tax Information System for the
Public Sector (SICONFI); Brazilian Institute of Geography and Statistics (IBGE), and Superior Electoral
Court (TSE). The data refer to the year 2019. The statistical methods used were normality and
homogeneity tests, correlation and multiple linear regression, with the aid of the IBM SPSS Statistics
Version 2.0 software.
Originality/relevance: It focuses on how contingency factors influence the implementation of smart
cities, producing quantitative evidence from the dependent variable with the independent variables.
Main results: Multiple linear regression showed that the selected variables explain 62.40% of what a
smart city is. It evidences the positive and significant influence of the ‘environment’; ‘organizational
structure’ and ‘size’ contingency factors for cities with more than 50,000 inhabitants.
Theoretical/methodological contributions: The results contribute to the gap in empirical studies
dealing with the contingency factors that affect municipalities in the sense of them becoming smart
cities, and in the understanding of how these factors are related.
Social/management contributions: The implications reach the definition of factors that affect public
policies, development of public governance practices and citizen engagement for the implementation of
smart cities.


ER  -