Exportar Publicação
A publicação pode ser exportada nos seguintes formatos: referência da APA (American Psychological Association), referência do IEEE (Institute of Electrical and Electronics Engineers), BibTeX e RIS.
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_1725172713362, 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 -