Influência de fatores contingenciais no desenvolvimento de cidades inteligentes no Brasil

IF 0.5 Q4 BUSINESS
Danilo Henrique Fagnani Rabito, Simone Letícia Raimundini Sanches, L. Carvalho, I. Paiva
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Abstract

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.
偶然性因素对巴西智慧城市发展的影响
本研究的目的:分析偶然因素(环境、结构、组织规模和组织文化)对2020年互联智能城市排名中排名最好的100个巴西城市的影响。方法/途径:数据收集自:巴西人类发展图谱(AtlasBR);联邦行政委员会;巴西公共部门会计和税务信息系统;巴西地理和统计研究所和高级选举法院。数据指的是2019年。使用的统计方法是正态性和同质性检验、相关性和多元线性回归,并借助IBM SPSS Statistics Version 2.0软件。原创性/相关性:关注偶然性因素如何影响智慧城市的实施,从因变量和自变量中产生定量证据。主要结果:多元线性回归表明,选择的变量解释了62.40%的智能城市,证明了“环境”的积极而显著的影响人口超过50000的城市的“组织结构”和“规模”应急因素。理论/方法学贡献:这些结果有助于填补经验研究中的空白,这些研究涉及影响城市成为智能城市的偶然因素,以及对这些因素如何相关的理解。社会/管理贡献:其影响达到了影响公共政策、公共治理实践发展和公民参与实施智能城市的因素的定义。
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