Modelo de Maturidade em Transparência Organizacional: Um Estudo Piloto em Organização Pública com foco nos Aspectos de Divulgação
Resumo
Transparência organizacional visa estabelecer as características que devem estar presentes nos processos e informações organizacionais de modo a torná-las transparentes. Este artigo descreve o primeiro estágio com o qual uma organização deve estar alinhada para seguir um caminho de maturidade em transparência. Este estágio é representado pelo Nível 2 do Modelo de Maturidade em Transparência Organizacional, que reúne as características e práticas a serem implementadas em uma organização de modo a torná-la divulgada (2o. Nível de maturidade do modelo proposto). O artigo também descreve, por meio de um exemplo, como as definições deste nível podem ser utilizadas para esta implementação e apresenta um cenário real de aplicação do modelo em uma organização pública.
Palavras-chave:
governo eletrônico
Referências
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Borgatti, Stephen P. (2005) Centrality and network flow, Social networks ,27, pp. 55- 71.
Chen, Bi Yu, Lam Willian H. K., Sumalee, A., Li, Qingquan, Li, Zhi-Chun (2012) Vulnerability analysis for large-scale and congested road networks with demand uncertainty, Transportation Research Part A: Policy and Practice 46.3, pp. 501-516
Easley, D., Jon K. (2010) “Networks, Crowds, and Markets”, Vol. 8, Cambridge:Cambridge University Press.
Ferber, C., Berche, B., Holovatch, T., Holovatch Yu. (2012) A tale of Two Cities Vulnerabilities of the London and Paris Transit Networks, J Transp. Secur. 5. 199/216.
Hagberg, A., Swart, P., Chult, D. (2008) “Exploring Network Structure, Dynamics, and Function Using NetworkX”. in Proceedings of the 7th Python in Science Conference (SciPy2008), pp. 11-15.
Jenelius, E., Mattsson L-G. (2015) Vulnerability and resilience of transport systems — A discussion of recent research, Transportation Research Part 4, 81, pp. 16-34.
Joanes, D. N., Gill, C. A. (1998). Comparing measures of sample skewness and kurtosis. Journal of the Royal Statistical Society: Series D (The Statistician) 47, pp. 183-189.
Latora, V., Marchiori, M. (2001) Efficient behavior of small-world networks. Physical review letters 87.19, pp.198701.
Nist (2003), “Engineering Statistics Hanbook”, https://www.itl.nist.gov/div898/handbook/eda/section3/eda35b.htm
Pearson, M., Sagastuy, J., Samaniego, S. (2017) “Traffic Flow Analysis Using Uber Movement Data”, htts:// snap.stanford.edu/class/projects/
Saramãki, J., Kivel, M., Onnela, J-P., Kaskil, K., Kertész, J. (2007) Generalizations of the Clustering Coefficient to Weighted Complex Networks, Physical Review E, 75.2,pp. 027105.
Sebastiani, M., Luders R., Fonseca K. (2016) “Evaluating electric bus operation for a real-world BRT public transportation using simulation optimization,” IEEE Trans. Intel. Transp. Sys., vol. 99, pp. 1-10.
Uber (2019) “Uber Movement”, https://movement.uber.com/
Vonu P., Tang L., Vassilakis W. (2011) “Spatio-temporal effects of bus arrival time information,” in Proceedings of the 4th ACM SIGSPATIAL International Workshopon Computational Transportation Science, CTS "11, (New York, NY, USA), pp. 6-11.
Balijepalli, Chandra, Oppong, Olivia (2014) Measuring vulnerability of road network considering the extent of serviceability of critical road links in urban areas, Journal of Transport Geography, 39, pp. 145-155.
Borgatti, Stephen P. (2005) Centrality and network flow, Social networks ,27, pp. 55- 71.
Chen, Bi Yu, Lam Willian H. K., Sumalee, A., Li, Qingquan, Li, Zhi-Chun (2012) Vulnerability analysis for large-scale and congested road networks with demand uncertainty, Transportation Research Part A: Policy and Practice 46.3, pp. 501-516
Easley, D., Jon K. (2010) “Networks, Crowds, and Markets”, Vol. 8, Cambridge:Cambridge University Press.
Ferber, C., Berche, B., Holovatch, T., Holovatch Yu. (2012) A tale of Two Cities Vulnerabilities of the London and Paris Transit Networks, J Transp. Secur. 5. 199/216.
Hagberg, A., Swart, P., Chult, D. (2008) “Exploring Network Structure, Dynamics, and Function Using NetworkX”. in Proceedings of the 7th Python in Science Conference (SciPy2008), pp. 11-15.
Jenelius, E., Mattsson L-G. (2015) Vulnerability and resilience of transport systems — A discussion of recent research, Transportation Research Part 4, 81, pp. 16-34.
Joanes, D. N., Gill, C. A. (1998). Comparing measures of sample skewness and kurtosis. Journal of the Royal Statistical Society: Series D (The Statistician) 47, pp. 183-189.
Latora, V., Marchiori, M. (2001) Efficient behavior of small-world networks. Physical review letters 87.19, pp.198701.
Nist (2003), “Engineering Statistics Hanbook”, https://www.itl.nist.gov/div898/handbook/eda/section3/eda35b.htm
Pearson, M., Sagastuy, J., Samaniego, S. (2017) “Traffic Flow Analysis Using Uber Movement Data”, htts:// snap.stanford.edu/class/projects/
Saramãki, J., Kivel, M., Onnela, J-P., Kaskil, K., Kertész, J. (2007) Generalizations of the Clustering Coefficient to Weighted Complex Networks, Physical Review E, 75.2,pp. 027105.
Sebastiani, M., Luders R., Fonseca K. (2016) “Evaluating electric bus operation for a real-world BRT public transportation using simulation optimization,” IEEE Trans. Intel. Transp. Sys., vol. 99, pp. 1-10.
Uber (2019) “Uber Movement”, https://movement.uber.com/
Vonu P., Tang L., Vassilakis W. (2011) “Spatio-temporal effects of bus arrival time information,” in Proceedings of the 4th ACM SIGSPATIAL International Workshopon Computational Transportation Science, CTS "11, (New York, NY, USA), pp. 6-11.
Publicado
22/05/2013
Como Citar
ARAUJO, Renata Mendes de; ENGIEL, Priscila; CAPPELLI, Claudia; TANAKA, Asterio; SANTOS, Gleison; LEITE, Julio Cesar Sampaio do Prado; MORAES, Miriam; NUNES, Vanessa.
Modelo de Maturidade em Transparência Organizacional: Um Estudo Piloto em Organização Pública com foco nos Aspectos de Divulgação. In: WORKSHOP DE COMPUTAÇÃO APLICADA EM GOVERNO ELETRÔNICO (WCGE), 5. , 2013, João Pessoa.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2013
.
p. 9-16.
ISSN 2763-8723.
