Analysis of the perception of digital government and artificial intelligence in the public sector in Jalisco, Mexico

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Abstract

The advancement and continuous development of information and communication technologies highlights the importance of analysing and monitoring the development and capabilities of governments to use and exploit the potential of new technologies. Digital government and artificial intelligence in the public sector are two trends associated with technological development. This research conducts an analysis of these elements from the perspective of public officials by conducting a factor analysis. The study is based on a survey administered to civil servants in the state of Jalisco in Mexico. An analysis was made of their training and knowledge of these aspects and whether there are significant differences according to gender, age, level of studies, area of knowledge and place of work. The results obtained show divergences and coincidences between variables and factors. The findings are useful as a reference for the government of Jalisco to promote measures aimed at improving the situation of digital government and artificial intelligence in the short to medium term, in order to optimize administrative management. Knowing the perspective of public officials on digital government and artificial intelligence is important to evaluate and design a digital strategy in governments. Public officials in charge of technology areas in governments need constant training on emerging technological tools, mainly those related to artificial intelligence. There is a lack of knowledge about the use and implications of artificial intelligence in the public sector by public servants. Data interoperability and coordination between government agencies are relevant to the development of digital government.

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APA

Ruvalcaba-Gomez, E. A., & Cifuentes-Faura, J. (2023). Analysis of the perception of digital government and artificial intelligence in the public sector in Jalisco, Mexico. International Review of Administrative Sciences, 89(4), 1203–1222. https://doi.org/10.1177/00208523231164587

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