Statistical analysis of the process architecture through a computational tool

Authors

  • Puerta Ramírez, Jorge Eliécer

DOI:

https://doi.org/10.18687/LACCEI2023.1.1.1521

Keywords:

Cronbach's Alpha, Principal Component Analysis, Process Architecture, Computational Tool.

Abstract

The objective of the work is to evaluate the architecture of the judicial services management process of the Judicial Branch of Colombia through the analysis of factors that are considered critical for its performance. For the construction of the instrument, critical factors such as the organizational scheme, processes and procedures, information systems, adaptability, effectiveness and efficiency were identified, which were validated by experts and their reliability determined by using Cronbach's alpha coefficient. The statistical analysis was carried out through a computational tool that allowed the Principal Component Analysis to be applied to reduce the dimensions and to be able to obtain the factors that facilitated the analysis of the information. From the statistical analysis of critical factors, the positive correlations that exist between them were evidenced, which allows establishing a set of conditions to evaluate the architecture of the process. Based on the results, it is concluded that through the use of a computational tool, it is possible to carry out the analysis of factors considered critical for the improvement of the process.

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Published

2023-07-27

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Section

Articles

License

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

LACCEI retains copyright of all published articles under the terms of its copyright transfer agreement. As the copyright holder, LACCEI distributes the articles to the public under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License (CC BY-NC-SA 4.0).

How to Cite

Puerta Ramírez, Jorge Eliécer. (2023). Statistical analysis of the process architecture through a computational tool. LACCEI, 1(8). https://doi.org/10.18687/LACCEI2023.1.1.1521