The IRMA Community
Newsletters
Research IRM
Click a keyword to search titles using our InfoSci-OnDemand powered search:
|
Application of Machine Learning Techniques for Predicting Citizen Usage of Electronic Government Services
|
|
Author(s): Juan Carlos De la Cruz-Maldonado (Autonomous University of Tamaulipas, Mexico), Fernando Ortiz-Rodriguez (Autonomous University of Tamaulipas, Mexico)and Demian Abrego-Almazan (Autonomous University of Tamaulipas, Mexico)
Copyright: 2025
Pages: 18
Source title:
Reshaping the Economy With AI
Source Author(s)/Editor(s): Fernando Ortiz-Rodriguez (Tamaulipas Autonomous University, Mexico), Yuridia Mendoza (Tamaulipas Autonomous University, Mexico), Vicente Villanueva (Tamaulipas Autonomous University, Mexico)and Shwetambari Chiwhane (Symbiosis International University, India)
DOI: 10.4018/979-8-3693-8714-6.ch006
Purchase
|
Abstract
This study examines Mexican citizens' use of e-government services through advanced machine learning models. Based on data collected from the ENDUTIH 2022 and focusing on a sample representing 30% of the total, sociodemographic variables such as sex, social strata, age, and socioeconomic level were investigated, along with technological skills and internet access patterns. The algorithms applied include K Nearest Neighbor, Support Vector Machine, Random Forest, and XGboost, with Random Forest and XGboost standing out for their precision and sensitivity. The results show that the factors studied are significant predictors of user behavior in the context of e-government, suggesting that the government can improve strategies for implementing government digital services based on these findings. However, the study acknowledges limitations, such as its focus on data from Mexican users, and recommends further research to expand the range of variables and contexts analyzed.
Related Content
|
Frederic Andres.
© 2027.
14 pages.
|
|
Kalsoom Safdar, Khairul Najmy Abdul Rani, Mohd Aminudin Jamlos, Siti Julia Rosli, Muhammad Usman Younus, Zanab Safdar.
© 2027.
27 pages.
|
|
Bani Adam, Binastya Anggara Sekti, Muhammad Adi Zacky Zahran.
© 2027.
24 pages.
|
|
Swetha Margaret T. A., Renuka Devi D..
© 2027.
31 pages.
|
|
Maurice Saluschke, Michael Schulz.
© 2027.
30 pages.
|
|
Mirjam Sepesy Maučec, Gregor Donaj.
© 2027.
16 pages.
|
|
Jorge A. Ruiz-Vanoye, Ocotlan Diaz-Parra, Ricardo A. Barrera-Cámara, Alejandro Fuentes-Penna, Francisco R. Trejo-Macotela, Jaime Aguilar-Ortiz, Eric Simancas-Acevedo.
© 2027.
21 pages.
|
|
|