Abstract
This study uses data mining techniques to examine the effect of various demographic, cognitive and psychographic factors on Egyptian citizens' use of e-government services. Data mining uses a broad family of computationally intensive methods that include decision trees, neural networks, rule induction, machine learning and graphic visualization. Three artificial neural network models (multi-layer perceptron neural network [MLP], probabilistic neural network [PNN] and self-organizing maps neural network [SOM]) and three machine learning techniques (classification and regression trees [CART], multivariate adaptive regression splines [MARS], and support vector machines [SVM]) are compared to a standard statistical method (linear discriminant analysis [LDA]). The variable sets considered are sex, age, educational level, e-government services perceived usefulness, ease of use, compatibility, subjective norms, trust, civic mindedness, and attitudes. The study shows how it is possible to identify various dimensions of e-government services usage behavior by uncovering complex patterns in the dataset, and also shows the classification abilities of data mining techniques. © 2013 Elsevier B.V.
DOI
10.1016/j.ijinfomgt.2013.03.007
Publication Date
2013-08-01
Publication Title
International Journal of Information Management
Volume
33
Issue
4
Publisher
Elsevier BV
ISSN
0268-4012
Embargo Period
2024-11-19
First Page
627
Last Page
641
Recommended Citation
Mostafa, M., & El-Masry, A. (2013) 'Citizens as consumers: Profiling e-government services’ users in Egypt via data mining techniques', International Journal of Information Management, 33(4), pp. 627-641. Elsevier BV: Available at: https://doi.org/10.1016/j.ijinfomgt.2013.03.007
