Citizens as consumers: Profiling e-government services' users in Egypt via data mining techniques

M.M. Mostafa, Ahmed El-Masry

Research output: Contribution to journalArticlepeer-review

34 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)627-641
Number of pages15
Journal International Journal of Information Management
Issue number4
Early online date10 May 2013
Publication statusPublished - Aug 2013
Externally publishedYes


  • e-Government services
  • Consumer profiling
  • Neural networks
  • Data mining
  • Egypt


Dive into the research topics of 'Citizens as consumers: Profiling e-government services' users in Egypt via data mining techniques'. Together they form a unique fingerprint.

Cite this