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Journal of Software Engineering
  Year: 2014 | Volume: 8 | Issue: 4 | Page No.: 265-277
DOI: 10.3923/jse.2014.265.277
A Classification Model for Predicting Web Users Satisfaction with Information Systems Success using Data Mining Techniques
Kamal Mohammed Alhendawi and Ahmad Suhaimi Baharudin

Abstract:
A very few research studies discussed the employment of data mining techniques in the field of IS success/effectiveness assessment. For this reason, the purpose of this study is to employ data mining techniques in the evaluation of Information System (IS) effectiveness, particularly classification method. This important issue helps and supports decision makers and IT managers towards the development of information system quality in order to be consistent with user needs and expectations. A reasonable data set of 255 subjects are collected through using a questionnaire of six dimensions including five quality factors (system quality, information quality, service quality, user interface quality and communication quality) and user satisfaction. This study attempts to employ the data mining techniques to develop a model for supporting the prediction of the user satisfaction with IS inside the international organizations. To validate the generated model, several experiments were performed based on real data collected from the international organization employees. The encouraging results of experiments show that this model has a sound prediction to decide regarding the level of user satisfaction toward the employed IS. Also the results indicate that the tree classification algorithm J48 is the best in doing classification in case of supervised target of two values. It is important to mention that the results consistent with the regression analysis and could contribute to the related empirical studies.
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How to cite this article:

Kamal Mohammed Alhendawi and Ahmad Suhaimi Baharudin, 2014. A Classification Model for Predicting Web Users Satisfaction with Information Systems Success using Data Mining Techniques. Journal of Software Engineering, 8: 265-277.

DOI: 10.3923/jse.2014.265.277

URL: http://scialert.net/abstract/?doi=jse.2014.265.277

 
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