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Kosmos
Astronomia Astrofizyka
Inne

Kultura
Sztuka dawna i współczesna, muzea i kolekcje

Metoda
Metodologia nauk, Matematyka, Filozofia, Miary i wagi, Pomiary

Materia
Substancje, reakcje, energia
Fizyka, chemia i inżynieria materiałowa

Człowiek
Antropologia kulturowa Socjologia Psychologia Zdrowie i medycyna

Wizje
Przewidywania Kosmologia Religie Ideologia Polityka

Ziemia
Geologia, geofizyka, geochemia, środowisko przyrodnicze

Życie
Biologia, biologia molekularna i genetyka

Cyberprzestrzeń
Technologia cyberprzestrzeni, cyberkultura, media i komunikacja

Działalność
Wiadomości | Gospodarka, biznes, zarządzanie, ekonomia

Technologie
Budownictwo, energetyka, transport, wytwarzanie, technologie informacyjne

A Classification Model for Predicting Web Users Satisfaction with Information Systems Success using Data Mining Techniques

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.

Journal of Software Engineering 2014/06/21 - 11:19 Czytaj