The use of cascade-correlation neural networks in university fund raising
Document Type
Journal article
Source Publication
Journal of the Operational Research Society
Publication Date
1-1-2000
Volume
51
Issue
8
First Page
913
Last Page
920
Keywords
Cascade-correlation, Education, Neural network, University fund raising
Abstract
In recent years, many Colleges and Universities in the USA have been facing a serious financial crisis since many state governments have reduced their support for higher education. There is no doubt that one of the solutions to this crisis depends on the successful implementation of University fund raising programs. Identifying the potential donors is an important part of this process. The objective of this research was to develop a cascade-correlation neural network to predict the types of people who would most likely be potential donors. A comparison of the classification accuracy between neural networks and multiple discriminant analyses (MDA) was also conducted. Our results indicated that neural networks could perform as well as MDA in overall accuracy. Furthermore, neural networks could predict with a lot more accuracy the actual donor (Type I hit) than MDA. Our study is the first published case study on the use of artificial neural networks for University fund raising.
DOI
10.1057/palgrave.jors.2600996
Print ISSN
01605682
Publisher Statement
Copyright © 2000 Operational Research Society Ltd. All rights reserved. Access to external full text or publisher's version may require subscription.
Full-text Version
Publisher’s Version
Language
English