Prediction of CASE adoption: a neural network approach

The failure of organizations to adopt CASE tools has been an area of interest to business researchers for over a decade. The purpose of this study is to test whether the previous research provides a basis for predicting the current adoption of CASE tools by organizations. This study uses a neural network methodology to predict CASE tool adoption using factors that were previously identified in the literature. The model consisted of six variables: IS department stability, need to improve IS department performance, use of external sources of knowledge, job rotation, pressure to reduce development time, and CASE champion. The study found that all the variables were relevant in the prediction of CASE tool adoption with an average accuracy of 71.43 percent.