Analysing interaction effects on MRP implementation using ACE

Abstract

This research represents the first study to examine the significance of interactions among individual determinant variables in affecting MRP implementation outcome. A powerful statistical technique, alternating conditional expectations (ACE), was used to maximise the model fit and to uncover underlying non-linear relationships between implementation success and its determinant variables. The final ACE model indicates that interaction effects are significant in affecting MRP implementation outcome. The analytic transformations from the ACE model presented new insights and information into the MRP implementation process. The managerial implications arising for the findings are also discussed.