| Abstract: | Abstract This study examines the export performance of 350 randomly selected Japanese engineering service firms. Inquiry is made regarding environmental, organizational, managerial, strategic, and functional determinants of these exporting firms. Two primary questions are addressed. Can performance category membership of engineering service exporters be predicted? Second and foremost, which determinants are the most effective in differentiating between the export performance categories? An Artificial Neural Network is selected as the statistical method because of the different perspective it provides for a highly non-linear function having many variables, offering results that consistently prove to numerically approximate such functions much easier than conventional methods, together with the ability to dependably and accurately predict membership classification while providing weighted analyses of input variables. Findings suggest that each group's membership can be consistently and accurately predicted and further identify which determinants dominantly impact category membership as supported by differences in the feature extraction phase of the neural network approach. |
