Predicting open IOS adoption in SMEs: An integrated SEM-neural network approach

This research examines the predictors of open interorganizational systems (IOS) adoption by using RosettaNet as a case study. The model used in this research derived its theoretical supports from literature related to interorganizational relationships and knowledge management studies. A sequential, multi-method approach integrating both structural equation modeling (SEM) and neural network analysis was employed in this research. Data was collected from 136 small and medium sized enterprises (SME). Our result showed that interorganizational relationships such as communication, collaboration and information sharing play an important role in SMEs' RosettaNet adoption decisions. Knowledge management practices such as knowledge application, knowledge acquisition and knowledge dissemination also influenced SMEs' decision to adopt RosettaNet. The findings are useful for decision makers to understand how they can improve the adoption of RosettaNet in their organizations. Unlike previous studies, this research provided additional insights into what influence the adoption of RosettaNet by examining variables beyond the traditional technological attributes which have been studied quite extensively. By integrating SEM with artificial intelligence techniques such as neural network, this study also examined the non-linear and non-compensatory relationships involved in the adoption of RosettaNet.