A SEM-neural network approach for understanding determinants of interorganizational system standard adoption and performances

This research examines the adoption of an interorganizational system standard and its benefits by using RosettaNet as a case study. A comprehensive research framework derived from institutional theory and a Technology- Organization-Environment model was developed for this research. Data were collected from a sample of 212 Malaysian manufacturing firms. A multi-state analytic approach was proposed whereby the research model was tested using structural equation modeling (SEM), and the results from SEM were used as inputs for a neural network model for predicting RosettaNet adoption. The results showed that factors related to the environment, the Interorganizational Relationship (IOR) and an information sharing culture have a positive influence on the adoption of RosettaNet. In terms of organizational factors, top management support was found to have a positive and significant relationship with RosettaNet adoption. The results also showed that RosettaNet adoption has a significant and positive relationship with organizational performance. The research findings can also assist managerial decision making for those organizations planning to adopt RosettaNet. This research reduces the previous research gap by advancing understanding on the relationship of adoption factors and RosettaNet adoption, and extends previous approaches on RosettaNet adoption by investigating the relationships between RosettaNet adoption and organizational performance. Improved existing technology adoption methodology was achieved by integrating both SEM and neural network for examining the adoptions of RosettaNet.