The impact of partnership attributes on EDI implementation success
Information and Management
An empirical validation of a neural network model for software effort estimation
Expert Systems with Applications: An International Journal
A Comprehensive Examination of Internet-EDI Adoption
Information Systems Management
Leveraging Standard Electronic Business Interfaces to Enable Adaptive Supply Chain Partnerships
Information Systems Research
Assimilation of Interorganizational Business Process Standards
Information Systems Research
Predicting tourism loyalty using an integrated Bayesian network mechanism
Expert Systems with Applications: An International Journal
Sharing knowledge in a supply chain using the semantic web
Expert Systems with Applications: An International Journal
International Journal of Information Management: The Journal for Information Professionals
Hi-index | 12.05 |
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.