Bayesian networks are limited in differentiating between causal and spurious relationships among decision factors. Decision making without differentiating the two relationships cannot be effective. To overcome this limitation of Bayesian networks, this study proposes linking Bayesian networks to structural equation modeling (SEM), which has an advantage in testing causal relationships between factors. The capability of SEM in empirical validation combined with the prediction and diagnosis capabilities of Bayesian modeling facilitates effective decision making from identification of causal relationships to decision support. This study applies the proposed integrated approach to decision support for customer retention in a virtual community. The application results provide insights for practitioners on how to retain their customers. This research benefits Bayesian researchers by providing the application of modeling causal relationships at latent variable level, and helps SEM researchers in extending their models for managerial prediction and diagnosis.
All Science Journal Classification (ASJC) codes
- Computer Science(all)
- Modelling and Simulation
- Management Science and Operations Research
- Information Systems and Management