A BAYESIAN NETWORKS APPROACH TO MODELING FINANCIAL RISKS OF E-LOGISTICS INVESTMENTS

To evaluate whether the investments of e-logistics systems may increase financial risks, models of Bayesian networks are constructed in this study with the mechanism of structural learning and parameter learning. Empirical findings from the transport and logistics sectors suggest that the e-logistics investments generally do not increase the financial risks of companies except the implementation of computer aided picking systems and radio frequency identification. Meanwhile, only the investment of enterprise resource planning can reduce the financial risks and enhance the profitability at the same time. Generally speaking, most advanced e-logistics investments do not yield financial advantages for the transport and logistics companies from the perspective of Bayesian inference. Empirical study based on the proposed models also demonstrates the practicability of Bayesian models.