UNSTRUCTURED BIG DATA ANALYTICS FOR AIR CARGO LOGISTICS MANAGEMENT

Abstract

Air cargo logistics plays a crucial role in speedy distribution enabling products to be promptly shipped between locations, but few studies have explored the business knowledge of air cargo logistics by unstructured big data. Hence, this study develops a hybrid data mining model to tackle critical issues of air cargo logistics as well as to generate operational strategies for those who would like to manage aviation logistics. Eight vital themes of air cargo logistics from the analytical results of the proposed hybrid data mining model are runway expansion, the air cargo platform, special logistics and certification, e-commerce logistics, cooperation, network expansion, market observation, and airplane conversion. Furthermore, innovation diffusion theory and resource dependence theory are incorporated into the empirical results of a hybrid data mining model to generate the following air cargo logistics strategies: "develop innovative aviation logistics in a suitable manner" and "establish strategic cooperation to enhance aviation logistics performance."