The value of the blockchain platform for shipping logistics considering the perceived utility of consumers

Abstract

Blockchain has been widely used to trace logistics information from sources to destinations in shipping logistics practices. Motivated by market practices, this study intends to discuss the potential application value of blockchain platforms in shipping logistics, considering the perceived utility of consumers. For this purpose, to investigate the shipping logistics supply chain ( SLSC ) of a shipping company (SC) and a freight forwarding company (FFC), in which FFC decides whether to introduce a blockchain platform and its introduction channel. Specifically, three channel models are considered: Model T (i.e., traditional logistics supply chain channel), Model B (i.e., FFCs applied a blockchain platform via a self-built platform channel), and Model J (i.e., FFCs applied a blockchain platform via joining a third-party platform channel). Meanwhile, the Nash and Stackelberg game are used to characterize the companies’ equilibrium outcomes under different power structures. By comparing these three modes, some interesting results are found: consumer perception of the level of service and privacy protection and consumer sensitivity to price has critical effects on the value of blockchain platforms. Results also show that the blockchain platform does not always motivate the members of the SLSC to become better. Whether the power structure is symmetrical or asymmetrical, FFC adopts Model B instead of Model J, in which the blockchain platform can help FFC increase profits, and the value of blockchain is significant. Moreover, the FFC can obtain at least approximately 78% more benefit when the power structure is symmetrical than asymmetrical. However, the value of blockchain platforms to SC is not necessarily significant. The value of the blockchain platform for SC is significant only in Model B with the power structure asymmetric. Therefore, the value of the blockchain platform in Model B with the power structure asymmetrical for both FFCs and SCs is significant, achieving a win-win situation. Also, an extensive numerical analysis showing the optimal channel strategy in the three models generated additional management insights.

Trusted Computing and Privacy Protection of Computer Internet of Things Nodes Based on Deep Fuzzy Control of Dynamic Learning Rate

“…Dynamic learning means that the system can guarantee that the information at a certain location cannot be matched with the exact individual or organization through reasoning attacks (Chen & Li, 2023). Dynamic node learning means that when the position of a mobile user cannot be distinguished from the positions of other (k-1) users, the position satisfies the dynamic node learning.…”