Delivering the finest products to consumers at the optimal time, in the perfect location, at the right price, and with the highest quality - these are well-known requirements for logistics and transportation. However, in a dynamic context, it is getting more difficult to achieve these needs. There is a transition from traditional to smart supply chains. The highly dynamic logistics market and the complexity of supply chains require new methods, and services. Aspects such as flexibility, adaptability and traceability are becoming more important and can only be accomplished through the integration of new technologies, especially Blockchain and the Internet of Things (IoT), and artificial intelligence( AI). Therefore, this paper aims to conduct a systematic review of the academic literature on Blockchain, IoT and AI in the context of Smart Logistics.
Social media has emerged as a pivotal marketing instrument and a platform that facilitates connections between consumers and businesses. In the intense market competition among peer airlines and high-speed rail services, the effective and efficient use of social media can provide a competitive edge for airlines in China. This paper constructs a partial least squares structural equation modelling (PLS-SEM) and examines the impact of the perceived ease of use of the social media platform, perceived usefulness of information, and marketing strategies on customer perceived value and purchase intention for Chinese airlines using multigroup analysis. The results suggest that social media marketing can positively influence on customers’ purchase intention, and perceived value plays a mediating role between social media marketing and customers’ purchase intention. Perceived usefulness of information is the most important factor contributing to the perceived value of airline products while marketing strategy is the most significant factor that can increase purchase intention. The research indicates a clear need to tailor marketing strategies for passengers based on factors such as gender, income, social media usage, and travel frequency.

