Integrated usage of artificial intelligence, blockchain and the internet of things in logistics for decarbonization through paradox lens

The objective of this study was to identify, assess, and prioritize resolution strategies to address paradoxes associated with the integrated use of artificial intelligence (AI), blockchain, and the Internet of Things (IoT) in logistics for decarbonization. To achieve these objectives, a four-phase mixed-methods approach was employed, involving the identification, prioritization, and verification of resolution strategies for their significance in managing paradoxes. A panel of 10 experts with experience in adopting AI, blockchain, and IoT in logistics participated in the study. The study identified ten key resolution strategies: education & training, coordination & collaboration, government regulations & support, standardized data format and protocol, modular architecture, interoperability standard, process optimization, security auditing, management participation, and man-machine interactions. Using an integrated multicriteria decision-making approach, the study presented compromised rank of these strategies based on their effectiveness in addressing paradoxes. The most significant resolution strategy identified was modular architecture. This was followed by a second compromise solution set of interoperability standards, process optimization, management participation, coordination & collaboration, and man-machine interactions, and a third compromise solution set including education & training, government regulation & support, standardized data format, and security auditing. The derived solutions were then verified by logistics domain experts. This study provided the first empirical investigation of paradox theory related to the adoption of AI, blockchain, and IoT for decarbonization in logistics. Overall, the study enhanced the understanding of competing demands, tensions, and complexities involved in adopting digital technologies for decarbonization using paradox theory.