Leveraging technology and sustainability practices for smart mobility and green logistics: a dual-theoretical approach to adoption dynamics

This study explores the adoption of smart technologies in sustainable logistics and mobility systems, focusing on the operational and environmental impacts, barriers to adoption, and key facilitators. The research is grounded in two prominent theoretical frameworks: the Technology Acceptance Model (TAM) and institutional theory. These frameworks provide a comprehensive lens to examine both internal organisational factors (e.g. perceived usefulness and ease of use) and external institutional pressures (e.g. regulatory mandates and industry norms) that influence the adoption of technologies such as IoT, Big Data Analytics, AI/Machine Learning, blockchain, and Electric Vehicles (EVs). Using a survey-based methodology, data were collected from organisations within the logistics and mobility sectors. The findings reveal that IoT and Big Data Analytics are the most widely adopted technologies, driven by their perceived operational benefits and ease of use. However, adoption of AI/Machine Learning, blockchain, and EVs remains constrained by high costs and infrastructural limitations, particularly for small and medium-sized enterprises (SMEs). Regulatory incentives, stakeholder collaboration, and public-private partnerships were identified as key facilitators of adoption, highlighting the importance of addressing both financial and technical barriers.