Improving productivity helps the supply chain to gain a competitive advantage, reduce operating costs, and enhance the customer experience. Past research has concluded that advanced technologies and digital adoption have a positive impact on productivity in large organisations, but there are still challenges to introducing new technologies and their benefits for logistics operations within SMEs. These challenges include competencies in management, access to resources, and leadership decision-making to invest and facilitate process reconfiguration, or redesign as a result of technology adoption.
This project aims to develop a deeper understanding of management competencies, decision making, and how organisations use their resources when investing in technology and leading process and people change. This is important in order to pursue productivity growth in logistics operations of SMEs. The project will focus on key factors for productivity growth in logistics operations through technology adoption and the effects of management competencies and strategic decisions.
It will be developed based on technology acceptance theories and their relationship with productivity growth and will employ a mixed-methods strategy to collect social big data and survey with the support of an industrial partner. Structural Equation Modelling (SEM) will then be employed to examine the relationships between organisational resources, management competencies, decision-making, and their effects on the long-term growth of productivity.
The project will contribute to the existing literature on technology adoption and productivity and bridge the current research gap surrounding supply chain technology advancements in SMEs. It will help enlarge the vision and insight of SME managers in the supply chain and other sectors for various operations. It will also provide a comprehensive understanding of how organisational resources as well as decision-makers’ knowledge and actions have influenced long-term productivity growth and improvement in logistics operations.
Lead researcher Jie Ma, Northumbria University