Determinants of human-machine interaction technology usage: An automated machine learning approach

The advent of Industry 4.0 technologies has reshaped modern manufacturing. Human-machine interaction (HMI) technologies are essential to this transformation, as they facilitate communication between people and machines, bridge the digital and physical worlds, improve decision-making, and increase overall productivity. However, the diffusion of these cutting-edge technologies varies greatly, possibly resulting in persistent geographical disparities over time. Moreover, our understanding of the factors determining the use of HMI technologies is still limited. Our goal is to investigate the factors that influence manufacturing firms’ use of these technologies, providing a comprehensive perspective. Combining insights provided by economic geography and innovation studies, we take a holistic approach that includes a wide range of technological, organizational, and environmental (TOE) factors. Using Automated Machine Learning (AML), we identify non-linear relationships between key predictors and the usage of HMI technology. Our analysis highlights the importance of geographical and organizational proximities in absorbing local external knowledge and coordinating long-distance knowledge pipelines alongside traditional factors influencing the rate of technology use. • Artificial Machine Learning is used to identify predictors of HMI technology usage. • Technological intensity of industries is the most influential predictor. • Heterogeneous effects of predictors of HMI technology usage are demonstrated. • Our analysis highlights the importance of geographical and organizational proximities. • Organizations absorb knowledge locally and via long-distance coordinated pipelines.