A Technology Acceptance Model to Explore Factors of Acceptance of Emotional Monitoring Technologies in the Logistics Industry

The integration of emotional monitoring technologies in logistics represents a significant leap in enhancing operational efficiency and worker well-being. As the logistics evolves, understanding the acceptance and adoption of such technologies becomes crucial. This study employs the extended Technology Acceptance Model (TAM) as a framework to investigate the determinants influencing logistics workers’ acceptance of emotional monitoring technologies, with a focus on facial expression recognition (FER), electroencephalography (EEG) and a bundle of physiological measures including Electromyography (EMG), Electrocardiography (ECG), and Galvanic Skin Conductance (GSC). We aim to explore how perceived usefulness (PU) and perceived ease of use (PEOU), core constructs of TAM, along with external variables belonging to social, individual and system levels, contribute to this acceptance. The questionnaire was administered to 45 warehouse operators from a logistics company in Italy. Results show that TAM models are able to explain the acceptance of these technologies in the examined working environment.