Modeling human–technology interaction in isolated, confined, and extreme environments: a PLS-SEM perspective for Human–Computer Interaction research

Human performance, psychological well-being, and behavioral adaptation in isolated, confined, and extreme (ICE) environments—such as intensive care units, remote healthcare settings, disaster zones, and space-analog missions—are increasingly mediated through digital and interactive technologies. Understanding how users perceive, experience, and sustain engagement with such systems requires methodological approaches capable of modeling complex psychosocial constructs under constrained conditions. This paper positions Partial Least Squares Structural Equation Modelling (PLS-SEM) as a robust and practical analytical framework for Human–Computer Interaction (HCI) research in ICE contexts. We review the methodological limitations of traditional statistical approaches and demonstrate how PLS-SEM enables the simultaneous modeling of latent experiential, cognitive, and behavioral variables central to technology use in extreme environments. Using an illustrative case involving a mobile health (mHealth) system designed for remote and resource-constrained settings, we examine how user experience, user satisfaction, perceived usefulness, perceived ease of use, and system quality influence behavioral intention. The findings indicate that experiential and affective factors—particularly user experience and satisfaction—play a dominant role in shaping continued usage intentions, while functional attributes exert their influence primarily through indirect pathways. By reframing PLS-SEM as an enabling methodology for ICE research, this paper contributes a theoretically grounded and practically applicable roadmap for researchers investigating human–technology interaction in extreme environments.