During this era of transition from Industry 4.0 to 5.0, technological success depends not only on innovation but increasingly on the ability of workers to appropriate and adapt to industrial transformations. As Industry 5.0 places humans at the center of production systems, it becomes crucial to move beyond traditional approaches—namely individual-level acceptance models (TAM/TAM3, UTAUT) and technology-driven Industry 4.0 roadmaps (automation, IoT, AI)—to fully capture the cognitive, organizational, and emotional dimensions of human–machine interaction. In this perspective, we propose an integrative theoretical model that combines insights from cognitive ergonomics, technological stress management, and the appropriation dynamics of digital tools. This model aims to identify the concrete levers driving acceptance, or conversely, resistance, in highly automated industrial environments. Our refection is grounded in an in-depth field immersion within a tire manufacturing plant undergoing a major modernization phase, where human-machine interfaces, cognitive workload, organizational adaptation, and employee well-being emerge as key factors. This empirical context illustrates how technological changes, often framed as purely technical improvements, must be reconsidered as profound human challenges. By reframing technological acceptance not as a simple adoption of new tools but as a reconstruction of work relations and practices, this theoretical framework seeks to contribute to a more sustainable, human-centered industrial transition, capable of creating durable value. The remainder of the paper is structured as follows. Section 2 reviews the state of the art on industrial modernization and technology acceptance, motivates the research question, and maps the three dimensions introduced in Fig. 1. Section 3 presents the proposed methodological framework and develops the Human–Technology–Organization (H-T-O) Acceptance Model, detailing its constructs and mediating mechanisms (Fig. 3), together with the mixed-methods design for its future empirical validation (Fig. 2) and the measurement instruments used (Fig. 4). Section 4 concludes by summarizing contributions, limitations, and outlining next steps toward multi-site validation and practical recommendations.
Capet et al. (Thu,) studied this question.