Empirical Study of Additive Manufacturing Adoption in the Ethiopian Textile Industry: An Integrated DOI–TOE Structural Modeling Approach

The global textile and apparel sectors are undergoing rapid technological transformation driven by digital manufacturing tools such as 3D printing. However, in Ethiopia, the level of adoption of these technologies remains largely unexplored. To investigate the factors influencing 3D printing adoption intention, this study employed an integrated framework of Diffusion of Innovation (DOI) and technology–organization–environment (TOE). A structured questionnaire was distributed to 101 textile professionals, including managers, engineers, technicians, and designers, across textile factories in Addis Ababa and surrounding industrial zones. Data were analyzed using descriptive statistics and Partial Least Squares Structural Equation Modeling (PLS-SEM) with SmartPLS 4 and IBMSPSS 27. The results indicate that relative advantage (β = 0.189, p = 0.001), compatibility (β = 0.286, p < 0.001), environmental factors (β = 0.148, p < 0.001), organizational factors (β = 0.144, p = 0.006) and technology readiness (β = 0.217, p < 0.001) significantly influence adoption intention. Positively influenced adoption (β = 0.153, p = 0.011) was also observed in awareness and knowledge. In contrast to predictions, adoption intention was positively impacted by complexity and trialability (β = 0.108, p = 0.015; β = 0.096, p = 0.090, respectively). Nevertheless, trialability showed a positive but statistically non-significant relationship with adoption intention, and complexity had a positive and statistically significant effect on adoption intention; however, Hypothesis H4 was not supported, because the observed relationship was opposite to the hypothesized negative effect. These unexpected results would suggest that, rather than being obstacles to the adoption of technology, users in Ethiopia view technical complexity and trialability as desirable tasks. The complete structural model explained 87.7% of the variance in adoption intention (R2 = 0.877). These findings highlight the need for targeted training programs, financial incentives, pilot projects, and supportive government policies to promote 3D printing adoption in Ethiopia’s textile sector.