This paper develops an empirically validated conceptual framework, which redefines construction inspection technology selection as a context-specific optimization problem. The framework models inspection performance as the generation of interactions between technological capabilities and environmental constraints, rather than universal technical superiority. An experiment was conducted in a mixed-factorial design with 72 observations at different levels of ecological fidelity. The data were analyzed using GLMMs with a negative binomial distribution with appropriate error counts. The findings indicate a significant main effect of technological medium (χ² (4) = 85.23, p < 0.001) and Interactions between medium and experiment Type (χ² (2) = 11.47, p = 0.003). Mixed reality (HoloLens) was much more effective than paper-based methods in laboratory (IRR = 0.28, p < 0.001) and realistic site (IRR = 0.36, p = 0.005) conditions, but remained superior to tablet-derived methods in all settings (Laboratory: IRR = 0.54, p < 0.001; Realistic site: IRR = 0.54, p =0.005). Digital inspection methods reduced inspection times by 60–78%. The research provided a tested, computationally based approach to aligning inspection technology in operational settings. Results revealed that strategic alignment between technology and the situation is the most effective way to achieve optimal performance. This provides construction managers with evidence-based decision support for quality control investment and implementation.

Mixed reality, Computational framework, Rebar inspection, Digital construction, GLMM, Context-aware systems, BIM