Robotic technology adoption in prefabricated and modular construction: integrating TOE factors and stakeholder interaction quality

Purpose The prefabricated and modular construction context involves dynamic, multi-actor interactions that shape technology adoption. Yet, these critical dynamics are often overlooked in conventional adoption models. This research, therefore, has a twofold purpose: first, to establish a foundational Technology-Organization-Environment (TOE) model that identifies core factors influencing robotic adoption in prefabricated and modular construction; and second, to advance theory by investigating the moderating effects of SIQ in the effect mechanism of TOE factors on robotic adoption. Design/methodology/approach A multi-stage mixed-methods sequential design was used to develop and test the framework. Initial factors were systematically identified via bibliometric analysis and refined through expert interviews. The resulting hypotheses were then tested on survey data from prefabricated and modular construction contractors through a comparative analysis of a base TOE model against the proposed, extended TOE-S model, using Partial Least Squares Structural Equation Modeling (PLS-SEM). Findings The analysis first confirmed several TOE factors, such as Prefabricated Characteristics (as a key driver) and Compatibility Issues (as a key barrier), as significant direct predictors of adoption. Crucially, the extended TOE-S model (R2 = 0.549, Q2 = 0.377) demonstrated a substantial and statistically significant increase in explanatory power over the base model (R2 = 0.451, Q2 = 0.323). Importantly, SIQ has no direct effect on adoption but functions as a powerful negative moderator, amplifying the deterring impact of Compatibility Issues. Originality/value This research integrating SIQ into the TOE framework and empirically demonstrates its moderating role in contractors' adoption of construction robotics. Beyond static factor analysis, the TOE-S conceptualizes adoption as a dynamic, socially mediated process and characterizes SIQ not as a direct driver or a simple facilitator of robotic adoption, but as a context shaper that modulates how other organizational and technological factors influence adoption outcomes. The model also uncovers a counterintuitive causal mechanism: high-quality collaboration increases transparency and risk visibility, which in turn encourages more cautious and realistic adoption decisions.