AI Technology Transfer: A Five-Dimension Taxonomy and Absorptive Capacity Framework

Artificial intelligence (AI), a general-purpose technology (GPT) with infrastructure and human-capital demands exceeding prior information technology, challenges existing technology transfer theory in ways that current frameworks do not adequately capture. This paper extends the canonical technology transfer taxonomy to the AI era through a five-dimension framework — Technoware-AI, Humanware-AI, Orgaware-AI, Inforware-AI, and Ethiware-AI — introducing Ethiware-AI as a novel construct addressing algorithmic accountability, bias, data sovereignty, and cultural alignment, dimensions absent from prior transfer taxonomies. Drawing on absorptive capacity theory, and in particular Zahra and George's (2002) distinction between potential and realized absorptive capacity, the paper develops six formal theoretical propositions specifying the conditions under which AI transfer generates realized absorptive capacity (RACAP), each stated to be falsifiable and accompanied by candidate operationalizable measures. The framework's application is illustrated through four transfer models — India, Rwanda/Kenya, multilateral and open-source, and Chinese Belt and Road — offered as theoretical illustration rather than empirical validation, that show how the propositions' logic plays out across contrasting transfer mechanisms in developing-economy contexts. The paper contributes to technology transfer theory by extending it to a technology whose properties violate the artifact-transfer assumption underlying prior frameworks, and to absorptive capacity theory by identifying Ethiware-AI as a boundary condition on RACAP sustainability not previously theorized.