Abstract
The integration of small and medium sized enterprises into global value chains is a complex phenomenon, requiring a holistic systems approach for comprehension purposes. Although literature provides widely available qualitative information on the subsystems constituting global value chains, a research gap exists regarding unpacking and utilizing this information for quantitative modeling, in a structured construct. This study thus establishes a systems-based approach, utilizing a 2-tier structured protocol of qualitative to quantitative methods, to extract key variables and corresponding interdependencies required to develop a global value chain model. The first tier utilizes a systematic literature review to qualitatively extract 12 key subsystems from Scopus for the duration 2015 to 2023. In the second tier, machine learning techniques are used via Word2Vector analysis to quantitatively map the respective subsystems' variables. The results indicate that this method is critical in characterizing essential subsystems that constitute a holistic global value chain model system, and the significance of this study lies in presenting a protocol towards the transformation of extant literature into structured data to support evidence-based decision-making for global value chains.