Mapping the Knowledge Landscape of Country-of-Origin Research: A Computational Review

This study employs advanced natural language processing (NLP) techniques to conduct a comprehensive computational literature review within the domain of country-or-origin (COO) research. The aim is to identify, cluster, and subsequently analyze relevant studies of COO research based on semantic similarity, uncovering the evolution and structure of extant research communities that developed over time. We apply cutting-edge NLP methodologies to extract and process textual data from scholarly articles spanning several decades. Through semantic clustering, we delineate prominent research themes and their development trajectories within the published COO research domain. Our findings reveal several distinct communities of studies and their evolution, highlighting pivotal studies and influential development trends. We conclude by proposing a future research agenda based on gaps identified through our computational analysis. This study contributes to both methodological advancements in computational literature reviews and substantive insights into the evolving landscape of COO research.