Utilizing text analysis in systematic review design: Perceptual and cognitive barriers to adoption of robotic and automated systems inconstruction

Construction, the last major analog and craft manufacturing industry, is showing early signs of industrialization through the emergence of new robotic and automated systems that can perform construction tasks in situ. While much is understood about the technical and economic challenges to be overcome for widespread adoption of robotics, less is known about the human barriers to adoption, and much less is summarized. Considering the amount of human cooperation required by existing robotic applications, a comprehensive review of barriers that are cognitive or perceptual in nature using a systematic literature assessment methodology is warranted. However, such a review is not straightforward to design. While matters of cognition and perception as pertinent to construction and automation may be queried directly from the literature, there is no certainty that a review based on directly querying abstract phenomena (i.e., perception) could be comprehensive. Thus, systematically reviewing this topic calls for a robust methodology for the design of database queries. In this paper, we perform text analysis with the quanteda package for R in order to (1) understand the language composition of an initial review corpus, and (2) with that understanding design further queries to capture additional articles otherwise not possible through standard query design. Findings indicate that performing text analysis on a systematic review design can produce valuable insight into a review corpus and inform queries that capture additional unique literature relevant to the review.