Structural equation modeling, often referred to as SEM, is a well‐established, covariance‐based multivariate method used in Human Resource Development (HRD) quantitative research. In some research contexts, however, the rigorous assumptions associated with covariance‐based SEM (CB‐SEM) limit applications of the method. An emergent complementary SEM approach, partial least squares structural equation modeling (PLS‐SEM), is a variance‐based SEM method that provides valid solutions and overcomes several limitations associated with CB‐SEM. Despite PLS‐SEM's increasing popularity in many social sciences disciplines, the method has yet to gain traction in the field of HRD. An accessible overview of the method, including potential advantages for HRD research and extant methodological advancements, is provided in this article with the goal of encouraging productive dialogue in the field of HRD surrounding the PLS‐SEM approach. We present an emergent analytical tool for quantitative HRD research, offer practical guidelines for researchers to consider when selecting a SEM method, and clarify assessment stages and up‐to‐date evaluation criteria through an illustrative example.

Background: An abundance of rapidly accumulating scientific evidence presents novel opportunities for researchers and practitioners alike, yet such advantages are often overshadowed by resource demands associated with finding and aggregating a continually expanding body of scientific information. Across social science disciplines, the use of automation technologies for timely and accurate knowledge synthesis can enhance research translation value, better inform key policy development, and expand the current understanding of human interactions, organizations, and systems. Ongoing developments surrounding automation are highly concentrated in research for evidence-based medicine with limited evidence surrounding tools and techniques applied outside of the clinical research community. Our objective is to conduct a living systematic review of automated data extraction techniques supporting systematic reviews and meta-analyses in the social sciences. The aim of this study is to extend the automation knowledge base by synthesizing current trends in the application of extraction technologies of key data elements of interest for social scientists. Methods: The proposed study is a living systematic review employing a partial replication framework based on extant literature surrounding automation of data extraction for systematic reviews and meta-analyses. Protocol development, base review, and updates follow PRISMA standards for reporting systematic reviews. This protocol is preregistered in OSF: (Semi)Automated Approaches to Data Extraction for Systematic Reviews and Meta-Analyses in Social Sciences: A Living Review Protocol on August 14, 2022. Conclusions: Anticipated outcomes of this study include: (a) generate insights supporting advancement in transferring existing reliable methods to social science research; (b) provide a foundation for protocol development leading to enhancement of comparability and benchmarking standards across disciplines; and (c) uncover exigencies that spur continued value-adding innovation and interdisciplinary collaboration for the benefit of the collective systematic review community.

Background: An abundance of rapidly accumulating scientific evidence presents novel opportunities for researchers and practitioners alike, yet such advantages are often overshadowed by resource demands associated with finding and aggregating a continually expanding body of scientific information. Across social science disciplines, the use of automation technologies for timely and accurate knowledge synthesis can enhance research translation value, better inform key policy development, and expand the current understanding of human interactions, organizations, and systems. Ongoing developments surrounding automation are highly concentrated in research for evidence-based medicine with limited evidence surrounding tools and techniques applied outside of the clinical research community. Our objective is to conduct a living systematic review of automated data extraction techniques supporting systematic reviews and meta-analyses in the social sciences. The aim of this study is to extend the automation knowledge base by synthesizing current trends in the application of extraction technologies of key data elements of interest for social scientists. Methods: The proposed study is a living systematic review employing a partial replication framework based on extant literature surrounding automation of data extraction for systematic reviews and meta-analyses. Protocol development, base review, and updates follow PRISMA standards for reporting systematic reviews. This protocol is preregistered in OSF: (Semi)Automated Approaches to Data Extraction for Systematic Reviews and Meta-Analyses in Social Sciences: A Living Review Protocol on August 14, 2022. Conclusions: Anticipated outcomes of this study include: (a) generate insights supporting advancement in transferring existing reliable methods to social science research; (b) provide a foundation for protocol development leading to enhancement of comparability and benchmarking standards across disciplines; and (c) uncover exigencies that spur continued value-adding innovation and interdisciplinary collaboration for the benefit of the collective systematic review community.