Methodological research on partial least squares structural equation modeling (PLS-SEM)
By Gohar Feroz Khan, Marko Sarstedt, Wen-Lung Shiau, Joseph F. Hair, Christian M. Ringle, Martin Paul Fritze. Internet Research, 2019
376 citations recorded by the source corpus.
Purpose The purpose of this paper is to explore the knowledge infrastructure of methodological research on partial least squares structural equation modeling (PLS-SEM) from a network point of view. The analysis involves the structures of authors, institutions, countries and co-citation networks, and discloses trending developments in the field. Design/methodology/approach Based on bibliometric data downloaded from the Web of Science, the authors apply various social network analysis (SNA) and visualization tools to examine the structure of knowledge networks of the PLS-SEM domain. Specifically, the authors investigate the PLS-SEM knowledge network by analyzing 84 methodological studies published in 39 journals by 145 authors from 106 institutions. Findings The analysis reveals that specific authors dominate the network, whereas most authors work in isolated groups, loosely connected to the network’s focal authors. Besides presenting the results of a country level analysis, the research also identifies journals that play a key role in disseminating knowledge in the network. Finally, a burst detection analysis indicates that method comparisons and extensions, for example, to estimate common factor model data or to leverage PLS-SEM’s predictive capabilities, feature prominently in recent research. Originality/value Addressing the limitations of prior systematic literature reviews on the PLS-SEM method, this is the first study to apply SNA to reveal the interrelated structures and properties of PLS-SEM’s research domain.
Subfields: Experimental and Cognitive Psychology, Statistical and Nonlinear Physics
View Paper: https://www.semanticscholar.org/paper/6c6d242d7928f7af2d650f23512b7be43c70b5a7
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In Experimental and Cognitive Psychology
In Statistical and Nonlinear Physics
Citations in the corpus
- Factors Influencing Entrepreneurial Intention of University Students in China: Integrating the Perceived University Support and Theory of Planned Behavior (2021, neutral): “In contrast to conventional covariance-based SEM, whose use of maximum-likelihood estimation requires assumptions of normal distribution, SEM–PLS requires a relative sample size to estimate a complex model [10,74,75].”
- Cultural intelligence, global mindset, and cross-cultural competencies: a systematic review using bibliometric methods (2020, methodology): “…for measuring the appeal of a topic in research literature over time is Kleinberg’s (2003) burst detection algorithm, which is well recognised on different fields using bibliometric methods (e.g., Khan et al., 2019; Zhu et al., 2019; Chen et al., 2018; Song et al., 2016; Guo et al., 2011).”
- When to use and how to report the results of PLS-SEM (2019, background): “In the past decade, there has been a considerable debate about which situations are more or less appropriate for using PLS-SEM (Goodhue et al., 2012; Marcoulides et al., 2012; Marcoulides and Saunders, 2006; Rigdon, 2014a; Henseler et al., 2014; Khan et al., 2019).”
- Structural model robustness checks in PLS-SEM (2020, background): “In addition, the many controversial debates about the method’s merits and limitations, witnessed in different research fields (Khan et al., 2019), have increased awareness of it (Petter, 2018).”