Methodological research on partial least squares structural equation modeling (PLS-SEM): An analysis based on social network approaches

Gohar F. Khan (Waikato Management School, University of Waikato, Hamilton, New Zealand)
Marko Sarstedt (Faculty of Economics and Management, Otto-von-Guericke-University Magdeburg, Magdeburg, Germany) (School of Business and Global Asia in the 21st Century (GA21) Research Platform, Monash University Malaysia, Subang Jaya, Malaysia)
Wen-Lung Shiau (School of Management, Zhejiang University of Technology, Hangzhou, China)
Joseph F. Hair (Faculty of Marketing and Quantitative Methods, University of South Alabama, Mobile, Alabama, USA)
Christian M. Ringle (Department of Management Science and Technology, Hamburg University of Technology (TUHH), Hamburg, Germany) (Waikato Management School, University of Waikato, Hamilton, New Zealand)
Martin P. Fritze (Faculty of Management, Economics and Social Sciences, University of Cologne, Cologne, Germany)

Internet Research

ISSN: 1066-2243

Publication date: 3 June 2019



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.


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.


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.


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.



Even though this paper does not use the statistical software SmartPLS (, Ringle acknowledges a financial interest in SmartPLS.


Khan, G.F., Sarstedt, M., Shiau, W.-L., Hair, J.F., Ringle, C.M. and Fritze, M.P. (2019), "Methodological research on partial least squares structural equation modeling (PLS-SEM): An analysis based on social network approaches", Internet Research, Vol. 29 No. 3, pp. 407-429.

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