Methodological research on partial least squares structural equation modeling (PLS-SEM)

Khan, Gohar F.; Sarstedt, Marko ; Shiau, Wen-Lung; Hair, Joseph F.; Ringle, Christian M. und Fritze, Martin P. (2019): Methodological research on partial least squares structural equation modeling (PLS-SEM). In: Internet Research, Bd. 29, Nr. 3: S. 407-429

Abstract

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.

• Dokumententyp: Zeitschriftenartikel
• Keywords: Structural equation modeling; social network analysis; Partial least squares; PLS-SEM; Co-authership networks; knowledge networks
• Fakultät: Betriebswirtschaft > Institut für Marketing
• Themengebiete: 300 Sozialwissenschaften > 330 Wirtschaft
• ISSN: 1066-2243
• Sprache: Englisch