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A concept analysis of methodological research on composite-based structural equation modeling: bridging PLSPM and GSCA

Abstract

Partial least squares path modeling (PLSPM) and generalized structural component analysis (GSCA) constitute composite-based structural equation modeling (SEM) methods, which have attracted considerable interest among methodological and applied researchers alike. Methodological extensions of PLSPM and GSCA have appeared at rapid pace, producing different research streams with different foci and understandings of the methods and their merits. Based on a theoretical comparison of PLSPM and GSCA in terms of model specification, parameter estimation, and results evaluation, we apply a text analytics technique to identify links between dominant topics in methodological research on both methods. We find that researchers have put effort on clearly distinguishing factor and composite models and their implications for the methods’ performance. We also identify an increasing interest in more complex model specifications such as mediating effects and higher-order models. The evidence of converging and diverging PLSPM and GSCA streams of research points out opportunities for advancing the evolution of composite-based SEM.

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Notes

  1. 1.

    Throughout the manuscript, we use the terms composites and components interchangeably.

  2. 2.

    Note that the original presentation of the PLSPM algorithm also considers a third stage, which deals with the estimation of location parameters of the indicators and latent variables. We refer to Lohmöller et al. (1989) and Tenenhaus et al. (2005) for a detailed description of the PLSPM algorithm (also see Hair et al. 2017b; Hwang et al. 2015; Wold 1982).

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Acknowledgements

Even though this research does not explicitly refer to the use of the statistical software SmartPLS (http://www.smartpls.com), Ringle acknowledges a financial interest in SmartPLS.

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Hwang, H., Sarstedt, M., Cheah, J.H. et al. A concept analysis of methodological research on composite-based structural equation modeling: bridging PLSPM and GSCA. Behaviormetrika 47, 219–241 (2020). https://doi.org/10.1007/s41237-019-00085-5

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Keywords

  • Composites
  • GSCA
  • Leximancer
  • PLSPM
  • Scientometrics
  • Text analytics