Call for Papers on "Advances in Composite-based Structural Equation Modeling"

Guest Editors:
Heungsun Hwang, McGill University (heungsun.hwang@mcgill.ca)
Marko Sarstedt, Otto-von-Guericke-University Magdeburg (marko.sarstedt@ovgu.de)

Motivation and Aim of the Special Issue

Structural equation modeling (SEM) has become a quasi-standard tool for analyzing complex inter-relationships between observed and latent variables. Two conceptually different approaches to SEM have been proposed: factor- vs. component-based SEM. Factor-based SEM approximates latent variables by common factors as in common factor analysis, whereas component-based SEM regards them as weighted composites of observed variables as in multivariate statistics such as canonical correlation analysis and principal component analysis. Factor-based SEM is represented by covariance structure analysis, whereas composite-based SEM includes generalized structured component analysis (GSCA; Hwang and Takane 2004), partial least squares (PLS; Lohmöller 1989), regularized generalized canonical correlation analysis (Tenenhaus and Tenenhaus 2011), and several others. Although factor-based SEM remains prevalent in practice, there have been both methodological and empirical advances in component-based SEM (e.g., Hwang et al., 2010; Suk and Hwang, 2016; Schlittgen et al., 2016), contributing to its growing popularity in recent years.

While there has always been a controversy between factor-based and composite-based approaches to SEM, recently, the tenor of this controversy has become more intense. Whereas some researchers strongly advocate the use of component-based SEM (Sarstedt et al., 2016), others believe that this approach should be abandoned (Rönkkö et al., 2016). The debates also led to a diversification of the composite-based SEM community, with differing viewpoints on the nature of measurement, the role of model fit, and the methods’ scope of application. For example, Dijkstra and Henseler (2015) introduced consistent PLS and Hwang et al. (2017) proposed GSCA with measurement errors incorporated, called GSCAM, which aim to estimate the parameters of factor-based SEM via PLS or GSCA. A divergent stream of research questions the universal validity of the factor model, instead emphasizing PLS’s nature as a composite-based approach to SEM (e.g., Rigdon, 2012; Rigdon et al., 2017). Similarly, whereas some researchers stress the need to consider model fit metrics (Henseler et al., 2016), others emphasize statistics for assessing a model’s out-of-sample predictive accuracy (Shmueli et al., 2016).

In light of these controversies and debates, composite-based SEM is at the crossroads. The following years will show under which conditions composite-based-SEM methods will routinely be used and how sustainable their current popularity will be. With these developments in mind, this special issue of Behaviormetrika seeks to serve as a platform for advancing and furthering our understanding of composite-based SEM methods. Topics of interest of the special issue include, but are not limited to the following:

  • Metrics for goodness-of-fit assessment
  • Predictive model evaluation metrics
  • Model comparison metrics
  • Measurement issues in composite-based SEM
  • Analysis of complex model relationships involving nonlinear effects, multiple mediation, and/or moderated mediation
  • Invariance assessment and multigroup analysis
  • Latent class analysis
  • Common method bias assessment
  • Endogeneity assessment and treatment
  • Longitudinal data analysis

We look forward to receiving your papers!

Deadlines

  • Submission due date: March 1st , 2019
  • First round of reviews: May 15th , 2019
  • Revisions due: July 15th , 2019
  • Second round decision: September 15th , 2019
  • Revisions due: November 15th , 2019
  • Final editorial decision: December 15th , 2019
  • Publication of the special issue: Late 2020

Please see the journal’s author guidelines for more details and submission instructions. Submissions to Behaviormetrika are made using Editorial Manager. Please specify that you submit your paper to the special issue “Advances in Composite-based Structural Equation Modeling” in the cover letter.

Contact Details

Queries regarding the special issue can be directed to Heungsun Hwang (heungsun.hwang@mcgill.ca) or Marko Sarstedt (marko.sarstedt@ovgu.de).

References

  • Dijkstra, T. K., & Henseler, J. (2015). Consistent partial least squares path modeling. MIS Quarterly, 39(2), 297–316.
  • Henseler, J., Hubona, G., & Ray, P. (2016). Using PLS path modeling in new technology research: Updated guidelines. Industrial Management & Data Systems, 115(1), 2–20.
  • Hwang, H., Takane, Y. (2004). Generalized structured component analysis. Psychometrika, 69(1), 81–99.
  • Hwang, H., Ho, M.-H. R., & Lee, J. (2010). Generalized structured component analysis with latent interactions. Psychometrika, 75(2), 228–242.
  • Hwang, H., Takane, Y., & Jung, K. (2017). Generalized structured component analysis with uniqueness terms for accommodating measurement error. Frontiers in Psychology, 8, 2137.
  • Lohmöller, J.-B. (1989). Latent variable path modeling with partial least squares. Berlin: Springer.
  • Rigdon, E. E. (2012). Rethinking partial least squares path modeling: in praise of simple methods. Long Range Planning, 45(5–6), 341–358.
  • Rigdon, E. E., Sarstedt, M., & Ringle, C. M. (2017). On comparing results from CB-SEM and PLS-SEM. Five perspectives and five recommendations. Marketing ZFP – Journal of Research and Management, 39(3), 4–17.
  • Rönkkö, M., McIntosh, C. N., Antonakis, J., & Edwards, J. R. (2016). Partial least squares path modeling: time for some serious second thoughts. Journal of Operations Management, 47-48(November), 9–27.
  • Sarstedt, M., Hair, J. F., Ringle, C. M., Thiele, K. O., & Gudergan, S. P. (2016). Estimation issues with PLS and CBSEM: where the bias lies! Journal of Business Research, 69(10), 3998–4010.
  • Schlittgen, R., Ringle, C. M., Sarstedt, M., & Becker, J.-M. (2016). Segmentation of PLS path models by iterative reweighted regressions. Journal of Business Research, 69(10), 4583–4592.
  • Shmueli, G., Ray, S., Velasquez Estrada, J. M., & Chatla, S. B. (2016). The Elephant in the Room: Evaluating the Predictive Performance of PLS Models. Journal of Business Research, 69(10), 4552–4564.
  • Suk, H. W., & Hwang, H. (2016). Functional generalized structured component analysis. Psychometrika, 81(4), 940–968.
  • Tenenhaus, A., & Tenenhaus, M. (2011). Regularized generalized canonical correlation analysis. Psychometrika, 76(2), 257–284.