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  1. Borsboom, D., Mellenbergh, G. J., & Van Heerden, J. (2004). The concept of validity. Psychological Review, 111(4), 1061–1071.

  2. Bollen, K. A. (1989). Structural equations with latent variables. New York, NY: Wiley.

  3. Bollen, K. A. & Bauldry, S. (2011) Three Cs in measurement models: Causal indicators, composite indicators, and covariates. Psychological Methods, 16, 265–284.

  4. Jöreskog, K. G., & Wold, H. (1982). The ML and PLS techniques for modeling with latent variables: Historical and comparative aspects. In H. Wold & K. G. Jöreskog (Eds.), Systems under indirect observation: Causality, structure, prediction, part I (pp. 263–270). Amsterdam, Netherlands: North Holland.

  5. Rigdon, E. E. (2012). Rethinking partial least squares path modeling: In praise of simple methods. Long Range Planning, 45, 341–358.

  6. Tenenhaus, M. (2008). Component-based structural equation modelling. Total Quality Management and Business Excellence, 19(7–8), 871–886.

  7. Jöreskog, K. G. (1970). Estimation and testing of simplex models. British Journal of Mathematical and Statistical Psychology, 23, 121–145.

  8. Bollen, K. A. (1996). An alternative two stage least squares (2SLS) estimator for latent variable equations. Psychometrika, 61, 109–121.

  9. Bollen, K. A. (2019). Model implied instrumental variables (MIIVs): An alternative orientation to structural equation modeling. Multivariate Behavioral Research, 54, 31–46.

  10. Dijkstra, T. K., & Henseler, J. (2015). Consistent partial least squares path modeling. MIS Quarterly, 39, 297–316.

  11. Hwang, H., Takane, Y., & Jung, K. (2017). Generalized structured component analysis with uniqueness terms for accommodating measurement error. Frontiers in Psychology, 8, 2137.

  12. Hwang, H., & Takane, Y. (2004). Generalized structured component analysis. Psychometrika, 69, 81–99.

  13. Hwang, H., & Takane, Y. (2014). Generalized structured component analysis: A component-based approach to structural equation modeling. New York, NY: Chapman and Hall/CRC Press.

  14. Lohmöller, J.-B. (1989). Latent variable path modeling with partial least squares. Heidelberg, Germany: Physica.

  15. Wold, H. (1982). Soft modeling: The basic design and some extensions. In K. G. Jöreskog & H. Wold (Eds.), Systems under indirect observation: Causality, structure, prediction, part II (pp. 1–54). Amsterdam, Netherlands: North Holland.

  16. Hair, J. F., & Sarstedt, M. (2019). Factors versus composites: Guidelines for choosing the right structural equation modeling method. Project Management Journal, 50, 619–624.

  17. 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, 39, 4–16.

  18. 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, 3998–4010.

  19. Cho, G., Hwang, H., Sarstedt, M., & Ringle, C. M. (2020). Cutoff criteria for overall model fit indexes in generalized structured component analysis. Journal of Marketing Analytics, 8, 189–202.

  20. Cho, G., Jung, K., & Hwang, H. (2019). Out-of-bag prediction error: A cross validation index for generalized structured component analysis. Multivariate Behavioral Research, 54, 505–513.

  21. Hair, J.F., Risher, J. J., Sarstedt, M., & Ringle, C.M. (2019). When to use and how to report the results of PLS-SEM. European Business Review, Vol. 31 No. 1, pp. 2–24.

  22. Sarstedt, M., Ringle, C.M., and Hair, J.F. (2017) Partial least squares structural equation modeling. In: C. Homburg, M. Klarmann, and A. Vomberg (eds.). Handbook of Market Research. Cham: Springer, Cham, pp. 1–40.

  23. Hair, J.F., Hult, G.T.M., Ringle, C.M. and Sarstedt, M. (2021). A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM), 3rd ed., Thousand Oaks: Sage.

  24. Cho, G., & Choi, J. Y. (2020). An empirical comparison of generalized structured component analysis and partial least squares path modeling under variance-based structural equation models. Behaviormetrika, 47, 243–272.

  25. Hair, J. F., Hult, G. T. M., Ringle, C. M., Sarstedt, M., & Thiele, K. O. (2017). Mirror, mirror on the wall: a comparative evaluation of composite-based structural equation modeling methods. Journal of the Academy of Marketing Science, 45(5), 616–632.

  26. Hwang, H., Cho, G., Jung, K., Falk, C., Flake, J., & Jin, M. (2021). An approach to structural equation modeling with both factors and components: Integrated generalized structured component analysis. Psychological Methods, 26(3), 273–294.

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