• Carlos Monge Perry Universidad Autónoma de Nuevo Léon (UANL), Mexico
  • Jesús Cruz Álvarez Universidad Autónoma de Nuevo Léon (UANL), Mexico
  • Jesús Fabián López Universidad Autónoma de Nuevo Léon (UANL), Mexico
Keywords: structural equations modeling, multiple partial least squares, PLS, SEM


Structural equation modeling (SEM) has traditionally been deployed in areas of marketing, consumer satisfaction and preferences, human behavior, and recently in strategic planning. These areas are considered their niches; however, there is a remarkable tendency in empirical research studies that indicate a more diversified use of the technique.  

This paper shows the application of structural equation modeling using partial least square (PLS-SEM), in areas of manufacturing, quality, continuous improvement, operational efficiency, and environmental responsibility in Mexico’s medium and large manufacturing plants, while using a small sample (n = 40).  The results obtained from the PLS-SEM model application mentioned, are highly positive, relevant, and statistically significant. Also shown in this paper, for purposes of validity, reliability, and statistical power confirmation of PLS-SEM, is a comparative analysis against multiple regression showing very similar results to those obtained by PLS-SEM.  This fact validates the use of PLS-SEM in areas of untraditional scientific research, and suggests and invites the use of the technique in diversified fields of the scientific research


Bagozzi, R., (1994). Structural equation models in marketing research: Basic principles. In P. Richard, & P. Bagozzi (Eds.), Principles of Marketing Research. Oxford, UK: Blackwell.

Coelho, P., & Henseler, J. (2012). Creating customer loyalty through service customization. European Journal of Marketing, 46(3), 331-356.

Hair, J., Ringle, C. & Sarstedt, M. (2011). PLS-SEM: Indeed a silver bullet. Journal of Marketing Theory and Practice, 19(2), 139-151.

Hair, J., Sarstedt, M., & Ringle, C. (2012). An assessment of the use of partial least squares structural equation modeling in marketing research. Journal of the Academy of Marketing Science, 40, 414-433.

Henseler, J., Ringle, C., & Sarstedt, M. (2011). Using partial least squares path modeling in advertising research: Basic concepts and recent issues. In Handbook of research on international advertising, Northampton, USA: Edward Elgar.

Henseler, J., Ringle, C., & Sinkovics, R. (2009). The use of partial least squares path modeling in international marketing. Advances in International Marketing, 20, 277-319.

Hölck, C, Ringle, C., & Sarstedt, M. (2010). Management of multi-purpose stadiums: Importance and performance measurement of services interfaces. International Journal Services Technology and Management, 14(2/3), 188-204.

Lee, S. (2012). The impact of manufacturing practices on operational performance. Review of Business Research, 12(5), 184-189.

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

Monge, C., Cruz, J., & López, F. (2013). Impacto de la manufactura esbelta, manufactura sustentable y mejora continua en la eficiencia operacional y responsabilidad ambiental en México. Información Tecnológica, 24(4), 15-31.

Reinartz, W., Haenlein, M., & Henseler, J. (2009). An empirical comparison of efficacy of covariance-based and variance-based SEM. International Journal of Research in Marketing, 26, 332-344.

Ringle, C., Wende, S., & Will, A. (2005). Smart PLS 2.0M3; Next generation path modeling software. Retrieved from:

Vinohd, S., & Dino, J. (2012). Structural equation modeling of lean manufacturing practices. International Journal of Production Research, 50(6), 1598-1607.