Abstract
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
Highlights
IntroductionThe structural equation modeling with latent variables (SEM), within the modalities covariance based (CB-SEM) (Bagozzi, 1994) and support of statistical software packages AMOS, EQS, LISREL and others or partial least squares based (PLS-SEM) (Reinartz, Haenlein, & Henseler, 2009; Lohmöller, 1989) while using the SMART PLS software package, has been considered a quasi-standard statistical method for studies in the areas of management, market research, organizational behavior, management information systems, and consumer behavior (Hair, Ringle, & Sarstedt, 2011; Hair, Sarstedt, & Ringle, 2012; Henseler, Ringle, & Sinkovics, 2009), as well as in advertising research
The quantities shown on the arrows, external measurement model and structural model, represent the values of the Student T test of interrelations within latent variables and test of latent variables and their indicators, which indicates that the level of significance of cause-effect relations are at a level not less than 95%
The use of structural equation modeling with latent variables (SEM) in research in these areas can generate positive, relevant, and statistically significant results, such as the empirical study presented in this article, which deals with the study of cause-effect relationships of lean manufacturing, sustainable manufacturing, continuous improvement on operational efficiency, and environmental responsibility in the case of medium-sized and large discrete manufacturing plants of Mexico
Summary
The structural equation modeling with latent variables (SEM), within the modalities covariance based (CB-SEM) (Bagozzi, 1994) and support of statistical software packages AMOS, EQS, LISREL and others or partial least squares based (PLS-SEM) (Reinartz, Haenlein, & Henseler, 2009; Lohmöller, 1989) while using the SMART PLS software package, has been considered a quasi-standard statistical method for studies in the areas of management, market research, organizational behavior, management information systems, and consumer behavior (Hair, Ringle, & Sarstedt, 2011; Hair, Sarstedt, & Ringle, 2012; Henseler, Ringle, & Sinkovics, 2009), as well as in advertising research. These values were obtained by using the bootstrap algorithm in SMART PLS 2.0 with 5000 samples (Ringle et al, 2005). We have decided to include supplementary studies of multiple linear regression with two purposes: (1) to test the statistical power of the model PLS-SEM using the Fisher F that cannot be obtained directly from the software PLS SMART 2.0 (Ringle et al, 2005), and (2) to develop a model to predict the behavior of OE&ERI, from statistically significant indicators of latent variables LME, CIE, SME and OE&ERI, which will further enable a third purpose—to predict the latent construct OE&ERI using the unstandardized scores of relevant indicators for each construct.
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