Introduction to SmartPLS software

SmartPLS is a software with graphical user interface for variance-based structural equation modeling (SEM) using the partial least squares (PLS) path modeling method. Besides estimating path models with latent variables using the PLS-SEM algorithm, the software computes standard results assessment criteria (e.g., for the reflective and formative measurement models, the structural model, and the goodness of fit) and

Chapter 1: Introduction to PLS-SEM

1. The Research Dilemma In any marketing research project, an ideal data set should have a large sample size and be normally distributed. Unfortunately, the reality is that many applied research projects have limited participants because of the nature of the project. Insufficient resources and tight project timelines further prevent researchers from obtaining a

Chapter 2: Understanding the PLS-SEM Components

1. Inner (Structural) and Outer (Measurement) Models Partial Least Squares Structural Equation Modeling (PLS-SEM) is a second-generation multivariate data analysis method that is often used in marketing research because it can test theoretically supported linear and additive causal models (Chin, 1996; Haenlein & Kaplan, 2004; Statsoft, 2013). With PLS-SEM, marketers can visually examine the

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Chapter 3: Using SmartPLS Software for Path Model Estimation

1. Introduction to the SmartPLS Software Application There are currently different versions of SmartPLS in circulation. The previous version, 2.0M3, is a freeware that has all of the core functionalities for PLS-SEM. Although it is only available on the Windows platform, this freely available version 2 is still used by thousands of researchers around

Chapter 4: Evaluating PLS-SEM Results in SmartPLS

1. The Colorful PLS-SEM Estimations Diagram SmartPLS presents path modeling estimations not only in the Modeling Window but also in a text-based report31 which is accessible via the “Report” menu. In the PLS-SEM diagram, there are two types of numbers: Numbers in the circle: These show how much the variance of the latent variable

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Chapter 5: Evaluating Model with Formative Measurement

1. Different Things to Check and Report As described earlier in this book, a model does not necessarily have reflective measurements. When working with model that utilizes a formative measurement scale, we do not analyze indicator reliability, internal consistency reliability, or discriminant validity because the formative indicators are not highly correlated together. Instead, we

Chapter 6: Determining Measurement Model Using Confirmatory Tetrad Analysis (CTA-PLS)

1. Formative or Reflective? Determining the Measurement Model Quantitatively Designing the measurement model in PLS-SEM is not always a straight-forward task. It has been a challenge for some researchers to determine if the relationship between the indicators and the latent variable is formative or reflective. This is particularly the case in Marketing when researchers

Chapter 7: Handling Non-Linear Relationship Using Quadratic Effect Modeling (QEM)

1. Non-linear Relationship Explained Experienced researchers would agree that variables do not always hold a linear relationship as in a straight line (Hay & Morris, 1991; Eisenbeiss, CorneliEen, Backhaus, & Hoyer, 2014). For example, in the field of marketing, advertising activities and sales revenue often hold a non-linear, quadratic relationship.48 Consumers may be eager

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Chapter 8: Analysing Segments Using Heterogeneity Modeling

1. Something is Hiding in the Dataset In exploratory research, marketing researchers may have little knowledge about the nature of the underlying data. They need to consider the potential problem of heterogeneous data structures in PLS-SEM modeling because the data do not necessarily come from a homogeneous population (see Figure 42). If heterogeneity is

Chapter 9: Estimating Complex Models Using Higher Order Construct Modeling (HCM)

1. Case Study: Customer Survey in a Photocopier Manufacturer (B2B) Since PLS-SEM is a relatively new approach to modeling, researchers who are not familiar with it may find the analytical and reporting aspects challenging, especially in the areas of higher-order constructs modeling, mediation analysis, and categorical moderation analysis. Chapter 9, 10 and 11 are

Chapter 10: Mediation Analysis with SmartPLS Software

1. Customer Satisfaction (SATIS) as a Mediator The relationships among constructs in PLS-SEM can be complex and not always straightforward. To gain a better understanding of the role of SATIS in our model, its potential mediating effect on the linkage between REPUT and LOYAL (see Figure 92), and those between PRICE and LOYAL (see

Chapter 11: Comparing Groups Using Categorical Moderation Analysis (PLS-MGA)

1. Multi-group Analysis – “Business Type” in the Photocopier Manufacturer Example Before starting this research project, Susan’s colleagues in the sales department keep telling her that non-profit business customers often behave very differently from for-profit ones in their decision-making processes. To confirm such insights, the last hypothesis is built as follows: H8: There is

Chapter 12: New Techniques in PLS-SEM

1. Estimating Factor Models Using Consistent PLS (PLSc) The traditional PLS algorithm has its shortcomings. Dijkstra & Schermelleh-Engel (2014) argue that it overestimates the loadings in absolute value and underestimates multiple and bivariate correlations between latent variables. It is also found that the R2 value of endogenous latent variables is often underestimated (Dijkstra, 2010).

Chapter 13: Recommended PLS-SEM Resources

1. Books Handbook of Partial Least Squares (PLS) and Marketing: Concepts, Methods, and Applications Editors: Vincenzo Esposito Vinzi, Wynne W. Chin, Jorg Henseler, & Huiwen Wangalso Publisher: Springer, 2010 Partial Least Squares Structural Equation Modeling: Basic Concepts, Methodological Issues and Applications Editors: Latan, H., & Noonan, R. Publisher: Springer, 2017 A Primer on Partial

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