1 using the smartpls software “structural model assessment” all rights reserved ©. cannot be...

Download 1 Using the SmartPLS Software “Structural Model Assessment” All rights reserved ©. Cannot be reproduced or distributed without express written permission

Post on 14-Dec-2015

213 views

Category:

Documents

1 download

Embed Size (px)

TRANSCRIPT

  • Slide 1

1 Using the SmartPLS Software Structural Model Assessment All rights reserved . Cannot be reproduced or distributed without express written permission from Sage, Prentice-Hall, SmartPLS, and session presenters. Joe F. Hair, Jr. Founder & Senior Scholar Joe F. Hair, Jr. Founder & Senior Scholar Slide 2 2 Structural Model Assessment All rights reserved . Cannot be reproduced or distributed without express written permission from Sage, Prentice-Hall, SmartPLS, and session presenters. Once the construct measures have been confirmed as reliable and valid, the next step is to assess the structural model results. This involves examining the models predictive capabilities and the relationships between the constructs. Exhibit 6.1 (next slide) shows a systematic approach to the assessment of structural model results. Before assessing the structural model, you must examine the structural model for collinearity (Step 1). The reason is that the estimation of path coefficients in the structural model is based on OLS regressions of each endogenous latent variable on its corresponding predecessor constructs. Just as in a regular multiple regression, the path coefficients may be biased if the estimation involves significant levels of collinearity among the predictor constructs. After checking for collinearity, the key criteria for assessing the structural model in PLS-SEM are: Step 2 the significance of the path coefficients, Step 3 the level of the R values, Step 4 the f effect size, and Step 5 the predictive relevance (Q & the q effect size). After checking for collinearity, the key criteria for assessing the structural model in PLS-SEM are: Step 2 the significance of the path coefficients, Step 3 the level of the R values, Step 4 the f effect size, and Step 5 the predictive relevance (Q & the q effect size). Slide 3 Slide 4 4 Step 1: Collinearity Assessment All rights reserved . Cannot be reproduced or distributed without express written permission from Sage, Prentice-Hall, SmartPLS, and session presenters. To assess collinearity, we apply the same measures as in the evaluation of formative measurement model indicators (i.e., tolerance and VIF values). To do so, we need to examine each set of predictor constructs separately for each subpart of the structural model. For instance, in the model shown in Exhibit 6.2 (next slide), Y1 and Y2 jointly explain Y3. Likewise, Y2 and Y3 act as predictors of Y4. Therefore, you need to check whether there are significant levels of collinearity between each set of predictor variables (constructs). In other words, you need to check the collinearity between Y1 and Y2 as well as between Y2 and Y3. Similar to the assessment of formative measurement model indicators, we consider tolerance levels below 0.20 (VIF above 5.00) in the predictor constructs as indicative of collinearity that is too high. If collinearity is exceeds these thresholds, you should consider eliminating constructs, merging predictors into a single construct, or creating higher-order constructs to deal with collinearity problems. Slide 5 5 All rights reserved . Cannot be reproduced or distributed without express written permission from Sage, Prentice-Hall, SmartPLS, and session presenters. Slide 6 6 Evaluating Construct Collinearity Extended Reputation Model The following sets of (predictor) constructs are run to assess collinearity: (1) ATTR, CSOR, PERF, and QUAL as predictors of COMP; (2) COMP and LIKE as predictors of CUSA; and (3) COMP, LIKE, and CUSA as predictors of CUSL. Slide 7 7. All rights reserved . Cannot be reproduced or distributed without express written permission from Sage, Prentice-Hall, SmartPLS, and session presenters. To assess construct collinearity, run the extended reputation model and open the default report by going to Menu Report Default Report. Next, you need to extract the latent variable scores from the default report, which you can find under PLS Calculation Results Latent Variable Scores. The scores are shown on this slide. Copy these scores to an SPSS file to run this analysis (right click to highlight scores, left click to copy, then paste in SPSS). Using the SPSS linear regression option, the following sets of (predictor) constructs are run to assess collinearity: (1) ATTR, CSOR, PERF, and QUAL as predictors of COMP; (2) COMP and LIKE as predictors of CUSA; and (3) COMP, LIKE, and CUSA as predictors of CUSL. The SPSS steps for testing collinearity are shown on the next slide for the first regression run. Evaluating Construct Collinearity Extended Reputation Model Slide 8 8. All rights reserved . Cannot be reproduced or distributed without express written permission from Sage, Prentice-Hall, SmartPLS, and session presenters. Using SPSS to Assess Collinearity These are the four exogenous constructs that are being tested for multicollinearity. Slide 9 9 Below are the SPSS collinearity results from using ATTR, CSOR, PERF, and QUAL as predictors of COMP. All rights reserved . Cannot be reproduced or distributed without express written permission from Sage, Prentice-Hall, SmartPLS, and session presenters. Evaluating Collinearity Among Exogenous Constructs All VIF values are clearly below the threshold of 5. Slide 10 10 All rights reserved . Cannot be reproduced or distributed without express written permission from Sage, Prentice-Hall, SmartPLS, and session presenters. Below are the collinearity results from using COMP and LIKE as predictors of CUSA (top table), and COMP, LIKE and CUSA as predictors of CUSL (bottom table). All VIF values are well below the threshold of 5. Evaluating Collinearity Among Exogenous Constructs Slide 11 11 Step 2: Assess Significance and Relevance of the Structural Model Relationships All rights reserved . Cannot be reproduced or distributed without express written permission from Sage, Prentice-Hall, SmartPLS, and session presenters. After applying the PLS-SEM algorithm, estimates are obtained for the structural model relationships (the path coefficients), which represent the hypothesized relationships between the constructs. The path coefficients for the structural model are shown in the next several slides. These results were obtained from the SmartPLS Default Report with the following sequence: PLS Algorithm Calculation Results Path Coefficients Before examining the sizes of the path coefficients we will first examine their significance. To do so, we must first run the Bootstrapping option. Slide 12 12 All rights reserved . Cannot be reproduced or distributed without express written permission from Sage, Prentice-Hall, SmartPLS, and session presenters. Reputation Model Results Path Coefficients Slide 13 13 All rights reserved . Cannot be reproduced or distributed without express written permission from Sage, Prentice-Hall, SmartPLS, and session presenters. Click here to run Bootstrapping Bootstrapping Which path coefficients are significant? When you run bootstrapping select mean replacement for missing data, no sign changes, your actual sample size = cases, and 5,000 samples. Slide 14 . All rights reserved . Cannot be reproduced or distributed without express written permission from Sage, Prentice-Hall, SmartPLS, and session presenters. Bootstrapping Default Report Significance of Path Coefficients The above results show the significance of the path coefficients. Recall that a T Statistic > 1.96 is significant with a two-tailed test, and >.98 is significant for a one-tailed test. Note: path coefficients are shown in Original Sample column. The results indicate that all paths are statistically significant using a one-tailed test except COMP CUSL. But eight of the thirteen structural paths are significant based on a two-tailed test. 14 T Statistics are found beside the tab below. Slide 15 15 All rights reserved . Cannot be reproduced or distributed without express written permission from Sage, Prentice-Hall, SmartPLS, and session presenters. Click here to obtain Default Report Obtaining the Default Report to Evaluate Path Coefficients Slide 16 16 All rights reserved . Cannot be reproduced or distributed without express written permission from Sage, Prentice-Hall, SmartPLS, and session presenters. SmartPLS Default Report Path Coefficients After examining the significance of relationships, it is important to assess the relevance of significant relationships. Path coefficients in the structural model may be significant, but their size may be so small that they do not warrant managerial attention. Structural model path coefficients can be interpreted relative to one another. If one path coefficient is larger than another, its effect on the endogenous latent variable is greater. More specifically, the individual path coefficients of the path model can be interpreted just as the standardized beta coefficients in an OLS regression. These coefficients represent the estimated change in the endogenous construct for a unit change in a predictor construct. Slide 17 17 All rights reserved . Cannot be reproduced or distributed without express written permission from Sage, Prentice-Hall, SmartPLS, and session presenters. Looking at the relative importance of the exogenous driver constructs in predicting the dependent construct perceived competence (COMP), we see that customer perceptions of the companys quality of products and services (QUAL = 0.4297) is most important, followed by performance (PERF = 0.2955). In contrast, the perceived attractiveness (ATTR = 0.0861) and degree to which the company acts in socially conscious ways (CSOR = 0.0589) have very little influence on COMP. These last two drivers are, however, more important in predicting a companys likeability (LIKE) ATTR = 0.1671 and CSOR = 0.1784, but the most important predictor of LIKE is QUAL (0.3800). When you examine the endogenous likability construct (LIKE) you see that it is the primary driver (predictor) of customer satis

Recommended

View more >