### SEM Model in Smart PLS

# SEM Model in Smart PLS

Theoretical model developed by the study is tested using Structural Equation Modelling (SEM). SEM is capable of testing complicated relationships among variables. Here, Job characteristic is considered as an exogenous (independent) variable and Job satisfaction is considered as an endogenous (dependent) variable. The relationship between the exogenous and endogenous variables was assessed through the Smart PLS software.

To create a new project in Smart PLS, click on ‘Select File > Create new project’ or click on ‘New Project’ icon in the top.

A dialog box would open, as shown in the following image. Then, name the project and click on Ok.

Double click on the option ‘Double-click to import data!’ to import the data. Then, browse the folder where the data file is located.

Here, SEM_Model_Data.csv file is imported.

Click on Ok after the Import Data file dialog box is opened.

Click on the highlighted icon to create the path model.

Then, select the variables from the indicators by pressing the “shift” button.

Drop the variables in the path model screen.

Then, rename the latent variable through the ‘Rename’ option.

Here, the latent variable is renamed as Job characteristics. Then, repeat the same procedure to create the other latent variables.

Here, two latent variables, namely, Job characteristics and Job satisfaction, are created.

Use the ‘Connect’ icon to connect the two latent variables.

The latent variable turns into blue colour when the two variables are connected.

Click on the ‘Calculate’ icon and select ‘PLS Algorithm; option to get the measurement model.

Then, Partial Least Squares Algorithm dialog box would be opened. Select ‘Factor’ option in weighting scheme and mention the maximum iterations. Here, 500 maximum iterations are considered. Now, click on ‘Start Calculation’ button.

The result for the PLS Algorithm would be obtained.

Then, check all the results of the PLS algorithm by clicking these options.

Here, AVE value for Job satisfaction is less than 0.5. Thus, delete some items from the latent variable of Job satisfaction.

Here, outer loading value for JS_11 and JS_3 is less than 0.6. Thus, delete these two items.

After deleting JS_11 and JS_3, we get 0.533 as the AVE value for Job satisfaction.

The result can be saved in excel sheet by clicking on ‘Excel’ option.

Click on ‘Calculate’ icon and select ‘Bootstrapping’ to get the structural model.

Then, the Bootstrapping dialog box would open. Select ‘Path’ option in weighting scheme and mention the maximum iterations. Here, 500 maximum iterations are considered. Now, click on ‘Start Calculation’ button.

The result for Bootstrapping would be obtained.

Click on ‘Calculate’ icon and select ‘Blindfolding’ option to get predictive relevance.

Then, the Blindfolding dialog box would open. Select ‘Path’ option in weighting scheme and mention the maximum iterations. Here, 500 maximum iterations are considered. Now, click on the ‘Start Calculation’ button.

The result for Blindfolding would be obtained.

# Output of SEM model in Smart PLS

## Assessment of measurement model

In the Construct Reliability and Validity table, Cronbach’s Alpha, rho_A and Composite Reliability values for all study variables are greater than the cut-off value of 0.7. Therefore, it can be inferred that reliability is established. Average Variance Extracted (AVE) values for the two variables are higher than the cut-off value of 0.5. This satisfies the convergent validity of the model.

The table for Fornell-Larcker Criterion is obtained under discriminant validity. Here all values were higher than the highest correlation of the specific variable with the other variables.

The values are less than the cut-off value of 0.85 in the HTMT table. This assures the optimal difference established between the constructs and the validity of factors.

## Evaluation of model fitness

R Square and R Square Adjusted values are obtained for the dependent variable (Job satisfaction).

Here, the R Square value of 0.175 indicates that 17.5% of the variation is explained by Job characteristics.

Then, f Square value is obtained. Here, f Square value is equal to 0.212, which indicates that Job characteristics had a medium impact on Job satisfaction.

## Collinearity Assessment

Here, all VIF values are less than 5, implying the nonappearance of collinearity among the indicators.

The Construct Cross validated Redundancy table is obtained when the blindfolding algorithm is run. The value for Q2 is obtained from this table. Predictive relevance of the model is estimated using the Q2 value. The model’s significantly predictive Q2 value is greater than 0. Here Q2 = 0.086; thus it is evident that the model had a good predictive relevance.

## Testing of the model

The table for Path Coefficients is obtained when the Bootstrapping model is run.

It can be inferred from this table that Job characteristics had a significant influence on Job satisfaction (t=7.256, p=0.000).

Data: SEM_Model_Data.csv