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SEM Mediation Model in Smart PLS

SEM Mediation 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. An intermediate variable, called the mediator, is considered in mediation.

Here, Job Characteristic is considered as an exogenous (independent) variable, Job Satisfaction is considered as an endogenous (dependent) variable and Psychological Contract is considered as a mediator. Smart PLS software is used to o test whether psychological contract mediates the relationship between the exogenous and endogenous variables.

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_Mediation_Data.csv file is imported.

Then, the ‘Import Datafile’ dialog box would open. Click on Ok.

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, three latent variables, namely, Job characteristics, Job satisfaction and Psychological contract, are created.

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

Here, Psychological contract is the mediator variable. Thus, direct the arrows from Independent variable (Job characteristics) -> Mediator variable (Psychological contract) -> Dependent variable (Job satisfaction). The latent variable turns into blue colour when the variables are connected.

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

Then, Partial Least Squares Algorithm dialog box would open. 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 PLS Algorithm would be obtained.

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

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

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

After deleting JS_11 and JS_3, 0.534 is obtained as AVE value for Job satisfaction.

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

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

Then, 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, 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 ‘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 could 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 are higher than the highest correlation of the specific variable with the other variable.

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 Job satisfaction and Psychological contract.

Here, R Square value for Job satisfaction is equal to 0.231, which indicates that 23.1% of the variation is explained by Job characteristics and Psychological contract. The variation of 11.5% in Psychological contract is explained by Job characteristics.

Then, f Square value would be obtained. Job characteristics showed a small impact on Job satisfaction (0.122) and Psychological contract (0.130). Psychological contract showed a small impact on Job satisfaction (0.078).

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. The model’s significantly predictive Q2 value is greater than 0. Here Q2 = 0.118 and 0.059; thus, it is evident that the model had a good predictive relevance.

Testing of the model

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

It can be inferred from this table that Job characteristics had a significant influence on Job satisfaction (t=4.625, p=0.000) and Psychological contract (t=5.297, p=0.000). Psychological contract had a significant influence on Job satisfaction (t=3.593, p=0.000).

Specific indirect effects would then be obtained.

It can be inferred from this table that indirect effect is significant (t=2.530, p=0.000). Therefore, it can be concluded that relationship between Job characteristic and Job satisfaction is mediated by Psychological contract.

Data: SEM_Model_Mediation_Data.csv

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