1. IS6000 – Class 10 Introduction to SmartPLS (&SPSS)

2. Agenda A typical structure of an IS paper (Quantitative)
Research Model
Methodology
Data Analysis
Q&A
11/18/2018
@CO Guest Talk at CityU

3. A Typical Structure of an IS Paper (Quantitative)
Introduction
Literature review
Research model
Methodology
Data analysis
Key findings and discussion
Conclusion
11/18/2018
@CO Guest Talk at CityU

4. Based on “common sense” and the literature!
Research Model - 1
What factors may influence your intention to buy a car?
Your need
Your buying power
The cost of parking place(s)
Social (family’s or friends’) norm
…….
Based on “common sense” and the literature!
11/18/2018
@CO Guest Talk at CityU

5. Research Model - 2 Methodology: Measures >> Survey
Need
Buying Power
Purchase Intention
The Cost of Parking Place(s)
Social Norm
Methodology: Measures >> Survey
Data analysis (A simple regression ): PurchaseIntention = a + b1 Need + b2 BuyingPower + b3 ParkingPlaceAvailable + b4 SocialNorm
Tools for data analysis: SmartPLS (SPLS), SPSS, ….
11/18/2018
@CO Guest Talk at CityU

6. Research Model – 3 – An Example
11/18/2018
@CO Guest Talk at CityU

7. Methodology - 1 Measures Data collection
How to measure the factors (IVs and DVs)
Items (survey questions)
Where do those items come from?
From literature
Newly developed
Data collection
Survey administration
Demographic data
11/18/2018
@CO Guest Talk at CityU

8. Methodology - 2
11/18/2018
@CO Guest Talk at CityU

9. Data Analysis - 1 Tools for data analysis SmartPLS SPSS
SmartPLS and SPSS
SmartPLS
SmartPLS is a software application for (graphical) path modeling with latent variables (LVP). The partial least squares (PLS)-method is used for the LVP-analysis in this software.
SPSS
SPSS Statistics is a software package used for statistical analysis. It is now officially named "IBM SPSS Statistics".
Partial least squares regression (PLS regression) is a statistical method that bears some relation to principal components regression; it finds a linear regression model by projecting the predicted variables and the observable variables to a new space.
11/18/2018
@CO Guest Talk at CityU

10. Data Analysis - 2 Typical information to be reported
Validating the measures (constructs)
Analyzing the research model
11/18/2018
@CO Guest Talk at CityU

11. Data Analysis – 3
Validating the measures , i.e., checking the construct validity and reliability
Factor analysis results: Loadings of 0.40 are acceptable and cross loadings can be differentiated; Each indicator should load highest on the construct it is intended to measure
Composite reliability scores: Composite reliability ≥0.70 (in exploratory research 0.60 is considered acceptable)
The cross correlations : the score and significant levels
AVE: Convergent validity Average Variance Extracted (AVE) ≥ 0.50; The square roots of the AVE should be greater than all other cross correlations.
The square roots of the Average Variance Extracted (AVE) are all above 0.80, which is greater than all other cross correlations.
This shows that all constructs capture more construct-related variance than error variance.
11/18/2018
@CO Guest Talk at CityU

12. Data Analysis – 4 Analyzing the research model
Item loadings: Significance of weights
The path coefficients and their significant level (p, t values): Critical t-values for a two-tailed test are 1.65 (p significance level = 10 percent), 1.96 (p significance level = 5 percent),and 2.58 (p significance level = 1 percent).
The R2: Whereas R² results of 0.20 are considered high in disciplines such as consumer behavior. In marketing (driver) studies, R ² values of 0.75, 0.50, or 0.25 for endogenous latent variables in the structural model can be described as substantial, moderate, or weak, respectively.
Showing the whole model
Whereas R² results of 0.20 are considered high in disciplines such as consumer behavior, R² values of 0.75 would be perceived as high in success driver studies. In marketing research studies, R² values of 0.75, 0.50, or 0.25 for endogenous latent variables in the structural model can, as a rule of thumb, be described as substantial, moderate, or weak, respectively.
11/18/2018
@CO Guest Talk at CityU

13. Data Analysis – 5 How to obtain the above information from SPLS?
Use the example project or create a new project
Use the SmartPLS 2.0 function "Project Import" to import the following examples.
Project File Remarks ecsi.splsp: A model for the "European Customer Satisfaction Index"
Link to the data set: xxx.txt or xxx.cvs (mobi_250.txt)
Draw the research model (by creating and linking constructs)
Assign the corresponding items to the constructs
Calculate “PLS Algorithm”
Calculate “Bootstrapping”
Report the information shown in the “Html Report”
11/18/2018
@CO Guest Talk at CityU

14. Checking the construct validity and reliability
Data Analysis – 6
Checking the construct validity and reliability
11/18/2018
@CO Guest Talk at CityU

15. Data Analysis – 7 Loading, Path Coefficients, R2
11/18/2018
@CO Guest Talk at CityU

16. Data Analysis – 8 Significant Level (T Value)
11/18/2018
@CO Guest Talk at CityU

17. How about your data?
Q & A
11/18/2018
@CO Guest Talk at CityU

18. Reference http://www.smartpls.de/forum/
Hair, J.F., Sarstedt, M., Ringle, C.M., and Mena, J.A. (2012) An assessment of the use of partial least squares structural equation modeling in marketing research, Journal of the Academy of Marketing Science (40), pp. 414–433.
Hair, J.F., Ringle, C.M., and Sarstedt, M. (2011) PLS-SEM: Indeed a silver bullet, Journal or Marketing Theory and Practice (19:2), pp. 139–151.
11/18/2018
@CO Guest Talk at CityU