IS6000 – Class 10 Introduction to SmartPLS (&SPSS)

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

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

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

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

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

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

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

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

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

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

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

Data Analysis – 5 How to obtain the above information from SPLS? Use the example project or create a new project http://www.smartpls.de/forum/downloads.php#samples 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

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

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

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

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

Reference http://www.smartpls.de/forum/ http://www.smartpls.de/forum/downloads.php#samples http://www.smartpls.de/forum/downloads.php#manual 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