Joe F. Hair, Jr.Joe F. Hair, Jr.Founder & Senior Scholar, DBA Founder & Senior Scholar, DBA

ProgramProgram

Joe F. Hair, Jr.Joe F. Hair, Jr.Founder & Senior Scholar, DBA Founder & Senior Scholar, DBA

ProgramProgram

PLS-SEM: Introduction (Part 1)PLS-SEM: Introduction (Part 1)

Sewall Wright, Correlation and Causation, Sewall Wright, Correlation and Causation, Journal Journal of Agricultural Researchof Agricultural Research, Vol. XX, No. 7, 1921. , Vol. XX, No. 7, 1921.

SEM Model:SEM Model:Predicting the Birth Weight Predicting the Birth Weight

of Guinea Pigsof Guinea Pigs

X & Y = different outcomesX & Y = different outcomesB, C & D = common causesB, C & D = common causesA & E = independent causesA & E = independent causes

The greatest interest in any factor solution centers on the correlations between the original variables and the factors. The matrix of such test-factor correlations is called the factor structure,

and it is the primary interpretative device in principal components analysis. In the factor structure the element rjk gives the correlation of the jth test with the kth factor. Assuming that the

content of the observation variables is well known, the correlations in the k th column of the structure help in interpreting, and perhaps naming, the kth factor. Also, the coefficients in the jth

row give the best view of the factor composition of the jth test.

Another set of coefficients of interest in factor analysis is the weights that compound predicted observations z from factor scores f. These regression coefficients for the multiple regression of

each element of the observation vector z on the factor f are called factor loadings and the matrix A that contains them as its rows is . . . . .

Source: Cooley, William W., and Paul R. Lohnes, Multivariate Data Analysis, John Wiley & Sons, Inc., New York, 1971, page 106.

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Structural Equations Structural Equations ModelingModeling

What comes to mind?What comes to mind?

CB-SEMCB-SEM

LISREL LISREL

AMOS ?AMOS ?PLS-SEMPLS-SEM

CB-SEM (Covariance-based SEM) CB-SEM (Covariance-based SEM) – – objective is to reproduce the theoretical objective is to reproduce the theoretical covariance matrix, without focusing on covariance matrix, without focusing on explained variance. explained variance.

PLS-SEM (Partial Least Squares SEM) PLS-SEM (Partial Least Squares SEM) – objective is to maximize the – objective is to maximize the explained variance of the endogenous explained variance of the endogenous latent constructs (dependent variables). latent constructs (dependent variables).

CB-SEM ModelCB-SEM Model

HBAT, HBAT, MDAMDA database database

Covariance Matrix = HBAT 3-Construct modelCovariance Matrix = HBAT 3-Construct model

CB-SEM CB-SEM – evaluation focuses on goodness of – evaluation focuses on goodness of fit = minimization of the difference fit = minimization of the difference between the observed covariance matrix between the observed covariance matrix and the estimated covariance matrix.and the estimated covariance matrix.

Research objective: testing and confirmation where Research objective: testing and confirmation where prior theory is strong. prior theory is strong.

• Assumes normality of data distribution, Assumes normality of data distribution, homoscedasticity, large sample size, etc.homoscedasticity, large sample size, etc.

• Only reliable and valid variance is useful for Only reliable and valid variance is useful for testing causal relationships. testing causal relationships.

• A “full information approach” which means small A “full information approach” which means small changes in model specification can result in changes in model specification can result in substantial changes in model fit.substantial changes in model fit.

PLS-SEM PLS-SEM – objective is to maximize the – objective is to maximize the explained variance of the endogenous explained variance of the endogenous latent constructs (dependent variables).latent constructs (dependent variables).

Research objective: theory development and Research objective: theory development and prediction.prediction.

• Normality of data distribution not assumed.Normality of data distribution not assumed.• Can be used with fewer indicator variables (1 or 2) Can be used with fewer indicator variables (1 or 2)

per construct.per construct.• Models can include a larger number of indicator Models can include a larger number of indicator

variables (CB-SEM difficult with 50+ items).variables (CB-SEM difficult with 50+ items).• Preferred alternative with formative constructs.Preferred alternative with formative constructs.• Assumes all measured variance (including error) is Assumes all measured variance (including error) is

useful for explanation/prediction of causal useful for explanation/prediction of causal relationships.relationships.

PLS Path ModelPLS Path Model

X1

X2

X3

X4

X5

X6

X7Y2

Y1

Y3

W1

W2

W3

W4

W5

W6

W7

P1

P2

Indicator Variable

Latent VariableLatent ConstructLatent Construct

Multivariate MethodsMultivariate Methods

Should SEM Be Used?Should SEM Be Used?

Considerations:Considerations:

1.1.The VariateThe Variate

2.2.Multivariate MeasurementMultivariate Measurement

3.3.Measurement ScalesMeasurement Scales

4.4.CodingCoding

5.5.Data DistributionData Distribution

Variate = Variate = a linear combination of several variables, a linear combination of several variables, often referred to as the fundamental building block often referred to as the fundamental building block

of multivariate analysis. of multivariate analysis.

Variate value = xVariate value = x11ww11 + x + x22ww22 + . . . + x + . . . + xkkwwk k

Data MatrixData Matrix

Multiple Regression ModelMultiple Regression Model

Variate = xVariate = x11 + x + x22 + x + xkk + e + e

Multivariate MeasurementMultivariate MeasurementMeasurement = the process of assigning numbers to a Measurement = the process of assigning numbers to a variable/construct based on a set of rules that are used to assign variable/construct based on a set of rules that are used to assign the numbers to the variable in a way that accurately represents the the numbers to the variable in a way that accurately represents the variable. variable.

When variables are difficult to measure, one approach is to When variables are difficult to measure, one approach is to measure them indirectly with proxy variables. If the concept is measure them indirectly with proxy variables. If the concept is restaurant satisfaction, for example, then the several proxy restaurant satisfaction, for example, then the several proxy variables that could be used to measure this might be:variables that could be used to measure this might be:

1.1.The taste of the food was excellent.The taste of the food was excellent.2.2.The speed of service met my expectations.The speed of service met my expectations.3.3.The wait staff was very knowledgeable about the menu items.The wait staff was very knowledgeable about the menu items.4.4.The background music in the restaurant was pleasant.The background music in the restaurant was pleasant.5.5.The meal was a good value compared to the price.The meal was a good value compared to the price.

Multivariate measurement involves using several variables to Multivariate measurement involves using several variables to indirectly measure a concept, as in the restaurant satisfaction indirectly measure a concept, as in the restaurant satisfaction example above. It also enables researchers to account for the error example above. It also enables researchers to account for the error in data.in data.

Data Characteristics – PLS-SEMData Characteristics – PLS-SEMSample Size No identification issues with small sample sizes (35-50).

Generally achieves high levels of statistical power with small sample sizes (35-50).

Larger sample sizes (250+) increase the precision (i.e., consistency) of PLS-SEM estimations.

Data Distribution

No distributional assumptions (PLS-SEM is a non-parametric method; works well with extremely non-normal data).

Missing Values

Highly robust as long as missing values are below reasonable level (e.g., up to 15% randomly missing data points).

Use mean replacement (sub-groups) and nearest neighbor. Measurement

Scales Works with metric, quasi-metric (ordinal) scaled data, and

binary coded variables (~only exogenous variables). Limitations when using categorical data to measure

endogenous latent variables. Suggest using binary variables for multi-group comparisons.

Model Characteristics – PLS-SEMModel Characteristics – PLS-SEM

Number of Items in each Construct

Measurement Model

Handles constructs measured with single and multi-item measures.

Easily handles 50+ items (CB-SEM does not). Single item scales OK.

Relationships between Latent Constructs and their Indicators

Easily incorporates reflective and formative measurement models.

Model Complexity

Handles complex models with many structural model relationships.

Larger numbers of indicators are helpful in reducing “consistency at large”.

Model Set-up Causal loops not allowed in the structural model (only recursive models).

Algorithm Properties – PLS-SEMAlgorithm Properties – PLS-SEMObjective Minimizes the amount of unexplained variance (i.e.,

maximizes the R² values). Efficiency Converges after a few iterations (even in situations with

complex models and/or large sets of data) to the global optimum solution; efficient algorithm.

Latent Construct

Scores

Estimated as linear combinations of their indicators. Used for predictive purposes. Can be used as input for subsequent analyses. Not affected by data inadequacies.

Parameter Estimates

Structural model relationships underestimated (PLS-SEM bias).

Measurement model relationships overestimated (PLS-SEM bias).

Consistency at large (minimal impact with N = 250+). High levels of statistical power with smaller sample

sizes (35-50).

Model Evaluation Issues – PLS-SEMModel Evaluation Issues – PLS-SEM

Evaluation of Overall Model

No global goodness-of-fit criterion.

Evaluation of Measurement

Models

Reflective measurement models: reliability and validity assessments by multiple criteria.

Formative measurement models: validity assessment, significance of path coefficients, multicollinearity.

Evaluation of Structural

Model

Significance of path coefficients, coefficient of determination (R²), pseudo F-test (f² effect size), predictive relevance (Q² and q² effect size).

Additional Analyses

Mediating effects Impact-performance matrix analysis Higher-order constructs Multi-group analysis Measurement mode invariance Moderating effects Uncovering unobserved heterogeneity: FIMIX-PLS

Rules of Thumb: PLS-SEM or CB-SEM?Rules of Thumb: PLS-SEM or CB-SEM?

Use PLS-SEM when: Use PLS-SEM when: •The goal is predicting key target constructs or identifying The goal is predicting key target constructs or identifying key “driver” constructs.key “driver” constructs.•Formative constructs are easy to use in the structural Formative constructs are easy to use in the structural model. Note that formative measures can also be used with model. Note that formative measures can also be used with CB-SEM, but doing so requires construct specification CB-SEM, but doing so requires construct specification modifications (e.g., the construct must include both modifications (e.g., the construct must include both formative and reflective indicators to meet identification formative and reflective indicators to meet identification requirements).requirements).•The structural model is complex (many constructs and The structural model is complex (many constructs and many indicators). many indicators). •The sample size is small and/or the data is not-normally The sample size is small and/or the data is not-normally distributed, or exhibits heteroskedasticity.distributed, or exhibits heteroskedasticity.•The plan is to use latent variable scores in subsequent The plan is to use latent variable scores in subsequent analyses.analyses.

Use CB-SEM when: Use CB-SEM when: •The goal is theory testing, theory The goal is theory testing, theory confirmation, or the comparison of alternative confirmation, or the comparison of alternative theories.theories.•Error terms require additional specification, Error terms require additional specification, such as the covariation.such as the covariation.•Structural model has non-recursive Structural model has non-recursive relationships.relationships.•Research requires a global goodness of fit Research requires a global goodness of fit criterion.criterion.

Rules of Thumb: PLS-SEM or CB-SEMRules of Thumb: PLS-SEM or CB-SEM

Specifying the Structural Model

Specifying the Measurement Models

Data Collection and Examination

PLS-SEM Model Estimation

Assessing PLS-SEM Results for ReflectiveMeasurement Models

Assessing PLS-SEM Results for Formative Measurement Models

Assessing PLS-SEM Results for the StructuralModel

Interpretation of Results and Drawing Conclusions

Stage 1

Stage 2

Stage 3

Stage 4

Stage 5a

Stage 5b

Stage 6

Stage 7

Systematic Process for applying PLS-SEM Systematic Process for applying PLS-SEM

Should You Use SEM?Should You Use SEM?Journal reviewers rate SEM papers more favorably Journal reviewers rate SEM papers more favorably

on key manuscript attributes . . on key manuscript attributes . . . .

Mean ScoreMean Score

AttributesAttributes SEMSEM No SEMNo SEM p-valuep-value Topic RelevanceTopic Relevance 4.2 4.2 3.83.8 .182 .182 Research MethodsResearch Methods 3.5 3.5 2.72.7 .006.006 Data AnalysisData Analysis 3.5 3.5 2.82.8 .025.025 ConceptualizationConceptualization 3.1 3.1 2.52.5 .018.018 Writing QualityWriting Quality 3.9 3.9 3.03.0 .006.006 Contribution Contribution 3.1 3.1 2.82.8 .328 .328 Note: scores based on 5-point scale, with 5 = more favorableNote: scores based on 5-point scale, with 5 = more favorable

Source: Babin, Hair & Boles, Publishing Research in Marketing Journals Source: Babin, Hair & Boles, Publishing Research in Marketing Journals Using Structural Equation Modeling, Using Structural Equation Modeling, Journal of Marketing Theory and Journal of Marketing Theory and PracticePractice, Vol. 16, No. 4, 2008, pp. 281-288., Vol. 16, No. 4, 2008, pp. 281-288.

PLS-SEM Stages 1, 2 & 3: Design IssuesPLS-SEM Stages 1, 2 & 3: Design Issues

1.1. Scale MeasuresScale Measures

• Scale selection/designScale selection/design

• Reflective vs. FormativeReflective vs. Formative

2.2. Common Methods VarianceCommon Methods Variance

• Harmon Single Factor TestHarmon Single Factor Test

• Common Latent FactorCommon Latent Factor

• Marker ConstructMarker Construct

3.3. Missing Data, outliers, etc.Missing Data, outliers, etc.

Scale DesignScale Design

1.1. Revise/UpdateRevise/Update

• Established scales – how old?Established scales – how old?

• Double barreled; negatively wordedDouble barreled; negatively worded

2.2. Number of Scale PointsNumber of Scale Points

• More scale points = greater variabilityMore scale points = greater variability

3.3. Single Item Scales Single Item Scales

Single Item Scales ?Single Item Scales ? Single-item measures Multi-item measures

Theoretical Aspects

Reliability

no adjustment of random error

assessing reliability is problematic

allows for random error adjustment

determination of reliability by means of internal consistency

Validity

lower construct validity – does not account for all facets of a construct

decreased criterion validity

assessing validity is more problematic

higher construct validity – different facets of a construct can be captured

increased criterion validity validity measures based on

item-to-item correlations

Partition-

ing

Partitioning solely based on the single variable

more precise partition possible

Missing Values

very difficult to resolve

imputation methods based on correlations between indicators of the same construct

Use in Academic Research

very uncommon (publication problematic)

generally accepted

Single Item Scales ?Single Item Scales ?

Single-item measures Multi-item measures

Practical Aspects

Costs

lower costs associated with scale development, questioning, and data analysis

higher costs associated with scale development, questioning, and data analysis

Non-

response

increased survey response rate

lower item nonresponse

lower survey response rate higher item nonresponse

Burden of

Question-ing

little burden: simple, fast, and comprehensible

increased burden: longer, likely more boring and tiring

Reflective (Scale) Versus Formative Reflective (Scale) Versus Formative (Index) Operationalization of Constructs (Index) Operationalization of Constructs

A central research question in social science research, particularly marketing A central research question in social science research, particularly marketing and MIS, focuses on the operationalization of complex constructs:and MIS, focuses on the operationalization of complex constructs:

Are indicators causing or being caused by Are indicators causing or being caused by

the latent variable/construct measured by them?the latent variable/construct measured by them?

Construct

Indicator 1 Indicator 2 Indicator 3

Construct

Indicator 1 Indicator 2 Indicator 3

?

Changes in the latent variable Changes in the latent variable directly cause changes in the directly cause changes in the

assigned indicatorsassigned indicators

Changes in one or more of the Changes in one or more of the indicators causes changes in indicators causes changes in

the latent variable the latent variable

Example: Reflective vs. Formative World Example: Reflective vs. Formative World ViewView

DrunkennessDrunkenness

Can’t walk a straight Can’t walk a straight lineline

Smells of alcoholSmells of alcohol

Slurred speechSlurred speech

Example: Reflective vs. Formative World ViewExample: Reflective vs. Formative World View

DrunkennessDrunkenness

Consumption of beerConsumption of beer

Consumption of wineConsumption of wine

Consumption of hard Consumption of hard liquorliquor

Basic Difference Between Reflective and Basic Difference Between Reflective and Formative Measurement ApproachesFormative Measurement Approaches

““Whereas reflective indicators are essentially interchangeable (and Whereas reflective indicators are essentially interchangeable (and therefore the removal of an item does not change the essential therefore the removal of an item does not change the essential nature of the underlying construct), with formative indicators nature of the underlying construct), with formative indicators ‘omitting an indicator is omitting a part of the construct’.” ‘omitting an indicator is omitting a part of the construct’.”

(DIAMANTOPOULOS/WINKLHOFER, 2001, p. 271)(DIAMANTOPOULOS/WINKLHOFER, 2001, p. 271)

The The reflective measurementreflective measurement approach approach focuses on focuses on maximizingmaximizing the the overlapoverlap between interchangeable indicatorsbetween interchangeable indicators

The The formative measurementformative measurement approach approach generally generally minimizesminimizes the the overlapoverlap

between complementary indicatorsbetween complementary indicators

Construct Construct domaindomain

Construct Construct domaindomain

Exercise: Satisfaction in Hotels as Formative Exercise: Satisfaction in Hotels as Formative and Reflective Operationalized Constructand Reflective Operationalized Construct

I am comfortable with I am comfortable with this hotelthis hotel

I appreciate this hotelI appreciate this hotel

I am looking forward to I am looking forward to staying overnight in staying overnight in

this hotelthis hotel

The rooms‘ furnishings The rooms‘ furnishings are goodare good

The rooms are quietThe rooms are quiet

The hotel‘s personnel The hotel‘s personnel are friendlyare friendly

The hotel’s service is The hotel’s service is goodgood

The hotel’s cuisine is The hotel’s cuisine is goodgood

The hotel’s recreation The hotel’s recreation offerings are goodofferings are good The rooms are cleanThe rooms are clean

Taking everything into Taking everything into account, I am satisfied account, I am satisfied

with this hotelwith this hotel

The hotel is low-pricedThe hotel is low-pricedSatisfaction Satisfaction with Hotelswith Hotels

Formative Constructs – Two TypesFormative Constructs – Two Types

1.1. Composite (formative) constructs Composite (formative) constructs – – indicators completely indicators completely determine the “latent” construct. They share similarities because determine the “latent” construct. They share similarities because they define a composite variable but may or may not have they define a composite variable but may or may not have conceptual unity. In assessing validity, indicators are not conceptual unity. In assessing validity, indicators are not interchangeable and should not be eliminated, because removing interchangeable and should not be eliminated, because removing an indicator will likely change the nature of the latent construct. an indicator will likely change the nature of the latent construct.

2.2. Causal constructs Causal constructs – – indicators have conceptual unity in that indicators have conceptual unity in that all variables should correspond to the definition of the concept. In all variables should correspond to the definition of the concept. In assessing validity some of the indicators may be assessing validity some of the indicators may be interchangeable, and also can be eliminated.interchangeable, and also can be eliminated.

Bollen, K.A. (2011), Evaluating Effect, Composite, and Causal Indicators in Bollen, K.A. (2011), Evaluating Effect, Composite, and Causal Indicators in Structural Equations Models, Structural Equations Models, MIS QuarterlyMIS Quarterly, Vol. 35, No. 2, pp. 359-372., Vol. 35, No. 2, pp. 359-372.

PLS-SEM ExamplePLS-SEM Example

CUSLCUSA

LIKE

COMP

Reflective Measurement Model

Reflective Measurement Model

Single-Item Construct Reflective Measurement

Model

COMP

comp_1

comp_2

comp_3

LIKE

like_1

like_2

like_3

CUSAcusa CUSL

cusl_1

cusl_2

cusl_3

Types of Measurement ModelsTypes of Measurement ModelsPLS-SEM ExamplePLS-SEM Example

Indicators for SEM Model ConstructsIndicators for SEM Model ConstructsCompetence (COMP)

comp_1 [company] is a top competitor in its market.

comp_2 As far as I know, [company] is recognized world-wide.

comp_3 I believe that [company] performs at a premium level.

Likeability (LIKE)

like_1 [company] is a company that I can better identify with than other companies.

like_2 [company] is a company that I would regret more not having if it no longer existed than I would other companies.

like_3 I regard [company] as a likeable company.

Customer Loyalty (CUSL)

cusl_1 I would recommend [company] to friends and relatives.

cusl_2 If I had to choose again, I would chose [company] as my mobile phone services provider.

cusl_3 I will remain a customer of [company] in the future.

Satisfaction (CUSA)

cusa If you consider your experiences with [company] how satisfied are you with [company]?

Data Matrix for Indicator VariablesData Matrix for Indicator Variables

Column Number and Variable Name

Case

Number

1 2 3 4 5 6 7 8 9 10

comp_1 comp_2 comp_3 like_1 like_2 like_3 cusl_1 cusl_2 cusl_3 cusa

1 4 5 5 3 1 2 5 3 3 5

2 6 7 6 6 6 6 7 7 7 7

. . .

344 6 5 6 6 7 5 7 7 7 7

Getting Started with the SmartPLS SoftwareGetting Started with the SmartPLS SoftwareThe next slide shows the graphical interface for the SmartPLS

software, with the simple model already drawn. We describe in the following slides how to set up this model using the SmartPLS software program. Before you draw your model, you need to have data that serves as the basis for running the model. The data we will use to run our example PLS model can be downloaded either as comma separated values (.csv) or text (.txt) data files at the following URL: http://www.smartpls.de/cr/. When you get to the website scroll down to the Corporate Reputation Example where it says Click on the following links to download Click on the following links to download filesfiles..

SmartPLS can use both data file formats (i.e., .csv or .txt). Follow the onscreen instructions to save one of these two files on your hard drive. Click on Save Target As… to save the data to a folder on your hard drive, and then Close. Now go to the folder where you previously downloaded and saved the SmartPLS software on your computer.  Click on the file that runs SmartPLS ( ) and then on the Run tab to start the software. You are now ready to create a new SmartPLS project.

SmartPLS Graphical Interface SmartPLS Graphical Interface

Example with Names and Data AssignedExample with Names and Data Assigned

Brief Instructions: Using SmartPLSBrief Instructions: Using SmartPLS

1.1. Load SmartPLS software – click onLoad SmartPLS software – click on

2.2. Create your new project – assign name and data.Create your new project – assign name and data.

3.3. Double-click to get Menu Bar.Double-click to get Menu Bar.

4.4. Draw model – see options below:Draw model – see options below:

• Insertion mode = Insertion mode =

• Selection mode = Selection mode =

• Connection mode = Connection mode =

5.5. Save model.Save model.

6.6. Click on calculate icon and select PLS algorithm on Click on calculate icon and select PLS algorithm on

the Pull-Down menu. Now accept the default options by the Pull-Down menu. Now accept the default options by

clicking Finish.clicking Finish.

To create a new project, click on → File → New → Create New Project. To create a new project, click on → File → New → Create New Project. The screen below will appear. Type a name in the window. Click The screen below will appear. Type a name in the window. Click

Next.Next.

You now need to assign a data file to the project, in our case, data.csv (or You now need to assign a data file to the project, in our case, data.csv (or whatever name you gave to the data you downloaded). To do so, click on whatever name you gave to the data you downloaded). To do so, click on the dots tab (…) at the right side of the window, find and highlight your data the dots tab (…) at the right side of the window, find and highlight your data folder, and click Open to select your data. Once you have specified the data folder, and click Open to select your data. Once you have specified the data file, click on Finish. file, click on Finish.

SmartPLS Software Options SmartPLS Software Options

Find your new project in window, expand list of projects to get project Find your new project in window, expand list of projects to get project details (see below), click on the .splsm file for your projectdetails (see below), click on the .splsm file for your project

Double click on your new model to get the menu Double click on your new model to get the menu bar to appear at the top of the screen.bar to appear at the top of the screen.

Selection modeSelection mode

Draw constructsDraw constructs

Draw structural pathsDraw structural paths

Initial Structural Model – No Indicator VariablesInitial Structural Model – No Indicator Variables

Structural Model with Names and PathsStructural Model with Names and Paths

Name Constructs, Align Indicators, Etc. . . .Name Constructs, Align Indicators, Etc. . . .

Start calculation

Change reflective to formative

Show measurement model

Rename Construct

Hide used indicators

How to Run SmartPLS SoftwareHow to Run SmartPLS Software

Default Settings for Example – Click Finish to runDefault Settings for Example – Click Finish to run

Trade-off in missing value Trade-off in missing value treatment:treatment:

Case wise replacement can Case wise replacement can greatly reduce the number of greatly reduce the number of

cases but sample mean cases but sample mean imputation reduces variables’ imputation reduces variables’

variance.variance.

Preferred approach to deal Preferred approach to deal with missing data is combination with missing data is combination

of sub-group and nearest of sub-group and nearest neighbor, or use EM imputation neighbor, or use EM imputation

using SPSS.using SPSS.

Always use path weighting schemeAlways use path weighting scheme

PLS Results for ExamplePLS Results for Example

SmartPLS Calculation Reports – OverviewSmartPLS Calculation Reports – Overview

Quality Criteria Report – SmartPLSQuality Criteria Report – SmartPLS

The composite reliability is The composite reliability is excellent – almost .90 for all excellent – almost .90 for all

three constructs.three constructs.

The AVEs for all three constructs are The AVEs for all three constructs are well above .50.well above .50.

Summary of PLS-SEM FindingsSummary of PLS-SEM Findings

1.1.The direct path from COMP to CUSA is 0.162 and the direct path The direct path from COMP to CUSA is 0.162 and the direct path

from COMP to CUSL is 0.009.from COMP to CUSL is 0.009.

2.2.The direct path from LIKE to CUSA is 0.424 and the direct path The direct path from LIKE to CUSA is 0.424 and the direct path

from LIKE to CUSL is 0.342.from LIKE to CUSL is 0.342.

3.3.The direct path from CUSA to CUSL is 0.504.The direct path from CUSA to CUSL is 0.504.

4.4.Overall, the model predicts 29.5% of the variance in CUSA, and Overall, the model predicts 29.5% of the variance in CUSA, and

56.2% of the variance in CUSL.56.2% of the variance in CUSL.

5.5.Reliability of constructs is excellent.Reliability of constructs is excellent.

6.6.Constructs achieve convergent validity (AVE > 0.50)Constructs achieve convergent validity (AVE > 0.50)

To determine significance levels, you must run Bootstrapping To determine significance levels, you must run Bootstrapping option. Look for under the calculate option.option. Look for under the calculate option.