learn about structural equation modeling in smartpls with

Learn About Structural Equation

Modeling in SmartPLS With Data

From the Customer Behavior in

Electronic Commerce Study in

Ecuador (2017)

© 2019 SAGE Publications, Ltd. All Rights Reserved.

This PDF has been generated from SAGE Research Methods Datasets.

Learn About Structural Equation

Modeling in SmartPLS With Data

From the Customer Behavior in

Electronic Commerce Study in

Ecuador (2017)

How-to Guide for SmartPLS

Introduction

Partial Least Squares Structural Equation Modeling (PLS-SEM) is useful when

the research needs to predict a set of dependent variables from a large set

of independent variables (Abdi, 2007). This example shows in which situations

researchers should use this technique with respect to other predictive multivariate

techniques. We illustrate PLS-SEM using a subset of 2017 Customer Behavior in

Electronic Commerce Study in Ecuador.

Specifically, we test whether Behavioral Intention (BI) is predicted by Performance

Expectancy (PE), Effort Expectancy (EE), Social Influence (SI), and Facilitating

Conditions (FC). According to the UTAUT Model, BI is an indicator of how people

are willing to shop online. PE is defined as the degree to which e-commerce will

provide benefits to consumers for shopping. SI is the extent to which consumers

perceive that importance others (e.g., family and friends) believe they should use

Internet for shopping. FC refer to consumers’ perceptions of the resources and

support available to shop online. The last variable, EE, is the degree of ease

associated with consumers’ use of Internet for shopping. This example is useful if

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2019 SAGE Publications, Ltd. All Rights Reserved.

SAGE Research Methods Datasets Part

2

of 19 Learn About Structural Equation Modeling in SmartPLS With Data From the

Customer Behavior in Electronic Commerce Study in Ecuador (2017)

we want to understand what drives the intention to shop online.

Contents

1. Partial Least Square Structural Equation Modeling (PLS-SEM)

2. An Example in SmartPLS: Behavioral Intention to Shop Online in

Ecuador

2.1 Estimating SEM-PLS With SmartPLS

2.2 The SmartPLS Procedure

2.3 Presenting Results

3. Your Turn

1 Partial Least Square Structural Equation Modeling (PLS-SEM)

Structural equation modeling (SEM) is a multivariate statistical technique that

allows researchers to estimate and test causal relationships. This method

originated in the context of genetics, to examine the joint effect of one or more

independent variables, which were represented in a path diagram, which is why it

is also sometimes called broadly, path analysis.

In these models, the types of variables are distinguished according to their

measurement or role in the model: (i) latent variables: also known as constructs,

factors, concepts, or conceptual variables—they are the model features of direct

interest, but they are unobservable elements that can only be inferred from

those observed; (ii) observed variables, also called indicators, inputs, or simply

measures, and are distinguished because they can be measured and are known

or thought to be related to the latent concepts. An example is intelligence, which

constitutes the construct and can be observed through the measurement of

variables observed as verbal and quantitative reasoning test scores, for instance,

among other measurable indicators.

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In turn, these latent variables, depending on their role in the model, can be

exogenous and endogenous. The exogenous affect others and do not receive

influence, while the endogenous ones receive influence from other variables

but also can affect each other. Conventionally, graphically, these models are

represented with the latent variables in Greek letters within circles, and the

observed variables are represented in Latin letters within rectangles. The

relationship between the observed variables or indicators and the latent variables

is known as the measurement model or outer model, while the relationship

structure between the latent variables or concepts of the model is called the

structural model or inner model. Both models are represented graphically by

arrows in trajectory diagrams (Henlein & Kaplan, 2004). The direction of the

arrows between the observed and latent variables, theoretically, indicates whether

the observed measurements are reflective indicators (each indicator is a reflection

or direct observation of the latent variable or construct) or formative indicators

(where some set of indicators together jointly determine the latent variable). The

entire process to establish or represent the structural model is known as the

specification or identification of the model.

Next, a graphic representation of a model of structural equations is shown in

which it is established that the intent of online purchase—endogenous conceptual

variable—is predicted by Performance Expectancy, Effort Expectancy, Social

Influence, and Facilitating Conditions, exogenous conceptual variables (see

Figure 1). In turn, each of these latent variables is measured by sets of indicators

or observed variables, specifically the score of the items on the scale of each of

these variables. The hypotheses are established as the predicted relationships

between the latent variables, specifically by the expected effect of the four

exogenous variables on the endogenous one.

In this example, the relationships of all the indicators with their respective latent

variables are presented as reflective indicators, since each survey question

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measure is only one of a set of indicators of their respective concept.

Figure 1: Path Diagram of a Structural Equations Model.

2 An Example in SmartPLS: Behavioral Intention to Shop Online in Ecuador

2.1 Estimating SEM-PLS With SmartPLS

The estimation of structural models consists of two stages—first, estimation of

the measurement model or outer model, and second, estimation of the structural

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model or inner model—as described below:

• Measurement model assessment: The fundamental purpose of this stage

is to evaluate the assumptions related to the reliability and validity of the

structural measurement model, identified in the SmartPLS program as

“PLS Algorithm.” When running the PLS algorithm, the initial weights of

all indicators in determining their construct within the PLS path model are

set to +1 (the default SmartPLS setting). Also, by default, the program

estimates these weights, called the “Path” weighting scheme, to maximize

the values of R2 or variance explained, sets the maximum iterations in

weights estimation to 300 (which is useful for exploratory models, but

as the estimation becomes more confirmatory, at least 1,000 and up to

5,000 iterations are recommended); and set the Stop Criterion to 10−7;

this setting is generally recommended as adequately fine tolerance (if

convergence problems arise, reducing tolerance to 10−5 is often

suggested).

• Structural model assessment: The structural model is evaluated with

respect to the estimates and hypothesis tests regarding the causal relations

between exogenous and endogenous variables specified in the path

diagram. Standard errors and test statistics for the relevant parameters are

estimate in SmartPLS with the Bootstrapping option. (The measurement

model estimation and the structure of the SEM causal model imply that

analytic standard errors derived from normal assumptions or

approximations, as used in regression analysis as an example, would be

inappropriate in this context; thus, bootstrapping is strongly advised.)

In bootstrapping, subsamples are randomly drawn observations from the original

set of data (with replacement). Each subsample is then used to estimate the PLS

path model. This process is repeated until many random subsamples have been

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created (e.g., 5,000). The variation across these many (e.g., 5,000) estimations

from the bootstrap subsamples is used to derive standard errors for the PLS-

SEM results. With this information, standard errors, Beta coefficients, t-values,

p-values, and confidence intervals can be calculated to assess the PLS-SEM

estimation results.

The (fit) quality criteria are the most relevant output values for assessing both

phases of the PLS-SEM estimation. Specifically, the criteria and generally

suggested interpretation are summarized in Table 1.

Table 1: Criteria for Evaluating the Quality of the Measurement Model

(Outer Model).

Quality

criteria Description

R2

Coefficient of determination. Indicates the percentage of the variance in the endogenous variable that the

exogenous variables explain collectively. Can take values between 0 and 1, where values closer to 0 represent

poor fit and values closer to 1 represent a better fit.

f2

Measures effect size and the strength of the relationship between the variables on a numeric scale related to

total, explained, and error variances. Higher is generally considered better, although “sufficiently high” values

will vary considerably across contexts. Nonetheless, these general guidelines have been offered:

Above 0.35 large effect size

Ranging from 0.15 to 0.35 medium effect size

Between 0.02 and 0.15 small effect size

Values less than 0.02 are considered essentially zero effect size

Construct

Reliability

and

Validity

Cronbach’s Alpha is a measure of internal consistency or reliability of a construct’s measure, that is, how

closely related the set of items comprising the construct are as a group. The result is usually a number from 0

to 1, but a negative Cronbach’s Alpha can also occur, suggesting something seriously wrong with the operation

(e.g., if some score items have polarity reversed relative to some others, the mean of all the inter-item

correlations can be negative: items’ polarity should always be aligned). General guidelines on Cronbach’s

Alpha for Construct Reliability and Validity are:

Below 0.60 unacceptable

0.60–0.70 minimally acceptable

0.70–0.80 respectable

0.80–0.90 very good

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Above 0.90 strong

Composite reliability (CR), also referred to as McDonald’s coefficient, is obtained by combining all the true

score variances and covariances in the composite of indicator variables related to constructs and by dividing

this sum by the total variance in the composite. Like Cronbach’s Alpha, CR is a reliability indicator, but

Cronbach’s Alpha assumes factor loadings to be the same for all items, whereas CR takes into consideration

the varying factor loadings of the items. Acceptable values of CR are generally considered 0.7 and above.

Average of variance extracted (AVE) is an indicator of convergent validity that measures the amount of

variance that is captured by a construct in relation to the amount of variance due to measurement error.

Generally, AVE of at least 0.5 or higher is demanded, otherwise variance of error is more than variance

explained, which is considered unacceptable.

Discriminant validity, finally, determines whether the constructs in the model are highly correlated among

themselves or not. It compares the Square Root of AVE of a particular construct with the correlation between

that construct with other constructs. It is generally suggested that the Square Root of AVE should be higher

than the correlation of the construct with others (If not, the individual construct does not provide much

discrimination, i.e., unique explanatory power).

Collinearity

Statistics

(VIF)

Variance inflation factor is a measure of the amount of multicollinearity among a set of multiple regression

variables. Values greater than 4 are generally considered to indicate problematically high multicollinearity.

Model fit

Standardized Root Mean Square Residual is an index of the average of standardized residual between the

observed and the hypothesized covariance matrices. Although recommendations vary across sources, a good

adjustment is generally considered to be less than 0.10 or 0.08.

Chi Square (χ2), the model is generally considered to have an acceptable fit if the Chi-square/df values are

from 2 to 3 and with limits of up to 5.

Outer

loadings

For reflective models, these are the key indicators showing the trajectory of the Latent variable towards the

observed variables. Therefore, they show how much each observable variable or item contributes absolutely to

the definition of the construct or latent variable. Loadings are generally expected to be greater than .6.

Outer

weights

They are indicators typical of the formative models, since they illustrate the trajectory from the Observed

variable to the Latent variables. These indicate the relative contribution of an indicator to the definition of its

corresponding variable. It is also expected to be greater than .7.

Residue Analogous to regression residuals, the residue indicates the variance that does not ultimately go into

explaining the factor. As this “waste” is lower, a better fit of the model is indicated.

2.2 The SmartPLS Procedure

We have summarized the process for the calculation of PLS-SEM in SmartPLS in

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five stages, as shown in Figure 2.

Figure 2: Summary of the Process for the PLS-SEM in SmartPLS Software.

Step 1—Prepare Your Data

• The database must be loaded in Excel

• Files with more than one worksheet are not processed

• All information must be expressed in numeric language

• Subject by row and variables by columns

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• Save in CSV format delimited by commas

• All variables must be of numeric

• If you have inverse items, they must be corrected prior to being exported to

the program

• To optimize the work of reporting the data, use the coding as it will appear

in the final work

Step 2—Create Your Project

Open the software (available in https://www.smartpls.com/) (Ringle, Wende, &

Becker, 2015) and follow the following commands: New Project → Create New

Project → naming the Project. As shown in Figure 3.

Figure 3: Screenshots of the Project Creation Process in SmartPLS Software.

Upon conclusion of the project creation, the command to import the data will

appear in the upper right panel. Double-click as indicated and look in the library of

your computer for the data file, select it and press open (see Figure 4).

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https://www.smartpls.com/

Figure 4: Screenshots to Import Data in SmartPLS Software.

Step 3—Explore Your Data

Once the data have been imported, you can view the descriptive statistics of the

variables under study in the lower right panel (e.g., Mean, Medium, Min, Max,

Standard deviation, Excess Kurtosis, and Skewness) (see Figure 4, Step 5).

Step 4—Specify the Theoretical Model

To specify the model, use the superior command “new path model.” Clicking this

will bring up a dialog box to name your path diagram. Now you can visualize the

graphics resources in the upper part that will allow you to draw it. First, place

the latent variables and then the arrows as theoretically established. Initially, they

appear as red circles (see Figure 5).

Figure 5: Description of the Model Specification in SmartPLS Software.

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With the right button on each variable latent, you can label the variables. With the

“connect” command, you can identify with an arrow and establish the relationships

between the variables (see Figure 5, Step 7). Then, load the observed variables

or indicators to the model. To do so, select the lower right panel identified with the

indicator name and drag the corresponding items to each variable and drop it over

the corresponding latent circle or variable. Progressively, as the indicators of each

variable are loaded, originally in red circles, they will turn blue, and the indicators

or items will be reflected in yellow rectangles. Now you can see the model in the

following way, as it was seen in Figure 1 previously described.

Step 5—Calculate the Model

As previously described, the calculation process involves two moments, the

evaluation of the measurement model or outer model and assessment of the

structural model or inner model. To evaluate the measurement model, follow the

following commands:

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Calculate → PLS Algorithm → Basic Setting (Path) – Maximum Iterations

(300) Stop Criterion (7) → Start Calculation

At the conclusion of the calculation, you can see all the indicators associated

with the measurement model in the lower right panel. In the upper tabs of this

quadrant, you can review this information numerically and graphically (see Figure

6).

Figure 6: Screening of the Data Output of the Measurement Model.

Subsequently, for the calculation of the structural model, a similar process is

followed, whose commands are described below:

Calculate → Bootstrapping → Basic Setting (Subsamples 500) –

Advanced Setting (Confidence Interval Method – Bias-Corrected and

Accelerate (BCa) Bootstrap) – Two-Tailed - Significance Level → Start

Calculation

Similarly, in the lower right panel, you can see the indicators associated with the

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evaluation of the structural model hypotheses.

2.3 Presenting Results

The results from a PLS-SEM should be reported according to the processing

structure as described below:

Measurement Model Assessment

All constructs in this research model are first-order reflective. Measurement quality

being verified by examining convergent validity, discriminant validity, and internal

consistency. Convergent validity was assessed as follows: item reliability was

inspected for each Convergent item, validity requires indicator loadings to be 0.6

or more. All indicators had loadings well above 0.6.

Remaining item loadings (see Table 2) demonstrated the acceptable convergent

validity and were retained for subsequent analysis. Composite reliability indicators

were higher than 0.7, and internal consistency was assessed via Cronbach’s

Alpha Coefficient, and all values were above 0.8, indicating excellent (1.0–0.90)

reliability for all the constructs. The average of variance extracted (AVE) was also

examined for each construct, and values were substantially higher than Chin’s

(1998) suggested 0.5 thresholds.

Table 2: Summary Results for Outer Model.

Construct Indicator Outer

loading

Composite

reliability

Cronbach’s

Alpha

Average of variance

extracted

Performance Expectancy

(PE)

PE1 0.807

0.92 0.89 0.64

PE2 0.772

PE3 0.815

PE4 0.818

PE5 0.770

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PE6 0.831

Effort Expectancy (EE)

EE1 0.837

0.91 0.86 0.71

EE2 0.890

EE3 0.763

EE4 0.865

Social Influence (SI)

SI1 0.821

0.91 0.88 0.72

SI2 0.865

SIC3 0.888

SI4 0.822

Facilitating Conditions

(FC)

FC1 0.775

0.93 0.91 0.73

FC2 0.897

FC3 0.873

FC4 0.875

Behavioral Intention (BI)

INT1 0.904

0.94 0.90 0.83 INT2 0.923

INT3 0.904

In regard to the discriminant validity, we compared all items loaded in which

we expected a higher value with the same construct than other variables (see

Table 3). This comparison satisfying discriminant validity suggested by Chin’s

criteria (2010). Second, the square root of AVE for each construct was higher

than the inter-scale correlation (see Table 4). In summary, these results indicate

satisfactory reliability and convergent validity.

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Table 3: Cross Factor Loadings and Reliability of Constructs.

Performance

Expectancy

Effort

Expectancy

Social

Influence

Facilitating

Conditions

Behavioral

Intention

PE1 0.81 0.64 0.34 0.63 0.51

PE2 0.77 0.59 0.30 0.56 0.47

PE3 0.82 0.57 0.36 0.50 0.44

PE4 0.82 0.56 0.41 0.49 0.43

PE5 0.77 0.59 0.45 0.51 0.44

PE6 0.83 0.66 0.44 0.57 0.49

EE1 0.65 0.84 0.46 0.60 0.50

EE2 0.71 0.89 0.44 0.69 0.56

EE3 0.50 0.76 0.44 0.54 0.37

EE4 0.64 0.86 0.45 0.72 0.55

SI1 0.36 0.39 0.82 0.27 0.22

SI2 0.47 0.55 0.87 0.54 0.45

SI3 0.38 0.42 0.89 0.35 0.29

SI4 0.37 0.38 0.82 0.34 0.26

FC1 0.59 0.60 0.49 0.77 0.53

FC2 0.61 0.67 0.43 0.86 0.54

FC3 0.58 0.68 0.40 0.90 0.57

FC4 0.57 0.65 0.34 0.87 0.54

FC5 0.57 0.68 0.37 0.88 0.56

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INT1 0.51 0.56 0.34 0.60 0.90

INT2 0.54 0.55 0.33 0.59 0.92

INT3 0.53 0.53 0.39 0.56 0.90

Note: Bold values indicate higher factorial loads.

Table 4: Construct Correlation Matrix.

Performance Expectancy Effort Expectancy Social Influence Facilitating Conditions Behavioral Intention

PE 0.80

EE 0.75 0.84

SI 0.47 0.53 0.85

FC 0.68 0.77 0.47 0.86

BI 0.58 0.59 0.39 0.64 0.91

Note: Bold values indicate higher factorial loads.

Structural Model and Hypothesis Testing

Based on SEM-PLS, we used the following criteria to assess the hypothesis

model: R2 adjusted value, Beta Coefficient, and f2 effect size. Before testing the

structural model, fit adjustment with Standardized Root Mean Square Residual

value was evaluated. The result was 0.06, which indicated a good fit adjustment.

In respect to the predictive power of the model provided for Behavioral Intention,

R2 adjusted value indicates that the model explains 45% of the variance in

BI. Bootstrapping was performed to provide a significance level for each

hypothesized relationship, parameter settings for bootstrapping included no sign

changes, and the 500 samples. According to the results, PE, EE, and FC predict

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significantly BI to use online methods to shop (see Table 5), in particular, FC

showed to be the best predictor, followed by the PE. According to f2, values of

0.02, 0.15, and 0.35 represent small, medium, and large effect sizes, respectively.

The f2 effect size on the BI was small for all the variables, which implies a small

but significant contribution of the variables whose hypotheses were confirmed. All

these results are summarized in Table 5.

Table 5: Summary of Hypothesis Testing.

Path Beta coefficient t-value p-value Result

H1: Performance Expectancy → Behavioral Intention 0.133 1.847 .066 Not confirm

H2: Social Influence → Behavioral Intention 0.020 0.394 .694 Not confirm

H3: Facilitating Conditions → Behavioral Intention 0.298 4.298 .000 Confirm

H4: Hedonic Motivation → Behavioral Intention 0.123 2.436 .015 Confirm

Note: Standardized Root Mean Square Residual = 0.06; Chi-square = 2.243.

3 Your Turn

You can download this sample dataset along with a guide showing how to conduct

a PLS-SEM. The sample dataset also includes demographic variables such as

gender and age. See whether you can reproduce the results presented here, and

try to conduct your own PLS-SEM: (1) for men and women separately and (2) for

young people (18–25 years old) and young adults (26–35 years old) separately.

References

Abdi, H. (2007). Partial least squares regression. Encyclopaedia of measurement

and statistics, 2, 740–744.

Chin, W. W. (1998). The partial least squares approach for structural equation

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modeling. In G. A. Marcoulides (Ed.), Modern methods for business research

(pp. 295–336). Mahwah, NJ: Lawrence Erlbaum.

Chin, W. W. (2010). How to write up and report PLS analyses. In Handbook of

partial least squares (pp. 655–690). Berlin, Germany: Springer.

Hair, J., Jr., Sarstedt, M., Hopkins, L., & Kuppelwieser, V. G. (2014). Partial

least squares structural equation modeling (PLS-SEM): An emerging tool in

business research. European Business Review, 26(2), 106–121.

Henlein, M., & Kaplan, A. M. (2004). A beginner’s guide to partial least squares

analysis. Understanding Statistics, 3(4), 283–297.

Ringle, C. M., Wende, S., & Becker, J.-M. (2015). “SmartPLS 3.” Boenningstedt,

Germany: SmartPLS GmbH. Retrieved from http://www.smartpls.com

Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User

acceptance of information technology: Toward a unified view. MIS Quarterly,

27(3), 425–478.

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