1. The document discusses terms and concepts related to structural equation modeling (SEM) using SmartPLS software. 2. It provides details on normality assessment, internal consistency, convergent validity, and discriminant validity tests that were performed on survey data to evaluate the measurement model in SmartPLS. 3. Assessment of the structural model included calculating p-values and confidence intervals to test hypotheses and determine the significance and relevance of relationships between latent variables.

1. Terms for smartPLS

2. Some facts about SEM - AMOS
• “IBM® SPSS® Amos is a powerful structural equation modeling
software that enables you to support your research and theories
by extending standard multivariate analysis methods, including
regression, factor analysis, correlation, and analysis of
variance. With SPSS Amos, one can build attitudinal and
behavioral models that reflect complex relationships more
accurately than with standard multivariate statistics techniques
using either an intuitive graphical or programmatic user
interface.” (IBM, 2017).

3. Smart PLS and CB SEMM
• “The philosophical distinction between CB-SEM and PLS-SEM
is straightforward. If the research objective is theory testing
and confirmation, then the appropriate method is CB-SEM.
• In contrast, if the research objective is prediction and theory
development, then the appropriate method is PLS-SEM.
Conceptually and practically, PLS-SEM is similar to
using multiple regression analysis.

4. • The primary objective is to maximize explained variance in the
dependent constructs but additionally to evaluate the data
quality based on measurement model characteristics.”
• Amos is more stringent (strict/precise) compared to Smart
PLS (SPLS). If formal theory and the appropriate sample size
are not available, SPLS can work, but Amos does not give a
proper model fit.

5. Working terms for smart PLS
• 10 times rule: one way to determine the minimum sample size specific to the
PLS path model that one needs for model estimation (i.e., 10 times the
number of independent variables of the most complex ordinary least squares
regression in the structural model or any formative measurement model).
• Hair, J. F., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2022). A Primer on
Partial Least Squares Structural Equation Modeling (PLS-SEM), 3rd ed.
Thousand Oaks, CA: Sage.

6. • The 10 times rule is not a reliable indication of sample size requirements in
PLS-SEM and should at best be seen as a rough estimate. While statistical
power analyses provide more reliable minimum sample size estimates,
researchers should primarily draw on the inverse square root method, which
stands out in terms of precision and ease of use.

7. Minimum sample size requirements
• the number of observations needed to represent the underlying population
and to meet the technical requirements of the multivariate analysis method
used.

8. Latent variables
• elements of a structural model that are used to represent theoretical concepts
in statistical models.
• A latent variable that only explains other latent variables (only outgoing
relationships in the structural model) is called exogenous (independent and
able to affect outside result),
• while latent variables with at least one incoming relationship in the structural
model are called endogenous. (like dependent variable)

9. Constructs
• measure theoretical concepts that are abstract, complex, and cannot be
directly observed by means of (multiple) items. Constructs are
represented in path models as circles or ovals and are also referred to as
latent variables.

10. Mediating effect
• occurs when a third construct intervenes between two other related
constructs.
• Mediator construct: a construct that intervenes between two other directly
related constructs.

11. Artifacts
• human-made concepts that are typically measured with formative indicators.

12. Path model
• a diagram that visually displays the hypotheses and variable relationships that
are examined when structural equation modeling is applied.

13. Sample of formative and reflective construct

14. p value
• in the context of structural model assessment, it is the probability of error for
assuming that a path coefficient is significantly different from zero. In
applications, researchers compare the p value of a coefficient with a
significance level selected prior to the analysis to decide whether the path
coefficient is statistically significant.

15. Outer loadings
• The bivariate correlations between a construct and the indicators. They
determine an item’s absolute contribution to its assigned construct. Loadings
are of primary interest in the evaluation of reflective measurement models but
are also interpreted when formative measures are involved.

16. • Average variance extracted (AVE): a measure of convergent validity. It is the
degree to which a latent construct explains the variance of its indicators; see
Communality (construct).

17. • Blindfolding: a sample reuse technique that omits singular elements of the
data matrix and uses the model estimates to predict the omitted part. It is
used to compute the Q² statistic.

18. Bootstrap samples
• the number of samples drawn in the bootstrapping procedure. Generally,
10,000 or more samples are recommended.

19. Bootstrapping
• a resampling technique that draws a large number of subsamples from the
original data (with replacement) and estimates models for each subsample. It
is used to determine standard errors of coefficients to assess their statistical
significance without relying on distributional assumptions.

20. Causal indicators
• A type of indicator used in formative measurement models. Causal indicators
do not fully form the latent variable but “cause” it. Therefore, causal indicators
must correspond to a theoretical definition of the concept under investigation.

21. Causal links
• are directed relationships between constructs, which can be interpreted as
causal if supported by strong theory.

22. Composite reliability
• (ρA): A measure of internal consistency reliability, which considered a sound
tradeoff between the conservative Cronbach's alpha and the liberal composite
reliability (ρC)
• (ρC): a measure of internal consistency reliability, which, unlike Cronbach’s
alpha, does not assume equal indicator loadings. It should be above 0.70 (in
exploratory research, 0.60 to 0.70 is considered acceptable).

23. Convergent validity
• the degree to which a reflectively specified construct explains the variance of
its indicators (see Average variable extracted). In formative measurement
model evaluation, convergent validity refers to the degree to which the
formatively measured construct correlates positively with an alternative
(reflective or single-item) measure of the same concept (see Redundancy
analysis).

24. Exogenous latent variables
• latent variables that serve only as independent variables in a structural model.

25. f² effect size
• a measure used to assess the relative impact of a predictor construct on an
endogenous construct in terms of its explanatory power.

26. Fornell-Larcker criterion
• a measure of discriminant validity that compares the square root of each
construct’s average variance extracted with its correlations with all other
constructs in the model. The Fornell-Larcker criterion is largely unsuitable for
detecting discriminant validity problems.

27. Heterotrait-monotrait ratio (HTMT)
• a measure of discriminant validity. The HTMT is the mean of all correlations of
indicators across constructs measuring different constructs (i.e., the
heterotrait-heteromethod correlations) relative to the (geometric) mean of the
average correlations of indicators measuring the same construct (i.e., the
monotrait-heteromethod correlations).

28. Hypothesized relationships
• proposed explanations for constructs that define the path relationships in the
structural model. The PLS-SEM results enable researchers to statistically test
these hypotheses and thereby empirically substantiate the existence of the
proposed path relationships.

29. Kurtosis
• is a measure of whether the distribution is too peaked (a very narrow
distribution with most of the responses in the center).

30. 1
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6.1
6.2
6.3

31. 1) Normality assessment
• Due to the fact that SEM needs data that do not contradict the
premise of normality, the data's normality was examined (Ali et al.,
2016). Results for normality assessment showed that the skewness
statistics of the survey questionnaire.
• Based on Kline (2009), skewness values larger than 3.0 are regarded
extreme, while values greater than 10 are considered delinquent,
becoming more serious when the value exceeds 20.

32. 2) Internal consistency
• Internal consistency to measure reliability through conbrach’s alpha and
the value calculated is 0.908, depending on the application, the expected
value of alpha range is between 0.70 – 0.95 (J Martin Bland, 1997;
Thorndike, 1995; Rice, 2015) .

33. 3) Convergent validity
• Measurement model was tested for convergent validity. The
reliability is measures using the factor loadings, Composite
Reliability (CR), and Average Variance Extracted (AVE) as
proposed by (Fornell & Larcker, 1981; Henseler et al., 2014;
Fauri, 2017).
• Rule of thumb, can’t delete more than 20% of the items.
However, composite reliability values, as an additional to the
measurement of reliability, have only recycling behaviour
and subjective norm over the recommended value of 0.7 and
all AVE are over the recommended value of 0.5

34. 4) Discriminant validity
• refers to the extent to which the measures are not a reflection of some other variables;
this is indicated by low correlations between the measure of interest and the measures
of other constructs.
• Discriminant validity is established to ascertain the distinctiveness of the constructs in
the study. It shows that constructs in the study have their own individual identity and are
not too highly co-related with other constructs in the study. Discriminant validity in
SMART-PLS is established using three different techniques.
1.Fornell and Larcker Criterion(AVE(Average Variance Extracted), CR(composite reliability))
2.Cross Loadings
3.Heterotrait-Monotrait (HTMT) - Ratio HTMT values obtained for all the constructs in the
study were lower than 0.85, indicating that discriminant validity was satisfactory and
posed a lesser threat to this study (Kline, 2011).

35. 5) Assessment of structural model
• Structural estimates hypothesis testing
• Basically, it is to calculate the P values and confidence interval.

37. 6) Assessment of the significance and
relevance of structural model
• A. R2 assessment
• B. Effect size (f2 ) assessment
• C. Predictive relevance (Q2 ) assessment

38. R square
• R Square statistics explains the variance in the endogenous variable explained by
the exogenous variable(s).
• Cohen (1988) suggested R2 values for endogenous latent variables are assessed
variables are assessed as follows: 0.26 (substantial), 0.13 (moderate), 0.02
(weak).
• Hair et al. (2011) & Hair et al. (2013) suggested in scholarly research that focuses
research that focuses on marketing issues, R2 values of 0.75, 0.50, or 0.25 for
endogenous latent variables can, as a rough rule of thumb, be respectively
described as substantial, moderate or weak.

39. •A variable in a structural model may be affected/influenced by a number of different variables.
•Removing an exogenous variable can affect the dependent variable.
•F-Square is the change in R-Square when an exogenous variable is removed from the model.
•f-square is effect size (>=0.02 is small; >= 0.15 is medium;>= 0.35 is large) (Cohen, 1988).
F square

40. Q square
• Q-square is predictive relevance, measures whether a model has predictive
relevance or not (> 0 is good).
• Further, Q2 establishes the predictive relevance of the endogenous
constructs.
• Q-square values above zero indicate that your values are well reconstructed
and that the model has predictive relevance.
• A Q2 above 0 shows that the model has predictive relevance.
• In order to find out the Q Square value, Run Blindfolding procedure in
SMART-PLS.