This document provides an agenda for a workshop on structural equation modeling using SmartPLS2.0. The morning session will cover basic concepts, components of structural and measurement models, and the PLS algorithm. After lunch, the analysis method for reflective models will be discussed. The afternoon will conclude with examining the analysis method for formative models. Hugo Watanuki is the PhD student leading the workshop at Napier University.

1. Workshop
Introduction to Structural Equation Modeling with
SmartPLS2.0
PhD Student Hugo Watanuki
Research and Innovation Office (RIO)
Napier University, UK
17th May 2018
1

2. Agenda
▪ Morning schedule
▪ 09:30 to 11:00
✓ Welcome
✓ Basic concepts and application
▪ 11:00 to 11:15
✓ Coffee-break
▪ 11:15 to 13:00
✓ Main components: structural and measurement models
✓ The PLS algorithm
▪ 13:00 to 13:30
✓ Lunch
▪ Afternoon schedule
▪ 13:30 to 15:00
✓ The analysis method: reflective model
▪ 15:00 to 15:15
✓ Tea
▪ 15:15 to 16:30
✓ The analysis method: formative model
✓ Adjourn
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3. About me
• PhD student @University of São Paulo, Brazil
• Research interest: team collaboration in virtual workplaces
• Teaching associate @Fundação Vanzolini, Brazil
• Research methods for MBA students
• Background: Mechanical (BSc) / Production Engr. (MSc)
• ~13 years as IT professional
Now, it’s your turn …
3

4. Basic concepts and
application
4

5. Contextualization
Scientific research
Quantitative approach Qualitative approach
Perspectives/environmentRelationship/variables
Miguel (2010)
5
• Methodological approaches

6. Contextualization
• Quantitative methods
• Simulation
• Experiment/Quasi-experiment
• Survey
Miguel (2010)
6

7. Contextualization
• Multivariate data analysis
7
• Multiple dependent relationships
between indirectly observed
variables
Hair et al. (2014)

8. SEM Definition
Structural Equation Modeling (SEM):
• Multiple regression technique -> Modeling
• Simultaneous dependent relationships -> Structural equations
• Separate treatment of measurement errors
Hair et al (2010)
8

9. PLS-SEM x CB-SEM
• PLS=Partial Least Squares
• Ordinary least squares (OLS) regression based
• Exploration
• SMARTPLS, ADANCO
• CB=Covariance-Based
• Maximum likelihood (ML) estimation procedure
• Testing
• LISREL, AMOS
• Complementary methods!
9
https://www.unc.edu/courses/2007spring/biol/145/001/docs/lectures/Nov3.html
https://www.youtube.com/watch?v=XepXtl9YKwc

10. PLS-SEM, a viable approach...
• Initial theoretical explorations
• Small sample sizes
• Data is nonnormally distributed
• Formative measurement models
10
Hair et al. (2014)

11. More details ...
• HAIR, J. F., HULT, G. T. M., RINGLE, C. M., & SARSTEDT, M. (2014). A
primer on partial least squares structural equation modeling (PLS-SEM).
Thousand Oaks: Sage.
• HENSELER, J., RINGLE, C. M., & SINKOVICS, R. R. (2009). The use of
partial least squares path modeling in international marketing. Advances
in International Marketing, 20, 277-319.
• TENENHAUS, M., VINZI, V. E., CHATELIN, Y., & LAURO, C. (2005). PLS path
modeling. Computational Statistics & Data Analysis, 48(1), 159-205.
• VINZI, V. E., CHIN, W. W., HENSELER, J., & WANG, H. (2010). Handbook of
partial least squares: Concepts, methods and applications. Berlin:
Springer.
11

12. SmartPLS2.0
download and installation
12

13. SmartPLS2.0 – download and installation
13

14. SmartPLS2.0 – download and installation
14

15. SmartPLS2.0 – download and installation
15

16. SmartPLS2.0 – download and installation
16

17. Main components
17
Structural and measurement models

18. The path model concept
Hair et al. (2010)
18

19. Simbology
Ringle et al (2014)
Hair et al. (2014)
Satisfaction
with hotel
19

20. A path model example...
20https://help.xlstat.com/customer/en/portal/articles/2062365-pls-path-modeling-in-excel-moderating-effects?b_id=9283

21. ... its measurement (outer) model ...
21

22. ... and its structural (inner) model
22

23. Types of variables
• Structural model:
• Endogenous variables (or dependent): determined/influenced by others.
• Exogenous variables (or independent): determine/influence others.
Vinzi et al. (2010)
23

24. Types of variables
• Measurement model:
• Latent variables (or construct): concepts that cannot be measured directly.
• Observed variables (or indicator): observed values that can be used to
measure a LV.
Vinzi et al. (2010)
24

25. Indicators can be either..
• Formative:
25
Hair et al. (2014)
• Reflective:
Henseler et al. (2009)

26. Exercise
Albers (2010)
26

27. Exercise
Albers (2010)
27

28. SmartPLS2.0
Creating a new project/model
28

29. SmartPLS2.0 – Creating project/model
Hair et al. (2014)
29
Research question: “What determines
Customer Loyalty (CUSL)?” Competence (COMP)
comp_l [The company] is a top competitor in its market.
comp_2 As far as I know, [the company] is recognized worldwide.
comp_3 I believe that [the company] performs at a premium level.
Likeability (LIKE)
like_l [The company] is a company that I can better identify with than other companies.
like_2
[The 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 [the company] as a likeable company.
Customer Satisfaction (CUSA)
cusa If you consider your experiences with [company], how satisfied are you with [company]?
Customer Loyalty (CUSL)
cusl_l I would recommend [company] to friends and relatives.
cusl_2 If I had to choose again, I would choose [company] as my mobile phone services provider.
cusl_3 I will remain a customer of [company] in the future.

30. SmartPLS2.0 – Creating project/model
• Sample data file (double click the “paper clip” icon below, open it in
Excell and save it to your PC as a “.csv” file):
Or
• https://www.pls-sem.net/downloads/1st-edition-a-primer-on-pls-sem/
30

31. SmartPLS2.0 – Creating project/model
31

32. SmartPLS2.0 – Creating project/model
32

33. SmartPLS2.0 – Creating project/model
33
Project
Model
Dataset

34. SmartPLS2.0 – Creating project/model
34
Double click

35. SmartPLS2.0 – Creating project/model
35
Projects
List of latent
variables from
your model
List of observed
variables from
your model
Double click

36. SmartPLS2.0 – Creating project/model
To import/export projects
To export images from the
model
36

37. SmartPLS2.0 – Creating project/model
Work modes
Visualization settings
37

38. SmartPLS2.0 – Creating project/model
Editting/organizing
tools for the path
model
38

39. SmartPLS2.0 – Creating project/model
39
Algorithms
Sampling procedures

40. SmartPLS2.0 – Creating project/model
40
Report view
options

41. SmartPLS2.0 – Creating project/model
41
Window
options
User
preferences

42. SmartPLS2.0 – Creating project/model
42

43. SmartPLS2.0 – Creating project/model
43

44. SmartPLS2.0 – Creating project/model
44
Select the
model to
activate the
work area and
menus

45. SmartPLS2.0 – Creating project/model
45
Use the insertion mode to create a new LV in the work area

46. SmartPLS2.0 – Creating project/model
Repeat the same process
to all LV’s in the model...
Select the appropriate
indicators and drag/drop
them over the LV
Switch to selection
mode in order to
move the LV in the
work area
46

47. SmartPLS2.0 – Creating project/model
47

48. SmartPLS2.0 – Creating project/model
Switch to connection mode to connect LV’s
48

49. SmartPLS2.0 – Creating project/model
49

50. SmartPLS2.0 – Creating project/model
LV’s
50

51. SmartPLS2.0 – Creating project/model
OV’s
51

52. SmartPLS2.0 – Creating project/model
Structural model
52

53. SmartPLS2.0 – Creating project/model
Measurement
model
53

54. SmartPLS2.0 – Creating project/model
Exogenous LV’s
54

55. SmartPLS2.0 – Creating project/model
Endogenous LV’s
55

56. SmartPLS2.0 – Creating project/model
Reflective model
56

57. The PLS algorithm
57

58. The algorithm
Hair et al. (2014) 58
Iterative process:

59. SmartPLS2.0
Running the algorithm
59

60. SmartPLS2.0 – Running the algorithm
60
- Path Weighting Scheme: uses regression.
- Factor Weighting Scheme: uses correlation.
- Centroid Weighting Scheme: uses only correlation signs “+/-
1” (older method to be used as a last resort).
Ringle et al. (2014)

61. SmartPLS2.0 – Running the algorithm
61

62. The analysis method
Reflective model
62

63. Two stage process
1. Assessment of the measurement model
• Are the measurement errors under control?
• Every measure is subject to errors (specially when measuring feelings, emotions,
competences, etc).
• Quality of measures affects quality of research.
2. Assessment of the structural model
• How well does the model explain the behavior of the target variable?
• How generalizable is the model?
63

64. 1. Assessment of the measurement model
Measurement errors can be of two types:
• Systematic (bias) - Are we measuring the right thing (validity)?
• Random – Are the measures reliable (reliability)?
Bido (2012) 64

65. 1. Assessment of the measurement model
Our goal ...
65Bido (2012)

66. 1. Assessment of the measurement model
Type of Assessment Level Criteria name Value
i) Reliability Construct Cronbach’s alpha (α),
Composite reliability (ρc)
> 0.70
ii) Reliability Indicator Outer loading relevance Ideally > 0.70
iii) Validity (convergent) Construct Average Variance Extracted
(AVE)
>= 0.50
iv) Validity (discriminant) Indicator Cross-loadings an indicator's outer loading on the
associated construct should be greater
than all of its loadings on other
constructs (i.e., the cross loadings)
v) Validity (discriminant) Construct Fornell-Larcker construct should share more variance
with its associated indicators than with
any other construct (i.e, the square
root of each construct's AVE should be
greater than its highest correlation
with any other construct)
66Hair et al. (2014)

67. 1. Assessment of the measurement model
67
What if something goes wrong?
Check your indicators...

68. 1. Assessment of the measurement model
68
Example:
Watanuki (2014)

69. SmartPLS2.0
Assessing the measurement model
69

70. SmartPLS2.0 – Assessing the measurement...
The algorithm has converged
within the appropriate number
of iterations-> OK!
70

71. SmartPLS2.0 – Assessing the measurement...
Criteria: AVE > 0.50 Criteria: ρc > 0.70 Criteria: α > 0.70
i) Reliability (constructs) ok!iii) Validity (convergent) ok!
71

72. SmartPLS2.0 – Assessing the measurement...
ii) Reliability
(indicators) ok!
Criteria: Outer loading
relvance (ideally > 0.70)
72

73. SmartPLS2.0 – Assessing the measurement...
iv) Validity
(discriminant) ok!
73
Criteria: Cross-loadings

74. SmartPLS2.0 – Assessing the measurement...
v) Validity (discriminant) ok!
74
Criteria: Fornell-Larcker

75. 2. Assessment of the structural model
Most common methods:
• Preditive accuracy of the model (coefficient of determination - R2)
• Relevance (signal/magnitude) and significance (ρ) of path coefficients
(β)
75
Bido (2012)

76. 2. Assessment of the structural model
Hair et al. (2014)
76
• Boostrapping

77. 2. Assessment of the structural model
Student’s t distribution
77https://www.statmethods.net/advgraphs/probability.html
t=β/SE , SE=std error

78. 2. Assessment of the structural model
Student’s t test
78http://magiamax.ml/qysul/level-of-confidence-chart-vaz.php
https://pt.wikipedia.org/wiki/Teste_t_de_Student

79. SmartPLS2.0
Assessing the structural model
79

80. SmartPLS2.0 – Assessing the structural model
80

81. SmartPLS2.0 – Assessing the structural model
81
R2
β

82. SmartPLS2.0 – Assessing the structural model
R2
82

83. SmartPLS2.0 – Assessing the structural model
Number of cases in
the original sample
Number of samples
to be generated
83

84. SmartPLS2.0 – Assessing the structural model
84

85. SmartPLS2.0 – Assessing the structural model
85
t > 1.96
There is less than 5% of possibility
of being wrong when affirming
these coefficients are different
from zero in the population!!!
COMP->CUSL effect can be either null in
the population or the sample selected has
low statiscal power.
t < 1.96

86. SmartPLS2.0 – Assessing the structural model
Original research question: “What determines Customer Loyalty (CUSL)?”
86
Conclusion: COMP->CUSL path coefficient (β) can be either null in
the population or the sample selected has low statiscal power.

87. The analysis method
Formative model
87

88. Two stage process
1. Assessment of the measurement model
• Are the measurement errors under control?
• Every measure is subject to errors (specially when measuring feelings, emotions,
competences, etc).
• Quality of measures affects quality of research.
2. Assessment of the structural model
• How well does the model explain the behavior of the target variable?
• How generalizable is the model?
88

89. 1. Assessment of the measurement model
It is formative, so what?...
Key distinction: indicators do not necessarilly correlate.
89
Items to be assessed:
- At the construct level: “Is there any portion of the focal construct not being
captured by the indicators?” (validity)
- At the indicator level: “Does every indicator really contribute to formative
index?”
Henseler et al. (2009)

90. 1. Assessment of the measurement model
“Is there any portion of the focal construct not being captured by the indicators?”
R.: Redundant analysis, i.e., the formative index should explain at least 64% of an
alternative reflective index for the same construct.
90
Hair et al. (2014)
Must be included a
priori in the surveyβ >= 0.80

91. 1. Assessment of the measurement model
“Does every indicator really contribute to a formative index?”
R.: Assess the relevance (signal/magnitude) and significance (ρ) of the formative
indicators.
91
Hair et al. (2014)
Must be relevant and
statistically significant

92. 1. Assessment of the measurement model
Type of Assessment Level Criteria name Value
i) Validity (convergent) Construct Redundancy analysis The path coefficient between the
formative and reflective constructs
should be >= 0.80
ii) Relevance and
significance
Indicator Outer weight significance
AND/OR
Outer loading relevance
Outer weight is significant AND/OR
Outer loading >= 0.50
92
Hair et al. (2014)

93. SmartPLS2.0
A mixed model example
93

94. SmartPLS2.0 – A mixed model example
Hair et al. (2014)
94
Research question: “Which marketing
strategy should be prioritized for a larger
increase in Customer Loyalty (CUSL)?”
Quality (QUAL)
qual_1 The products/services offered by [the company] are of high quality.
qual_2 In my opinion [the company] tends to be an innovator, rather than an imitator with respect to [industry].
qual_3 I think that [the company]'s products/services offer good value for money.
qual_4 The services [the company] offers are good.
qual_5 Customer concerns are held in high regard at [the company].
qual_6 [The company] seems to be a reliable partner for customers.
qual_7 I regard [the company] as a trustworthy company.
qual_8 I have a lot of respect for [the company].
Performance (PERF)
perf_1 [The company] is a very well-managed company.
perf_2 [The company] is an economically stable company.
perf_3 I assess the business risk for [the company] as modest compared to its competitors.
perf_4 I think that [the company] has growth potential.
perf_5 [The company] has a clear vision about the future of the company.
Corporate Social Responsibility (CSOR)
csor_1 [The company] behaves in a socially conscious way.
csor_2 I have the impression that [the company] is forthright in giving information to the public.
csor_3 I have the impression that [the company] has a fair attitude toward competitors.
csor_4 [The company] is concerned about the preservation of the environment.
csor_5 I have the feeling that [the company] is not only concerned about profits.
Attractiveness (ATTR)
attr_1 In my opinion [the company] is successful in attracting high-quality employees.
attr_2 I could see myself working at [the company].
attr_3 I like the physical appearance of [the company] (company, buildings, shops, etc.).

95. SmartPLS2.0 – A mixed model example
95

96. SmartPLS2.0 – A mixed model example
96

97. SmartPLS2.0 – A mixed model example
97

98. SmartPLS2.0 – A mixed model example
Tip: use this option to
setup the proper
measurement model for
formative LV’s
98

99. SmartPLS2.0 – A mixed model example
99

100. SmartPLS2.0 – A mixed model example
100

101. SmartPLS2.0 – A mixed model example
101
Reflective LV’s

102. SmartPLS2.0 – A mixed model example
102
Formative LV’s

103. SmartPLS2.0 – A mixed model example
103

104. SmartPLS2.0 – A mixed model example
The algorithm has converged
within the appropriate number
of iterations-> OK!
104

105. SmartPLS2.0 – A mixed model example
105
Criteria: AVE > 0.50 Criteria: ρc > 0.70 Criteria: α > 0.70
i) Reliability (constructs) ok!iii) Validity (convergent) ok!

106. SmartPLS2.0 – A mixed model example
106
ii) Reliability
(indicators) ok!
Criteria: Outer loading
relevance (ideally > 0.70)

107. SmartPLS2.0 – A mixed model example
107
iv) Validity
(discriminant) ok!
Criteria: Cross-loadings

108. SmartPLS2.0 – A mixed model example
108
v) Validity
(discriminant) ok!
Criteria: Fornell-Larcker

109. SmartPLS2.0 – A mixed model example
109

110. SmartPLS2.0 – A mixed model example
110

111. SmartPLS2.0 – A mixed model example
111

112. SmartPLS2.0 – A mixed model example
112

113. SmartPLS2.0 – A mixed model example
113
i) Validity
(discriminant) ok!
Criteria: Redundancy
analysis (β >= 0.80)
Repeat the same steps for the
other formative LV’s ....

114. SmartPLS2.0 – A mixed model example
114
i) Validity
(discriminant) ok!
Criteria: Redundancy
analysis (β >= 0.80)

115. SmartPLS2.0 – A mixed model example
115
Now, go back to
the original model
and perform a
bootstrapping

116. SmartPLS2.0 – A mixed model example
Number of cases in
the original sample
Number of samples
to be generated
116

117. SmartPLS2.0 – A mixed model example
All outer weights are significant
(t>1,96), except four...
117

118. SmartPLS2.0 – A mixed model example
118
ii) Relevance ok!
Criteria: Outer loading
relevance (>=0.50)

119. SmartPLS2.0 – A mixed model example
119
Now, assess the
structural
model...
R2
β

120. SmartPLS2.0 – A mixed model example
120
All outer weights are significant
(t>1,96), except four...

121. SmartPLS2.0 – A mixed model example
121
Original research question: “Which marketing strategy should be
prioritized for a larger increase in Customer Loyalty (CUSL)?”

122. SmartPLS2.0 – A mixed model example
122
Quality is the marketing
strategy with largest
influence in customer
loyalty and ....

123. SmartPLS2.0 – A mixed model example
123
... marketing strategy should
focus on the increase of
customer’s perception
regarding reliability of both
products and services.

124. Main limitations
Structural Equation Modeling with SmartPLS2.0
124

125. Main limitations
• Cannot be applied to non-recursive models
• Less precise compared to CB-SEM (in the ideal sample/scenario)
• Needs to be complemented by non-parametric methods
(bootstrapping)
• Minimal sample size requirements:
• 10 times the maximum number of arrowheads pointing at a latent variable
anywhere in the PLS path model
• Cannot easily assess multicollinearity issues
• Absence of a global evaluation parameter for the model (GoF) ->
resolved in SmartPLS3.0
125Henseler et al. (2009) Hair et al. (2014)Streiner (2004)

126. Practical aspects
126

127. Practical aspects
Operationalization issues:
https://webofknowledge.com/
http://www.scopus.com/home.url
127

128. Practical aspects
128
Electronic questionnaires:
https://www.surveymonkey.com/
https://surveys.google.com/

129. Practical aspects
Advertising the survey:
• Email, social media, class organizations,
etc
• Direct mail/advertising companies
129https://thenextweb.com/contributors/2017/08/21/blockchain-can-make-social-networks-private-profitable/

130. Practical aspects
130
Preliminary data treatment:

131. Workshop
Introduction to Structural Equation Modeling with
SmartPLS2.0
PhD Student Hugo Watanuki
Research and Innovation Office (RIO)
Napier University, UK
17th May 2018
131

132. Let’s stay in touch...
• http://lattes.cnpq.br/8871662215636083
• https://www.researchgate.net/profile/Hugo_Watanuki
• https://www.linkedin.com/in/hugo-watanuki-82ba53a/
• https://www.slideshare.net/HugoWatanuki
• hwatanuki@usp.br
• @hugowtnk
• +44 (0) 794 419 1980
• +55 (11) 94142 0218
132

133. References
• ALBERS, S. (2010). PLS and Success Factor Studies in Marketing. In: VINZI, V. E., CHIN, W. W., HENSELER, J., & WANG, H. (Org.), Handbook of partial
least squares: Concepts, methods and applications (pp. 409-425). Berlin: Springer.
• BIDO, D. S. (2012). Modelagem em equações estruturais com estimação PLS (partial least squares-path modeling). Workshop presented at Encontro
Nacional da ANPAD. Available at: http://www.anpad.org.br/diversos/enanpad2012/
• HAIR JR., J. F., BLACK, W. C., BABIN, B. J., & ANDERSON, R. E. (2014). Multivariate Data Analysis. New Jersey: Prentice Hall.
• HAIR, J. F., HULT, G. T. M., RINGLE, C. M., & SARSTEDT, M. (2014). A primer on partial least squares structural equation modeling (PLS-SEM). Thousand
Oaks: Sage.
• HENSELER, J., RINGLE, C. M., & SINKOVICS, R. R. (2009). The use of partial least squares path modeling in international marketing. Advances in
International Marketing, 20, 277-319.
• MIGUEL, P. A. C. (2010). Metodologia de Pesquisa em Engenharia de Produção e Gestão de Operações. Rio de Janeiro: Editora Campus.
• RINGLE, C. M., SILVA, D., & BIDO, D. (2014). Modelagem de equações estruturais com utilização do SmartPLS. Revista Brasileira de Marketing, 13, 54-
71.
• RINGLE, C. M., WENDE, S., & WILL, A. (2005). Smart PLS 2.0 M3. Hamburg University. Available at: http://www.smartpls.de
• STREINER, D. L. (2005). Finding Our Way: An Introduction to Path Analysis. The Canadian Journal of Psychiatry, 50 (2), 115-122.
• TENENHAUS, M., VINZI, V. E., CHATELIN, Y., & LAURO, C. (2005). PLS path modeling. Computational Statistics & Data Analysis, 48(1), 159-205.
• VINZI, V. E., TRINCHERA, L., & AMATO, S. (2010). PLS Path Modeling: From Foundations to Recent Development and Open Issues for Model
Assessment and Improvement. In: VINZI, V. E., CHIN, W. W., HENSELER, J., & WANG, H. (Org.), Handbook of partial least squares: Concepts, methods
and applications (pp. 47-82). Berlin: Springer.
• WATANUKI, H.M. (2014). Desempenho de equipes virtuais no multisourcing de serviços de tecnologia da informação (Dissertation, Polytechnic School,
University of São Paulo. Available at: http://www.teses.usp.br
133

134. Backup slide
Exercise:
• Evaluate the European consumer satisfaction index (ECSI) using SmartPLS
134
Tenenhaus et al. (2005)