Hair, J. F. Jr., Hult, G. T. M., Ringle, C. M., Sarstedt, M. (2014). A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM). Sage Publications. ISBN: 978-1-4522-1744-4. 307 pp.

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BOOK REVIEW 211
Hair, J. F. Jr., Hult, G. T. M., Ringle, C. M., Sarstedt, M. (2014).
A Primer on Partial Least Squares Structural
Equation Modeling (PLS-SEM). Sage Publications.
ISBN: 978-1-4522-1744-4. 307 pp.
Reviewed by Lawrence Hoc Nang Fong1 and Rob Law1
Received: 12/07/2013
1 School of Hotel and Tourism Management, The Hong Kong Polytechnic University, 17 Science Museum
Road, TST East, Kowloon, Hong Kong; e-mails: lawrence.fong@connect.polyu.hk; rob.law@polyu.edu.hk
© 2013 International University College. All rights reserved
Citation: Hair, J. F. Jr., Hult, G. T. M., Ringle, C. M., Sarstedt, M. (2014). A Primer on Partial Least
Squares Structural Equation Modeling (PLS-SEM). Sage Publications. ISBN: 978-1-4522-1744-4.
307 pp. Reviewed by Lawrence Hoc Nang Fong and Rob Law, European Journal of Tourism
Research 6(2), pp. 211-213
In view of its essential role in knowledge
creation, multivariate data analysis prevails in
the social sciences literature. The field of
tourism is not an exception, specifically in the
widely adoption of structural equation
modeling (SEM), a multivariate technique, by
tourism researchers over the past decade.
While there are two major types of SEM
including covariance-based SEM (CB-SEM)
and variance-based SEM (PLS-SEM), the
former dominated previous tourism research.
However, increasing use of PLS-SEM in
tourism research has been witnessed in
recent years. This upward trend is likely to
persist in the near future given the growing
popularity of PLS-SEM in other social
sciences domains like marketing, strategic
management, and management information
system, as specified in the preface of the
book. Indeed, PLS-SEM, in relative to CB-
SEM, provides more flexibility in handling of
data. For instance, PLS-SEM is well-suited for
accommodating small sample sizes and
complex model, for testing a model containing
both formative and reflective constructs, and
for handling single-item measures. To this
end, the timely introduction of the book “A
Primer on Partial Least Squares Structural
Equation Modeling (PLS-SEM)” helps tourism
researchers stand at the front edge of the
SEM technique and make effective use of the
PLS-SEM in data analysis. Additionally, the
book illustrates the application of PLS-SEM
with a free downloadable software namely
SmartPLS which is essential to extend the
application of PLS-SEM in tourism research.
Authored by Hair, Hult, Ringo, and Sarstedt,
the book consists of eight chapters. To equip
the readers with the basic knowledge of PLS-
SEM, Chapter 1 delineates the meaning of
SEM and its relationship with multivariate
data analysis, followed by a description of the
major elements in multivariate data analysis.
Then the basic elements of PLS-SEM are
explained. Finally, PLS-SEM is distinguished

Hair, J. F. Jr., Hult, G. T. M., Ringle, C. M., Sarstedt, M. (2014). A Primer on Partial Least Squares Structural Equation
Modeling (PLS-SEM). Sage Publications. ISBN: 978-1-4522-1744-4. 307 pp.
212
from its counterpart namely CB-SEM while
the major characteristics of PLS-SEM and the
conditions where the PLS-SEM are more
adequate than CB-SEM and vice versa are
discussed. To step in the application of PLS-
SEM, Chapter 2 firstly explicates the concepts
in structural model specification including
mediation, moderation, and higher-order
models. Then specification of measurement
model is explained with a special focus on the
differences between reflective and formative
measures. After that, the issues that need to
be addressed after data collection are
discussed. The chapter ends by creating the
model in the SmartPLS is illustrated. With an
established model, Chapter 3 focuses on
model estimation. The chapter explains the
algorithm underpinning the estimation and the
statistical properties of the PLS-SEM method,
as well as the options and parameter settings
for running the algorithm. Following that, the
issues about interpretation of results are
explained. The final section illustrates the
execution of model estimation in the
SmartPLS.
Based on the model estimation, empirical
measures of the measurement and structural
models are derived, where evaluation of the
models takes place. Chapter 4 exhibits the
major steps in model evaluation in the
beginning. Thereafter, the chapter explains
the evaluation of reflective measurement
models according to three major criteria
including internal consistency reliability,
convergent validity, and discriminant validity,
followed by an illustration with the SmartPLS.
Chapter 5 explains the assessment of
formative measurement models with respect
to the criteria of convergent validity,
collinearity, and significance and relevance of
the formative indicators. The chapter also
elucidates the basic concepts of
bootstrapping which is used to examine the
statistical significance of estimates in PLS-
SEM. An illustration of the assessment of
formative measurement model in the
SmartPLS follows. Chapter 6 continues the
topic on model evaluation by focusing on the
assessment of structural model. First, the five
major assessment criteria including
collinearity, significance of the structural
model relationships, coefficient of
determination, effect size, and predictive
relevance are detailed. Then, the importance
of heterogeneity in structural model
evaluation is highlighted and discussed. By
the end of the chapter, an illustration of how
to report the structural model results in the
SmartPLS is provided.
Chapter 7 focuses on three advanced topics
of PLS-SEM. First, PLS-SEM importance-
performance matrix analysis (IPMA) is
introduced, followed by a demonstration on
how to execute the analysis. Second,
mediator analysis is explained, followed by an
illustration of how it is performed in PLS-SEM.
Third, four types of hierarchical component
models (HCM) including reflective-reflective,
reflective-formative, formative-reflective, and
formative-formative are elaborated. Finally, an
application of the reflective-reflective HCM in
PLS-SEM is illustrated.
Chapter 8 continues the introduction of
advanced topics of PLS-SEM. Initially the
chapter highlights the importance of modeling
heterogeneity in PLS-SEM, setting the ground
to explain moderator effects thereafter. The
chapter introduces and illustrates a
parametric approach to examine categorical
moderator effects via PLS-SEM multigroup
analysis (PLS-MGA), followed by an
illustration on performing the analysis. The
chapter also emphasizes the importance of
handling unobserved heterogeneity and
provides corresponding suggestions. Finally,
the chapter explains the examination of
continuous moderating effects in PLS-SEM
with special focuses on three-way interactions
and creation of interaction variables. An
illustration of the examination of continuous
moderating effects in the SmartPLS follows.
The organization of book chapters is well
designed. Given the basic knowledge of SEM
in Chapter 1, chapters 2 through 6 lead the
reader through all stages of performing PLS-
SEM analyses while illustrations are provided
subsequent to the explanations and
discussions of concepts. The advanced topics
were covered in the last two chapters of the
book. The language used in the book is

Reviewed by Lawrence Hoc Nang Fong and Rob Law, European Journal of Tourism Research 6(2), pp. 211-213
213
simple and straightforward, facilitating the
reader to comprehend the contents.
Mathematical equations and formulas, which
usually demand painstaking efforts from non-
statisticians, are limitedly used in the book.
Rule-of-thumbs, which are always the
information that social sciences researchers
look for in a statistics text, are clearly
exhibited in the book. However, a few typos
are found. In general, the content
presentation of the book facilitates readers’
learning process, leading toward the
achievement of the authors’ goal of
communicating the PLS-SEM method to
broad audience.
Illustration is a major component that
determines the value of an application-
oriented statistics text. The authors refer all
illustrations to a single case, so that readers
do not need to expend great efforts on
comprehending and thinking about a variety
of cases and, thus making the learning
process more efficient. Another credit for the
authors is on their selection of a free
downloadable software namely SmartPLS to
illustrate the analyses. Their choice is likely to
reinforce the popularity of PLS-SEM.
Although there is a plethora of articles and
edited book regarding PLS-SEM, they were
not dedicated to novices of PLS-SEM. Many
issues in the application of PLS-SEM are still
debatable which may confuse researchers
when applying the technique. These gaps,
however, leave rooms for the book. The
authors made a systematic and
comprehensive review of the recent literature
and thorough discussions on those
controversial issues in order to provide
general guidelines for the readers. In this
regard, contribution of the book to the
advancement of knowledge is notable,
especially in the field of tourism where the
application of PLS-SEM is still at the infant
stage.
Although the book has a lot of merits, there
are still rooms for improvement. While the
authors have introduced four types of HCM in
Chapter 7, they only illustrate one type in the
chapter. In their future edition, if any, the
authors may cover all types of HCM in the
part of illustration. Additionally, Chapter 8
stated that there are several approaches of
PLS-MGA. However, the authors merely
incorporate one of the approaches without
providing justifications on excluding the
others. In the future, the authors may explain
and illustrate all the approaches and discuss
the pros and cons of these approaches.
In summary, the book is of good quality as its
content is easy-to-read, comprehensive, and
up-to-date. Tourism researchers are highly
recommended to read this book if they want
to catch up with the latest trend of application
of SEM techniques.