PLS Equation Model of Student Loyalty based on Gender in IR 4.0 Environment

Abstract

Students loyalty and attrition is an important issue for university authorities. Earlier studies have discovered that many factors influencing student loyalty towards their higher learning institutions such as student satisfaction, university image, student trust, and service quality. However, these factors have high relationship correlated with each other. Therefore, this study used the Partial Least Square (PLS) to create a path model which shows the relationship between all factors related to student loyalty. The results from this study revealed that student satisfaction is the most important factors that influence student loyalty, followed by image of university and student commitment. Analysis based on gender shows that female student model have a same pattern with overall student loyalty model but the male student loyalty model is simpler just consist student satisfaction. On the other hand, factors technology, social environment and quality of instructor gave a great influence towards student satisfaction. Therefore, in order to improve student loyalty, university should keep on improving to satisfy students' requirement.

Full text

Int. J Sup. Chain. Mgt Vol. 8, No.5, October 2019
447
PLS Equation Model of Student Loyalty
based on Gender in IR 4.0 Environment
Bahtiar Jamili Zaini #1, Rosnalini Mansor#2, Norhayati Yusof #3, Mohammad Nizam Sarkawi#4
1-3School of Quantitative Sciences
4School of Business Management
Universiti Utara Malaysia, 06010 UUM Sintok Kedah Malaysia
1bahtiar@uum.edu.my
2rosnalini@uum.edu.my
3norhayati@uum.edu.my
4drnizam@uum.edu.my
Abstract— Students loyalty and attrition is an
important issue for university authorities. Earlier
studies have discovered that many factors influencing
student loyalty towards their higher learning
institutions such as student satisfaction, university
image, student trust, and service quality. However,
these factors have high relationship correlated with
each other. Therefore, this study used the Partial
Least Square (PLS) to create a path model which
shows the relationship between all factors related to
student loyalty. The results from this study revealed
that student satisfaction is the most important factors
that influence student loyalty, followed by image of
university and student commitment. Analysis based
on gender shows that female student model have a
same pattern with overall student loyalty model but
the male student loyalty model is simpler just consist
student satisfaction. On the other hand, factors
technology, social environment and quality of
instructor gave a great influence towards student
satisfaction. Therefore, in order to improve student
loyalty, university should keep on improving to satisfy
students’ requirement.
Keywords— Partial Least Squares, Structural equation
Modelling, Student Loyalty, Undergraduate Students
1. Introduction
It is vital for university management to know what
factors lead to student loyalty based on gender.
This study is important due to it is important issues
for higher institution that facing such as the budget
constraints, accommodation, competitors with
other universities and reduction in student
enrolment. Therefore, student loyalty is an
important issue to be considered by the university
authorities on long-term strategic planning. Beside
that perceptions of student loyalty varied
significantly among student of different
background such as ages, ethnic background and
those studying different courses.
Student attraction and anti-attrition can help
management of higher education institution to
make better decisions concerning the allocation of
resources. Hence, the insight concerning these
factors become a crucial issue for determining the
most appropriate strategic management in order to
ensure long term successful performance for higher
education. Past studies indicated that when
students were satisfied with their institution, they
would display positive attitudes and behaviour
towards the institution. However, there are still a
high percentage of Malaysian students who decide
to pursue their degree in foreign countries.
According to UNESCO, Malaysia was one of the
top ten countries which have most students study
abroad. It is estimated that almost 60,000
Malaysian students choose to study abroad in 2014.
With the increasing competition in higher
education sector, the university authorities should
come out with a strategy not to only attract new
students, but also retain the current students.
Thus, building up the student loyalty based on
gender is the most important key for any successful
higher education institutions. Therefore, this
research will explore student loyalty model based
on gender in IR4.0 environment.
2. Literature Review
Higher learning institutions is critical to the
development of a country and the student is the
major human capital for the nation [1]. As such the
student loyalty is influenced by many factors such
as student satisfaction, service quality and
university image [2]. In IR 4.0 environment, the
student satisfaction can be defined in many ways,
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Int. J Sup. Chain. Mgt Vol. 8, No.5, October 2019
448
depending on the requirements of students on the
university. Service quality is defined as the extent
that the service provided fulfils the customer’s
expectation. University image is an impression that
a student has about their university. Its image has a
direct influence in student loyalty ([3], [4].
[4] indicates that student loyalty is the
intention to continue education at the same
university and also prefer the same institute for
future educational needs. Student loyalty have an
attitudinal component such as cognitive, affective
and conative [5], [6]. Their behaviours were
manifest variables about commitment such as
repurchase, patronize, recommendation of the
university to others, returning to repeat in the
institution such as pursuing another level of study,
and returning to join activity with the institution
under Alumni. Meanwhile, student satisfaction or
dissatisfaction leads quit which in turn leads to
student attrition [7]. This means that student
satisfaction has an important antecedence and a
major driver of student loyalty[8].
In higher educational institutions, satisfaction
may increase loyalty predictor of student loyalty.
[3] on their study has indicates that student
satisfaction has the highest degree of association
with student loyalty both directly and totally,
representing total effect about three times higher
than the effect of image university. [9] indicates
that just like any form of business, factors related
to satisfaction levels and students’ perceptions of
quality will attract and retain students.
There are many analytical methods that could
be used to analyse the relationships between
factors, for instance, multiple linear regression,
canonical correlation analysis and principal
component analysis. However, the partial least
squares (PLS) path model is proposed by [10] can
also be implemented to analyse the relationship
between variables. It has been widely used in many
fields, such as safety and health [11], marketing
([12], organization [13], management information
system [14], behavioural sciences [15], business
strategy [16], etc. PLS are more appropriate when
the interest is in prediction and theory development
rather than in theory testing [12], [14]. Thus, there
is a significant need to further study on factors
influencing student loyalty based on gender in
higher education.
3. Methodology
3.1 Data Collection
The data for this study is collected through a paper
questionnaire. The questionnaire used for this study
will be modified from past studies such as [17]. It
will consist of demographic information of
students, perception on student satisfaction based
on instructor, administration, curriculum, physical
environment and technology, perception of students
loyalty based on student’s satisfaction, university’s
service quality, university image and student’s
commitment.
Figure 1. Proposed Student Loyalty Model
Therefore, the 9 following hypotheses have been
formulated:
H1. Student satisfaction will have an effect on
student loyalty.
H2. University image will have an effect on
student loyalty.
H3. Student commitment will have an effect
on student loyalty.
H4. Instructor will have an effect on student
satisfaction.
H5. Administration quality will have an effect
on student satisfaction.
H6. Curriculum will have an effect on student
satisfaction.
H7. Physical environment will have an effect
on student satisfaction.

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449
H8. Social environment will have an effect on
student satisfaction.
H9. Technologies facility will have an effect
on student satisfaction.
3.2 Method of Data Analysis
A PLS structural equation modelling is a statistical
method which used to create structural models of
the relationships between every variables
simultaneously. The aim to conduct PLS to reduce
the number of variables to a smaller set of
uncorrelated components and performs least
squares regression on these components. Therefore,
PLS method can be applied to theory development,
as it tests and validates exploratory models and can
estimate complex models with several latent and
manifest variables [12], [18], [19].
The PLS path model is defined by 2 sub-
models: (1) a measurement model to assess and
develop the reliability and validity of the
instrument and testing the goodness of measure and
(2) a structural model to assess the hypothesized
relationship among constructs in the conceptual
model. There are a few assumptions to be checked
before using PLS method, which are indicator
reliability, internal consistency reliability,
convergent validity and discriminant validity. This
study will use a software named SmartPLS to test
the relationship between variable and to create the
PLS path model.
4 Results and Discussion
4.1 Descriptive Analysis
Initially, results from description of respondents
show that 69.5% of respondents were females and
30.5% were male students. Besides that, 98.7% of
the respondents are Malaysian and 1.3% are Non-
Malaysian. Respondents of the questionnaire
survey are represented by various ethnicities.
64.7% of the respondents were Malay, with the rest
of the sample being 24.1% Chinese, 6% Indian and
2.6% others. 61.8% of the respondents are
employed while 38.2 are unemployed.
Path coefficient and Hypothesis Testing
This study proposed 9 research hypotheses testing,
which were analysed using PLS structural model.
The path significance levels are estimated by the
bootstrapping method using SmartPLS 3.0. The
results for t-statistics are shown in Figure 2. Table
1 indicates the results for path coefficient for each
hypothesis. Based on Table 1, 6 out of 9
hypotheses were supported. The results show that
hypotheses H1, H2, H3, H4, H8 and H9 are
significant.
Figure 2. The Structural Model and Path Analysis
between Constructs.
Table 1. Path Coefficients and Hypothesis Testing.
Mean
Standard
Deviation
T
Values
p-
values
H1
Satisfaction ->
Loyalty 0.59 0.059 10.02 0
H2
Image ->
Loyalty 0.179 0.049 3.681 0
H3
Commitment ->
Loyalty 0.163 0.058 2.764 0.006
H4
Instructor ->
Satisfaction 0.17 0.062 2.761 0.006
H5
Admin ->
Satisfaction 0.078 0.049 1.602 0.109
H6
Curriculum ->
Satisfaction 0.033 0.046 0.726 0.468
H7
Physical ->
Satisfaction 0.105 0.059 1.774 0.076
H8
Social ->
Satisfaction 0.237 0.052 4.547 0
H9
Technology ->
Satisfaction 0.323 0.053 6.136 0
Based on Table 1, 3 hypothesis for student
satisfaction were significant which are instructor,
social environment and technology since the p-
values were less than 0.05. However, factors such
as administration, curriculum and physical
environment are not significance. Thus, the final

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450
PLS model is constructed again after remove
insignificant factors, as given in Figure 3. Figure 3
also give the R2 value for satisfaction, student
commitment and university image. The R2 value for
student loyalty is 0.777 suggest that 77.7%
variation in student loyalty can be explained by
student satisfaction, university image and student
commitment. These R2 value of student loyalty path
showed a good predictive ability to describe the
behaviour of student loyalty. Meanwhile, the R2 of
student satisfaction value is 0.625. Therefore only
62.5% variation in student satisfaction is explained
by instructor, social environment and technology.
Figure 3. The Structural Model and Path Analysis
between Constructs.
Student loyalty Model based on Gender
Previously, we model the overall student loyalty
model. Next, we look for different student loyalty
model based on their gender. Figure 4 and 5 display
the student loyalty model for male and female,
respectively. Their path coefficients are given in
Table 2 and 3 for male and female, respectively.
Based on Table 2, for male student loyalty model, 4
paths are statistically significant, where only path
student satisfaction is statistically significant to
student loyalty. Although only path student
satisfaction is significant, this path model still give
a high R2 value, which is 0.772. Meanwhile, the R2
value for satisfaction path model is 0.676.
Table 2. Path Coefficients for Male Student
Loyalty Model
Mean
Standard
Deviation
T
Values
p-
values
Admin ->
satisfaction -0.135 0.084 1.671 0.095
Commitment -
> loyalty 0.119 0.103 1.068 0.286
Curriculum ->
satisfaction 0.160 0.109 1.483 0.138
Image ->
loyalty 0.098 0.100 1.020 0.308
Instructor ->
satisfaction 0.118 0.088 1.439 0.150
Physical ->
satisfaction 0.185 0.078 2.280 0.023
Satisfaction ->
loyalty 0.698 0.111 6.342 0.000
Social ->
satisfaction 0.267 0.082 3.245 0.001
Technology ->
satisfaction 0.381 0.069 5.518 0.000
Figure 4. The Student Loyalty Model for Male.
On the other hand, female student loyalty
model more complicated compared to male student
loyalty model. Figure 5 display the student loyalty
model for female. The path coefficients for female
model are given in Table 3. For female student
loyalty model, all 3 paths towards student loyalty
are statistically significant with R2 is 0.78. This R2
value is slightly difference with R2 for male model.
The R2 value for male is 0.763 compared to female
is 0.78, although the model for male is simpler
compared to female model. In addition, male model
consist only student satisfaction instead of all 3
factors. On the other hand, for paths female student
satisfaction, the value of R2 is 0.62 that consist of
administration, social environment, technology and
physical environment. Again, male model is simple
to female model that consist only 3 factors which
are technology, social, and physical environment.

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451
Table 3. Path Coefficients for Female Student
Loyalty Model
Mean
Standard
Deviation
T
Values
p-
values
Admin ->
satisfaction 0.138 0.058 2.392 0.017
Commitment ->
loyalty 0.177 0.071 2.473 0.013
Curriculum ->
satisfaction 0.001 0.048 0.027 0.978
Image ->
loyalty 0.201 0.056 3.602 0.000
Instructor ->
satisfaction 0.210 0.077 2.739 0.006
Physical ->
satisfaction 0.096 0.073 1.338 0.181
Satisfaction ->
loyalty 0.558 0.070 7.970 0.000
Social ->
satisfaction 0.194 0.066 2.938 0.003
Technology ->
satisfaction 0.301 0.069 4.307 0.000
Figure 5. The Student Loyalty Model for Female
.
4 Conclusion
This research successfully achieves all the
research objective by presenting student loyalty
model using PLS. This study pursued to identify
significant factors that influence student loyalty
based on gender in IR4.0 environment. The
results shows that the key factors of student
loyalty are the student satisfaction, followed by
image of university and student commitment.
Female student loyalty model also have a same
pattern with overall student loyalty model but the
male student loyalty model is simpler just consist
only student satisfaction. The outcomes also
showed that the most important student
satisfaction that students emphasizes is on
technology, followed by social environment and
quality of instructor.

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452
Acknowledgments
The authors acknowledge to Universiti Utara
Malaysia by funded this research under Research
Grant (S/O Code: 13877).
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