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Talent Development & Excellence 1087
Vol.12, No.2s, 2020, 1087-1100
ISSN 1869-0459 (print)/ ISSN 1869-2885 (online)
© 2020 International Research Association for Talent Development and Excellence
http://www.iratde.com
Satisfaction and Loyalty Model for University Students Based on
Industrial Revolution 4.0 Management
Bahtiar Jamili Zaini#1 , Rosnalini Mansor#2 , Norhayati Yusof#3 , Nizam Sarkawi#4
1,2,3,School of Quantitative Sciences
4School of Business Management
Universiti Utara Malaysia 06010 UUM Sintok, Kedah. Malaysia.
Abstract
Students’ satisfaction and loyalty are one of issues that the university’s management need to address. There are
many factors influence both values to the students. Factors influenced students’ satisfaction such as such as
instructor, administration, curriculum, physical and social environment, and technology facilities while students’
satisfaction, university’s image, student’s trust, and service quality influence students’ loyalty. However, these
factors have high correlated among them. In addition, students’ satisfaction and loyalty are also influenced by the
student's background such as gender and the type of program occupied. Thus, this research shall use Partial Least
Square structural modelling to construct different model based on field of study and their gender. This study used
469 undergraduate student in one of University in Malaysia. Results from this study revealed that satisfaction
from students is the most important elements that contribute students’ satisfaction, followed by the university
image and students’ commitment. Based on different field of study model, analysis shows that technology is very
important in order to make sure that the students’ are satisfied and loyal to their university. On the other hand,
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. As overall, factors technology,
social environment and quality of instructor gave a great influence towards student satisfaction. Therefore, so as
to improve student satisfaction, those institutions need to keep improving to satisfy students’ requirement.
Keywords: Partial Least Squares, Structural Equation Modelling, Students’ Loyalty, IR 4.0
Introduction
At present, there are 20 government funded institution and 443 private institutions in Malaysia.
This shows that the learning environment in this country is fast growing. From the growing
institutions of higher learning, there is difficulty to identify appropriate students to match the
programs offered. Therefore, college and university management should be able to use a variety
of strategies to not only attract new students, but also to engage current students in furthering
their education. Many factors that influence current students to pursue their studies at the same
university such as their satisfaction and loyalty. Student loyalty is readiness to pursue their
learning at the same institutions as well as provide their views of evaluation on university to
others such as family, friends, community, industry, and organization (Kunanusorn &
Puttawong, 2015; Mohamad & Awang, 2009).
Student satisfaction is an important issue that university authorities need to consider. It is very
important because it can provide a conducive environment for students. When the level of
satisfaction is high, students will have good academic performance, enjoy their studies, and
live a comfortable life. In addition, they will definitely show the right attitude and behavior
towards their university, especially in terms of student loyalty (Kunanusorn & Puttawong,
2015; Mohamad & Awang, 2009; Zaini, Mansor, Yusof, & Sarkawi, 2019). Thus, it is essential
for university management to identify the factors that influence student satisfaction and loyalty.
However, there is still increasing number of students who decide on pursue their studies, not
only at different universities but also in foreign countries. With the growing demand in the high
learning environment and market demand in IR4.0 in the teaching environment, the
university’s management should devise strategies that not only attract new students to enroll
Talent Development & Excellence 1088
Vol.12, No.2s, 2020, 1087-1100
ISSN 1869-0459 (print)/ ISSN 1869-2885 (online)
© 2020 International Research Association for Talent Development and Excellence
http://www.iratde.com
but at the same time keep existing students continuing their studies at the same university.
Therefore, students’ loyalty is an issue to be considered by the university authorities for long-
term strategic planning. Hence, the growth of the student loyalty and satisfaction is crucial for
any successful higher learning institutions. Additionally, perceptions of loyalty varied
significantly among students of different background, such as age, ethnic background, and
those studying different courses (Annamdevula, 2016; Yusof, Zaini, & Mansor, 2019).
Universiti Utara Malaysia is the leading management institution of higher learning in Malaysia.
In UUM, there is 3 colleges that emphasize on IR4.0 environment on their delivering the
knowledge towards the students. The 3 colleges are COB (College of Business), CAS (College
of Arts and Science) and COLGIS (College of Law, Government and International Study).
COB comprises networking for in highest standards for successful entrepreneurs and business
leaders in various industry. It consists of technology management, operations management,
muamalat, Islamic banking and finance, insurance and risk management, human resource
management, banking, marketing, economics, accountancy, finance and business
administration which are integrated with innovative ideas and methods to nurture
entrepreneurial potential as well as business leaders. In a latest development, this college is a
a member in AACSB (Association to Advance Collegiate Schools of Business) and actively
pursue to be accredited internationally. Meanwhile, CAS serves as a centre of excellence in
the fields of social science and arts as well as to develop committed and competent human
capital to drive the nation and humanity. It has a diverse mix of expertise in the fields of
General Studies, Education and Modern Languages, Quantitative Sciences, Multimedia
Technology and Communication, Social Development and Computing. COLGIS offers
programmes of study in urban planning, international business, international affairs, law and
public management. It is also offering joint degree programmes as well as cross-university
initiatives. Thus, with the variety of programmes offered by these colleges, it is important to
foster student loyalty and meet their level of satisfaction. Therefore, this study identified
different models between the 3 colleges at the local university to show the element that
contribute satisfaction and loyalty for Student University within IR 4.0 Management. In
addition, this study will explore student loyalty models based on their gender.
Literature Review
In Malaysia, higher learning institutions are critical on the development of student as major
human capital for the nation in this new technology of teaching (Annamdevula, 2016). As such,
the students’ loyalty to higher learning institution is greatly influenced by various factors,
including the degree of satisfaction from the students, the services of quality given, and the
university’s goodwill (Yusof et al., 2019). In IR 4.0 environment, the students’ satisfaction can
be defined in many ways, taking consideration from the requirements of students on the
university. The quality of service is referring as the extent to which the provided services fulfils
stakeholder expectation. The image of the university reflects the students, who studied and are
still studying there. This image contribute significant on the loyalty of student (Helgesen &
Nesset, 2007; Mohamad & Awang, 2009). The loyalty of student is defined as the earnestness
by giving assess whether is a good and positive about their learning institution to other persons.
It can also be showed by the willingness of students to choose the same learning institution
should they decided to embark next level on post graduate study after they finish undergraduate
study there (Sarkawi, Shamsuddin, Jaafar, & Rahim, 2020). Mohamad & Awang (2009)
demonstrated that the loyalty of students is the desire to pursue their study at the same
institution and fore choose the same institution for post graduate needs. Students’ loyalty has
an attitudinal component, such as cognitive, affective, and conative (Hennig-Thurau, Langer,
Talent Development & Excellence 1089
Vol.12, No.2s, 2020, 1087-1100
ISSN 1869-0459 (print)/ ISSN 1869-2885 (online)
© 2020 International Research Association for Talent Development and Excellence
http://www.iratde.com
& Hansen, 2001; Marzo-navarro, Pedraja-iglesias, & Rivera-torres, 2005). Students will
demonstrate their loyalty by recommending their university to others, returning to the
institution by pursuing another level of learning, and continue the activities with university
under Alumni. This was shown by personal account about the goodwill and for the institution
which will be significant evident that the learner shall pursue their embark of knowledge at the
same institution. A student that loyal shall proceed to hold up the university even after they
finish their study there through “word of mouth promotion” to various level such as financial
sponsorship of any event organized by the institution. As well as prospective, current, or former
students. Meanwhile, students’ satisfaction also leads to termination, which in turn leads to
dissatisfaction (Kara & Deshields, 2004). In a sense, satisfaction from students’ is key
important factor and driven to students’ loyalty (Thomas, 2011). In university environment,
high levels of pleasure can increase the students’ loyalty. (Helgesen & Nesset, 2007; Zaini,
Mansor, Yusof, & Sarkawi, 2019) in their study showed that high levels of students' satisfaction
are directly related to loyalty of student, and that contribute impactful than the effect of
university’s image. (Astin, 1993) indicated that just like any others business modus operandi,
the contribution level of satisfaction and the perceptions of students on quality will sustain the
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, PLS (Partial Least Squares) path model develop by (Wold,
Ruhe, Wold, & W. J. Dunn, 1984) can also be implemented to analyse the relationship between
variables. It has been widely used in many fields, such as safety and health (Ramli, Akasah,
Idrus, & Masirin, 2013), marketing (Henseler, Ringle, & Sinkovics, 2009), organisation (Sosik,
Kahai, & Piovoso, 2009), management information system (Chin, Marcolin, & Newsted,
2003), behavioural sciences (Bass, Avolio, & Jung, 2003; Zaini, Mansor, Yusof, Sulaiman, &
Rhu, 2019), business strategy (Hulland, 1999), etc. PLS is more appropriate when the interest
is in prediction and theory development afore testing in theory (Chin et al., 2003; Henseler et
al., 2009).
Methodology
Data Collection and Questionnaire Development
This study comprised of 469 respondents who have completed their studies in higher learning
institutions. The respondents are classified into 3 main fields of study, which categorised as
CAS, COB and COLGIS. This study uses data collected from the questionnaire form. The
questionnaire is constructed commensurate with several past research, for instance (Clemes,
Gan, & Kao, 2008; Taecharungroj, 2014). The questionnaire consists of 5 parts, which are
Part A about the demographic of respondents, Part B shall cover the students’ satisfaction on
University based on instructor, administration, curriculum, physical and social environment,
and technology, while Part C is about the university’s image, meanwhile in Part D is about
commitment, finally in Part E is about the students’ loyalty towards the university. Based on
questions in Parts B, C, D, and E, we proposed a model of students’ satisfaction and students’
loyalty in higher learning institution as displayed in Figure 1. The students’ satisfaction and
students’ loyalty model consists of 3 explanatory variables; the university’s image, students’
satisfaction, and students’ commitment to the university. At the same time, students’
satisfaction is a mediator to students’ loyalty, and it has 6 explanatory variables, which are
instructor, administration, curriculum, physical and social environment, and technology.
Talent Development & Excellence 1090
Vol.12, No.2s, 2020, 1087-1100
ISSN 1869-0459 (print)/ ISSN 1869-2885 (online)
© 2020 International Research Association for Talent Development and Excellence
http://www.iratde.com
Figure 1: Proposed Students’ Satisfaction and Students’ Loyalty Model
Method of Data Analysis
The PLS structural equation model is conducted to test the proposed model. This approach lets
us test the complex relationships of the theoretical model as shown in Figure 1 simultaneously.
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 (Henseler et al.,
2009; Wong, 2013). Two parts of the analysis will be conducted in the construction of the
students’ satisfaction and loyalty model using the PLS model: 1) validating measurement
model, and 2) validating structural models. There are a few assumptions to be checked before
using PLS method, which are indicator reliability, internal consistency reliability, convergent
validity and discriminant validity. Both of the reliability should be 0.7 or higher. The
convergent validity should be 0.5 or higher. While in order to pass the discriminant validity
test, the square root of average variance extracted (AVE) of each latent variable should be
greater than the correlations among the latent variables. The SmartPLS software is also used to
validate the measurement and structural model of students’ satisfaction and loyalty model in
Higher Learning Institutions.
Results and Discussion
Descriptive Analysis
In this study result indicate that out of the 469 respondents, 69.5% were female while the
remaining 30.5% were male. Survey respondents were represented by various ethnic groups.
67.4% of respondents were Malay, 24.1% Chinese, 6% Indian, and 2.6% others. 61.8% of the
respondents were employed while 38.2 were unemployed. In order to identify the model
differences between the fields of study at the university, the survey respondents also comprised
41.8% of CAS students, 44.8% COB students, and only 13.4% COLGIS students. Table 1 gives
a summary of the results from respondents.
Talent Development & Excellence 1091
Vol.12, No.2s, 2020, 1087-1100
ISSN 1869-0459 (print)/ ISSN 1869-2885 (online)
© 2020 International Research Association for Talent Development and Excellence
http://www.iratde.com
Table 1: Descriptive Analysis based on Respondent Profile
Frequency
Percent
Gender
Male
143
30.5
Female
326
69.5
Race
Malay
316
67.4
Chinese
113
24.1
Indian
28
6
Others
6
1.3
Foreigner
6
1.3
Field
CAS
196
41.8
COB
210
44.8
COLGIS
63
13.4
Total
469
100
Validating Measurement Model
This section provides an evaluation of validating the measurement model using PLS.
Validating measurement models should be evaluated via internal consistency reliability
through Cronbach’s Alpha (CA), composite reliability (CR), convergent and discriminant
validity (Hair, J. F., Ringle, C. M., & Sarstedt, 2011; Henseler et al., 2009; Urbach &
Ahlemann, 2010).
Table 2: Results Summary for Reflective Measurement Model.
Cronbach's
Alpha (CA)
Composite
Reliability (CR)
Average Variance
Extracted (AVE)
Admin
0.921
0.944
0.809
Commitment
0.93
0.95
0.826
Curriculum
0.876
0.924
0.801
Image
0.919
0.94
0.757
Instructor
0.874
0.923
0.799
Loyal
0.923
0.942
0.765
Physical
0.88
0.926
0.806
Satisfaction
0.916
0.937
0.747
Social
0.896
0.935
0.828
Technology
0.871
0.907
0.663
Talent Development & Excellence 1092
Vol.12, No.2s, 2020, 1087-1100
ISSN 1869-0459 (print)/ ISSN 1869-2885 (online)
© 2020 International Research Association for Talent Development and Excellence
http://www.iratde.com
From the Table 2, the accepted value of CA is above 0.8 that indicate good reliability of the
measurement items of each construct. The CR values showed that the study’s data is
consistently above 0.9. Thus, the indicator reliability is confirmed. The convergent validity was
tested using the value of average variance extracted (AVE). The results showed that all AVE
values exceeded the recommended value of 0.5 and above to indicate sufficient convergent
validity. Discriminant validity can be measured by Fornell-Larcker criterion (Fornell, C., &
Larcker, 1981). Fornell and Lacker (1981) suggested that the square root of AVE in each latent
variable must be higher than the correlation between it and the other constructs. Table 3
revealed that all construct measurements have adequate discriminant validities.
Table 3: Discriminant Validity of Fornell-Larcker Criterion.
ADMIN
COMMIT
CURRI
IMAGE
INST
LOYAL
PHY
SATIS
SOCIAL
TECH
ADMIN
0.90
COMMIT
0.48
0.91
CURRI
0.61
0.53
0.90
IMAGE
0.64
0.74
0.65
0.87
INST
0.59
0.60
0.69
0.70
0.89
LOYAL
0.52
0.79
0.57
0.77
0.62
0.88
PHY
0.59
0.54
0.56
0.69
0.61
0.59
0.90
SATIS
0.55
0.83
0.58
0.80
0.65
0.87
0.64
0.87
SOCIAL
0.48
0.61
0.61
0.66
0.64
0.61
0.66
0.67
0.91
TECH
0.55
0.65
0.57
0.74
0.61
0.64
0.65
0.70
0.61
0.81
Validating Structural Model
In validating the structural model, we applied the PLS standard by bootstrapping 1000
resamples and examined the significance of the path coefficients. The first essential criterion
for judging the structural model is the determination coefficient, R2. The total of variance
explained variance of the R2 value for dependent construct that measures the relationship of
latent variables. The R2 value shown in Figure 2 demonstrated that university image, student
satisfaction and commitment can be explained by the model of student loyalty. The R2 values
of 0.776 suggest that 77.6% of the variance in student loyalty can be explained by university
image, student satisfaction and commitment. On the other hand, the R2 values of 0.625 suggest
that 62.5% of the variance in student satisfaction can be explained by factors instructor,
administration quality, curriculum, physical environment, social environment and technology.
Talent Development & Excellence 1093
Vol.12, No.2s, 2020, 1087-1100
ISSN 1869-0459 (print)/ ISSN 1869-2885 (online)
© 2020 International Research Association for Talent Development and Excellence
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Figure 2: The Structural Model with Results of the R2 Value and Path Analysis with T-
Values between Constructs.
Path coefficient and Hypothesis Testing
This study proposed 9 research hypotheses testing, which were analysed using PLS structural
model. The results for t-statistics and path coefficient for each hypothesis are shown in Table
4. Based on Table 4, 6 out of 9 hypotheses were supported. The results show that hypotheses
H1, H2, H3, H4, H8 and H9 are significant.
Table 4: Path Coefficients and Hypothesis Testing
Sample
Mean
Standard
Deviation
T Statistics
P Values
H1
Satis -> Loyal
0.59
0.059
10.02
0
H2
Image -> Loyal
0.179
0.049
3.681
0
H3
Commitment -> Loyal
0.163
0.058
2.764
0.006
H4
Instructor -> Satis
0.17
0.062
2.761
0.006
H5
Admin -> Satis
0.078
0.049
1.602
0.109
H6
Curriculum -> Satis
0.033
0.046
0.726
0.468
H7
Physical -> Satis
0.105
0.059
1.774
0.076
H8
Social -> Satis
0.237
0.052
4.547
0
H9
Technology -> Satis
0.323
0.053
6.136
0
Based on the results, 3 paths (administration, curriculum, and physical environment) for student
satisfaction model were not significant. Thus, we removed these 3 insignificant path and rerun
analysis as shown in Figure 3. The R2 value for satisfaction path model is 0.613 which can be
considered as moderate predictive ability. Meanwhile, the student loyalty path model showed
a high predictive ability with R2 value is 0.777.
Talent Development & Excellence 1094
Vol.12, No.2s, 2020, 1087-1100
ISSN 1869-0459 (print)/ ISSN 1869-2885 (online)
© 2020 International Research Association for Talent Development and Excellence
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Figure 3: The Structural Model with Results of the R2 Value and Path Analysis with T-
Values between Constructs for Reduced Model.
Student loyalty Model based on Field of Study
3 types of students’ loyalty models will be developed based on 3 fields of studies and colleges,
such as CAS, COB and COLGIS. Figures 4-6 display significant factors that contribute to
students’ loyalty model for CAS, COB, and COLGIS, respectively. The R2 value for loyalty
path for CAS, COB, and COLGIS are 0.799, 0.787, and 0.694, respectively. For CAS models,
there is a 79.9% variance in students’ loyalty, which can be explained by students’ satisfaction,
university’s image, and commitment. For COB models, there is a 78.8% variance in students’
loyalty, which can be explained by students’ satisfaction, university’s image, and commitment.
Meanwhile only 69.4% variance in students’ loyalty can be explained by students’ satisfaction,
university’s image, and commitment towards the COLGIS model.
Figure 4: The Students’ Loyalty Model for CAS
Talent Development & Excellence 1095
Vol.12, No.2s, 2020, 1087-1100
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Figure 5: The Students’ Loyalty Model for COB
Figure 6: The Students’ Loyalty Model for COLGIS
This study also shown that for students’ satisfaction, only paths from social environment and
technology were significant, and their R2 value is 0.63.4. On the other hand, for COLGIS
students’ loyalty model, path from students’ satisfaction and university’s image are significant
to students’ loyalty with an R2 value of 0.694. Same as COB model, only paths from social
environment and technology to students’ satisfaction were significant with an R2 value of
0.565. Tables 5–7 give a path coefficient and statistical test values for CAS, COB, and COLGIS
students’ loyalty model, respectively. COB and COLGIS models proved that paths technology
and social environment are statistically significant to students’ satisfaction. For CAS, paths
from instructor and technology to students’ satisfaction were found statistically significant.
This finding showed that technology is very important in order to make sure that the students’
are satisfied and loyal to their university. This is consistence with a study from Annamdevula
Talent Development & Excellence 1096
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(2016) indicate that higher learning institutions are critical on the development of student as
major human capital for the nation in this new technology of teaching that could contribute
student loyal and satisfied with their institution.
However, these 3 models projected a difference in terms of their path towards students’
satisfaction. For CAS students’ loyalty model, path students’ satisfaction and university image
are statistically significant to students’ loyalty and this model showed a high predictive ability
with coefficient of determination R2 value, which is 0.799. 2 paths from instructor and
technology to students’ satisfaction are also significant with moderate predictive ability R2
value, which is 0.586. Meanwhile, for COB students’ loyalty model, all 3 paths (students’
satisfaction, university’s image, and commitment) are significant to students’ loyalty with R2
value of 0.787. This is also synchronise from a study from Helgesen & Nesset (2007);
Mohamad & Awang (2009) where image has a direct influence on student’s loyalty.
Table 5: Path Coefficients for CAS Students’ Loyalty Model
Sample
Mean
Standard
Deviation
T Statistics
P Values
Admin -> satisfaction
0.095
0.079
1.217
0.224
Commitment -> loyalty
0.187
0.092
1.952
0.051
Curriculum -> satisfaction
0.011
0.085
0.128
0.898
Image -> loyalty
0.149
0.077
1.973
0.049
Instructor -> satisfaction
0.241
0.088
2.749
0.006
Physical -> satisfaction
0.171
0.103
1.654
0.098
Satisfaction -> loyalty
0.602
0.079
7.614
0.000
Social -> satisfaction
0.121
0.082
1.449
0.147
Technology -> satisfaction
0.316
0.080
3.891
0.000
Table 6: Path Coefficients for COB Student Loyalty Model
Sample
Mean
Standard
Deviation
T Statistics
P Values
Admin -> satisfaction
0.052
0.059
0.846
0.398
Commitment -> loyalty
0.170
0.082
2.014
0.044
Curriculum -> satisfaction
0.027
0.067
0.405
0.685
Image -> loyalty
0.129
0.065
2.013
0.044
Instructor -> satisfaction
0.134
0.087
1.610
0.108
Physical -> satisfaction
0.061
0.076
0.883
0.377
Satisfaction -> loyalty
0.632
0.089
7.114
0.000
Social -> satisfaction
0.317
0.073
4.278
0.000
Technology -> satisfaction
0.344
0.088
3.813
0.000
Talent Development & Excellence 1097
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Table 7: Path Coefficients for COLGIS Student Loyalty Model
Sample
Mean
Standard
Deviation
T Statistics
P Values
Admin -> satisfaction
0.050
0.199
0.304
0.761
Commitment -> loyalty
0.112
0.158
0.752
0.452
Curriculum -> satisfaction
0.131
0.148
0.771
0.441
Image -> loyalty
0.339
0.135
2.455
0.014
Instructor -> satisfaction
0.082
0.197
0.177
0.860
Physical -> satisfaction
0.105
0.120
0.830
0.407
Satisfaction -> loyalty
0.476
0.164
2.902
0.004
Social -> satisfaction
0.303
0.163
2.074
0.038
Technology -> satisfaction
0.341
0.144
2.448
0.014
Student loyalty Model based on Gender
Figure 7 and 8 display the student loyalty model for male and female, respectively. Their path
coefficients are given in Table 8 and 9. Based on Figure 7 and Table 8, for male student loyalty
model, 3 paths from physical environment, social environment and technology to student
satisfaction and student satisfaction to student loyalty were found statistically significant with
R2 value is 0.763. On the other hand, female student loyalty model more complicated compared
to male student loyalty model. For female student loyalty model, seven paths out of nine paths
are statistically significant. The R2 for female loyalty path is 0.78 showed a high predictive
ability. There is only small difference in term of R2 value between male and female student
loyalty 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.
Figure 7: The Student Loyalty Figure 8: The Student Loyalty Model
Model for Male for Female
Talent Development & Excellence 1098
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Table 8: Path Coefficients for Male Student Loyalty Model
Sample
Mean
Standard
Deviation
T Statistics
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
Table 9: Path Coefficients for Female Student Loyalty Model
Sample
Mean
Standard
Deviation
T Statistics
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
CONCLUSION
This study has able to achieved all the objectives of the research by presenting 3 models of
Higher Learning Institutions students’ satisfaction and loyalty model based on their field of
Talent Development & Excellence 1099
Vol.12, No.2s, 2020, 1087-1100
ISSN 1869-0459 (print)/ ISSN 1869-2885 (online)
© 2020 International Research Association for Talent Development and Excellence
http://www.iratde.com
studies. This study used the PLS structural equation models to model the relationship among
several independent variables to identify factors that influence students’ satisfaction and loyalty
towards higher learning institution. 3 different models between 3 colleges in a local university
were identified in order to recognise the satisfaction factors influencing students’ loyalty
towards Higher Learning Institution in IR 4.0 environment. The image of university,
commitment, and student satisfaction are also significant factors towards students’ loyalty. The
findings also show that technology factors are a major contributor to students’ satisfaction and
contribute significantly to students’ loyalty.
Acknowledgments
The authors acknowledge Universiti Utara Malaysia, which funded this research under the
Research Generation University Grant (S/O Code: 13877).
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