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International Journal of Advanced Research and Publications ISSN: 2456-9992
Volume 1 Issue 4, Oct 2017 www.ijarp.org
200
Predictive Drivers Of Students‟ Satisfaction In Open
Distance Learning In Sri Lanka
MJR Perera, Nalin Abeysekera, S.R.S.N. Sudasinghe, Isuri Roche Dharmaratne
Management and Science University of Malaysia, Graduate School of Management (GSM), University Drive, Seksyen 13,40100, Shah
Alam Selangor, Malaysia, PH-00940718258172
Open University of Sri Lanka, Department of Management Studies, Nawala, Nugegoda,Sri Lanka,PH-0094773028690
Sri Lanka Institute of Development Administration, School of Postgraduate Studies, 28/10, MalalasekaraMawatha, Colombo 7, Sri Lanka.
PH-0094712322314
Management and Science University, Malaysia, Colombo Learning Centre, 300,Colombo 3, Sri Lanka , PH-0094773822946
Abstract: The predictive drivers of students‟ satisfaction in Open Distance Learning (ODL) has become an important and competitive
criterion in service quality perspectives in higher education (HE). The customer service quality act as an antecedent of customer satisfaction,
and their relationship has beenempirically proved by many of the researchers following literature based onmarketing.Distance Education
(DE) is introduced as a supplementary method in the higher education system for the disadvantaged, and for those who have missed
entering the traditional university system of higher education due to restrictions on the number of students allowed for university admission.
The DE system removed the barriers of minimum qualifications for the courses giving moreopportunity to the public to obtain higher
educational qualifications from recognised universities operating under the Open Distance Learning (ODL) concept. The ODL system
developed rapidly with the increase in number of students, but does not reflect the number of students passing out as graduates. Just as in,
„consumer behavior‟ in marketing literature, most of the researchers have addressed the relationships between students‟ perceived service
quality, satisfaction, students‟ persistence and attrition. The development of information technology influences ODL in a positive way by
giving more interaction with web based on-line and e-learning systems. The problem of student dropouts or students‟ attrition encountered
not in the early DE environment days,even in the on-line courses have been observed as high. Many research studies have disclosed the
reasons for this situation and recommendations made to increase and assess the quality of the programmes. This studywill examine the
relationships between the independent variables of Reliability, Cost &Time and Website Content and dependent variable of Students‟
Satisfaction. This study was conducted in the six main regional centers of the OUSL spread across the island. The sample size used was 760
undergraduate students who have more than,or equaled at least one year‟s exposure with the ODL system. The statistical analysis revealed
all the independent variables were significantly related with the Students‟ Satisfaction.
Keywords: Distance Education, Open Distance Learning, Satisfaction, Service Quality
1. Introduction
Education is a significant factor to enlighten the life of the
nation (Susanti, Sule, & Sutisna, 2015). Distance
Education (DE) has originated as an alternative to
traditional education (Kutluk & Gulmez, 2012 ).DE
isdefined as,“an educational process and system in
which all or a significant proportion of the teaching is
carried out by someone or something removed in space
and time from the learner. Distance education requires
structured planning, well-designed courses, special
instructional techniques and methods of
communication by electronic and other technology, as
well as specific organizational and administrative
arrangements” (Higher Education for the Twenty First
Century (HETC) Project Ministry of Higher Education
and Research Sri Lanka & University Grants Commission,
April 2015). In recent times, a growth of distance
education could bea more flexible way (Butcher & Rose-
Adams, 2015) to acquire the higher education deprived of
time and space constraintswhich is restricted with a face to
face learning system.The DE is more economically
beneficial and desirable especially with the employed
students (Kutluk & Gulmez, 2012 ).DE is expanding and
(Khan & Iqbal, 2016) the rapid growth of distance
learning hasresulted in web based e-learning or on-line
learning. When the studentsare converted from the
traditional face-to-face to DE programs, changes should
be made to engage the learners proficiently (Khan &
Iqbal, 2016). To study the performanceof the students
with the unfamiliar new environment it is important to
study student satisfaction with distance learning programs
and how these relate to their academic achievement.
(Khan & Iqbal, 2016). The student persistence and
completion rates have faced problems with student
attrition or dropouts in DE (Simpson, Does distance
education do more harm than good?, 2010; Simpson, E-
Learning and the Future of Distance Education, 2013;
Simpson, Student Support Service for Success in Open
and Distance Learning, Feb 17, 2016; Tinto, 1975).The
enrollment in online courses is rapidly increasing due to
its flexibility, convenience and easy access but the
attrition rates remain high (Croxton, 2014).Internal,
External, and contextual factors could be influenced
dropout decisions (Croxton, 2014; Lee & Choi, 2011;
Parker, 1999; Park & Choi, 2009). Quality is important in
the input and output of higher education especially in open
and distance learning (Inegbedion & Adeyemi, Cost
indices in open and distance education in Nigerian
universities, 2013).The improvement of the quality of the
output of the educational process and the quality
International Journal of Advanced Research and Publications ISSN: 2456-9992
Volume 1 Issue 4, Oct 2017 www.ijarp.org
201
indicators must be managed in a systematic way,
(Peterson, Kovel‐Jarboe, & Schwartz, 1997). “The quality
of teaching depends on the quality of teachers in the
system” (Lekamge, 2010, p. 1). The prominent
SERVQUAL instrument with the dimensions of
Assurance, Empathy, Responsiveness, Reliability, and
Tangibilityhas been used in many educational
backgrounds to assess the service quality (SQ) and to find
the significant SQ dimensions and significant
relationships with students„satisfaction which leads to
behavioral intention (Malik, Danish, & Usman, 2010;
Mansori, Vaz, & Ismail, 2014; Pohyae, Romle, Darus,
Saleh, Saleh, & Mohamood, 2016; Stodnick & Rogers,
2008; Sembiring, 2015; Wei & Ramalu, 2011). The
student satisfaction is a multidimensional construct
(Montinaro & Chirico, 2006 ; Parasuraman, Zeithaml, &
Berry, A Conceptual Model of Service Quality and Its
Implications for Future Research, 1985), and four out of
the five constructs of SERVPERF (Cronin & Taylor,
1992), which include: responsiveness, reliability,
assurance and empathy were found to have significant
influence on satisfaction (Noor, Masuod, Said,
Kamaruzaman, & Mustafa, 2016 ). “Customer satisfaction
is a goal and an essential factor in an organization
success” (Khattab & Fraij, 2011). “Importance to conduct
research on students‟ satisfaction with distance learning
because differences in students‟ satisfaction might
influence educational opportunities for learning in a
relevant Web-based environment” (Horvat, Krsmanovic,
& Djuric, 2012, p. 1).Consequently the blended learning
courses which is a mix of face to face and online sessions
introduced as an online learning method would reduce the
cost to the students (Liyanagunawardena, Adams,
Rassool, & Williams, 2014). Universities can attract more
students to apply, if the higher education institutionsfocus
on reducing the cost of fees charged (Yusuf, Ghazali, &
Abdullah, 2017). The importance of the service
provided for students will influence the outcome of the
graduates and “ the output can be generated from the
quality of the lecturers who provide good teaching when
universities provide facilities and services to support the
advancement of knowledge and expertise” (Susanti, Sule,
& Sutisna, 2015, p. 1). Quality is very important as input
and output of higher education especially in an ODL
environment (Inegbedion & Adeyemi, Cost indices in
open and distance education in Nigerian universities,
2013)
1.1 Problem Statement
The OUSL has faced the problem of high attrition or
dropouts in the student population which has resulted in a
low graduation rate (The Open Unviersity of Sri Lanka
Corporate Plan 2011-2016 for Achieving Excellence,
Efficiency and Equity in Open Distance Learning, 2011).
Studies of the OUSL have revealed this situation
(Ariyaratne, Munasinghe, Seneviratne, Rajapaksha, &
Dediwala, 2014; Liyanagama, 2014)and recommended to
implement quality enhanced programmes to recoverfrom
this type of situation. The reliability of the service as a
promised service, the fees or cost structure of the
programmes, the content in the educational websites are
very important as service quality dimensions when
considering the student satisfaction based on the
literature (Ana Horvat, 2012; Hasan, Ilias, Rahman, &
Razak, 2008; Kutluk & Gulmez, 2012 ; Gruber, Fuß,
Voss, & Glaeser-Zikuda, 2010; Inegbedion & Adeyemi,
Cost indices in open and distance education in Nigerian
universities, 2013; Simpson, Student Support Service for
Success in Open and Distance Learning, Feb 17,
2016).This study will be used to analyse the significant
factors which affect students‟ satisfaction to complete the
courses through persistence in ODL in the OUSL.
1.2 The Significance of the study
This study is designed based on the threevariables of,
Reliability, Cost, Time and Website Content, and the
independent variable of Student Satisfaction in ODL in
the OUSL. The results of the analysis given are the
predictable variables and their related significant items.
The University‟s Academic staff, higher management and
policy makers can understand the importance of these
service quality dimensions and how they impact on
student satisfaction. Strategic decisions could be
implemented to fulfill the desired service levels of the
students
2. Literature Review The SERVQUAL, the prominent service quality
instrument was developed by Parasuraman et al (1988)
(Parasuraman, Zeithaml, & Berry., SERVQUAL: A
Multiple-Item Scale for Measuring Consumer Perceptions
of Service Quality, 1988), which consist of five
dimensions namely tangibles, reliability, responsiveness,
assurance and empathy.Reliability defined as,“the ability
to perform the promised service dependably and
accurately” (Parasuraman, Zeithaml, & Berry.,
SERVQUAL: A Multiple-Item Scale for Measuring
Consumer Perceptions of Service Quality, 1988), means
that “the organization delivers on its promises regarding
delivery, service provision, and problem resolution”
(Khattab & Fraij, 2011). In 2012,Mantovani (Mantovani,
2012) has found the significant relationship between
Reliability and Service Qualityin e-learning inODL. In
2013 Shah (Shah, 2013) has studied customer service
quality dimensions which lead to customer satisfaction in
the higher education sector in Pakistan by using
SERVQUAL instrument andshowed that the customer
satisfaction is significantly related to reliability (Shah,
2013).In 2015 Sembiring has examined the students‟
persistence with the Students‟ satisfaction with
SERVQUAL dimensions and found the dimension of
Reliability is siginificant with the students‟ satisfaction
(Sembiring, 2015). Cost can be defined as “an amount that
has to be paid or given up by a person in order to get
something that he/she desired” (Yusuf, Ghazali, &
Abdullah, 2017).In 1997 Joseph and Joseph has examined
students‟ perceptions of service quality in education and
identified seven determinants of service quality including
Cost/Time in New Zealand. He has used an
importance/performance-based approach to evaluate
service quality dimensions in education and Cost/Time
was ranked as the fourth place of the list of factors (Joseph
& Joseph, 1997). In 2004 Rashid & Harun (Rashid &
Harun, 2004) has catagorised 8 key characteristics of
service quality in the ODL in Learners‟ Perspective in
Malaysia including cost/ fees of the courses. The
cost/fees were significant with Ethnic Groups, type of
academic programs and distance between learning centres
International Journal of Advanced Research and Publications ISSN: 2456-9992
Volume 1 Issue 4, Oct 2017 www.ijarp.org
202
and home.In 2013, Sia (Sia, 2013) and in 2017 Yusuf,
Ghazali, & Abdullah (Yusuf, Ghazali, & Abdullah, 2017)
showed that the cost is very important when “college
choice” decision process.The study was conducted under
the statements of financial aids, available university
scholarships or loans, reasonable charges for the education
and accommodation, and flexible payment schemes. The
students were very much concernedabout full or partial
scholarships which depend on a student‟s results as entry
requirements,and othersnot getting the scholarships
whohave to apply for a bank loan or other bank facilites
(Sia, 2013). In 2017 Yusuf, Ghazali, & Abdullah (Yusuf,
Ghazali, & Abdullah, 2017) had found that there is a
significant relationship between cost and a student‟s
decision in selectingHE institutions in Malaysia.“If the
institution of higher education focuses on reducing the
cost of fee in the university, then it can attract more
students to apply” (Yusuf, Ghazali, & Abdullah, 2017, p.
33). The location of the institute is also considered as a
time factor. This factor was studied as the ideal location of
the university; convenience, accessibility, campus layout
and the attractiveness of the university itself. In 2008
Hasan, Ilias, Rahman, & Razak (Hasan, Ilias, Rahman, &
Razak, 2008) did not find a significant relationship
between Reliability and the student satisfaction in private
higher education institutions in Malaysia.Consequently in
2011 Udo, Bagchi, & Kirs (Udo, Bagchi, & Kirs, Using
SERVQUAL to assess the quality of e-learning
experience, 2011) also used the SERVQUAL intrument to
investigate service quality dimensions in e-learning in
ODL in the USA but did not find it significant with the
dimension of Reliability. The significant relationship
between students‟ satisfaction and Reliablility was
examined and concluded that enhancing the trust in the
students that their institution is very much active in
providing the quality education and learning environment
for their academic development (Malik, Danish, &
Usman, 2010).In 2008 Udo, Bagchi, & Kirs (Udo, Bagchi,
& Kirs, Assessing Web Service Quality Dimensions: The
E- Servperf Approach, 2008) assessed web service
quality dimensions by using E- SERVPERF approach and
Website Content was one of the significant
dimension.Consequently, the findings indicated that web
service quality is asignificant driver of behavioral
intentions, its indirect effect through customer satisfaction
is also equally important. In 2011Udo, Bagchi, & Kirs
(Udo, Bagchi, & Kirs, Using SERVQUAL to assess the
quality of e-learning experience, 2011), have used
SERVQUAL to assess the quality of e-learning
experience, in ODL and found Website Content is a
significant dimension with the student‟s perceived
service quality.
3. Research Methodology The research Methodology is mainly dependent on the
conceptual frame work, Research instrument, Sample
frame work, Data collection and it‟s methods. It consists
of three (3) independent variables of service quality
dimensions of, Reliability, Website Content andCost&
Time. The independent variable is the Students‟
Satisfaction in ODL in the OUSL. The objectives of the
study; To examine the significant factors of Student
Satisfaction in ODL in the OUSL and confirm the
conceptual model. The research question for the study;
What significant factors do Students Satisfaction influence
ODL in the OUSL?
3.1 Conceptual Frame work and the Hypotheses
The conceptual frame work for this study is mainly based
on literature review. It consists of three (3) independent
variables of, service quality dimensions of Reliability,
Website Content and Cost& Time. The independent
variable is Student Satisfaction in ODL in the OUSL. The
research hypotheses: -
There are three research hypotheses built into this study;
1. H1: There is an association between Reliability and
Student Satisfaction in ODL in the OUSL.
2. H2: There is an association between Website Content
and Student Satisfaction in ODL in the OUSL.
3. H3: There is an association between Cost &Time and
Student Satisfaction in ODL in the OUSL.
Figure 1: Conceptual Frame work
3.2 Research Instrument
3.2.1 Data Collection
The validated questionnaire through a pilot test was
administered to collect the data. The sample of 760
undergraduate students followed a stratified sampling
procedure covering the central university and 5 of their
main regional centersisland wide which has a population
of 38,000 students.The respondents were selected from the
Regional Centers randomly as those who were willing to
participate in this survey provided they had a learning
experience with the university of more than, or equal
toone year at least.The 744 valid responses were used for
the analysis which covered 98% of the original size of the
sample.
3.2.2 Procedure
The Structural Equation Modeling (SEM) analysis was
adopted for this study which is considered as the second-
generation multivariate data analysis method (Gye-Soo,
International Journal of Advanced Research and Publications ISSN: 2456-9992
Volume 1 Issue 4, Oct 2017 www.ijarp.org
203
2016; Hair, Hult, Ringle, & Sarstedt, 2017). The main two
methods of SEM, covariance-based SEM (CB-SEM) and
variance based approach of Partial Least Squares (PLS)
can be carried out using the PLS-Graph, VisualPLS,
SmartPLS, and WarpPLS (Hair, Hult, Ringle, & Sarstedt,
2017; Wong, 2013). SEM is used to solve the complex
research problem situations with multiple dependent and
independent variables (Akter, D'Ambra, & Ray, 2011).
PLS is a soft modeling approach to SEM with no
assumptions about data distribution (Akter, D'Ambra, &
Ray, 2011; Vinzi, Chin, Henseler, & Wang, 2010). The
advantages of SEM is when handling a small sample
size in the non-availability of sufficient theory, the
accuracy of the predictive power and confirming the
correct model specification (Wong, 2013). The software
package of SmartPLS3.2.6 was used in this current study
(Hair, Hult, Ringle, & Sarstedt, 2017). This SEM based
package used different types of fields. In the field of ODL,
service quality and student satisfaction (Mantovani, 2012;
Ribeiro & Gouvêa, 2013; Udo, Bagchi, & Kirs, Using
SERVQUAL to assess the quality of e-learning
experience, 2011), Students‟ Satisfaction and continuance
intention in HE institutes (Al-Rahmi, Othman, & Yusuf,
2015; Chow & Shi, 2014 ; Ibrahim, Rahman, & Yasin,
2014; Shahijan, Rezaei, & Amin, 2015), Repurchase
Intention in Banking (Ibrahim, Rahman, & Yasin, 2014),
Satisfaction Industry in hospitality industry (Ali, Amin, &
Cobanoglu, 2015; Izogo, 2016), customer satisfaction
(Gye-Soo, 2016; Ringle, Sarstedt, & Zimmermann, 2011).
3.3 Survey material
Based on the conceptual model the questionnaire was
developed to collect data to test the research hypotheses.
The research instrument was based on literature and most
of the time the validated questions were used as the survey
items by modifying it more to reflect the HE and ODL
system. The questionnaire consisted of two (2) sections.
The first section was the demographic data and the second
section consistedof 27questions which have been rated on
a5- point Likert scale.
3.3.1 Reliability
The questions for this variable ismainly based on
SERVQUAL (Parasuraman, Zeithaml, & Berry.,
SERVQUAL: A Multiple-Item Scale for Measuring
Consumer Perceptions of Service Quality, 1988),
modified SERVQUAL (Udo, Bagchi, & Kirs, Using
SERVQUAL to assess the quality of e-learning
experience, 2011) and other literature based HE studies of
(Aghamolaei & Zare, 2008; Al-Rahmi, Othman, & Yusuf,
2015; Mantovani, 2012) .There were six(7) questions to
evaluate the service quality dimension of Reliability under
the Likert scale 1-5. The Table 1 has tabulated the
Questions for the construct of Reliability.
Table 1 Questions for the construct of Reliability
3.3.2 Website Content
The eight (8) items (Table 2) were constituted for this
item based on the study of (Udo, Bagchi, & Kirs, Using
SERVQUAL to assess the quality of e-learning
experience, 2011) rated on the 1-5 Likert scale. These
items were mainly based on (Cao, Zhang, & Seydel, 2005;
Wang, Wang, & Shee, 2007; Zhang, Prybutok, & Huang,
2006). These items are set to measure the usefulness of the
information with relation to the lessons, accuracy, quality
of information, and the applications of audio-video,
multimedia and graphics. The Table 2 has tabulated the
Questions for the construct of Website Quality.
3.3.3 Cost & Time
There were only 3 items (Table 2) for this construct and
use Likert scale (1.5) for the rating of the responses. This
construct measures the allocated of the time periods for
the courses, the payments and the available payment
schemes for the programmes. Questions were mainly
Item Question Source
REL1
REL2
REL3
The instructor consistently
provides good lectures.
The instructor is dependable.
The instructor reliably
corrects information when
needed.
(Mantovani, 2012;
Udo, Bagchi, &
Kirs, Using SERVQUAL to
assess the quality of
e-learning experience, 2011)
Item Question Source
WSC1.
WSC2.
WSC3.
WSC4.
WSC5.
WSC6.
WSC7.
WSC8.
The web site provides
useful information.
The web site provides
accurate information.
The website provides
high quality
information.
The information on the
web site is relevant to
my lessons.
The web site uses multimedia features
properly.
The web site uses
animations/graphics
properly.
The web site uses audio
elements properly.
The web site uses video elements properly.
(Udo,
Bagchi, &
Kirs, Using SERVQUAL
to assess the
quality of e-learning
experience,
2011)
(Udo,
Bagchi, &
Kirs, Using
SERVQUAL to assess the
quality of e-
learning experience,
2011)
(Udo,
Bagchi, & Kirs, Using
SERVQUAL to assess the
quality of e-
learning
experience,
2011)
(Udo, Bagchi, &
Kirs, Using
SERVQUAL to assess the
quality of e-
learning experience,
2011)
International Journal of Advanced Research and Publications ISSN: 2456-9992
Volume 1 Issue 4, Oct 2017 www.ijarp.org
204
based on (Joseph & Joseph, 1997). The Table 3 has
tabulated the Questions for the construct of Cost & Time.
Table 3: Questions for the construct of Cost & Time
3.3.4 Satisfaction
There were nine (9) (Table 4) questions that were set out
for this construct to measure the overall satisfaction of
their perceived service experience. The items were
adopted from the (Gruber, Fuß, Voss, & Glaeser-Zikuda,
2010; Mantovani, 2012; Udo, Bagchi, & Kirs, Using
SERVQUAL to assess the quality of e-learning
experience, 2011; Zhang, Prybutok, & Huang, 2006) using
the 5-point Likert scale for the response rating.
Table 4: Questions for the construct of Satisfaction
4 Data Analysis and Results The instrument used for this study has been validated
through a pilot test (Table 5). The pilot test was verified
with 50 undergraduate students selected randomly from
the central university premises. The Cronbach‟s alpha and
Kaiser-Meyer-Olkin (KMO) values revealed that the
required standard of the values is greater than 0.7000
except the dimension of Cost &Time (KMO= 0.584)
(Field, 2011, p. 647; Hair, Black, Babin, Anderson, &
Tatham, 2011, p. 139) . The Cost & Time dimension has
shown the law value of Cronbach‟s alpha and KMO since
it depends on the number of items and has only 3 items
(Devi, 2015). In PLS-SEM data analysis mainly in two
Parts; Measurement Model analysis and Structural Model
analysis. Measurement Model represents the relationships
between constructs and their corresponding indicators and
is generally referred to as an outer model. The Structural
Model describes the relationships between latent variables
or constructs and referred to as the inner model (Hair,
Hult, Ringle, & Sarstedt, 2017).
Table 5: Pilot Test Results
4.1 Measurement Model Analysis
There are two measurement model specifications based on
the development of the constructs; Reflective and
Formative. The Reflective Model representingthe
causality is from the constructs to its measures. The
Formative Model which is based on the causal indicators
form the construct (Hair, Hult, Ringle, & Sarstedt, 2017).
As the first step for the reflective Measurement Model the
outer loadings (Appendix A) of the indicators were
estimated. The threshold value of 0.7 was not to be found
in the items of REL5P REL6P, REL7P, SAF8P and
SAF9P and they were removed from the constructs. An
increase in the value of Average Variance Extracted
(AVE) and R2were observed. The Indicator Reliability
(square value of outer loadings) is also another indicator
of the Measurement Model and the highest (0.812) is
given by the largest value (CT1P = 0.901) of the outer
loading. The threshold value for the indicator reliability
must be greater than 0.700 (Hair, Hult, Ringle, & Sarstedt,
2017). The quality criteria values of construct reliability
and validity were given the composite reliability and
Cronbach's Alpha. The threshold value for these values
must be greater than 0.700. The convergent validity
indicator of AVE must be greater than 0.500. The results
of Cronbach's Alpha, Composite Reliability and AVE for
this study have reached the required standard (Appendix
B). The discriminant validity indicators of theFornell-
Larcker Criterion, cross loadings and Heterotrait-
Monotrait (HTMT) Ratio Confidence Intervals (CI) in
Appendix C, Appendix D and Appendix E respectively.
The results of the Fornell-Larcker Criterion (Appendix D)
showed all the diagonal values which is equal to the
square root of the AVE and is higher than the values in the
off diagonal positions; row andcolumn. This confirms that
all these constructs are valid measures of unique concepts.
The second discriminant validity indicator of Cross
Loadings (Appendix E) showed that the indicators‟
loading on its assigned construct is higher than all its
cross-loadings with other constructs. The third indicator of
Item Question Source
CT 1
CT 2
CT 3
Satisfied with the allocated time
period for the degree programme.
Satisfied with the Cost of
accommodation.
Satisfied with the Cost structure
allocated for my program. ( part time
payments)
(Joseph &
Joseph,
1997) modified
(Joseph & Joseph,
1997)
modified
(Joseph &
Joseph, 1997)
modified
Item Question Source
SAT1
SAT2
SAT3
SAT4
SAT5
SAT6
SAT7
SAT8
SAT9
Would you agree to say that „„I am
satisfied with my decision to enroll with this distance program‟‟?
Would you agree to say that „„My choice to enroll in this program was a
wise one?‟‟
Would you agree to say that „„I think I
did the right thing when I paid for this
learning service?‟‟
Would you agree to say that „„I feel
that my experience with distance learning has been enjoyable?‟‟
This distance learning course meets my expectations.
My Overall experience is better than I originally anticipated.
I am satisfied overall with the programs and services offered by the
Institute.
I am satisfied with the distance course
since it will give me a better chance to
further my career development.
I am delighted with the distance
course and its contents.
(Udo,
Bagchi, & Kirs, Using
SERVQUA
L to assess the quality
of e-learning
experience, 2011)
(Udo,
Bagchi, & Kirs, Using
SERVQUA
L to assess the quality
of e-learning
experience, 2011)
(Udo,
Bagchi, & Kirs, Using
SERVQUA
L to assess the quality
of e-learning
experience,
2011)
(Udo,
Bagchi, & Kirs, Using
SERVQUA
L to assess the quality
of e-learning
experience, 2011)
Variables Cronbach's Alpha
KMO value
No. of Items
Reliability.
0.732 0.630 7
Website Content
0.807 0.786 8
Satisfaction 0.899 0.828 8
Cost and Time 0.673 0.584 3
International Journal of Advanced Research and Publications ISSN: 2456-9992
Volume 1 Issue 4, Oct 2017 www.ijarp.org
205
HTMT ratios and the confidence intervals must be
examined and the ratios must be lower than the threshold
value of 0.85, and in the bootstrap results of HTMT,
confidence intervals must significantly be different from
1. In the (Appendix F) all the HTMT ratios have reached
the standard of less than 0.85. In (Appendix F: HTMT
Confidence Intervals Bias Corrected) the columns labelled
2.5% and 97.5% showed the lower and upper boundaries.
The value of 1 must not be included in these intervals
(Hair, Hult, Ringle, & Sarstedt, 2017). The summary of
the results of the Reflective Measurement Model is
shownin(Table 6).
Table 6: The summary of the results of the Reflective
Measurement Model
Note:
(Latent Variable(LV), Loadings (L), Indicator Reliability
(IR), Composite Reliability (CR), Cronbach‟s Alpha
(CA), Confidence Interval (CI) must not include
1(HTMT)).
4.2 Structural Model Analysis
The Variance Inflation Factor (VIF) is above 5 in the
constructs as the critical level of the multicollinearity The
results of VIF between dependent and independent
variables in (Table 7) and all values are less than 1.2 and
no issues of multicollinearity (Hair, Hult, Ringle, &
Sarstedt, 2017; Izogo, 2016) .
Table 7: The Collinearity Statistics (VIF) of the Structural
Model
The Structural Model relationships represent the
hypothesised relationships among the constructs. The path
coefficients (p values) can be obtained from the
bootstrapping test which are inbetween -1 and +1. The
bootstrap standard error enables computing the empirical t
values and p values for all path coefficients (Hair, Hult,
Ringle, & Sarstedt, 2017). The critical t value is 1.96 if the
significance level is 5% for the two-tailed test and the p
values are less than the 0.05 then the relationship can be
concluded as the significant under the considerationof 5%
confidence level (Hair, Hult, Ringle, & Sarstedt, 2017).
Table 8: The Bootstrapping results of path coefficients
Note: Original Sample (OS), Standard Deviation
(STDEV), Sample Mean (SM),
The output results (Table 8) confirmed thatall the
relationships are significant. The final model in Fig (2).
The variance extracted for the model which is considered
as a predictive power of the model is R2
is 0.5222
(52.2%). The R Square Adjusted is 0.520 (52.0%) which
is the unbiased R2 value after removing the nonsignificant
exogenous constructs (Hair, Hult, Ringle, & Sarstedt,
2017).
Figure 2: Final Structural Model
LV Indicat
ors
Convergent Validity Internal Consistency Reliability
DV
L
IR AVE
CR CA HTMT
>0.7 >0.5 >0.5
0.6-0.9 0.6-0.9 CI
Cost &
Time
CT1P 0.904 0.817
0.681 0.864 0.801 Yes CT2P 0.796 0.634
CT3P 0.769 0.591
Reliability
REL1P 0.848 0.720
0.636 0.874 0.809 Yes REL2P 0.758 0.575
REL3P 0.830 0.689
REL4P 0.748 0.559
Satisfactio
n
SAF1P 0.793 0.628
0.568 0.902 0.873 Yes
SAF2P 0.743 0.552
SAF3P 0.758 0.575
SAF4P 0.733 0.538
SAF5P 0.776 0.603
SAF6P 0.741 0.549
SAF7P 0.729 0.532
Website
Content
WCS1P 0.788 0.622
0.613 0.927 0.910 Yes
WCS2P 0.796 0.634
WCS3P 0.801 0.642
WCS4P 0.728 0.531
WCS5P 0.791 0.626
WCS6P 0.797 0.636
WCS7P 0.767 0.589
WCS8P 0.789 0.623
Construct Satisfaction
Cost & Time 1.129
Reliability 1.239
Satisfaction
Website Content 1.280
Construct Satisfaction
Cost & Time 1.129
Reliability 1.239
Satisfaction
Website Content 1.280
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Volume 1 Issue 4, Oct 2017 www.ijarp.org
206
The Effect size (f2) of the relationships between
endogenous variable of Satisfactionand the exogenous
variables of Cost& Time is 0.024(small in size),Reliability
is 0.195(medium) and Website Content is 0.346 (large)
(Hair, Hult, Ringle, & Sarstedt, 2017). The Blindfolding
procedure Q2(Construct Cross Validated Redundancy
approach) suggests the value of the predictive relevance
of the model. The Q2
value is greater than zero
recommends the predictive relevance of the model for a
certain endogenous construct. With this model, the Q2 is
0.275 for the endogenous variable of Satisfaction (Akter,
D'Ambra, & Ray, 2011; Henseler, Hubona, & Ray,
2016).The values of the model fit summary; “SRMR is a
measure of approximate fit of the researcher‟s model. It
measures the difference between the observed correlation
matrix and the model-implied correlation matrix” (Garson,
2016, p. 68; Hair, Hult, Ringle, & Sarstedt, 2017).The
Standardized Root Mean Square Residual (SRMR) is
0.074 and Root Mean Square Residual Covariance
(rmsTheta) is 0.129. The threshold values are SRMR is less
than 0.08 (Hu & Bentler, 1999) and rmsThetais 0.12. The
results revealed the fitness of the final model (Garson,
2016; Hair, Hult, Ringle, & Sarstedt, 2017; Henseler, et
al., 2014)
5 Discussion and Recommendation
The research objective of this study is to examine the
significant factors of Student Satisfaction in ODL in the
OUSL and finalise the conceptual model. The three
significant factors were tested and concluded that all three
constructs can be included in the model as significant
predictors. The three hypotheses are supported from the
results of the study. The construct of the Reliability was
significant and this was consistent with the results of the
studies of (Hasan, Ilias, Rahman, & Razak, 2008; Malik,
Danish, & Usman, 2010; Sembiring, 2015; Stodnick &
Rogers, 2008)and inconsistent withthe study in Malaysia,
investigation of relationship between Service quality and
students‟ satisfaction (Wei & Ramalu, 2011). The
construct of Website Quality was significant and this
result is consistent with the study of (Mason & Weller,
2000)in a very large web-based course presented by the
Faculty of Technology in the Open University of the UK.
The results of this study were examined as a qualitative
research which uncovered the factors of the web based
course content and presentation fitness as
thestudents‟expectations and learning style. This mostly
affected students‟satisfaction. The study of (Ramayah,
2006) has showed by using extended the Technology
Acceptance Model that the perceived ease of use was
positively related to perceived usefulness of the course
website. The study of (Zhang, Prybutok, & Huang, 2006)
is also consistent with the current study results and
conformed to the Web site service quality which has
affected the consumers‟satisfaction level and further
enhancement of consumer intention. The other significant
construct is Cost & Time. The importance and
performance analysis of (Joseph & Joseph, 1997; Lim,
Yap, & Lee, 2011)confirmed the importance and it‟s4th
place in the ranking order of Cost and Time in service
quality factors in the higher education sector. In the
hospital industry results showed a significant relationship
between service experience and customer satisfaction,
which influences price acceptance of customers. (Ali,
Amin, & Cobanoglu, 2015). The cost (financial aid) is an
important decision when they select a higher education
institute (Sia, 2013). The promotions which affect cost
structures significantly influence the university selection
by the students and their parents (Zain, Jan, & Ibrahim,
2013). The recommendations on all three factors since
they are significant with the student satisfaction and
outcome would lead to positive and favorable decisions of
continuance of the studies, with the same institute. The
cost factor is very important when selecting a university
by the parents and the students. The feasible and reliable
cost structure and promotions will motivate both students
and parents and give them the impressionofeducation as
an investment for a life time (Lim, Yap, & Lee, 2011; Sia,
2013). The Factor of reliability is dependent on the sound
knowledge of the subjects, successful lectures, and the
quality of information. The quality must be assessed with
the lecturers, their professional knowledge and how they
treat their students. All the staff must have a very good
understanding of ODL delivery methods and their distance
education clientele, without frustrating the students who
have face to face sessions, by recommending to them the
hybrid method which is a mixture face to face, and on-line
learning. Visiting lecturersand other teaching staff must
offer quality enhanced trainingto continue with the
student‟s reliability in the promised service. “In the
Distance Education mode, whilst catering to larger
numbers, it also attempts to improve the quality of
training provided for teachers” (Lekamge, 2010, p.
4).Partial or full scholarships could be offered depending
on the results of the academic year. The quality of the
Website will enhance giving more reliable and a speedy
service to the students. The policy makers must take
strategic decisions to enhance the current service as an
attractive, reliable and uniquely recognised service. Future
research should be handled as a qualitative and
longitudinal way (Tinto, 1975) to understand other service
quality factors that influence the students‟ satisfaction
since the R2
has covered only 52%of this study. The term
„quality assurance‟ refers to a process of defining and
fulfilling a set of quality standards consistently and
International Journal of Advanced Research and Publications ISSN: 2456-9992
Volume 1 Issue 4, Oct 2017 www.ijarp.org
207
continuously with the goal of satisfying all consumers,
producers and the other stakeholders (Inegbedion & Adu,
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Appendix A
Outer Loadings
Items Cost &
Time
Reliability Satisfaction Website
Content
CT1P 0.901
CT2P 0.799
CT3P 0.773
REL1P 0.802
REL2P 0.706
REL3P 0.796
REL4P 0.718
REL5P 0.666
REL6P 0.591
REL7P 0.686
SAF1P 0.771
SAF2P 0.718
SAF3P 0.736
SAF4P 0.713
SAF5P 0.779
SAF6P 0.749
SAF7P 0.732
SAF8P 0.669
SAF9P 0.668
WCS1P 0.786
WCS2P 0.795
WCS3P 0.799
WCS4P 0.725
WCS5P 0.793
WCS6P 0.800
WCS7P 0.771
WCS8P 0.791
Appendix B
Construct Reliability and Validity
Construct Cronbach's
Alpha
Composite
Reliability
Average
Variance
Extracted
(AVE)
Cost &
Time 0.801 0.864 0.681
Reliability 0.809 0.874 0.636
Satisfaction 0.873 0.902 0.568
Website
Content 0.910 0.927 0.613
Appendix C
Discriminant Validity (Fornell-Larcker Criterion)
Construct Cost &
Time
Reliability Satisfaction Website
Content
Cost & Time 0.825
Reliability 0.255 0.797
Satisfaction 0.341 0.561 0.754
Website
Content
0.308 0.419 0.637 0.783
Appendix D
Discriminant Validity (Cross Loadings)
Item Cost &
Time
Reliabili
ty
Satisfacti
on
Website
Content
CT1P 0.904 0.303 0.392 0.339
CT2P 0.796 0.127 0.185 0.180
CT3P 0.769 0.100 0.149 0.157
REL1P 0.165 0.848 0.529 0.379
REL2P 0.261 0.758 0.369 0.212
REL3P 0.214 0.830 0.465 0.360
REL4P 0.194 0.748 0.402 0.364
SAF1P 0.300 0.471 0.793 0.506
SAF2P 0.268 0.365 0.743 0.537
SAF3P 0.258 0.391 0.758 0.451
SAF4P 0.202 0.377 0.733 0.489
International Journal of Advanced Research and Publications ISSN: 2456-9992
Volume 1 Issue 4, Oct 2017 www.ijarp.org
211
SAF5P 0.237 0.518 0.776 0.473
SAF6P 0.210 0.418 0.741 0.458
SAF7P 0.322 0.408 0.729 0.444
WCS1P 0.232 0.462 0.593 0.788
WCS2P 0.255 0.371 0.538 0.796
WCS3P 0.282 0.346 0.528 0.801
WCS4P 0.293 0.275 0.436 0.728
WCS5P 0.215 0.297 0.478 0.791
WCS6P 0.242 0.284 0.440 0.797
WCS7P 0.208 0.257 0.447 0.767
WCS8P 0.203 0.281 0.489 0.789
Appendix E
Discriminant Validity (Heterotrait-Monotrait Ratio
(HTMT))
Construct Cost &
Time
Reliability Satisfaction Website
Content
Cost & Time
Reliability 0.268
Satisfaction 0.343 0.656
Website
Content
0.313 0.472 0.707
Appendix F
Discriminant Validity (Heterotrait-Monotrait(HTMT)
Confidence Intervals Bias Corrected
Relationship Origina
l
Sample
(O)
Sampl
e
Mean
(M)
Bias 2.5
%
97.5
%
Reliability -> Cost &
Time
0.268 0.271 0.00
3
0.19
8
0.347
Satisfaction -> Cost
& Time
0.343 0.344 0.00
1
0.26
9
0.413
Satisfaction->
Reliability
0.656 0.657 0.001
0.578
0.725
Website Content ->
Cost & Time
0.313 0.313 0.00
1
0.23
4
0.396
Website Content ->
Reliability
0.472 0.472 0.000
0.391
0.551
Website Content ->
Satisfaction
0.707 0.707 0.00
0
0.64
7
0.761
Appendix G
Collinearity Statistics (VIF)
Construct Cost
&
Time
Reliability Satisfaction Website
Content
Cost & Time 1.129
Reliability 1.239
Satisfaction
Website
Content
1.280