Content uploaded by Heather Harvey
Author content
All content in this area was uploaded by Heather Harvey on Feb 05, 2020
Content may be subject to copyright.
Full Terms & Conditions of access and use can be found at
http://www.tandfonline.com/action/journalInformation?journalCode=wmhe20
Download by: [Heather Harvey] Date: 23 November 2015, At: 19:42
Journal of Marketing for Higher Education
ISSN: 0884-1241 (Print) 1540-7144 (Online) Journal homepage: http://www.tandfonline.com/loi/wmhe20
Designing a predictive model of student
satisfaction in online learning
Sanjai K Parahoo, Mohammad Issack Santally, Yousra Rajabalee & Heather
Lea Harvey
To cite this article: Sanjai K Parahoo, Mohammad Issack Santally, Yousra Rajabalee & Heather
Lea Harvey (2015): Designing a predictive model of student satisfaction in online learning,
Journal of Marketing for Higher Education, DOI: 10.1080/08841241.2015.1083511
To link to this article: http://dx.doi.org/10.1080/08841241.2015.1083511
Published online: 21 Oct 2015.
Submit your article to this journal
Article views: 19
View related articles
View Crossmark data
Designing a predictive model of student satisfaction in online
learning
Sanjai K Parahoo
a
, Mohammad Issack Santally
b
, Yousra Rajabalee
b
and Heather
Lea Harvey
c
a
HBMSU, Business School, Dubai, United Arab Emirates;
b
Centre for Innovative and Lifelong Learning,
University of Mauritius, Reduit, Mauritius;
c
Health Department, Thamassat University, Bangkok, Thailand
ABSTRACT
Higher education institutions consider student satisfaction to be
one of the major elements in determining the quality of their
programs. The objective of the study was to develop a model of
student satisfaction to identify the influencers that emerged in
online higher education settings. The study adopted a mixed
method approach to identify issues perceived by students as
affecting their satisfaction, using focus groups followed by
exploratory and confirmatory factor analyses to develop the study
model. Data were collected using an online questionnaire from a
campus-wide sample of 834 students enrolled in a generic online
course at the University of Mauritius. Using structural equation
modeling, the study identified four significant determinants of
student satisfaction in decreasing importance: the marketing
construct of university reputation; physical facilities; faculty
empathy; and student–student interactions. Various theoretical
and managerial implications are discussed and directions for
further research are proposed.
ARTICLE HISTORY
Received 7 February 2015
Accepted 20 May 2015
KEYWORDS
satisfaction; online learning;
reputation; interactions;
facilities
Introduction
Higher education (HE) institutions consider student satisfaction to be one of the major
elements in determining the quality of their programs in today’s markets (Yukselturk & Yil-
dirim, 2008), as student satisfaction is considered to be an important indicator of the
quality of academic experiences (Kuo, Walker, Belland, & Schroder, 2013; Yukselturk & Yil-
dirim, 2008). In this regard, the focus by universities on the marketing construct of student
orientation has been justified on academic grounds, since student satisfaction is related to
several desirable consequences for the students: quality of academic experiences (Allen &
Seaman, 2008; Gibson, 2010; Kuo et al., 2013), persistence (Allen & Seaman, 2008; Gibson,
2010), self-confidence (Letcher & Neves, 2010), and retention (Debourgh, 1999).
From a business perspective, globalization and increasing competition among univer-
sities to attract students have led universities to adopt customer-oriented business models
in order to compete effectively (Newman & Jahdi, 2009; Parahoo, Harvey & Tamim, 2013).
Satisfied students are likely to engage in desirable behavior such as: spreading positive
© 2015 Taylor & Francis
CONTACT Sanjai K Parahoo s.parahoo@hbmeu.ac.ae
JOURNAL OF MARKETING FOR HIGHER EDUCATION, 2015
http://dx.doi.org/10.1080/08841241.2015.1083511
Downloaded by [Heather Harvey] at 19:42 23 November 2015
word-of-mouth, collaborating with the institution after they graduate (Alves & Raposo,
2009), and acting as potential donors as alumni (Parahoo et al., 2013). This makes these
satisfied students an asset to the university. As a result, the strategic focus of HE insti-
tutions has shifted from a teaching-oriented model to a customer-oriented model (Kuo
et al., 2013; Parahoo et al., 2013) so that universities may be considered as a provider of
products and services to their customers, namely students.
This shift in strategic orientation by universities is increasingly occurring within a new
paradigm of delivery of teaching and learning. There is a growing student request for
online courses, leading to online learning in higher education becoming a major mode
of delivery in today’s technology-driven environment (Croxton, 2014; Platt, Raile, & Yu,
2014). In the USA, for example, demand for online courses has exceeded that for face-
to-face courses (Kuo et al., 2013). A similar trend is emerging in other markets, as
evidenced by the growing geographical distribution in the active membership of the Inter-
national Council of Distance Education, and the Commonwealth of Learning. The flexibility
in participation, ease of access, and convenience offered by online learning to students
(Croxton, 2014), as well as a growing need for continuous and lifelong learning among
the working population, could be identified among the factors behind this surge in
demand.
Unsurprisingly, the global growth of HE has contributed to the development of a
rich student satisfaction literature. As a recognition of the emergence of online learning,
the empirical settings in recent studies have shifted from physical classes to online deliv-
ery. The issue of student satisfaction in online settings has emerged because the new tech-
nologies have altered the way that students interact with instructors and classmates
(Parahoo & Tamim, 2012; Yukselturk & Yildirim, 2008). This raises the question: Are the con-
siderations surrounding student satisfaction different between physical and online
settings?
In this regard, Platt et al. (2014) reported that students did not perceive online and
physical classes as being equivalent, as online courses were associated with fewer oppor-
tunities for interaction compared to face-to-face courses. McFarland and Hamilton (2005)
compared the determinants of satisfaction across traditional and online classes and found
that some determinants of satisfaction were specific to online learning: the effectiveness
of the discussion board and that of interactions with other students to support learning.
Other authors have focused on modeling student satisfaction in online learning using
multivariate linear regression to identify what antecedents of satisfaction emerged (e.g.
Beqiri, Chase, & Bishka, 2009; Kuo et al., 2013; Sher, 2009; Thurmond, Wambach,
Connors, & Frey, 2002). Given the variety of empirical settings and courses involved in
these studies, a number of antecedents emerged as affecting satisfaction, and these are
discussed in the next section. However, it is fair to state that consensus has yet to
emerge on a generic framework for predicting student satisfaction in online learning.
Further, the bulk of these studies have been undertaken in Western contexts and the find-
ings may not be extensible to developing countries, which have different cultural, econ-
omic, and technological environments. It would therefore be pertinent to undertake
such studies in developing country environments.
The objectives of the study may therefore be stated as developing a model of student
satisfaction to identify the antecedent factors that emerge in online settings. More specifi-
cally, the research objectives may be stated as:
2S.K. PARAHOO ET AL.
Downloaded by [Heather Harvey] at 19:42 23 November 2015
(a) To identify the factors that influence student satisfaction in online settings in a devel-
oping country.
(b) To develop and empirically validate a model of student satisfaction in such a setting.
(c) To identify the relative importance of the factors that influence student satisfaction.
Given the dearth of empirical studies on student satisfaction in developing countries,
Mauritius was selected as an empirical setting for the study. Mauritius has a multi-
ethnic population of 1.3 million inhabitants comprising people of Indian, African,
Chinese, and French origins. Building on its infrastructure and a business-friendly
climate, the country successfully diversified its economy from agriculture and manufactur-
ing to a service economy based on tourism, financial services, and technology. It has a
vibrant HE system with four public universities, including the University of Mauritius set
up in 1968, and the Open University of Mauritius which officially started its operations
in February 2013. The Tertiary Education Commission (TEC) is responsible for promoting
and managing the HE sector and the implementation of an overarching regulatory
quality assurance framework. As of December 2012, 67 private tertiary education insti-
tutions were registered with the TEC, many of which offered programs using distance edu-
cation or blended modes of delivery. The tertiary education sector employment is
estimated at 2700 persons, of which 30% are academic staff (Santally et al., 2013). The
gross tertiary enrollment rate, including Mauritian students enrolled in both local and
overseas HE institutions, as a percentage of the population aged 20–24 years, stood at
46.6% in 2012 (Santally et al., 2013).
After decades of providing and refining online learning in Mauritius, this was the first
research focused on developing a predictive model of student satisfaction in online learn-
ing. While generalizability of the findings to other contexts should always be done with
circumspection, nonetheless it is hoped that the findings will provide some broad direc-
tions to university administrators in developing countries, particularly those with compar-
able socio-economic contexts.
Literature review: student satisfaction and its predictors
Student satisfaction
Customer satisfaction occupies a central role in marketing. Desirable business imperatives
such as loyalty intentions (Alves & Raposo, 2009; Parahoo et al., 2013) have been found to
impact on the quality of academic experiences (Gibson, 2010; Kuo et al., 2013), leading to
student satisfaction (Letcher & Neves, 2010). Satisfaction thus constitutes the outcome vari-
able of the study model. Satisfaction has been defined as: ‘A judgment that a product or
service feature, or the product or service itself, provides a pleasurable level of consump-
tion-related fulfillment’(Oliver, 1999, p. 34). Given the emergence of higher education as
a service experience, this definition is logically extendable to university contexts.
In a comprehensive review of the satisfaction literature in traditional face-to-face HE
settings, Gibson (2010) reviewed major studies and classified the antecedents of
student satisfaction across nine factors: academic staff/teaching; classes/curriculum; advis-
ing support; skills developed by students; preparation for future; services/facilities; social
integration; student centeredness/responsiveness; and pre-enrollment factors.
JOURNAL OF MARKETING FOR HIGHER EDUCATION 3
Downloaded by [Heather Harvey] at 19:42 23 November 2015
During the past decade, various scholars have investigated student satisfaction in an
online setting and they identified interactivity as one of its key predictors. Kuo et al.
(2013) surveyed 102 HE students in the USA, and using regression analysis, determined
that the interactions of students with instructors, with the course materials, as well as inter-
net self-efficacy, determined their satisfaction. Similarly, Sher (2009) surveyed HE students
in Washington, DC, and using linear regression, found that interactions among students
and between students and faculty were the two factors affecting student satisfaction.
Endres, Chowdhury, Frye, and Hurtubis (2009) reported similar findings. Endres et al.
(2009) surveyed 277 students in an online MBA program in the Midwest (USA) and
using discriminant analysis, found that student satisfaction was determined by five
factors: satisfaction with faculty practices, learning practices, course materials, student-
to-student interaction, and course tools. Bollinger and Martindale (2004) conducted a
study involving 105 graduate students enrolled in multiple online courses in instructional
technology at a regional university in the USA to identify factors influencing student sat-
isfaction. Using factor analysis, they unearthed three components affecting student satis-
faction: the instructor; technology; and interactivity. Finally, in professional online courses,
the design of the course work, assessments, and providing timely feedback on assign-
ments were found to affect student satisfaction (Thurmond et al., 2002).
Other authors have focused on the demographic profiles and personal attributes that
would help to predict student satisfaction in an online HE context. For example, Beqiri et al.
(2009) surveyed 240 business students in the USA and they determined that male and
married postgraduates were more likely to be satisfied, and with regression analysis, it
was further found that factors such as the appropriateness of and student familiarity
with the course also influenced their satisfaction.
Interactions
With the growth of technology-enabled learning environments, online learning platforms
now enable considerable scope for synchronous interactions among students as well as
between the students and the instructor. Alternatively, in asynchronous mode, threaded
discussions are often used to support interactive discussions and exchange of ideas
among students or between students and the instructor. It has been proposed that
online courses with high levels of interactivity result in higher levels of student motivation,
improved learning and satisfaction, as compared to less interactive learning environments
(Croxton, 2014).
Moore (1989) examined the issue of interactions and proposed a framework compris-
ing three types of interactions that are important in online education: student–student,
student–teacher, and student–content. Student–student interaction refers to two-way
reciprocal communication among learners who exchange information, knowledge,
thoughts, or ideas regarding the course (Moore, 1989). Student–teacher interaction con-
sists of two-way communications between the teacher and students, while student–
content interaction is a process of individual students elaborating and reflecting on
the subject matter or the course content (Moore, 1989). Numerous recent studies
have shown that these interactions play a critical role in the student academic experi-
ence and student satisfaction (Endres et al., 2009;Kuoetal.,2013;Parahoo&Tamim,
2012;Sher,2009). Although interactions are recognized as critical in online education,
4S.K. PARAHOO ET AL.
Downloaded by [Heather Harvey] at 19:42 23 November 2015
the literature is inconclusive regarding which type(s) of interaction would be more
important in predicting student satisfaction (Bray, Aoki, & Dlugosh, 2008).
In support of the impact of student–student interactions on satisfaction, Ivankova and
Stick (2007) surveyed 207 doctoral students and determined that those who successfully
completed their studies received more meaningful and constructive peer feedback than
those who dropped out. Similarly, Einarson and Matier (2005) used multiple linear
regression and found that students who had a sense of belonging and benefitted from
social engagement were more likely to be satisfied with their educational experience.
Finally, in a qualitative study of marketing students, Hollenbeck, Mason, and Song
(2011) found that student–student interactions reduced the perceived threat of poor per-
formance in their courses.
While student–student interactivity plays an important role in online student satisfac-
tion, it has been proposed that quality and timeliness of student–teacher communication
is also a major predictor of student satisfaction (Croxton, 2014), supported by various
empirical studies. Such examples include Walker and Kelly (2007) where online under-
graduate and graduate students reported that the timeliness of instructor feedback influ-
enced overall course satisfaction. Parahoo et al. (2013) found similar results in Saudi Arabia,
using multiple regression analysis showing that various elements of faculty interactions
(including empathy, availability of faculty, and promptness of faculty feedback) had a sig-
nificant impact on satisfaction for males.
The interactions between student–student and student–teacher in online learning
have been investigated more commonly than student–content interaction (Kuo et al.,
2013), and have been found to be more predictive of student satisfaction than
learner-content interaction (Bolliger & Martindale, 2004). On this basis, the focus in this
study was therefore on student–student and student–teacher interactions. Support for
such interactions is found in the service-dominant logic literature, which argues for
the involvement of customers in co-creating value in a service. In essence, facilitating
effective interactions of students among themselves and with faculty would support
higher involvement of the students in the teaching and learning process.
University reputation
An often cited definition of corporate reputation is the ‘“observers”collective judgments of a
corporation based on assessments of the financial, social, and environmental impacts attrib-
uted to the corporation over time’(Barnett, Jermier, & Lafferty, 2006, p. 34). Achieving a posi-
tive reputation is crucial to a business as it represents the most valuable intangible asset
(Vidaver-Cohen, 2007). The importance of reputation to organizations seems to have
extended to universities, as supported by the recent research findings (Gibson, 2010; Helge-
sen & Nesset, 2007; Parahoo et al., 2013; Sung & Yang, 2009).
While Gibson (2010) found that only two studies identified reputation as a determinant of
satisfaction, other recent studies have established the relevance of reputation in satisfaction
models. For example, in a study in Dubai, Parahoo and Tamim (2012) surveyed 99 students
enrolled in an online program and found that university reputation significantly affected
student satisfaction. Kuo and Ye (2009) used structural equation modeling (SEM) and found
a positive relationship between university image and student satisfaction at a Taiwanese
vocational institute.In Norway, Helgesen and Nesset (2007) used SEM to establish a significant
JOURNAL OF MARKETING FOR HIGHER EDUCATION 5
Downloaded by [Heather Harvey] at 19:42 23 November 2015
relationship betweenreputation and loyalty, for online undergraduates. Similar findings relat-
ing to the university reputation-student satisfactionrelationship have been reported by Sung
and Yang (2009) and Parahoo et al. (2013).
Assessing reputation from student perceptions presents a difficulty. Students are in
essence being required to assess their own university post-purchase emotions. Cognitive
dissonance theory would suggest that students at a lower ranked university would not
be comfortable in reporting their university reputation as ‘second rate’, so they would
tend to over-rate the reputation of their university to compensate (Lake Wobegon
effect). To work around this dilemma, this study identified a range of diverse items
that would collectively and comprehensively constitute the perceived reputation of a
university.
It is recognized that an organization’s reputation has been built over time by con-
sistently meeting its stated objectives. Consequently, the students’perception of the
reputation of a HE institution would be used by the students as a proxy to reduce
the risk of having an unsatisfactory academic experience. This would be particularly
relevant in online learning environments where students interact at a distance with
the organization, and hence would rely on more intangible cues to make satisfaction
judgments. In this regard, the lack of research emphasis on reputation in the HE litera-
ture, particularly in an online context, may be a bit surprising, and it would be useful to
investigate the construct of reputation and its role in satisfaction models for online
learning.
Physical facilities
A construct that complements reputation is the quality of the physical facilities of the uni-
versity. The facilities dimension is broadly referred to as tangibles, physical features, and
physical issues by various researchers (Farahmandian, Minavand, & Afshardost, 2013). It
is associated with accessibility of physical facilities by students to support academic and
nonacademic activities. In their seminal work on services, Parasuraman, Zeithaml, and
Berry (1988) identified ‘tangibles’as one of the five dimensions of service quality, a con-
struct closely associated with satisfaction.
Subsequent research has supported this proposition. For example, Gibson (2010)
surveyed the student satisfaction literature and found strong evidence for the effect of
physical facilities on satisfaction. Similarly, Helgesen and Nesset (2007) established a sig-
nificant relationship between ‘facilities’and satisfaction, and Arambewela and Hall
(2009) found a positive association between physical infrastructure and satisfaction
among international postgraduate business students in Australia. Elsewhere, Farahman-
dian et al. (2013) surveyed postgraduate students in Malaysia and established that facilities
significantly affected student satisfaction, as did Parahoo et al. (2013) in a survey of stu-
dents in Saudi Arabia, where a factor (labeled reputation) which included items relating
to physical infrastructure had a significant effect on satisfaction both for male and
female students.
Research evidence thus suggests that the academic experience of students extends
beyond human interactions to other aspects that affect student life, such as physical
characteristics of university facilities (Parahoo et al., 2013). Although the facilities-satisfac-
tion linkage has been supported by the literature on service quality (e.g. Parasuraman
6S.K. PARAHOO ET AL.
Downloaded by [Heather Harvey] at 19:42 23 November 2015
et al., 1988), other researchers have not identified such a relationship. For example, O’Dris-
coll (2012) used factor analysis and multiple regression analysis in a study of 263 under-
graduate hospitality students in Ireland and found that physical facilities did not have a
significant impact on satisfaction.
Of particular relevance is the fact that most of the evidence pertains to traditional phys-
ical settings. Therefore, it would be pertinent to examine to what extent online setting
impacts on the importance of physical facilities in influencing student satisfaction. Does
the fact that the teaching and learning now takes place in a virtual context influence
the perceived importance of facilities by students?
In conclusion, it would be useful to examine and quantify the impact the variables of
interactions, reputation, and physical facilities have on student satisfaction in an online
setting by designing a model of satisfaction and testing it using SEM.
Methodology
Empirical setting
In line with the objectives of the study, a generic course offered by the University of
Mauritius General Education module (GEM) was selected as the sampling frame for a
number of reasons. First, the GEM had an enrollment of over 800 undergraduate stu-
dents from the different schools on campus, thereby representing a university-wide
sample. Walker and Kelly (2007)identified behavioral differences in interactions
between undergraduate and postgraduate students. Therefore, focusing on UG stu-
dents in the present study enabled one to control the possible confounding effect of
level of study. Further, the course was offered fully online, thereby leading the students
to be fully immersed in the online learning experience and hence to be primed to
report on their satisfaction. An online survey was administered for convenience and
anonymity at the end of the online course. The students were assured of the confiden-
tiality of their responses, and that any analysis would be done on an aggregate basis
without identifying individual respondents. This approach worked well, and at the
end of the survey period, a total of 834 usable responses were received, representing
a response rate of 90%.
Mixed method: combining qualitative and quantitative approaches
The study used a ‘mixed method’approach to achieve its objectives. Such an approach
has been recognized by Johnson, Onwuegbuzie, and Turner (2007) as ‘the third major
research approach’, as it combines the benefits of both qualitative and quantitative
methodologies. In the present exploratory study, this approach enabled identifying com-
prehensive issues affecting student satisfaction, developing a conceptual model, and
then validating the model empirically.
The first stage involved a qualitative phase using two focus groups, each containing
eight undergraduate students. This enabled detailed information to be obtained about
individual and group feelings, perceptions, and opinions as well as seeking clarifica-
tions about the ideas expressed by the students. The discussions were digitally
recorded for ease of transcription. The discussions were then analyzed in depth inde-
pendently by each author to ensure reliability and minimize researcher bias. A
JOURNAL OF MARKETING FOR HIGHER EDUCATION 7
Downloaded by [Heather Harvey] at 19:42 23 November 2015
meeting was then held between the researchers to resolve discrepancies and reach
agreement regarding the emerging themes affecting student satisfaction and hence
to complete the coding process. The major themes affecting satisfaction that
emerged were:
.Quality of students’interactions with faculty;
.Quality of students’interactions with other students;
.Quality of students’interactions with IT staff;
.Quality of students’interactions with administrative staff;
.Faculty profile in terms of academic competence, teaching experience, and compe-
tence in using technological tools;
.Quality of physical facilities of the university;
.Corporate reputation of the university.
Development of conceptual model and hypotheses
This qualitative phase was followed by a quantitative one involving exploratory and
confirmatory factor analyses to determine and confirm the factor structure of
student satisfaction. Six variables influencing satisfaction emerged: IT/Administrative
staff interactions;Faculty empathy;Reputation of university;Student interactions;Phys-
ical facilities;and Faculty feedback.
Four of these factors (IT/Administrative staff interactions;Faculty empathy;Student inter-
actions;and Faculty feedback) related to different types of interactions that students were
involved in. Based on Moore (1989), student–student interactions represented one of the
three types of interactions that led to satisfaction, and this has been supported by
various empirical studies discussed in the literature review section (e.g. Einarson &
Matier, 2005; Endres et al., 2009; Hollenbeck et al., 2011; Ivankova & Stick, 2007; Sher,
2009). Therefore, the first hypothesis is:
H1: Student–student interactions have a positive influence on student satisfaction
Similarly, the wide empirical support for student–teacher interactions (represented in the
present study by two distinct factors faculty empathy and faculty feedback) constituted
major antecedents of student satisfaction (see literature review section for discussion of
supporting studies by Croxton, 2014; Thurmond et al., 2002; Walker & Kelly, 2007;
Parahoo et al., 2013). Consequently, it is proposed:
H2: Faculty empathy has a positive influence on student satisfaction
H3: Faculty feedback has a positive influence on student satisfaction
The online setting supported the emergence of a related type of interaction that was not
strictly part of Moore’s(1989) framework. Student–administrative/IT staff interactions
emerged as a determinant of student satisfaction during the qualitative phase and
was confirmed in the exploratory factor analysis (EFA) and confirmatory factor analysis
(CFA). This factor seems to be subsumed into the ‘facilities/services’classification of
Gibson (2010) which was identified in other studies, among them LeBlanc and
Nguyen (1997), Thomas and Galambos (2004)andParahooandTamim(2012)provided
further support for the relationship between student interactions with IT/administrative
8S.K. PARAHOO ET AL.
Downloaded by [Heather Harvey] at 19:42 23 November 2015
staff and student satisfaction, when they used multiple regression and empirically
determined a significant relationship between the two constructs. Thus the fourth
hypothesis is:
H4: Student–administrative/IT interactions have a positive influence on student satisfaction
A positive association between reputation of universities and student satisfaction was
demonstrated by various researchers (Kuo & Ye, 2009; Parahoo et al., 2013; Parahoo &
Tamim, 2012; Sung & Yang, 2009). Therefore:
H5: University reputation has a positive influence on student satisfaction
Helgesen and Nesset (2007) used SEM and demonstrated that university facilities exerted a
significant effect on student satisfaction. Similar findings were determined by LeBlanc and
Nguyen, (1997) and Thomas and Galambos, (2004), while Parasuraman et al. (1988) estab-
lished a positive relationship between facilities and service quality (a key antecedent of
satisfaction). It is therefore proposed that:
H6: Physical facilities at a university have a positive influence on student satisfaction
These relationships may be summarized as per Figure 1.
Figure 1. Conceptual model.
JOURNAL OF MARKETING FOR HIGHER EDUCATION 9
Downloaded by [Heather Harvey] at 19:42 23 November 2015
Developing pool of items
For each variable identified, validated measures were sourced from the literature and fine
tuned to the context of the present study. For example, measures pertaining to the differ-
ent types of interactions students had with other stakeholders, as well as their perceptions
of the university’s physical facilities, were sourced from SERVPERF (Cronin & Taylor, 1992)
and HEdPERF (Firdaus, 2005). For university reputation, items from Sultan and Wong (2012)
and Parahoo et al. (2013) were selected, while for satisfaction, common satisfaction scales
(e.g. Butcher, Sparks, & O’Callaghan, 2001; Sivadas & Baker-Prewitt, 2000) were used. At the
conclusion of this process, a battery of 34 items deemed to influence student satisfaction
with their university was drafted. These items were pilot tested with a sample of 10 stu-
dents, which resulted in additional refinement, yielding the draft study questionnaire,
ready for item purification.
Item purification and finalization of measurement scales
The data collected were cleaned and coded, and the analysis comprised of four stages.
First, an EFA was conducted using SPSS 20 to identify the number of distinct factors
involved (Hair, Black, Babin, & Anderson, 2010). The emerging factor structure was then
confirmed by CFA using LISREL. Items having loadings of less than 0.5 on their respective
factors were considered for omission, thereby achieving purification of scales (Hair et al.,
2010).
The internal consistency of the different study scales was measured through composite
reliability (CR) using a value of 0.70 as a threshold (Hair et al., 2010). Face, convergent, and
discriminant validity were also assessed. Face validity was incorporated into the measures
by using existing validated measures. Convergent and discriminant validities were
assessed by using average variances extracted (AVE) statistics and squared correlations
between study variables. Finally, the study hypotheses were tested by structural equation
modeling (SEM) using LISREL. Full details of these statistical techniques are described in
the next section.
Exploratory factor analysis
A sample size of 834 respondents exceeded the minimum size required for EFA as per Hair
et al. (2010). The assumptions underlying EFA were confirmed with the Kaiser–Meyer–
Olkin measure of sampling adequacy being 0.930, while the Bartlett’s test of sphericity
had a p-value < .001. The EFA was conducted using principal component analysis for
extraction followed by varimax rotation. Seven factors involving 29 items emerged with
eigenvalues exceeding 1.0, which cumulatively accounted for 59.1% of the variance (see
Table 1). The factors were named as follows: Student satisfaction;Student–IT/Administrative
staff interactions;Faculty empathy;Reputation of university; Student–student interactions;
Physical facilities;and Faculty feedback.
Since the loading and variance for satisfaction were substantially larger than those of
other factors, the possibility of common method bias (CMB) was tested, as its presence
would threaten the validity of the conclusion drawn upon statistical results (Podsakoff,
Mackenzie, & Podsakoff, 2012). Harman’s test was undertaken with the number of
10 S.K. PARAHOO ET AL.
Downloaded by [Heather Harvey] at 19:42 23 November 2015
factors to be extracted constrained to one, and assessing the value of percentage of var-
iance explained by the first component. The results showed a variance of 31.9%, well
below the threshold of 50%, thereby confirming the absence of CMB in the data.
Scale construction: unidimensionality
The 29 scale items representing 7 measurement scales were tested for the unidimension-
ality of each individual scale using confirmatory factor analysis (Hair et al., 2010). The fit
indices for the overall models were satisfactory (chi-square/degrees of freedom ratio =
2.90). On analyzing the measurement models, it was observed that while all the paths
were statistically significant (p< .01), and the vast majority of 29 paths (lambdas) had
Table 1. Rotated factor solution.
Item description (simplified)
Component
1234567
Student satisfaction
Very satisfied with university services .692
University met my expectations .808
University fulfilled my aspirations .803
University met my needs .751
Student–IT/administrative staff interactions
IT Staff are accessible .698
Administrative staff provide dependable information .693
IT staff provide good technical support .817
IT staff provide good technical training .769
Administrative staff provide timely support .524
Faculty empathy
Faculty are caring .702
Faculty respond promptly to requests for assistance .690
Faculty are interested to solve my problems .724
Faculty display a positive attitude towards me .701
Reputation of university
Faculty are competent .458
Faculty have long teaching experience
a
.460
University is known for excellent quality programs .517
University is known for its reputable programs .593
University has a good reputation in market .513
Faculty use technology effectively
a
.496
Faculty communicate using electronic tools
a
.561
Student–student interactions
There is good collaboration among students during
assignments
.861
There is good communication among students for
group assignments
.853
There is good collaboration among students during the
course
.735
Physical facilities
University campus layout is attractive .778
University facilities are visually appealing .750
University has a comfortable physical environment .732
Faculty feedback
Faculty provides timely feedback .781
Faculty provides detailed feedback .798
Faculty are available for consultation
a
.468
Eigen value 10.5 2.1 1.8 1.6 1.2 1.1 1.1
Cumulative percentage of variance (%) 31.9 38.2 43.8 48.5 52.2 55.7 59.1
a
Items with loadings < 0.50 on their construct (subsequently deleted after CFA).
JOURNAL OF MARKETING FOR HIGHER EDUCATION 11
Downloaded by [Heather Harvey] at 19:42 23 November 2015
loadings above the desirable value of 0.70, four of the path loadings had values below the
minimum prescribed threshold of 0.50 (Hair et al., 2010). Three of these paths pertained to
reputation (relating to faculty experience, faculty using technology effectively and faculty
communicating electronically) and one pertained to faculty feedback (available for consul-
tation). After consultations among the authors and with colleagues from the Admissions
Department, it was considered that these four items could be deleted without affecting
the conceptualization of the related constructs. Consequently, these four items were
omitted and the CFA was run again with 25 measures. As expected, the chi-square/
degrees of freedom ratio (= 2.50) and the fit indices improved: SRMR = 0.050; RMSEA =
0.032; NNFI = 0.98; CFI = 0.99.
Examining reliability and validity
As illustrated in Table 2, the CR of each of the seven scales was good (> 0.70 as
recommended by Hair et al., 2010). Face validity was established by using validated
measures from the literature. Convergent validity was established using the criteria
of Hair et al. (2010) by examining the path loadings, which were all high. Further,
the AVE at least equaled 0.5 for all constructs (see Table 2), thereby confirming con-
vergent validities. Discriminant validities between a pair of latent variables were estab-
lished by comparing the two respective AVE values to the square of the respective
correlation estimates between the same two variables. Since the AVE values exceeded
the squared correlation estimates, discriminant validity was established.
Having established unidimensionality, reliability and validity for each of the measure-
ment scales, it was appropriate to investigate the study hypotheses.
Profile of respondents
All the undergraduate students surveyed were relatively young, with 72.5% being below
20 years, and 99% below 25. Most of the students were female (62.4%) as compared to
male (37.6%), and the students were primarily single (98.1%).
Testing of hypotheses
The conceptual model shown in Figure 1 above was tested by SEM. It was observed that all
paths pertaining to the 25 items (see Table 1) in the measurement models were significant
at p< .01, while in the structural model, all paths were similarly significant (p< .01) except
Table 2. Summary statistics for scale items (AVE values on diagonal, squared correlation estimates
below diagonal).
Item description #Items CR 1234567
1 Student satisfaction 4 0.92 0.74
2 Student–IT/admin Interactions 5 0.84 0.26 0.52
3 Faculty empathy 4 0.80 0.30 0.34 0.50
4 Reputation of university 4 0.80 0.48 0.31 0.31 0.50
5 Student–student interactions 3 0.84 0.15 0.14 0.13 0.15 0.64
6 Physical facilities 3 0.81 0.44 0.24 0.22 0.40 0.44 0.59
7 Faculty feedback 2 0.73 0.16 0.22 0.30 0.04 0.04 0.11 0.58
12 S.K. PARAHOO ET AL.
Downloaded by [Heather Harvey] at 19:42 23 November 2015
for two paths: administrative/IT interactions–student satisfaction (gamma = 0.01, t=
−0.19) and faculty feedback–student satisfaction (gamma = 0.03, t= 0.70). Consequently,
these two non-significant paths were omitted and the revised model containing four inde-
pendent variables reflected by 18 items was again tested by SEM.
The model fit was good, and supported by a chi-square/degrees of freedom ratio = 2.60,
with the following fit indices: SRMR = 0.035; RMSEA = 0.044; NNFI = 0.99; CFI = 0.99, all well
within the threshold proposed by Hair et al. (2010). Consequently, hypotheses H3 and H4
were rejected, while H1, H2, H5, and H6 could not be rejected (Figure 2).
Figure 2. Final structural and measurement models (p< .01 for all model paths).
JOURNAL OF MARKETING FOR HIGHER EDUCATION 13
Downloaded by [Heather Harvey] at 19:42 23 November 2015
Discussion
Theoretical implications
The study used EFA to identify six factors that influenced student satisfaction in online set-
tings in Mauritius: Student–IT/Administrative staff interactions; Faculty empathy; Repu-
tation of university; Student–student interactions; Physical facilities; and Faculty
feedback. These factors confirm those identified from three preceding studies that used
a similar methodology (summarized in Table 4 of Parahoo et al., 2013). However, there
is one notable addition: Faculty empathy.
Empathy had a significant effect on satisfaction, and relates to the following attributes
displayed by the faculty: caring approach and positive attitude towards students, prompt-
ness in responding to students’queries, and interest to help them (see Table 1). While it
would be anticipated that students would expect some empathy from their faculty in an
educational setting, it seems that the physical separation of students from their peers and
instructors in the online course, in Mauritius, magnified students’expectations of empathy,
allowing it to emerge as a separate factor. The reasons for this expectation may be inves-
tigated further using qualitative methodologies.
The modeling of student satisfaction presented additional interesting findings. Four dis-
tinct variables significantly affected satisfaction (p< .01). In decreasing order of impor-
tance, they were identified as: Reputation of university (gamma = 0.47); Physical facilities
(gamma = 0.27); Faculty empathy (gamma = 0.17), and Student–student interactions
(gamma = 0.06).
The key role of reputation is noteworthy, particularly since Gibson (2010) in his literature
review found that reputation/image was identified in only two student satisfaction studies.
Therefore, this study highlights the emerging role of university corporate reputation in
affecting student satisfaction and confirms similar findings (e.g. Kuo & Ye, 2009;
Parahoo et al., 2013; Parahoo & Tamim, 2012; and Sung & Yang, 2009). It is interesting
to note that in addition to university and program quality/image, the reputation construct
comprised one item relating to perceived faculty competence (see Table 1). This highlights
the fact that in online education, the contribution of faculty in delivering teaching and
learning service retains an important role, unaffected by technology and the availability
of digitized content.
Physical facilities had a significant effect on satisfaction in an online context, thereby
supporting similar findings determined previously (e.g. LeBlanc & Nguyen, 1997;
Thomas & Galambos, 2004). The relevance of physical facilities (defined by esthetic
design and layout and comfortable facilities, see Table 1) is interesting, as it demonstrates
that despite the online setting, physical facilities retain their importance in the minds of
students. This might imply that in an intangible service, the students used quality of phys-
ical facilities as a proxy for quality of academic experience. In this regard, the precise role of
facilities in online learning contexts as compared to brick and mortar universities should
be further investigated.
Finally, the present study found that student–student interactions had a significant
effect on satisfaction. This finding supports those of preceding researchers who estab-
lished similar findings (e.g. Einarson & Matier, 2005; Hollenbeck et al., 2011; Ivankova &
Stick, 2007). The comparatively lower effect of this type of interaction on satisfaction
14 S.K. PARAHOO ET AL.
Downloaded by [Heather Harvey] at 19:42 23 November 2015
may be due to the online nature of the course which limits opportunities for interaction
among students. Further, student–student interaction and student–instructor interaction
were empirically determined to be more predictive of student satisfaction than
student–content interaction in most studies of online learning (Kuo et al., 2013). It
would therefore seem that students attached more importance to interactions with
their faculty members and with fellow students as compared to provided course
content. Understanding why student–content interactions did not emerge as a factor
influencing satisfaction brings about further questions as to whether such a situation
emerges due to open educational resources available freely on the internet, so that stu-
dents look more for human interactions (with faculty and fellow students) to share experi-
ences, discuss and comprehend the course concepts.
Conclusion
In line with the research objectives, the study developed and empirically validated a model
of student satisfaction in an online setting in a developing country. It was determined that
university reputation was the most important factor to achieve student satisfaction, fol-
lowed by physical facilities, faculty empathy, and student–student interactions. Based
on these findings, university management may develop a marketing strategy to
enhance student satisfaction in its online courses. Student satisfaction has desirable con-
sequences both from an academic perspective, including student persistence, self-confi-
dence, performance, retention and matriculation rates, as well as from the business
perspective of student loyalty, positive word of mouth, support from alumni and future
endowments.
Limitations and further research
While a large sample size, survey of the whole course population, and the scientific meth-
odology used give increased confidence in the findings, the results nonetheless need to
be treated with caution. The limitations include the fact that it was undertaken among
undergraduate students in a single university in a developing country, and may not be
generalizable to other contexts. Therefore, extension to other regional and global contexts
would need to be undertaken with utmost care due to differences in socio-cultural
environments. For this reason, it would be useful to replicate the study in other universities
to establish generalizability in a broader context. The key roles of reputation and physical
facilities on student satisfaction also warrant more in-depth investigation, as well as how
these issues fit in solely online learning institutions. Finally, since the present study focused
on undergraduate students, it would also be useful to determine whether postgraduate
students demonstrate similar determinants of their satisfaction.
Managerial implications
It would make sense for administrators of the university to recognize the key role that
reputation plays and hence invest resources in developing desirable images projected
by their university in the higher education market, thereby benefiting from desirable con-
sequences. Since the reputation construct included an item related to the perceived
JOURNAL OF MARKETING FOR HIGHER EDUCATION 15
Downloaded by [Heather Harvey] at 19:42 23 November 2015
competence of faculty, the administrators need to invest in faculty recruitment, develop-
ment and retention.
The physical facilities of the university retained an important effect on student satisfac-
tion, despite the online context of the course. Therefore, university administrators should
ensure both esthetic and comfortable physical environments. While this may seem para-
doxical, particularly for online learning, it seems that the physical facilities represent a reas-
surance to students in terms of being a proxy for quality academic experiences.
Furthermore, the findings showed that in the online setting particularly, a need for the
faculty to demonstrate empathy towards their students was critical for student satisfaction
and took on several forms, including adopting a caring approach and positive attitude
towards the students, promptness in responding to students’queries and exhibiting a
genuine interest to help. This requires that online faculty demonstrate a different set of
social traits as compared to those in traditional settings. It may require a special effort
for faculty to adapt to this new social requirement brought by the online course
context. Therefore, faculty who teach online courses may need to discuss with their
course coordinators the perceived need for empathy by online students, so that faculty
expectations may be reconciled with those of students, and a common ground be deter-
mined that is mutually beneficial.
While the impact of student–student interactions on their satisfaction is well documen-
ted (e.g. Moore, 1989), and university administrators need to ensure there are ample
opportunities for such interactions, in the current study, such interactions had by far
the lowest impact on satisfaction. This is somewhat surprising, as quality of peer
support and interactions have been established to have a strong effect on student satis-
faction in online learning. These findings would tend to point to a gap in opportunities for
student interactions, so that university management may consider setting up active online
forums, blogs and wikis, designing part of the formative assessments around group work,
and stimulating physical interactions to ‘break the ice’, particularly at university orientation
and during other events.
The study provides management with a predictive model of student satisfaction which
supports various academic benefits (quality of learning, self-confidence, retention and
matriculation rates). The established consequences of satisfaction in terms of student
loyalty are equally important, as satisfied students spread positive word of mouth and con-
stitute the foundation of a strong relationship with their university. As alumni, they
support the establishment of strong industry linkages (for placements, collaborative
research, and curriculum review), and may further support the university through future
endowments.
On a concluding note, it seems that the recent focus by universities on student orien-
tation by ‘viewing themselves as providers of a quality service in order to attract prospec-
tive students’(Kuo & Ye, 2009) has altered the dynamics of university-student relationships
and expectations. This fits with the strategy of developing a strong corporate reputation
that has been well established in the business satisfaction literature (Vidaver-Cohen, 2007).
The corollary of this change in student expectations is that students now frame their
behavior within a business service perspective, probably as they would for any other com-
mercial service. In line with the contentious debate about customer orientation in acade-
mia, universities need to give serious thought to reconciling academic requirements (e.g.
expecting learners to invest time and effort required in developing knowledge, skills, and
16 S.K. PARAHOO ET AL.
Downloaded by [Heather Harvey] at 19:42 23 November 2015
competencies) and service imperatives (based on a logic of minimum customer efforts in
enjoying a service). Striking this right balance is crucial to universities for maintaining aca-
demic excellence while meeting service imperatives.
Acknowledgements
The authors would like to thank the two anonymous reviewers and the editor for their insightful
comments which helped us to improve the article.
Disclosure statement
No potential conflict of interest was reported by the author(s).
References
Allen, I. E., & Seaman, J. (2008). Staying the course: Online education in the United States, 2008, The
Sloan consortium. Babson Survey Research Group, 23 p. Retrieved from http://www.sloanc.
Alves, H., & Raposo, M. (2009). The measurement of the construct satisfaction in higher education.
The Service Industries Journal,29(2), 203–218.
Arambewela, R., & Hall, J. (2009). An empirical model of international student satisfaction. Asia Pacific
Journal of Marketing and Logistics,21(4), 555–569.
Barnett, M. L., Jermier, J. M., & Lafferty, B. A. (2006). Corporate reputation: The definitional landscape.
Corporate Reputation Review,9(1), 26–38.
Beqiri, M. S., Chase, N. M., & Bishka, A. (2009). Online course delivery: An empirical investigation of
factors affecting student satisfaction. Journal of Education for Business,85(2), 95–100.
Bolliger, D. U., & Martindale, T. (2004). Key factors for determining student satisfaction in online
courses. International Journal on E-Learning,3(1), 61–67.
Bray, E., Aoki, K., & Dlugosh, L. (2008). Predictors of learning satisfaction in Japanese online distance
learners. International Review of Research in Open & Distance Learning,9(3), 1–24.
Butcher, K., Sparks, B., & O’Callaghan, F. (2001). Evaluative and relational influences on service loyalty.
International Journal of Service Industry Management,12(4), 310–327.
Cronin, J. J. Jr., & Taylor, S. A. (1992). Measuring service quality: A re-examination and extension.
Journal of Marketing,56(3), 33–55.
Croxton, R. A. (2014). The role of interactivity in student satisfaction and persistence in online learn-
ing. MERLOT Journal of Online Learning and Teaching,10(2), 314–325.
Debourgh, G. (1999). Technology is the tool, teaching is the task: Student satisfaction in distance learn-
ing. Paper presented at the Society for Information and Technology & Teacher Education
International Conference, San Antonio, TX.
Einarson, M. K., & Matier, M. W. (2005). Exploring race differences in correlates of seniors’satisfaction
with undergraduate education. Research in Higher Education,46(6), 641–676.
Endres, M. L., Chowdhury, S., Frye, C., & Hurtubis, C. A. (2009). The multifaceted nature of online MBA
student satisfaction and impacts on behavioral intentions. Journal of Education for Business,84(5),
304–312.
Farahmandian, S., Minavand, H., & Afshardost, M. (2013). Perceived service quality and student sat-
isfaction in higher education. IOSR Journal of Business and Management,12(4), 65–74.
Firdaus, A. (2005). The development of HEdPERF: A new measuring instrument of service quality for
the higher education sector. International Journal of Consumer Studies,30, 569–581.
Gibson, A. (2010). Measuring business student satisfaction: A review and summary of the major pre-
dictors. Journal of Higher Education Policy and Management,32(3), 251–259.
Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2010). Multivariate data analysis. Englewood Cliffs,
NJ: Prentice Hall.
JOURNAL OF MARKETING FOR HIGHER EDUCATION 17
Downloaded by [Heather Harvey] at 19:42 23 November 2015
Helgesen, O., & Nesset, E. (2007). Images, satisfaction and antecedents: Drivers of student loyalty? A
case study of a Norwegian university college. Corporate Reputation Review,10(1), 38–59.
Hollenbeck, C. R., Mason, C. H., & Song, J. H. (2011). Enhancing student learning in marketing courses:
An exploration of fundamental principles for website platforms. Journal of Marketing Education,33
(2), 171–182.
Ivankova, N. V., & Stick, S. L. (2007). Students’persistence in a distributed doctoral program in edu-
cational leadership in higher education: A mixed methods study. Research in Higher Education,48
(1), 93–135.
Johnson, R. B., Onwuegbuzie, A. J., & Turner, L. A. (2007). Toward a definition of mixed methods
research. Journal of Mixed Methods Research,1(2), 112–133.
Kuo, Y. C., Walker, A. E., Belland, B. R., & Schroder, K. E. (2013). A predictive study of student satisfac-
tion in online education programs. The International Review of Research in Open and Distributed
Learning,14(1), 16–39.
Kuo, Y. K., & Ye, K. D. (2009). The causal relationship between service quality, corporate image and
adults’learning satisfaction and loyalty: A study of professional training programmes in a
Taiwanese vocational institute. Total Quality Management,20(7), 749–762.
LeBlanc, G., & Nguyen, N. (1997). Searching for excellence in business education: An exploratory
study of customer impressions of service quality. International Journal of Educational
Management,11(2), 72–79.
Letcher, D. W., & Neves, J. S. (2010). Determinants of undergraduate business student satisfaction.
Research in Higher Education Journal,6(1), 1–26.
McFarland, D., & Hamilton, D. (2005). Factors affecting student performance and satisfaction: Online
versus traditional course delivery. Journal of Computer Information Systems,46(2), 25–32.
Moore, M. G. (1989). Three types of transaction. In M. G. Moore & G. C. Clark (Eds.), Readings in prin-
ciples of distance education (pp. 100–105). University Park: The Pennsylvania State University.
Newman, S., & Jahdi, K. (2009). Marketisation of education: Marketing, rhetoric and reality. Journal of
Further and Higher Education,33(1), 1–11.
O’Driscoll, F. (2012). What matters most: An exploratory multivariate study of satisfaction among first
year hotel/hospitality management students. Quality Assurance in Education,20(3), 237–258.
Oliver, R. L. (1999). Whence consumer loyalty? Journal of Marketing,63(Special Issue), 33–44.
Parahoo, S. K., Harvey, H. L., & Tamim, R. M. (2013). Factors influencing student satisfaction in univer-
sities in the Gulf region: Does gender of students matter? Journal of Marketing for Higher Education,
23(2), 135–154.
Parahoo, S. K., & Tamim, R. M. (2012). Determinants of student satisfaction in higher education: An
empirical study in Dubai. International Journal of Services, Economics and Management,4(4),
282–297.
Parasuraman, A., Zeithaml, V. A., & Berry, L. L. (1988). Servqual. Journal of retailing,64(1), 12–40.
Platt, C. A., Raile, A. N., & Yu, N. (2014). Virtually the same? Student perceptions of the equivalence of
online classes to face-to-face classes. Journal of Online Learning & Teaching,10(3), 489–503.
Podsakoff, P. M., MacKenzie, S. B., & Podsakoff, N. P. (2012). Sources of method bias in social science
research and recommendations on how to control it. Annual Review of Psychology,63, 539–569.
Santally, M., Sungkur, R., Fagoonee, I., Halkhoree, R., Swarts, & P. Mikkonen, J. (2013). Assessment of
environmental, institutional and individual leadership capacity needs for the knowledge society in
Mauritius. Global e-Schools and Communities Initiative, (pp. 10–21). Retrieved from http://www.
gesci.org/assets/files/ALICT_Mauritius_Sit.pdf.
Sher, A. (2009). Assessing the relationship of student-instructor and student-student interaction to
student learning and satisfaction in web-based online learning environment. Journal of
Interactive Online Learning,8(2), 102–120.
Sivadas, E., & Baker-Prewitt, J. L. (2000). An examination of the relationship between service quality,
customer satisfaction, and customer loyalty. International Journal of Retail and Distribution
Management,28(2), 73–82.
Sultan, P., & Wong, H. Y. (2012). Service quality in a higher education context: An integrated model.
Asia Pacific Journal of Marketing and Logistics,24(5), 755–784.
18 S.K. PARAHOO ET AL.
Downloaded by [Heather Harvey] at 19:42 23 November 2015
Sung, M., & Yang, S. U. (2009). Student–university relationships and reputation: A study of the links
between key factors fostering students’supportive behavioral intentions towards their university.
Higher Education,57(6), 787–811.
Thomas, E. H., & Galambos, N. (2004). What satisfies students? Mining student-opinion data with
regression and decision-tree analysis. Research in Higher Education,45(3), 251–269.
Thurmond, V. A., Wambach, K., Connors, H. R., & Frey, B. B. (2002). Evaluation of student satisfaction:
Determining the impact of a web-based environment by controlling for student characteristics.
The American Journal of Distance Education,16(3), 169–190.
Vidaver-Cohen, D. (2007). Reputation beyond the rankings: A conceptual framework for business
school research. Corporate Reputation Review,10(4), 278–304.
Walker, C. E., & Kelly, E. (2007). Online instruction: Student satisfaction, kudos, and pet peeves.
Quarterly Review of Distance Education,8(4), 309–319.
Yukselturk, E., & Yildirim, Z. (2008). Investigation of interaction, online support, course structure and
flexibility as the contributing factors to students’satisfaction in an online certificate program.
Educational Technology & Society,11(4), 51–65.
JOURNAL OF MARKETING FOR HIGHER EDUCATION 19
Downloaded by [Heather Harvey] at 19:42 23 November 2015
