Critical Success Factors for E-learning: An Indian Perspective

Abstract

Being free from the constraints of time and place, the importance of e-learning is increasing in the education system across the globe. This paper attempts to identify the critical factors attributing to the success of e-learning in higher education sector through a student perspective. It aims at contributing to the limited knowledge base on the efficiency of e-learning in India. A questionnaire is constructed based on the previous research. Using a convenience sampling technique, the data is collected from the undergraduate, postgraduate, and doctoral students of CHRIST (Deemed to be University), Bangalore, India. The participants in the survey were invited to provide their views about the importance of a number of factors attributing to different dimensions in the success of e-learning. An exploratory factor analysis is conducted by employing Principal Component Analysis to assess the factor loading of each variable onto different factors. The study identifies five factors as critical in the success of e-learning viz. technological support, e-learning resources, e-learning support and training, characteristics of student, and characteristics of instructor in their order of relative importance with technological support being the most critical factor.

Full text

Critical Success Factors for E-learning: An Indian Perspective
Namitha K Cheriyan
Abstract
Being free from the constraints of time and place, the importance of e-learning is increasing
in the education system across the globe. This paper attempts to identify the critical factors
attributing to the success of e-learning in higher education sector through a student
perspective. It aims at contributing to the limited knowledge base on the efficiency of e-
learning in India. A questionnaire is constructed based on the previous research. Using a
convenience sampling technique, the data is collected from the undergraduate, postgraduate,
and doctoral students of CHRIST (Deemed to be University), Bangalore, India. The
participants in the survey were invited to provide their views about the importance of a
number of factors attributing to different dimensions in the success of e-learning. An
exploratory factor analysis is conducted by employing Principal Component Analysis to
assess the factor loading of each variable onto different factors. The study identifies five
factors as critical in the success of e-learning viz. technological support, e-learning resources,
e-learning support and training, characteristics of student, and characteristics of instructor in
their order of relative importance with technological support being the most critical factor.
Keywords: E-learning system; critical success factors; higher education; exploratory factor
analysis
1. Introduction
The pedagogy and the learning styles have evolved over time. They continue to evolve even
today given the developments in the technology being used. The educational institutions
undergo unprecedented transformations in their teaching-learning practices with the influence
of information technology. Such transformations are of significant importance as they add
different dimensions to education with the help of electronically mediate tools in order to
smoothen and enhance the process of learning (Piccoli et. al, 2001). This revolution in
educational sector is referred to as e-learning. It refers to the usage of information technology
blended with communication technology to enable the access to quality and real-time
resources that enrich the teaching-learning systems in educational institutions. It cannot be

considered as a mere substitute of the traditional methods of teaching-learning with the help
of technology, but reinforces and magnifies the reach of learning (Islam & Azad, 2015). The
most commonly used e-learning tools include Blackboard, Moodle, and Sakai. With the
growing importance of e-learning, it has been implemented in many educational institutions
across the globe (Garrison, 2011), which requires huge investments to be made.
The significance of e-learning systems is that they offer learning opportunities beyond the
constraints of time and place. They also support to have experiments in teaching-learning by
having new approaches to teaching and learning. However, the literature provides that the
extent to which these systems are being used by the students are often low, despite of the
huge cost involved in putting them in place (Bhuasiri et al., 2012). There are a number of
studies addressing this issue by contributing possible solutions. Most of them focuses to
identify the factors affecting the implementation of e-learning (King & Boyatt, 2015). The
studies were also attempted to measure the satisfaction of users with regard to the e-learning
platforms (González-Gómez et al., 2012; Teo & Wong, 2013). Another significant dimension
widely studied in e-learning literature is measuring and modelling the influence of e-learning
on the students learning process (Mohammadyari & Singh, 2015).
A potential aspect of e-learning which is not widely studied is to identify the critical success
factors for e-learning. This essentially relates to the evaluation of the participants’ experience
of e-learning which can be used as a benchmark for the enhancement of e-learning systems.
Such critical success factors measure the best or essential characteristics of e-learning
systems from the perspective of its users attributing to their success. A critical success factor
approach to the evaluation of e-learning systems not only produces a vibrant agenda for the
management but also enhances the system as such (Sun et. al., 2008).
There are a limited number of studies that are attempted to identify the critical success factors
for e-learning. These studies have been attempted in a broad range of circumstances that
include schools (Taha, 2014) and higher educational institutions (Selim, 2007; Puri, 2012).
The studies also vary significantly based on the country in which they have been conducted.
They also differ according to the stakeholder group on which the studies were focused with
most of them being focused on students (Selim, 2007; Puri, 2012; Musa & Othman, 2012).
There are studies also focused on the perspective of academic staff or instructor (Ahmed,
2013, Naveed et al., 2017). However, it is found a lack of empirical attempts to identify the

critical factors attributing to the success of e-learning systems in India being one of the
leaders of developing countries in the world. Given this, this study attempts to examine and
identify the factors affecting the success of e-learning from a student perspective.
2. Data and Methodology
2.1 The participants
In order to achieve the objectives of the study, a convenience sampling survey is used
employing a self-administered questionnaire. The samples selected consist of the
undergraduate, postgraduate and doctoral students of CHRIST (Deemed to be University),
Bangalore, India. The responses are gathered from 158 students.
Table 1 exhibits the demographic data of the participants in terms of gender, academic degree
which is currently pursuing and their current academic year. It shows that more than half of
the participants are females (52.53%). Majority of the survey participants are pursuing their
Master’s degree and Bachelor degree (89.87%) on years 1 and 2 of their course (69.62%).
Table 1. Demographic data of the participants
Frequency
Percent
Gender
Male 75 47.47
Female 83 52.53
Academic
Degree
Bachelor degree 34 21.52
Master’s degree 108 68.35
Ph.D 16 10.13
Academic Year
1 41 25.95
2 69 43.67
3 24 15.19
4 24 15.19
2.2 Procedures adopted
A questionnaire is designed to collect the relevant data from the population of the study
which is the students of CHRIST (Deemed to be University). A number of critical success
factors related to e-Learning are identified from the literature and are used to prepare the

questionnaire. These factors were grouped into different categories viz. those related to the
instructor’s characteristics, the participant’s characteristics as a student, the role of
technology infrastructure available, the importance of online learning resources, and the role
of support and training as exhibited in Table 2. Though the critical success factors identified
vary from study to study, they possess some common patterns based on which they are
categorized. The list of these factors as presented in Table 2 is used as the basis for designing
the questionnaire. The fundamental of the designed questionnaire was thirty four five-point
Likert scale statements relating to various aspects of e-Learning, for which each participant is
asked to express his/her view about their significance as factors contributing to the success of
e-Learning.
Table 2. E-learning critical success factors from literature
Categories Variables
The instructor’s
characteristics
Enthusiasm of the instructor while teaching aiding e-learning tools
Ability of the instructor to motivate his/her students
Clarity of instructor’s explanation
Capability of the instructor for using the e-learning system
efficiently
The style of teaching of the instructor aiding e-learning
technologies
The approachability of instructor in general and during teaching
Participant’s
characteristics as a
student
My readiness to take part in e-learning
My learning style
My aptitude to find topics in e-learning system
My knowledge and acquaintance about computers
The extent of my satisfaction while using technology
My understanding about the use of various parts of the e-learning
systems
The role of
technology
infrastructure
available
Easy access to internet
Easiness in browsing
Accessibility of online communication tools
Speed of internet
Accessibility to multimedia technologies

Facility to explore learning material using the e-learning system
Adequate computer labs
Reliability of technical infrastructure
The importance of
online learning
resources
Easiness in registration for the e-learning course
Accessibility to the e-learning resources while being in and out of
the campus
The design and layout in which the information is provided
Adequacy of the learning materials provided
Interaction of the course
Adequacy of communication with the instructor in the e-learning
system
Accessibility to and adequacy of online test/quizzes
Possibility to return to uncompleted tasks
Ability to measure the progress of learning
Up-to-dateness of the learning materials
The role of support
and training
Availability of offline technical support
Openness and sociability of the support team
Accessibility to the online help desk
Adequacy of training sessions on the usage of e-learning systems
2.3 Analysis of the data
The data collected was imported to IBM SPSS for the purpose of analysis. The data is
cleaned for missing values and the questionnaires with incomplete responses were removed
from the analysis. Descriptive statistics were calculated for the demographic factors to
understand the basic nature of the sample considered for the study. Exploratory factor
analysis is carried out to identify the factors that are considered as critical in the success of e-
learning by the students of CHRIST (Deemed to be University).
3. Results and Discussions
This study intends to identify the critical success factors for the effectiveness of e-learning
system among the students. It adds to the limited literature base on the effectiveness of e-

learning systems among the student community of India. This section presents the major
findings of the study and discusses about their implications.
Kaiser-Meyer-Olkin (KMO) measure of sampling adequacy and Bartlett's Test of Sphericity
are employed in order to ensure that the data collected is suitable and adequate for
exploratory factor analysis. Table 3 reports the results of KMO and Bartlett’s Test. While the
Bartlett’s test assesses the overall significance of correlation matrix, Kaiser-Meyer-Olkin
(KMO) measure of sampling adequacy measures the factorability of the variables
individually as well as together as a group which are basic assumptions of factor analysis.
From the result of Bartlett's Test of Sphericity presented in Table 3 it can be inferred that the
correlations between the variables considered in the study, when taken collectively are
significant at one percent level. This indicates that there exists nonzero correlations among
the variables selected. However, the test does not provide the pattern of these correlations. On
the other hand the measure of sampling adequacy takes into account not only the correlations,
but also the patterns of such correlations between the variables. The table shows an overall
measure of sampling adequacy value of 0.920 which falls in the acceptable range i.e. above
0.50. As both these test are fulfilling the basic assumptions of exploratory factor analysis, the
data is deemed suitable for exploratory factor analysis.
Table 3. KMO and Bartlett's Test
Kaiser-Meyer-
Olkin Measure of Sampling
Adequacy.
.920
Bartlett's Test of
Sphericity
Approx. Chi-Square 3206.969
df 561
Sig. .000
A Principal Component Analysis is employed to find out the factors which explain the most
variance in the data used. The Eigenvalues are considered as the criterion to decide the
number of factors to be identified along with the cumulative percentage of variance
explained. Only those factors which are having Eigenvalue above 1 are extracted. This
criterion has provided for five factors from the set of thirty five variables.
Varimax rotation is used to generate the component matrix. As an orthogonal rotation, it
ensures that the factors remain uncorrelated throughout the process of rotation. The

component matrix displays loading for the rotated factor matrix. The factor loadings
represent the extent to which a variable is associated with a factor. The variables are
examined for their factor loadings. All the factor loadings are found to be above the value of
0.5. Therefore none of the variables are excluded from the analysis and all the variables are
retained with their corresponding factors. As a final step of Exploratory Factor Analysis, the
five factors derived are named in such a way that the name reflect the characteristics of the
variables loaded to the factor. Table 4 exhibits the final factors along with the variables
loaded onto them. The five factors derived are found to be explaining a total of 78.064% of
the variances in the data considered for the study.
Table 4. Final factors extracted
Factor Variables Component
Technological
Support
Accessibility to multimedia technologies 0.834
Accessibility of online communication tools 0.802
Adequate computer labs 0.798
Reliability of technical infrastructure 0.794
Facility to explore learning material using the e-
learning system 0.765
Speed of internet 0.751
Easiness in browsing 0.74
Knowledge and acquaintance about computers 0.611
Easy access to internet 0.604
e-Learning
Resources
Interaction of the course 0.776
Accessibility to the e-learning resources while
being in and out of the campus 0.772
Adequacy of communication with the instructor
in the e-learning system 0.72
Adequacy of the learning materials provided 0.667
The design and layout in which the information
is provided 0.666
Easiness in registration for the e-learning
course 0.663
Accessibility to and adequacy of online
test/quizzes 0.659
Possibility to return to uncompleted tasks 0.599

e-Learning Support
and Training
Adequacy of training sessions on the usage of e-
learning systems 0.842
Openness and sociability of the support team 0.816
Accessibility to the online help desk 0.791
Availability of offline technical support 0.685
Up-to-dateness of the learning materials 0.649
Ability to measure the progress of learning 0.626
Characteristics of
Student
Readiness to take part in e-learning 0.762
Learning style 0.748
Aptitude to find topics in e-learning system 0.689
The extent of my satisfaction while using
technology 0.685
Understanding about the use of various parts of
the e-learning systems 0.658
Characteristics of
Instructor
Clarity of instructor’s explanation 0.699
The style of teaching of the instructor aiding e-
learning technologies 0.679
Capability of the instructor for using the e-
learning system efficiently 0.641
Ability of the instructor to motivate his/her
students 0.638
The approachability of instructor in general and
during teaching 0.61
Enthusiasm of the instructor while teaching
aiding e-learning tools 0.605
The questionnaire used for the study had five categories to which variables were broadly
classified. The results of exploratory factor analysis also provided five factors with almost the
same variables loaded to each factor. The factors identified are named as technological
support, e-learning resources, e-learning support and training, characteristics of student, and
characteristics of instructor.
The factor technological support includes the variables such as accessibility to multimedia
technologies, accessibility of online communication tools, adequate computer labs, reliability
of technical infrastructure, facility to explore learning material using the e-learning system,
speed of internet, easiness in browsing, knowledge and acquaintance about computers, and
easy access to internet. Among these variables loaded to the critical success factor viz.

technological support, all the variables except the knowledge and acquaintance of the
participant about computers was categorised as the role of technology infrastructure available
as identified by the literature (Selim, 2007; Masrom, 2008; Musa & Othman, 2012; Ahmed,
2013). This indicates that in line with the trend across the globe, Indian students also consider
technological support as one of the most critical factor in the success of e-learning system in
the country. Though the variable the knowledge and acquaintance of the participant about
computers was initially categorised as participant’s characteristics as a student, its loading to
the technological support is reliable as it is also related to the technological aspect.
Accessibility to multimedia technologies and easy access to internet are found to be having
the maximum and minimum association with the factor technological support (83.4% and
60.4%).
The variables viz. interaction of the course, accessibility to the e-learning resources while
being in and out of the campus, adequacy of communication with the instructor in the e-
learning system, adequacy of the learning materials provided, the design and layout in which
the information is provided, easiness in registration for the e-learning course, accessibility to
and adequacy of online test/quizzes, and possibility to return to uncompleted tasks which
were initially categorized as the importance of online learning resources are loaded to one
common factor which is renamed as e-learning resources. This is in line with the findings of
Selim (2007). Among these eight variables, interaction of the course is found to be
recognized by the students as highly associated with the factor (77.6%) followed by
accessibility to the e-learning resources while being in and out of the campus (77.2%).
Menchaca & Bekele (2008) and Musa & Othman (2012) also highlighted the importance of
these variables in determining the e-learning resources as a factor critical for the success for
the e-learning. Therefore it can be inferred that ensuring the interaction with the learner and
providing access to the resources when the learner is in and out of the campus are important
to successfully implement and operate the e-learning system.
The third critical success factor explored from the exploratory factor analysis is e-learning
support and training with six variables viz. adequacy of training sessions on the usage of e-
learning systems, openness and sociability of the support team, accessibility to the online
help desk, availability of offline technical support, up-to-dateness of the learning materials,
and ability to measure the progress of learning, loaded to it. Among these the first four were
initially categorized as the role of support and training whereas the last two were categorized

originally as the importance of online learning resources but loaded to e-learning support and
training. However, the misclassification of the last two variables can be validated with the
help of correlation matrix of the variables used in the study which exhibits statistically high
correlation of the up-to-dateness of the learning materials and ability to measure the progress
of learning with the variables representing support and training aspects. On the other hand,
the loadings of adequacy of training sessions on the usage of e-learning systems, openness
and sociability of the support team, accessibility to the online help desk, and availability of
offline technical support to the e-learning support and training factor can be supplemented
with the findings Masrom (2008), Menchaca & Bekele (2008), Puri (2012), and Alhomod &
Shafi, (2013) who identify them as critical factors under the category of support and training
in determining the success of e-learning system.
The readiness to take part in e-learning, learning style, aptitude to find topics in e-learning
system, the extent of my satisfaction while using technology, and understanding about the use
of various parts of the e-learning systems which were categorized as the participant’s
characteristics as a student as identified from the literature are loaded together to form a new
factor (Selim, 2007; Musa & Othman, 2012; Puri, 2012). As all these variables are focused
towards the same dimension, it is renamed considering their original categorization as
characteristics of student. Among these student characteristics, his/her readiness to take part
in e-learning is found to be having the maximum association with the factor (76.2%)
followed by the learner’s style of learning (74.8%). understanding about the use of various
parts of the e-learning systems is identified as the least associated variable (65.8%).
The fifth and the last critical success factor identified from the study is named as
characteristics of instructor as all the variables initially categorized as those related to the
instructor’s characteristics are loaded to this factor. They include the clarity of instructor’s
explanation, the style of teaching of the instructor aiding e-learning technologies, capability
of the instructor for using the e-learning system efficiently, ability of the instructor to
motivate his/her students, the approachability of instructor in general and during teaching,
and enthusiasm of the instructor while teaching aiding e-learning tools. However, the loading
of these variables to the factor is comparatively lower with a maximum value of 69.9% for
the clarity of instructor’s explanation and the minimum value of 60.5% for enthusiasm of the
instructor while teaching aiding e-learning tools. Comparing this with the factor loadings for
the previous factors, it can be inferred that rather than the characteristics of the instructor, it is

the technological support, the availability of e-learning resources, and e-learning support and
training are critical in the success of e-learning systems in Indian higher education sector
though demands for empirical confirmation.
Finally, Table 5 presents the relative ranking of critical success factors for e-learning among
the students pursuing higher education in India. The most important three critical success
factors (in order of importance reflected by the percentage variance they explain) are
technological support, e-learning resources, and e-learning support and training. This
empirically confirms the significantly high factor loadings of the variables onto these factors.
Table 5. Total variance explained
Component Factor name Eigenvalue % of Variance
1 Technological Support 19.565
57.543
2 E-learning resources 2.89
8.499
3 E-learning support and training 1.536
4.518
4 Characteristics of student 1.376
4.047
5 Characteristics of instructor 1.176
3.458
Total 78.064
The factors presented in Table 5 are regarded as the most important factors for the success of
e-learning as identified by the students pursuing higher education in India with special
reference to CHRIST (Deemed to be University). This is a substantial indicator of their
perspective on e-learning systems. For instance, the participants who are students, prioritizing
the technology infrastructure, consider technological support and the e-learning resources as
important over other factors. In other words, while they acknowledge the importance of their
own characteristics or the characteristics of instructors, they regard the technological support
and the e-learning resources as of prime importance as this system of learning cannot exist
and grow without the most suitable and relevant technology and learning resources rather
than the instructor. Through this they reflect on their own experience with the technological
aspects and prioritize the factors related to technology such as adequacy of training sessions
on the usage of e-learning systems, accessibility to multimedia technologies, openness and
sociability of the support team, accessibility of online communication tools, adequate
computer labs, reliability of technical infrastructure, and accessibility to the online help desk.
These insights are found missing in earlier studies as most of them have focused on the
school students (Taha, 2014) or those pursuing their education through the distance mode
(Menchaca & Bekele, 2008).

The critical success factors identified in this study are unique, like many other earlier studies.
There are a good number of studies attempted to identify the factors critical for the success of
e-learning, but these factors vary substantially from a study to another. For instance, Selim
(2007) acknowledged seven different factors with three derived from the characteristics of
students. The remaining four are technology, characteristics of instructor, e-learning system
being followed, and the support system in place. On the other hand, Taha (2014) identified
just four factors viz. the characteristics of students, characteristics of the instructor, the
technology in use, and the content and design. Even though there are a number of persistent
factors identified in different studies, there lacks a consensus on the number of factors critical
for the success of e-learning. This study is of no difference. It suggests five factors among
which majority are identified as important individually in different papers. The potential
reasons for such differences can be attributed to the differences of studies in terms of their
objectives, nature, approach, environment in which they are conducted, and so on. Further, if
there is any consensus on the number of factors and their categories, their relative importance
may differ from study to study. Therefore, as a whole, there is an ample scope for future
research to identify the critical success factors for e-learning. The further studies in Indian
context can be attempted to identify the critical success factors for e-learning from the
instructors’ point of view. Attempts can also be made to model the factors critical for the
success of e-learning which can be generalized.
4. Conclusion
This study attempted to identify the critical success factors for e-learning in Indian higher
education sector with special reference to the students of CHRIST (Deemed to be
University), Bangalore, India. Using a convenience sampling technique, data is collected
from 158 respondents and the exploratory factor analysis is employed to identify the
important categories of factors critical for the success of e-learning in Indian context. It
reports five factors viz. technological support, e-learning resources, e-learning support and
training, characteristics of student, and characteristics of instructor as critical for the success
of e-learning in their order of importance as identified by the students pursuing various higher
educational courses ranging from undergraduate programmes to doctoral programmes.
The study contributes significantly to the existing literature as it identifies the factors that can
have impact on the success of e-learning. This is of immense use for the policy makers in

such a way that this provides first-hand information about the areas to be focused to make e-
learning successful in the country’s higher education sector. It enables to design and
implement the e-learning system in a better way and to have systematic investments in e-
learning. This systematic approach not only saves the resources of educational institutions in
terms of labour, money, and time but also enhances the image of such institutions (Taha,
2014). Therefore, it is suggested to focus on these factors before designing new e-learning
systems or while improvising or enhancing the existing e-learning system.
References
Ahmed, T. T. (2013). Toward Successful E-Learning Implementation in Developing
Countries: A Proposed Model for Predicting and Enhancing Higher Education
Instructors' Participation. International Journal of Academic Research in Business and
Social Sciences, 3(1), 422.
Alhomod, S., & Shafi, M. M. (2013). Success Factors of E-Learning Projects: A Technical
Perspective. Turkish Online Journal of Educational Technology-TOJET, 12(2), 247-
253.
Bhuasiri, W., Xaymoungkhoun, O., Zo, H., Rho, J. J., & Ciganek, A. P. (2012). Critical
success factors for e-learning in developing countries: A comparative analysis between
ICT experts and faculty. Computers & Education, 58(2), 843-855.
Garrison, D. R. (2011). E-learning in the 21st century: A framework for research and
practice. Routledge.
González-Gómez, F., Guardiola, J., Rodríguez, Ó. M., & Alonso, M. Á. M. (2012). Gender
differences in e-learning satisfaction. Computers & Education, 58(1), 283-290.
Islam, A. N., & Azad, N. (2015). Satisfaction and continuance with a learning management
system: Comparing perceptions of educators and students. The International Journal of
Information and Learning Technology, 32(2), 109-123.
King, E., & Boyatt, R. (2015). Exploring factors that influence adoption of e‐learning within
higher education. British Journal of Educational Technology, 46(6), 1272-1280.
Masrom, M. B. (2008). Critical success in e-learning: an examination of technological and
institutional support factors. International Journal of Cyber Society and
Education, 1(2), 131-142.
Menchaca, M. P., & Bekele, T. A. (2008). Learner and instructor identified success factors in
distance education. Distance education, 29(3), 231-252.

Mohammadyari, S., & Singh, H. (2015). Understanding the effect of e-learning on individual
performance: The role of digital literacy. Computers & Education, 82, 11-25.
Musa, M. A., & Othman, M. S. (2012). Critical success factor in e-Learning: an examination
of technology and student factors. International Journal of Advances in Engineering &
Technology, 3(2), 140.
Naveed, Q. N., Muhammad, A., Sanober, S., Qureshi, M. R. N., & Shah, A. (2017). A Mixed
Method Study for Investigating Critical Success Factors (CSFs) of E-Learning in Saudi
Arabian Universities. Methods, 8(5).
Piccoli, G., Ahmad, R., & Ives, B. (2001). Web-based virtual learning environments: A
research framework and a preliminary assessment of effectiveness in basic IT skills
training. MIS quarterly, 401-426.
Puri, G. (2012). Critical success Factors in e-Learning–An empirical study. International
Journal of Multidisciplinary Research, 2(1), 149-161.
Selim, H. M. (2007). E-learning critical success factors: an exploratory investigation of
student perceptions. International Journal of Technology Marketing, 2(2), 157-182.
Sun, P. C., Tsai, R. J., Finger, G., Chen, Y. Y., & Yeh, D. (2008). What drives a successful e-
Learning? An empirical investigation of the critical factors influencing learner
satisfaction. Computers & education, 50(4), 1183-1202.
Taha, M. (2014). Investigating the success of E-learning in secondary schools: The case of
the Kingdom of Bahrain (Doctoral dissertation).
Teo, T., & Wong, S. L. (2013). Modeling key drivers of e-learning satisfaction among
student teachers. Journal of Educational Computing Research, 48(1), 71-95.