Demographic implications for the user perceptions of e-learning in higher education institutions of n-w.f.p, pakistan

EJISDC (2010) 41, 5, 1-17
DEMOGRAPHIC IMPLICATIONS FOR THE USER-PERCEPTIONS OF E-LEARNING
IN HIGHER EDUCATION INSTITUTIONS OF N-W.F.P, PAKISTAN
Allah Nawaz
Dept. of Public Administration
Gomal University,
Khyber Pakhtunkhwa
Pakistan
profallahnawaz@yahoo.com
Ghulam Muhammad Kundi
Dept. of Public Administration
Gomal University,
Khyber Pakhtunkhwa
Pakistan
kundi@gu.edu.pk
ABSTRACT
There are documented differences between the success and failure factors in the developed
and developing countries with regard to the development and use of eLearning in Higher
Education Institutions (HEIs). These differences are widely attributed to the demographic
variations in the context of eLearning development and use. Unless these differences are
skillfully identified and accommodated as such into the development and use models,
eLearning efforts are reported to be associated with problems in the construction, use and
progress of the eLearning environments in the institutions for teaching, learning and
administrative purposes. This study is an exploration of the demographic diversities in the
HEIs of NWFP, Pakistan to document their impacts on perceptions of the teachers, students
and administrators about the nature and roles of eTeaching, eLearning and eEducation.
Keywords: Educational Technologies, HEIs, User-Perceptions, eTeaching, eLearning,
eEducation, eMaturity, Demographics-impacts
1. INTRODUCTION
There is a continuum of perceptions and attitudes of eLearning-users, with those who dislike
information and communication technologies (ICTs) on one extreme and those who are their
promoters on the other end, while many groups can be located at different points between the
two extremes. The research reveals that these differences in attitudes stem from the
contextual factors relating to individual, group and organizational characteristics. Mehra &
Mital (2007) note that within a university, the context for eLearning consists of human
attributes of teachers, students and administrators (age, gender, qualification, perceptions,
experience with computers etc.) and organizational characteristics (policies, resources,
management, culture and age of the institute).
In the context of developing countries, the results are quite similar to developed states
in many ways, but different at the broader level. In a study of Jordanian University, it was
found that use of computers seems to have little or no effect on teachers' beliefs about their
use of technology in their actual teaching practice (Bataineh & Abdel-Rahman, 2006). In
Barbados, there are favorable attitudes toward using ICTs as a supplement and not as a
replacement to traditional teaching (Gay et al., 2006). In Malaysia, ICT is more commonly
used as a source of information and support learning in the traditional classroom as a tool. It
is reported that the use of ICT for communication between students and lecturers, and
between lecturers is still not widespread (Mokhtar et al., 2007). For example, “HEIs use
computers every day to send e-mail, compose texts with word processing, and search the
Web, however, very few use it to enhance their teaching” (Kopyc, 2007).
The study under discussion records the impacts of user-characteristics on his/her
perceptions and thus attitudes in relation to the educational technologies. The impacts of six
The Electronic Journal on Information Systems in Developing Countries
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EJISDC (2010) 41, 5, 1-17 2
demographic groupings have been tested on the six research variables through the application
of t-Test procedures to determine the significance of group-mean-differences for recording
the impacts of user-attributes on their perceptions and attitudes about the eLearning tools and
techniques in HEIs.
2. LITERATURE REVIEW
The educational applications of ICTs are multiple; starting from a simple information
delivery (for example, accessing an online catalogue of a digital library) and ending with
modern uses of cognitive tools (Web 2.0 technologies) and ‘adaptive technologies to support
and enhance the learning process (Sirkemaa, 2001). eLearning covers a continuum of
educational applications with Word, Excel, Access and PowerPoint as the main gadgets on
one end with no or little impact on teaching, learning and administrative practices. On the
other extreme are the virtual learning environments with web-based applications and virtual
lecture halls having far reaching impacts on teaching, learning and education management
(Sife et al., 2007).
2.1 eLearning in HEIs
eLearning is widely researched in the perspectives of “higher education as well as corporate
training (Tinio, 2002)” and explained as the 'application of electronic technologies’ in
supporting, enhancing and delivering teaching and learning (Gray et al., 2003). ICTs
represent computers, networks, software, Internet, wireless and mobile technologies to
access, analyze, create, distribute, exchange and use facts and figures in a manner that has
been unimaginable hitherto (Beebe, 2004). A number of concepts are interchangeably used to
represent eLearning: computer-based instruction, computer-assisted instruction, web-based
learning, electronic learning, distance education, distance learning, online instruction,
multimedia instruction, online courses, networked learning, virtual classrooms, computer
mediated communication, Technology supported education/learning and technology uses in
education (Tinio, 2002; Abrami et al., 2006; Baumeister, 2006; Manochehr, 2007; Sife et al.,
2007; Wikipedia, 2009).
HEIs have passed through three stages of evolution and growth: 1. traditional
computer-based learning (supplemental use of ICTs); 2. blended (mixed) learning; and 3
virtual learning. These lie on the continuum of two extremes with one extreme of low-tech
applications and on the other end are hi-tech environments while in the middle are different
forms of blended learning (see for example, Oliver, 2002; Young, 2003; Beebe, 2004; Heinze
& Procter, 2006; Manochehr, 2007). HEIs, particularly in developing countries, are voyaging
through different levels of blended learning with multiple experimental applications of ICTs
in teaching, learning and education management due to the varying availability and
accessibility of educational technologies and professionalism of their use.
2.2 User-Perceptions
2.2.1 Educational Technologies
Dinevski & Kokol (2005) give a broader classification of educational technologies into:
1. Infrastructure including Computers, Networks; Internet, Intranet and facilities for
offline/online access and user interfaces.
2. Content management systems (CMS) for delivery, tracking, management and
reporting of online content.

EJISDC (2010) 41, 5, 1-17 3
3. Learning management systems (LMS) for performance management, employee
development plans, financial and activity tracking/reporting, and integration with
other systems.
4. Learning technologies for mentoring, chatting, forums, discussions, Web seminars,
online meeting and virtual classroom sessions.
5. m(mobile)-learning technologies are enabling learning anywhere and anytime.
With the Internet and ICTs available to teachers and students, its integration into
education becomes increasingly indispensable (Oh & French, 2004). Internet-based education
can contribute to substantial improvements in education (Laffey and Musser, 2006). New
communication-tools like e-mail, bulletin boards, etc., provide more reflective and useful
interactions among learners, instructors and resources (Arulchelvan & Viswanathan, 2006).
Internet tools like Web 2.0, virtual reality applications, videogames and mobile devices are
commonly used for communication, entertainment, and learning (Chan & Lee, 2007).
Internet provides access to online information along with traditional technologies (Klamma et
al., 2007). The multiplicity of perceptions about the nature and role of ICTs in HEIs can be
grouped into two broad user-theories or beliefs, which guide most of the eLearning
development and use practices around the globe:
1. Instrumental theory: It is the most commonly held belief to view ICTs as a ‘tool’
without any inherent value (neutral) and its value lies in how is it used so a one-size-
fits-all policy of universal employment of ICTs (Macleod, 2005; Radosevich & Kahn,
2006). Instrumental education is based on the premise that education serves society so
emphasis is placed on the relevance, utility, and contribution of education to the
economy. The risk of this approach is that students simply meet some identified need,
rather than think critically to achieve personal or communal advancement (Ezer,
2006).
2. Substantive theory: This is a determinist or autonomous approach, which argues that
technology is not neutral, rather has positive or negative impacts. Technological
determinism suggests that mere presence of technology leads to its standard
applications, which in turn bring social change (Macleod, 2005; Radosevich & Kahn,
2006). The substantive theory matches with the ‘liberal theory’ of education (Ezer,
2006), which views learning as an active and interconnected experience and not
simply a recollection of facts.
2.2.2 Development and Use Practices
The effective integration of ICTs into the educational system is a complex, multifaceted
process that involves not just technology but also curriculum and pedagogy, institutional
readiness, teacher competencies, and long-term financing, among others (Tinio, 2002). The
growth of innovative practices in eLearning has contributed to the development of new skills
and competencies and novel ways of using them within project teams (Gray et al., 2003).
However, design and development principles need to be aligned with teachers’ understanding
of student requirements (Young, 2003; Nyvang, 2006). Given the differences of perceptions
about ICTs, eProjects for eLearning become challenging and uphill adventures where
developers have to consider a variety of factors (variables) including development
approaches and attitudes, project management techniques, user participation, user training,
change management and the context within which the development and use of eLearning is
about to happen.
Research is going on about the factors that affect the user decisions in using
computers in teaching, learning and education management (Marcella & Knox, 2004;

EJISDC (2010) 41, 5, 1-17 4
Bataineh & Abdel-Rahman, 2006; Martin & Dunsworth, 2007; Mehra & Mital, 2007).
Several factors have been identified as the determinants of the volume and intensity of using
eLearning tools by teachers, students and administrators, for example:
1. Demographic factors (like age, educational background, gender etc);
2. Experience in use of instructional technology, perception about
3. Perceived Usefulness: encouraging interaction, teaching more systematic, creativity
in the faculty and the students, intellectual enhancement of the faculty and students
(Bataineh & Abdel-Rahman, 2006; Mehra & Mital, 2007)
4. Perceived Ease of use: teaching process less personal, intimidating, highly training
intensive, time saved in lecture preparation) (Marcella & Knox, 2004; Mehra &
Mital, 2007).
2.2.3 Problems Associated with eLearning
“More than half of all information technology projects become runways – overshooting their
budgets and timetables while failing to deliver on their goals” (McManus & Wood-Harper,
2004:3). Similarly, “While networked learning is making its appearance in universities, its
overall impact is, as yet, rather limited” (Baumeister, 2006). Several researchers have
identified problems associated with the development, use and integration of ICTs into
teaching, learning and educational management (see for example, Drinkwater et al., 2004;
Bondarouk, 2006; Vrana, 2007; Kanuka, 2007; Sife et al., 2007; Wells, 2007) such as:
1. Inertia of behavior of users, like their resistance to changes, etc.
2. Underestimation, lack of awareness and negative attitudes towards ICTs.
3. Lack of systemic approach to implementation and lack of follow-up.
4. High rates of system non-completion and user dissatisfaction with new systems
5. Lack of user-training and lack of administrative and technical end-user support.
6. Mismatches between technologies and the context, culture and work practices.
2.2.4 Prospects Associated with eLearning in HEIs
Education determines, more than anything else, a country's prospects for human development
and competitiveness. Fortunately, the information revolution offers some extraordinary
opportunities in education (MoST, 2000). The perceptions about the nature and role of ICTs
are very positive because several existing opportunities support this optimism (Tinio, 2002).
The existing opportunities include the global availability of digital technologies; free and
open source systems (FOSS); growing local ICT industry and professionals; local, national
and international partnerships; emergence of information societies and digital cultures. For
example, universities and even smaller departments within organizations are becoming able
to afford sophisticated digital systems (Ezziane, 2007).

EJISDC (2010) 41, 5, 1-17 5
2.3 List of Variables
Research Variables
Variables Attributes (32 + 6 demographic Questions) Code
1 Perceptions about
Educational Technologies
ICTs can solve the problems of teaching, learning and
administrative); Reduce digital divide, Enhance academics
and can bring social change. 8 Questions
PET
2 Perceptions about
Development Practices
Availability of Hardware & Software; Availability of
Networks, Internet, Technical Support and Adequate
Training. 7 Questions
PDP
3 Perceptions about Use of
eLearning
Perceived-use –PU; Perceived ease of use – PEU; Types of
uses. 6 Questions
PUE
4 Perceptions about Problems
of eLearning
Development and Use problems related to hardware, software,
networks, training, technical support and cultural issues. 6
Questions
PPE
5 Perceptions about Prospects Expected role of ICTs in teaching, learning and education
management. 5 Questions
PRS
Demographic Variables
1 Respondent Type Teacher, Student, & Administrator RTP
2 Subject Computer & Non-Computer CNC
3 Sector Public & Private PPS
4 Gender Male & Female GDR
5 City Big and Small cities CTY
6 Experience Experience with computer EXP
2.4 Theoretical Framework & Hypotheses
Figure 1: Theoretical Framework
Note. There are 6 demographics and 5 research variables. Therefore 30 applications of tests
of significance are used. H1 is computed by applying relevant test of significance (ANOVA)
5-times (can be considered as having 5 sub-hypotheses). If most of the results out of 5 are
substantiated, the hypothesis is accepted. As in case of H1 4 out of 5 tests nullify the null
hypothesis of no difference thus, H1 is accepted because 4/5 tests are successful.

EJISDC (2010) 41, 5, 1-17 6
H1: Teachers score higher than students and administrators.
H2: Non-Computer respondents score lower than those with computer as subject.
H3: Public sector respondents score lower than those from Private sector.
H4: Male respondents score higher than Female.
H5: The respondents from Big city score higher than those from Small city.
H6: The greater the experience with computers, the greater will be the score.
3. RESEARCH DESIGN
3.1 Survey Approach
There is a huge body of studies both in developed and developing countries about the theories
and practices of eLearning in HEIs both from qualitative and quantitative perspectives. The
quantitative studies, which used survey approach to access the problem situation are many for
example, by Irons et al., 2002, Luck & Norton 2005, Marcella & Knox (2004), Abrami et al.,
2006, Johnson et al., 2006, Radosevich & Kahn (2006), Bataineh & Abdel-Rahman (2006),
Thomas & Allen 2006, Mehra & Mital (2007), Martin & Dunsworth 2007, Garcia & Qin
(2007), and DiCerbo (2007) – which are a few from a long list. Likewise, there are qualitative
studies based purely on the secondary sources, for example, studies by Sasseville (2004),
Valdez et al., (2004), and Davey & Tatnall (2007) are some of the examples.
3.2 Sample and Sampling Procedure
The ‘Sample-Population’ for the study included all the HEIs in the cities of Peshawar and
Dera Ismail Khan. These two cities were selected as the best representative samples because:
1. Peshawar represents a big city while D I Khan is an example of a small city – with 1.5
million inhabitants in comparison to Peshawar with 4 million inhabitants but with
strong educational bases in the province.
2. Both the cities host two of the oldest universities of the province (University of
Peshawar – 1950 and Gomal University - 1974). Similarly, they have the oldest as
well as new universities in both the public and private sectors.
3. These institutions are populated with students, teachers and administrators from
almost all cities and areas of the province.
For the development of research project (synopsis), a pilot study was conducted to test
the instrument and determine the appropriate sample size using a standard procedure – the
researchers used formula (available in several books on social research) given for the
purpose. 388 copies of the questionnaire were distributed among the teachers, students and
administrators. The response rate was: teachers 137; students 132 and administrators 85 =
354 (92%).
3.3 Data Collection
Literature survey was conducted to examine the existing research on the topic and extract
variables and their mutual relationships from: Books (hard copies) and eBooks (off-line on
CDs and online); Free and Open Source Systems (FOSS), i.e., eJournals. The researcher used
the ‘Directory of Open Access Journals’ (doaj.org) and Wikipedia.org, Blogs and facebooks;
and the websites of United Nations, International universities, Government of Pakistan and
websites of the Universities’ in NWFP. For empirical data, a structured questionnaire was
constructed based on the extracted variables.

EJISDC (2010) 41, 5, 1-17 7
3.4 Data Analysis and Validity
SPSS 12.0 has been used to create a database of primary data for applying statistical
procedures to generate descriptive and inferential statistics and test the hypothesis. t-Tests
were used to determine the significance of demographic impacts on the attitudes of different
groups of respondents. The overall reliability of Cronbash’s alpha was estimated at 0.9288,
with 354 cases and 38 survey items. This value exceeds the required minimum threshold
suggested for the overall reliability test, i.e. 0.7 (Koo, 2008).
4. FINDINGS
4.1 Respondents’ Profile
Following is the classification of the respondents according to their major demographic
characteristics:
Table 1: Demographic Data
Demographic Groupings
1 Type of Respondents: Students = 132, Teacher = 137, Administrators = 85
2 Subject: Computer = 101, Non-Computer = 253
3 Sector: Public Sector = 180, Private Sector = 174
4 Gender=GDR): Male = 241, Female = 113
5 City (code=CTY): Dera Ismail Khan = 145, Peshawar = 209
6 Experience with Computer (code=EXP): (>=5) = 163, (<5) = 190
4.2 Summary Statistics of the Research Variables
Table 2 Descriptive Statistics of Research Variables (n=354)
Min Max Mean Std. D
Perceptions about the Educational Technologies 3.18 6.55 4.7779 .57637
Views about the development practices 3.00 5.89 4.3082 .52236
Opinion about the use practices 3.10 6.75 4.7961 .58463
Perceptions of the eLearning problems 3.60 6.34 4.8207 .47971
Perceptions about the Prospects of eLearning 2.17 6.12 5.7359 .89704
Chart 1 Mean Scores on Demographic Groupings
Average Scores on Demographic Groupings
4.92 5.01
4.63
5.55
4.87 4.81
4.97 4.98
4.69 4.73
4.95
5.05
4.75
4.00
4.20
4.40
4.60
4.80
5.00
5.20
5.40
5.60
5.80
StudentTeacher
Adm
inistratorC
om
puter
N
on-Com
puter
Public
Private
M
ale
Fem
ale
D
IKPeshaw
ar
>=
5
<
5
Groups
Scale

EJISDC (2010) 41, 5, 1-17 8
Testing of Hypothesis about the Demographic Impacts (Six Hypotheses about the
impact of Demographic-Groupings. The impacts of demographics on the user perceptions,
theories and attitudes on the development and use of eLearning in higher education are well
documented (see for example, Valcke, 2004; Gay et al., 2006; Wims and Lawler, 2007;
Moolman and Blignaut, 2008). The developers of eLearning environments are constantly
advised by the researchers to address demographic differences regarding the use of ICTs and
develop strategies for generating and sustaining positive learner attitudes for effective uses of
eLearning environments (Gay et al., 2006). The differences are based on the user-
characteristics of gender, age, educational level, computer skills, previous experience with
eLearning as well as learning styles, personal goals and attitudes, preferences, cultural
background, experience, motivation (Moolman & Blignaut, 2008).
Hypothesis # 1 Teachers score higher than students and Administrators.
Table 3 Results of ANOVA Applications on Respondent Type (RTP)
Sum of
Squares
df Mean
Square
F Sig.
PET Between Groups 4.748 2 2.374 7.406 .001
Within Groups 112.519 351 .321
PDP Between Groups 16.783 2 8.391 37.032 .000
PUE Between Groups .561 2 .280 .820 .441
PPE Between Groups 10.651 2 5.326 26.485 .000
PRS Between Groups 17.329 2 8.665 11.402 .000
Hypothesis 1 was accepted in the sense that teachers score significantly higher than
students and administrators. The ANOVA application gives significant differences of means
between the three groups on all the variables except Use practices (F = .82 with p = .441, far
more than required significance of .05) where they all hold the same opinion.
Hypothesis # 2 Non-Computer respondents score lower than those who study computer.
Table 4 T-tests on Computer and Non-Computer (CNC)
T Sig. (2-tailed)
Perceptions about the Educational Technologies 12.946 .000
Perceptions about the Development Practices 8.504 .000
Perceptions about the Use Practices 14.656 .000
Perceptions of the Problems of eLearning 12.955 .000
Perceptions about the Prospects of eLearning 6.547 .000
IMPACTS 100%
Table-value at 0.05 Significance level with 352 df = 1.96
This classification brings out very visible influences on the groups. The p-values on
all the tests are .000 which confirms the impact of groupings on the respondents. Thus,
respondents with computer background are totally different from the non-computer users of

EJISDC (2010) 41, 5, 1-17 9
eLearning in their perceptions and attitudes because respondents with computer as a subject
have scored higher than their counterparts on all five research variables, so Null hypothesis
stands ineffective.
Hypothesis # 3 Public sector respondents score lower than the private.
Table 5 T-tests on Public & Private Respondents (PPR)
t Sig. (2-tailed)
Perceptions about the Educational Technologies -3.938 .000
Perceptions about the Development Practices -2.375 .018
Perceptions about the Use Practices -4.984 .000
Perceptions of the Problems of eLearning -2.740 .006
Perceptions about the Prospects of eLearning -.765 .445
IMPACTS 80%
The difference of being in public or private sector is also significant. 4 out of 5 tests
are positive showing changes in the responses except on ‘Perceptions about the prospects of
eLearning’. Null hypothesis is therefore rejected because private sector respondents score
higher than the public sector on all the variables. It also indicates that private sector
respondents are more involved in eLearning than the public sector subjects. The alternative
hypothesis is thus substantiated.
Hypothesis # 4 Males score higher than female respondents.
Table 6 T-tests on Males and Females (GDR)
t Sig. (2-tailed)
IMPACTS 80%
Gender effects have very widely been identified by the researchers from developed
and developing countries with positive results showing that gender is a good predictor of
using eLearning tools in HEIs round the globe (Graff et al., 2001; Irons et al., 2002; Thomas
& Allen, 2006; Abrami et al., 2006; Cagiltay et al., 2006; Tubaishat et al., 2006). In this
research, Male and Female respondents have significant difference of perceptions about four
of the research variables except ‘Views about the Development Practices’. Therefore, Null
hypothesis is rejected.
Hypothesis # 5 Respondents from big city score higher than those from small city.
Table 7 T-tests on Big and Small City Respondents (CTY)
T Sig. (2-tailed)
Perceptions about the Educational Technologies -3.546 .000
Perceptions about the Development Practices -.695 .488

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Perceptions about the Use Practices -1.725 .085
Perceptions of the Problems of eLearning -3.761 .000
Perceptions about the Prospects of eLearning -2.637 .009
IMPACTS 60%
The respondents from both the cities have no difference of opinion about the
‘development and use practices’ (two variables) indicating that the same type of development
and use practices is prevalent irrespective of big and small cities. However, the respondents
have significantly different views on three of the research variables: Perceptions about
educational technologies, problems and prospects showing the difference of eLearning
facilities and environment or greater eMaturity of big city (Moolman & Blignaut, 2008).
Thus, we reject the Null hypothesis.
Hypothesis # 6 Greater the experience with computers, greater will be the score.
Table 8 T-tests on Experience of User (EXP)
T Sig. (2-tailed)
IMPACTS 100%
Experience with computers have positive impacts on the user perceptions about
educational technologies and adopting eLearning in the sense that greater the experience,
more positive is the attitude of users (Blázquez & Díaz, 2006; Manochehr, 2007; Bondarouk,
2006; Mehra & Mital, 2007; Ezziane, 2007; Moolman & Blignaut, 2008). In the current
study, the experience with computers has significant impact on all the variables because
experienced users have higher perceptions and understanding of the eLearning issues than
their counterparts. Given the significance of the impacts, alternative hypothesis is accepted.
5. DISCUSSION
Contextual and demographic impacts on user perceptions and attitudes have widely been
investigated and reported as critical points for the decision makers to consider when planning
and implementing eLearning solutions in the HEIs. Researchers are constantly identifying the
incompatibility of eLearning models with contextual requirements of specific countries,
particularly in the developing world.
The impacts of demographics on the user perceptions and attitudes about the
development and use of eLearning tools in higher education are well reported (Valcke, 2004;
Gay et al., 2006; Wims & Lawler, 2007; Mehra & Mital, 2007). The developers of eLearning
environments are frequently insisted by the researchers to consider demographic diversities
while developing strategies for generating and sustaining positive learner attitudes for
eLearning environments (Buzhardt & Heitzman-Powell, 2005; Gay et al., 2006). These
differences emerge from user-characteristics such as gender, age, educational level, computer
skills, previous experience with eLearning as well as learning styles, personal goals and
attitudes, preferences, cultural background, experience, motivation (Luck & Norton, 2005;
Moolman & Blignaut, 2008).

EJISDC (2010) 41, 5, 1-17 11
Furthermore, the change management aspect of eLearning occurs in a particular
context. The contextual factors influence the eLearning theories and practices, which has to
be understood and handled by both the developers and users of eLearning environments. The
context is multifaceted and includes community, culture and technology and becomes very
important when understanding implementation of the ICTs in education (Nyvang, 2003). The
context of an organization like university is made of internal and external contextual elements
(Loing, 2005). Internal contextual factors are the human characteristics and the organizational
attributes while, external context is made of government ICT-policies and the broader social
environment.
The role of context in eLearning is consistently identified by almost every research
study concerning the integration of educational technologies (Tinio, 2002; Oliver, 2002;
Nyvang, 2003; Aaron et al., 2004; Loing, 2005; Cawson, 2005; Macleod, 2005; Ehlers, 2005;
Baumeister, 2006; Stephenson, 2006; Hameed, 2007). In traditional computer-enhanced
learning, the computer was used as a tool to complete a task or get something done so there
was not need to address the broader environmental context of the individual (Young, 2003).
Even today, Ezer (2006), in a study of Indian universities, found that “most IT education is
ineffective because it is too technical and not at all concerned with local contexts and real
world problems.” Similarly, another research reveals that despite the best of intentions,
efforts and resources, many of the eLearning projects end in failure primarily because they
are not undertaken in the perspectives of existing and changing social and political context
(Wells, 2007). Thus, much more research is needed regarding teaching orientations, personal
traits and the construction of online learning environments (Phillips et al., 2008).
To cut short, context is either a support or a barrier for eLearning project management
(Nyvang, 2003; Sasseville, 2004). System developers need to design an eLearning model
within the context of the existing support and resource infrastructures (Tran et al., 2005).
There is no universal eLearning-model to fit every context rather learning has to be
conducted within the culturally familiar contextual frameworks (Stephenson, 2006).
Baumeister (2006) asserts that eLearning is a multi-dimensional phenomenon which needs to
be understood in terms of its relationship with the societal environment within which it is
applied, meaning that a successful eLearning model in Atlanta, USA may be “ineffective or
inappropriate” for students in Kuala Lumpur (Stephenson, 2006). Research indicates that
failure to establish effective communication environment during ISD process usually ends up
into a kind of IS failure (Nawaz et al., 2007).
In this study, all hypotheses (alternative) have been substantiated thereby confirming
different degrees of impacts of all six demographic attributes on the responses of the
respondents. Table 5.1 gives the summary of results and decisions about the acceptance and
rejection of hypotheses.
Table 9 Results and Decisions about Hypothesis on Demographic Impacts
Hypotheses Results Accepted
Impact %age
1 Student/Teacher/Administrator 4/5 80 H1
2 Computer/Non-Computer 5/5 100 H2
3 Public/Private 4/5 80 H3
4 Male/Female 4/5 80 H4
5 Big/Small Cities 3/5 60 H5
6 Experience with Computers 5/5 100 H6

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6. CONCLUSIONS
The research reveals that demographic factors play a moderating role in the relationships
between the research variables. Extreme cases in demographic diversities disturb the normal
relationships between different factors of a topic therefore, measurement and assessment of
demographic variations is indispensable for getting the real picture of the phenomena in
practice. For example, every demographic feature tested in this research comes up with one
or another degree of impact. Computer/non-computer and experience are leading attributes of
the respondent which are obviously changing their responses to extent of opposition.
Thus, the eLearning solutions must be compatible with the human and contextual
factors of any country. Furthermore, domestic digital models need to be developed through
domestic research because the policy of ‘one-for-all’ paradigm is proving problematic in
several countries. This research confirms the need for local research by unearthing the
‘existing-diversities’ among the eLearning users of HEIs, who differ in their perceptions
about various aspects of educational technologies and their applications in education. There is
need to develop domesticated and customized models of eLearning for every single but
different group of users. It means that different groups need differing eLearning facilities and
training to reach the same levels of eMaturity in the institute. For example, female
respondents who score low on all research variables need more resources and attention their
male counterparts for ‘equal-digital-growth’.
Addressing these diverse groups of users, there are several opportunities available to
the authorities, which can help in serving each different group through multiple digital
devices and services. For example, Web 2.0 technologies are interactive and come in a
variety of interactions and interfaces between the user-groups and the new technologies or
work environments.
The problems from demographic dimensions are universal however; their intensity is
more implicative in the developing and poor countries than the advanced states with high
levels of education, economic equalities and availability of government services for the
masses. In developing states such as Pakistan, the situation is not so promising in terms of
demographic groupings. Here the groups are not only highly dissimilar but also the number
of groups is greater. Thus, knowledge about the impacts of user-characteristics in the
development and use of eLearning environments in HEIs of a developing country is the
prerequisite to introduce successful educational DOI in higher education.
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EJISDC (2010) 41, 5, 1-17 17
Appendix 1 List of Research & Demographic Variables
Research Variables
1. Perceptions about the Educational Technologies: Questions were asked about the
availability and accessibility of hardware/software, network; and Internet
(code=PET).
2. Views about the Development Practices: Questions were raised about ICT Policies,
User Needs; User Participation, Training and Satisfaction; Implementation;
Maintenance; Evaluation (code=PDP)
3. Views about the Use Practices: Questions included perceived ease of use, perceived
usefulness, types of use; experience with computers, and user-developer-
communication (code=PUP)
4. Perceptions about the eLearning Problems: The questions were asked about the
problems related to the development, use and user-satisfaction (code=PRB)
5. Perceptions about the Prospects of eLearning: The respondents were inquired about
what prospects they foresee for eLearning in the Prospects of eLearning based on the
existing opportunities in HEIs of NWFP (code=PRS)
Demographic Variables
1. Respondent-Type: Teachers and Students (code=RTP)
2. Sector: Public and Private (code=PPR)
3. Gender: Male/Female (code=GDR)
4. Subject: Computer/Non-Computer (code=CNC)
5. City Peshawar/Dera Ismail Khan CTY
6. Experience with Computers: (code=EXP)

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