Investigating the factors affecting the adoption of online tertiary education in Kazakhstan
Table of contents: The Kazakh-American Free University Academic Journal №5 - 2013
Author: Kim-Choy Chung, KIMEP University, Kazakhstan
1.
Introduction
Rapid proliferation of the Internet and demand for
knowledge in newly emerging economies has created opportunities for
higher institutions to access potential students who otherwise have no
opportunity for tertiary education because of their geographic distance from
physical campuses or their full-time work commitments. Potentially, this would
be advantages for Kazakhstan (16 million people in a large land mass) to
achieve its goal of being world’s top 30 most developed states by 2050 via
economic diversification, industry innovation, modernize production and skilled
workforce etc (Kazakhstan 2050 strategy). Kazakhstan’s Internet penetration
rate is 34.3% with 5,300,000 users (Internet World Stats, 2010). However,
Internet-based education is still at its infancy in Kazakhstan and there is a
lack of study in this area. This study investigates the following:
i) What factors affect the adoption of online tertiary
education in Kazakhstan?
ii) Which factors are the most influential in the
adoption of online tertiary education in Kazakhstan?
Online tertiary education is defined here as
university’s undergraduate and post-graduate education via the Internet/Web.
Adoptions may refer to actual or intended enrolment in online tertiary education.
2. Literature review
According to the Diffusion of Innovation [DoI]
theory (Rogers, 1980), technological
innovation is communicated through particular channels, among members of a
social system over
time, working through the following stages:
- Knowledge (awareness): Exposure to the existence and
understanding of the functions of the innovation.
- Persuasion (the forming of a favourable attitude to the
innovation): Here the individual is interested in the innovation and actively seeks
information/detail about the innovation.
- Decision: In this stage the individual weighs the
advantages/disadvantages of using the innovation and then decides whether to
adopt or reject the innovation. Decisions may be collective (among members of a
system), or authority-based (where a decision is imposed by another
person/organisation which possesses requisite power, status or technical
expertise).
- Implementation: During this stage, the individual determines
the usefulness of the innovation and may search for further information about
it.
- Confirmation: In this stage the individual finalizes their
decision to continue using the innovation and may use the innovation to its
fullest potential.
This study focuses on the “decision stage” of the DoI theory (Rogers 1980), investigating
factors influencing the adoption of online tertiary education in Kazakhstan. The following are variables that potentially influence
the adoption of online education.
2.1
Pull factors
This concept originates from Mazzarol
and Soutar’s (2002) ‘push-pull’ theory of factors influencing international
student destination choice. Pull factors in this study refers to the
characteristics of online tertiary education that provide utility or are
relatively attractive to potential students to meet their personal motives.
Identified factors under this category include: convenience-based delivery for
learning and teaching; suitability for certain learning styles of students (Di
Bartola, Miller & Turley, 2001); interactive and peer collaboration
(Kearsley, 2000); and less stringent course entry requirement for adults
(Philips 2007). Dunning and Vijayaraman (2001) highlight the advantages of
Internet-based education: no spatial constraints (24 hours access) for
study/learning/teaching, appealing to potential students who otherwise have
work or family commitments, permitting flexible class schedule and reduced
commuting time for students and faculties. Di Bartola et al. (2001)
reports that who exhibits divergent learning style (strong imaginative ability
and holistic outlook) performed well in an online environment.
2.2
Individual competency factors
This refers to the
skills or inadequate knowledge that impedes an individual from studying online
or adopting online tertiary education. It includes
ones’ self-motivation for independent learning, writing and computing skills.
Learners who procrastinate and have low self-motivation for independent
learning may not be suitable for online education (Berge & Huang, 2004). Ragan and White (2001)
articulate that the unusual demand for written communication in term of speed,
volume and clarity in a Web environment, presents a great potential for
miscommunication and could make students unnecessarily frustrated, leading to
low student retention in the Web-Based environment. Further, Alexander (1999)
asserts that computing skills affect students’ satisfaction with online
learning, impeding its popularity.
2.3
Courseware design factors
This refers to the design of the Web-based
program that facilitates student-learning outcomes (i.e. acquiring new
knowledge, skills, & experience). Among these are good structure and
clarity of design, availability of good technical support/helpdesk (and
self-checking activities. Easy
and immediate access by learners to up-to-date information could motivate
people to learn and apply their knowledge and skills to improve their learning
(Levesque & Kelly, 2002). Thus, a user-friendly (structured) format that
facilitates easy navigation through the content; book marking that allows the
student to return to the last page studied are ideal. The availability of
technical helpdesk facilitates student satisfaction amongst online learners
while self-checking activity encourages self-directed learning (Leiblein,
2000).
2.4
Institutional competency factors
There is a need for government recognition of
online degrees to ensure credibility and quality of online degrees (Alhabshi,
2002). Accreditation of online course or degrees by the relevant home
government of the course provider or professional bodies can reduce the problem
of the ‘certificate mill’ (Philips, 2007). A certificate mill refers to the
provision of worthless education degrees for a fee without active study
participation. The lack of industry collaboration in most online program
impedes its uptake as an alternative medium to the traditional classroom (Ryan,
2001).
2.5
Trust factor
The concept ‘trust’ has aroused intense interest across
difference disciplines, resulting in various definitions in the literature.
Despite this, there is a common practice of defining trust in terms of ‘having
confidence’, or ‘willingness to rely on the other party’ or ‘willingness to
take risk’ in existing literature (Gefen et al., 2003). Trust is important in facilitating “relationship enhancement” in buyer-seller
interactions and for reducing perceived risk of using
services. Since online learners
have no direct contact (virtual buyer-seller relationship) with the education providers, trust plays an
important role in online tertiary education settings. From a student’s
perspective, “perceived risk” of adopting online tertiary education could be
time risk- Time spent studying for a degree will be wasted if industry or government
does not recognize the online degree for employment. Offering
customer-centric program that are relevant to industry’s needs and career
advancement helps to reduce “perceived
risk” in adopting online tertiary
education (Wong et al., 2003). Assessment of online faculty’s
qualifications are another way to assure confidence in online education
(Philips, 2007). Further, good security system provides confidentiality and
integrity by confirming the identity of the people who are attempting to access
the computer or network, and protects against inappropriate access by others.
Thus, identity verification to prevent exam fraud or information theft are
another basis for trust building in Web-based learning environment.
3. Methodology
Given the lack of information about the adoption of online tertiary
education in Kazakhstan, a two-stage research design was utilised. Stage 1
involved exploratory research using semi-structured in-depth interviews with
the aim of confirming variables identified in the literature review were valid
in the Kazakhstan context and to identify any other variables specific to the Kazakhstan environment not identified in the literature. Opinions of online education
professionals, members of the teaching profession and past users of online
programs were sought in this stage. Stratified snowball sampling was used. The
results from stage 1 found agreement that most of the factors identified in
literatures were valid as influence factors on the adoption of online education
in Kazakhstan. Only pre-assessment of student for suitability for online
learning was deemed irrelevant in the Kazakhstani context.
Extra findings from the in-depth interviews were:
- Internet speed in Kazakhstan is the main concerns among the
interviewees;
- A university’s reputation had an over-riding importance over cost
of education as decision-making factor in the enrolment in online degree;
- Government’s recognition of online degrees influences the adoption
of online degree in Kazakhstan;
- Ease of finance (loans for online tertiary education) can increase
the adoption of online tertiary education in Kazakhstan;
- Authentication and security issues affect public trust of online
education.
Findings from
stage 1 led to a hypothetical model (Figure 1) and the following:
Hypothesis 1: Pull
factors have a positive influence on the adoption of online tertiary education
in Kazakhstan.
Hypothesis 2: Courseware
design competency factors have a positive influence on the adoption of online
tertiary education in Kazakhstan.
Hypothesis 3: Individual
competency factors have a positive influence on the adoption of online tertiary
education in Kazakhstan.
Hypothesis 4: Institutions
competency factors have a positive influence on the adoption of online tertiary
education in Kazakhstan.
Hypothesis
5: Trust factors have a positive influence on the adoption of online tertiary
education in Kazakhstan.
The second stage of this study involved designing questionnaire
(survey) and data sampling to test the hypothetical model/hypotheses. The
questionnaire utilised a seven-point numerical rating scales (from not at all
importance to very important). A pre-test of the questionnaire wording found no
discrepancy. The final questionnaire comprised the following:
• Section one: demographic profile (education, age, computing
skills).
• Section two asked respondents to rate the importance of
attributes that may affect the adoption of online tertiary education in
Kazakhstan.
Figure 1. Hypothetical model of factors influencing
the adoption of online tertiary education in Kazakhstan
Samplings
The pre-tested questionnaires were randomly distributed in two
universities (English as medium of instruction) in Almaty and Astana. Overall,
645 questionnaires were returned (response rate = 71.6%). Two hundreds and
eight returned questionnaires were rejected for further analysis because
respondents expressed non-interest in university studies. The resulting 437
survey comprised 53% males and 47% females; 28% high schools students, 29%
undergraduate students and 43% working adult. Most of the respondents rated
themselves as frequent users of the Internet (85%). The survey data was
normally distributed.
Statistical
tests
To validate the hypothetical model, a series of exploratory factor
analyses and confirmatory factor analyses was conducted to test the
reliability, and convergent validities of the measures (Anderson & Gerbing,
1988). All measurement variables were subjected to principal component analysis
(varimax rotation) using SPSS v.14. This procedure extracted five factors with
factor loadings (Table I) ranging from 0.58 to 0.785 (pull factor); 0.757 to
0.843 (courseware design competency factor), 0.774 to 0.863 (individual competency
factor); 0.641 to 0.792 (trust factor); 0.883 to 0.909 (institutional
competency factor). The KMO value for sampling adequacy was 0.859, and the Bartlett’s Test of Sphericity was significant (Sig= 0.0), indicating their appropriateness
for factor analysis. The loaded components in the five extracted factors
tallied with those of the proposed hypothetical model (Figure 1). All extracted
factors have Composite Reliability (CR) higher than 0.70, the benchmark for
internal consistency and Average Variance Extracted (AVE) >0.5 for
satisfactory convergent validity (Hair et al., 2010). Thus, all the measurement
scales used in the five-factor model were statistically valid.
Table 1. PCA
component scores of the five extracted factors
Figure
2. SEM statistics of the proposed hypothetical model
Next,
Anderson and Gerbing's (1988) two-step Structural Equation Modeling (SEM) procedures using AMOS 7.0 was used as a further
validity test of the measurement model and structural model test for hypothesis
testing. All
Confirmatory Factor Analysis (CFA) estimation processes for individual
measurement model showed sufficient evidence of goodness-of-fit between the measuring model
and the sample data [Comparative-Fit-Index (CFI)>.95 and Goodness-of-fit Index (GFI)>.95]. The structural test revealed sufficient goodness-of-
fit statistics for hypotheses testing. The structural model test statistics
are: CMIN/DF=2.67, RMSEA= 0.05 and CFI=0.92 (Figure 2).
4. Findings and
Managerial implications
As shown in Figure 2, there was sufficient evidence to
support all five hypotheses in this study. That is, the following have
significant impact on the adoption of online tertiary education in Kazakhstan: trust factors (regression wt= 0.70), individual competency factors (regression
wt= 0.77) and institutional competency factors (regression wt= 0.76).
Courseware design competency factors had the most significant influence
(highest regression wt of 0.77) while pull factors had the least significant
influence (least regression wt of 0.65) on the adoption of online tertiary
education in Kazakhstan.
Online tertiary providers are advised to
collectively lobby the Kazakhstan Government to recognize online degree given
that its recognition can influence its adoption or draw more interest among the
working adults. While
governmental support and industry collaboration were indicated as important
factors for the propagation of the online education in literature (Tan &
Lambe, 2002), the issue of course accreditation was not seriously examined.
This study found it a highly rated factor for choice of online tertiary
education among its survey respondent. Online tertiary providers should
seriously consider accredit ting their program with foreign regulatory bodies
or accreditation bodies. Online tertiary providers should also focus on
customer centric solutions (skills needed by specific industries) by collaborating
with employers to enhance their graduate’s career prospects. A list of their
online graduates in prominent institutions (for marketing promotion) and strong
alumni network would greatly enhance the teaching institution’s reputation.
Prior studies
(Berge et al., 2004; Chung, 2012) had indicated the importance of good
courseware design for student retention among online learners. Consistent with
this observation, noted complaints about web-based education during the
exploratory study (stage 1) are the concerns about intrusion of pop-up advertisements
or slow screen loading of multimedia contents, the lack of student support
online indicating the needs for provision of technical support/helpdesk;
self-checking activities; and consideration for fast access when implementing
visualisation technologies (Web or courseware design issue). Thus, online
instructional designers should not introduce intrusive banners or large clip
media (pictures, audio/videos
clips) in the courseware that can slow down user’s screen loading. This study also showed that
individual competency factors (user’s writing and computing skills, and
self-motivation for independent learning) could help students to adapt the
virtual environment for learning. Online tertiary providers are advised to hire
motivated online tutors who are well-versed in computer-based learning and who
are sympathetic of online learners issues (eg. time-pressed, difficulties with
internet technology) to help alleviate the problem of student’s drop-out.
The preference for
mixed-mode instruction was emphasised quite strongly in this study (mean score
of 6.1 out of max 7). A regular classroom session in the form of block teaching
to complement online teaching is suggested. The venue could be on-campus,
hotels or community halls that are central to groups of students. Investing in
appropriate security measures by online tertiary providers to prevent exam
fraud and the tempering of exam records is another issue worth considering.
Online tertiary providers are also advised to avoid being perceived as a
certificate mills (education
degrees for a fee). Better
control of their course entry requirements, stricter rules on online attendance
and coursework completion would ally this fear. Overall, the trust factor
affects the reputation of the online tertiary providers and the public opinion
on the quality of the degree on offered. Thus, marketing efforts must be
centred on these trust factors. Empirical evidence showed that there was a
desire for financial institutions to extend credits or relax loan requirements
for online tertiary studies.
5. Conclusions
This study adopted a customer focus with respect to the
adoption of online education in Kazakhstan, using the decision-making stage of Rogers DOI (1980).
The factors influencing the adoption of online tertiary education in Kazakhstan are trust factors,
courseware design competency factors, individual competency
factors, institutional competency factors and pull factors. However, the
courseware design competency factors (measured by variables such as good
structure and charity of design, good technical support or helpdesk, provision
of self-checking activities) are most influential among the surveyed
respondents. This study also showed respondents showing a strong preference for
mixed-mode instructions. Overall, this study contributes
to our understanding of student’s decision-making regarding online tertiary
education in Kazakhstan.
BIBLIOGRAPHY
1. Alexander,
J. (1999), “Collaborative design, constructivist learning, information
technology immersion, and electronic communities: A case study”, Interpersonal
Computing and Technology 7 (1/2), 23-31.
2. Alhabshi,
S. O. (2002), ‘E-Learning: A Malaysian case study’, Paper presented at the Africa-Asia
Workshop on Promoting Co-operation in Information and Communication Technologies
Development, National Institute of Public Administration, Kuala Lumpur, 26th March 2002.
3. Anderson, J.C., & Gerbing, D. W. (1988), “Structural equation modeling in practice: A
review and recommended two-step approach”, Psychological Bulletin, 103 (3),
411-423.
4. Berge,
Z. L. & Huang, Yi-Ping (2004), “A Model for Sustainable Student Retention:
A Holistic Perspective on the Student Dropout Problem with Special Attention to
e-Learning”, http://www. ed. psu.edu/
5. Chung, K.
C. (2012), “Antecedents of brand trust in online tertiary education: a
tri-nation study”, Journal of Global Scholars of Marketing Science: Bridging Asia and the World 22 (1), 24-44.
6. DiBartola, L. M., Miller, M.
K. & Turley, C. L. (2001), “Do learning style and learning environment affect learning outcome?”, Journal
of Allied Health 30 (2), 112-115.
7. Dunning,
K. A. & Vijayaraman, B. S. (2001), Motivational factors, characteristics
and computer skills of MBA students in Web-based courses, Ohio: University of Akron Press.
8. Gefen,
D., Karahanna, E. & Straub, D. W. (2003), “Trust and TAM in online
shopping: An integrated model”, MIS Quarterly 27 (1), 1-90.
9. Hair, J. F., Black, W. C., Babin, B. J. and Anderson, R.
E. (2010), Multivariate Data Analysis: A Global Perspective, 7th Ed., NJ: Pearson.
10. Internet World Stats (2010), “Kazakshtan Internet
usage, broadband and telecommunications”, http://www. internetworldstats. com/ asia/kz.htm.
11. Kazahktan 2050 Strategy, “President sets out new economic policy”, http:// www.kazakhembus.com/
12. Kearsley, G. (2000), Online education: Learning and
teaching in Cyberspace, Montreal, Canada: Wadsworth.
13. Levesque, D. R & Kelly, G. (2002), “Meeting the
challenge of continuing education with e-learning”, Radiology Management 24 (2), 40-43.
14. Lieblein, E. (2000), “Critical factors for successful
delivery of online programs”, Internet and Higher Education 3 (1),
161-174.
15. Mazzarol, T. & Soutar, G. N. (2002), “Push-pull
factors influencing international student destination choice”, The
International Journal of Educational Management 16 (2), 82-90.
16. Philips, V.
(2007). Consumer Alert: Top 10 signs online
diploma mills and degree mills, http://www. geteducated. com/
17. Ragan, T. J. & White, P. R. (2001), “What we have
here is a failure to communicate: The criticality of writing in online
instruction”, Computers and Composition 18 (1), 399-409.
18. Rogers, E., M. (1980), Diffusion of Innovations (3rd Ed), Glencoe: Free Press.
19. Ryan, Y. (2001), “Online education: Are universities
prepared?”, Proceeding, Online Learning in Borderless Market Conference,
Griffith University, Gold Coast campus, 15-16 February 2001.
20. Tan,
E. & Lambe, P. (2002), “E-learning effectiveness from the learner’s point
of view in Singapore”, Singapore: Straits Knowledge Publication.
21. Wong,
Y. Y., Gerber, R., & Toh, K. A. (2003), A comparative study of diffusion
of Web-based education in Singapore and Australia, Hershey, PA, USA:
IGI Publishing.
Table of contents: The Kazakh-American Free University Academic Journal №5 - 2013
|