Investigating the factors affecting the adoption of online tertiary education in 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.

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