Direct Determinants of User Acceptance and Usage behavior of eLearning System in Nigerian Tertiary Institution of Learning
Olabode Olatubosun Computer Science Dept
Federal University of Technology,Akure, Ondo State, Nigeria [email protected]
Fasoranbaku Olusoga Statistics Dept
Federal University of Technology,Akure, Ondo State, Nigeria Shemi A.P
Business Information System University of Botswana, South Africa
Abstract.
Innovations in Information Technology have contributed to new forms of Teaching and Learning of tertiary institutions in most part of the developed countries of the world. However, the adoption and implementation of Web-Based course management and learning tool in most developing countries like Nigeria is still in the infancy. In this paper, an attempt is made to appraise the eLearning Management System and the evaluation of the student eLearning readiness in tertiary institutions of Nigeria. The research subjects were accessible students of the Federal University of Technology, Akure and the Nigerian Open University, Nigeria. In all 627 students participated in the survey. This includes 296 males and 181 females for Federal University of Technology, Akure while 108 males and 42 females are from the Nigerian Open University, Nigeria. The students of the two Universities were selected randomly and were given the questionnaires to record their responses. The questionnaire consisted of two parts. In part I, questions were related to age, gender, computer literacy level, type of smart system and level of power supply. Part II, consisted of questions relating to technology access, online skills and relationship, motivation, foreseen reasons for not willing to use eLearning tools and modified UTAUT constructs. The UTAUT model captures all the essential elements, i.e. performance expectancy, effort expectancy, social influence, facilitating condition, Behavioural Intention, self- efficacy, attitude toward using technology and anxiety. Data were statistically analyzed using both descriptive and qualitative approach. The result suggests that, students in tertiary institution in Nigeria are ready to accept and use eLearning systems
Keywords: eLearning, UTAUT Models, Pedagogical, Web-Based Course Management System, Traditional Teaching and Learning, asynchronous, constructivist
1.0 Introduction
The EU eLearning action defines eLearning as ‘the use of new multimedia technologies and the Internet to improve the quality of learning by facilitating access to resources and services as well as remote exchanges and collaboration’ (CEC, 2001). Moreover, eLearning initiative places emphases on creating appropriate conditions for the development of content, services and learning environments which are sufficiently advanced and relevant to education in terms of both the market and the public sphere.
eLearning is facilitated by different types of communication technologies where especially the use of online access of the Internet provides unique possibilities to deliver eLearning across space and support interaction-based learning types., which, for example, CD-ROMS do not (Morten and Hanne, 2008).
eLearning is not confined to any particular part of the educational system, rather the contrary. One of
the advantages of eLearning is that it makes it possible to extend the reach of educational and training
systems into new areas. Thus eLearning can be applied both in the formal educational system (public schools, colleges, universities, etc.), as well as for vocational training. It can be used both for private use as well as in the public and the private sector (Morten and Hanne, 2008).
.
The success with regards to the readiness and implementation of this eLearning is dependent on stakeholder’s support as well as student adoption of the eLearning services. Learning Management System (LMS) is an important and popular course management software application in higher education providing a number of learning tools, including an online discussion board, course content management, a course calendar, information announcement, electronic mail, review, auto-marked quizzes and exams, navigation tools, access control, grade maintenance and distribution, student progress tracking, etc. With the use of LMS learning, student stands to benefit high level of interactivity, reflectivity and collaborative learning, a great level of enthusiasm, and high level of satisfaction (Seamus and Kay, 2003). This paper presents the assessment of students of higher educational system on their attitude and behavior towards eLearning system using the Unified Theory of Acceptance and Use of Technology Model (UTAUT) .
2.0 Theoretical Background of eLearning
In the review paper of Jack and Kurt (2007), it was informed that, over the past several years, institutions of higher education have increasingly invested in course management software to provide a virtual learning environment designed to enhance student learning and to assist in the administration of the course itself. In addition, the need for integration of education, practice, and information technology is growing. University and other instructors are often encouraged to find ways to help their students improve their learning skills both inside and outside of the classroom.
Learning is truly a lifelong experience that offers many benefits. It can work to enrich the work life and social life, and can offer a great personal enjoyment and a sense of purpose to learners. The commonest form of learning in most Nigerian tertiary institution is the traditional approach.
Traditional learning is what we are all most familiar with. It is how most of learners in elementary and secondary school, and this type of learning is still used by most colleges, polytechnic and universities across the developing countries. Traditional learning usually awards credits based on student performance, which is measured through assignments, tests, and exams. These credits are usually put together towards a certificate, diploma, or degree, which is to be completed later on.
Traditional learning typically takes place in an identifiable classroom space, usually in a school or in an institution dedicated to learning. The traditional learning paradigm has a number of specific features, including: an instructor who delivers information to students, a number of students who are all physically present in the classroom and regularly meet at a specific time and student participation in lectures and discussions. Many learners favor traditional learning while others find that it is more restrictive and lacks flexibility.
In Kim (2007), the traditional instructor-led classroom learning is a proven and effective means of learning, with full opportunities for interaction between the instructor and students, the learning- inducing stress of exams and homework, and relationship forming among students, etc. However, the requirement for the students to be in the classroom on designated days and times makes it difficult for certain students. Further, the lack of equipment in the classroom may make it difficult for the instructor to teach certain topics effectively. It has been suggested that, the technology-enabled eLearning can help address such difficulties posed by the limitations of the traditional classroom learning.
The presence of computer and information technologies in today’s organizations has expanded dramatically. Westland and Clark (2000) decried that some estimates indicated that, since the 1980s, about 50 percent of all new capital investment in organizations has been in information technology.
However, Viswanath et. al (2003) observed that, for technologies to improve productivity, users must
be willing to accept and used by them. One of such technology is online/eLearning technology.
Online learning also known as eLearning is quickly becoming one of the most popular options when it comes to continuing your education. eLearning involves learning materials over the computer, with the help of the internet/intranet/extranet. In an online course, there may be no physical classroom. The novelty of online learning is apparent in the diversity of names given to the phenomenon: Web-based learning, eLearning, and asynchronous learning networks among others, (Charles et. al. 2004). These efforts have focus primarily on off-campus student populations. With the more recent on-campus emphasis, yet another set of labels has appeared, including hybrid learning, blended learning, and mixed-mode instruction. The mere existence of so many names for what is essentially a single concept suggests that no dominant model has yet been accepted as a definition of standard practice, (Charles, et. al, 2004). The trend toward conversion from traditional classroom to online courses follows the shift of learning theories from the behaviorist orientation that portrays learning as a primarily passive activity to theorist orientation which emphasize the active, reflective and social nature of learning.
(Barbara et. al 2005).
The transition from the traditional face-to-face teaching-learning system to the web enhanced solution was introduced in order to reach different goals. For what concerns students these goals are, (Silva et al 2009):
a. To foster individual study and self-assessment as prerequisites for a more constructivist approach to laboratory activities;
b. To encourage them to become responsible for their own learning;
c. To offer the opportunity to engage in online activities, synchronous and asynchronous, acquiring experience in the use of different software tools;
d. To offer them the opportunity to work collaboratively online, experiencing situations similar to what they will probably meet in their future work;
e. To facilitate meaningful learning through an improved graphical interface and interactive learning units.
From the institutional and lecturer’s perspective the goals are also:
a. To test the efficiency and performance of the available eLearning tools in view of further development of new online courses;
b. To introduce progressively information and communication technologies (ICT) tools in traditional courses, avoiding quality gaps in the learning-teaching process of different academic years.
Falch (2004) proposes four types of eLearning classifications: eLearning without presence and without communication, eLearning without presence but with communication, eLearning combined with occasional presence, and eLearning used as a tool in classroom teaching. Following Falch’s (2004) presence/communication classification, Solomon and Marlene (2008) have redefined the terms
“presence” and “communication” and expanded the classifications to six in order to make a distinction between physical presence and virtual presence. The six classifications are outlined are:
a. eLearning with physical presence and without e-communication (face-to-face) b. eLearning without presence and without e-communication (self-learning) c. eLearning without presence and with e-communication (asynchronous) d. eLearning with virtual presence and with e-communication (synchronous)
e. eLearning with occasional presence and with e-communication (blended/hybrid- asynchronous)
f. eLearning with presence and with e-communication (blended/hybrid-synchronous).
The scope of a eLearning program, would normally be an entire curriculum or at least a number of courses. Appropriate objectives must be selected and used to guide the design and implementation of a eLearning program. There are various potential objectives for implementing a eLearning program, as suggested by (Kim 2007):
a. Increased learning effectiveness for the students or lecturer, over either pure traditional classroom
learning or pure eLearning.
b. Increased convenience for the students or lecturer. In the case of student, the eLearning component of a learning program can make it easier for the lecturer when on field trips or high priority lectures or practicals come up to prevent them from attending scheduled in-class training.
c. Enhanced image for the school or the corporation. The progressive image may be projected both internally to own students or lecturer and externally the general public, customers, the government, news media, the financial analysts, etc..
d. Cost savings for the school or the Government. The cost savings may result from possibly reducing the number of instructors, which are usually scarce.
e. Classroom space savings for the school or the Institution. The eLearning component of a learning program can help ease the classroom space needs by having students and lecturer learn more from outside the classrooms. The freed-up classroom space can potentially be used for other purposes.
f. Reduced traffic and parking congestion on the campus coursed by students, lecturers and visitors.
Learning management systems (LMS) facilitate the planning, management, and delivery of content for eLearning; it is therefore important to mention them here briefly. LMSs can maintain a list of student enrollment in a course, manage course access with logins, lecture files and lecture notes, support quizzes and assessments, schedule assignments, support e-mail communication, manage discussion forums, facilitate project teams, and support chat. These systems support many-to-many communication among learners and between learners and instructors (Solomon and Marlene, 2008) Kin (2007) analysis the basic requirement for the design of the Learning Management System to consist of the following ingredients:
Thier paper Barbara (1005) evaluated nine learning management systems (LMS) programs which are WebCT, v.4.1; BlackBoard, v. 6.1; Jones E-education; Educator; Angel; .LRN; McGraw Hill Pageout;
Moodle; and e-College AU. Each LMS program was based on the ability to accommodate different active learning experiences in online courses. The paper highlighted the following essential features a good MLS should have to accommodate active learning, viz:
Content development for effective and efficient course content
Bulletin Board/ Discussion to enhance dynamic element to the online class
Group participation to share documents, chart, send emails and work together in synchronous and asynchronous environment.
Calendar to monitors due date of assignment, cancellation of classes, discussion time with lecturers and students, exam time table, etc
Chart/whiteboard/e-mail for interactivity online. Instructors can incorporate synchronous leaning through chart rooms and whiteboards
Student study tools for note taking and lecture content attachment
Audio/Video for student to sometimes hear material to minimize skim and video to virtually see motion of lectures is been delivered
Monitoring to see how the students interact with course contents
Navigate and Interface for ease of use of MLS components
Site Administration to provide the instructors with full range of flexible teaching and learning tools that can meet the need of diverse students
3.0 Methodology
User acceptance and intentions to adopt technology has been frequently studied topic in different
disciplines (for instance in Psychology & Information Systems). In the past several decades, many
studies have been made to explain, predict, and enhance user acceptance of different information
system technologies. These studies were based on a modified version of UTAUT theoretical
approaches (Venkatesh 2003).
Venkatesh et al. (2003) then proposed the Unified Theory of Acceptance and Use of Technology (UTAUT) that include eight different models in order to get an integrated view of user acceptance of the eLearning approach. The UTAUT model is one of the most comprehensive, robust, and powerful model up-todate. Venkatesh et al. (2003) identified seven constructs that appeared to be significant direct determinants of intention or usage in one or more of the individual. Of these models, they theorize that four constructs will play a significant role as direct determinants of user acceptance and usage behavior: performance expectancy, effort expectancy, social influence, and facilitating conditions. Attitude toward using technology, self efficacy, And anxiety are theorized not to be direct determinants of intention. The labels used for the constructs describe the essence of the construct and are meant to be independent of any particular theoretical perspective. Figure 1 present the UTAUT structure model.
The explanation of Dillon and Morris (1996) and Charles (2006) on the philosophy of UTAUT model presented by Venkatesh et al. (2003) is modified to explain the models to show the behavioural intention to use a technology such as the eLearning system as in Table 1 3.1 Aims and Objectives of the study
The present study adopts the Unified Theory of Acceptance and Use of Technology (UTAUT) model proposed by Venkatesh et al., (2003) and deals with the following aims and objectives:
a) To define various constructs that would describe readiness of student of tertiary institution in Nigeria on the adoption of eLearning and assess their reliability.
b) To assess the degree of relationship between the defined constructs and statistically test their significance.
c) To establish the association between constructs of the UTAUT model and demographic variables such as age, gender etc.
d) To identify student’s of tertiary institution in Nigeria foreseen reasons for not willing to use eLearning system and examine if there is any gender or age differential vis-à-vis stated foreseen reasons.
Performance Expectation
Effort Expectancy
Social Influence
Facilitating Conditions Self Efficacy
Anxiety Online Skill Willingness
&Age&Gender
Motivation Behavioral Intention
Use Behavior
Figure 1 Modified version of UTAUT
e) To measure the extent of opinion responses on the UTAUT determinants among the sampled student of tertiary institution in Nigeria.
f) To assess whether the effects of UTAUT model constructs are homogeneous in explaining the preparedness of student of tertiary institution in Nigeria on their readiness of adoption of eLearning system.
g) To assess whether opinion responses of student of tertiary institution in Nigeria as measured in percentages differ significantly over the different constructs of the UTAUT model.
h) To ascertain whether the significant determinants of UTAUT constructs are associated within themselves.
Table 1: Items Used to Estimate UTAUT Model in determining the eLearning Readiness of Students of Higher Institutions in Nigeria (Venkatesh et al. 2003) and definitions.
Construct Construct Descriptions
Performance Expectancy: is the degree to which an individual believes that using the eLearning system will help him or her to attain gains in job performance (Venkatesh et. al 2003). Thus, it is of interest to examine whether performance expectancy is influenced either by gender or the age of the respondents.
PE1 PE2 PE3 PE4
I would find the eLearning system useful in my studies and assignments.
Using the eLearning system enables me to accomplish tasks more quickly.
Using the eLearning system increases my productivity.
If I use the eLearning system, I will increase my chances of getting a raise.
Effort expectancy: is the degree of ease associated with the use of the system. Venkatesh et. al (2003) propose that effort expectancy will be most salient for women, particularly those who are older and with relatively little experience with the system
EE1 EE2 EE3 EE4
My interaction with the eLearning system would be clear and understandable to me.
It would be easy for me to become skillful at using the eLearning system for my studies.
I would find the eLearning system easy to use.
Learning to operate the eLearning system is easy for me.
Attitude toward using technology: is an individual’s overall affective reaction to using a system. Given that we expect strong relationships in UTAUT between performance expectancy and intention, and between effort expectancy and intention, (Venkatesh et. al 2003) believe that, consistent with the logic developed here, attitude toward using technology will have a direct or interactive influence on intention
AT1 AT2 AT3 AT4
Using the eLearning system is a good idea.
The eLearning system makes work more interesting.
Working with the eLearning system is fun.
I like working with the eLearning system.
Social influence: is the degree to which an individual perceives that how important others believe he or she should use the new system (Venkatesh and Davis, 2000).
SI1 SI2 SI3 SI4
People who influence my behavior think that I should use the eLearning system People who are important to me think that I should use the eLearning system.
The senior management of this business has been helpful in the use of the eLearning system.
In general, the organization has supported the use of the eLearning system.
Facilitating Condition: is the degree to which an individual believes that an organizational and technical infrastructure exists to support use of the system
FC1 FC2 FC3 FC4
I have the resources necessary to use the eLearning system I have the knowledge necessary to use the eLearning system.
The eLearning system is not compatible with other systems I use
A specific person (or group) is available for assistance with eLearning system difficulties.
Self-efficacy: Self-efficacy refers to the beliefs in one’s capabilities to organize and execute the courses of action required producing given attainments (Bandure, 1997). Self-efficacy and anxiety have been modeled as indirect determinants of intention fully mediated by perceived ease of use (Venkatesh, 2000). Consistent with this, it is believed that self-efficacy and anxiety appear to be significant determinants of intention, i.e., without controlling for the effect of effort expectancy. Therefore the self-efficacy and anxiety are expected to behave
similarly, that is, to be distinct from effort expectancy and to have direct effect on intention above and beyond effort expectancy.
SE1 SE2 SE3 SE4
I could complete my studies or assignment using the eLearning system…
If there was no one around to tell me what to do as I go.
If I could call someone for help if I got stuck.
If I had a lot of time to complete the job for which the software was provided.
If I had just the built-in help facility for assistance.
Anxiety: Evoking anxious or emotional reactions when it comes to performing a behavior (e.g., using a computer).
AN1 AN2 AN3 AN4
I feel apprehensive about using the eLearning system.
I hesitate to use the eLearning system for fear of making mistakes I cannot correct.
The eLearning system is somewhat intimidating to me the most.
It scares me to think that I could lose a lot of information using the eLearning system by hitting the wrong key.
Behavioral intention to use the system: Behavioural intention consistent with the underlying theory is perceived to hold for all of the intention models. It is expected that behavioral intention will have a significant positive influence on technology usage. The research hypothesis in this case would be
BI1 BI2 BI3
I intend to use the eLearning system in the next semester and sessions.
I predict I would use the eLearning system in the next semester and sessions.
I plan to use the eLearning system in the next semester and sessions.
3.2 Hypotheses
This study is conducted at the Federal University of Technology, Akure and the National Open University, Nigeria. This research work attempt to find confirmation to the basic form of the UTAUT model as modified in Troy Lenandlar and Kemuel (2013). The following hypotheses, which are consistent with the projections based on the UTAUT model was considered
H1: Performance expectancy is positively related to perceived behavioural intention and will be moderated by gender and age.
H2: Effort expectancy is positively related to perceived behavioural intention.
H3: Social factors are positively related to perceived behavioural intention.
H4: Attitude towards the use of the technologies for learning is positively related to perceived behavioural intention.
H5: Performance expectancy is positively related to attitude towards technology.
H6: Effort expectancy is positively related to attitude towards technology.
H7: Social factors are positively related to attitude towards technology.
H8: Facilitating conditions are positively related to attitude towards technology.
H9: Facilitating conditions are positively related to perceived behavioural intention. OS H9: Anxiety are positively related to perceived behavioural intention.
4 Experiment
4.1 Procedure and Samples A well structure questionnaire was developed based on the modified version of the UTAUT model
developed by Venkatesh, et al. (2003). The research subjects were accessible students of the Federal University of Technology, Akure and the Nigerian Open University, Nigeria. In all 627 students participated in the survey. This includes 296 males and 181 females for Federal University of Technology, Akure while 108 males and 42 females are from the Nigerian Open University, Nigeria.
The students of the two Universities were selected based on accessibility and commitment of
respondents. The questionnaire was web based and compulsory for respondents to complete all
questions before such respondent’s responses can be accepted. The questionnaire consisted of two
parts. In part I, questions were related to age, gender, Computer literacy level, type of smart system
and level of power supply. Part II, consisted of questions relating to technology access, online skills
and relationship, motivation, foreseen reasons for not willing to use eLearning tools and modified
UTAUT constructs. The respondents ’responses were measured on a five-point likert type scale
(extending from 1 (completely disagree) to 5 (completely agree). Data was collected from January 2014 to August 2014.
Table 2 Characteristic of the sample (N=627)
Demographic variables Attribute Measure
School: FUTA Male
Female
47.2%
28.9%
NOA Male
Female
17.2%
6.7%
Age Median 18-25
Computer literacy level
Very low Low Medium High Very High
2.6%
15.9%
35.9%
28.7%
16.9%
Type of Smart system
None Smart Phone Computer System
5.1%
49.6%
45.3%
Table 2 depicts the demographic characteristics of the 627 respondents. About 47.2% males and 28.9%
females for Federal University of Technology, Akure while 17.2% males and 6.7% females are from the Nigerian Open University, Nigeria. The median age group was 18-25 years, with all the respondents below the age of 35 years. More than 64%f of the respondents was computer literate.
About 95% of the respondents had either a computer system or smart phone or both systems for eLearning access. In the subsequent analysis of data, of the four demographic variables, gender, age, motivation to use eLearning and willingness not to use eLearning technology will be associated with the eLearning readiness of the students in higher institutions
4.2 Data Analysis
In Table 3, the reliability for the various construct using the Cronbach’s Alpha and Chi Square are presented. For comparing items in a construct, alpha values greater than or equal to 0.7 are regarded as satisfactory (Hair, Black, Babin, Anderson, and Tatham 2006). As in the table, most of the scales in the construct appear to have a good degree of reliability since the corresponding the computed Cronbach’s alpha statistics is above the value 0.7. However, it appears that the items that make up the perceived behavior control construct lack internal consistency as its test statistic falls below 0.70.
However, the statement of the construct is then negated to improve is reliability. The 𝜒
2values of all
the items in the construct are significant, thus shows there is association between the items of each
constructs
Table 3 Reliability Analysis for the constructs
Constructs Abbreviat
ion
Items# N Cronbach’s Alpha
Chi Square D.F Sig
Age AG 3 627
Gender GE 2 627
Access to Computer or Smart Systems AC 3 627
Technology Access TA 5 627 0.770 125.368 4 .000
Online Skills and Relationship OS 9 627 0.900 269.005 4 .000
Motivation MO 4 627 0.779 257.378 4 .000
Willingness not to use eLearning tools WL 10 627 0.807 35.577 3 .000
Performance Expectancy PE 4 627 0.901 371.812 4 .000
Effort Expectancy EE 4 627 0.904 332.609 4 .000
Attitude Toward Technology AT 4 627 0.860 348.494 4 .000
Social Influence SI 4 627 0.801 47.282 4 .000
Facilitating Condition FC 4 627 0.857 413.486 4 .000
Self Efficacy SE 4 627 0.805 374.842 4 .000
Anxiety AN 4 627 0.835 18.447 4 .001
Behavour Intention BI 4 627 0.787 163.758 4 .000
Table 4 presents a summary of the Spearman correlations and their significance, and is used to indicate the strength of relationship among the constructs. Of the 105 pair-wise Spearman’s correlations, 62 pairs of constructs are significantly correlated. As one may expect, age is significantly correlated with technology and Social Influence. The technology access is correlated with all the constructs except the willingness not to use eLearning technology, Social Influence and anxiety. The online skill is significantly related with all the constructs except the Social Influence. Similar conclusions can be drawn on the significance of the pair-wise correlation between the other constructs. Finally, anxiety is negatively correlated with most of the constructs namely, gender, willingness not to use eLearning, Online skill and relationship, performance expectation, attitude towards the use of technology, social influence, facilitating condition and self efficacy.
Table 4: Correlation of the Demographic variables and the UTAUT construct
Correlations
Age GEN SYS TA OS MO WT PE EE AT SI FC SE AN BI
Age 1 GEN .006 1
.887
SYS -.007 -.137** 1 .865 .001
TA -.112** .030 .264** 1 .005 .449 .000
OS -.050 -.197** .465** .350** 1 .208 .000 .000 .000
MO -.053 -.031 .149** .140** .236** 1 .184 .431 .000 .000 .000
WT .005 .040 -.099* -.059 -.117** -.019 1 .900 .316 .013 .138 .003 .632
PE -.062 -.055 .113** .184** .265** .211** .016 1 .122 .171 .005 .000 .000 .000 .689
EE -.039 -.023 .230** .160** .289** .235** .048 .560** 1 .329 .561 .000 .000 .000 .000 .226 .000
AT -.055 -.004 .163** .185** .226** .266** -.007 .491** .510** 1 .171 .911 .000 .000 .000 .000 .851 .000 .000
SI .092* .009 .038 .048 -.014 .054 .107** -.049 .032 .032 1 .021 .825 .337 .234 .719 .175 .007 .220 .418 .427
FC -.016 -.019 .193** .199** .253** .191** .048 .569** .487** .418** .066 1 .690 .632 .000 .000 .000 .000 .226 .000 .000 .000 .101
SE .004 -.066 .206** .161** .262** .232** .054 .510** .554** .620** .021 .540** 1 .912 .098 .000 .000 .000 .000 .177 .000 .000 .000 .599 .000
AN .020 -.079* .041 -.038 .122** -.019 -.130** -.103** -.074 -.126** -.100* -.091* -.096* 1 .616 .047 .300 .348 .002 .635 .001 .010 .064 .002 .012 .023 .016
BI -.069 -.064 .228** .165** .216** .176** -.009 .261** .316** .349** .145** .309** .377** -.122** 1 .083 .109 .000 .000 .000 .000 .820 .000 .000 .000 .000 .000 .000 .002
**. Correlation is significant at the 0.01 level (2-tailed).
*. Correlation is significant at the 0.05 level (2-tailed).
Table 5: Summary Finding of Chi-square Test of Independent between Constructs, Age and Gender
Construct Age Gender
Sample Size
Chi- Square
D.F p- value
Sample Size
Chi- Square
D.F p-value
Technology access 627 12.071 8 0.148 627 2.671 4 0.614
Access to Smart System 627 15.334 8 0.053 627 14.366 4 0.000
Online Skills and Relationship 627 8.575 8 0.017 627 26.473 4 0.376
Motivation 627 16.494 8 0.036 627 4.225 4 0.428
Wiliness to eLearning 627 18.950 8 0.004 627 2.774 3 0.428
Considering the readiness of the student, it should be noted that adoption of eLearning technology
actually depends on several factors such (i) accessibility to the technology at their various schools, (ii)
conversance of the students to the online skills and relationship with other systems, and (iii) motivation
to be a part of the eLearning drive. Table 5 provides the summery finding of Chi-square test of independence between the rating of the constructs and the age and gender categories. The ratings of technology access by students of both Universities do not seem to be depending on their age and the gender of the students. It turns out that conversance with online skills rating are statistically dependent of the age but independent for gender of the students. Quite interestingly, motivation on the part of the civil servants to be aligned with the eLearning drive is independent of the age but significantly depends on the gender factor.
The study attempted to identify the perception of the students as what could be their foreseen reasons for not willing to use e-government tools. Their perceptions on ten important reasons such as lack of tutor training, unreliability of the technology, lack of tutor support/contact, difficulties of use of the online government system, lack of time to prepare/access services, non-availability of additional resources required for use information technology tool, issues related to management encouragement, lack of understanding of what is available, citizens attitudes and other issues were measured on a three- point likert type scale (extending from 1=significant, 2= very significant, 3= most significant). It has been revealed that all the 10 reasons that the students perceived to be the main hindrance for their unwillingness to use eLearning is associated with the age (
82=18.950, p-value=0.004) but has no association with the gender (calculated 𝜒
32=2.774, p-value=0.428) of the students.
Table 6: Summary of Frequency for the UTAUT model based on Responses and in Percentage
Likert Scale Frequency of Responses in %
UTAUT Construct Strongly
Disagree Disagree Undecided Agree
Strongly Agree Technology Access
14.47 20.10 34.85 17.27 13.32
Online Skills and Relationship 3.97 6.47 28.46 30.82 30.29
Motivation 7.50 11.20 36.20 26.63 18.46
Willingness not to use eLearning tools 3.43 5.90 12.52 33.57 44.58
Performance Expectancy 3.99 5.58 18.61 40.67 31.15
Effort Expectancy 13.44 16.35 29.79 28.59 11.84
Attitude Toward Technology 3.89 7.18 21.53 39.11 28.29
Social Influence 9.98 10.69 21.24 34.99 23.09
Facilitating Condition 3.79 4.39 14.59 35.85 41.39
Self Efficacy 10.21 18.07 20.10 19.34 32.29
Anxiety 10.21 18.10 20.06 19.34 32.30
Perceived Behavour Intention 3.30 6.27 15.36 39.13 35.94
In Table 6, we summarize percent option responses of University students on the eight constructs that are known to be significant direct determinants of intention or usage in one or more of the individuals.
Table 7, presents the two-way ANOVA table for testing the hypotheses that (i) the eight constructs do
not differ significantly as determinants of UTAUT, (ii) the opinion ratings are homogeneous. The
dependent variable considered for the ANOVA was the average opinion rating of respondents. From
the ANOVA table it is clear that eight constructs do not differ significantly and therefore are indeed
significant determinants of UTAUT. However, the opinion rating differs significantly from each other.
Table 7: Two-way ANOVA Table
Source of Variation S.S D.F M.S.S F ratio p-value
Determinants 69.08 7 9.869 0.12 0.894
Opinion Ratings 5282.30 4 1320.58 15.484 < 0.0001
Error 2388.39 28 85.2857
Total 7739.77 39
Table 8: Summary findings of Chi-square Tests of Independence between UTAUT Constructs, Age and Gender
Construct Age Gender
Sample Size
D.F Chi- Square
p- value
Sample Size
D.F Chi- Square
p- value
Technology access 627 8 12.071 0.148 627 4 2.671 0.614
Online Skill and Relation 627 8 18.575 0.017 627 4 26.473 .000
Motivation 627 8 16.494 0.036 627 4 4.225 0.376
Performance Expectancy 627 8 13.153 0.107 627 4 7.803 0.099
Effort Expectancy 627 8 11.076 0.197 627 4 0.694 0.952
Social Influence 627 8 10.690 0.220 627 4 5.417 0.247
Attitude towards Technology 627 8 18.205 0.020 627 4 5.060 0.281
Facilitating Condition 627 8 24.121 0.002 627 4 3.494 0.479
Self –Efficacy 627 8 9.213 0.325 627 4 5.641 0.228
Anxiety 627 8 6.924 0.545 627 4 4.822 0.306
Behaviour Intention 627 8 18.205 0.020 627 4 5.060 0.281
Table 8 summarizes the findings of Chi-square tests of independence between UTAUT constructs and age/gender of the sample University students. A few of the research hypotheses being tested were outlined in section 4.1 through
H1to
H3.The following broad conclusions can be made about the validation of the stated hypotheses related to the UTAUT constructs exhibited in the table. It is revealed that
i. There is no significant association between the performance expectancy and age and gender ii. The effort expectancy does not seem to be associated with either the age or the gender of the
University students.
iii. Social influence seems to be independent of the age and also independent on the gender.
iv. Perceived behavior intention of the University students appears to be independent of both age and gender in their behavioral intention to use eLearning technology.
v. Attitude behavior towards the use of technology does seem to be associated with the age but not significantly associated with gender.
vi. Self- efficacy does not seem to be associated with age and with gender.
vii. Anxiety seems not to be associated with age and the gender.
The hypotheses
H4to
H7refer to the association between a few of the UTAUT constructs and these
hypotheses help to establish constructs’ direct impact on behavioural intention to use e-governance
technology. The summary findings of the statistical analysis are reported in Table 10.
Table 9: Summary findings of Chi-square Tests of Independence between UTAUT Constructs, Motivation and Online Skill and Relations
Construct Motivation Online Skill and Relations
Sample Size
D.F Chi- Square
p- value
Sample Size
D.F Chi- Square
p- value Performance Expectancy 627 16 74.100 0.000 627 16 74.684 0.000
Effort Expectancy 627 16 82.517 0.000 627 16 111.879 0.000
Attitude towards Technology 627 16 98.289 0.000 627 16 88.542 0.000
Social Influence 627 16 15.380 0.497 627 16 31.850 0.010
Facilitating Condition 627 16 70.449 0.000 627 16 108.010 0.000
Self- Efficacy 627 16 77.249 0.000 627 16 101.934 0.000
Anxiety 627 16 18.091 0.319 627 16 48.144 0.000
Behaviour Intention 627 16 70.710 0.000 627 16 79.837 0.000
Table 10: Summary findings of Chi-square tests of Independence between UTAUT constructs, Willingness and Technology Access
Construct Willingness Technology Access
Sample Size
D.F Chi- Square
p- value
Sample Size
D.F Chi- Square
p- value Performance expectancy 627 12 18.615 0.098 627 16 51.539 0.000
Effort expectancy 627 12 33.520 0.001 627 16 53.619 0.000
Attitude towards Technology 627 12 36.455 0.059 627 16 54.497 0.000
Social influence 627 12 26.738 0.008 627 16 26.141 0.044
Facilitating Condition 627 12 23.583 0.059 627 16 82.127 0.000
Self –efficacy 627 12 20.475 0.059 627 16 49.134 0.000
Anxiety 627 12 37.871 0.000 627 16 42.818 0.000
Behaviour Intention 627 12 30.905 0.002 627 16 51.979 0.000
Table 9 Summary findings of Chi-square tests of independence between UTAUT constructs, Attitude Towards Behaviour and Perceived behaviour Intention
Construct Attitude Towards Technology Perceived Behavior Intention Sample
Size
D.
F
Chi- Square
p- value
Sample Size
D.
F
Chi- Square
p- value Performance Expectancy 627 16 259.338 0.000 627 16 121.850 0.000
Effort Expectancy 627 16 324.777 0.000 627 16 161.719 0.000
Attitude Towards Technology 627 16 228.505 0.000
Social Influence 627 16 56.820 0.000 627 16 88.204 0.000
Facilitating Condition 627 16 218.86 0.000 627 16 129.598 0.000
Self –efficacy 627 16 559.104 0.000 627 16 250.406 0.000
Anxiety 627 16 131.868 0.000 627 16 113.953 0.000
Behaviour Intention 627 16 228.505 0.000
From Table 9, it is seen that the research hypothesis H4 related to the association between computer self- efficacy and behavioral intention is accepted, while the research hypothesis H5 related to association between computer anxiety and behavioral intention is rejected.
From Table 10, it is seen that the research hypothesis H5 Performance expectancy is positively related to attitude towards technology is accepted while the hypothesis H6: Effort expectancy is positively related to attitude towards technology is also accepted. . Turning to the research hypotheses H6 andH7, H10 all the hypotheses seem to be validated based on the information furnished by the sample data. Finally, there appears to be association between the use of technology and the attitude toward behavior with chi square value 𝜒162 228.505, p-value=0.000).
