Keywords: elearning, UTAUT Models, Pedagogical, Web-Based Course Management System, Traditional Teaching and Learning, asynchronous, constructivist

Transcription

1 Journal of IT and Economic Development 5(2), , October 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 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, selfefficacy, 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

2 Journal of IT and Economic Development 5(2), , October 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 learninginducing 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.

3 Journal of IT and Economic Development 5(2), , October 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/hybridasynchronous) 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.

4 Journal of IT and Economic Development 5(2), , October 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 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 s 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/ 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).

5 Journal of IT and Economic Development 5(2), , October 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. Performance Expectation Effort Expectancy Behavioral Intention Use Behavior Social Influence Facilitating Conditions Self Efficacy Anxiety Online Skill &Age&Gender Motivation Willingness Figure 1 Modified version of UTAUT 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 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.

6 Journal of IT and Economic Development 5(2), , October 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 I would find the elearning system useful in my studies and assignments. PE2 Using the elearning system enables me to accomplish tasks more quickly. PE3 Using the elearning system increases my productivity. PE4 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 People who influence my behavior think that I should use the elearning system SI2 People who are important to me think that I should use the elearning system. SI3 The senior management of this business has been helpful in the use of the elearning system. SI4 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 I have the resources necessary to use the elearning system FC2 I have the knowledge necessary to use the elearning system. FC3 The elearning system is not compatible with other systems I use FC4 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

7 Journal of IT and Economic Development 5(2), , October similarly, that is, to be distinct from effort expectancy and to have direct effect on intention above and beyond effort expectancy. I could complete my studies or assignment using the elearning system SE1 If there was no one around to tell me what to do as I go. SE2 If I could call someone for help if I got stuck. SE3 If I had a lot of time to complete the job for which the software was provided. SE4 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

8 Journal of IT and Economic Development 5(2), , October (extending from 1 (completely disagree) to 5 (completely agree). Data was collected from January 2014 to August 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 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 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 However, the statement of the construct is then negated to improve is reliability. The χ 2 values of all the items in the construct are significant, thus shows there is association between the items of each constructs

9 Journal of IT and Economic Development 5(2), , October Table 3 Reliability Analysis for the constructs Constructs Abbreviat Items# N Cronbach s Chi Square D.F Sig ion Alpha Age AG Gender GE Access to Computer or Smart Systems AC Technology Access TA Online Skills and Relationship OS Motivation MO Willingness not to use elearning tools WL Performance Expectancy PE Effort Expectancy EE Attitude Toward Technology AT Social Influence SI Facilitating Condition FC Self Efficacy SE Anxiety AN Behavour Intention BI 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.

10 Journal of IT and Economic Development 5(2), , October Table 4: Correlation of the Demographic variables and the UTAUT construct

11 Journal of IT and Economic Development 5(2), , October Correlations Age GEN SYS TA OS MO WT PE EE AT SI FC SE AN BI Age 1 GEN SYS ** TA ** ** OS **.465 **.350 ** MO **.140 **.236 ** WT * ** PE **.184 **.265 **.211 ** EE **.160 **.289 **.235 ** ** AT **.185 **.226 **.266 ** **.510 ** SI.092 * ** FC **.199 **.253 **.191 ** **.487 **.418 ** SE **.161 **.262 **.232 ** **.554 **.620 ** ** AN * ** ** ** ** * * * BI **.165 **.216 **.176 ** **.316 **.349 **.145 **.309 **.377 ** ** **. 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 Chi- D.F p- Sample Chi- D.F p-value Size Square value Size Square Technology access Access to Smart System Online Skills and Relationship Motivation Wiliness to elearning 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

12 Journal of IT and Economic Development 5(2), , October 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 threepoint 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 2 unwillingness to use elearning is associated with the age ( 8 =18.950, p-value=0.004) but has no association with the gender (calculated χ 2 3 =2.774, p-value=0.428) of the students. Table 6: Summary of Frequency for the UTAUT model based on Responses and in Percentage UTAUT Construct Likert Scale Frequency of Responses in % Strongly Disagree Disagree Undecided Agree Strongly Agree Technology Access Online Skills and Relationship Motivation Willingness not to use elearning tools Performance Expectancy Effort Expectancy Attitude Toward Technology Social Influence Facilitating Condition Self Efficacy Anxiety Perceived Behavour Intention 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.

13 Journal of IT and Economic Development 5(2), , October Table 7: Two-way ANOVA Table Source of Variation S.S D.F M.S.S F ratio p-value Determinants Opinion Ratings < Error Total 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 Online Skill and Relation Motivation Performance Expectancy Effort Expectancy Social Influence Attitude towards Technology Facilitating Condition Self Efficacy Anxiety Behaviour Intention 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 H 1 to H 3.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 4 H to 7 H refer 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.

14 Journal of IT and Economic Development 5(2), , October 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 Effort Expectancy Attitude towards Technology Social Influence Facilitating Condition Self- Efficacy Anxiety Behaviour Intention 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 Effort expectancy Attitude towards Technology Social influence Facilitating Condition Self efficacy Anxiety Behaviour Intention 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 Effort Expectancy Attitude Towards Technology Social Influence Facilitating Condition Self efficacy Anxiety Behaviour Intention From Table 9, it is seen that the research hypothesis H 4 related to the association between computer selfefficacy and behavioral intention is accepted, while the research hypothesis H 5 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 H 6 and H 7, 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 χ , p-value=0.000).

15 Journal of IT and Economic Development 5(2), , October Conclusion E-learning is important for education because, it can improve the quality of the learning experience, and extend the reach of every lecturer and tutor. help remove barriers to achievement, by providing new and creative ways of motivating and engaging pupils and learners of all abilities, enabling and inspiring everyone to attain their educational potential. support learning by offering differentiated learning, particularly for those who need support in literacy, numeracy and ICT. offers a wide range of tools to enable teachers and learners to be innovative, creative and resourceful in all learning activities. Teachers and learners can easily customise digital learning resources to suit pace and level, appropriate to any learning style and ability. creates on-line communities of practice. The Internet can bring learners, teachers, specialist communities, experts, practitioners and interest groups together to share ideas and good practice. For Learners E-learning can provide an individualized learning experience for all learners, including those who are disadvantaged, disabled, exceptionally gifted, have special curriculum or learning needs or who are remote or away from their usual place of learning. facilitate wider participation and fairer access to further and higher education by creating the opportunity to start learning and to choose courses and support according to the learners needs. Provide personalised learning support through information, advice, and guidance services. It can help learners find the course they need, with a seamless transition to the next stage of their learning, including online application or enrolment and an electronic portfolio of their learning to take with them. provides virtual learning worlds where learners can take part in active and creative learning with others through simulations, role-play, remote control of real-world tools and devices, online master classes, or collaboration with other education providers In order assess the readiness, attitude and behavior of students of tertiary institution on elearning, the study 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 were interviewed and their responses recorded using a webbased structured questionnaire developed based on the modified version of the UTAUT model of Venkatesh, et al. (2003).The strength of relationship among the constructs of UTAUT model was assessed using Spearman s correlation coefficient. 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. Further, 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 ( =18.950, p-value=0.004) but has no association with the gender (calculated χ 3 2 =2.774, p-value=0.428) of the students. 2 8

16 Journal of IT and Economic Development 5(2), , October The analysis of variance indicates that seven constructs are indeed significant determinants of UTAUT model, but the opinion ratings of civil servants across the constructs differ significantly from each other. The association between the constructs of UTAUT on one hand and the age/gender of the respondents on the other has been discussed in terms of Chi-square test of independence. Finally, the association between the constructs of UTAUT per se has been studied. This survey has established the perspectives of students on the use of elearning. The results suggest that there are good logistics of implementation but the services are far from reach, though, they admitted that this innovation will enhance their capacity, make them more effective and efficient. Productivity will improve, cost will drastically reduce and more importantly all the negative attribute of the traditional learning system will reduce hence creating a good study environment. The survey findings make a strong case for the replication of the similarly designed study in other University of Nigeria and other Universities in other countries. References Bandura, A. (1997). Self-efficacy. Exercise of control. NY: W.H. Freeman and Company. Barbara A. Lewis, Virginia M. MacEntee, Shirley DeLaCruz, Catherine Englander, Thomas Jeffrey, Elizabeth Takach, Sharon Wilson, and Jason Woodall (2005)Learning Management Systems Comparison, Proceedings of the 2005 Informing Science and IT Education Joint Conference, Flagstaff, Arizona, USA. Bernie O Hare (2006), ELearning Strategy For the Further Education Sector, A Learning Revolution A Pre-strategy Consultation Document, CEC (2001), The elearning action plan: Designing Tomorrow s Education Commission of the European Communities (2001) 172 Final Brussels, 28, 3, Charles Dziuban, Joel Hartman and Patsy Moskal, (2004), Blended Learning, University of Central Florida, Educause Center for Applied Research, Research Bulletin, vol. 2004, number 7, March 30, LearningUCF Study.pdf. Charles Graham (2006) Blended Learning Systems: Definition, Current Trends, and Future Directions, in Handbook of Blended Learning: Global Perspectives, Local Designs, Pfeiffer Publishing, San Francisco, USA, blended_learning_systems.pdf. Dillon, A. and Morris, M. (1996) User acceptance of new information technology: theories and models. In M. Williams (ed.) Annual Review of Information Science and Technology, Vol. 31, Medford NJ: Information Today, Epic Group plc., Blended Learning Epic white paper, white_papers/epic_whtp_blended.pdf. Falch, M. (2004). A Study on Practical Experiences with using ELearning Methodologies and Cooperative Transnational Development Methodology. CTI Working Paper, no. 97, 2004, Center for Tele-Information, Technical University of Denmark. Retrieved February 17, 2007 from Hair, J. F., Black, B., Babin, B. J., Anderson, R. E., & Tatham, R. L. (2006), Multivariate Data Analysis (6th ed.). New Jersey: Pearson Prentice Hall. Hutchins, H.M (2001), Enhancing the Business Communication Course through WebCT Business Communication Quarterly, 64, Pg

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