PERPETUATION INTENTION OF USING
E-LEARNING AMONG UNIVERSITIES STUDENTS IN NIGERIA
Abdulhamid Tahir Hamid
1, Shafiu Muhammad Tahir
2, Murtala Aminu
31 2Department of Engineering & Technology, Sunrise University Alwar Rajasthan (India)
3Department of Library and Information Science, NIMS University Jaipur (India)
ABSTRACT
This paper considers an element of perpetuation intention to use e-learning among Universities students In Nigeria. The elements used in this paper are embraced from Technology Acceptance Model (TAM) and Expectancy Disconfirmation Theory (EDT). The assessable homework working for accidental control group where the email invitation which controlled a hypertext link to the survey page, enables the participants to access to the survey hosted in Google Drive. Out of 760 sample size, a total of 650 responses were congregated with 509 valid answers used for investigation. The information was investigated to test the relationship between elements of perpetuation intention to use e- learning among Universities students In Nigeria. The judgment determines that determinant of perpetuation intention to use e- learning are based on Fulfillment, while Fulfilment is powerfully strong-minded by Observed Simplicity of Use and Endorsement of using e- learning.
Internet self-efficacy is testified to have a heavy-duty result on Fulfillment through Observed Simplicity of Use, while Observed Excellence affects Fulfillment of using e- learning through Endorsement. In general discoveries from this study are useful for Universities students in that determinant of perpetuation intention of using e- learning could also be used in choosing and assessing e- learning landscapes before donation. Moreover, e- learning vendors may well use these discoveries to progress and improve landscapes of their e- learning services.
Keywords: Perpetuation intention; Academic libraries; E-learning; Electronic books; Universities students In Nigeria; Technology acceptance model; E-Learning Acceptance Model [ELAM]
I. INTRODUCTION
Currently, we are existing in an age of information Technology in the era of Digital life everywhere computer technology plays a major role. The tradition of technology to facilitate learning is established to be of importance across educational institutions. The speedy progress of information, communication and technologies (ICT), internet Technologies and Web-based applications have introduced incomparable change in universities All over the world (Cheng 2010). Electronic learning (e-learning) is changing the way teaching and Learning is taking place on university campuses (Ahmed, 2010). However the up-scale of e-Learning In developing countries especially in Africa is slow compared to their Western colleagues, the last decade has observed some strong-minded efforts on the part of university administrators to implement e-learning strategies
in order to catch up with their colleagues in the developed countries. Fundamentally, e-learning is usually defined as a type of learning supported by information and communication technology (ICT) via the internet, intranets, extranets or many others to progress the feature of teaching and learning.
E- Learning has become a projecting service in universities as it provides the user with convenient means of accessing resources in the digital era. Nowadays, most of the universities including academic libraries have included E- Learning in their collections. This has interested researcher to promote understand user acceptance and perception towards E- Learning for their reading or referring, as well as to inspect usage pattern of E- Learning. The E- Learning service offered by the library is likely to have comfortable users in which the fulfillment could cause users’ intention to continuously use it for various purposes. Previous research by Patterson and Spreng (1997) showed that fulfillment has a positive effect on future intention, while Bhattarcherjee (2001) reported an essential link between fulfillment and perpetuation intention to use E- Learning. Actual slight gains have been made with respect to incorporating e-learning into teaching and Learning at the Universities in Nigeria.
This situation is not particular to Nigeria. Most African universities are still perpetuation intention struggling to incorporate E-learning into teaching and learning.
II. LITERATURE REVIEW
Perception and Intention of E-learning Users
Electronic is at the very of foundation of information and computer age now in which we are living presently. E- learning services in academic Sectors (universities) have been inhabitation for years and were being accepted by users. Survey by UNESCO in Africa on e-learning concluded that “e-learning is still very much in its infancy across most of the continent,” though there is much enthusiasm amongst university
Administrators to fully develop e-learning systems. The developing of e-learning systems on university campuses,
E-Learning Acceptance Model [ELAM]
E-learning is defined as learning facilitated and supported through the utilization of information and communication technologies (Jenkins & Hanson 2003). Thus, e-learning includes the use of ICT tools (e.g. the Internet, computer) and content created with technology (e.g. animations, videos) to support teaching and learning activities.
As e-learning is associated with individualization of the teaching-learning process, the learning style of the student and teaching style of the teacher is an important factor affecting the embracing process. These factors are considered as intermediaries affecting the relation between performance expectancy beliefs and interactive perpetuation intention to use e-learning.
ELAM is illustrated in Figure 1.
* Terms shown in italics are additions to UTAUT
Figure 1: ELAM [E-learning Acceptance Model]
The Technology Acceptance Model (TAM), introduced by Davis (1989), is an adaptation of social psychology theory of reasoned action, specifically tailored for modeling user acceptance of information systems. The TAM, as shown in Figure 2, considers observed usefulness and observed ease of use as major determinants of intention to use a technology. The former refers to the extent to which a person believes that using the system will enhance task performance, while the latter refers to the degree to which the user expects the target system to be free of effort. Across studies, observed usefulness is highlighted as the most significant determinant of performance intention to the technology.
Source: Davis, F. (1989) Observed Usefulness, Ease of Use, and User Acceptance of Information Technology Figure 2: Technology Acceptance Model
Externa l variable
Perceived usefulness
Perceived ease of
use
Attitude towards using
Behavioral intention to
use
Actual system
use Performance
expectancy - Perceived usefulness
- Interactivity * - Flexibility
Effort expectancy - Perceived ease of use - Ease of learning - Perceived efficacy
beliefs
Actual usage Performance
intention Teaching
Style Learning
Style
Social influence - Subjective norm - Image
Facilitating conditions - ICT infrastructure - Institutional policies - Training and support - Leadership
Nearly 70 percent of Academic universities owned or subscribed to e- learning in 2007, and more recent estimates range from 94 to 97 percent (Walters 2013). Several studies such as Borchert et al. (2009) and Noorhidawati and Gibb (2008) investigated awareness level of e- learning collection. On the other hand, awareness, in fact, motivates people’s intention to use, which ultimately leads to E-Learning usage and preference among the user. For example, Oliveira (2012) investigated student’s perceptions, performance, and attitudes towards E-Learning in universities library. His finding reported that users preferred to use printed book rather than e-learning although he showed that e-learning users were satisfied with the feature of the e-learning.
In addition, Nicholas et al. (2008) reported 61.8 percent of their respondents use an e-learning for various purposes. Another report on a large-scale survey that was carried out to assess academic awareness, perceptions and usage levels of e-learnings in a university in UK, found a significant number of reading media preference (Rowlands et al. 2007). The result showed48 percent of the respondents prefer to read an e-learning on screen, only 13 percent prefer to read on paper, while percent vary in using the screen, paper or other media to read.
This is probably because the choice of using either e-learning or printed book depends on the availability of information needed by the user. A very recent survey study in India, on the other hand, concluded that users are not significantly concerned by the choice of e-learning or printed book, but on the availability of information source regardless of whether it is in digital or printed format (Ramaiah 2012).
Richardson and Mahmood (2012) conducted a study to investigate user fulfillment when using different e- readers (iPad, Barnes and Noble’s Noke, Border’s Kobo Reader and Sony Digital Reader) for reading an e- learning. They found that portability, ease of use and collections provided are the most desirable features.
Another study related to perpetuation use of e-reader found that user intention to use is solely strong-minded by attitude, which refers to the perception of content enrichment and device personalization (Chou, Stu, and Lin 2010). These two studies were conducted mainly to investigate factors that influence people to continue to use a specific e-learning reader, which in turn would help the practitioners, researcher or stakeholder in understanding what are the desirable design, method, and features to generally satisfy the users.
III. RESEARCH MODELS TO UNDERSTAND E-LEARNING USAGE
Available research models have been reviewed systematically to examine elements that could develop valuable insight in understanding e-learning users particularly related to perpetuation intention of using of e-learning.
Several models have been developed with the intention of explaining and predicting usage of information technology they include the theory of reasoned action, theory of planned performance- TPB, technology acceptance model-TAM, theory of diffusion of innovation-DOI. This study used the Technology Acceptance Model (TAM) to explain students’ attitude to e-learning in selected Nigerian universities. TAM adapted TRA by replacing several of its measures with two key constructs: observed usefulness (PU) and observed ease of use (PEOU). TAM has been widely used to predict user acceptance and use, based on observed usefulness and ease of use. Davis (1989) and Davis et al. (1989) developed TAM by adapting the Theory of Reasoned Action (TRA) (Ajzen & Fishbein, 1980; Fishbein & Ajzen, 1975), to understand the causal chain linking external variables to IT usage intention and actual use in a workplace.
There are few recent studies focused on concepts such as observed feature, subjective norms, fulfillment and their relationships on continuous intention use of e-Learning. The perpetuation intention to use a particular
product is by the level of fulfillment to the product (Al-Maghrabi and Dennis 2011; Chen et al. 2009; Zhao and Cao 2012). The study by Roca et al. (2006) reported observed feature has significant effect to the confirmation.
They also showed that interpersonal and external influence (subjective norm) had no significant impact on the fulfillment of using e-learning; while observed feature including system feature and information feature could become the determinants of e-learning fulfillment by mean of confirmation on using e-learning. On the other hand, Joint (2010) found that e-learning has neither proven itself as a transformational technology in the context of library services, nor as a consumer product to be sold directly to end users. It does have the potential to be so, if certain problems related to usability, business models and library finding tools are addressed.
In accordance with technology implementation, the Technology Acceptance Model (TAM) is the most common model being used in understanding the acceptance of a technology being applied and accepted in a specific environment. In e-learning research, TAM has been used in several studies. For example, Tsai (2012) conducted a study to understand user performance as a primary key in understanding the e-learning market. The findings showed brand, service, trust, and observed usefulness to have a positive effect on the attitude towards using e- learning, while attitudes towards using e-learning has significant effect on the perpetuation intention to use.
Other than TAM, there is also a reliable framework proposed by Roca, Chiu and Martinez (2006), investigating the perpetuation intention of using an e-learning system. The framework consists of TAM as well as a combination model of Theory of Planned performance (TPB), TAM and Expectancy Disconfirmation Theory (EDT). Although a number of studies were conducted to evaluate e-learning usage, there has been no study to date that specifically investigates perpetuation intention to use e-learning in an academic library. Therefore a combination of perpetuation intention model (using TPB, TAM and EDT) as suggested by Roca, Chiu and Martínez (2006) is adopted in this study.
IV. OBJECTIVE AND METHOD
The main objective of this study is to identify the factors that influence continuation use of e-learning among Universities students In Nigeria. The following research questions were addressed:
a) What are the factors that affect students’ perception on e-learning usability?
b) What are the factors that influence students’ fulfillment in using e-learning?
c) What are the factors that encourage confirmation of using e-learning among students?
d) What are the factors that influence e-learning perpetuation intention?
This study employed a quantitative research method, which included design of the research instrument for data collection (questionnaire) adapted from Roca, Chiu and Martínez’s (2006), and data analysis.
The questionnaire was divided into three sections:
(a) Respondent's demographic information, (b) e-learning usage, and(c) Perception on perpetuation intention in using e-learning.
For measuring respondents perpetuation intention in using e-learning, this study used seven-point Likert-type measurement scale, decoded as 1=strongly disagree, 2=disagree, 3=somewhat disagree, 4=undecided, 5=somewhat agree, 6=agree and 7=strongly agree.
V. OBJECTIVES AND HYPOTHESES
As showed in the academic structure, the use of TAM has been applied to the recognition of technology in many universities and among students.
The study investigates the factors that influence perpetuation intention of using e-learning among higher education students, adopting a research model proposed by Roca, Chiu and Martinez (2006). The model is based on the following three theoretical frameworks: TPB, TAM and EDT. The model includes six main constructs in investigating the perpetuation intention to use e-learnings, which are observed feature, observed usability, observed control, subjective norms, confirmation and fulfillments shown in Figure 1. For the purpose of this study, user fulfillment is a determinant of continuation intention in using e-learning where the satisfaction’s predictors are the observed feature, observed usability,
Confirmation and subjective norms. This study is conducted to provide evidence to the following hypotheses:
H1: there is a statistically significant relationship between observed feature and confirmation of using e- learnings.
H2: there is a statistically significant relationship between observed feature and fulfillment of using e- learnings.
H3: there is a statistically significant relationship between confirmation and observed usability of using e- learnings.
H4: there is a statistically significant relationship between confirmation and fulfillment in using e-learnings.
H5: there is a statistically significant relationship between subjective norms and fulfillment in using e-learnings.
H6: there is a statistically significant relationship between observed control and observed usability of using e- learnings.
H7: there is a statistically significant relationship between observed usability and fulfillment in using e- learnings.
H8: there is a statistically significant relationship between fulfillment and perpetuation intention of using e- learnings
Figure 1: The Research Model for Perpetuation Intention to Use E-learning
VI. DATA COLLECTION
The review utilized arbitrary inspecting procedure where email welcome to the overview connection was disseminated to respondents' email address. The email locations were acquired from the college's scholarly division. The specimen size was solid disapproved of in view of Krejcie and Morgan (1970) populace and the example table. With a populace of 23,000 understudies (as detailed by college recorder at the purpose of information gathering), the specimen size was solid disapproved as 760 (Confidence Level = 95%, Margin of Error = 3.5%). The email welcome, which contained a hypertext interface, empowers the members to connection to the review database facilitated in Google Drive.
Table 1 demonstrates the study reaction rate. After two rounds of conveyances, reactions were gotten from 650 respondents out of which, 508 (78.15%) were finished and utilized for investigation. The reaction rate is extraordinarily useful for the online review as Gravetter and Forzano (2008) demonstrated an average reaction rate for online study is just around 18 percent. This is most likely because of the motivator is given to the members who might win a fortunate draw in the wake of finishing the study. This was done utilizing an arbitrary number generator in Microsoft Excel spreadsheet to create numbers that were already allocated to the email addresses.
Table 1: Survey Response Rate
VII. DATA ANALYSIS
The scale things in the survey were subjected to component examination test and unwavering quality investigation. For the variable examination to be viewed as proper, Bartlett's trial of Sphericity ought to be noteworthy at p < .05, and estimations of the Kaiser-Meyer-Olkin (KMO) measure of inspecting ampleness ought to be in the vicinity of 0.6 and 1.0 as appeared in Table 2. For this review, scale things that recorded component stacking of under 0.4 were not acknowledged. The inside consistency of each scale was measured utilizing Cronbach's alpha. Measures of unwavering quality range from 0 to 1, and each scale ought to display satisfactory dependability with Cronbach's alpha near or over the prescribed 0.70 level as appeared in Table 3.
To answer the examination questions, we connected the non-parametric tests Spearman's rho the various direct relapse investigation. Table 4 demonstrates the methods and standard deviation values decided for the six develops examined in this paper.
Total Population 23,000
Total sample 760
Clicked on survey link 650 Incomplete survey 141 Non-completion rate 21.7%
Completed survey 509
Response rate 78.3%
Table 2: Bartlett's Test and KMO Measure
Constructs KMO Bartlett’s Percentage of Variance t test Sig explained
Observed Usefulness .732 .000 81.775
Observed Cognitive Absorption .782 .000 53.77
Observed Ease of Use .723 .000 79.26
Observed Internet Self-efficacy .712 .000 77.57
Observed Computer Self-efficacy .770 .000 63.17
Interpersonal Influence .663 .000 79.65
External influence .717 .000 78.09
Information feature .781 .000 35.22
System feature .836 .000 50.90
Confirmation .736 .000 77.98
Fulfillment .761 .000 85.59
Perpetuation intention .747 .000 87.04
Table 3: Cronbach’s Alpha and Factor Loading
Observed Usefulness: Cronbach’s α = 0.89 Loading
1. Using e-learning can improve my study performance .923 2. Using e-learning can increase my study effectiveness .915
3. I find e-learning is useful to me .874
Observed Cognitive Absorption: Cronbach’s α = 0.81 Loading 4. Time files when I am reading/referring e-learning .460 5. Most times when I read e-learning, I end up spending more time than I had planned
.722
6. When I am accessing e-learning, I am able to block out most other distractions
.732
7. While using e-learning, I am absorbed in what I am doing .811
8. I have fun interacting with the e-learning .820
9. I enjoy using the e-learning .792
Observed Ease of Use: Cronbach’s α = 0.87 Loading
10. Learning to use e-learning is easy for me .866
11. It is easy for me to become skillful at using the e-learning system .916 12. My interaction with e-learning system is clear and understandable .888 Observed Internet Self-efficacy: Cronbach’s α = 0.85 Loading 13. I feel confident in navigating the e-learning by following hyperlinks .903 14. I feel confident in the e-learning system searching information .900
15. I feel confident in the e-learning system downloading e-learning .837 Observed Computer Self-efficacy: Cronbach’s α = 0.81 Loading 16. I could complete my reading/referring activities using e-learning if I had
never used any e-learning system like it before
.665
17. I could complete my reading/referring activities using e-learning if I had only the e-learning system manuals for reference
.806
18. I could complete my reading/referring activities using e-learning if I had seen someone else using it before trying it myself
.845
19. I could complete my reading/referring activities using e-learning if I had just the built-in-help facility for assistance
.848
Interpersonal Influence: Cronbach’s α = 0.87 Loading
20. My family thought I should use e-learning .818
21. My colleagues thought I should use e-learning .943
22. My friends thought I should use e-learning .911
External influence: Cronbach’s α = 0.85 Loading
23. I read/saw news reports that using e-learning was a good way of reading .870 24. Expert opinions depicted a positive sentiment for using e-learning .913 25. Mass media reports convinced me to use e-learning .868
Information feature : Cronbach’s α = 0.70 Loading
26. The e-learning provides relevant information for my study .704 27. The e-learning does not provide easy-to-understand information (R) .745 28. The output e-learning from the e-learning system is not clear (R) -
29. The e-learning system presents the e-learning in an appropriate format .623 30. The information content in the e-learning is very good .773 31. The e-learning from the e-learning system is up-to-date enough for my
purposes
.747
32. The collection of e-learning that the e-learning system delivers is not sufficient for my purposes (R)
-
33. The reliability of output information from e-learning system is high .723 34. The e-learning system provides the information I need in time .745
System feature : Cronbach’s α = 0.72 Loading
35. Number of steps to access an e-learning in the e-learning system are too many
-
36. Steps to access & navigate e-learning in the e-learning system follow a logic sequence
.817
37. Navigating e-learning in the e-learning system always leads to a predicted result
.813
38. The organization of information on the e-learning system screens is clear .799
39. The e-learning system has natural and predictable screen changes .813 40. The e-learning system responds quickly during the busiest hours of the
day
.641
Confirmation: Cronbach’s α = 0.86 Loading
41. My experience with using the e-learning was better than I expected .888 42. The services provided by e-learning system was better than I expected .881 43. Overall, most of my expectations from using the e-learning system were
confirmed
.880
Satisfaction: Cronbach’s α = 0.92 Loading
44. I am satisfied with the performance of the e-learning .927 45. I am pleased with the experience of using the e-learning .925 46. My decision to use the e-learning was a wise one .923
Perpetuation Intention: Cronbach’s α = 0.92 Loading
47. I will use the e-learning on a regular basis in the future .948 48. I will frequently use e-learning in the future .942
49. I will strongly recommend others to use it .908
Notes:
The item statements that didn’t load are showed as (-) and will not be used in the correlation and regression analysis
(R) Reverse Item
Scale: 1 = Strongly Disagree; 2 = Disagree; 3 = Somewhat Disagree; 4 = Undecided; 5 = Somewhat Agree; 6
= Agree; 7 = Strongly Agree
Table 4: Descriptive Statistics of the Constructs
Constructs Mean SD
Perceived Usefulness 4.36 0.81
Perceived Cognitive Absorption 4.20 0.84
Perceived Ease of Use 4.34 0.81
Perceived Internet Self-efficacy 4.25 0.79
Perceived Computer Self-efficacy 4.05 0.95
Interpersonal Influence 3.48 0.99
External Influence 3.79 0.97
Information Quality 4.44 0.60
System Quality 4.23 0.67
Confirmation 4.04 0.78
Satisfaction 4.23 0.79
Continuance Intention 4.28 0.83
VIII. FINDINGS AND DISCUSSION
Demographic InformationOut of 509 aggregate reactions, 349 (68.6%) were students while 160 (31.4%) were postgraduate understudies.
253 respondents (49.7%) were male and the other 256 (50.3%) were female. Reactions were assembled from 18 resources in the University of Malaya that incorporate science and expressions and sociology train. A high number of reactions were gotten from three resources which gave over 49% of the general reaction. The workforce of Science came beat with 106 (20.8%), Faculty of Engineering with 94 reactions (18.5%), and Faculty of Computer Science and Information Technology with 51 (10.0%). The number or respondents from every workforce is an impression of its all-inclusive community.
Table 5:
Spearman's rho
Confirmation Satisfaction Continuance Intention
Perceived Control
Subjective Norms
Perceived Usability
Perceived Quality Confirmation 1.000 .750** .594** .451** .517** .564** .667**
Satisfaction .750** 1.000 .743** .474** .495** .602** .684**
Continuance Intention
.594** .743** 1.000 .467** .493** .573** .590**
Perceived Control
.451** .474** .467** 1.000 .461** .501** .508**
Subjective Norms
.517** .495** .493** .461** 1.000 .533** .550**
Perceived Usability
.564** .602** .573** .501** .533** 1.000 .536**
Perceived Quality
.667** .684** .590** .508** .550** .536** 1.000
.Correlation is significant at the 0.01 level (2-tailed)
IX. E-LEARNING USAGE
As far as digital book utilization, 184 (36.1%) respondents revealed that they utilized digital book at any rate once per week; 117 (23.0%) said that they read digital book in any event once every month while just 95 (18.7%) individuals utilized digital book regularly; and 113 respondents (22.2%) infrequently read digital book.
When contrasting amounts of E-learning read in the previous 6 months, 305 (59.9%) of the respondents read up to 10 E-learning , 132 (25.9%) read in the vicinity of 11 and 30 E-learning , while the adjust of 72 (14.2%) of them were perusing more than 30 E-learning amid the period.
At the point when gotten some information about how they became more acquainted with about digital book, 312 (61.3%) of the respondents knew from the college library while 275 (54.0%) respondents caught wind of it from their nearby family, companions, and partners; 172 (33.8%) of them knew about digital book from their instructors; and 149 (29.3%) of them just knew about digital book from the news and magazines (printed media). Another 81 (15.9%) respondents provided other resources for example from online interactions, various s and search engines.
For e-book category, 395 (77.6%) respondents usually downloaded self-managed e-books from the Internet or other sources; 229 (45%) of them downloaded from other providers such as Amazon, Google Books, iBook, Kobo and Scribd; 204 (40.1%) of them read e-books from the e-book service subscribed by the university
library. Regarding devices used to access e-books, 460 (90.4%) respondents used computers and laptops; 187 (36.7%) of them used smartphones to access while some 113 (22.2%) also used tablets like iPad, Samsung Galaxy, and Playbook. Only 20 (3.9%) respondents used dedicated e-book readers such as Amazon Kindle, Nook and Sony Reader.
Table 6: Result of Regression Analysis for the Research Model
The last research model is appeared in Figure 2 demonstrated that Satisfaction, Perceived Usability, and Subjective Norms altogether had influenced on Continuous Intention of utilizing e-book. In spite of the fact that Confirmation, Perceived Quality, and Perceived Control are not measurably critical in the regression test, there are huge relationships between these elements with e-book Continuous Intention usage. In general, Continuance Intention to use e-book could be derived from user experience where fulfillment of e-book service influences user to constantly use e-book as their preference. Fulfillment has real effect in the intention of constant usage as reported by Zhao and Cao (2012). Therefore Continuance Intention to use e-book is driven by Satisfaction, Perceived Usability, and Subjective Norms that could be used to predict user decision to constantly use the E- learning.
R2 = .582, Adjusted R2 = .577, p< .01, F = 116.321
Figure 2: Final Research Model with Factors that significantly influenced Perpetuation
intention of using e-learning
X. CONCLUSION
This research recognizes factors that affect perpetuation intention to use e-learning among Universities students In Nigeria. The conclusions are useful to provide information to both library and e-learning service providers to improve their services. In that for students to continually using e-learning, it would be strong-minded by several factors such as Fulfillment, Confirmation, Observed Usability, Observed Control and Observed feature.
Although conclusions have supported the hypotheses, further investigations may be conducted to focus on an individual factor or on another specific group of user. This conclusion would be useful and beneficial for e- learning implementation in Universities setting. Since the data gathered are adequate to demonstrate significance discoveries, these recommendations would hopefully bring some progress in the services and on e- learning provision in Universities. Having said that, this study does not refer to any specific e-learning systems or services, as the respondents generally used several e-learning types and systems or platforms. Therefore, this finding is only limited to the reflection of their experiences.
For further and stronger recommendation of the determinant for perpetuation intention to use e-learning, more empirical works and replication of this research need to be carried out which focus on certain type of e-learning system or service, as well as on a different group of the population. This could map and identify the antecedent of perpetuation intention to use a particular e-learning system for certain group of user. It could also use the same research model for a different system or technology implementation in information science research. This would enrich the knowledge and findings of different disciplines and could also provide different determinant pattern especially for perpetuation intention to use certain system or technology.
REFERENCES
[1] Bharati, P. and Chaudhury, A. 2004. An empirical investigation of decision-making fulfillment in web-based decision support systems. Decision support systems,
[2] Bhattacherjee, A. 2001. Understanding information systems continuance: an expectation-confirmation model. MIS quarterly,
[3] Borchert, M., Hunter, A., Macdonald, D. and Tittel, C. 2009. Study on student and staff awareness, acceptance and usage of e-learnings at two Queensland universities. In 14th ALIA Information Online Conference & Exhibition, 20-22 January 2009, Darling Harbour Exhibition and Convention Centre, Sydney. Available at: http://eprints.qut.edu.au/20379/.
[4] Afari-Kumah, E & Achampong, A. (2010). Modelling computer usage intentions of tertiarystudents in a developing country through the Technology Acceptance Model. International Journal of Education and Development using Information and Communication Technology (IJEDICT), 6, 1, 1-15.
[5] Saade, R., Nebede, F & Tan, W (2007). Viability of the “Technology Acceptance Model” inmultimedia learning environments: A comparative study. Interdisciplinary Journal ofKnwoledge and Learning Objects, 3, 175-183.
[6] Selim, H. (2007). Critical success factors for e-learning acceptance: Confirmatory factor models.
Computer & Education, 49, 396-413.
[7] Teo, T. (2010). Establishing gender structural invariance of Technology Acceptance Model (TAM). The Asia-Pacific Education Researcher, 19, 2, 311-320.
[8] University of Ghana (2007) ICT Strategic Plan (2005-2010). Version 3.0. October.
[9] Yee, H., Luan, W., Ayub, A & Mahmud, R. (2009). A review of the literature: determinants ofonline learning among students. European Journal of Social Sciences, 8, 2, 246-252.
