This document summarizes a study that investigated factors affecting acceptance of e-learning among students at Universiti Utara Malaysia (UUM). The study tested a model combining the Technology Acceptance Model (TAM) with variables from the Theory of Planned Behavior and Social Cognitive Theory. It found that perceived usefulness and perceived ease of use positively influenced e-learning usage, but social pressure and internet self-efficacy did not. The implications are that universities should focus on developing useful e-learning content to encourage student adoption. A survey of 256 business students found that most used e-learning a few times a week, indicating a moderate overall usage level.

1. 2nd International Malaysian Educational Technology Convention
An Investigation of Factors Affecting E-Learning Acceptance
Among Student In UUM: An Empirical Analysis
Khairol Anuar bin Ishak
College of Business (COB)
Universiti Utara Malaysia, 06010 Sintok, Kedah
khairol@uum.edu.my
Abstract
The purposes of this paper are to study the usage trend and determine the driving factors that effect
towards acceptance of e-learning in a public university. This paper present and empirically test a model of
Technology Acceptance Model (TAM) in e-learning acceptance. The model was combined with two
variables for social pressure and internet self-efficacy derived from Theory of Planned Behavior (TPB)
and Social Cognitive Theory (SCT) respectively. The nature of study for this research is quantitative
method. There are two types of research design been used in this research including descriptive and
causal (correlation) study. The model access the direct influence of perceived usefulness, perceived easy
to use, social pressure and internet self-efficacy towards e-learning usage among student in UUM. The
findings found that e-learning usage trend among student was found at moderate level. The results also
indicated that two prominent factors that derived from TAM; perceived usefulness and perceived easy to
use had direct and positive effects on e-learning usage. However, social pressure and internet self-
efficacy had insignificant relationship with e-learning usage. The implications drawn from this research is
important for the organisation especially the management team to focus on useful contents in order to
attract users to use and utilise e-learning.
Introduction
The National IT Agenda (NITA), launched in December 1996 by the National IT Council (NITC), provides
the foundation and framework for the utilisation of information and communication technology (ICT), to
transform Malaysia into a values-based Knowledge Society by the year 2020. NITA focuses on the
development of people, infrastructure, content and applications to create value; and provide equity and
access to all Malaysians. The agenda contained a broad outline for a national framework aimed at
providing balanced IT development for Malaysians, Malaysia's infrastructure and the applications found
within (Gilbert, 2001). The Strategic Agenda highlights the need to address five areas critical to our
migration to the E-World, namely E-Community, E-Public Services, E-Learning, E-Economy and E-
Sovereignty. (Maznah Buyung, 2001)
Universiti Utara Malaysia (UUM) had implemented Learning Management System LearnCare or e-
learning application as learning medium with student through intranet, extranet and Internet facilities.
UUM had appointed University Teaching and Learning Centre (UTLC) to implement e-learning in the
university through the Learningcare Learning Management System and related platforms. This system
became operational in May 2002/2003 Semester. UTLC operates wholly and collectively as a teaching
and learning unit of the university playing active roles related to the conduct of training and courses for
academic staff, such as those on evaluation, e-learning, and quality assurance. It is module within UUM
Group Web Communication application. E-learning allow instructors to create courses and student can
access their approach adopted by Learning Care for the creation of materials and uploading of
documents makes crating a portfolio in Learningcare an easy task. E-learning was fully implemented in
UUM throughout all academic staff started from January 2003 until December 2004.
Objectives of the Study
The objectives of this study are specifically:
• To identify the e-learning usage pattern among BBA programme student in UUM
• To determine the relationship between driving factors towards acceptance of e-learning
• To examine the predictor factors that will have direct effect towards acceptance of e-learning
Theory Development
The research was based on three major theories in Technology Adoption; Theory of Planned Behavior
(TBP), Technology Acceptance Model (TAM) Theory and Social Cognitive Theory (SCT). The four major
information issues that support the research questions for understanding and potentially predicting user

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acceptance and usage of e-learning will be presented. The four major areas from which the research
literature is drawn are the perceived of usefulness, perceived of easy to use, social pressure and internet
self-efficacy.
Resernberg (2001) defined e-learning as “the use of internet technologies to deliver a broad array of
solutions that enhance knowledge and performance”. Resenberg also stated that different organisational
define e-learning in various ways and this is usually a reflection of the organisational e-learning direction.
In the current study, the researcher defines e-learning as is a form of learning that can be delivered
electronically, in part or wholly via a web browser and it includes the delivery of content via the internet,
intranet or extranet which managed by UTLC, UUM. E-learning application and processes include web-
based learning computer based learning and digital collaboration.
Theoretical Framework
The Theory of Planned Behavior (TPB) was chosen as the guiding framework for the development the
research model. The TPB theorises that individual’s behavior (i.e. decision) was determined by perceived
behavioral control and behavior intention. TPB also has been applied to explain an individual’s adoption
and usage of new technology (e.g. Mathieson, 1991; Taylor and Todd, 1995; Venkatesh et al., 2000;
Bhattacherjee, 2001).TPB defines relationship between attitudes, norms, and perceived behavioral
control as determinants of intentions and behavior.
The important of subjective norm can be assumed to be related to an individualism-collectivism dimension
in Hofsfede’s framework (Igbaria & Iivari, 1995). Hofstede (1991, 1994) recognise that many popular
management and motivations theories such as Hertzberg’s and Maslow’s Hierarchy of needs theories
reflect the North American culture and argues that their applicability in other cultures is questionable.
Ahorani and Burton (1994) suggested that more research is needed to address the generalisability of
management science, where our knowledge in many ways is specific and limited to a given country or
culture.
Technology Acceptance Model (TAM) has been validated as powerful and parsimonious framework for
explaining the adoption of Information Technology (IT) by the users (Davis, 1989; Davis, Bagozzi and
Warshaw, 1989). TAM is an adaptation of TRA specifically tailored for modeling user acceptance of
information system (Davis et al. 1989). The two main construct of TAM are perceived of usefulness and
perceived ease of use.
Perceived usefulness is defined as the extent to which a person believes that using a particular system
will enhance his or her performance. Perceived ease to use is defined as the degree to which the
prospective user expects the potential system to be free of effort (Davis et al., 1989). TAM posits that
behavioral intention determined by actual system use and behavioral intention is determined by both
attitude and perceived usefulness. Based on the TAM, perceived of usefulness and perceived ease to use
both have effect on behavioral attention. Perceived of ease to use also have affect on perceived on
usefulness.
Gist (1989) suggests that self-efficacy is an important motivational variable, which influences individual
effect, effort persistence and motivation. Individual who feel less capable of handling a situation may
resist it because of feeling of inadequacy or discomfort which may result from expected changes (Igbaria
& Iivari, 1995). On the other hand, individuals with self-efficacy will perceive the system to be easy and
useful due to the effect of self-efficacy on the degree of effort, the persistence and the level of learning
which takes place and will be less resistant to changes (Bandura, 1977). Therefore, self-efficacy is likely
to effect belief and behavior.
In this study, the researcher will examine the factor of perceived behavior control which to internet self-
efficacy (ISE) towards the acceptance of e-learning. An important of theoretical of self-efficacy is that is
concerned not the skills a person has; rather, it is what individual believe they can do with the skill they
posses. In discussing ISE, Eastin and Larose (2000) distinguished between a person’s skill at performing
specific internet related tasks, such as writing HTML, using a browser, or transferring files and his or her
ability to apply skill in a more encompassing mode, such as finding information or troubleshooting search
problems.
User Acceptance and Usage of System
User acceptance is defined as “the demonstrable willingness within a user group to employ information
technology for the tasks it is designed to support” (Dillon & Morris, 1996, p.4). Although this definition
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focuses on planned and intended uses of technology, studies report that individual perceptions of
information technologies are likely to be influenced by the objective characteristics of technology, as well
as interaction with other users. For example, the extent to which one evaluates new technology as useful,
she/he is likely to use it).
System usage has been widely used in Information System (IS) research as an indicator of IS success
and computer acceptance. On the basis of previous studies on computer and information system
acceptance, the use of information system was chosen to be indicator for success (David et al., 1989;
Davis, 1993; Al-Ghathani, 2001). The e-learning usage was chosen as the dependent variables in the
model. This is in line with other studies, that in which actual usage has been selected as the measure of
use (Thompson et. al (1991), Davis (1993), Igbaria et al. (1996), Al-Ghatani & King (1999), Anandarajan
et al. (2000) and Legris, Ingham & Collerette, 2003).
Theoretical Framework
Based on the literature review, the proposed theoretical framework for the study can be shown in Figure
1. The research model for this study was derived from three theories of TAM (perceived usefulness;
perceived easy to use), TPB (social pressure) and SCT model (self efficacy). The student usage behavior
of e-learning was conceptualised to be a function of perceived of easy to use, perceived usefulness,
social pressure and internet self-efficacy.
The Hypotheses of Study
Based on the theoretical framework (figure 1), the following hypotheses were developed in order to relate
with the objectives of this study:
H1: Perceived usefulness will have a direct effect on user acceptance of e-learning
H2: Perceived ease of use will have a direct effect on user acceptance of e-learning
H3: Social pressure will have a direct effect on user acceptance of e-learning
H4: Internet Self-Efficacy will have a direct effect on user acceptance of e-learning
Methodology
The population for this research is BBA undergraduate fulltime final year student. The final year student
will select from semester 5 and the above. The student data will be generated from record of Academic
Affair Department, UUM. Krejcie & Morgan (1970) had simplified the sample size decision by providing a
table that ensures a good decision model. Based on table for the nearest population of 836, the proposed
sample size is 263. The details of final year undergraduate student for BBA programme was tabulated in
Table 1.
Independent Variables Dependent Variables
Perceived Usefulness
Perceived Ease to
Use
Acceptance e-learning
- Usage of e-learning
Social Pressure
Internet Self
-Efficacy
Figures 1: Theoretical framework for the study
Table 1 Population size for undergraduate BBA Programme for final year student
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(semester 5 and the above)
Gender No of student %
Male 216 26%
Female 620 74%
Total 836 100%
The student was selected based on non-probabilistic purposive sampling. The purposive sampling
method was used in sample selection to ensure that only students who have participated in the e-learning
programme in UUM were included. To qualify for inclusion, student must spend at least a semester in
university prior to the introduction of e-learning. This will ensure that respondent have fair knowledge of
university programme, infrastructure and academics to help in their judgment.
Instrument And Measurement
The structured of survey questionnaire adopted in this study will covered from five different topics which
include users usage behavior, user perceived ease to use, usefulness, social pressure, internet self-
efficacy and demographic measurement. Based on several studies (David et al. 1989; Thompson et al.
1991; Igbaria et al., 1996; Anandarajan et al. 2000), system usage was selected as the primary indicator
of e-learning acceptance. Two indicator of e-learning usage will be included in this study:
i) Self reported frequency of use e-learning
ii) The number of different e-learning applications used
The frequency of use will measure on six point of scale ranging from 1 (less than once a month) to 6
(several times per day). Number of e-learning applications usage will use as an indicator of overall
e-learning usage. Respondent shall be asked to indicate, on scale of 1 (not at all) to 5 (very intensively),
the extent to which they used the e-learning to perform nine (9) different applications programme of
e-learning; announcement, note, documents, forum, links, reference, news, assignment and online quiz.
The perceived of usefulness scales were adapted from Davis, at el. (1989) and Chau (1996). The items
used to construct this were adapted from prior research with modifications to make them relevant to e-
learning systems The scales of perceived ease to use were adapted from Davis, at el. (1989) and Moore
& Benbasat (1991). The user perceived of ease to use was measured by the four (4) items scale
developed by Davis (1989) with two (2) items added to the scale by Moore & Benbasat (1991).
The measures for social pressure were adapted from Igbaria et al. (1996) and Anandarajan et al. (2000).
The measures for social pressure was operationalised in accordance with guidelines suggested by
Fishbein & Ajzen (1975) and used in examining microcomputer usage (Igbaria et al., 1996 and
Anandarajan et al., 2000).
The Internet Self-efficacy (ISE) scale was adapted from Hsu & Chiu (2004). They were developed the
instrument of General Internet Self-efficacy which was adapted from Torkzadeh and Van Dyke’s ISE
instruments (2001) scale by considering its limitation and the processes involved in the WWW
applications. The ISE scale was identified 19 general processes that user usually perform activities on the
World Wide Web (WWW). The scales in this study were mainly used a five-point Likert-type scale (1,
strongly disagree; 2, disagree; 3, neutral; 4, agree; 5, strongly agree). Refer table 2 for the list of
instruments for research construct.
Table 2 Questionnaire Elements
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Variable No of item
E-learning Usage 11 items
Perceived Usefulness 6 items
Perceived Ease to use 6 items
Social Pressure 3 items
Internet Self-efficacy 19 items
Findings
Profiles of the respondent obtained which included gender, age, education level, semester and majoring
studies that were used in the current study is showed in Table 2. For the purpose of this study, only 256
from 270 were proceeding for data analysis, yielded the return rate of 94.8%. Another 14 questionnaires
were considered as damaged or incomplete appropriately by the respondent. According to David et al.
(1989), the respond rate of over 30% meets the requirement to run statistical tests. The details of
respondent are tabulated in Table 3.
Table 3 The profile of respondent
Variable Frequency Valid Percentage
Gender
Male 56 21.9%
Female 200 78.1%
Age
20-21 42 16.4%
22-23 194 75.8%
24-25 16 6.3%
Above 26 4 1.6%
Race
Malay 116 45.3%
Chinese 110 43.0%
Indian 27 10.5%
Others 3 1.2%
Semester
5-6 236 92.2%
7-8 19 7.4%
More than 11 1 0.4%
Source: Survey
User Usage Behavior of E-Learning
The findings for e-learning usage are tabulated in Table 7, 67% of respondents were found used e-
learning for a few times a week and 19% of them found used a few time a month. This finding explains
that the highest usage of e-learning is very high in week compare with the month. The highest usage of e-
learning was found in several times per day 3.9% and the lowest usage of e-learning is only 1.6% for less
than once month.
Table 4 Frequently used of e-learning
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Frequency use of e-learning Frequency Percent
less than once month 4 1.6
once a month 7 2.7
a few times a month 49 19.1
a few time s a week 171 66.8
about once a day 15 5.9
several times a day 10 3.9
Total 256 100.0
Source: Survey
The e-learning system that used in UUM was divided into 9 applications for online note, download
documents, assignment, news, announcement, online quiz, reference, website address link and on-line
forum. The usage trend for e-learning application were tabulated in Table 9 . From the table, the online
note which stated mean score 3.89 was identified the highest usage compared with others application in
e-learning system. Moreover, the application of document download which mean score 3.77 and the
assignment guideline with mean score 3.48 were identified as second and third highest usage of e-
learning.
Further, the application for news, announcement and on line quiz with mean score of 3.15, 3.12 and 2.96
respectively, were found in moderate level in user’s usage of e-learning. Finally, the application of
reference, website links and online forum with mean score 2.76, 2.45 and 1.99 respectively, were found
the low usage of e-learning.
Table 5 Mean Rating for the usage of application e-learning
Usage of e-learning Mean Ranking
Online notes 3.89 1
Download documents 3.77 2
Check Assignment 3.48 3
Check News 3.15 4
Check Announcement 3.12 5
Online quiz 2.96 6
Check References 2.76 7
Navigate the links 2.45 8
Participate online forum 1.99 9
Source: Survey
In conclusion, the finding shows that the average of e-learning usage by BBA student are in moderate
level (mean=3.141). Overall the usage of e-learning is not constant for all applications available in e-
learning portfolio. The finding also highlighted that the highest usage for e-learning application is on-line
notes. The second highest is download document that normally contains of slide presentation and
additional notes related the subject matters. Meanwhile, the application for checking the assignment, new
and announcement were found used at moderate level. Furthermore, the lowest usage for e-learning
application are online quiz, navigate the link, references and online forum.
The Pearson Correlation Coefficient analysis was conducted in order to verify the relationship of among
the variables and was used to reveal various “pattern” in the relationship between the usage behavior as
dependent variable and the users’ perceived easy to use, perceived usefulness, social pressure and inter
self-efficacy as dependent variables. The result of analysis is presented in Table 7.
Table 7 Correlation for independent and dependent variables
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Variables Perceived Perceived Social Internet Usage of e-
usefulness easy to Pressure Self- learning
use efficacy
Perceived 1
usefulness
Perceived easy to .420** 1
use
Social Pressure .483** .418** 1
Internet Self- .247** .380** .265** 1
efficacy
Usage of e- .367** .323** .270** .240** 1
learning
** Correlation is significant at 0.01 level (2 tailed).
Based on the result as per above table, the relationship between perceived easy to use, perceived
usefulness, social pressure and Internet self-efficacy are statistically show significant correlated on usage
behavior of e-learning. The result based on Table 7, stated that user acceptance of e-learning is positively
correlated with perceived usefulness, perceived easy of use, social pressure and internet self-efficacy.
(r =0.367, 0.323, 0.270 and 0.240, P = 0.001, respectively).
Moreover, perceived usefulness was found to be strongly correlated with e-learning usage. However
based on rules of thumb by Burns and Bush (2000), all significant relationships show a very weak
correlation on usage behavior of e-learning with the r value range between “0.367 to 0.240”. All the
significant relationships show a positive correlation with dependent variable (usage of e-learning).
In conclusion, all independent variables showed significantly correlated with user acceptance of e-
learning. However, the finding also showed that all independent variables had weak relationship with
user’s acceptance of e-learning. The finding also supported that perceived usefulness and perceived easy
of use play an important determinant in e-learning acceptance. (Davis et al., 1989; Igbaria and Iivari,
1995; Igbaria at el., 1996; Lopez and Manson, 1997)
Regression Analysis
Based on table 8, the result from multiple regression has shown that the model provide a little support to
all the hypothesis relationship, which indicated that 18.2% of independents variables collectively
explaining the variation in the e-learning usage. The standardised beta coefficient between user’s
perceived usefulness and E-learning acceptance is 0.248 (p < 0.01). This provides strong support for
hypothesis H1. The greater the user’s perceived of usefulness, the more extensively an application of e-
learning being used by student. This finding is consistent with previous studies which indicate that
perceived usefulness play an important factor that affect usage behavior (e-learning). (Davis et al., 1989;
Igbaria and Iivari, 1995; Igbaria at el., 1996; Lopez and Manson, 1997).
Table 8 Summary of Multiple Regression Result. Model Summary
Adjusted Std. Error of
Model R R Square R Square the Estimate
Model
.426(a) .182 .169 .49917
ANOVA b
Sum of
Squares df Mean Square F Sig.
Regression 13.879 4 3.470 13.925 .000(a)
Residual 62.541 251 .249
Total 76.419 255
Coefficients a
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Unstandardised Standardised
Coefficients Coefficients t Sig.
B Std. Error Beta
(Constant) 0.618 0.355 1.739 0.083
Perceived Usefulness 0.287 0.079 0.248 3.654 0.000
Perceived ease of use 0.190 0.084 0.155 2.273 0.024
Social Pressure 0.063 0.075 0.057 0.841 0.401
Internet Self-Efficay 0.138 0.082 0.105 1.679 0.094
a
Notes: Predictors: (Constant), perceived useful, perceived easy of use, social pressure and internet
self-efficacy.
b
Dependent Variable: E-learning Usage
The standardised beta coefficient between user’s perceived ease of use and e-learning usage is 0.155
(p<0.05). Thus, the result supported the H2. That mean that the greater of perceived of ease of use, the
more extensively an application is used for e-learning. Thus, the finding is consistent with prior studies
that identified perceived ease of use as determinants factor for usage behavior. (e-learning). (Davis,
1989; Davis, et., 1989; Moore and Benbasat, 1991; Adams at el., 1992; Igbaria at el., 1997).
The standardised beta coefficient between social pressure and e-learning usage is 0.057 (p<0.05).Thus,
the result not supported the H3. The study did not show any statistically significant relationship between
social pressure and user’s usage of e-learning. Thus, the finding is inconsistent from previous studies
done by Igbaria at el., 1996 and Anandarajan et al., 2000. Igbaria at el. (1996) found that social pressure
is positively related to microcomputer usage and identified its’ effect on usage is much smaller.
Additionally, Anandarajan et al., (2000) found that the social pressure is a strong motivating factor of
microcomputer usage in Nigeria.
The standardised beta coefficient between user’s internet self-efficacy and e-learning usage is 0.105
(p<0.05).Thus, the result was not supported the H4. That self efficacy was found statistically insignificant
relationship with e-learning usage. Surprisingly, the finding is contrary to the previous study done by
Eastin and LaRose (2000) that found the Internet self-efficacy have significant relationship with internet
usage.
In conclusion, the findings from regression analysis show that, perceived usefulness was significantly
strongly linked to e-learning usage than was perceived easy of use. Thus, the hypothesis H1 and H2 are
substantiated and demonstrated that user perceived usefulness and perceived easy of use plays
important roles and were significant independent variables for user acceptance of e-learning. Meanwhile,
another hypothesis for H3 (social pressure) and H4 (internet self-efficacy) were found not significant with
e-learning usage.
Discussion
In conclusion, the findings from regression analysis show that, perceived usefulness was significantly
strongly linked to e-learning usage than was perceived easy of use. Thus, the hypothesis H1 and H2 are
substantiated and demonstrated that user perceived usefulness and perceived easy of use plays
important roles and were significant independent variables for user acceptance of e-learning. Meanwhile,
another hypothesis for H3 (social pressure) and H4 (internet self-efficacy) were found not significant with
e-learning usage.
User Perceived Usefulness and Acceptance of E-Learning
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The study was found that user perceived usefulness have a strong effect and positive relationship with
usage of e-learning. The finding form this research was confirms with Davis (1989), that the most
significant finding is relatively strength of the usefulness-usage relationship compared to the ease of use-
usage relationship. The finding also confirm that perceived usefulness is the strongest predictor of user
acceptance across a diverse area of research settings at the expense of perceived ease of use and
perceived enjoyment (Adam et. al. 1992; Taylor and Todd 1995; Venkatesh and Davis 2000).
User Perceived Easy of Use and User Acceptance of E-Learning
Perceived easy of use was found positively relationship and significant effect with usage of e-learning.
Perceived easy of use was found second stronger than perceived of usefulness with relationship usage of
e-learning. This finding was aligned with Anandarajan et al. (2000) that found easy to use is play the
important factors affecting system usage in Nigeria. The finding also aligned with Coopers (1997) which
founds that ease of adoption as one the important characteristic form customer’s perspective for adoption
of innovative services.
Social Pressure and User Acceptance of E-Learning
Social pressure was found positively and significant correlate with the usage of e-learning. Social
pressure was found not significant relationship towards e-learning usage. It means that users’ decision to
use e-learning were not influenced by student peer, lecturer and UUM in general. The finding for social
pressure was not inline with finding Anandarajan et al. (2000) who found that social pressure is motivating
factor of microcomputer usage in Nigeria. The finding also not consistent with previous study that (Igbaria
at el., 1996; Malhotra and Galletta,1999) that found that social pressure is positively related to usage
behavior of new technologies.
The possible explanation with the result of social pressure did not have a significant with e-learning
acceptance is that because the implementation of e-learning has past the early stage of innovation
diffusion process in which social influences have significant effect on usage. Based on theory of
innovation diffusion theory, an IT adoption creates uncertainty about it expected consequences for the
potential adopters. Since the level of uncertainty declines and individual move through stage of the
adoption process, the impact of social norms will declined and diminish to non significant over the time
(Hsu and Chiu, 2004).
The finding was inline with Karahanna at el. (1999) that found subjective norm for past-adoption user
does not have a significant relationship with intention to continue using Microsoft Windows and for
potential adopters the subjective norm was found significant effect on intention to adopt Windows. This
finding may suggest that social pressures from organisation environment may be effective mechanism to
overcome adopter initial in adopting IT.
The use of social norm may be important in inducing initial use and subsequent development of
perceptions (Argawal & Prasad, 1997). In addition, Thompson at el. (1994) found that the influence of
social norms and affect on usage were greater for inexperienced than for users. Thus, this finding was
supported with past studies that social pressure was not important factor for student to continuously use
the e-learning in UUM.
Internet Self-Efficacy and User Acceptance of E-Learning
Internet self-efficacy was found positively and significant correlate with the usage of e-learning. However,
the Internet self–efficacy was found insignificant relationship with usage of e-learning compared with other
variables. The result of this research was found contrary with previous studies that supported for the
relationship between self-efficacy and decision involving computer usage and adoption (Compeau &
Higgins, 1995; Compeau & Higgins, 1999; Igbaria & Iivari, 1995).
Anyway, the finding was found to be partly supported by Igbaria & Iivari (1995) that found self-efficacy
had no direct effect on usage but it had a strong indirect effect on usage, mainly through perceived ease
of use and usefulness. Compeau and Higgins (1999) suggested that the linkages between cognitive
factors (self-efficacy with outcome expectations), affective factors (affect and anxiety) and behavioral
reactions (system usage) to information technology.
In conclusion, the finding found that internet self-efficacy showed that no influence with e-learning usage.
Its mean that the student either low or high internet self-efficacy will have no affect to use the e-learning.
Even though, self-efficacy was found not significant with system usage in this study, but prior Information
System research that has found a significant effect of computer self-efficacy on behavioral intention to
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use through user’s perceived of easy of use and perceived usefulness. (Davis, 1989; Mathieson, 1991;
Wang et al., 2003). Thus, self-efficacy shall consider as an important factor for individual differences in
order to influence outcomes expectation towards usage behavior and user acceptance.
Conclusion
The result of statistical analysis was conducted on the four factors indicate that perceived usefulness and
perceived easy of use were found to be most influential factors explaining the use of e-learning. This
finding refers to the fact that student who used e-learning for the benefits and convenience rather than
traditional method. This finding is in line with other TAM studies (e.g. Davis, 1989; Davis et al., 1989)
which found that PEOU has less impact on technology acceptance than PU. PEOU was found the
second influential factor explaining the user acceptance of e-learning. Thus, the easier the technology is
to use and the more useful it is perceived to be, the usage of technology shall be increased.
Meanwhile, the social pressure and internet self efficacy were found to have relatively weak relationship
with the e-learning acceptance. Furthermore, social pressure and internet self-efficacy were found did not
have a significant direct effect on e-learning usage. For social pressure, it means that student’s decisions
to use in e-learning usage are not influenced by peers, lecturer and university. Thus the finding in this
study was fully supported TAM which suggested that PEOU and PU are the most important factors in
explaining system use. The finding was able to determinant factors for e-learning acceptance among BBA
students in UUM. However, future research shall be conducted to find the unexplained of 80% for
additional variables to understand and explain use behavior in e-learning acceptance.
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