P r o c e d i a - S o c i a l a n d B e h a v i o r a l S c i e n c e s 1 1 6 ( 2 0 1 4 ) 1 3 7 8 – 1 3 8 2
1877-0428 © 2013 The Authors. Published by Elsevier Ltd. Open access under CC BY-NC-ND license.
Selection and/or peer-review under responsibility of Academic World Education and Research Center.
World Conference on Educational Sciences -WCES 2013
Acceptance and Intention to Use the iLearn System in an Automotive
Semiconductor Company in the Northern Region of Malaysia
, Mona Masood
Centre for Instructional Technology and Multimedia, Universiti Sains Malaysia, Malaysia
This study investigates the factors affecting employees’ acceptance and intention to use the e-Learning system called the iLearn. Combining the
Innovation Diffusion Theory (IDT) with Technology Acceptance Model (TAM), the present study proposes an extended technology acceptance
model. The proposed model was tested with data collected from 100 employees with various position backgrounds from one of the Automotive
Semiconductor Company in Northern Malaysia who currently uses the iLearn (an e-Learning gateway) training system. The results indicated
gender, age, position and comfortableness of using computer are the factors affecting the employees’ acceptance and intention towards the use
of the iLearn system. The employees behavioural intention (BI) was shown to be significantly influenced by perceived usefulness (PU) and
perceived ease of use (PEU) towards the iLearn system. Specifically, IDT has been utilized to examine factors affecting the adoption of the
iLearn system by employees for the purpose of job enhancement. The relative advantage (ADV) shows a positive effect on the PU, PEU and
BI. Meanwhile, the result shows that compatibility (CPA) had negative effect on the PU, PEU and BI. Meanwhile, trialability (TRI) and
complexity (CPL) had positive effect on PEU, but negative effect on PU and BI. Observability (OBS) had positive effect on PU but negative
effect on PEU and BI. These findings suggest that Human Resource Training and Development of the company should consider factors of
technology acceptance and employee’s intention towards the iLearn system to enhance their skills and knowledge in order to achieve excellent
Keywords: Technology Acceptance Model, Innovation Diffusion Theory, iLearn, Training
In current globalization, the Training & Development Department (T&D), like all other departments within organizations,
are undergoing vital transformations in the quest to stay competitive in today’s global economy. The main objective of the T&D
is to help develop key competencies which enable employees to perform current or future jobs successfully. This can be done
through strengthening the job skills/knowledge of employees, improving operational efficiency and productivity and developing
the potential of employees to maximize mutual benefit of individuals and the company itself. In order to achieve these objectives
most of the corporate sectors are integrating various types of “technology-enabled learning” interventions in employee’s learning
and development activities, one of which is e-Learning (Borotis & Poulymenakou, 2009). Therefore, in this context, e-Learning
has gained attention as a means for delivering education more efficiently.
In the last few years, companies across the globe realize the benefits of providing online training to employees. People have
been identified as the key to the success of organizations and businesses in the knowledge-based economy. Thus, to succeed in
such an economy, organizations and businesses need to recruit, retain, and update highly skilled people (Harun, 2002). The
present challenge is more than moving learning seamlessly through an enterprise; it is to ensure that the right skills and
competencies play key elements of the organization. To thrive in such situations, organizations need to provide the means to
develop the skills and education to workers. Driving each of these developments, combined with the new technological
infrastructure, e-Learning has been identified as the enabler for people and organizations to keep up with changes.
Currently there are many e-Learning software systems developed to be adapted by corporate sectors to train their employees
via online basis. In this study, an e-Learning system known as iLearn has been selected. This system is used by one of the leading
* Mona Masood. Tel.: +604-653-2619
E-mail address: firstname.lastname@example.org
Available online at www.sciencedirect.com
© 2013 The Authors. Published by Elsevier Ltd. Open access under CC BY-NC-ND license.
Selection and/or peer-review under responsibility of Academic World Education and Research Center.
Rekah Veloo and Mona Masood / Procedia - Social and Behavioral Sciences 116 ( 2014 ) 1378 – 1382
automotive semiconductor companies in the Northern Region of Malaysia. This iLearn system has been established in this
organization since 2008.
The iLearn system is a web application which allows employees to view or take part in e-Learning modules as part of their
skill and knowledge enhancement. This is required to support and secure business success through an efficient, transparent and
homogeneous training and development infrastructure. If the employees are well educated and aware about the company’s status,
they will be motivated to perform better. The reasons for using the iLearn system are because, the employees would be able to
learn any time and place by eliminating the need for physical attendance, strict scheduling and travel difficulties. The iLearn also
allow for instant scoring and feedback of results to learners or the employees and repeated access if they have to retrain
2. Statement of the Problem
Organizations today face a highly competitive global marketplace. Those organizations that can provide knowledge and
information on time to their employees, customers, partners, and suppliers expeditiously gain a competitive advantage. Therefore
iLearn gives an organization the tools to accomplish this goal. However, the success of an iLearn initiative depends as much on
the people and culture of the organization as it does on the technology used. This is the most important factor to ensure that the
iLearn cause significant growth in the organization such as cost reduction, improved quality of the product or services, reduced
employee turnover, or increased profitability (McIntosh, 2006). If learners fail to use the iLearn system, the benefits will not be
Numerous researches have been conducted and there is no doubt that e-Learning is able to demonstrate cost-savings and
broader benefits, develop integrated offerings, and propose innovative ways of applying e-Learning (Ong, Lai, & Wang, 2004;
Teodora & Nicolae, 2009). But an insufficient number of primary studies relates to the individual information systems
acceptance that integrates the organizational and work-context dimensions in e-Learning. Furthermore, the individual level,
perceived usefulness and ease of use, personal innovativeness with information technology, computer anxiety, self-efficacy, and
intrinsic motivation to learn affect in broad terms the attitude employees formulates towards e-Learning in the organization are
also rare in studies (Borotis & Poulymenakou, 2009).
The various aforementioned levels must be taken into account when designing and maintaining technology-supported
training interventions, in order to mitigate resistance and maximize the potential benefits. Therefore, the study is focused on the
acceptance and the intention to use the iLearn system in the automotive semiconductor company in the Northern Region of
Malaysia. Although the system has been established since 2008, the usage among the employees is still low. The iLearn usage
report (between 2008 till mid 2012) which was extracted from a few selected optional training modules in the iLearn system
shows that only 50 percent (400) of the employees accessed the iLearn system. Therefore, we need to investigate if the reasons
for non usage include diffusion issue, acceptance, and effectiveness of the e-Learning system.
This study aimed to determine how well employees in selected automotive semiconductor company in the Northern Region
of Malaysia accept and intend to use the iLearn as a training system of job enhancement modules. Specifically, the research
objectives of this study were to:
• Identify factors affecting the iLearn acceptance among the employees in an automotive semiconductor company.
• Investigate the existence of any relationship among the five characteristics of Innovation Diffusion Theory (IDT);
relative advantage (ADV), compatibility (CPA), complexity (CPL), trialability (TRI) and observability (OB) with Technology
Acceptance Model (TAM) variables; Perceived usefulness (PU), Perceived Ease of Use (PEU) and Behavioral Intention (BI) to
use the iLearn.
• Investigate the existence of any relationship between PU with employees’ BI to use the iLearn system.
3. Theoretical Framework
The theoretical framework for this study is build by combining Innovation Diffusion Theory (IDT) with the Technology
Acceptance Model (TAM). It was clearly explain that TAM has been widely used as the theoretical basis for many empirical
studies to explained the user‘s acceptance of Information System (IS) or information technology (IT) (Taylor & Todd, 1995;
Venkatesh & Davis, 2000). In the other hand, Agarwal (2000) explained that in IDT, “potential users make decisions to adopt or
reject an innovation based on beliefs that they form about the innovation”. As this study aim to investigate factors affectin
employee’s acceptance and intention to use the iLearn system, thus this study proposes integrated theoretical framework, which
In this framework we engaged five factors or variables to examine acceptances and intentions to use the selected iLearn
system: relative advantage (ADV), compatibility (CPA), complexity (CPL), trial ability (TRI) and observability (OB) as
determinants of perceived usefulness (PU), perceived ease of use (PEU) and behavioral intention to use (BI). According to Lee,
Hsieh, & Hsu (2011) this type of theoretical framework could be useful to develop and test the iLearn system, as well as to
experts for understanding strategies to design and promote the iLearn system. The theoretical framework for the study is shown
in Figure 1.3 which is adopted from Lee, Hsieh, and Hsu (2011).
1380 Rekah Veloo and Mona Masood / Procedia - Social and Behavioral Sciences 116 ( 2014 ) 1378 – 1382
In this theoretical framework, employees’ PU of the iLearn system is defined as the perception of degrees of improvement in
learning because of adoption of such a system. PEU of the iLearn system is the users’ perception of the ease of adopting the
iLearn system. We made assumptions that the more end-users who perceive usefulness of the iLearn system within an
organization, the more positive their acceptance of the iLearn system, consequently increasing their chances for future usage of
the iLearn systems (Arbaugh & Duray, 2002; Pituch & Lee, 2006). Furthermore, technology acceptance is determined by
behavioral intention to use (Ajzen & Fishbein, 1980). Therefore, within an organizational context adoption of the iLearn system
is a positive function of the behavioural intention to accept the systems.
Moore and Benbasat (1991) have stated that theoretically, the diffusion of an innovation perspective does not have any
explicit relation with the TAM, but both share some key constructs. The relative advantage construct in IDT is similar to the
concept of the PU in TAM, and the complexity construct in IDT captures the PEU in the technology acceptance model, although
the sign is the opposite. Simply, the less complex the system to use, the more likely an individual is to accept it. Compatibility is
connected with the fit of a technology with prior experiences, while the ability to try and observe are associated with the
availability of opportunities available for relevant experiences. These constructs relate to prior technology experience or
opportunities for experiencing the technology under consideration. Compatibility, and the ability to try and observe can be
treated as external variables, which directly affect the constructs in the technology acceptance model. After the initial adoption,
the effects of these three constructs could be diminished with continuous experience and reduced over time (Karahanna et al.,
Thus, in this research, by combining TAM with IDT characteristics, the additional research constructs can increase the
credibility and effectiveness of the study.
4.1. Research Procedure
The research procedure (Figure 1) were categorized into 5 steps, exploring related concept of the background study and
literature review, identify the sample for the research, prepare the instrument for survey questionnaire for data collection, validate
and check internal consistency of the questionnaire, analyze the data and finally discuss the result.
Figure 1. The Research Procedure
A random sample of 100 (10%) employees with various position backgrounds (Technician, Executive, Engineer, and
Manager) were invited to participate in answering the questionnaires which was posted online. All completed the survey
questionnaires and the data were analyzed.
The survey questionnaire consists of three sections. The first section is the demographic data, such as gender, educational
level, work experience, and prior experience using computers based on a 4-point Likert scale of “Strongly disagree” (1),
“Disagree” (2), “Agree” (3) or “Strongly Agree”(4) with given statements.
Section 2 of the questionnaire was based on the constructs of PU, PEU, BI in the TAM model and was adapted from the
measurement defined by Davis, Bagozzi, and Warshaw (1989) and Venkatesh and Davis (2000). There were 11 items for the
above constructs. The questions were updated according to the need of the iLearn system.
Rekah Veloo and Mona Masood / Procedia - Social and Behavioral Sciences 116 ( 2014 ) 1378 – 1382
Section 3 of the questionnaire was prepared based on IDT including CPA, CPL, ADV, OB, and TRI. Sixteen questions were
adapted from the previous studies and modified according to the context of the iLearn system (Davis, Bagozzi, & Warshaw,
1989; Karahanna, Straub, & Chervany, 1999; Moore & Benbasat, 1991; Taylor & Todd, 1995).
5.1. Employees Acceptance and Intention towards iLearn based on Gender
The sample consists of 56 (56%) males and 44 (44%) females. Results show that there were statistically significant
differences in the acceptance levels of employees based on gender. The PU means for the male is 3.14 and for female is 3.09.
This would indicate that males, t(56) = 45.50, p < .05, are significantly more positive in their rating of the usefulness of iLearn
more than females. Meanwhile, the mean of PEU for the female is 3.17 and male is 3.08. This would indicate that women, t(46)
= 46.72, p < .05 are significantly more positive in their perceptions of the PEU of iLearn system more than men. However, the
BI shows that the average scores for the male is 2.99 and for female is 2.80. This would indicate that males t(56) = 45.50, p <
.05 are significantly more positive in their rating of their intention to use iLearn again more than women.
Table 1. Employees Acceptance of iLearn based on Gender
95% Confidence Interval
Male 3.14 .48129 45.502 .000 3.002 3.275
Female 3.09 .55757 39.650 .000 2.931 3.240
Male 3.08 .36960 46.717 .000 2.961 3.200
Female 3.17 .53585 51.206 .000 3.036 3.305
Male 2.99 .56315 36.322 .000 2.830 3.158
Female 2.80 .67941 30.142 .000 2.618 2.988
5.2. The relationships of IDT characteristics with the PEU, PU, and BI and the relationships between PU with BI towards the
The result shows that strongest positive correlation shared between OB and CPL, r(100) = 0.825, p < 0.01 and between CPL
and PEU was r(100) = 0.788, p < 0.01. However, correlation between TRI and CPL is relatively low compared to the other 7
variables, with r(100) = 0.282, p < .01.
The regression analyses were performed to examine the significance and strength of relationship in the research model.
Three dependent variables (PU, PEU and BI) were tested with independent factors (ADV, CPA, CPL, TRI, and OB) in the
model. The results showed that relative advantage (ADV) was significantly (p < .05) influenced by PU, β = .270, t(100) = 3.07,
p = .03, PEU, β = .243, t(100) = 2.56, p = .12, and BI, β = .425, t(100) = 4.07, p = .00. However, the compatibility (CPA) was
not influenced by PU, β = -.072, t(100) = -.737, p = .463, PEU, β = .022, t(100) = .203, p = .840, and BI, β = .156, t(100) =
1.38, p = .171, as the significances indicates p > .05. The results showed that CPL was significantly influenced by PEU, β =
.532, t(100) = 4.50, p = .001. However, CPL had negative effect on PU, β = -.009, t(100) = -
, p = .94 and BI, β = -.107,
t(100) = -.86, p = .387. Meanwhile, the TRI was found not significantly influenced by PU, β = .000, t(100) = .003, p = .998,
PEU, β = .089, t(100) = 1.36, p = .178, and BI, β = .069, t(100) = 1.03, p = .307, as the significances indicates p > .05.
Moreover, OB had a positive effect on PU, β = 0.387, t(100) = 3.73, p = .001. However, OB was found not significantly
influenced by PEU, β = .045, t(100) = .384, p = .702, and BI, β = .080, t(100) = .626, p = .533. The results showed that PU
significantly influenced behavioral intention β = .295, t(100) = 2.68, p = 0.09. The perceived ease of use was significantly
influenced by perceived usefulness β = 0.370, t(100)=p = 0.00.
PU was found to be determined by six variables (PEU, ADV, CPA, CPL, TRI, and OB), resulting in an R
of 0.74. This
meant that the above variables accounted for 74% of variance in PU. Likewise, PEU was found to be determined by five
independent variables (ADV, CPA, CPL, TRI, and OB), resulting in an R
of 0.67. This meant that the above variables explained
for 67% of variance in PEU. BI was determined by ADV, CPA, CPL, TRI, OB and PU, resulting in an R
=0.66. In other words,
the variables described above explained 66% of the variance of BI.
1382 Rekah Veloo and Mona Masood / Procedia - Social and Behavioral Sciences 116 ( 2014 ) 1378 – 1382
Figure 2. The Theoretical Framework with Significant Path
The importance of the five innovative characteristics in affecting behavioral intention had several implications for
researchers and practitioners. Firstly, TAM can be used as a cost-effective measurement to effectively predict the future use of
iLearn systems. Secondly, according to the innovation diffusion theory (Rogers, 2003), adoption is not a snapshot and one-time
decision, but rather a continuously staged process that can be investigated and boosted (Leonard-Barton, 1988). Potential users
must first learn about the innovative tool and be persuaded to try it out before they decide whether to adopt it. Therefore, this
study suggested that well-designed trainings should be provided for the employees to familiarize themselves with the
fundamental knowledge about how to use the iLearn systems as well as the trial opportunities to build a better understanding in
the operational functions. The trainers’ frequent demonstrations of the use of the iLearn system help the employees form positive
beliefs and attitudes, which in turn influences their behavioral intention and actual use of the iLearn systems. Meanwhile, the
trainers should introduce and describe the benefits of iLearn systems and their relevance to their job performances.
Additionally, training and development department and system designers of the iLearn systems should carefully consider the
needs of iLearn system for users or the employees and ensure that the e-learning systems effectively meet their job needs and
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