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International Journal of Innovative Technology and Creative Engineering (ISSN:2045-8711) vol.4 issue. 5
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INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY AND CREATIVE ENGINEERING (ISSN:2045-8711) VOL.4 NO.5 MAY 2014
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INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY AND CREATIVE ENGINEERING (ISSN:2045-8711) VOL.4 NO.5 MAY 2014
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UK: Managing Editor
International Journal of Innovative Technology and Creative Engineering 1a park lane, Cranford London TW59WA UK E-Mail: [email protected] Phone: +44-773-043-0249
USA: Editor
International Journal of Innovative Technology and Creative Engineering Dr. Arumugam Department of Chemistry University of Georgia GA-30602, USA. Phone: 001-706-206-0812 Fax:001-706-542-2626
India: Editor
International Journal of Innovative Technology & Creative Engineering Dr. Arthanariee. A. M Finance Tracking Center India 17/14 Ganapathy Nagar 2nd Street Ekkattuthangal Chennai -600032 Mobile: 91-7598208700
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INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY AND CREATIVE ENGINEERING (ISSN:2045-8711) VOL.4 NO.5 MAY 2014
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IJITCE PUBLICATION
International Journal of Innovative Technology & Creative Engineering
Vol.4 No.5
May 2014
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INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY AND CREATIVE ENGINEERING (ISSN:2045-8711) VOL.4 NO.5 MAY 2014
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From Editor's Desk
Dear Researcher, Greetings! Research article in this issue discusses about motivational factor analysis. Let us review research around the world this month; Researchers in California have identified a plausible biomarker that can be used to predict long-term MS progression. Using proton magnetic resonance spectroscopy (MRS), researchers measured metabolite abnormalities known to be associated with neurodegeneration. N-acetylaspartate (NAA) is an amino acid generated by mitochondria that is confined almost exclusively to neurons and axons in the CNS. A reduction of NAA will occur when there is loss of neurons and axons—thus reflecting neurodegeneration—but may also decrease when there is potentially reversible neuroaxonal dysfunction. Myo-inositol (mI) is generated by glial cells, in particular astrocytes. An increase of mI in white matter lesions and normal-appearing white matter (NAWM) in MS may reflect astrocyte proliferation. International Energy Agency (IEA), published Monday, looks at the cost of converting to green electricity in order to keep global warming below 2 °C. The agency found that it will cost $44 trillion more than keeping the current mix, where most electricity comes from fossil fuels. The extra money will go on building wind and solar power stations, efficient grids, electric vehicles and infrastructure. Although going green costs more upfront, it brings long-term savings. That is primarily because a more efficient infrastructure uses less fuel. The IEA says that, at $115 trillion by 2050, those savings more than offset the initial investment. Google unveils design for its own self-driving car. Revealed at the inaugural Code Conference in California this week, Google's two-person driverless car is an all-electric design with a top speed of 25 miles an hour. It incorporates a range of pedestrian, road and environmental sensing technologies based on lasers, radar and cameras. The firm has perfected these during 1 million kilometres of autonomous driving tests on the streets of California.Driverless operation seems to be a natural evolution of current systems like lane-keeping technology, adaptive cruise control and self-parking, which can all be activated at the touch of a button. It has been an absolute pleasure to present you articles that you wish to read. We look forward to many more new technologies related research articles from you and your friends. We are anxiously awaiting the rich and thorough research papers that have been prepared by our authors for the next issue. Thanks, Editorial Team IJITCE
INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY AND CREATIVE ENGINEERING (ISSN:2045-8711) VOL.4 NO.5 MAY 2014
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Editorial Members
Dr. Chee Kyun Ng Ph.D Department of Computer and Communication Systems, Faculty of Engineering, Universiti Putra Malaysia,UPM Serdang, 43400 Selangor,Malaysia. Dr. Simon SEE Ph.D Chief Technologist and Technical Director at Oracle Corporation, Associate Professor (Adjunct) at Nanyang Technological University Professor (Adjunct) at Shangai Jiaotong University, 27 West Coast Rise #08-12,Singapore 127470 Dr. sc.agr. Horst Juergen SCHWARTZ Ph.D, Humboldt-University of Berlin, Faculty of Agriculture and Horticulture, Asternplatz 2a, D-12203 Berlin, Germany Dr. Marco L. Bianchini Ph.D Italian National Research Council; IBAF-CNR, Via Salaria km 29.300, 00015 Monterotondo Scalo (RM), Italy Dr. Nijad Kabbara Ph.D Marine Research Centre / Remote Sensing Centre/ National Council for Scientific Research, P. O. Box: 189 Jounieh, Lebanon Dr. Aaron Solomon Ph.D Department of Computer Science, National Chi Nan University, No. 303, University Road, Puli Town, Nantou County 54561, Taiwan Dr. Arthanariee. A. M M.Sc.,M.Phil.,M.S.,Ph.D Director - Bharathidasan School of Computer Applications, Ellispettai, Erode, Tamil Nadu,India Dr. Takaharu KAMEOKA, Ph.D Professor, Laboratory of Food, Environmental & Cultural Informatics Division of Sustainable Resource Sciences, Graduate School of Bioresources, Mie University, 1577 Kurimamachiya-cho, Tsu, Mie, 514-8507, Japan Mr. M. Sivakumar M.C.A.,ITIL.,PRINCE2.,ISTQB.,OCP.,ICP Project Manager - Software, Applied Materials, 1a park lane, cranford, UK Dr. Bulent Acma Ph.D Anadolu University, Department of Economics, Unit of Southeastern Anatolia Project(GAP), 26470 Eskisehir, TURKEY Dr. Selvanathan Arumugam Ph.D Research Scientist, Department of Chemistry, University of Georgia, GA-30602, USA.
Review Board Members
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Prof & Head,Depaartment of CSE/IT, INFO Institute of Engineering,Coimbatore.
Dr. T. Christopher, Ph.D.,
Assistant Professor & Head,Department of Computer Science,Government Arts College(Autonomous),Udumalpet, India.
Dr. T. DEVI Ph.D. Engg. (Warwick, UK),
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Dr. Renato J. orsato
Professor at FGV-EAESP,Getulio Vargas Foundation,São Paulo Business School,Rua Itapeva, 474 (8° andar),01332-000, São Paulo (SP), Brazil
Visiting Scholar at INSEAD,INSEAD Social Innovation Centre,Boulevard de Constance,77305 Fontainebleau - France
Y. Benal Yurtlu
Assist. Prof. Ondokuz Mayis University
Dr.Sumeer Gul
Assistant Professor,Department of Library and Information Science,University of Kashmir,India
Dr. Chutima Boonthum-Denecke, Ph.D
Department of Computer Science,Science & Technology Bldg., Rm 120,Hampton University,Hampton, VA 23688
Dr. Renato J. Orsato
Professor at FGV-EAESP,Getulio Vargas Foundation,São Paulo Business SchoolRua Itapeva, 474 (8° andar),01332-000, São Paulo (SP), Brazil
Dr. Lucy M. Brown, Ph.D.
Texas State University,601 University Drive,School of Journalism and Mass Communication,OM330B,San Marcos, TX 78666
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Katedra materiálu a strojírenské technologie Technická fakulta,Ceská zemedelská univerzita v Praze,Kamýcká 129, Praha 6, 165 21
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Professor - Department of Computer Science and Engineering,Tagore Engineering College, Chennai
Dr. H. S. Fadewar B.sc,M.sc,M.Phil.,ph.d,PGDBM,B.Ed.
Associate Professor - Sinhgad Institute of Management & Computer Application, Mumbai-Banglore Westernly Express Way Narhe, Pune - 41
Dr. David Batten
Leader, Algal Pre-Feasibility Study,Transport Technologies and Sustainable Fuels,CSIRO Energy Transformed Flagship Private Bag 1,Aspendale,
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(MTech & PhD(IITM);Ex-Faculty (Curtin Univ Tech, Perth, Australia))Scientist CLRI (CSIR), Adyar, Chennai - 600 020,India
Miss Jing He
PH.D. Candidate of Georgia State University,1450 Willow Lake Dr. NE,Atlanta, GA, 30329
Jeremiah Neubert
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Hui Shen
Mechanical Engineering Dept,Ohio Northern Univ.
Dr. Xiangfa Wu, Ph.D.
Assistant Professor / Mechanical Engineering,NORTH DAKOTA STATE UNIVERSITY
Seraphin Chally Abou
Professor,Mechanical & Industrial Engineering Depart,MEHS Program, 235 Voss-Kovach Hall,1305 Ordean Court,Duluth, Minnesota 55812-3042
Dr. Qiang Cheng, Ph.D.
Assistant Professor,Computer Science Department Southern Illinois University CarbondaleFaner Hall, Room 2140-Mail Code 45111000 Faner Drive,
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Y. Benal Yurtlu
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Dr. Paul Koltun
Senior Research ScientistLCA and Industrial Ecology Group,Metallic & Ceramic Materials CSIRO Process Science & Engineering
Dr.Sumeer Gul
Assistant Professor,Department of Library and Information Science,University of Kashmir,India
Dr. Chutima Boonthum-Denecke, Ph.D
Department of Computer Science,Science & Technology Bldg., Rm 120,Hampton University,Hampton, VA 23688
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Dr. Renato J. Orsato
Professor at FGV-EAESP,Getulio Vargas Foundation,São Paulo Business School,Rua Itapeva, 474 (8° andar)01332-000, São Paulo (SP), Brazil
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Department Head-Electronics Engineering Technology Dept.School of Engineering Technology ECPI College of Technology 5501 Greenwich Road -
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Assistant Professor of Computer Science, Government Arts College(Autonomous), Salem-636 007, India.
Dr. P. Kamakkannan,M.C.A., Ph.D .,
Assistant Professor of Computer Science, Government Arts College(Autonomous), Salem-636 007, India.
Dr. V. Karthikeyani Ph.D.,
Assistant Professor of Computer Science, Government Arts College(Autonomous), Salem-636 008, India.
Dr. K. Thangadurai Ph.D.,
Assistant Professor, Department of Computer Science, Government Arts College ( Autonomous ), Karur - 639 005,India.
Dr. N. Maheswari Ph.D.,
Assistant Professor, Department of MCA, Faculty of Engineering and Technology, SRM University, Kattangulathur, Kanchipiram Dt - 603 203, India.
Mr. Md. Musfique Anwar B.Sc(Engg.)
Lecturer, Computer Science & Engineering Department, Jahangirnagar University, Savar, Dhaka, Bangladesh.
Mrs. Smitha Ramachandran M.Sc(CS).,
SAP Analyst, Akzonobel, Slough, United Kingdom.
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Director, Department of MCA, Vivekanandha Business School For Women, Elayampalayam, Tiruchengode - 637 205, India.
Mr. M. Moorthi M.C.A., M.Phil.,
Assistant Professor, Department of computer Applications, Kongu Arts and Science College, India
Prema Selvaraj Bsc,M.C.A,M.Phil
Assistant Professor,Department of Computer Science,KSR College of Arts and Science, Tiruchengode
Mr. G. Rajendran M.C.A., M.Phil., N.E.T., PGDBM., PGDBF.,
Assistant Professor, Department of Computer Science, Government Arts College, Salem, India.
Dr. Pradeep H Pendse B.E.,M.M.S.,Ph.d
Dean - IT,Welingkar Institute of Management Development and Research, Mumbai, India
Muhammad Javed
Centre for Next Generation Localisation, School of Computing, Dublin City University, Dublin 9, Ireland
Dr. G. GOBI
Assistant Professor-Department of Physics,Government Arts College,Salem - 636 007
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Pulau Pinang-11800,[PENANG],MALAYSIA.
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Manoj Sharma
Associate Professor Deptt. of ECE, Prannath Parnami Institute of Management & Technology, Hissar, Haryana, India
RAMKUMAR JAGANATHAN
Asst-Professor,Dept of Computer Science, V.L.B Janakiammal college of Arts & Science, Coimbatore,Tamilnadu, India
Dr. S. B. Warkad
Assoc. Professor, Priyadarshini College of Engineering, Nagpur, Maharashtra State, India
Dr. Saurabh Pal
Associate Professor, UNS Institute of Engg. & Tech., VBS Purvanchal University, Jaunpur, India
Manimala
Assistant Professor, Department of Applied Electronics and Instrumentation, St Joseph’s College of Engineering & Technology, Choondacherry Post,
Kottayam Dt. Kerala -686579
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Control Engineer Synchrotron-light for Experimental Sciences and Applications in the Middle East (SESAME),P. O. Box 7, Allan 19252, Jordan
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Professor, Department of CSE,Kongu Engineering College,Perundurai-638 052
Dr. (Mrs.) R. Uma Rani
Asso.Prof., Department of Computer Science, Sri Sarada College For Women, Salem-16, Tamil Nadu, India.
MOHAMMAD YAZDANI-ASRAMI
Electrical and Computer Engineering Department, Babol "Noshirvani" University of Technology, Iran.
Dr. Kulasekharan, N, Ph.D
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GE India,John F Welch Technology Center, Plot # 122, EPIP, Phase 2,Whitefield Road,Bangalore – 560066, India.
Dr. Manjeet Bansal
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Contents
Does self-efficacy effect on knowledge sharing intention in e-learning system? A motivational factor analysis in Open University Malaysia (OUM) by Dr. seyed mohammad bathaei, Seyed Ali Hosseini……………………….[206]
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Does self-efficacy effect on knowledge sharing intention in e-learning system? A motivational factor analysis in Open
University Malaysia (OUM) Dr. seyed mohammad bathaei
Ministry of education Islamic republic of Iran Deputy of minister of education in Islamic republic of Iran
Seyed Ali Hosseini International Business School University Technology Malaysia (UTM) Malaysia
Jalan Semarak 50400 Kuala Lumpur, MALAYSIA -019-4616464 [email protected]
Abstract— The purpose of this paper is to examine the
self-efficacy factor influence intention to share
knowledge in E-Learning system within Open University
Malaysia (OUM). Moreover, its objective is to identify
the relationship between self-efficacy and perceived
behaviour control (PBC) and intention to share in E-
learning system. The current research expands a
theoretical framework of online knowledge sharing
factors with the Theory of Planned Behaviour (TPB). An
online questionnaire survey was applied to collect data
and the analysis was completed according to 583
responses from students who act in EL system of Open
University Malaysia (OUM). A semi-structured interview
was constructed with 10 participants who were
facilitators and teachers in EL system of OUM as the
case study to achieve knowledge sharing
comprehensible and understandable intention. The
outcomes of the study survey and interview support the
fundamental statement that self-efficacy direct to
influence intention to share knowledge well.
Key words — knowledge sharing (KS), E-learning (EL),
knowledge sharing behavior, theory of planned behavior
(TPB), self-efficacy (SE), intention to share, perceived
behavior control (PBC).
I. INTRODUCTION
The new learning methods assist cooperative
experience and virtual students’ society improvement
(Bransford et al., 2000), and valuable knowledge via
knowledge sharing (Zhang et al., 2007). Nevertheless,
these profits cannot be apprehended lacking of the
energetic participation of people. Latest empirical
research disclose little points of participation and
unwillingness to share knowledge in EL systems and
environments (Carr and Chambers, 2006; Ng, 2001),
and research of crucial factors influencing participation
in some systems disclose that this problem rests
unsolved (Xie et al., 2006). E-learning system structure
confirmed through constructivist theories of learning and
behavior (Prawat, 1996), and assists learning process
via increasing knowledge sharing behavior in learning
environment (Honebein, 1996; Wilson, 1996). As Hiltz
and Turoff (2005) referred, ‘‘EL is a modern
communicative procedure which gives enjoyment to
distance learning students, compared to traditional
classrooms (face to face)”. In spite of the attractiveness
of the EL environments, there are still major challenges
in the willingness to use EL systems, one of which is
that the students do not want to share their knowledge
and experiences (Zhang and Faerman, 2007).
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II. Operational definition
E-Learning system
EL systems are the principal learning surroundings in
Higher Education. EL is recognized as new applications
by Learning Management System (LMS) and also
Course Management System (CMS) in distance learning
universities and institutions (Artino, 2010). Kanuka and
Rourke (2008) discover the modifications that are
happening in higher education (HE) as an outcome of
the use of EL system. They find that some technical
system prepares chances for developed accessibility,
compatible with the objective in application of modern
technology in education environment. Nevertheless, it is
also related to negative influences, as teachers and
learners experience to gather a lock of a feel of
belonging and responsiveness of borders.
Self-efficacy
Self-efficacy indicates the degree of an individual’s
confidence to perform and to coordinate the knowledge
and activities in daily educational tasks as required to
obtain knowledge, experiences, and successful
performance in the EL system environment. As Ormrod
(2006) defined it, “Self-Efficacy is the measure of the
belief in one's own ability to complete tasks and reach
goals". Bandura (1995) also believed that "Self-Efficacy
is individual’s beliefs about their competencies to create
success that impact on procedures growing their lives’
performance". A suggestion is that PBC might be
divisible into two separate constituents. These would be
“SE beliefs”, which is defined as the level of enactment
of the behaviour which is easy or difficult for the
individual. The next constituent is “perceived control
over the behaviour”, which reveals the level at which the
individual perceives the enactment of his/her behaviour
is under his/her control (Tavousi et al., 2009).
Theory of planned Behaviour (TPB)
Theory of Planned Behaviour (Ajzen, 1985) is
recognized as an advanced version of the theory reason
action (TRA). Fishbein and Ajzen (1991) completed
obligatory by the second representation ‘incapability to
contract with Behaviours done which individuals have
imperfect optional control. TPB recognizes actual
performed Behaviour as a people's effort of a convinced
Behaviour is performed by his or her/his intention to fulfil
that Behaviour. Attitude towards the actual Behaviour,
Subjective Norm (SN) about involving in the Behaviour,
and perceived Behaviour control (PBC) are supposed to
impact intention and online learning by Behaviour in
TPB producer (Baker and White, 2010). Moreover, a
number of current researches have shown the
substantial connection between SE and Perceived
Behavioural Control (PBC) based on TPB (Taylor and
Todd, 1995; Sheng and Noe, 2010) then SE can help
turn the person's behaviour towards KS (Lin et al., 2009;
Wasko and Faraj, 2005; Lin, 2007).
Intention to Share Knowledge
According to Ajzen (1991), the Intention is the most
important cause of people’s Behaviour. The
sophisticated purpose will be achieving certain
Behaviour, the advanced chances of the authentic
enactment of that exact Behaviour.
Perceived behaviour control (PBC)
PBC includes some features that affect the KS intention
in producing the actual behaviour in terms of individual’s
abilities, accessibility, skills, and feelings; also it is
supposed that PBC is recognized by the whole complex
of accessible control beliefs (Ajzen, 1991). In this
research, PBC is associated with electronic materials,
accessibility to an EL system, a technical support
system as facility conditions, and self-efficacy in the use
of an EL system.
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iii. Significant of the Study
Chiu et al. (2006) believed that the most important
problem in predicating the online learning communities
is the lock of contributions and willingness to share
knowledge in the online communities. It is necessary to
recognize the motivational factors in order to encourage
students in performing and sharing their knowledge and
experiences in learning environment (Ma, 2009; Ellis et
al., 2002; Liu, 2008). Self-efficacy is the one of important
factor that effects on knowledge sharing behavior (Lin et
al., 2009; Wasko and Faraj, 2005; Lin, 2007; Chen et
al., 2009). The current research creates empirical and
theoretical contributions about SE and KS in EL
environment. The conclusions have empirical
consequences for on-line KS in an EL system. The
examination of the practical research of EL shows that a
few studies have been funded to increase KS by
behavioral mechanisms like SE (Chen et al., 2009),
such as the requirement of students to use the
interactive connections between students in EL systems.
Previous research has concentrated on gaps in
interaction due to the lack of physicality or wave signals
compared with face-to-face communication.
Nevertheless, current, practical research indicates that
the web is an intermediate instrument that encourages
the quick construction of neighboring connections that
support the above period, and even promote
involvement in the global geography. Prior EL and KS
research has concentrated on the influence of technical
factors on the adoption and continue behavior of EL and
KS, and a have rarely explored the students’ self-
efficacy factor influencing the promotion and
encouragement offered to students regarding
participation in EL activities (Bibi Alajmi, 2008; Kamarul,
2012). This research is also significant as it utilizes both
deductive and inductive tactics. On the one hand, it
produces a theoretical structure and arranges the
constructs under the categories within that framework
while on the other hand, through an inductive approach,
it validates the constructs that could be combined in the
online KS model.
IV. Research Questions
According to the statement of the research problem
explained before, the research questions have
developed the following questions:
1. Does self-efficacy influence on knowledge sharing
intention in E-learning system?
V. Research Objectives
The purpose of the research is to discover the
relationship between the motivational knowledge sharing
factors and intention to share knowledge in an EL
system. In connection to this, the researcher’s other
purpose is to achieve the following objectives:
1. To identify the influence of self-efficacy on knowledge
sharing behaviour in E-learning system.
VI. Research Hypotheses
The questions and objectives of the current study can
be further studied through the following hypotheses:
H1. Self-Efficacy has a positive effect on Perceived
Behavioural Control in the EL system.
H2. Perceived Behavioural Control has a positive effect
on the intention to share knowledge in the EL system.
Figure 1.1 Research Model and Hypothesis
VII Open University Malaysia (OUM)
OUM was named as Open Distance Learning (ODL) in
2000. It is the seventh private university in Malaysia and
it is owned by Malaysia’s eleven public university
associations. Based on the philosophy that learning is
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supposed to be democratized, OUM has concentrated
on generating a reasonable and easy corridor to Higher
Education system with significant position on flexible
entrance necessities like a student-friendly education
system, and a blended learning method with mixtures of
dissimilar forms of education. This system was planned
to perform different features based on the student’s
requirements, information and communication
technologies and internet tools.
VIII Research Method
Research methodology has many classifications, such
as, research methodology in terms of qualitative and
quantitative methods (Williams, 2007). These
approaches can be used as single and mixture method
by investigators linked in OUM. For this study, mixed
method is performed. Researcher has used both
qualitative and quantitative approaches and he also
examines and establishes the data gathered from
selected cases (Creswell, 2012). Studies show that
Quantitative and qualitative approaches are appropriate
to grow the fortes and the reducing of weaknesses of
the research methods (Johnson and Onwuegbuzie,
2004). Thus, in this method we can say that the findings
and outcomes are more valid. Furthermore,
Quantitative study shows the actuality of the cause and
effectiveness of relationships among variables. On the
other hand, qualitative study approaches to discover the
implications and outlines, consider to particularly the
activities and records carefully. There are many
researches in the Knowledge management area in
which utilized the qualitative and quantitative methods
as their key resources to collect data and to use the
features of individual behavior constantly, which are
frequently veiled (Nonaka and Takeuchi, 1995;
Chennamaneni, 2006; Jones, 2007; Bock et al., 2005;
Ma, 2009; Stewart, 2008; Vashisth et al., 2010; Jewels
and Ford, 2006).
IX Instrument Development
Researcher gathered the data in two steps. Firstly, the
online questionnaire as a survey instrument is
distributed. After the online questionnaire, the interview
questions followed to improve the survey, and to
evaluate more appropriate outcomes and to develop the
survey's validity and reliability. In addition, the
investigator applied online questionnaire as pre-test to
evaluate the 25 questions and to enhance its value. In
this research, a pre-test investigation was applied with
emphasizing on the validity of citing knowledge sharing
in the EL.
X. Interview
This research has shown the semi-structure interview
questions that are comprised with; the research was
created in 6 knowledgeable interviews selected online
and off line by the facilitators and teachers in different
faculties of OUM. The data have been gathered during
two semesters in 2012-2013 academic years. In semi-
structured interviews where some facilitators, technical
administrators, and some online teachers were in
charge of distance learning system or EL. As revealed,
the questions on the interview were open-ended; and
each interview was about 10 to 15 minutes created on
their arrangement, while some of the interviews were
voice-recoded. The knowledge sharing intention factors
and the effects of the knowledge sharing on the success
of the EL were discussed in the case study area. Table
1.1 shows the interview questions.
XI. Questionnaire
Instrument Plan
Questionnaire survey based on the research
conducted in DTPB is composed of two main sections.
In the first part of the questionnaire, respondents were
asked questions about personal characteristics, such as
sex, age, educational level, duration of study, study
courses. This section uses EL system as a moderating
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variable in the study. The second section of the
questionnaire is extracted from the DTPB model.
Question components are measured as well as what the
students ask and a Likert scale was used to rank it from
very low to very high. The investigator as a viewer can
approve examination methods of collecting data and
assessments by viewing how the learners engaged in
learning activities in EL system. The five-point Likert
scale was applied to measure each paradigm which
comprised 1= strongly disagree to 5= strongly agree.
The Likert response allocated numeric values to help the
analysis of the answers.
Table 1.1 The interview questions
NO. QUESTIONS
1
Are the students learning in the EL system, frequently exchange and share
knowledge and experiences with others? If not, what factors and situations preventing
them? If you think yes, why? And what factors affect it?
2 In your opinion, are the students in E-Learning system interested in participating and
willing to share their knowledge and courses with other students?
3 In your opinion, do they (students) share knowledge and develop learning
experiences within EL system easily? Do they think that knowledge sharing is useful?
4 In your opinion, what are the individual factors that effect on the student’s attitude
toward knowledge sharing?
5 Do they (students) think that they are comfortable and can trust and share better by
EL system?
6 Is EL system compatible with students’ values, current requirements and previous
experiences?
Table 1.2 Statistics for Self-Efficacy (SE)
Table 1.3 Descriptive Statistics for Perceived Behavioral
Control
Table 1.4 Descriptive Statistics for Intention to KS (IS)
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Table 1.5 the learning centers that collect questionnaire
NO. LEARNING
CENTER (BRANCH) STUDENTS
1 Bangi Learning centre 70
2 Ipoh Learning centre 85
3 Johor Learning centre 80
4 Kelantan Learning centre 68
5 OUM KL center 180
6 OUM Petaling Jaya (PJ) Learning centre
100
583
XII Construct Measures
The research theoretical model of this research is
comprised of following paradigms, the independent
variables which comprise two factors (self-efficacy,
perceived behavior control (OBC)). Additionally,
dependent variable which intention to share knowledge,
contributes in this study, There are also thirteen main
constructions of the hypothetical model that are used in
this study. Knowledge sharing intention plays a double
role: dependent and independent paradigms. The 43
questionnaire items measured the relationship between
variables; these statements were calculated according
to a 5-point Likert scale which include 1=strongly
disagree to 5= strongly agree.
XIII Descriptive statistic of the questions and variables
Descriptive statistic questioner includes quantity value,
value, mean, standard deviation drawing was taken for
each question that its results is visible in table 1.6.
Table 1.6 Descriptive statistics of the questions
Q? Min Max Mean Std. Deviation
Skewness
Kurtosis
IS1 1 5 3.94 0.77 0.778 0.819
IS2 1 5 3.73 0.88 0.882 0.121
IS3 1 5 4.14 0.68 0.681 0.722
IS4 1 5 3.61 0.85 0.853 0.48
IS5 1 5 3.44 0.83 0.837 0.646
IS6 1 5 3.71 0.82 0.822 0.525
IS7 1 5 3.46 0.86 0.868 0.436
PBC1
1 5 3.69 0.76 -0.644 1.157
PBC2
1 5 4.02 0.69 -0.524 0.755
PBC3
1 5 3.69 0.73 -0.601 1.251
PBC1
1 5 3.69 0.76 -0.644 1.157
SE1 1 5 3.72 0.76 -0.625 1.322
SE2 1 5 3.53 0.82 -0.689 0.998
SE3 1 5 3.64 0.83 -0.643 0.843
SE1 1 5 3.72 0.76 -0.625 1.322
IS: Intention to knowledge sharing Behavior, PBC:
perceived behavior control, SE: self-efficacy,
XIV Descriptive statistic of variables
Dimensions of each question are made then descriptive
statistic include standard deviation and mean is gotten
by compute device in SPSS software that it’s results are
presents in table 1.7 the results of above table defines
the people are studies have gotten advantages more
than supposed mean, once these aren’t limitation for
their learning as electronic and their condition is suitable
in terms of the dimensions, special in subjects like
attitude toward knowledge sharing (AI) with mean 4.02,
succession in electronic teaching system (ES) with
mean 3.94.
Table 1.7 Descriptive statistical of variables
Variables Mean Std.
Deviation
Intention to knowledge
sharing Behavior (IS) 3.56 0.70
Perceived Behavior
Control (PBC) 3.80 0.56
Self-Efficacy (SE) 3.63 0.70
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XV Qualitative Analysis
Interviews were carried out on the system facilitators
and teachers in OUM learning system. As mentioned
earlier, the interview was done after the results from the
survey research. In order to verify the research results
from the survey method about the antecedents was
used qualitative method as a supplementary technique
with the answers from the case study that cooperate the
self-efficacy effects on the knowledge sharing intention
in the EL system. This part of the study will report the
responses of the participants in the interview and how
those responses give answers to research questions.
XVI Participant Information
According to the quantity of individual interviews that
was dependent on participants from the OUM as case
study. Since the research needs some explanations
about details of study dimensions, the interview
questions were requested that contribute by a few
participants in OUM as case study. Choosing
interviewees were beneficial and only integrated a small
from the case study (Saunders et al., 2007). The
interviewees who were conducting as individual
interviews are recommended around 10 to 15 (Hill et al.,
1997). Participants in this study consisted of 10 system
facilitators and teachers engaging in the learning
practice within the OUM e-learning system.
XVII Conclusions and recommendations
To answer the questions related to hypotheses, four
main questions were proposed and investigated. These
research questions are:
Does self-efficacy effect on knowledge
sharing intention in e-learning system?
Self-Efficacy (SE), the first effective factor of PBC, refers
to individuals’ belief that their own educational
competences, to a greater extent than others’, have a
critical function in motivating them to accomplish and
control their behaviour. Investigators have proceeded to
prove its information validity as well as its convergent
validity in predicting general motivational findings, in
terms of learners’ practice selections, effort, persistence,
and emotional reactions. SE attitude has been found to
be responsive to understated modifications in learners’
performance background, to become involved with self-
regulated education procedures, and to mediate
learners’ educational accomplishment (2000 Academic
Press). Supporting this, the eleventh hypothesis is
designed to examine the effect of Self-Efficacy on
Perceived Behavioural Control.
H1: Self-Efficacy has a positive effect on Perceived
Behavioural Control in the EL system.
Perceived Behavioural Control (PBC) is the third
effective factor which determines students’ desire for KS
in the EL system. It refers to people's perceptions of
their ability to perform a given behaviour. It also points
to the degree of individual control over transferral of
information through the EL system. The higher the
degree of control, the greater will be the desire for KS in
the EL system. In order to examine the effect of
Perceived Behavioural Control on KS among the users
of the EL system, the next and fourth hypotheses of the
research were designed.
H2. Perceived Behavioural Control has a positive
effect on the intention to share knowledge in the EL
system.
Table 1.8 Hypothesis Testing Results
NO. Relationship Hypotheses Results
1
Perceived behavioral control and intention to share knowledge
H2. The perceived behavioral control has a positive effect on the intention to share knowledge in EL system.
The findings of the research indicate that perceived behavioral control has a positive impact on intention to share knowledge.
2
Knowledge Self-Efficacy and perceived behavioral control
H1. Self-Efficacy has a positive effect on perceived behavioral control in EL system.
The findings of the research supported that Self-Efficacy has a positive impact on perceived behavioral control.
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Figure 1.2 Results of Structural Modelling Analysis
XVIII Restrictions of the study
There were some limitations in the research method and
data collecting. This often happens in similar studies
done in behavioral field. There are also restrictions in
the examination of EL system in OUM as Open
University which educates through online system where
students are not required to attend classes in the
traditional classroom as in face to face education.
Secondly, the investigation of some variables in this
research like intention to share knowledge is sometimes
very difficult because the control on effective changes
on this structure is hard, for example, the application of
the whole factors of knowledge sharing Behavior in
research model. Another research limitation is the lack
of examination tools for all main factors on students’
knowledge sharing behavior in EL system. The third
limitation was to adjust factors in relation to the intention
to share knowledge in online environment that students
can use many tools and technologies based on their
skills and abilities.
XIX Recommendations for Further Study
In the other aspect of this research it is also
recommended that the relation among research
structures must be investigated for successful results as
what this current study did, for example, the
investigation of the main factors on intention to share,
where it introduced two levels of intent to share. There
are three more effective structures in each level. Thus,
the effective examination of the factor of the variable
such as Self-Efficacy on this study will approve the
student’s intent to share knowledge in EL system if
these factors and variables will be applied. And because
of this, the need to work on more studies and examining
the related theories and models in the future can be
much easier.
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