1. KAE 3013
KPT 6044 : PEMBELAJARAN
BERASASKAN ELEKTRONIK DAN WEB
TUGASAN INDIVIDU
ULASAN JURNAL
DISEDIAKAN OLEH :
i-THINK
NAMA
SUZANA BT. SAUDIN
NO. MATRIK
NO. TELEFON
M20121000514 0122391423
PENSYARAH : PROF.MADYA DR. ABD LATIF

2. SYSTEM CHARACTERISTICS,
SATISFACTION AND E-LEARNING
USAGE: A STRUCTURAL
EQUATION MODEL (SEM) 1
T. Ramayah
School of Management
Universiti Sains Malaysia
Jason Wai Chow Lee
School of Business
Nilai University College, Malaysia
aramayah@usm.my
bwclee@nilai.edu.my

3. 1.0 PENGENALAN
Pembelajaran elektronik (e-learning) adalah didokumenkan dalam kesusasteraan IT kerana
mengikut Roca et al. (2006), ia semakin diberikan "persekitaran yang baru dan pengalaman
pembelajaran yang berlaku juga di luar bilik darjah, kurikulum dan format berasaskan teks".
E-pembelajaran biasanya melibatkan penyampaian kandungan kursus dengan menggunakan
media elektronik, seperti Internet, Intranet, extranet, siaran satelit, pita audio / video , TV
interaktif dan CD-ROM (Urdan & Weggen, 2000). Khan (2001)menerangkan e-pembelajaran
sebagai sinonim dengan pembelajaran berasaskan web (Jejaring), latihan berasaskan Internet
(IBT), maju pembelajaran diedarkan (ADL), Arahan berasaskan web (WBI), pembelajaran
dalam talian (OL) dan pembelajaran terbuka / fleksibel (OFL ). Ramayah et. al.(2010)
menyatakan bahawa di institusi pengajian tinggi di Malaysia, langkah-langkah pelaksanaan epembelajaran yang berjaya adalah kepuasan pengguna dan penerusan penggunaan kemudahan
untuk penyelidikan dan pengajaran dan pembelajaran.
Wang et al. (2007) berhujah bahawa ia adalah sukar untuk menangkap dimensi penuh sistem
kejayaan e-pembelajaran dalam sesebuah organisasi kerana banyak kombinasi langkah-langkah
individu, pengurusan dan organisasi boleh diguna pakai. Tambahan pula, pemeriksaan sistem epembelajaran yang berjaya dalam konteks IS adalah sukar kerana pemain yang berbeza atau
pihak berkepentingan yang berbeza melihat manfaat daripada sistem (DeLone & McLean, 2003).
Kajian ini adalah dari perspektif pelajar yang menggunakan sistem e-pembelajaran yang pada
asasnya berasaskan web dalam alam dan kerana ia juga merupakan fenomena sistem komunikasi
dan maklumat (IS) Wang et al. (2007), penulis berpendapat bahawa adalah wajar untuk mengkaji
pelaksanaannya yang berjaya diperluaskan dengan menggunakan DeLone & McLean’s (2003)
Model Kejayaan IS.Ia telah dicadangkan bahawa "walaupun sifat multidimensi dan jangka IS
kejayaan, usaha hendaklah dibuat untuk mengurangkan dengan ketara bilangan langkah-langkah
yang digunakan untuk mengukur kejayaan IS, supaya hasil penyelidikan boleh dibandingkan dan
penemuan disahkan" (DeLone & McLean, 2003). Oleh itu, kajian ini melaksanakan model
dipermudahkan DeLone dan ini McLean (2003) model dilanjutkan untuk memeriksa melalui
model persamaan struktur (SEM), peranan kualiti (kualiti perkhidmatan, maklumat yang
berkualiti dan kualiti sistem) dalam mempengaruhi kepuasan pengguna dan penggunaan
berterusan daripada sistem e-pembelajaran di universiti awam di Malaysia.

4. 2.0 Persoalan Kajian
H1: System quality has a positive relationship with user satisfaction.
H2: Information quality has a positive relationship with user satisfaction.
H3: Service quality has a positive relationship with user satisfaction.
H4: User satisfaction is positively related to usage continuance.
H5: System quality is positively related to intention to use.
H6: Service quality is positively related to intention to use.
3.0 RESEARCH METHOD
3.1 Data Collection
Data was collected from 250 students from a public university in Penang, Malaysia using a
structured questionnaire which was derived from the literature. The questionnaire consisted of 4
sections. The first section collected the demographic data, the second section elicited information
about information quality, service quality and system quality, section three measured user
satisfaction and the last section measured continuance intention. Since there was no list
available, non-probability convenient purposive sampling method was used. The sample selected
were students who have used the e-learning system as the measures required them to rate the
system, information and service quality as well as the satisfaction and continuance intention.
3.2 Measures
The measures were all adapted from published literature. The measures for service quality,
information quality and system quality were from Lee and Lee (2008). Satisfaction measures
were adapted from Spreng et al. (1996) whereas intention to use was adapted from Venkatesh et
al. (2003).
3.3 Sample Profile
The demographics of the respondents tabulated in Table 1 were derived from descriptive
analysis. Females (69.6%) outnumber males (30.4) in this study which somewhat reflects the
gender ratio of undergraduates for public universities in Malaysia. About 70% of the students
were from the Arts stream while 30% were from Science. More than 66% of students stayed in

5. the campus and the rest outside the campus. About 50% of students used the e-learning system
for between 1-5 hours per day while about a quarter used the system for less than an hour per
day. Twenty-eight percent of students claimed they belonged to the slightly frequent to
extremely frequent user group of the system.
Table 1: Demographics of respondents
Gender
Frequency
Percent
Male
76
30.4
Female
174
69.6
Malay
72
28.8
Indian
24
9.6
Ethnicity
Chinese
Others
148
59.2
6
2.4
Stream
Arts
Science
174
69.6
76
30.4
166
66.4
84
33.6
Residence
In campus
Outside campus

6. Hours
Frequency
Percent
Almost never
6
2.4
< 1 hour
62
24.8
1 – 5 hours
124
49.6
6 – 10 hours
38
15.2
11 – 15 hours
14
5.6
6
2.4
Extremely infrequent
16
6.4
Quite infrequent
50
20.0
Slightly infrequent
64
25.6
Neither infrequent nor frequent
50
20.0
Slightly frequent
46
18.4
Quite frequent
14
5.6
Extremely frequent
10
4.0
More than 20 hours
Frequency of use
4.0 DATA ANALYSIS
AMOS version 16.0 was used to analyze the hypotheses generated. AMOS and LISREL are the
most widely used Structural Equation Modeling (SEM) software available in the market. Since
we considered AMOS 16.0 to be more user friendly this software was adopted. We followed the
2-step analytical procedure suggested by Hair et al.(2010) whereby the measurement model was
evaluated first and then the structural model was assessed next.

7. 4.1 Measurement Model
Convergent validity measures the extent to which the items of a scale that are theoretically
related are correlated. According to Hair et al. (2010) a composite reliability of 0.70 or above
and an average variance extracted of more than 0.50 are deemed acceptable. As can be seen from
Table 2, all the composite reliability values are above 0.70 except for intention which is
acceptable as there are only 2 measurement items. The average variance extracted is all above
0.50. Therefore, we can conclude that convergent validity has been established.
Next, we assessed the discriminant validity which is the extent to which a measure is not a
reflection of some other variable. This can be established by low correlations between the all the
measure of interest and the measure of other constructs. Also according to Fornell and Larcker
(1981) when the square root of the average variance extracted is greater than its correlations with
all other constructs then discriminant validity has been established. (see Table 3)
Table 2: Result of CFA for measurement model
Convergent validity
Factor
Internal reliability loading
Construct
Information Quality
Item
0.896
0.66
0.78
0.54
0.74
SQ1
0.901
0.73
SQ2
0.68
SERQ2
0.77
0.53
0.74
SERQ3
0.51
0.76
SERQ1
0.75
0.64
SQ3
User Satisfaction
variance
0.80
IQ3
Servis Quality
reliability
extracted
IQ2
System Quality
Average
Cronbach alpha
IQ1
Composite
0.77
US1
US2
0.911
0.67
0.79
0.76
0.76

8. US3
Intention to Use
BI1
0.71
0.837
0.71
BI2
0.68
0.52
0.73
Table 3: Discriminant validity of constructs
Constructs
(1)
(2)
(3)
(4)
(5)
(1)
Information Quality
System Quality
Service Quality
User Satisfaction
Intention
0.734
0.250
0.146
0.130
0.082
(2)
(3)
0.714
0.166
0.232
0.104
0.728
0.090
0.063
(4)
(5)
0.872
0.229
0.721
_____________________________________________________________________________________
4.2 Structural Model
The structural model was estimated using the maximum likelihood method (MLE). Fig. 2
presents the results. The fit statistics are presented in Table 3. All the fit measures from this
study are above the recommended values suggesting a good model fit. The model accounts for
45% of the variance explained in user satisfaction and 44% of the variance in user intention. All
the paths are significant at the 0.01 level. Information quality has the strongest effect on user
satisfaction whereas user satisfaction has the strongest effect on user intention. Thus the results
of the structural model have established support for H1, H2, H3, H4, H5 and H6 (See Table 4).
Table 3: Fit indices
Fit Measures
df
x2
x2/df
GFI
AGFI
CFI
RMSEA
NNFI (TLI)
Study
1
2.595
2.595
0.996
0.978
0.997
0.080
0.972
Recommended values
≤ 3.00
≥ 0.90
≥ 0.80
≥ 0.90
≤ 0.08
≥ 0.90

9. Table 4 summarizes the results of hypotheses testing in this study.
Table 4: Hypotheses testing
Hypothesis
Critical ratios (CR)
p-value
Decision
H1: System quality has a positive relationship with
3.256
0.001
Supported
5.399
0.000
Supported
2.948
0.003
Supported
5.069
0.000
Supported
2.837
0.005
Supported
4.697
0.000
Supported
user satisfaction.
H2: Information quality has a positive relationship
with user satisfaction.
H3: Service quality has a positive relationship with
user satisfaction.
H4: User satisfaction is positively related to usage
continuance.
H5: System quality is positively related to intention
to use.
H6: Service quality is positively related to intention
to use.
5.0 CONCLUSION
In this study, we found that system quality, information quality and service quality are significant
factors influencing user satisfaction in using an e-learning system. User satisfaction is also found
to be significant in affecting user’s intention to use. The findings provided by the study may
enable the creators of e-learning systems to think seriously on these factors that will affect user
satisfaction. In addition, this study may provide a direction as to how satisfaction can be
cultivated among users in order to encourage them to use the e-learning system. The findings
provided by the study may give empirically justified foundation for the creators to develop
strategies to enhance their e-learning system’s quality by focusing on the user satisfaction. By
understanding the determinants of user satisfaction, appropriate actions can be taken to increase
the users’ perceptions of their experience on adoption of the e-learning system. In short,
continued research is needed to improve this study and to address its limitations. It is hoped that
this study will give a preliminary insight and understanding on user satisfaction and behavioral
intention in order to maximize the actual use of the e-learning system.