PhD Presentation (Doctorate)

1
PhD. – Information System
Thesis Viva
By: Sharif Omar Salem
Supervisor: Dr. Khaironi Yatim Sharif
Co-Supervisor: Dr. Ilham Sentosa
1
The design and format is done by me, feel free to use the same format. But I am expecting
appreciation notification.

Research Title
Developing a Hybrid Success Model for Different
Content Management Systems in Higher
Education:
A Comparative Analysis of Students’ Perspective
on Traditional and SNS systems.
2

Contents
Subtitle Slide No.
Introduction 4
Theoretical Framework and Proposed Model 12
Research Methodology 18
Findings 30
Hypothesis Discussion 36
Contributions, and Recommendation 41
Publications 46
3

Research Brief
This academic research Investigated the learners’ outcome
and its determinants via experiencing two different
treatments.
First treatment by using traditional CMS and second
treatment by using FB-based CMS.
A survey based on a developed hybrid eLearning success
model is used to collect data.
Findings analyzed to assess the relations in the causal
model and to compare the outcome constructs acquired
via experiencing the two different systems.
5

Research Motivations
 During the last five years; many researchers
announced and recommend for further and future
research to fill up two gaps:
6
Theoretical gap:
The need for a
revised e-
Learning system
success model
Practical gap:
The need for
more
understanding
of the FB effect
in learning
outcome.

7
Theoretical gap: The need for a revised e-
Learning system success model
• “A goal of continuing research would be an exploration
of how the ISS model would be supplemented in order
to more accurately reflect the E-learning
environment”.
In 2010; Freeze, Alshare, Lane, & Wen state that
• Factors from the community of inquiry frameworks
such as metacognitive, motivational, and behavioral
traits of active online students may be a valuable add-
on to the eLearning success model
In 2010; Shea & Bidjerano state that
• Considering the perspective of all players of the
eLearning system and including additional different
factors is important for a proper representation of the
system success
In 2012; Bhuasiri et al. state that
• System success dimensions are not technology only,
the revolution of web 2.0 and the uniqueness of
eLeaning environment especially the different
stakeholders guide the researchers to seek for new
revised model
Cheng 2012; Lee et al. 2009; Chen 2010; Keramati et al. 2011; Sun et
al. 2008; Hassanzadeh et al. 2012; Wang & Chiu 2011

8
Practical gap: The affect of using Facebook in
learning outcome.
• Consolidating the Facebook in the learning and
teaching process is very important for the students’
education lives and further research is needed to
understand this phenomenon
In 2011; Bicen & Cavus
• Future research is needed to investigate more the
usability of FB in education
In 2012; LaRue state that
• There is a need for understanding the relation
between the learners’ interaction level in the
Facebook and their academic success
In 2012; Junco stated that
• Considering the perspective of all players of the
eLearning system and including additional different
factors is important for a proper representation of the
system success
In 2013; Ng & Wong stated that

Problem Statement:
Content Management System (CMS) is widely used in most
of the universities worldwide to facilitate higher education
stakeholders’ communications.
The challenge is whether Facebook environment “as a CMS
system” is favorable and more effective than the traditional
CMS system and what determinants/constructs affect the
eLearning success.
Recently, few academic studies begin investigating this field
of application. Further investigation is needed to fill up the
illustrated gaps.
9

Research Objectives:
To identify the dimensions of the information system success
in the modern e-learning environment, and propose a
modified model for e-learning system success.
To implement and test the proposed model when the
implemented system is Moodle based.
To implement and test the proposed model when the
implemented system is Facebook based.
To compare between the findings of the Moodle-based
system analysis and the findings of the Facebook-based
system analysis.
10

Scope of the Study
The research use
Facebook as a
social network
system and
Moodle as a
traditional CMS
system.
The research
focus in the
application of the
system to
facilitate higher
education.
Respondents for
the application of
the system are
from the LUCT
University,
Malaysia.
Students are
participants for
the master
degree level.
11

Theoretical Framework and Proposed
Model
12

Learning
System
Outcome
Net Benefits
Information
Quality
Service
Quality
Intention to
Use/ System Use
User Satisfaction
System
Quality
13
Theory 1: Delone and McLean IS success model
- 2002

Learning
System
Outcome
Learning
System
Outcome
Teaching
Presence
Social
Presence
Cognitive
Presence
14
Theory 2: The Community of Inquiry - 2000

Learning System
Outcome
Community System
Success
Sociability
Usability
(System Dimension)
15
Theory 3: Preece’s sociability and usability
framework – 2001

Learner’s
Outcome
System
Sociability
Information
Quality
Service
Quality
Intention to
(Use)
User
Satisfaction
Teaching
Presence
Learner’s
Presence
System
Quality
16
Proposed Hybrid Model for IS Success of
Modern LMS
Learner’s
Outcome
Learner
Outcome
D&M IS Success Model
CoI Theory
Preece’s S&U Framework

17
Learning
System
Outcome
Learner
Outcome
System
Sociability
Information
Quality
Service Quality
Intention to
(Use)
User
Satisfaction
Teaching Presence
Learner Presence
System Quality
Research Hypothesis
HA6; HB6
HA9; HB9
HA16;HB16
HA##  Hypothesis Set of System 1
HB##  Hypothesis Set of System 2
HC##  Hypothesis Set of Comparison
• IU of the FB-based system is different from and higher than the Moodle-based system.
• US of the FB-based system is different from and higher than the Moodle-based system.
• LO of the FB-based system is different from and higher than the Moodle-based system.

19
Research Design
• The study starts up with a theory and ends up with testing
the hypothesis.
Deductive
Approach
• Statistical analysis based on descriptive measures,
variance, covariance techniques are used.
Quantitative
Research
• Literature review and systematic reviews techniques are
used to build the hybrid model.
Qualitative
Research
• This study aims to assess a desired field subjects in two
different treatments then compare the outcomes.
Field Experiment
Design
• “Counterbalanced Measures Design” technique is
performed by assigning participants in different groups
and applying treatments to each group in a different order.
Counterbalanced
Measure Design

Population and Sample
22
The population
is all the
Module-based
Master
students of
LUCT ~ 362.
The minimum
sample size for
SmartPls = 70.
the effective
sample size
based on the
statistical
power value =
153.
In reality, the
analysed
sample = 231.

Experimental Design (Counterbalanced
Measures Design)
This research is Semi-
Experimental design use
Field Experiment.
This approach is a mix
between the “Between
Subjects Design” and
“Repeated Measure
Design”
23
Group Pretest
Treatment
Jan-Feb 2014
Test
Treatment
Mar-Apr 2014
Posttest
Group A No Survey Moodle-Based Survey Facebook-Based Survey
Group B No Survey Facebook-Based Survey Moodle-Based Survey

24
Experimental Design (Treatment 1)
Top 5 Most Popular LMS software ranking

25
Experimental Design (Treatment 2)
Active Users of SNS Sites

Data Collection
Data collected
for the two
samples in two
different
empirical
conditions.
Direct collected
method is mainly
applied, but
under certain
conditions online
survey is applied.
Survey
distribution and
collection was
managed by the
researcher and
the module
lecturer.
Student
informed that
the survey is for
academic
purposes only
and it is optional
and confidential.
26
Group
Treatment
Jan-Feb 2014
Test
Treatment
Mar-Apr 2014
Posttest
Moodle Group A Survey Group B Survey
Facebook Group B Survey Group A Survey
Time

29
Pilot Study - Reliability Test (Cronbach's
Coefficient )
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
IQ
SQ
SrQ
TP
SP
SS
SU
US
LO
0.744
0.724
0.746
0.725
0.701
0.759
0.724
0.838
0.736
0.807
0.745
0.759
0.747
0.726
0.74
0.806
0.886
0.697
IQ SQ SrQ TP SP SS SU US LO
FB 0.744 0.724 0.746 0.725 0.701 0.759 0.724 0.838 0.736
Moodle 0.807 0.745 0.759 0.747 0.726 0.74 0.806 0.886 0.697
0.7
0.6

31
Data Screening
0
50
100
150
200
250
300
350
400
362
310
265
85% 229
86%
45
36
MOODLE-BASED SYSTEM
Retained Waived
0
50
100
150
200
250
300
350
400
363
308
262
85% 233
89%
46
29
FACEBOOK-BASED SYSTEM
Retained Waived

32
Demographic Analysis
0
20
40
60
80
100
120
Total Gender Age Race Nationality Academic Eng. Prof. Internet Prof.
100 %
231
61.3 (M)
51.5 (22-25)
33.1 (Arab)
14.7 (China)
69.5 (MBA)
16.5 (Excellent)
41 (Excellent)
38.7 (F)
35.9 (26-30)
18 (Chinese)
10 (Iran)
19.6 (MA)
44.5 (V. Good)
37.5 (V. Good)
12.1 (31-40)
14.5 (African)
9.1 (Yemen)
10.9 (MSc)
35.5 (Good)
20.6 (Good)
0.4 (>40)
13.2 (Persian)
9.1 (Syria)
3.5 (Poor) 0.9 (Poor)
6.1 (Indian)
8.5 (Malaysia)
5.9 (Malay)
6.7 (Nigeria)

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Carryover Effect Analysis
Group Phase System
The variance of the learner outcome is explained only by System
factor (different systems have a significant affect on LO)
0.05
0.10
P-Value
Source F Sig.
System 53.283 .000
Phase .096 .757
Group 1.997 .158
Dependent Variable: Learner Outcome.

34
Assessing PLS-SEM Results
Internal consistency reliability
Convergent validity
Outer model loadings and
significance
Outer
Loading
Composite Reliability
“AVE” numbers and Latent Variable
Correlations
Variance Inflation Factor (VIF)
• Predictive power (R2) and Predictive
relevance (Q2)
• ƒ² effect size
• P-Values, T Statistics, and Path Coefficient
Average Variance Extracted (AVE)
Discriminant validity
Collinearity Assessment
Indicator reliability

35
Significance of Construct Model Relations
IQ -> IU
IQ -> US
SQ -> IU
SQ -> US
SrQ -> IU
SrQ -> US
SS -> IU
SS -> LO
SS -> US
LP -> IU
LP -> LO
LP -> US
TP -> IU
TP -> LO
TP -> US
US -> IU
US -> LO
IU -> LO
Facebook
Coefficient T Value P Values
0.057 0.811 0.209
0.264 3.77 0***
0.156 2.039 0.021*
0.227 2.778 0.003*
0.093 1.28 0.1
-0.04 0.506 0.306
-0.039 0.655 0.256
0.088 1.73 0.042*
-0.012 0.201 0.42
0.298 4.759 0***
0.222 3.552 0***
0.075 1.029 0.152
-0.005 0.074 0.47
0.196 2.886 0.002**
0.355 4.628 0***
0.326 3.839 0***
0.321 5.006 0***
0.145 2.174 0.015*
Moodle
0.036 0.599 0.275
0.213 2.987 0.001***
0.142 2.059 0.02*
0.397 5.062 0.000***
0.041 0.653 0.257
-0.047 0.638 0.262
-0.07 1.689 0.046*
0.062 1.365 0.086
-0.01 0.19 0.425
0.209 3.85 0.000***
0.183 3.17 0.001***
0.015 0.234 0.407
0.125 2.036 0.021*
0.207 3.125 0.001***
0.334 4.721 0.000***
0.465 6.993 0.000***
0.263 3.351 0.000***
0.254 3.182 0.001***

37
Hypothesis Regarding Relations: Moodle-based System
Learner's
Outcome
0.704
SS  LO Yes* + 0.09 1.37 0.008 0.06
TP  LO Yes + 0.00 3.13 0.051 0.21
LP  LO Yes + 0.00 3.17 0.048 0.18
US  LO Yes + 0.00 3.35 0.068 0.26
IU  LO Yes + 0.00 3.18 0.060 0.25
UserSatisfaction
0.673
IQ  US Yes + 0.00 2.99 0.049 0.21
SQ  US Yes + 0.00 5.06 0.152 0.40
SrQ  US No Null 0.26 0.64 0.002 -0.05
SS  US No Null 0.43 0.19 0.000 -0.01
TP  US Yes + 0.00 4.72 0.122 0.33
LP  US No Null 0.41 0.23 0.000 0.02
IntentiontoUse
0.736
IQ  IU No Null 0.28 0.60 0.002 0.04
SQ  IU Yes + 0.02 2.06 0.021 0.14
SrQ  IU No Null 0.26 0.65 0.002 0.04
SS  IU No* - 0.05 1.69 0.011 -0.07
TP  IU Yes + 0.02 2.04 0.020 0.13
LP  IU Yes + 0.00 3.85 0.070 0.21
US  IU Yes + 0.00 6.99 0.268 0.47
Dependent
Variable
Predictive
Power R2 Hypothesis Sign P Value T Statistics f2
Values
Path
Coefficient

38
Hypothesis Regarding Relations: FB-based System
Learner's
Outcome
0.657
SS  LO Yes + 0.04 1.73 0.013 0.09
TP  LO Yes + 0.00 2.89 0.046 0.20
LP  LO Yes + 0.00 3.55 0.060 0.22
US  LO Yes + 0.00 5.01 0.127 0.32
IU  LO Yes + 0.02 2.17 0.027 0.15
UserSatisfaction
0.575
IQ  US Yes + 0.00 3.77 0.073 0.26
SQ  US Yes + 0.00 2.78 0.048 0.23
SrQ  US No Null 0.31 0.51 0.001 -0.04
SS  US No Null 0.42 0.20 0.000 -0.01
TP  US Yes + 0.00 4.63 0.109 0.36
LP  US Yes** + 0.15 1.03 0.006 0.08
IntentiontoUse
0.575
IQ  IU No Null 0.21 0.81 0.003 0.06
SQ  IU Yes + 0.02 2.04 0.022 0.16
SrQ  IU Yes* + 0.10 1.28 0.007 0.09
SS  IU No Null 0.26 0.66 0.002 -0.04
TP  IU No Null 0.47 0.07 0.000 -0.01
LP  IU Yes + 0.00 4.76 0.092 0.30
US  IU Yes + 0.00 3.84 0.106 0.33
Dependent
Variable
Predictive
Power R2 Hypothesis Sign P Value T Statistics f2
Values
Path
Coefficient

39
Re-specified Hybrid Model
Learning
System
Outcome
Learner
Outcome
System
Sociability
Information
Quality
Service
Quality
Intention to
(Use)
User
Satisfaction
Teaching
Presence
Learner Presence
System
Quality
Solid, Approved Relation
Mixed, Semi-Approved Relation
Rejected Relation
Moodle-based
Facebook-based
M
F
M
F

40
Hypothesis Regarding Comparison
3.3
3.4
3.5
3.6
3.7
3.8
3.9
4
4.1
4.2
4.3
Intention to Use User Satisfaction Learner Outcome
3.6288
3.8035
3.6559
4.0129
4.2575
4.0918
Intention to Use User Satisfaction Learner Outcome
Moodle-based System 3.6288 3.8035 3.6559
Facebook-based System 4.0129 4.2575 4.0918
MEAN VALUE
The two-sample t-test shows a significant difference
between the mean values of the two data sets for the
two systems with P-value > 0.5

Contributions, and Recommendation
41

RECOMMENDATIONS FOR FUTURE
RESEARCH
44

RECOMMENDATIONS FOR FUTURE
RESEARCH
45

Publications
47
Title
The role of System Sociability Factor in Modeling Learning Management
System Success in University Education.
Status Published; April 2015
Journal/Conference
International Conference on e-Commerce, e-Administration, e-Society, e-
Education, and e-Technology (e-CASE & e-Tech 2015)
Title
DEVELOPING A SUCCESS MODEL FOR CONTENT MANAGEMENT
SYSTEM IN HIGHER EDUCATION: ANALYSIS FROM STUDENTS’
PERSPECTIVE.
Status In Process. Initial Submission; Expected June 2016
Journal/Conference The Journal of the Association for Information Systems (JAIS)
Title
INVESTIGATION OF A MODIFIED INFORMATION SYSTEM SUCCESS
IN UNIVERSITY LEARNING SUCCESS – STUDENTS’ PERSPICTIVE.
Status In Process. Accept Manuscript; Expected Dec. 2015
Journal/Conference Journal of Technology; UTM

Publications
48
Title
Factors Influencing the Learning Management System ( LMS ) Success
Among Undergraduate Students in Limkokwing University of Creative
Technology , Malaysia.
Status Published; June 2015
Journal/Conference International Journal of Multicultural and Multireligious Understanding
Title
Learning Management System (LMS) Success: An investigation among
the university students.
Status Published; Aug. 2015
Journal/Conference
The IEEE Conference on e-Learning, e-Management and e-Services
(IC3e 2015)
Title
The effects of school management support on the use of interactive
whiteboard (IWB) in high school.
Status Published; 2015
Journal/Conference International Journal of Multicultural and Multireligious Understanding

End of Formal VIVA Presentation
50

51
The next section are not presented
It is for supporting during discussion

53
Population
Count of
Moodle-Based System FB-Based System
All
Phase1/
Group A
Phase2/
Group B
All
Phase1/
Group B
Phase2/
Group A
Registered Students 362 174 188 363 192 171

SmartPLS Minimum Sample Size
 Sample size should be at least 10 times the largest number
of formative measures of a particular construct, or 10 times
the largest number of structural paths points to a single
latent construct.
 In this study, the minimum sample size is 70.
54

55
Effective Sample Size
55
G*Power screenshot of the applied setting

56
Actual Respondents
Count of
Moodle-Based System FB-Based System
All
Phase1/
Group A
Phase2/
Group B
All
Phase1/
Group B
Phase2/
Group A
Registered Students 362 174 188 363 192 171
Distributed Survey 310 152 158 308 160 148
Collected Cases 265 128 137 262 130 132
Non-Fitted Cases -5 -2
Uncompleted Cases -11 -9
Initial Cases for Analysis 249 251
Unengaged Screening -7 -10
Univariate Screening -10 -6
Multivariate Screening -3 -2
Cleaned Cases for Analysis 229 112 117 233 118 115

Indicator reliability
(Outer Loading)
 Outer loading scale used in reflective models to
assure the proper loading of measures in its
construct.
 The acceptable level of outer loading is 0.708 and
above.
 Levels between 0.4 and 0.7 can be deleted if other
indicators reliability enhanced
(Hulland 1999; Hair et al. 2014).
58

59
Indicator reliability (Outer Loading) - Moodle
IQ IU LO LP SQ SS SrQ TP US
IQ1 0.783
IQ2 0.848
IQ3 0.839
IQ4 0.839
IQ5 0.792
IU1 0.773
IU2 0.857
IU3 0.860
IU4 0.815
LO1 0.874
LO2 0.893
LO3 0.861
LO4 0.810
LO5 0.840
LP1 0.844
LP2 0.740
LP4 0.755
LP5 0.685
SQ1 0.814
SQ2 0.839
SQ3 0.800
SQ4 0.847
SQ5 0.871
SS1 0.769
SS2 0.811
SS3 0.821
SS5 0.816
SrQ1 0.845
SrQ2 0.842
SrQ3 0.755
SrQ5 0.776
TP1 0.826
TP2 0.792
TP3 0.804
TP5 0.860
US1 0.893
US2 0.882
US3 0.905
US4 0.859

60
Indicator reliability (Outer Loading) - Facebook
IQ1 0.730
IQ2 0.811
IQ3 0.812
IQ4 0.808
IQ5 0.761
IU1 0.832
IU2 0.876
IU3 0.834
LO1 0.818
LO2 0.821
LO3 0.784
LO4 0.758
LO5 0.821
LP1 0.790
LP2 0.861
LP4 0.763
LP5 0.817
SQ1 0.754
SQ2 0.779
SQ3 0.747
SQ4 0.815
SQ5 0.851
SS1 0.711
SS2 0.839
SS3 0.808
SS5 0.774
SrQ1 0.820
SrQ2 0.785
SrQ3 0.796
SrQ5 0.768
TP1 0.727
TP2 0.772
TP3 0.801
TP4 0.815
TP5 0.696
US1 0.866
US2 0.832
US3 0.809
US4 0.833

Internal Consistency
(Composite Reliability)
 To show the consistency of items of the same
construct.
 Composite reliability should be 0.7 or higher.
 If it is an exploratory research, 0.6 or higher is
acceptable
(Bagozzi & Yi 1988; Hair et al. 2014)

62
Composite Reliability
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
IQ
SQ
SrQ
TP
LP
SS
SU
US
LO
0.889
0.892
0.871
0.874
0.883
0.864
0.884
0.902
0.899
0.912
0.92
0.88
0.892
0.843
0.88
0.896
0.935
0.932
IQ SQ SrQ TP LP SS SU US LO
FB 0.889 0.892 0.871 0.874 0.883 0.864 0.884 0.902 0.899 0
Moodle 0.912 0.92 0.88 0.892 0.843 0.88 0.896 0.935 0.932 0
0.7
0.6

Convergent Validity
(AVE value)
 Average Variance Extracted (AVE) scale used to
show that measure inside individual construct is
related.
 The acceptable level of AVE value is 0.5 and
above
(Bagozzi & Yi 1988; Hair et al. 2014).
63

64
Convergent Validity: (AVE) Average Variance
Extracted
FB
Moodle
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
IQ
SQ
SrQ
TP
LP
SS
SU
US
LO
0.616
0.624
0.628
0.583
0.654
0.616
0.718
0.698
0.642
0.674
0.696
0.649
0.673
0.575
0.647
0.684
0.783
0.733
IQ SQ SrQ TP LP SS SU US LO
FB 0.616 0.624 0.628 0.583 0.654 0.616 0.718 0.698 0.642
Moodle 0.674 0.696 0.649 0.673 0.575 0.647 0.684 0.783 0.733
0.5
AVE

Discriminant Validity (Fornell-Larcker
Criterion Analysis)
 “Correlation matrix of AVE values” scale used to
show that measures outside a specific construct is
not related to it.
 Rule of thumb is that the square root of AVE value
of a specific construct must be greater than all the
other values in the same column or row of the
correlation matrix
(Hair et al. 2014; Fornell & Larcker 1981).
65

66
Discriminant Validity (Fornell-Larcker
Criterion Analysis)
IQ 0.821
IU 0.679 0.827
LO 0.633 0.769 0.856
LP 0.664 0.672 0.683 0.758
SQ 0.724 0.75 0.689 0.654 0.834
SS 0.537 0.444 0.511 0.587 0.498 0.805
SrQ 0.726 0.662 0.618 0.635 0.752 0.532 0.805
TP 0.678 0.73 0.739 0.656 0.738 0.522 0.72 0.821
US 0.696 0.812 0.76 0.599 0.766 0.459 0.65 0.741 0.885
IQ 0.785
IU 0.587 0.848
LO 0.601 0.656 0.801
LP 0.603 0.64 0.675 0.809
SQ 0.652 0.628 0.635 0.595 0.79
SS 0.508 0.429 0.528 0.607 0.428 0.785
SrQ 0.67 0.583 0.613 0.61 0.675 0.57 0.792
TP 0.61 0.599 0.693 0.642 0.695 0.535 0.713 0.763
US 0.641 0.668 0.715 0.565 0.658 0.432 0.582 0.686 0.835
Fornell-Larcker
CriterionAnalysis
TheDiagonalValuemustbegreaterthanall
theothervaluesinthesamecolumnorraw.

Multicollinearity Analysis: (VIF) Variance
Inflation Factor
 Each set of exogenous latent variables in the inner
model is checked for potential collinearity
problem to see if any variables should be
eliminated, merged into one, or simply have a
higher-order latent variable developed.
 The acceptable level of VIF value is above 0.20
and less than 5.00.
(Wong 2013)
67

68
Multicollinearity Analysis: (VIF) Variance
Inflation Factor
5.0
0.2
VIF
IU LO US
IQ 2.939 2.801
IU 3.647
LO
LP 2.372 2.365 2.372
SQ 3.644 3.164
SS 1.675 1.622 1.675
SrQ 3.052 3.045
TP 3.141 2.826 2.8
US 3.057 3.427
IU LO US
IQ 2.423 2.258
IU 2.23
LO
LP 2.282 2.404 2.269
SQ 2.621 2.5
SS 1.813 1.682 1.813
SrQ 2.784 2.78
TP 3 2.422 2.704
US 2.355 2.352

69
Predictive Power (R2) & Predictive Relevance (Q2)
0.2
0.5
R2
0.75
0.02
0.15
Q2
0.35
R Square Q Square
Intention to Use (IU) 0.736 0.495
Learner Outcome (LO) 0.704 0.511
User Satisfaction (US) 0.673 0.516
R Square Q Square
Intention to Use (IU) 0.575 0.399
Learner Outcome (LO) 0.657 0.417
User Satisfaction (US) 0.575 0.393

70
The Effect Size – ƒ2
The ƒ² effect size measures the change in the R² value when
a specified exogenous construct is omitted from the model.
0.02
0.15
ƒ2
0.35
IU LO US
IQ 0.002 0.049
IU 0.06
LO
LP 0.07 0.048 0
SQ 0.021 0.152
SS 0.011 0.008 0
SrQ 0.002 0.002
TP 0.019 0.051 0.122
US 0.268 0.068
0.00
IU LO US
IQ 0.003 0.073
IU 0.027
LO
LP 0.092 0.06 0.006
SQ 0.022 0.048
SS 0.002 0.013 0
SrQ 0.007 0.001
TP 0 0.046 0.109
US 0.106 0.127

 Three values are used for the assessment that are
 significant level or probability estimate value (P value),
 the significance of path coefficient (T-statistics),
 and path coefficient.
 As Hair (2014), the rule of thumbs for assessing the
values is:
 P-value could be on three levels 1%, 5% or 10%, but the
popular level in psychological studies is 5% or (0.05).
 With 5% significance level, T statistic > 1.96 is significant
with a two-tailed test and T Statistics >.98 is significant
for a one-tailed test.
71

72
IQ -> IU
IQ -> US
SQ -> IU
SQ -> US
SrQ -> IU
SrQ -> US
SS -> IU
SS -> LO
SS -> US
LP -> IU
LP -> LO
LP -> US
TP -> IU
TP -> LO
TP -> US
US -> IU
US -> LO
IU -> LO
Facebook
0.057 0.811 0.209
0.264 3.77 0***
0.156 2.039 0.021*
0.227 2.778 0.003*
0.093 1.28 0.1
-0.04 0.506 0.306
-0.039 0.655 0.256
0.088 1.73 0.042*
-0.012 0.201 0.42
0.298 4.759 0***
0.222 3.552 0***
0.075 1.029 0.152
-0.005 0.074 0.47
0.196 2.886 0.002**
0.355 4.628 0***
0.326 3.839 0***
0.321 5.006 0***
0.145 2.174 0.015*
Moodle
0.036 0.599 0.275
0.213 2.987 0.001***
0.142 2.059 0.02*
0.397 5.062 0.000***
0.041 0.653 0.257
-0.047 0.638 0.262
-0.07 1.689 0.046*
0.062 1.365 0.086
-0.01 0.19 0.425
0.209 3.85 0.000***
0.183 3.17 0.001***
0.015 0.234 0.407
0.125 2.036 0.021*
0.207 3.125 0.001***
0.334 4.721 0.000***
0.465 6.993 0.000***
0.263 3.351 0.000***
0.254 3.182 0.001***

76
Research Variables and Literature Reference Models
References IQ SQ SrQ SS TP LP IU US LO
IS success model by Delone and McLean (2003) x x x x x x
ELearning system model by Freeze et al. (2010) x x x x x
ELearning system model by Wang et al. (2007) x x x x x x
Hexagonal eLearning assessment model by Ozkan et al. (2009) x x x x x
Revised community of inquiry model by Shea & Bidjerano (2010) x x x
eLearning acceptance framework by Selim (2007) x x x
Hierarchical model for eLearning CSF.. by Bhuasiri et al. (2012) x x x x x
Model of online community attributes&benefit by Kim, Park and Jin (2008) x x
Sociability and Usability Framework by Lambropoulos (2005) x x
Online Community framework by de Souza & Preece (2004) x x
(Garrison et al. 2010) x x x
(Arbaugh 2008) x x x x
(Daspit & D’Souza 2012) x x x
(Lambert & Fisher 2013) x x x
(Lee-post 2009) x x x x x x
(Keramati et al. 2011) x x x
(Gao et al. 2010) x x x
(Lin et al. 2007) x x x x
(Phang et al. 2009) x x

Information Quality (IQ)
 This study defines information quality as the level
of goodness of the information produced by the
system and assessed by using different measures
differ from system to another based on its nature
and functions.
 Those measures could include up to date,
relevance, accuracy and much more.
78

System Quality (SQ)
 This study defined system quality as the level of
goodness of the information system features and
tools excluding the output and assessed by using
different measures differ from system to another
based on its nature and functions.
 Those measures could include flexibility, response
time, system reliability and much more.
79

Service Quality (SrQ)
 This study defines service quality as the level of
goodness of the personnel support offered by the
administrative affairs to the system users.
 Those measures could include communication
quality, technical competence, and empathy of the
personnel staff and others.
80

System Sociability (SS)
 This study defined system sociability as the
system level of readiness and practice for the
interaction services and activities including
technology, policies and practice.
 Those measures could include system interactive,
members’ interaction, policies support, and others.
81

Teaching Presence (TP)
 This study defines teaching presence as the level
of instructor involvement and participation into
the system
 including content feeding quality and interaction
with the system and members in a synchronous
and asynchronous manner.
82

Learner Presence (LP)
 This study defines learner presence as the level of
learner readiness and participation in the system
 including learner confident interaction, confident
participation, ability to form an impression and
others.
83

Intention to Use (IU)
 This study defines intention to use as the level of
willingness to use the information system.
 Those measures could include frequency of use,
dependency, reusability and others.
84

User Satisfaction (US)
 This study defines user satisfaction as the level of
goodwill achieved after experiencing the system.
 Those measures could include system usefulness,
adequacy, effectiveness and others.
85

Learner Outcome (LO)
 This study defines learner outcome as the level of
studying outcome achieved by using the
information system.
 Those measures could include productivity,
performance, better thinking and others.
86

Community of Inquiry Framework
87

Brief of COI
 The community of inquiry framework is an instructional
design model for eLearning developed by Randy Garrison,
Terry Anderson et al. (2000).
 This framework is for educational context as it proposes a
framework for the use of computer-mediated
communication to support the education process.
 The framework has three essential elements cognitive
presence, social presence, and teaching presence
88
The Community of Inquiry (CoI) bundle is the
result of a project ran from 1997 to 2001.

Definitions of COI
 Social presence refers to the ability of learners to project
their personality into the community of inquiry, means
learners can introduce themselves as a real people within
the online communication or interaction.
 Teaching presence construct outlines task sets such as
organization, design, discourse facilitation, and direct
instruction and articulates the specific behaviours likely to
result in a productive community of inquiry.
 Cognitive presence refers to the extent to which the
participants in any particular configuration of a community
of inquiry are able to construct meaning through sustained
communication.
89

Preece’s Sociability and Usability
Framework
90

Brief of Preece’s S&U
 Preece (2000, 2001) proposed system usability and system
sociability as determinants of the online community
success. Goals, purposes and functions of the community
affect the needs of online communities.
 This framework used in many studies where the finding
mostly shows a relationship between sociability and social
benefits and usability and functional benefit.
91

Definitions of Preece’s S&U
 Sociability dimension involves the measures
related to the purpose, people, and policies.
Purpose factor refers to the interaction and
involvement levels of community participants.
 Usability dimension covers the measures related
to dialog and social interaction support,
information design, navigation, and access.
 Success definition of the online community differs
based on the perspective of whom.
92

Delone and Mclean Information
System Success Model
93

Brief of D&M IS Model
 D&M IS success model is a result of attempts to provide
an integrated scene of IS success that enables comparisons
between different studies. It propose a broad and
acceptable meaningful of the information system success.
 The founders of this famous theory are William H. Delone
and Ephraim R. McLean in 1992. Later on, the same
authors revised the original theory and proposed an
updated model after ten years in response to comments
announced by other different researchers.
 The updated model proposed six different dimensions of
the IS success and provided an identifying, describing, and
explaining the relationships among the six dimensions of
success.
94

Definitions of D&M IS Model
 System quality construct comprises the desirable characteristics
of the system itself and includes related measures of the IS
itself.
 Information quality construct comprises the desirable
characteristics of the information system output.
 Service quality construct characterizes the quality of the
support offered to system users by the IS department and IT
support workforce.
 The (intention to) use construct characterizes the user
utilization level of the desired information system.
 User satisfaction construct comprises the user’s level of
satisfaction when using the desired information system.
 Net benefits construct comprises the extent to which desired
information system are contributing to the success of the desired
users.
95

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