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the survey are being conducted or have used E- Learning. Primary data collection was done by
conducting interviews, observation and survey. 5.3.2 Secondary Data
The Secondary Data consist of data matching data in the form of documents that already exist with regard to
the application of E-Learning and data obtained from the literature review in the form of textbooks, journals,
the internet, the results of earlier research. 5.4 Data Collection Techniques
Form of research that will be used in this research using quantitative data analysis, statistics, interviews and
survey approach. Kuantitafif statistics data analysis is used to examine the relationship of the variables
research, by providing questionnaires to lecturer and students. Answers of the respondents to the
questionnaire that was distributed later, used to measure the variables contained in the research model.
Questionnaire types used are closed, meaning that the respondent must choose one of the answers that are
already available in which the respondent will provide qualitative answers as measured by likert scale
questionnaire for each question. Likert scale is a method of measuring attitude by stating his disapproval
of the subject agree or object, or certain events Indriantoro and Supomo, 1999. Likert scale each
question is measured with a scale interval of 1 to 5. Strongly disagree 1 score STS, Disagree TS score 2,
neutral N score 3, Agree S score 4, strongly agree SS 5. The Data will be analyzed using SEM
Structural Equation Modeling with AMOS Software version 16.0.
6. MEASUREMENTS OF OPERATIONAL
DEFINITIONS AND VARIABLES
The variable in this study were classified into exogenous
variables, variables
are endogenous
variables, and mediation. Exogenous variables consist of Information Quality of E-Learning, E-Learning
System Quality, Service Quality E-Learning. Variable mediation consists of Intention To Use and User
Satisfaction, and to endogenous variables is Net Benefit. The following are the operational definition of
variables:
Table. 1 Variable and Indicator
Kode Variable dan
Indicator
Adoption from
SQ System Quality
SQ1 Ease of use
Gable et al. 2008, McKinney et al. 2002
SQ2 System Flexibility
Gable et al. 2008, Iivari 2005
SQ3 Response Time
Livari 2005 SQ4
System Reliabilty Gable et al. 2008, Sedera and
Gable 2004b SQ5
Navigation McKinney et al. 2002
SQ6 Sophistication
Gable et al. 2008, Sedera and Gable 2004b
SQ7 System features
Gable et al. 2008, Sedera and Gable 2004b
SQ8 System Security
Hamilton dan Chervany 1981 IQ
InformationQuality
IQ1 Accuracy
Gable et al. 2008, Iivari 2005
IQ2 Timeliness
Gable et al. 2008, Iivari 2005
IQ3 Completeness
Bailey and Pearson 1983, Iivari 2005
IQ4 Format
Gable et al. 2008, Iivari 2005, Sedera and Gable
2004b IQ5
Reliability McKinney et al. 2002
IQ6
Availability
Gable et al. 2008, Sedera and Gable 2004b
IQ7
Usefulness
McKinney et al. 2002 SV
Service Quality
SV1 Tangible
Pit et al 1995 SV2
Service Reliability Pit et al 1995
SV3 Responsiveness
Chang and King 2005 SV4
Assurance Pit et al 1995
SV5 Empathy
Pit et al 1995 IU
Intention To Use
IU1 Frequency of use
Almutairi and Subramanian 2005, Iivari 2005
IU2 Intention to reuse
Davis 1989, Wang 2008 IU3
Dependence Wang Shee 2007
SU Satisfaction User
SU1 Effectiveness
Almutairi and Subramanian 2005
SU2 Enjoyment
Gable et al. 2008 SU3
Information satisfaction
Gable et al. 2008 SU4
System satisfaction Gable et al. 2008
SU5 Proudnes
McGill et al., 2003 SU6
Overall Satisfaction
Gable et al. 2008 NB
Net Benefit
NB1 Individual
productivity Gable et al. 2008, Sedera and
Gable 2004b NB2
Awareness Gable et al. 2008, Sedera and
Gable 2004b NB3
Usefulness Davis 1989, Iivari 2005
NB4 Decision
Effectiveness Almutairi and Subramanian
2005 NB5
Job E ffectiveness
Davis 1989, Iivari 2005 NB6
Cost reduction
Almutairi and Subramanian 2005, Gable et al.2008
NB7
Improved outcomesoutputs
Gable et al. 2008, Sedera and Gable 2004b
NB8
Competitive advantage
Almutairi and Subramanian 2005, Sabherwal 1999
NB9
Quality Improvement
Sabherwal 1999 NB10
Costumer Satisfaction
Torkzadeh and Doll 1999
7. QUALITY INSTRUMENTS TEST
In the measurements used to assess the validity of a valid or invalid at least one questionnaire. A
questionnaire as valid if the question of the questionnaire was able to reveal something that will be
the measure by a questionnaire. According to Umar 2005, the validity test is a test that is used to indicate
the extent to which a measuring device that is able to measure what you want to measure. Thus, the validity
of the tests carried out to ensure that the measurement
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results in accordance with what we want to measure. Test Validity done by looking at the output of the
program AMOS AMOS is the Estimate by comparing P values with an alpha of 5 if the P value 0.05 was
then declared invalid. Reliability test was conducted to test the interpretation of the respondents statement of
grains contained in the instrument of consistency shown by the answers given. To determine whether the data is
reliable or not it can be seen from the Cut-Off Value of contruct Reliability with the minimum provisions of 0.7
or views of Cut Off Value of Variance Extracted at least 0.5.
8. DATA ANALYSIS
The variables in this study were classified into exogenous
variables, mediating
variables, and
endogenous variables. Exogenous variables consist of Information Quality E-Learning, E-Learning System
Quality, Service Quality of E-Learning. Mediating variables consisting of Intention To Use and User
Satisfaction, and for the endogenous variables namely Net Benefit. The following is the operational definition
of variables. Perhaps there is also a double instrumental variables as independent variable in a relationship, but
became dependent on variables other relationships given the existence of a relationship of causality that is
berjenjan. Each of the dependent and independent variable is a factor or invalid constructs can be built
from a few variable indicators. Similarly among the variables that may take the form of a single variable in
observation or measured directly in a process of research. Modeling by using SEM research refers to
Ferdinand 2002.
Table. 2
Goodness of Fit Indicates
Goodness of Fit Index Cut Off Value
X
2
chi square
statistic
df den gan α =
0,005 Significancy
Probability ≥ 0,05
RMSEA ≤ 0,08
GFI ≥ 0,90
AGFI ≥ 0,90
CMINDF ≤ 2,00
TLI ≥ 0,95
CFI ≥0,95
Source : Hair, dkk Ferdinand, 2002
9. RESEARCH PROCESS