B. Measurement Model Analysis
3. Validity Analysis
Validity denotes how accurately latent constructs measure their concepts or attributes and how precisely the test tool developed for the measurement reflects the corresponding attribute (Kang, 2002). And, construct validity gets recognition by testing statistically whether an operationalized instrument actually gauges an abstract notion of a construct that it purports to measure (Straub, 1989).
According to Lewis and associates (2005), each of the items in an instrument should be representative of the construct and comprehensively cover all aspects of the construct. Using the construct validity test, this study was to investigate whether the measurement instrument extracted by the researcher condignly estimated the research field. For the construct validity to be confirmed, the measurement variables in the present study were checked in two types of validity: convergent and discriminant validity.
a. Convergent Validity Analysis
First, convergent validity signifies the extent to which multiple items for a potential concept are consistent. In estimating an identical concept, the values
estimated by different measures must have high correlation. To test the convergent validity, confirmatory factor analysis (CFA) was deployed using principal axis factoring and a varimax technique of orthogonal rotation. And the validity testing was identified by factor loadings of the measurement variables.
According to Steenkamp and van Trijip (1991), CFA is a technique that after establishing a specific hypothesis, analyzes and explains observable relationships in data, and regarded as highly useful to test validity of measures estimating a latent concept. There are several indicators in this authorized analysis method.
One of them is Construct Reliability (CR) referring to values of factor loadings, which should equal to or exceed 0.7 (Hair et al., 2010; Song, 2013). Another crucial indicator is Average Variance Extracted (AVE) meaning size or degree of attributes‘ variance, whose over 0.5 value is considered acceptable (Anderson &
Gerbing, 1988; Chin, 1998; Fornell & Larcker, 1981; Hair et al., 2010; Song, 2013). Also, standardized solutions and measurement errors should be consulted as well. Table 10 shows the results of convergent validity analysis.
As displayed in Table 10 below, all the values of factor loading exceed the threshold (0.7), ranging from 0.702 to 0.889. In addition, AVE and CR for all latent variables exceed the recommend value (0.5 and 0.7, respectively). The volume of AVE refers to sufficient values: motivation (0.626), anxiety (0.621), attitude (0.578), perceived ease of use (0.586), perceived usefulness (0.594), perceived interactivity (0.611), and variety (0.734). And the CR scores of individual variables are loaded at 0.9321 for motivation, 0.920 for anxiety, 0.872 for attitude, 0.850 for perceived ease of use, 0.880 for perceived usefulness, 0.886 for perceived interactivity, and lastly 0.892 for variety. Since all of the standardized parameter estimates of all indicators on their constructs achieved remarkably higher than the recommended standards, it can support the evidence that the factor structure of the observed variables is well-constructed. It means that the measurement variables are highly relevant to their respective latent constructs. Therefore, it is concluded that the convergent validity of the measurement model is guaranteed in the constructs.
Latent
Variable Item Std.
Error
Factor Loading
t-value (Critical Ratio)
Motivation AVE = 0.626 CR = 0.921
MOT1 0.107 0.749 16.036
MOT2 0.044 0.739 10.640
MOT3 0.069 0.808 11.108
MOT4 0.036 0.772 12.365
MOT5 0.095 0.871 9.691
MOT6 0.084 0.747 11.166
MOT7 0.059 0.843 9.455
Anxiety AVE = 0.621 CR = 0.920
ANX1 0.054 0.775 12.477
ANX2 0.061 0.842 10.008
ANX3 0.007 0.738 8.275
ANX4 0.079 0.780 10.600
ANX5 0.145 0.746 8.035
ANX6 0.002 0.734 10.682
ANX7 0.064 0.889 9.318
Attitude AVE = 0.578 CR = 0.872
ATT1 0.091 0.716 11.138
ATT2 0.071 0.739 14.166
ATT3 0.049 0.783 11.478
ATT4 0.083 0.808 12.566
ATT5 0.093 0.752 10.280
Perceived Ease of Use
AVE = 0.586 CR = 0.850
PEOU1 0.086 0.748 11.651
PEOU2 Removed
PEOU3 0.123 0.805 13.289
PEOU4 0.105 0.716 14.368
PEOU5 0.159 0.790 12.410
Perceived Usefulness
AVE = 0.594 CR = 0.880
PU1 0.091 0.792 17.108
PU2 0.099 0.757 14.455
PU3 0.117 0.807 10.815
PU4 0.143 0.765 17.811
PU5 0.124 0.731 12.142
Perceived Interactivity
AVE = 0.611 CR = 0.886
PI1 0.104 0.857 13.671
PI2 0.097 0.768 9.195
PI3 0.064 0.702 7.970
PI4 0.136 0.825 11.099
PI5 0.125 0.745 15.786
Variety AVE = 0.734 CR = 0.892
VAR1 0.136 0.868 9.660
VAR2 0.129 0.859 10.222
VAR3 0.095 0.843 9.451
VAR4 Removed
VAR5 Removed
[Table 10] Results of Convergent Validity Analysis
Note. MOT: Motivation; ANX: Anxiety; ATT: Attitude; PEOU: Perceived Ease of Use, PU;
Perceived Usefulness; PI: Perceived Interactivity; VAR: Variety.
b. Discriminant Validity Analysis
Second, discriminant validity signifies one construct should be empirically differentiated from other constructs that may be similar; i.e., the lack of a relationship among latent variables that theoretically should not be related. Thus, it is defined as the distinctive degree to which two similar constructs in their concepts are differentiated (Hair et al., 2010). To this end, the estimates between different constructs must be distinguished, and excessive overlap among different constructs must not exist. In order to demonstrate the discriminant validity, the correlation coefficient (
r
) among latent variables in the horizontal and vertical line should not exceed the square root of AVE (Chin, 1998; Fornell & Larcker, 1981). The bold figures on diagonal of the matrix, shows the square root of AVE, and the bottom shows the correlations. The significance level was set at 0.05 (p
< 0.05) to test the null hypotheses of no association. Table 11 presents the matrix showing the results of discriminant validity analysis.Latent
Variable MOT ANX ATT PEOU PU PI VAR
MOT 0.791
ANX .416** 0.788
ATT .361** .254** 0.760
PEOU .288** .284** .245** 0.766
PU .401** .380** .342** .326** 0.791
PI .456** .287** .155** .261** .397** 0.782
VAR .204* .190* .238** .256** .336** .249** 0.857
M 3.29 3.15 3.43 3.58 3.33 3.70 3.02
SD 0.627 0.743 0.641 0.720 0.600 0.622 0.763
[Table 11] Results of Discriminant Validity Analysis
Note. MOT: Motivation; ANX: Anxiety; ATT: Attitude; PEOU: Perceived Ease of Use, PU;
Perceived Usefulness; PI: Perceived Interactivity; VAR: Variety. p<0.05:*, p<0.01:**
As indicated on the matrix, the correlation coefficients (