Page 945 - MARSIUM'21 COMP OF PAPER

946 Teo & Dr Adaviah (2021)

Variable Skewness Kurtosis
Statistic Std. Error Statistic Std. Error
Brand Equity -0.171 0.212 -0.224 0.420
e-WOM 0.162 0.212 -0.369 0.420
User Generated Social Media 0.134 0.212 -0.166 0.420
Communication
Firm Generated Social Media -0.010 0.212 0.104 0.420
Communication
Repurchase Intention -0.264 0.212 0.089 0.420

Table 4.4: Skewness and Kurtosis Normality

4.3 VALIDITY ANALYSIS

4.3.1 CONSTRUCT VALIDITY

According to Mohajan (2017), construct validity is particularly essential for empirical measurements and testing of
hypotheses in theory creation. To determine the construct validity, researchers conducted a study in which they examined whether
the test's variables aligned with theoretical predictions (Sekaran & Bougie, 2010). Convergent and discriminant validity are the
two categories of construct validity. These two categories are aimed to ensure that the instruments are compatible with the concept
of speculation. For the best results, the loading value should be more than 0.5, as stated by Hair et al. (2010).

4.3.2 CONVERGENT VALIDITY

Convergent validity is being used to examine the extent to which numerous items evaluate the same idea (Ramayah et
al., 2011). It refers to the degree toward which results on one measure have a high, moderate, or low correlation with values on
another test that assesses the same construct. The loading value should be more than 0.5 to have the best results (Hair et al., 2010).
Based on Table 4.5, the result shows that all values are more than 0.5. It demonstrates the close connection between items that
measure the same construct and all items that fall into one component. Thus, the convergent validity was fulfilled.

BE E F R U
BE1 0.866
BE2 0.899
BE3 0.898
BE4 0.723
BE5 0.829
E1 0.82
E2 0.811
E3 0.791
E4 0.606
E5 0.547
E6 0.651
F1 0.942
F2 0.919
F3 0.949
F4 0.853
R1 0.879
R2 0.914
R3 0.908
U1 0.661
U2 0.899
U3 0.896
U4 0.8

Table 4.5: Result of Convergent Validity Test

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