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No. Variables Cronbach’s Alpha N of Items

1. Firm-created social media 0.796 4
communication

User-generated social media 0.828 4
2.
communication
3. Brand awareness (BA) 0.839 4

4. Perceived quality (PQ) 0.759 3

Table 3: Reliability Analysis

4.3 REGRESSION ANALYSIS

Regression analysis is a collection of statistical approaches for estimating relationships between variables (Thrane,
2019). The variables are not treated symmetrically (Angelini, 2019). There are several variations of regression, and the most
commonly used are linear regression and multiple regression (Morrissey & Ruxton, 2018). Frequently, linear regression was
employed to investigate the relationship between two variables (Schmidt & Finan, 2018). Linear regression is the simplest
model and functions as a linear mixture of predictors (Su et al., 2012). Linear regression was used in this study to investigate
the relationship between brand awareness and perceived quality.

Multiple regression is an extension of simple linear regression and used to forecast a variable's value using two or
more other variables (Bell et al., 2002). The multicollinearity test was used to verify that the tolerance value for each variable
is more than 0.1 and the variance inflation factor (VIF) value is less than 10. Multiple regression analysis examined the
connections between social media brand communication, brand awareness, and perceived value. The R2 values will determine
associations and the most potent predictors, and the beta value will indicate the strength of each independent variable to the
dependent variable, which is the higher, the stronger the relationship is. Besides, a variable relationship is statistically
significant when p-value < 0.05 (Dahiru, 2011). Hence, multiple regression was used to investigate the relationship between
FCC and UGC to brand awareness and perceived quality.

Table 4 shows that all hypotheses are supported. FCC and UGC are positively and significantly related to brand
awareness. UGC is the strongest predictor for brand awareness with a beta value of 0.444. The R2 value was 0.362. Hence,
36.2% of the variations in the dependant variables is explained by the linear relationship with the independent variable. In
addition, FCC and UGC have a significant and positive impact on perceived quality. Similarly, UGC is the strongest predictor
for perceived quality with a beta value of 0.531. The R2 value was 0.457. Thus, 45.7% of the variations in the dependant
variables is explained by the linear relationship with the independent variable. Finally, the relationship between brand
awareness and perceived quality is positive and significant.

Hypotheses Relationship Standardized Coefficients P- Value T-Value Result
Beta
H1a FCC → BA 0.198 0.037 2.105 Supported

H1b UGC → BA 0.444 0.000 4.714 Supported
H2a FCC → PQ 0.192 0.027 2.230 Supported
H2b UGC → PQ 0.531 0.000 6.152 Supported
H3 BA → PQ 0.612 0.000 9.403 Supported
Table 4: Multiple Regression Analysis

4.5 INTERVENTION FOR TOP ONE TECHNOLOGY SDN. BHD.

The intervention was conducted for Top One Technology Sdn. Bhd. for two months from 17th September 2021 to
16th November 2021. The findings show that FCC and UGC are critical to increase the brand awareness and brand equity of

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