## 3 Methodology
### Data Analysis and Results
#### 3.3.1 Structure Equation Model  
We employed the partial least squares (PLS) path analysis as implemented in SmartPLS 2.0 M3 to estimate the parameters in the measurement and structural part of the structural model. First, the SmartPLS 2.0 M3 is a good method for conducting both exploratory and confirmatory research. Second, the SmartPLS 2.0 M3 is more useful than the covariance-based approach for screening out factors that have an insignificant effect on the dependent variable. Moreover, we used SmartPLS 2.0 M3 for model analysis because our study attempted to predict the factors that influence brand community participation. It leaned more toward a predictive research model less toward a theory confirmatory model. We also wanted to simultaneously evaluate the reliability and validity of the measures of the constructs in the model, and estimate the relationships among these constructs. Thus, we used SmartPLS 2.0 M3 to test our model. (A). Measurement Model Test  
We first conducted the reliability and validity analysis to measure the model and summarised the scale properties in Table 2. The reliability of measurement was assessed using Cronbach's alpha. As shown in Table 2, all values were above the 0.70 threshold, indicating that all items reached the recommended levels of CR. In addition, all composite reliabilities (CR) were above 0.7, showing that the scales have a high internal consistency [51]. This finding demonstrated the reliability of the study measurement items. The standardised loading of items was mostly above 0.7. Table 3 shows that all the average variances extracted (AVEs) were above 0.5. The square root of AVE for each construct exceeded the correlations between constructs and all other constructs, demonstrating adequate discriminant validity of all constructs. \begin{table}
\begin{tabular}{l c c c} \hline \hline
**Factor** & **Cronbach’s Alpha** & **Composite Reliability** & **AVE** \\ \hline Information quality & 0.877 & 0.924 & 0.803 \\ Marketers-interactivity & 0.935 & 0.959 & 0.886 \\ Customers-interactivity & 0.889 & 0.930 & 0.817 \\ Platform- interactivity & 0.825 & 0.896 & 0.741 \\ Brand- interactivity & 0.794 & 0.880 & 0.710 \\ Flow & 0.780 & 0.871 & 0.693 \\ Customer’s participation intention & 0.795 & 0.867 & 0.619 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Scale properties. (B). Structural Model Test  
Table 4 presents three sets of index data to show the goodness of model fit (SRMR, d\({}_{\text{ULS}}\), and d\({}_{\text{G}}\)):original value, 95 percent bootstrap quantile (HI95) and 99 percent bootstrap quantile (HI99). For the criteria of model fit, the HI95 of SRMR, d\({}_{\text{ULS}}\) and d\({}_{\text{G}}\) were greater than the original values. Thus, the goodness of the model fit in this study meets all of the criteria. In this study, we tested the structural model and summarized the results with the SmartPLS coefficients shown in Figure 2. The results support all of our hypotheses (H1, H2, H3, H4, H5, H6a, H6b, H6c, H7a, H7b and H7c). As Figure 2 shows, the flow had a positive influence on customer's participation (\(\beta\) = 0.60, t = 11.29, \(p<\) 0.001). Thus, H1 is supported. Platform-interactivity (\(\beta\) = 0.22, t = 3.99, \(p<\) 0.001), marketers-interactivity (\(\beta\) = 0.11, t = 1.98, \(p<\) 0.05), customers-interactivity (\(\beta\) = 0.21, t = 3.24, \(p<\) 0.01), brand-interactivity (\(\beta\) = 0.33, t = 6.11, \(p<\) 0.001), and information quality (\(\beta\) = 0.15, t = 3.04, \(p<\) 0.01) positively affected flow. Thus, H6a, H7a, H3, H4 and H2 are supported. In the model, the platform-interactivity is shown. In addition, marketers-interactivity is positively affected by platform-interactivity (\(\beta\) = 0.31, t = 3.96, \(p<\) 0.001) and brand-interactivity (\(\beta\) = 0.12, t = 1.97, \(p<\) 0.05); customers-interactivity is positively affected by platform-interactivity (\(\beta\) = 0.13, t = 1.96, \(p<\) 0.05), brand-interactivity (\(\beta\) = 0.26, t = 4.16, \(p<\) 0.001), and marketers-interactivity (\(\beta\) = 0.34, t = 5.02, \(p<\) 0.001).