# User Acceptance of Information Feed Advertising: A Hybrid Method Based on SEM and QCA
## 5 Qualitative Comparative Analysis (QCA)  
Based on the SEM analysis, we have obtained the influencing factors (i.e., consistency, accuracy, informativeness, sociality, advertising reward, and perceived advertising clustering) of information feed advertising. Next, we will apply the fuzzy set qualitative comparative analysis (fsQCA) to study the effect of the combination of these factors and try to understand how the different combinations will impact the users' willingness to accept information feed advertising. The fsQCA method includes the following processes [28]: extraction of dependent variables; data acquisition; data calibration, necessity analysis, and adequacy analysis. We will set up different constructs of variables and measure their impact on user acceptance of information seed advertising. (1) Data calibration  
In this step, we establish the combinations of various factors that affect the user acceptance of information feed advertising [40]. Since this study's sampling data is a numerical type of 5-point Likert, we need to convert data to the value between 0 and 1 scale before analysis. All the variables are directly calibrated, and three anchors need to be set, namely full membership, intersection, and total non-subordination [41]. Many studies choose to use percentiles to calibrate the scale [42], and it is necessary to deal with the relationship between the measurement scale and the actual distribution of the sample [43]. In this study, the sample data score distribution is relatively concentrated. The calculation is completed by the calibrate function in the fsQCA software. (2) Necessity analysis  
Based on data calibration, we test the necessity of a single antecedent variable. The consistency of the necessity fuzzy subset relation is shown in Formula (1), and coverage is shown in Formula (2):  
\[consistency(X_{i}\leq Y_{i})=\frac{\sum\ \min(X_{i},Y_{i})}{\sum\ X_{i}} \tag{1}\] \begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline
**Statistical Test** & **x2/df** & **SMRM** & **RMSE** & **AGFI** & **NFI** & **RFI** & **CFI** & **IFI** & **PGFI** & **PNFI** & **PCFI** \\ \hline Ideal Value & \(<\)2.00 & \(<\)0.08 & \(<\)0.05 & \(>\)0.80 & \(>\)0.90 & \(>\)0.90 & \(>\)0.90 & \(>\)0.50 & \(>\)0.50 & \(>\)0.50 \\ Acceptable Value & \(<\)3.00 & \(<\)0.1 & \(<\)0.08 & \(>\)0.70 & \(>\)0.80 & \(>\)0.80 & \(>\)0.80 & \(>\)0.80 & & & \\ Our Value & 2.345 & 0.0549 & 0.077 & 0.812 & 0.871 & 0.883 & 0.921 & 0.922 & 0.628 & 0.697 & 0.737 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Model fitting degree. \begin{table}
\begin{tabular}{c c c} \hline \hline
**No.** & **Hypothesis** & **Validation Result** \\ \hline H1 & Consistency has a positive impact on the user acceptance & Established \\  & of information feed advertising. & \\ H2 & Accuracy has a positive impact on the user acceptance of & Not Established \\  & information feed advertising. & \\ H3 & Informativeness has a positive impact on the user & Established \\  & acceptance of information feed advertising. & Established \\ H4 & Sociality has a positive effect on the user acceptance of & Established \\ H5 & Advertising reward has a positive impact on the user & Established \\  & acceptance of information feed advertising. & Established \\ H6 & Perceived advertising clustering has a negative impact & Established \\  & on information feed advertising acceptance intention. & \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Summary of hypotheses validation in the SEM analysis. \[coverage(X_{i}\leq Y_{i})=\frac{\sum\ \min(X_{i},Y_{i})}{\sum\ Y_{i}} \tag{2}\]  
Here, \(Y_{i}\) is the result set and as \(X_{i}\) is the condition set. When the consistency is over 0.9, the condition is necessary and becomes a superset of the result set [44].