# User Acceptance of Information Feed Advertising: A Hybrid Method Based on SEM and QCA
## Research Design
### Research Method  
This paper proposes a two-stage hybrid research method, which combines the Structural Equation Model (SEM) with the Qualitative Comparative Analysis (QCA). The SEM's primary purpose is to analyze the influence of a single factor on the user acceptance of information feed advertisements. The purpose of QCA is to analyze the impact of different variable combinations on the user acceptance of information feed advertisements. It is necessary to carry out qualitative comparative analysis in selecting the actual information feed advertising strategy. First of all, the positive or negative effects of a single factor on the user acceptance of information feed advertising can only show the impact of a single factor. However, they can not explain how to combine multiple factors in the actual information feed advertising. Secondly, not all information feed advertising enterprises can maximize all the influencing factors. For example, suppose some enterprises are unwilling to advertise rewards and advertise on social networks, the two factors of sociality and advertising rewards will be missing. How to maximize the user acceptance of information feed advertising becomes challenging. The introduction of the qualitative comparative analysis method can solve the above problems. Basically, our research model consists of the following stages. (1) Stage 1: Performing the SEM Analysis  
In the first stage, the SEM model is used to study the influence of a single factor on the user acceptance of information feed advertising. Structural equation modeling (SEM) is a method to establish, estimate, and test causality. The model contains both observable and potential variables. The SEM model can use multiple regression, path analysis, factor analysis, covariance analysis, and other methods to clearly analyze the effect of a single factor on the overall and the relationship between individual indicators. (2) Stage 2: Performing the QCA Analysis  
The QCA analysis is a research method between the case-oriented approach and the variable-oriented approach [24; 25]. This method combines the advantages of the traditional qualitative research methods, taking the membership relationship between sets as the primary means. It uses Boolean algebra to explore how antecedents' combination causes observable changes or discontinuities in the interpreted results [26]. The QCA method systematically examines the conditions for an event's occurrence and the interaction and possible relationship combinations between the internal generating conditions. It attempts to explain the core conditions contributing to the event's occurrence, the interrelationship between the conditions, and the complex combination of conditions that stimulate the event's occurrence. Thus, it can deepen the understanding of the complicated causal relationship of the event [27]. The basic principle of Boolean algebra used by qualitative comparative analysis in data coding is to use dichotomy to deal with different conditions by 0/1. If a condition appears, it is indicated by 1, and, if a condition does not occur, it is represented by 0. However, in the actual case analysis, some conditional variables cannot be clearly coded as 0 or so the fuzzy-set QCA method (fsQCA) is typically used. For conditions that cannot be clearly coded, there is a membership score between 0 and 1, dealing with degree change and partial membership [28]. Compared to the SEM analysis, the fsQCA method is asymmetric, i.e., the relationship between cause and condition is asymmetric. Thus, multiple approaches or solutions may lead to the same result and causal complexity, indicating the interdependence of cause conditions and multiple concurrent causalities formed by different combinations. The fsQCA method is suitable for studying social sciences, including economics and management [25]. It has been rapidly applied in management research in recent years and has been used in questionnaire design, second-hand data, and case studies [28].