3.1. Variables and Hypothesis
- (1)
Variables
There is only one dependent variable in this study: the user’s willingness to accept information feed advertising. The independent variables include the factors that affect the user acceptance of information feed advertising. Since the characteristics of advertising and the related operations of advertising publishing platforms significantly impact the user acceptance of advertising, we define the variables from the related advertising attributes of information feed advertising and advertising publishing platforms. These factors include consistency, accuracy, informativeness, sociality, advertising reward, and perceived advertising clustering. The basis for the selection of these variables is detailed below:
Consistency. Information feed advertising is an Internet display advertisement embedded in information streaming media platforms. Information feed advertising has characteristics consistent with social media platforms and context information. Previous research showed that the design of information feed advertising is compatible with the social media platform [
15,
16], which weakens the interference to users’ browsing or reading information. The consistency of information feed advertising positively impacts user perception of information processing fluency. The information processing fluency theory points out that people’s perception of the difficulty of processing information affects their response to information, and believes that, when people perceive information with high fluency when processing information, it is easier to cause positive feedback from users [
17].
Accuracy. Based on big data, information feed advertising accurately depicts users, analyzes user behavior, accurately captures user needs, and realizes accurate delivery to users. With the development of information feed advertising, big data, and machine learning technology, it is possible to realize intelligent collaborative recommendation of information feed advertising and improve information feed advertising accuracy. In the modern era, with more and more advanced technologies, users are increasingly pursuing convenient operation and access to useful information to avoid being trapped in miscellaneous details. However, when users see that the pushed information feed advertisement matches their interests or needs, it saves users’ efforts and time for searching. Therefore, the information feed advertising that meets the user’s needs has won their initial recognition to browse or further accept advertisements.
Informativeness. Informativeness refers to the content of effective products or services provided by advertisements. Advertising information will directly affect users’ cognition and understanding of products or services. The most basic advertising function is to provide users with the information content of products or services. Ducoffe pointed out that the primary reason to support advertising was that users could obtain effective advertising content from advertising [
18]. Information feed advertising could further provide users with high-quality and exciting products or services. Thus, users can get greater harvest and satisfaction from the effectiveness of information seed advertising.
Sociality. Sociality refers to the degree to which the computer media communication environment can promote social space’s emergence by allowing social support [
19]. Information feed advertising integrates the page interaction technology of social function, making users actively participate in the interaction with information feed advertising. Therefore, in the context of information feed advertising, users can click to view, like, and comment on information feed advertising. In addition, other users’ participation behavior can be observed, which meets users’ social needs, so it has the characteristics of sociality. Specifically, information feed advertising’s sociality represents the degree to which users can meet their social impulses by interacting with other users through information feed advertisements. According to the social existence theory, if social media can give individuals the same feeling as a face-to-face conversation, individuals will have better psychological feelings. That is to say, they will experience a sense of social existence, and sociality can enhance the perceived pleasure of users and create an immediate sense of belonging among individuals [
20]. Moreover, the sociality of information feed advertising can meet the psychological needs of users for social interaction. The interactive media experience constructs a learning community based on real society for information feed advertising, dramatically improving users’ participation and activity [
21].
Advertising reward. Advertising reward refers to the incentive measures that advertisers provide users with product gifts, coupons, etc. Differing from users’ active search, information feed advertising is passively accepted by users. Therefore, it may cause a particular aversion. In practice, to attract users and reduce users’ aversion, some information feed advertisements will provide advertising, such as coupons and gifts. These reward measures can let users experience practical benefits. Users are generally rational, and advertising rewards can improve users’ perceived benefits [
22], so they may accept information feed advertising because of advertising rewards.
Perceived advertising clustering. Too much advertising and too high advertising frequency will cause cognitive pressure on users, which will cause an aversion to advertising. Perceived advertising clustering is used to describe the user’s perception of the number of platform ads and the media attributes of platform advertising [
23]. It is generally embedded in information for information feed advertising and is displayed one by one according to the platform information presentation mode. Therefore, if the social platform pushes too many information feed advertisements to the users, forming a perceptual advertising cluster, it will worsen users’ perception of advertising and cause boredom to information.
- (2)
Hypothesis
In this study, based on the above six variables, we aim to verify the following hypotheses:
Hypothesis 1 (H1). Consistency has a positive impact on the user acceptance of information feed advertising.
Hypothesis 2 (H2). Accuracy has a positive impact on the user acceptance of information feed advertising.
Hypothesis 3 (H3). Informativeness has a positive impact on the user acceptance of information feed advertising.
Hypothesis 4 (H4). Sociality has a positive effect on the user acceptance of information feed advertising.
Hypothesis 5 (H5). Advertising reward has a positive impact on the user acceptance of information feed advertising.
Hypothesis 6 (H6). Perceived advertising clustering has a negative impact on the user acceptance of information feed advertising.
Hypothesis 7 (H7). Different groups of variables have different impacts on the user acceptance of information feed advertising.
Note that hypotheses 1 to 6 are toward the impact of a single factor on the user acceptance of information feed advertising, while hypothesis 7 is toward the impact of various groups of multiple factors. In this study, we use separate methods to verify these hypotheses. For verifying hypotheses 1 to 6, we use the SEM model, and, for hypothesis 7, we use the fsQCA approach. Next, we detail the research method of this study.
3.2. 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]. Moreover, the effective integration of the fsQCA method and mainstream statistical analysis methods have provided a broad opportunity to expand the social science theory’s descriptive, predictive, and explanatory power [
29]. For example, the previous research analyzed the influencing factors of customer complaint behavior on customer loyalty in B2C E-commerce. It used the SEM model and fsQCA to test the relationship among distribution, interaction and procedural justice, positive and negative emotions, service compensation satisfaction, and trust [
30]. With the help of fsQCA, another work analyzed the influencing of user characteristics and intensive factors on user behavior [
31]. Alonso dos Santos et al. (2018) used the SEM model and fsQCA to analyze the cancellation of sports sponsorship in sports virtual brand community [
32]. The study of Duarte et al. (2019) showed that six combinations of seven antecedent variables were sufficient for users’ willingness to accept mobile medical care [
33]. Gligor et al. (2020) considered customers’ background and explored two methods of qualitative comparative analysis, including fsQCA and the multiple regression analysis, to explain the factors influencing customers’ participation in the brand advertising [
34].
Based on the SEM analysis and the summary of different levels of influencing factors, this paper explores the combination of factors influencing user acceptance of information feed with the QCA method’s help. The introduction of the QCA method makes us not limited to studying a single factor, but also does not rely on researchers’ subjective will. The conclusion is more objective and exploratory. Through the combination of the SEM model and QCA method, we can not only find the independent influence of a single factor but also mine the combination effect of user’s intention, which can further analyze the user’s willingness to accept information feed advertising.
3.3. Questionnaire Design and Data Collection
The questionnaire in this study was measured by the Likert 5 scale. The respondents chose 1 (very disagree) to 5 (very agree) to rate the questions. The observation indicators are set based on classic literature and users’ willingness to accept information feed advertisements. The questionnaire consists of three parts: sample screening questionnaire, demographic questionnaire, independent variable, and dependent variable questionnaire:
- (1)
Sample questionnaire. This part of the questionnaire mainly detects the useful samples, ensuring that the users are experiencing information feed advertising.
- (2)
Demographic questionnaire. This part mainly measures the gender, age, and educational background of the respondents.
- (3)
Independent and dependent variable questionnaire. This part of the questionnaire is the core of the whole questionnaire design, as shown in
Table 1.
In this study, 262 questionnaires were collected through an online survey. After eliminating invalid questionnaires such as data missing, 229 valid questionnaires were obtained, with an effective rate of 87.4%. The sample size is more than 200, and the ratio of a sample size to measurement items is 10.9:1, which meets the analysis requirements of the SEM model.
The descriptive statistical results of the sample are as follows: the proportion of male and female is 47.6% and 52.4%; the age group is mainly between 18 and 30 years old, accounting for 88.2% of the total sample; the number of undergraduates and above accounts for 89.9%, and the sample of master’s degree or above accounts for 49.7%; in the occupation, the proportion of students studying is 55.4%, and that of enterprise employees is 32.7%.