User Acceptance of Information Feed Advertising: A Hybrid Method Based on SEM and QCA

Jie Zhao and Can Yan
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Jie Zhao: School of Business, Anhui University, Hefei 230601, China
Can Yan: School of Business, Anhui University, Hefei 230601, China

Future Internet, 2020, vol. 12, issue 12, 1-17

Abstract: It is of great significance for enterprises’ development to effectively use mobile Internet to carry out information feed advertising. This paper aims to study the influence factors and effect of the users’ acceptance intention of information feed advertising through empirical analysis to provide references for further optimizing information feed advertising strategy. Traditional quantitative analysis methods, such as the Structural Equation Model (SEM), can only measure a single factor’s influence from an individual perspective. Therefore, we introduce the Qualitative Comparative Analysis (QCA) and present a two-stage hybrid method based on SEM and QCA. In the first stage, we analyze the influence of a single variable on the acceptance intention of information feed advertising by SEM. Then, in the second stage, we analyze the impact of different variable combinations by QCA. Based on the actual questionnaire data, we define six independent variables and use AMOS, SPSS, and fsQCA to carry out SEM analysis and the fuzzy-set-based QCA analysis, respectively. The SEM analysis results show that the four factors (including consistency, informativeness, sociability, and advertising reward) have a significant positive impact on user acceptance of information feed advertising. On the contrary, perceived advertising clustering has a significant negative impact. In addition, accuracy has no considerable effect. The QCA analysis reveals that seven constructs of six variables can all significantly enhance information feed ads’ acceptance intention.

Keywords: information feed advertising; user acceptance; structural equation model; qualitative comparative analysis (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2020
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