Based on triadic reciprocal determinism, in this study, we adopted the fuzzy-set qualitative comparative analysis (fsQCA) method to conduct configuration analysis on the factors that influence the continuous use of knowledge payment platforms and explain the mechanism of the configuration effect of different influencing factors. The research included six casual variables: perceived value, platform quality, satisfaction, trust, subjective norms, and habits. Continuous use intention and continuous use behavior were used as the outcome variables. By discussing the action paths of six casual variables on two outcome variables, it is suggested that the operators of knowledge payment platforms should select different influencing factor configuration paths to improve the service and promote the continuous use of the knowledge payment platform by users.
From 2015 to the end of 2016, the Internet set off a wave of payment for knowledge. Pay-for-knowledge platforms such as Fenda, iGet, and Qianliao went online quickly, and platforms such as Himalaya FM, Zhihu, and Qingting FM gathered to launch paid columns. The number of users increased rapidly, and payment for knowledge was considered to have reached the trend of development. This article aims to study the introduction of edge computing in the mobile information system into the existence and inevitability of the knowledge payment platform; analyze the advantages, dilemmas, and optimization paths of the knowledge payment platform; and try to provide a theoretical reference for promoting its development. This article puts forward an explanation of the related content of mobile edge computing and RFID technology overview, using comparative experiment and behavior analysis methods. The experimental results show that there are 98 people under the age of 18 in the questionnaire, accounting for 19.1% of the total, 201 people aged 18–29, accounting for 39.1% of the total, 142 people aged 30–39, accounting for 27.6% of the total, and 73 people over 40 years old, accounting for 14.2% of the total number. It can be seen from the data that the sample age is mainly concentrated in the 18–29 years old, followed by the 30–39 years old; the sample age is biased towards young people. It has well completed the continuous use behavior analysis of the knowledge payment platform based on edge computing under the mobile information system.
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