Modeling the Use of Online Knowledge Community: A Perspective of Needs-Affordances-Features.

Comput Intell Neurosci

Kyonggi University, Suwon 443-760, Gyeonggi-do, Republic of Korea.

Published: January 2022

With the support of network information technology, the Online Knowledge Community (OKC) has emerged. Among different OKC applications, some entered into the new era of popular knowledge production, while others experienced the process to decline. In order to solve the dilemma faced by the OKC platforms, the needs-affordances-features (NAF) perspective on OKC is proposed by taking Zhihu, one of the most popular OKC applications in China as an example. According to NAF, the psychological needs of individuals motivate the use of Zhihu to the extent to which Zhihu offers affordances that satisfy the individuals' needs. By collecting data through questionnaires and using SPSS and AMOS for data analysis, the relationship between individuals' psychological needs and Zhihu affordances is identified. This paper generates two aspects of implications. In terms of theoretical implications, a more comprehensive framework is developed for the affordances of OKC as a whole, and the NAF model is leveraged to identify related psychological needs which motivate the use of a specific OKC application-Zhihu. Further research can leverage NAF to identify different OKC platform features which satisfy the psychological needs of their targeting users to optimize the system of OKC platforms. As for practical implications, by building the relationship between the affordances of OKC platforms and users' psychological needs, marketers have to realize that although the digital platform system has a certain degree of imitability, the value positioning, user community, and core capabilities behind those platforms are all different. Thus, the platform system must also be differentiated. In order to determine the appropriate business model, a clear understanding of these organizational features should be identified.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8723843PMC
http://dx.doi.org/10.1155/2021/3496807DOI Listing

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