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An Empirical Study on the Network Model and the Online Knowledge Production Structure

An Empirical Study on the Network Model and the Online Knowledge Production Structure

Quan Chen, Jiangtao Wang, Ruiqiu Ou, Sang-Bing Tsai
Copyright: © 2019 |Volume: 12 |Issue: 4 |Pages: 12
ISSN: 1938-7857|EISSN: 1938-7865|EISBN13: 9781522564775|DOI: 10.4018/JITR.2019100109
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MLA

Chen, Quan, et al. "An Empirical Study on the Network Model and the Online Knowledge Production Structure." JITR vol.12, no.4 2019: pp.171-182. http://doi.org/10.4018/JITR.2019100109

APA

Chen, Q., Wang, J., Ou, R., & Tsai, S. (2019). An Empirical Study on the Network Model and the Online Knowledge Production Structure. Journal of Information Technology Research (JITR), 12(4), 171-182. http://doi.org/10.4018/JITR.2019100109

Chicago

Chen, Quan, et al. "An Empirical Study on the Network Model and the Online Knowledge Production Structure," Journal of Information Technology Research (JITR) 12, no.4: 171-182. http://doi.org/10.4018/JITR.2019100109

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Abstract

Mass production has attracted much attention as a new approach to knowledge production. The R software system is a typical product of mass production. For its unique architecture, the R software system accurately recorded the natural process of knowledge propagation and inheritance. Thus, this article established a dynamic complex network model based on the derivative relationship between R software packages, which reflects the evolution process of online knowledge production structure in R software system, and studied the process of knowledge propagation and inheritance via the dynamic complex network analysis method. These results show that the network size increases with time, reflecting the tendency of R software to accelerate the accumulation of knowledge. The network density and network cohesion decrease with the increase of scale, indicating that the knowledge structure of R software presents a trend of expansion. The unique extension structure of R software provides a rich research foundation for the propagation of knowledge; thus, the results can provide us a new perspective for knowledge discovery and technological innovation.