Application and Analysis of an Interspecific Competition Model in a Digital Economy Ecosystem

Application and Analysis of an Interspecific Competition Model in a Digital Economy Ecosystem

Xiangdong Yang
Copyright: © 2024 |Pages: 22
DOI: 10.4018/IRMJ.339919
Article PDF Download
Open access articles are freely available for download

Abstract

Expanding industrial digitization is vital for a thriving digital economy. This article delves into the application of interspecies competition models within the digital economy ecosystem. Such models aid in analyzing competitive dynamics among enterprises and understanding innovation trends. Interspecies competition models offer insights into enterprise competition within the digital economy ecosystem. They illuminate competitive landscapes, enabling strategic planning for enterprises facing peer and cross-industry competition. Moreover, these models facilitate the study of innovation and evolution, predicting market trends and aiding decision-making for enterprises and policymakers. Empirical studies across e-commerce, fintech, and sharing economy sectors validate the effectiveness of interspecies competition models. Analysis using these models elucidates competitive dynamics and provides actionable insights for enterprises and policymakers. Furthermore, addressing data migration bandwidth issues in cloud processes fortifies digital ecosystem construction.
Article Preview
Top

Literature Review

According to gray system theory, the main research includes correlation analysis, cluster evaluation, prediction, decision-making, and control (Chen et al., 2018). In recent years, the theory has derived a variety of different gray correlation analysis models and algorithms, such as area correlation model, slope correlation model, A-type correlation model, B-type correlation model, parametric gray correlation model, generalized gray correlation model, absolute gray correlation model and relative correlation model, and other algorithms to improve the calculation of correlation between data series, as well as gray cloud optimization-based whitening model and multi-attribute decision making of fuzzy complementary judgment matrix and other improvements on gray decision algorithms, optimization of gray cluster analysis algorithm of stomach, and refinement of multivariate gray control model (Ferasso et al., 2020). The grey correlation analysis method is an important part of grey system theory, and its basic idea is to calculate the correlation between curves by comparing the proximity between them concerning the data series and comparing the geometry of the data series. Scholars combine their research directions and analyze the characteristics of gray correlation models to propose many innovative and feasible solutions (Li et al., 2020).

Complete Article List

Search this Journal:
Reset
Volume 37: 1 Issue (2024)
Volume 36: 1 Issue (2023)
Volume 35: 4 Issues (2022): 3 Released, 1 Forthcoming
Volume 34: 4 Issues (2021)
Volume 33: 4 Issues (2020)
Volume 32: 4 Issues (2019)
Volume 31: 4 Issues (2018)
Volume 30: 4 Issues (2017)
Volume 29: 4 Issues (2016)
Volume 28: 4 Issues (2015)
Volume 27: 4 Issues (2014)
Volume 26: 4 Issues (2013)
Volume 25: 4 Issues (2012)
Volume 24: 4 Issues (2011)
Volume 23: 4 Issues (2010)
Volume 22: 4 Issues (2009)
Volume 21: 4 Issues (2008)
Volume 20: 4 Issues (2007)
Volume 19: 4 Issues (2006)
Volume 18: 4 Issues (2005)
Volume 17: 4 Issues (2004)
Volume 16: 4 Issues (2003)
Volume 15: 4 Issues (2002)
Volume 14: 4 Issues (2001)
Volume 13: 4 Issues (2000)
Volume 12: 4 Issues (1999)
Volume 11: 4 Issues (1998)
Volume 10: 4 Issues (1997)
Volume 9: 4 Issues (1996)
Volume 8: 4 Issues (1995)
Volume 7: 4 Issues (1994)
Volume 6: 4 Issues (1993)
Volume 5: 4 Issues (1992)
Volume 4: 4 Issues (1991)
Volume 3: 4 Issues (1990)
Volume 2: 4 Issues (1989)
Volume 1: 1 Issue (1988)
View Complete Journal Contents Listing