Qihang, Zhang, Jiang, Jie (Eve) ORCID: 0000-0002-5461-1574 , Feng, Bo and Feng, Junwen (2023) Design of IPR evaluation system based on linear regression model. Applied Mathematics and Nonlinear Sciences, 9 (1). pp. 1-18. ISSN 2444-8656 (Online) (doi:https://doi.org/10.2478/amns.2023.2.00279)

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Abstract

This paper first defines the conceptual scope of intellectual property, and the intellectual property evaluation system analyzes the special characteristics of enterprise intellectual property evaluation and compares the differences between several intellectual property management models. Secondly, an open adaptive enterprise IPR evaluation system is constructed based on a linear regression model, and the system structure and the relationship between subsystems are analyzed in depth. Finally, based on the theory of adaptive evaluation management, the adaptive IPR evaluation system is constructed. The adaptive enterprise IPR evaluation model based on linear regression was constructed mainly from three dimensions, and the method to determine the development coordination index and early warning degree of the three dimensions was deduced. The results show that the average efficiency of the typical enterprise IPR evaluation system calculated based on the linear regression model is 0.86, which is 21.3% more efficient than the traditional model. Four of the decision units’ DEA is effective, 63% of the inputs are effective, and 37% of the input resources are wasted, which aligns with the actual enterprise. The adaptive IPR evaluation system based on the linear regression model proposed in this paper has theoretical innovation value and practical significance for enterprises to realize the transformation of IPR achievements.

Item Type:	Article
Uncontrolled Keywords:	intellectual property evaluation; linear regression model; adaptivity; coordination index; early warning degree
Subjects:	H Social Sciences > H Social Sciences (General) H Social Sciences > HB Economic Theory Q Science > QA Mathematics
Faculty / School / Research Centre / Research Group:	Faculty of Business
Last Modified:	25 Apr 2024 11:38
URI:	http://gala.gre.ac.uk/id/eprint/46847

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