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Indicator-Type Grey Structure Incidence Analysis Method for Panel Data and Its Application in Identifying Technological Innovation Factors

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Bibliographic Details
Title: Indicator-Type Grey Structure Incidence Analysis Method for Panel Data and Its Application in Identifying Technological Innovation Factors
Authors: Shuqin Gu, Yong Liu, Lu Yue
Source: Journal of Social Computing, Vol 6, Iss 2, Pp 145-157 (2025)
Publisher Information: Institute of Electrical and Electronics Engineers (IEEE), 2025.
Publication Year: 2025
Subject Terms: Social sciences (General), H1-99, scale volume, Electronic computers. Computer science, volatility, grey structural incidence analysis, QA75.5-76.95, development trend, time-lag effect
Description: There exist many panel data decision problems in real life, and they take on obvious structural similarities and lag effects among decision objects or indicators, which are difficult to solve effectively based on traditional panel data analysis methods. To deal with these problems, considering the structural characteristics of panel data and lag effect, from multiple structural dimensions such as scale volume, development trend, and volatility, we exploit grey incidence analysis and panel data to establish an indicator-type grey structural incidence analysis model, and utilize it to analyze and identify factors influencing technological innovation of industrial enterprises. The results show that the proposed method fully considers the structural characteristics of panel data and lag effect, and it can deal with panel data decision problems and provide a new methodological support for the grey incidence analysis.
Document Type: Article
ISSN: 2688-5255
DOI: 10.23919/jsc.2025.0004
Access URL: https://doaj.org/article/ebf4377c96da4a82a8ee47d9d1a3acf8
Accession Number: edsair.doi.dedup.....11a2f1ee2d176ef79cf42959773360dd
Database: OpenAIRE
Description
Abstract:There exist many panel data decision problems in real life, and they take on obvious structural similarities and lag effects among decision objects or indicators, which are difficult to solve effectively based on traditional panel data analysis methods. To deal with these problems, considering the structural characteristics of panel data and lag effect, from multiple structural dimensions such as scale volume, development trend, and volatility, we exploit grey incidence analysis and panel data to establish an indicator-type grey structural incidence analysis model, and utilize it to analyze and identify factors influencing technological innovation of industrial enterprises. The results show that the proposed method fully considers the structural characteristics of panel data and lag effect, and it can deal with panel data decision problems and provide a new methodological support for the grey incidence analysis.
ISSN:26885255
DOI:10.23919/jsc.2025.0004