Open AccessStudy on Enterprise Competitive Intelligence Evaluation Based on Mixed Uncertain Attribute Group Decision-making
Author(s) -
Jian Song,
Zhao Zhi-hao,
Yemeng Zhang
Publication year - 2020
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/790/1/012101
Subject(s) - ranking (information retrieval) , group decision making , computer science , construct (python library) , standardization , group (periodic table) , decision maker , management science , selection (genetic algorithm) , competitive intelligence , order (exchange) , variety (cybernetics) , knowledge management , data mining , artificial intelligence , engineering , psychology , business , social psychology , finance , programming language , operating system , chemistry , organic chemistry
Enterprise competitive intelligence evaluation has the characteristics of uncertainty and group decision. It is significant to construct the evaluation model based on the mixed uncertain attribute group decision-making. In this paper, we reviewed the algorithms, order relations and distance measures of the main uncertain parameters. Based on mixed uncertain attribute group decision-making, prospect theory and VIKOR theory, we constructed the evaluation model. We put forward the processes including standardization, group integration, value measurement and ranking. An example analysis shows that the evaluation model can integrate a variety of uncertain indicators, comprehensively reflect the evaluation opinions of each expert, and reflect the decision-maker’s risk psychology and preferences. The proposed model can provide more scientific and consistent conclusions for the evaluation and selection of competitive intelligence of enterprises.