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A Dempster-Shafer-theory-based entry screening mechanism for small and medium-sized enterprises under uncertainty.

Authors :
Wu, Xingli
Liao, Huchang
Source :
Technological Forecasting & Social Change; Jul2022, Vol. 180, pN.PAG-N.PAG, 1p
Publication Year :
2022

Abstract

• We propose an aggregation model to adjust the compensation effect among criteria. • We introduce three mutually independent coefficients in Dempster's rule. • We consider the reliability of information in the model. • We validate the method by a case study of the entry screening of SMEs. The evaluation of star-up small and medium-sized enterprises (SMEs) is an important part of ensuring the success of development, but it is not an easy task because of the multiple criteria to be considered and uncertainties caused by incomplete decision information, limited individual perceptions and differences in perceptions of experts. By integrating Dempster-Shafer theory into a probabilistic linguistic setting that takes into account the uncertainties in the evaluation process by combining linguistic terms with subjective probabilities, this study proposes a multi-criteria decision-making model for the management of SMEs. We incorporate different decision factors regarding (a) the risk attitude of decision makers, (b) the relative importance of criteria, (c) the unreliability and incompleteness of evaluation on alternatives in one preference model of multi-criteria decision making. The applicability of the proposed preference model is validated by a case study of the entry screening of SMEs for business incubators. Through comparative analysis and sensitive analysis, the reliability of the proposed model is verified, which can not only express uncertain evaluation information, but also avoid information loss and defuzzification in the aggregation process. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
00401625
Volume :
180
Database :
Supplemental Index
Journal :
Technological Forecasting & Social Change
Publication Type :
Academic Journal
Accession number :
157285430
Full Text :
https://doi.org/10.1016/j.techfore.2022.121719