Dynamics of stakeholder relations with multi-person aggregation

Publication Type:
Journal Article
Citation:
Kybernetes, 2018, 47 (9), pp. 1801 - 1820
Issue Date:
2018-10-01
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© 2018, Emerald Publishing Limited. Purpose: The purpose of this paper is to develop a novel method to analyse dynamic interactions of stakeholders to explain how a set of agents can act by considering the power/influence positions. Design/methodology/approach: A novel mathematical application uses the importance of characteristics algorithm in combination with composition max-min to compare, group and order information according to the importance of its characteristics. The mathematical application is focused on a strategic analysis, evaluating stakeholder dynamics through power relationships. Findings: The results show a comparison of the relationships among each of the stakeholders to obtain the relative intensity and importance of relationships between them, given by the fuzzy matrix FRInM and the fuzzy matrix FRIM, respectively. This application provides a useful tool for a dynamic analysis of stakeholders in a complex environment, where the best approach to performing a strategic analysis process is sought. Research limitations/implications: The main implication of the proposed approach is taking into account the importance of information to establish the boundaries and relationships of each characteristic according to its intensity. However, limitations are due to the nature of this research, based on theoretical assumptions regarding stakeholders and the use of a hypothetical example to show the operation of algorithms. Originality/value: The primary advantage of this proposition is that it takes into account the importance of information to establish the relationships among the characteristics according to their intensity. In addition, it performs multiple comparisons among each characteristic of the information. The interests and opinions of decision makers can be parameterised. A mathematical application shows how each interest group could be classified and related according to subjective information.
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