In this paper, we investigated into aggregated social influence. We adopted and modified the weighted TOPSIS approach to ascertain the overall social influences of management members in the banking network of Ghana. The weighted TOPSIS method employs a composite approach of classical centrality influence that uses the position of the actor in the network hierarchy, the intensity of his interaction, extent of his connectivity and flow of information within the network. The approach offers an extensive advantage in ensuring holistic decision making by implementing an algorithm that employs a multi-criteria approach. The study revealed that although most single attributes were significant in measuring the niched aspect of social influence, the closeness to ideal that was attained through a weighted TOPSIS algorithm showed stronger ties and was conclusive enough to judge the social influence of actors to warrant its sole application in the determination of spreaders or influential nodes in a network. To enhance efficiency in decision making in relation to employment and layoffs, it is recommended that a social network analysis which adapts a multi-attribute decision-making approach that reflects both individual strength and weaknesses in totality for all aspect of social influences should be employed. We recommend further studies into Actor Ranking and its impact on recruitment practices for organizational innovation.
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