An Application of TOPSIS Approach in Determination of Spread Influencers in a Competitive Industrial Space: Evidence from the Banking Network of Ghana

Abstract

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.

Acquisition of human capital for organizational sustainability: A BASS-SIR forecasting approach

“…In the view of Rizman Žalik 30 in a proximity-based cluster analysis, the cluster with the largest population has a higher level of interaction while the cluster with fewer population have less interaction. Three clusters were created as determined by the TOPSIS adopted from Kong et al 13…”

“…In this article, we, therefore, consider the susceptible population as the average of employee closeness to ideal (CI) below the infectious population ( I ), the infected population as the population average of the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) determined CI, and the average of the employee CI below the susceptible population ( S ) as the recovery ( R ) to potentially represent all individuals who fail to acquire or retain diffused human capital within the network in the study as elaborated in our earlier article. 13 Further, we assume the population that do not meet the desired performance benchmarks as having attained attrition status.…”

“…The population average of the TOPSIS determined CI was used as the infectious population ( I ), the average of employee CI below the infectious population ( I ) were employed as susceptible population ( S ), and the average of the employee CI below the susceptible population ( S ) as the recovery ( R ) in the study by Kong et al 13…”

“…where X ¼ individual CI scores. The population average of the TOPSIS determined CI was used as the infectious population (I), the average of employee CI below the infectious population (I) were employed as susceptible population (S), and the average of the employee CI below the susceptible population (S) as the recovery (R) in the study by Kong et al 13 Collected data were extrapolated from the curriculum vitae of actors as well as from industrial and government reports. The UCINET 6 for Windows version 6.658 was used as the analytical tool for the network.…”

“…The argument is that all things being equal, proximity, accessibility, and similarities are catalysts for establishing relationships between actors within a network. Referencing our earlier article, 13 the appropriate weight determination was done in Table 2.…”