Decision quality of the research project evaluation mechanism by using particle swarm optimization. Issue 4 (15th May 2017)

• Record Type: Journal Article
• Authors: Seo, Young Wook; Lee, Kun Chang; Lee, Sangjae
• Is Part Of: Management decision. Volume 55:Issue 4(2017)
• Page Start: 745
• Page End: 765
• Publication Date: 2017-05-15
• Subjects: Research performance -- Particle swarm optimization -- Research impact -- K-means clustering method -- Research fund -- Knowledge-based society; Management -- Periodicals; 658.403
• DOI: 10.1108/MD-03-2016-0141
• Languages: English
• ISSNs: 0025-1747

Abstract

Abstract : Purpose: For those who plan research funds and assess the research performance from the funds, it is necessary to overcome the limitations of the conventional classification of evaluated papers published by the research funds. Besides, they need to promote the objective, fair clustering of papers, and analysis of research performance. Therefore, the purpose of this paper is to find the optimum clustering algorithm using the MATLAB tools by comparing the performances of and the hybrid particle swarm optimization algorithms using the particle swarm optimization (PSO) algorithm and the conventional K-means clustering method. Design/methodology/approach: The clustering analysis experiment for each of the three fields of study – health and medicine, physics, and chemistry – used the following three algorithms: "K-means+Simulated annealing (SA)+Adjustment of parameters+PSO" (KASA-PSO clustering), "K-means+SA+PSO" clustering, "K-means+PSO" clustering. Findings: The clustering analyses of all the three fields showed that KASA-PSO is the best method for the minimization of fitness value. Furthermore, this study administered the surveys intended for the "performance measurement of decision-making process" with 13 members of the research fund organization to compare the group clustering by the clustering analysis method of KASA-PSO algorithm and the group clustering by research funds. The results statistically demonstrated that the group clustering by the clustering analysis method of KASA-PSO algorithm was better than the group clustering by research funds. Practical implications: This study examined the impact of bibliometric indicators on research impact of papers. The results showed that research period, the number of authors, and the number of participating researchers had positive effects on the impact factor (IF) of the papers; the IF that indicates the qualitative level of papers had a positive effect on the primary times cited; and the primary times cited had a positive effect on the secondary times cited. Furthermore, this study clearly showed the decision quality perceived by those who are working for the research fund organization. Originality/value: There are still too few studies that assess the research project evaluation mechanisms and its effectiveness perceived by the research fund managers. To fill the research void like this, this study aims to propose PSO and successfully proves validity of the proposed approach.