2016, Vol.33, No.2

Journal of the Korean Society for Information Management 2023 KCI Impact Factor : 1.31

pISSN : 1013-0799 / eISSN : 2586-2073
https://journal.kci.go.kr/kosim

This study aims to explore the effect of a librarian’s organizational commitment and Organization Citizenship Behavior (OCB) on organizational effectiveness in libraries. The following conclusions were drawn from the research procedures and data analysis. First, the librarian’s organizational commitment make no difference by age level. The librarian’s organizational commitment makes no difference by career. The librarian’s organizational commitment and organization citizenship behavior make no difference by employment status. Second, the highest correlation is maintain commitment factor and all of the commitment. The second high correlation is sportsmanship behavior factor and all of the organization citizenship behavior. The third high correlation is conscientiousness behavior factor and all of the organization citizenship behavior. Third, the organizational effectiveness is affected 13.4% by a librarian’s organization citizenship behavior, 5.6% by librarian’s organizational commitment.

This study examined the factors affecting the performance of automatic classification for the domestic conference papers based on machine learning techniques. In particular, In view of the classification performance that assigning automatically the class labels to the papers in Proceedings of the Conference of Korean Society for Information Management using Rocchio algorithm, I investigated the characteristics of the key factors (classifier formation methods, training set size, weighting schemes, label assigning methods) through the diversified experiments. Consequently, It is more effective that apply proper parameters (β, λ) and training set size (more than 5 years) according to the classification environments and properties of the document set. and If the performance is equivalent, I discovered that the use of the more simple methods (single weighting schemes) is very efficient. Also, because the classification of domestic papers is corresponding with multi-label classification which assigning more than one label to an article, it is necessary to develop the optimum classification model based on the characteristics of the key factors in consideration of this environment.

This study aims at proposing three prestige indices—closeness prestige, input domain, and proximity prestige- as useful measures for the impact of a particular node in citation networks. It compares these prestige indices with other impact indices as it is still unknown what dimensions of impact these indices actually measure. The prestige indices enable us to distinguish the most prominent actors in a directed network, similar to the centrality indices in undirected networks. Correlation analysis and principal component analysis were conducted on the author citation network to identify the differentiated implications of the three prestige indices from the existing impact indices. We selected simple citation counting, h-index, PageRank, and the three kinds of centrality indices which assume undirected networks as the existing impact measures for comparison with the three prestige indices. The results indicate that these prestige indices demonstrate distinct impact dimension from the other impact indices. The prestige indices reflect indirect impact while the others direct impact.