• The Journal of the Korea Contents Association
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• v.20 no.12
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• pp.406-416
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• 2020

The purpose of this study is to confirm the effect of parents' positive and negative parenting attitudes perceived by children on smartphone dependence. In addition, it is to verify whether aggression and social withdrawal play a mediating role in the relationship between parental attitude and dependence on smartphones. In order to achieve this goal, the data of the "Korean Children and Youth Panel Survey 2018(KCYPS 2018)" were used for analysis. The sample group is 2,399 "elementary school students 4 cohort". The research results of this study are as follows. First, it was found that autonomy support and coercion had a negative effect on aggression of children, but rejection and inconsistency had a positive effect on aggression. Second, it was found that inconsistency and rejection had a positive effect on children's social atrophy, but coercion had a negative effect. Third, it was found that aggression had a positive effect on children's dependence on smartphones, but social withdrawal had no significant effect. Fourth, it was found that autonomy support, rejection, coercion, and inconsistency indirectly affect children's dependence on smartphones through aggression. In this study's conclusion, practical implications for lowering children's dependence on smartphones were suggested.

• Korean Journal of Social Welfare Studies
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• v.41 no.4
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• pp.313-343
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• 2010

The aim of this study is to examine the factors influencing network centrality on women's welfare organizations, and to investigate how the level of network centrality influence the effectiveness of the organization. To achieve this goal, this study conducted a survey on women's welfare organizations in Seoul from March to June, 2009. Network analysis method was used to get each organization's network centrality value. Also, through the Structural Equation Modelling, organizational characteristics predicting network centrality and effect of network centrality on organizational effectiveness. The main results are as follows. First, the significant affecting factors were different between three types of centralities with regards to the type of organization, recognition of resource dependency, attitude of top manager, and established year. Second, the common factors affecting three network centralities were the number of informal ties, accepting feminism as the main organizational philosophy, and the number of qualified staffs. Third, only closeness centrality positively predicted the level of organizational effectiveness among three types of centralities. The faster the organization reaches to other organizations in a network, the organizational effectiveness becomes higher, which means high closeness centrality is more important factor than high degree centrality or high betweenness centrality to increase organizational effectiveness. This result shows social welfare organization should consider changing inter-organizational network strategy from quantity-focused to quality-focused.

• Journal of Intelligence and Information Systems
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• v.17 no.1
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• pp.111-125
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• 2011

Clustering is a process of grouping similar or relevant documents into a cluster and assigning a meaningful concept to the cluster. By this process, clustering facilitates fast and correct search for the relevant documents by narrowing down the range of searching only to the collection of documents belonging to related clusters. For effective clustering, techniques are required for identifying similar documents and grouping them into a cluster, and discovering a concept that is most relevant to the cluster. One of the problems often appearing in this context is the detection of a complex concept that overlaps with several simple concepts at the same hierarchical level. Previous clustering methods were unable to identify and represent a complex concept that belongs to several different clusters at the same level in the concept hierarchy, and also could not validate the semantic hierarchical relationship between a complex concept and each of simple concepts. In order to solve these problems, this paper proposes a new clustering method that identifies and represents complex concepts efficiently. We developed the Hierarchical Overlapping Clustering (HOC) algorithm that modified the traditional Agglomerative Hierarchical Clustering algorithm to allow overlapped clusters at the same level in the concept hierarchy. The HOC algorithm represents the clustering result not by a tree but by a lattice to detect complex concepts. We developed a system that employs the HOC algorithm to carry out the goal of complex concept detection. This system operates in three phases; 1) the preprocessing of documents, 2) the clustering using the HOC algorithm, and 3) the validation of semantic hierarchical relationships among the concepts in the lattice obtained as a result of clustering. The preprocessing phase represents the documents as x-y coordinate values in a 2-dimensional space by considering the weights of terms appearing in the documents. First, it goes through some refinement process by applying stopwords removal and stemming to extract index terms. Then, each index term is assigned a TF-IDF weight value and the x-y coordinate value for each document is determined by combining the TF-IDF values of the terms in it. The clustering phase uses the HOC algorithm in which the similarity between the documents is calculated by applying the Euclidean distance method. Initially, a cluster is generated for each document by grouping those documents that are closest to it. Then, the distance between any two clusters is measured, grouping the closest clusters as a new cluster. This process is repeated until the root cluster is generated. In the validation phase, the feature selection method is applied to validate the appropriateness of the cluster concepts built by the HOC algorithm to see if they have meaningful hierarchical relationships. Feature selection is a method of extracting key features from a document by identifying and assigning weight values to important and representative terms in the document. In order to correctly select key features, a method is needed to determine how each term contributes to the class of the document. Among several methods achieving this goal, this paper adopted the $x^2$�� statistics, which measures the dependency degree of a term t to a class c, and represents the relationship between t and c by a numerical value. To demonstrate the effectiveness of the HOC algorithm, a series of performance evaluation is carried out by using a well-known Reuter-21578 news collection. The result of performance evaluation showed that the HOC algorithm greatly contributes to detecting and producing complex concepts by generating the concept hierarchy in a lattice structure.