초록

본 연구는 지역사회 특성에 따른 지역 주민의 도서관 요구를 파악하여 지역 공공도서관의 운영방향과 정책을 수립하기 위한 방안 모색을 목적으로 인천광역시 8개 구와 2개 군의 기초자치단체를 중심으로 지역사회 조사를 실시하였고, 지역 특성에 따라 지역1(원도심 중심), 지역2(신도심 중심), 지역3(도․농 복합 지역), 3개의 지역 유형으로 나누어 그 지역에 속해 있는 공공도서관 이용자 300명을 대상으로 설문조사를 실시하여 도서관 이용행태, 도서관에 대한 인식 및 요구, 지역사회 평가, 서비스 만족도 등을 분석하였다. 그 결과 도서관 이용행태, 도서관에 대한 인식, 도서관 서비스에 대한 만족도에서 지역유형에 따라 부분적인 차이를 보였으며 그 결과를 토대로 지역 유형에 따른 공공도서관의 운영 방안에 대해 제안하였다.

Abstract

This study aims to explore measures to help establish a management direction and policies of local public libraries based on local residents’ needs for library services based on the community characteristics. To that end, a community survey was conducted focusing on eight urbans (gus) and two rural districts (guns) in Incheon Metropolitan City. The districts were categorized into three types, original urban center (Area 1), new urban center (Area 2), and urban-rural complex (Area 3). The residents in each type were analyzed in terms of library use, awareness, needs, and satisfaction. In terms of library use, awareness, and satisfaction, we identified differences among the area types. This paper suggested management directions of local public libraries based on the community characteristics.

초록

본 연구는 다양한 지식이 축적되고 공유되며 구성원 간의 관계와 상호작용을 바탕으로 지식이 생산되고 활용되는 공간인 소셜 러닝 커뮤니티에서 학습자의 지식소싱 행위가 지식활용 성과에 어떠한 영향을 미치는지를 실증적으로 분석하는 것을 목적으로 하였다. 이를 위해 한 대학의 사회과학분야 전공수업에 참여한 55명의 학습자들을 대상으로 소셜 러닝 커뮤니티를 구성하고 한 학기의 소셜 러닝 완료 후 설문조사를 통해 자료를 수집하였으며, 다중회귀분석을 사용하여 지식소싱 행위와 지식활용 성과의 영향관계를 규명하였다. 연구 결과, 양자 지식소싱과 문서 지식소싱은 지식재이용과 지식응용에 영향을 미치며, 그룹 지식소싱은 지식응용과 지식혁신에 영향을 미치는 것으로 나타났다. 본 연구의 결과는 소셜 러닝 커뮤니티에서 지식의 이용 목적에 따라 효율적인 지식소싱 행위를 선택하는데 유용할 것으로 기대된다.

Abstract

This research aims to analyze empirically the effects of learners’ knowledge sourcing behavior on the knowledge utilization outcomes in a social learning community. This kind of virtual community is of service to users who not only produce but also share a variety of valuable knowledge which is created based on relationships and interactions among learners. In order to conduct the study, a group of learners was made of 55 undergraduate students who were majoring in social science. The data was collected by online survey at the end of the term and multiple regression methods have been used for empirical analysis. The study shows that dyadic knowledge sourcing and published knowledge sourcing both have significant effects on knowledge reuse and knowledge adaptation. In addition, knowledge adaptation and knowledge innovation were affected by group knowledge sourcing. The research results help to select appropriate knowledge sourcing behavior depending on one’s purpose of knowledge use.

초록

Abstract

As co-authorship has been prevalent within science communities, counting the credit of co-authors appropriately is an important consideration, particularly in the context of identifying the knowledge structure of fields with author-based analysis. The purpose of this study is to compare the characteristics of co-author credit counting methods by utilizing correlations, multidimensional scaling, and pathfinder networks. To achieve this purpose, this study analyzed a dataset of 2,014 journal articles and 3,892 cited authors from the Journal of the Architectural Institute of Korea: Planning & Design from 2003 to 2008 in the field of Architecture in Korea. In this study, six different methods of crediting co-authors are selected for comparative analyses. These methods are first-author counting (m1), straight full counting (m2), and fractional counting (m3), proportional counting with a total score of 1 (m4), proportional counting with a total score between 1 and 2 (m5), and first-author-weighted fractional counting (m6). As shown in the data analysis, m1 and m2 are found as extreme opposites, since m1 counts only first authors and m2 assigns all co-authors equally with a credit score of 1. With correlation and multidimensional scaling analyses, among five counting methods (from m2 to m6), a group of counting methods including m3, m4, and m5 are found to be relatively similar. When the knowledge structure is visualized with pathfinder network, the knowledge structure networks from different counting methods are differently presented due to the connections of individual links. In addition, the internal validity shows that first-author-weighted fractional counting (m6) might be considered a better method to author clustering. Findings demonstrate that different co-author counting methods influence the network results of knowledge structure and a better counting method is revealed for author clustering.