• Journal of the Korean Society for Library and Information Science
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• v.25
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• pp.343-388
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• 1993

The ability of any organization to innovate itself in a rapid change of environment means the existence of the organization. Innovative activity is achieved in different ways according to the objectives of organization. the characteristics of external environmental factors. and various attributes in organization. In the present study. all the existing approaches to the innovative nature of organization were synthetically compared to each other and evaluated: then. for a more rational approach. a research model was built and suggested by establishing the inclusive variables of the innovative nature of library organization and categorizing the types of such nature. Additionally. an empirical. analytical study on such a model was done. That is. paying regard to the fact that innovation has basically a close relation with the circumstantial factors of organization. synthetic, circumstantial relations were clarified. considering the external environmental factors and internal characteristics of organization. In the study. the innovation of library organization was seen in two parts i.e .. the feasible degree of technical innovation and the feasible degree of administrative innovation. Regarding the types of innovative implementation. according to the feasible degree of innovation, four types such as a stationary type. technic-oriented type, organization-oriented type. and technical-socio systematic type were classified. There were nine independent variables-i.e., the scale of organization. available resources of the organization, formalization, differentiation, specialization. decentralization, recognizant degree of the technical attribute. degree of response to the change of technical environment, and professional activities. There were three subordinate variables - i.e., technical innovation, administrative innovation. and the performance of organization. Through establishment of such variables, the factors which might influence the innovation of library organization were understood, and with the types of the innovative implementation of library organization being classified according to the feasible degree of innovation. the characteristics of library organization were reviewed in the light of each type. Also. the performance of library organization according to the types of the innovative implementation of library organization was analyzed. and the relations between the types of innovative implementation according to circumstantial variables and the performance of library organization were clarified. In order to clarify the adequacy of the research model in the methodology of empirical study, data were collected from 72 university libraries and 38 special libraries. and for a hypothetical test of the research model. an analysis of correlations, a stepwise regression analysis. and One Way ANOVA were utilized. The following are the major results or findings from the study 1) It appeared there is a trend that the bigger the scale of organization and available resources are, the more active the professional activity of the managerial class is, and the higher the recognizant degree of technical environment (recognizant degree of technical attributes and the degree of response t9 the change of technical environment) is, the higher the feasible degree of innovation becomes. 2) It appeared that among the variables influencing the feasible degree of technical innovation, the order from the variable influencing most was first, the recognizant degree of technical innovation: second, the available resources of organization: and third, professional activity. Regarding the variables influencing the feasible degree of administrative innovation from the most influential variable, it appeared they were the available resources of organization, the differentiation of organization. and the degree of response to the change of technical environment. 3) It appeared that the higher the educational level of the managerial class is, the more active the professional activity becomes. It seemed there is a trend that the group of library managers whose experience as a librarian was at the middle level(three years to six years of experience) was more active in research activity than the group of library managers whose experience as a librarian was at a higher level(more than ten years). Also, it appeared there is a trend that the lower the age of library managers is, the higher the recognizant degree of technical attributes becomes. and the group of library managers whose experience as a librarian was at the middle level (three years to six years of experience) recognized more affirmatively the technical aspect than the group of library managers whose experience as a librarian was at a higher level(more than 10 years). Also, it appeared that, when the activity of the professional association and research activity are active, the recognizant degree of technology becomes higher, and as a result. it influences the innovative nature of organization(the feasible degree of technical innovation and the feasible degree of administrative innovation). 4) As a result of the comparison and analysis of the characteristics of library organization according to the types of innovative implementation of library organization. it was indicated there is a trend that the larger the available resources of library organization, the higher the organic nature of organization such as differentiation. decentralization, etc., and the higher the level of the operation of system development, the more the type of the innovative implementation of library organization becomes the technical-socio systematic type which is higher both in the practical degrees of technical innovation and administrative innovation. 5) As a result of the comparison and analysis of the relations between the types of innovative implementation and the performance of organization, it appeared that the order from the highest performance of organization is the technical-socio systematic type, then the technic-oriented type, the organization­oriented type, and finally the stationary type which is lowest in such performance. That is, it demonstrated that, since the performance of library organization is highest in the library of the technical-socio systematic type while it is lowest in the library whose practical degrees in both technical innovation and administrative innovation are low, the performance of library organization differs significantly according to the types of innovative implementation of library organization. The present study has extracted the factors influencing innovation, classified systematically the types of innovative implementation, and inferred the synthetical, circumstantial correlations between the types and the performance of organization, and empirically inspected those factors. However, due to the present study's restrictive matters and the limit of the research design, results from the study should be more prudently interpreted. Also, the present study, as an investigative study of the types of innovative implementation, with few preceding studies, requires more complete hypothetical inference based on the results of the present study. In other words, if more systematical studies are given to understanding the relations, it will devote the suggestion and demonstration of a more useful theory.

• Journal of Intelligence and Information Systems
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• v.26 no.3
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• pp.149-169
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• 2020

"The Urban Renewal New Deal project", one of the government's major national projects, is about developing underdeveloped areas by investing 50 trillion won in 100 locations on the first year and 500 over the next four years. This project is drawing keen attention from the media and local governments. However, the project model which fails to reflect the original characteristics of the area as it divides project area into five categories: "Our Neighborhood Restoration, Housing Maintenance Support Type, General Neighborhood Type, Central Urban Type, and Economic Base Type," According to keywords for successful urban regeneration in Korea, "resident participation," "regional specialization," "ministerial cooperation" and "public-private cooperation", when local governments propose urban regeneration projects to the government, they can see that it is most important to accurately understand the characteristics of the city and push ahead with the projects in a way that suits the characteristics of the city with the help of local residents and private companies. In addition, considering the gentrification problem, which is one of the side effects of urban regeneration projects, it is important to select and implement urban regeneration types suitable for the characteristics of the area. In order to supplement the limitations of the 'Urban Regeneration New Deal Project' methodology, this study aims to propose a system that recommends urban regeneration types suitable for urban regeneration sites by utilizing various machine learning algorithms, referring to the urban regeneration types of the '2025 Seoul Metropolitan Government Urban Regeneration Strategy Plan' promoted based on regional characteristics. There are four types of urban regeneration in Seoul: "Low-use Low-Level Development, Abandonment, Deteriorated Housing, and Specialization of Historical and Cultural Resources" (Shon and Park, 2017). In order to identify regional characteristics, approximately 100,000 text data were collected for 22 regions where the project was carried out for a total of four types of urban regeneration. Using the collected data, we drew key keywords for each region according to the type of urban regeneration and conducted topic modeling to explore whether there were differences between types. As a result, it was confirmed that a number of topics related to real estate and economy appeared in old residential areas, and in the case of declining and underdeveloped areas, topics reflecting the characteristics of areas where industrial activities were active in the past appeared. In the case of the historical and cultural resource area, since it is an area that contains traces of the past, many keywords related to the government appeared. Therefore, it was possible to confirm political topics and cultural topics resulting from various events. Finally, in the case of low-use and under-developed areas, many topics on real estate and accessibility are emerging, so accessibility is good. It mainly had the characteristics of a region where development is planned or is likely to be developed. Furthermore, a model was implemented that proposes urban regeneration types tailored to regional characteristics for regions other than Seoul. Machine learning technology was used to implement the model, and training data and test data were randomly extracted at an 8:2 ratio and used. In order to compare the performance between various models, the input variables are set in two ways: Count Vector and TF-IDF Vector, and as Classifier, there are 5 types of SVM (Support Vector Machine), Decision Tree, Random Forest, Logistic Regression, and Gradient Boosting. By applying it, performance comparison for a total of 10 models was conducted. The model with the highest performance was the Gradient Boosting method using TF-IDF Vector input data, and the accuracy was 97%. Therefore, the recommendation system proposed in this study is expected to recommend urban regeneration types based on the regional characteristics of new business sites in the process of carrying out urban regeneration projects."

• Journal of Intelligence and Information Systems
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• v.19 no.2
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• pp.1-20
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• 2013

Nowadays, social network is a huge communication platform for providing people to connect with one another and to bring users together to share common interests, experiences, and their daily activities. Users spend hours per day in maintaining personal information and interacting with other people via posting, commenting, messaging, games, social events, and applications. Due to the growth of user's distributed information in social network, there is a great potential to utilize the social data to enhance the quality of recommender system. There are some researches focusing on social network analysis that investigate how social network can be used in recommendation domain. Among these researches, we are interested in taking advantages of the interaction between a user and others in social network that can be determined and known as social relationship. Furthermore, mostly user's decisions before purchasing some products depend on suggestion of people who have either the same preferences or closer relationship. For this reason, we believe that user's relationship in social network can provide an effective way to increase the quality in prediction user's interests of recommender system. Therefore, social relationship between users encountered from social network is a common factor to improve the way of predicting user's preferences in the conventional approach. Recommender system is dramatically increasing in popularity and currently being used by many e-commerce sites such as Amazon.com, Last.fm, eBay.com, etc. Collaborative filtering (CF) method is one of the essential and powerful techniques in recommender system for suggesting the appropriate items to user by learning user's preferences. CF method focuses on user data and generates automatic prediction about user's interests by gathering information from users who share similar background and preferences. Specifically, the intension of CF method is to find users who have similar preferences and to suggest target user items that were mostly preferred by those nearest neighbor users. There are two basic units that need to be considered by CF method, the user and the item. Each user needs to provide his rating value on items i.e. movies, products, books, etc to indicate their interests on those items. In addition, CF uses the user-rating matrix to find a group of users who have similar rating with target user. Then, it predicts unknown rating value for items that target user has not rated. Currently, CF has been successfully implemented in both information filtering and e-commerce applications. However, it remains some important challenges such as cold start, data sparsity, and scalability reflected on quality and accuracy of prediction. In order to overcome these challenges, many researchers have proposed various kinds of CF method such as hybrid CF, trust-based CF, social network-based CF, etc. In the purpose of improving the recommendation performance and prediction accuracy of standard CF, in this paper we propose a method which integrates traditional CF technique with social relationship between users discovered from user's behavior in social network i.e. Facebook. We identify user's relationship from behavior of user such as posts and comments interacted with friends in Facebook. We believe that social relationship implicitly inferred from user's behavior can be likely applied to compensate the limitation of conventional approach. Therefore, we extract posts and comments of each user by using Facebook Graph API and calculate feature score among each term to obtain feature vector for computing similarity of user. Then, we combine the result with similarity value computed using traditional CF technique. Finally, our system provides a list of recommended items according to neighbor users who have the biggest total similarity value to the target user. In order to verify and evaluate our proposed method we have performed an experiment on data collected from our Movies Rating System. Prediction accuracy evaluation is conducted to demonstrate how much our algorithm gives the correctness of recommendation to user in terms of MAE. Then, the evaluation of performance is made to show the effectiveness of our method in terms of precision, recall, and F1-measure. Evaluation on coverage is also included in our experiment to see the ability of generating recommendation. The experimental results show that our proposed method outperform and more accurate in suggesting items to users with better performance. The effectiveness of user's behavior in social network particularly shows the significant improvement by up to 6% on recommendation accuracy. Moreover, experiment of recommendation performance shows that incorporating social relationship observed from user's behavior into CF is beneficial and useful to generate recommendation with 7% improvement of performance compared with benchmark methods. Finally, we confirm that interaction between users in social network is able to enhance the accuracy and give better recommendation in conventional approach.