• Journal of Korean Library and Information Science Society
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• v.50 no.3
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• pp.217-238
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• 2019

Recently, interest in Public Lending Right has increased in Korea. This study aims to collect valuable data necessary for the discussion of the introduction of Public Lending Right by analyzing the loan statistics of 820 public libraries nationwide for five years from 2014 to 2018. In order to analyze the loan statistics of Korean public libraries, 1,178,300,000 big data provided by 'Data for Library' operated by the National Library of Korea were used. Through the analysis of loan statistics, 125 books were identified, which have been lent the most in the last five years. The study examined the 125 books to find out who are authors and Japanese authors and authors' nationality. The study also analyzed publishers and number of lending of cartoons.

• Journal of the Korean Society for Library and Information Science
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• v.50 no.3
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• pp.429-453
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• 2016

This study analyzes circulation patterns of books with checkout transaction count by 11 subject areas, 5 positions, and 5 divisions with a Use Factor developed by Bonn in an Academic Library. 20% of the loan books occupies more than half of circulation and these are regarded as core collection. It proposes a 'Loan books 20/50 rule' that 20% core collection accounts for 50% of its circulation. It analyzes the proportion of core collection from the aspect of each subject area with a use factor, monthly change trend and loan period. It also defines 'book usage' considering checkout frequency of each title and loan period. Circulation patterns of core collection are compared and analyzed in terms of both checkout frequency and book usage. Core collection occupies about more than half of both total checkout transactions and total book usages and they all show a Power Law distribution.

• Journal of the Korean Society for Library and Information Science
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• v.56 no.4
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• pp.357-376
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• 2022

This study examines circulation status and trends of a public library during three year periods from January 2019 to December 2021. There was a statistically significant difference in the mean number of circulation between the pre-Covid-19 period and the Covid-19 period, and the Covid-19 period and the Covid-19 recovery period. However, no significant difference was found between the pre-Covid-19 period and the Covid-19 recovery period. Across three years, there was a significant difference in the distribution of circulation per month. Circulation distribution was also significantly different among different days of the week and different hours of the day. Monthly circulation distribution and hourly circulation distribution during the pre-Covid-19 period was similar to those of the Covid-19 recovery period, whereas those of the Covid-19 period differed from the pre-Covid-19 period and the Covid-19 recovery period. It is expected that the results of this study could contribute to the collection development, and the management and improvement of services of public libraries. It is also expected that the results of this study could contribute to the prediction of circulation patterns and information needs of public library users.

• Journal of Korean Library and Information Science Society
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• v.54 no.4
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• pp.1-24
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• 2023

In a situation with reducing numbers of students and local universities in crisis, this study aims to analyze collection and loan usage data in small-sized university libraries and present evidence for guiding their collection development policy. The data was collected from Rinfo from 2010 to 2022 regarding the numbers of enrolled students, collected books, book loans, and electronic resources and compared the average values of entire 4-year university libraries to those of small-sized ones by year and region. Results show that while the numbers of enrolled students and book loans have decreased in both of groups, the number of collected books has increased 30% more in small groups and the amount of electronic resource use has increased enormously as well. Correlation analysis presents that both of groups have the consistent result. It is suggested that small-sized university libraries should highly increase their budget ratio to electronic resources for collection development and build a consortium by university's speciality for mutual use of electronic resources.

• Journal of the Korean Society for Library and Information Science
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• v.56 no.4
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• pp.455-472
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• 2022

The purpose of this study is to analyze variables affecting the number of circulated books which is one of the indicators representing the library use behavior. For the analysis, 2015-2019 data for public libraries was acquired from the National Library Statistics System. The Latent Growth Model estimating a latent intercept and a latent slop based on the individual library trajectories was applied. The results are as followed; first, the circulation rate tends to be decreased. Second, the most affecting factor on the library circulation decrease was the collection budget. This study suggests increasing a collection budget in order to prevent the library circulation decrease while the library is operating in a daily routine.

• The Journal of Society for e-Business Studies
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• v.23 no.3
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• pp.207-224
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• 2018

In this article, an empirical study was conducted by using public dataset from Lending Club Corporation, the largest online peer-to-peer (P2P) lending in the world. We explore significant predictor variables related to P2P lending default that housing situation, length of employment, average current balance, debt-to-income ratio, loan amount, loan purpose, interest rate, public records, number of finance trades, total credit/credit limit, number of delinquent accounts, number of mortgage accounts, and number of bank card accounts are significant factors to loan funded successful on Lending Club platform. We developed online P2P lending default prediction models using discriminant analysis, logistic regression, neural networks, and decision trees (i.e., CART and C5.0) in order to predict P2P loan default. To verify the feasibility and effectiveness of P2P lending default prediction models, borrower loan data and credit data used in this study. Empirical results indicated that neural networks outperforms other classifiers such as discriminant analysis, logistic regression, CART, and C5.0. Neural networks always outperforms other classifiers in P2P loan default prediction.

• Journal of the Korean Society for Library and Information Science
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• v.57 no.1
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• pp.45-68
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• 2023

The purpose of this study is to longitudinally analyze impact factors on library use. One of library use indicators, the number of circulated books was statistically analyzed with latent growth model and growth mixture model. Library data from 2014 to 2019 were collected from the National Library Statistics System, and 846 public libraries were analyzed. As results, the number of circulated books were decreased, but it was tempered. Next, with controlling the factor affecting the dependent variable, the size of collection and the number of participants in reading programs provided by public libraries were statistically significant. Lastly, 5 classes were identified by applying the growth mixture model, and the number of librarians was significantly associated with trajectory class membership.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.18 no.5
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• pp.77-90
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• 2023

This study aims to investigate the potential of alternative credit assessment through Social Networking Sites (SNS) as a complementary tool to conventional loan review processes. It seeks to discern the impact of SNS usage characteristics and loan product attributes on credit loan repayment. To achieve this objective, we conducted a binomial logistic regression analysis examining the influence of SNS usage patterns, loan characteristics, and personal attributes on credit loan conditions, utilizing data from Company A's credit loan program, which integrates SNS data into its actual loan review processes. Our findings reveal several noteworthy insights. Firstly, with respect to profile photos that reflect users' personalities and individual characteristics, individuals who choose to upload photos directly connected to their personal lives, such as images of themselves, their private circles (e.g., family and friends), and photos depicting social activities like hobbies, which tend to be favored by individuals with extroverted tendencies, as well as character and humor-themed photos, which are typically favored by individuals with conscientious traits, demonstrate a higher propensity for diligently repaying credit loans. Conversely, the utilization of photos like landscapes or images concealing one's identity did not exhibit a statistically significant causal relationship with loan repayment. Furthermore, a positive correlation was observed between the extent of SNS usage and the likelihood of loan repayment. However, the level of SNS interaction did not exert a significant effect on the probability of loan repayment. This observation may be attributed to the passive nature of the interaction variable, which primarily involves expressing sympathy for other users' comments rather than generating original content. The study also unveiled the statistical significance of loan duration and the number of loans, representing key characteristics of loan portfolios, in influencing credit loan repayment. This underscores the importance of considering loan duration and the quantity of loans as crucial determinants in the design of microcredit products. Among the personal characteristic variables examined, only gender emerged as a significant factor. This implies that the loan program scrutinized in this analysis does not exhibit substantial discrimination based on age and credit scores, as its customer base predominantly consists of individuals in their twenties and thirties with low credit scores, who encounter challenges in securing loans from traditional financial institutions. This research stands out from prior studies by empirically exploring the relationship between SNS usage and credit loan repayment while incorporating variables not typically addressed in existing credit rating research, such as profile pictures. It underscores the significance of harnessing subjective, unstructured information from SNS for loan screening, offering the potential to mitigate the financial disadvantages faced by borrowers with low credit scores or those ensnared in short-term liquidity constraints due to limited credit history a group often referred to as "thin filers." By utilizing such information, these individuals can potentially reduce their credit costs, whereas they are supposed to accrue a more substantial financial history through credit transactions under conventional credit assessment system.

• Journal of the Korean Society for Library and Information Science
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• v.44 no.2
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• pp.333-356
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• 2010

Small libraries, with their insufficient quantities of materials and lack of diversity within the collection compared to larger general public libraries, may need to be assessed and develop collections based on empirical analysis. This study suggests a method for collection evaluation with other cases analyzing ILL (interlibrary loan) data, which is especially heavy in Small libraries in addition to the holdings and circulation data that are traditionally used in collection development. Collecting and analyzing materials proceeded from 14 Small libraries which operate ILL in a city and tried to figure out features of each library comparing collection statistics with usage statistics including circulation and interlibrary loans. It also identified subject areas heavily used in a Small library, based on the analysis of collection and usage statistics, for the purpose of formulating future policy.

• International Area Studies Review
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• v.16 no.3
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• pp.135-155
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• 2012

The objective of this study was to empirically analyze how Chinese urban residents use mortgage loan in their purchase of houses. For the research purpose, 205 households were surveyed on use of housing finance in Beijing, using prepared questionnaires in advance. The survey result showed that 39.5% of the respondents used mortgage loan in their housing purchase in Beijing. Most of the mortgage loan users got their loans from four state-owned commercial banks. This implies that the government could effectively govern the housing market by managing qualifications and interest rates under the government control. The households who did not use mortgage loan raised the fund mostly from own capital and their parents' money. In general, it was found that the Chinese, besides mortgage loan from commercial banks, rarely depend on outside sources. The dichotomous logistic analysis by logit model showed that socio-economic variables such as age, income, housing price and entry into the housing provident fund had significantly positive effects on the use of mortgage loan in the purchase of housing.