• Title/Summary/Keyword: Recency

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Effects of Innovation Characteristics on Spillover: An Empirical Evidence from US Semiconductor Industry (기술혁신의 특성이 파급효과에 미치는 영향에 대한 분석: 반도체산업의 실증분석)

  • Park, Young-Bin
    • Journal of the Korea Academia-Industrial cooperation Society
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    • v.18 no.6
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    • pp.145-154
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    • 2017
  • Technology innovation is regarded as the quintessential process to acquire a competitive advantage. This is especially true in high-tech industries, and firms that recognize the importance of technological innovation concentrate their capacities on developing new technologies, new products, and new processes. In general, such research requires many resources, but not all technological breakthroughs are followed by positive feedbacks. Consequently, the firms in high-tech industries are compelled to find new directions in acquiring technologies. This study examines the factors that influence technological innovation and empirically tests the effect these factors have on its diffusion. Radicality, discontinuity, and exploitation/exploration were selected as the factors from the previous literature on technological innovation and organizational learning. For the empirical test, patent data from the US semiconductor industry were used to describe innovation activities from various fields. From the result, these three factors (Ed- is this what you mean, i.e., radicality, discontinuity, and exploitation/exploration?)were found to have significant meaning as proxies for the diffusion of technological innovation.

Correlation Between Public Library Service User Satisfaction and Loyalty and Moderator Variables (공공도서관 서비스이용자 만족도와 충성도의 상관관계분석 및 매개변수)

  • Lee, Seongsin
    • Journal of the Korean BIBLIA Society for library and Information Science
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    • v.24 no.1
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    • pp.83-103
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    • 2013
  • According to the results from the recent research in Business field, the correlation between customer satisfaction and customer loyalty is not strong. Based on this understanding, the first purpose of this study was to investigate the correlation between public library service user satisfaction and public library service user loyalty. To achieve this purpose, the study conducted a survey of 240 public library users. The findings of this study are 1) the correlation between 'public library service user satisfaction' and 'public library service user loyalty' exists. However the strength of the correlation is moderate, 2) the correlation between 'public library service user satisfaction' and 'public library service users' intention to use new library services' is the weakest among the variables of 'public library service user loyalty', and 3) the correlation between 'public library service user satisfaction' and 'public library service users' intention to recommend library services to others is the strongest among the variables of 'public library service user loyalty'. The second purpose of this study was to find the moderator variables between public library service user satisfaction and public library service user loyalty. According to the study results, the following moderate variables are found: 1) physical accessibility, 2) lack of diversity in library service, 3) car-parking issues, 4) lack of diversity and recency in collections, and 5) lack of convenience in facilities.

A Study on the Evaluation and Improvement of the Korean National Assembly Digital Library by Applying ISA(Importance-Satisfaction Analysis) (ISA를 적용한 국회전자도서관 품질 평가와 개선방안 수립)

  • Kwak, Seung-Jin;Jung, Young-Mi;Kim, Jin-Mook;Bae, Kyung-Jae;Im, Mi-Kyung
    • Journal of the Korean Society for Library and Information Science
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    • v.45 no.3
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    • pp.327-343
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    • 2011
  • The goal of this study was to examine ways to improve the quality of databases of the Korean National Assembly Digital Library(KNADL). In order to achieve the goal, we first designed an assessment tool that could measure the quality of the 'contents,' the 'service,' and the 'system' of the KNADL's databases. Each of the three categories also had sub-categories-i.e., eight sub-categories for 'contents'(e.g., accuracy, recency, ${\cdots}$), seven sub-categories for 'service'(e.g., convenience for request, rapid response, ${\cdots}$), and seven sub-categories for 'system'(e.g., usability, response time, ${\cdots}$). We thenconducted a survey using the assessment tool we developed and gathered a total of 270 responses from users of KNADL's databases. We used Excel and PASW Statistics 18 for data analysis. Each sub-category was measured by its importance and by the level of satisfaction(implemented from the DigiQUAL project). Finally, we performed an importance-satisfaction analysis(ISA) to identify what action(i.e., maintain, concentrate, low priority, and exceed) needs to be made in each sub-category. We concluded the paper with some useful suggestions for improving the quality of KNADL's databases.

Metadata Management for E-Commerce Transactions in Digital Library (디지털 도서관에서 전자상거래 트랜잭션을 위한 메타데이타 관리 기법)

  • Choe, Il-Hwan;Park, Seog
    • Journal of KIISE:Databases
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    • v.29 no.1
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    • pp.34-43
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    • 2002
  • Since traditional static metadata set like Dublin Core has static metadata attributes about bibliography information, integration of metadata for various metadata, problems about standard and extension of metadata must be considered for applying it to new environment. Specially, as event-driven metadata write method included the notion of e-commerce come out for interoperability in digital libraries, traditional metadata management which cannot distinguish between different kinds of update operations to new extension of metadata set occurs unsuitable waiting of update operation. So, improvement is needed about it. In this paper, we show whether alleviative transaction consistency can be applied to digital library or not. Also it would divide newer metadata into static metadata attribute connected in read operation within user read-only transaction and dynamic metadata attribute in update operation within dynamic(e-commerce) update transactions. We propose newer metadata management algorithm considered in classfication of metadata attributes and dynamic update transaction. Using two version for minimal maintenance cost and ARU(Appended Refresh Unit) for dynamic update transaction, to minimize conflict between read and write operations shows fast response time and high recency ratio. As a result of the performance evaluation, we show our algorithm is proved to be better than other algorithms in newer metadata environments.

Development of Personalized Recommendation System using RFM method and k-means Clustering (RFM기법과 k-means 기법을 이용한 개인화 추천시스템의 개발)

  • Cho, Young-Sung;Gu, Mi-Sug;Ryu, Keun-Ho
    • Journal of the Korea Society of Computer and Information
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    • v.17 no.6
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    • pp.163-172
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    • 2012
  • Collaborative filtering which is used explicit method in a existing recommedation system, can not only reflect exact attributes of item but also still has the problem of sparsity and scalability, though it has been practically used to improve these defects. This paper proposes the personalized recommendation system using RFM method and k-means clustering in u-commerce which is required by real time accessablity and agility. In this paper, using a implicit method which is is not used complicated query processing of the request and the response for rating, it is necessary for us to keep the analysis of RFM method and k-means clustering to be able to reflect attributes of the item in order to find the items with high purchasablity. The proposed makes the task of clustering to apply the variable of featured vector for the customer's information and calculating of the preference by each item category based on purchase history data, is able to recommend the items with efficiency. To estimate the performance, the proposed system is compared with existing system. As a result, it can be improved and evaluated according to the criteria of logicality through the experiment with dataset, collected in a cosmetic internet shopping mall.

An empirical study on RFM-T model for market performance of B2B-based Technology Industry Companies (B2B 중심의 기술 산업 기업의 수익성 성과를 위한 RFM-T 모형 실증 연구)

  • Miyoung Woo;Young-Jun Kim
    • The Journal of the Institute of Internet, Broadcasting and Communication
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    • v.24 no.2
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    • pp.167-175
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    • 2024
  • Due to the Fourth Industrial Revolution, ICT(Information and Communication Technology) industry is becoming more important and sophisticated than ever. In B2B based ICT industry demand forecasting by analyzing the previous customer data is so important. RFM, one of customer relationship management models is a marketing technique that evaluates Recency, Frequency and Monetary value to predict customers behavior. RFM model has been studied focusing on the B2C based industry. On the other hand there is a lack of research on B2B based technology industry. Therefore this study applied it to B2B based high technology industry and considered T(technology collaboration) value, which are identified as important factors in the technology industry. To present an improved model for market performance in B2B technology industry, an empirical study was conducted on comparing the accuracy of the traditional RFM model and the improved RFM-T model. The objective of this study is to contribute to market performance by presenting an improved model in B2B based high technology industry.

The Prediction of Purchase Amount of Customers Using Support Vector Regression with Separated Learning Method (Support Vector Regression에서 분리학습을 이용한 고객의 구매액 예측모형)

  • Hong, Tae-Ho;Kim, Eun-Mi
    • Journal of Intelligence and Information Systems
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    • v.16 no.4
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    • pp.213-225
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    • 2010
  • Data mining has empowered the managers who are charge of the tasks in their company to present personalized and differentiated marketing programs to their customers with the rapid growth of information technology. Most studies on customer' response have focused on predicting whether they would respond or not for their marketing promotion as marketing managers have been eager to identify who would respond to their marketing promotion. So many studies utilizing data mining have tried to resolve the binary decision problems such as bankruptcy prediction, network intrusion detection, and fraud detection in credit card usages. The prediction of customer's response has been studied with similar methods mentioned above because the prediction of customer's response is a kind of dichotomous decision problem. In addition, a number of competitive data mining techniques such as neural networks, SVM(support vector machine), decision trees, logit, and genetic algorithms have been applied to the prediction of customer's response for marketing promotion. The marketing managers also have tried to classify their customers with quantitative measures such as recency, frequency, and monetary acquired from their transaction database. The measures mean that their customers came to purchase in recent or old days, how frequent in a period, and how much they spent once. Using segmented customers we proposed an approach that could enable to differentiate customers in the same rating among the segmented customers. Our approach employed support vector regression to forecast the purchase amount of customers for each customer rating. Our study used the sample that included 41,924 customers extracted from DMEF04 Data Set, who purchased at least once in the last two years. We classified customers from first rating to fifth rating based on the purchase amount after giving a marketing promotion. Here, we divided customers into first rating who has a large amount of purchase and fifth rating who are non-respondents for the promotion. Our proposed model forecasted the purchase amount of the customers in the same rating and the marketing managers could make a differentiated and personalized marketing program for each customer even though they were belong to the same rating. In addition, we proposed more efficient learning method by separating the learning samples. We employed two learning methods to compare the performance of proposed learning method with general learning method for SVRs. LMW (Learning Method using Whole data for purchasing customers) is a general learning method for forecasting the purchase amount of customers. And we proposed a method, LMS (Learning Method using Separated data for classification purchasing customers), that makes four different SVR models for each class of customers. To evaluate the performance of models, we calculated MAE (Mean Absolute Error) and MAPE (Mean Absolute Percent Error) for each model to predict the purchase amount of customers. In LMW, the overall performance was 0.670 MAPE and the best performance showed 0.327 MAPE. Generally, the performances of the proposed LMS model were analyzed as more superior compared to the performance of the LMW model. In LMS, we found that the best performance was 0.275 MAPE. The performance of LMS was higher than LMW in each class of customers. After comparing the performance of our proposed method LMS to LMW, our proposed model had more significant performance for forecasting the purchase amount of customers in each class. In addition, our approach will be useful for marketing managers when they need to customers for their promotion. Even if customers were belonging to same class, marketing managers could offer customers a differentiated and personalized marketing promotion.