• Journal of Distribution Science
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• v.15 no.1
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• pp.123-130
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• 2017

Purpose - This research aims to examine the effect of e-business adoption on firm's growth and profitability in the distribution industry. The value added from the distribution industry acts as the cost of other industries. As the distribution industry develops, its stage becomes shorter and the distribution margin becomes smaller. Therefore, e-business is expected to have a different effect on the distribution industry than other industries. Research design, data and methodology - The previous research generally used e-business adoption as an independent variable and firm's performance as a dependent variable. This study elaborated the model using a dynamic panel model that includes the performance variable of the previous year as an independent variable. By employing system GMM (Generalized Method of Moments), the endogeneity problem in the dynamic panel model can be solved. For the analysis, I extracted the distribution companies as the raw data in the National Statistical Office's Business Activity Survey over the period 2006 to 2012. Results - The growth rate of firms adopting e-business was 0.299%p higher than that of the non-adopter. However, only ERP (Enterprise Resource Planning), KMS (Knowledge Management System) and SCM (Supply Chain Management) contributed positively to the growth rate. In the case of profitability, it was 0.04%p higher than the distribution companies that did not adopt e-business. ERP and LMS (Learning Management System) improve profitability, while SCM reduces profitability. Consequently, while ERP improves both growth and profitability, SCM improves growth but reduces profitability. In addition, KMS improves firm's growth only, and LMS does only profitability, showing that each e-business has a differentiated effect. Conclusions - Since the distribution industry has different characteristics from manufacturing and other service industries, the introduction of e-business may not guarantee the growth and profitability of distribution companies. Careful introduction considering the characteristics of the distribution industry is required. In particular, it is necessary to select an e-business meeting the characteristics and needs of a distribution company, and thereafter, it is required for the company's own efforts to internalize it within the system.

• Journal of Digital Convergence
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• v.11 no.2
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• pp.235-242
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• 2013

Reputation systems aggregate users' feedback after the completion of a transaction and compute the "reputation" of products, services, or providers, which can assist other users in decision-making in the future. With the rapid growth of online e-Learning content providing services, a suitable reputation system for more credible e-Learning content delivery has become important and is essential if educational content providers are to remain competitive. Most existing reputation systems focus on generating ratings only for user reputation; they fail to consider the reputations of products or services(item reputation). However, it is essential for B2C e-Learning services to have a reliable reputation rating mechanism for items since they offer guidance for decision-making by presenting the ranks or ratings of e-Learning content items. To overcome this problem, we propose a novel collaborative filtering based reputation rating method. Collaborative filtering, one of the most successful recommendation methods, can be used to improve a reputation system. In this method, dual information sources are formed with groups of co-oriented users and expert users and to adapt it to the reputation rating mechanism. We have evaluated its performance experimentally by comparing various reputation systems.

• Journal of Internet Computing and Services
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• v.15 no.1
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• pp.143-156
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• 2014

The smart education has the goal of enhancing the capability of learners in the 21st century and especially address the improvement of the problem solving capability. This smart education based on the growth of smart devices and the effect of dramatical spread requires the ability of problem solving using the smart technology in accordance with time change. As the problem solving learning is a model used mainly for improving the capability of problem solving, this study develops the problem solving learning model focusing on the teaching-learning activity using the smart devices and also applies this model to the school field. As a result, the favorable response that using the smart devices is effective to the problem solving can be obtained. This study can contribute to achieve the goal of the smart education, and later can be effective to the successful smart education in the school field.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.10 no.8
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• pp.2068-2077
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• 2009

While six sigma project management systems have been widely used as a knowledge management systems, no one has proposed an empirical explanation for impacts of project management systems on project performance. This study proposes a structural equation model of the project management system that relates learning/growth, internal growth, customer performance, and financial effects based on six sigma project performance. The relationships are investigated using data collected from a sample of green and black belts. The results indicate that there are a causal relationship with use of project management and learning/growth and internal process, internal process and customer performance, and customer and financial performance. However, there is no relationship between internal process and financial effects. The results suggest that six sigma project system could effectively be implemented as a knowledge management system to improve six sigma performance of green an black belts. This study also compares index of SEM's model fit of research model and that of alternative models for further analysis. The result shows that index of research model of index is better than that of alternative model.

• Journal of the Korea Society of Computer and Information
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• v.25 no.12
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• pp.165-172
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• 2020

Recently, due to exponential growth of access information on the web, the importance of predicting a user's next web page use has been increasing. One of the methods that can be used for predicting user's next web page is deep learning. To predict next web page, web logs are analyzed by data preprocessing and then a user's next web page is predicted on the output of the analyzed web logs using a deep learning algorithm. In this paper, we propose a framework for web page prediction that includes methods for web log preprocessing followed by deep learning techniques for web prediction. To increase the speed of preprocessing of large web log, a Hadoop based MapReduce programming model is used. In addition, we present a web prediction system that uses an efficient deep learning technique on the output of web log preprocessing for training and prediction. Through experiment, we show the performance improvement of our proposed method over traditional methods. We also show the accuracy of our prediction.

• The KIPS Transactions:PartD
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• v.11D no.3
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• pp.597-604
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• 2004

Accurate software cost estimation is essential to both developers and customers. Most of the cost estimating models based on the size measure methods, such as LOC and FP, are obtained through size estimation. The accuracy of size estimation directly influences the accuracy of cost estimation. As a result, the overall structure of regression-based cost models applies the power function based on software size. Many growth phenomenon in nature such as the growth in living organism, performance of technology, and learning capability of human show an S-shaped curve. This paper proposes a model which estimates the developing effort by using the growth curve. The presented model assumes that the relation cost and size follows the growth curve. The appropriateness of the growth curve model based on Function Point, Full-Function Point and Use-Case Point, which are the general methods in estimating the software size have been confirmed. The proposed growth curve model shows similar performance with power function model. In conclusion, the growth curve model can be applied in the estimation of the software cost.

• The Journal of the Korea Contents Association
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• v.13 no.4
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• pp.459-468
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• 2013

With advancements in augmented reality technology, attempts to apply it in various fields have increased. With advancements in information technology and a growth in the number of Korean learners in Korea and abroad, there is a growing demand for state-of-the-art Korean learning contents. Because the Korean learning contents developed using augmented reality have been found insufficient, this paper investigates ways to develop better contents. This paper proposes ways to develop learning contents that can increase learners' interest in learning and lead to academic achievement, because the methods of education in Korea are limited to textbook learning and learning through the media. Because of the structure of fairytales and the educational lessons that can be learned from them, Korean learning contents are developed using fairytales. Additionally, Korean contents are designed to implement augmented reality technology, and learners need only have computers, webcams, and markers to make use of it. In consideration of Korean learners who do not have access to augmented reality technology, this paper clarifies that there should be a harmony between existing and new Korean learning contents. On the basis of this study, further studies on Korean education exploring the role of augmented reality should be conducted so that Korean learning contents that use diverse types of augmented reality technology can be will developed.

• The Journal of the Acoustical Society of Korea
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• v.42 no.5
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• pp.460-468
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• 2023

Developmental Dysplasia of the Hip (DDH) is a pathological condition commonly occurring during the growth phase of infants. It acts as one of the factors that can disrupt an infant's growth and trigger potential complications. Therefore, it is critically important to detect and treat this condition early. The traditional diagnostic methods for DDH involve palpation techniques and diagnosis methods based on the detection of keypoints in the hip joint using X-ray or ultrasound imaging. However, there exist limitations in objectivity and productivity during keypoint detection in the hip joint. This study proposes a deep learning model-based keypoint detection method using X-ray and ultrasound imaging and analyzes the performance of keypoint detection using various deep learning models. Additionally, the study introduces and evaluates various data augmentation techniques to compensate the lack of medical data. This research demonstrated the highest keypoint detection performance when applying the residual network 152 (ResNet152) model with simple & complex augmentation techniques, with average Object Keypoint Similarity (OKS) of approximately 95.33 % and 81.21 % in X-ray and ultrasound images, respectively. These results demonstrate that the application of deep learning models to ultrasound and X-ray images to detect the keypoints in the hip joint could enhance the objectivity and productivity in DDH diagnosis.

• Journal of The Korean Association of Information Education
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• v.13 no.1
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• pp.9-22
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• 2009

As information technology(IT) has been rapidly developed, e-learning is also growing to meet the need of lifelong education using internet. However, with the growth of e-learning has come the big problem of high dropout rates. The purpose of this present study was to identify the major factors influencing drop-out in corporate e-learning. 250 employees(persistence: n=157, dropout: n=93) who enrolled an e-learning course in S company were participated in this study. A logistic regression analysis was performed to identify predictors of dropout. It was determined that individual background(marriage, amount of study time, difficult to combine work and family), learners' characteristics and value of the course were able to predict dropout with nearly 75 percent accuracy.

• The Journal of the Convergence on Culture Technology
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• v.10 no.2
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• pp.459-463
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• 2024

Frequent closures among self-employed individuals lead to national economic losses. Given the high closure rates in the restaurant industry, predicting changes in this sector is crucial for business survival. While research on factors affecting restaurant industry survival is active, studies predicting commercial district changes are lacking. Thus, this study focuses on forecasting such alterations, designing a deep learning model for Seoul's administrative district commercial district changes. It collects 2023 and 2022 second-quarter variables related to these changes, converting yearly fluctuations into percentages for augmentation. The proposed deep learning model aims to predict commercial district changes. Future policies, considering this study, could support restaurant industry growth and economic development.