• Radiation Oncology Journal
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• v.10 no.1
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• pp.59-67
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• 1992

From March 1979 through December 1986, 124 patients with early stage carcinoma of the uterine cervix received curative radiation therapy. According to FIGO classification, 35 patients were stage IB and 89 were stge II A. In stage IB, five year locoregional control, five year disease free survival, and five year overall survival was $79.0\%$, $76.4\%$ and $81.8\%$, respectively. In stage II A, five year locoregional control, five year disease free survival, and five year overall survival were $78.0\%$, $66.8\%$, and $72.1\%$, respectively. To identify prognostic factors, pretreatment parameters including age, ECOG performance status, number of pregnancies, history of diabetes mellitus and hypertension, histology, size and shape of primary tumor, CT findings and blood parameters were retrospectively analyzed in terms of locoregional control, disease free survival and overall survival using univariate analysis and multivariate analysis. In univariate analysis, tumor size on physicai examination and rectal invasion on CT significantly affected locoregional control, disease free survival and overall survival. Parametrial involvement on CT was a significant prognostic factor on locoregional control and disease free survival. Hemoglobin level affected disease free survival and overall survival. Histology and age were significant prognostic factors on locoregional control. In multivariate analysis excluding CT finding, tumor size on physical examination was a significant factor in terms of locoregioal control and overall survival. Hemoglobin level was significant in terms of disease free survival. In multivariate analysis including CT, histology was a prognostic factor on locoregional control and disease free survival. Hemoglobin level and rectal invasion on CT were significant factors on locoregional control.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.12 no.1
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• pp.1-13
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• 2017

The purpose of this study is to find out which factors determine the success of start-up in the initial market and what are the most important determinants. For the empirical analysis, the questionnaire related to the analysis of success factors for start-up success was designed according to the quantitative analysis (AHP technique). First, we selected 8 representative success factors for successful start-up in the initial market. In order to determine the degree of priority among these factors, we surveyed 12 entrepreneurs who are interested in entrepreneurship, universities, research institutes, and public officials. As a result of the empirical analysis, 51% of the funds in the tier 1 were ranked as the top priority to determine success factors. Followed by research and development (32.5%), management (8.7%) and marketing (7.8%). In particular, when each of the four items is calculated as 100 according to the result of the tier 1, and the tier 2 is converted, the foreign investment is analyzed as 43.7%. It was followed by 15.14% of R & D facilities, 14.07% of ideas, 8.7% of managerial ability, 7.29% of domestic investment, 5.85% of buyer feedback, 3.3% of development strategy and 1.95% of marketing strategy. Among the eight success factors, overseas investment items showed the closest preference to half, and it was the most important variable that determines the success or failure of market entry. The implication of this study is that many start-ups in Korea expect to receive investment and support from overseas accelerators. This means that overseas investment itself has been recognized as a start-up that makes services and products that can be used in the global market. A high preference for attracting foreign investment is due to the fact that the amount of investment is larger than that of Korea and that it can flexibly cope with the pressure on the performance compared to domestic investors. In this study, it was meaningful that we could confirm this fact through questionnaires of start-up experts. In future research, we need to find a viable alternative through studying how to provide start-up to foreign direct investment at the national level.

• Journal of Distribution Research
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• v.6 no.2
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• pp.1-17
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• 2002

The recent dot-com craze has been one of the main causes that accelerated the growth of internet-related companies in diversity as well as in size. Meanwhile, the domestic market of supplies and equipment for internet businesses has been dominated by major foreign companies. To regain their market positions, the domestic manufacturers had to find the way to build up their competitive advantages, such as meeting their customers needs and reducing overall costs. In this study, one domestic PC server manufacturer, which competes fiercely with foreign manufacturers for the top place, has been chosen as a model to evaluate its current supply chain and to find an area that can be improved for a better performance. System Dynamics is used throughout the study. The central concept to system dynamics is understanding how all the objects in a system interact with one another. It focuses on feedback and secondary effects to think through how a strategy might or might not work, depending on how organizational changes are received, and what kinds of consequences emerge. Then, computerized models were built for simulations, each with different conditions, and, finally, the results were evaluated based on some criteria which are considered to be important and meaningful. The inefficiency that exists in the supply chain was proved to be a thirty-day long purchasing order leadtime, and it was expected that more effective supply chain could be formed if the leadtme were reduced to 14 days or 7 days. The results of simulations showed that the overall expected costs in supply chain was the least with the purchasing leadtime being 7 days. The lower average number of parts held as inventory, along with the reduced lost sales, acted as the factor reducing the expected overall costs. Although there was a slight increase in the average number of final products held as inventory and the total ordering cost, the benefits from lower parts inventory and reduced lost sales were large enough to justify the overall cost reduction.

• Journal of Intelligence and Information Systems
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• v.20 no.2
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• pp.179-192
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• 2014

Keeping in line with the recent changes in the information technology environment, the online learning environment that supports multiple users' participation such as MOOC (Massive Open Online Courses) has become important. One of the largest professional associations in Information Technology, IEEE Computer Society, announced that "Supporting New Learning Styles" is a crucial trend in 2014. Popular MOOC services, CourseRa and edX, have continued to build active learning environment with a large number of lectures accessible anywhere using smart devices, and have been used by an increasing number of users. In addition, collaborative web services (e.g., blogs and Wikipedia) also support the creation of various user-uploaded learning materials, resulting in a vast amount of new lectures and learning materials being created every day in the online space. However, it is difficult for an online educational system to keep a learner' motivation as learning occurs remotely, with limited capability to share knowledge among the learners. Thus, it is essential to understand which materials are needed for each learner and how to motivate learners to actively participate in online learning system. To overcome these issues, leveraging the constructivism theory and collective intelligence, we have developed a social bookmarking system called WeStudy, which supports learning material sharing among the users and provides personalized learning material recommendations. Constructivism theory argues that knowledge is being constructed while learners interact with the world. Collective intelligence can be separated into two types: (1) collaborative collective intelligence, which can be built on the basis of direct collaboration among the participants (e.g., Wikipedia), and (2) integrative collective intelligence, which produces new forms of knowledge by combining independent and distributed information through highly advanced technologies and algorithms (e.g., Google PageRank, Recommender systems). Recommender system, one of the examples of integrative collective intelligence, is to utilize online activities of the users and recommend what users may be interested in. Our system included both collaborative collective intelligence functions and integrative collective intelligence functions. We analyzed well-known Web services based on collective intelligence such as Wikipedia, Slideshare, and Videolectures to identify main design factors that support collective intelligence. Based on this analysis, in addition to sharing online resources through social bookmarking, we selected three essential functions for our system: 1) multimodal visualization of learning materials through two forms (e.g., list and graph), 2) personalized recommendation of learning materials, and 3) explicit designation of learners of their interest. After developing web-based WeStudy system, we conducted usability testing through the heuristic evaluation method that included seven heuristic indices: features and functionality, cognitive page, navigation, search and filtering, control and feedback, forms, context and text. We recruited 10 experts who majored in Human Computer Interaction and worked in the same field, and requested both quantitative and qualitative evaluation of the system. The evaluation results show that, relative to the other functions evaluated, the list/graph page produced higher scores on all indices except for contexts & text. In case of contexts & text, learning material page produced the best score, compared with the other functions. In general, the explicit designation of learners of their interests, one of the distinctive functions, received lower scores on all usability indices because of its unfamiliar functionality to the users. In summary, the evaluation results show that our system has achieved high usability with good performance with some minor issues, which need to be fully addressed before the public release of the system to large-scale users. The study findings provide practical guidelines for the design and development of various systems that utilize collective intelligence.

• 대한공업교육학회지
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• v.41 no.2
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• pp.119-133
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• 2016

This study is aimed at verifying the effect of scratch programming education for middle school students on their information science creative personality and technological problem solving tendency. The results of such study can be used as basic data for raising 'future creative talents' armed with problem-solving capability they honed in software education. The results of this research are as follows. First, a statistically significant difference was confirmed between ex ante and ex post samples in a t-test which was performed to verify information science creative personality of the middle school students (t(37)=4.305, p<.01). Their information science creative personality was high in the average score as it dropped from 3.00 in the ex-ante test to 2.51 in the ex post test. It was confirmed that the education of scratch programming influences information science creative personality for middle school students positively, suggesting that middle school students are interested in new problematic situations they found in information science and discover new problem-solving methods in the programming education, thereby showing positive feedback in the education performance. However, it was revealed that the middle school students were unable to immerse themselves in the scratch programming course completely and change their psychological states. Second, a statistically significant difference was confirmed between ex ante and ex post samples in a t-test which was performed to verify their technological problem solving tendency (t(37)=3.074, p<.01). Their technological problem solving tendency was high in the average score as it dropped from 4.06 in the ex-ante test to 3.55 in the ex post test. It was confirmed that the education of scratch programming influences technological problem solving tendency for middle school students positively: they understood problems associated with technology, explored diverse breakthroughs for the identified problems and assessed and improved resolutions. Third, a moderate correlation was confirmed between their information science creative personality and technological problem solving tendency (r=.343, p<.05). Therefore, it is judged that the middle school students who took scratch programming education demonstrated its influence in the correlation between the imagination for problem solving, positivity in the information science creative personality and the confidence for problem solving in the technological problem solving tendency.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.41 no.5
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• pp.581-589
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• 2021

With the recent increase in the introduction of micro-electric vehicles in Korea, interest in micro-electric vehicle user satisfaction is increasing to revitalize related markets. In this paper, a basic study was conducted on the development of public services using micro-electric vehicle based on the constituent factors of user satisfaction. The survey includes: ① 'Analytic Hierarchy Process (AHP) for selecting the priority factors of user satisfaction of micro-electric vehicles', ② 'A survey of micro-electric vehicles image' to collect data in advance for providing users' preferences and transportation services for micro-electric vehicles, ③ In order to investigate the user satisfaction level of users who actually operated micro-electric vehicles, the order of 'user satisfaction survey of micro-electric vehicle drivers' was conducted. In the Analytic Hierarchy Process (AHP) analysis, it was found that users regarded as important in the order of 'user utilization data', 'vehicle movement data', and 'charging service data'. In the micro-electric vehicle image survey, users perceived micro-electric vehicles more positively in terms of "safety", 'durability', 'Ride comfort', 'design', 'MOOE (Maintenance and other operating expense)', and 'environment-friendly' when comparing micro-electric vehicles with electric motorcycles. In the survey on the user satisfaction of micro-electric vehicle drivers, the use of micro-electric vehicle did not directly affect work performance efficiency, and there was an experience of being disadvantaged on the road due to the size of the micro-electric vehicle, and driving in a cluster of micro-electric vehicle for outdoor advertisements. The city's public relations effect was great, but it was concerned about safety. In the future, based on the results of this study, we plan to build a user satisfaction structural equation model, preemptively discover feedback R&D for micro-electric vehicle utilization services in the public field, and actively seek to discover new public mobility support services.

• Journal of Intelligence and Information Systems
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• v.25 no.1
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• pp.139-161
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• 2019

The utilization of the e-commerce market has become a common life style in today. It has become important part to know where and how to make reasonable purchases of good quality products for customers. This change in purchase psychology tends to make it difficult for customers to make purchasing decisions in vast amounts of information. In this case, the recommendation system has the effect of reducing the cost of information retrieval and improving the satisfaction by analyzing the purchasing behavior of the customer. Amazon and Netflix are considered to be the well-known examples of sales marketing using the recommendation system. In the case of Amazon, 60% of the recommendation is made by purchasing goods, and 35% of the sales increase was achieved. Netflix, on the other hand, found that 75% of movie recommendations were made using services. This personalization technique is considered to be one of the key strategies for one-to-one marketing that can be useful in online markets where salespeople do not exist. Recommendation techniques that are mainly used in recommendation systems today include collaborative filtering and content-based filtering. Furthermore, hybrid techniques and association rules that use these techniques in combination are also being used in various fields. Of these, collaborative filtering recommendation techniques are the most popular today. Collaborative filtering is a method of recommending products preferred by neighbors who have similar preferences or purchasing behavior, based on the assumption that users who have exhibited similar tendencies in purchasing or evaluating products in the past will have a similar tendency to other products. However, most of the existed systems are recommended only within the same category of products such as books and movies. This is because the recommendation system estimates the purchase satisfaction about new item which have never been bought yet using customer's purchase rating points of a similar commodity based on the transaction data. In addition, there is a problem about the reliability of purchase ratings used in the recommendation system. Reliability of customer purchase ratings is causing serious problems. In particular, 'Compensatory Review' refers to the intentional manipulation of a customer purchase rating by a company intervention. In fact, Amazon has been hard-pressed for these "compassionate reviews" since 2016 and has worked hard to reduce false information and increase credibility. The survey showed that the average rating for products with 'Compensated Review' was higher than those without 'Compensation Review'. And it turns out that 'Compensatory Review' is about 12 times less likely to give the lowest rating, and about 4 times less likely to leave a critical opinion. As such, customer purchase ratings are full of various noises. This problem is directly related to the performance of recommendation systems aimed at maximizing profits by attracting highly satisfied customers in most e-commerce transactions. In this study, we propose the possibility of using new indicators that can objectively substitute existing customer 's purchase ratings by using RFM multi-dimensional analysis technique to solve a series of problems. RFM multi-dimensional analysis technique is the most widely used analytical method in customer relationship management marketing(CRM), and is a data analysis method for selecting customers who are likely to purchase goods. As a result of verifying the actual purchase history data using the relevant index, the accuracy was as high as about 55%. This is a result of recommending a total of 4,386 different types of products that have never been bought before, thus the verification result means relatively high accuracy and utilization value. And this study suggests the possibility of general recommendation system that can be applied to various offline product data. If additional data is acquired in the future, the accuracy of the proposed recommendation system can be improved.

• Therapeutic Science for Rehabilitation
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• v.13 no.1
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• pp.9-21
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• 2024

Objective : The purpose of this study was to confirm the characteristics and quality of a single-subject research that conducted interventions to improve activities of daily living (ADL) in stroke patients. Methods : 'Stroke,' 'activities of daily living,' and 'single-subject studies' were searched as keywords among papers published in the last 15 years between 2009 and 2023 among Research Information Sharing Service, DBpia, and e-articles. A total of nine papers were examined for the characteristics and quality before analysis. Results : The independent variables applied to improve ADL included constraint-induced therapy, mental practice for performing functional activities, virtual reality-based task training, subjective postural vertical training without visual feedback, bilateral upper limb movement, core stability training program, traditional occupational therapy and neurocognitive rehabilitation, smooth pursuit eye movement, neck muscle vibration, and occupation-based community rehabilitation. Assessment of Motor and Process Skills was the most common evaluation tool for measuring dependent variables, with four articles, and Modified Barthel Index and Canadian Occupational Performance Measure were two articles each. As a result of confirming the qualitative level of the analyzed papers, out of a total of nine studies, seven studies were at a high level, two at a moderate level, and none were at a low level. Conclusion : Various types of rehabilitation treatments have been actively applied as intervention methods to improve the daily life activities of stroke patients; the quality level of single-subject studies applying ADL interventions was reliable.

• Journal of the Korean Society of Radiology
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• v.83 no.6
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• pp.1327-1341
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• 2022

Purpose To evaluate the pattern of use and the perception of digital breast tomosynthesis (DBT) among Korean breast radiologists. Materials and Methods From March 22 to 29, 2021, an online survey comprising 27 questions was sent to members of the Korean Society of Breast Imaging. Questions related to practice characteristics, utilization and perception of DBT, and research interests. Results were analyzed based on factors using logistic regression. Results Overall, 120 of 257 members responded to the survey (response rate, 46.7%), 67 (55.8%) of whom reported using DBT. The overall satisfaction with DBT was 3.31 (1-5 scale). The most-cited DBT advantages were decreased recall rate (55.8%), increased lesion conspicuity (48.3%), and increased cancer detection (45.8%). The most-cited DBT disadvantages were extra cost for patients (46.7%), insufficient calcification characterization (43.3%), insufficient improvement in diagnostic performance (39.2%), and radiation dose (35.8%). Radiologists reported increased storage requirements and interpretation time for barriers to implementing DBT. Conclusion Further improvement of DBT techniques reflecting feedback from the user's perspective will help increase the acceptance of DBT in Korea.

• Journal of Intelligence and Information Systems
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• v.25 no.1
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• pp.21-41
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• 2019

Thanks to the rapid development of information technologies, the data available on Internet have grown rapidly. In this era of big data, many studies have attempted to offer insights and express the effects of data analysis. In the tourism and hospitality industry, many firms and studies in the era of big data have paid attention to online reviews on social media because of their large influence over customers. As tourism is an information-intensive industry, the effect of these information networks on social media platforms is more remarkable compared to any other types of media. However, there are some limitations to the improvements in service quality that can be made based on opinions on social media platforms. Users on social media platforms represent their opinions as text, images, and so on. Raw data sets from these reviews are unstructured. Moreover, these data sets are too big to extract new information and hidden knowledge by human competences. To use them for business intelligence and analytics applications, proper big data techniques like Natural Language Processing and data mining techniques are needed. This study suggests an analytical approach to directly yield insights from these reviews to improve the service quality of hotels. Our proposed approach consists of topic mining to extract topics contained in the reviews and the decision tree modeling to explain the relationship between topics and ratings. Topic mining refers to a method for finding a group of words from a collection of documents that represents a document. Among several topic mining methods, we adopted the Latent Dirichlet Allocation algorithm, which is considered as the most universal algorithm. However, LDA is not enough to find insights that can improve service quality because it cannot find the relationship between topics and ratings. To overcome this limitation, we also use the Classification and Regression Tree method, which is a kind of decision tree technique. Through the CART method, we can find what topics are related to positive or negative ratings of a hotel and visualize the results. Therefore, this study aims to investigate the representation of an analytical approach for the improvement of hotel service quality from unstructured review data sets. Through experiments for four hotels in Hong Kong, we can find the strengths and weaknesses of services for each hotel and suggest improvements to aid in customer satisfaction. Especially from positive reviews, we find what these hotels should maintain for service quality. For example, compared with the other hotels, a hotel has a good location and room condition which are extracted from positive reviews for it. In contrast, we also find what they should modify in their services from negative reviews. For example, a hotel should improve room condition related to soundproof. These results mean that our approach is useful in finding some insights for the service quality of hotels. That is, from the enormous size of review data, our approach can provide practical suggestions for hotel managers to improve their service quality. In the past, studies for improving service quality relied on surveys or interviews of customers. However, these methods are often costly and time consuming and the results may be biased by biased sampling or untrustworthy answers. The proposed approach directly obtains honest feedback from customers' online reviews and draws some insights through a type of big data analysis. So it will be a more useful tool to overcome the limitations of surveys or interviews. Moreover, our approach easily obtains the service quality information of other hotels or services in the tourism industry because it needs only open online reviews and ratings as input data. Furthermore, the performance of our approach will be better if other structured and unstructured data sources are added.