• Journal of the Economic Geographical Society of Korea
• /
• v.7 no.1
• /
• pp.97-115
• /
• 2004

The purpose of this study is to investigate the spatial pattern of retail services and the consumer behaviors. For the purpose we select Jeju Island as the study area, because it retains relatively little distorted retail service systems by it's locational isolation. The retail service systems comprise three types: large-scale modern marts, conventional markets, and periodic markets. This study attempts to examine the interrelationships between these three different types, of retail services, and to figure out the spatial characteristics of consumer behaviors for each of them. We performed questionnaire surveys for getting the data of consumer behaviors. We applied several statistical methods to analyze the survey data. Most of retail services are located in two urban centers, Jeju City and Seoguipo City. We found that the locations of retail services are determined strongly by population size. The selection of market type and the location to go for shopping are related strongly with the types of goods. However, there is a wide difference in the consumer behaviors according to the consumer's socio-economic characteristics. Young wives tend to go shopping to large-scale marts in Jeju City which is the higher level central place, while old wives go shopping to conventional markets and periodic markets. They also show different shopping behaviors according to the household income levels. Low income groups prefer to go conventional markets located near to their residence, middle income groups go to large-scale marts in Jeju, and high income group go out of the Jeju Island. However, the consumer behavior does not show big difference according to the size of family. There are also no difference in the selection for shopping location according to the consumer's resident locations.

• The Korean Journal of Franchise Management
• /
• v.10 no.1
• /
• pp.43-52
• /
• 2019

Purpose - Many companies use the Internet to conduct their business to maintain and acquire their customers. SNS is used as a site where users can create profiles, build personal networks, and then share and exchange information with others. Not only do people use SNS for their self-promotion, but they also promote their services by creating SNS pages. SNS is recognized as a medium for implementing effective advertising strategies and is being used as an important means of promoting the company. Therefore, in this study, we investigate the effect of SNS marketing characteristics of restaurant franchise firms on utilitarian value and hedonic value and examine their effects on purchase intention. Research design, data, and methodology - The data were collected from 20s-60s respondents who have used SNS for restaurant visit using Google survey. A total of 159 responses were collected and used for final analysis. Smart PLS 3.0 was used for the hypothesis test. Results - As a result of an analysis, it was shown that the influence of the playfulness and affordability of information on the utilitarian value had a significant positive effect. Interaction and up-to-date did not have a positive effect on utilitarian value. Interaction, affordability, and up-to-date have no significant positive effects on hedonic value. The playfulness of information has a positive effect on the hedonic value. Both utilitarian value and hedonic value had positive effects on purchase intention. Conclusions - The findings of this study suggest that the SNS marketers of restaurant franchisors should focus on the playfulness, affordability, and up-to-date rather than the interactivity of SNS. In marketing through SNS, the act of presenting the basis of information and enhancing the provision of information through objective criteria makes it possible to experience the practical value of information. It is necessary to develop differentiated contents which cause customers interest and fun and to induce many customers' purchase intent by providing objective and realistic information. In order to increase the customers' repurchase intentions toward the food service business, customers should maximize the hedonic value and practical value felt through information. It should also focus on providing information that customers are receptive to, rather than providing prompt information.

• The Journal of the Korea Contents Association
• /
• v.21 no.3
• /
• pp.157-171
• /
• 2021

The purpose of this study is to assess the smart city for major cities in Korea. The assessment indicators are based on the STIM structure (Service, Technology, Infrastructure, and Management Layer Architecture) of the Multi-Layered Smart City Model. Assessment indicators are established through smart city concepts, case analysis, big data analysis, as well as weighted through expert AHP survey. For the assessment, seven major metropolitan cities are selected, including Seoul, and their data such as KOSIS, KISDISTAT from 2017 to 2019 is utilized for the smart city level assessment. The smart city level results show that the service, technology, infrastructure, and management levels were relatively high in Seoul and Incheon, which are metropolitan areas. Whereas, Busan, Daegu, and Ulsan, the Gyeongsang provinces are relatively moderate, while Daejeon and Gwangju, the South Chungcheong region and the Jeolla provinces, were relatively low. The overall STIM ranking shows a similar pattern, as the Seoul metropolitan area smart city level outperforms the rest of the analyzed areas with a large difference. Accordingly, balanced development strategies are needed to reduce gaps in the level of smart cities in South Korea, and respective smart city plans are needed considering the characteristics of each region. This paper will follow the literature review, assessment index establishment, weight analysis of assessment index, major cities assessment and result in analysis, and conclusion.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.13 no.4
• /
• pp.2060-2077
• /
• 2019

Recently, mobile healthcare services have attracted significant attention because of the emerging development and supply of diverse wearable devices. Smartwatches and health bands are the most common type of mobile-based wearable devices and their market size is increasing considerably. However, simple value comparisons based on accumulated data have revealed certain problems, such as the standardized nature of health management and the lack of personalized health management service models. The convergence of information technology (IT) and biotechnology (BT) has shifted the medical paradigm from continuous health management and disease prevention to the development of a system that can be used to provide ground-based medical services regardless of the user's location. Moreover, the IT-BT convergence has necessitated the development of lifestyle improvement models and services that utilize big data analysis and machine learning to provide mobile healthcare-based personal health management and disease prevention information. Users' health data, which are specific as they change over time, are collected by different means according to the users' lifestyle and surrounding circumstances. In this paper, we propose a prediction model of user physical activity that uses data characteristics-based long short-term memory (DC-LSTM) recurrent neural networks (RNNs). To provide personalized services, the characteristics and surrounding circumstances of data collectable from mobile host devices were considered in the selection of variables for the model. The data characteristics considered were ease of collection, which represents whether or not variables are collectable, and frequency of occurrence, which represents whether or not changes made to input values constitute significant variables in terms of activity. The variables selected for providing personalized services were activity, weather, temperature, mean daily temperature, humidity, UV, fine dust, asthma and lung disease probability index, skin disease probability index, cadence, travel distance, mean heart rate, and sleep hours. The selected variables were classified according to the data characteristics. To predict activity, an LSTM RNN was built that uses the classified variables as input data and learns the dynamic characteristics of time series data. LSTM RNNs resolve the vanishing gradient problem that occurs in existing RNNs. They are classified into three different types according to data characteristics and constructed through connections among the LSTMs. The constructed neural network learns training data and predicts user activity. To evaluate the proposed model, the root mean square error (RMSE) was used in the performance evaluation of the user physical activity prediction method for which an autoregressive integrated moving average (ARIMA) model, a convolutional neural network (CNN), and an RNN were used. The results show that the proposed DC-LSTM RNN method yields an excellent mean RMSE value of 0.616. The proposed method is used for predicting significant activity considering the surrounding circumstances and user status utilizing the existing standardized activity prediction services. It can also be used to predict user physical activity and provide personalized healthcare based on the data collectable from mobile host devices.

• Journal of Information Management
• /
• v.43 no.4
• /
• pp.191-210
• /
• 2012

Scholarly science citation data is typically of large volume and consists of a variety of data. Moreover, the volume of data is increasing more and more. Therefore, there are some requirements to store and manage the data efficiently and Korea Institute of Science and Technology Information (KISTI) develops Korea Science Citation Database (KSCD) which manage and serve very large-volume of korea science technique information including citation data. However, current services based on KSCD are not enough for various users. Thus, it is important issue to offer a variety of services using KSCD. For example, if a user searches articles described by a specific author, then a user may want to find not only the articles cited by a certain author but also those articles that study similar topics. However, it is not always easy to provide these services with citation data. Therefore, this paper surveys studies about services using citation data in order to find approaches for better utilizing KSCD. Especially, this paper considers data mining techniques, because data mining is one of the main techniques to extracting semantic information from big data. Therefore, this paper discusses methods for utilizing large volume of KSCD based on data mining technique.

• Journal of the Korea Society of Computer and Information
• /
• v.25 no.11
• /
• pp.229-238
• /
• 2020

Upcoming 4th industrial revolution era and the post-covid19 made procedure, contents, and the ways of education innovative changes. Thesis analyzed the changes of educational procedures of universities unsing Bigkinds of 'KPF', (which is Korea Press Foundation) and DataLab system of 'Naver'. The following three results were derived from relational analysis, monthly keyword trend, and related word analysis with 633 cases searched for the keyword of "university curriculum innovation, creativity, and convergence." Firstly, the frequency of relationship keyword analysis of recent 4 years(2016~2020) was ministry of education(190), industrial revolution(154), system(137), career(136), global(131), smart(97), and enrolled students(95) in order. Secondly, The frequency of keywords related to the related words was Human Resources Development (136), Industrial-Academic Cooperation (119), Education Ministry (86), Specialization (69), and LiNC (62), which showed the importance of supporting the government (Ministry of Education) and fostering human resources, industry-academic cooperation, LiNC, and characterization in order to foster human resources in universities. According to this study, the paradigm of education is the artificial intelligence environment of the fourth industrial revolution, which is meaningful in presenting the direction of specialization, industry-academic cooperation, smart, and globalization, and the future direction of education that fosters creative talent in the era of the fourth industrial revolution.

• The Journal of the Korea Contents Association
• /
• v.22 no.4
• /
• pp.222-230
• /
• 2022

Recently, Korea's cultural content industry is developing, and behind the growing recognition around the world is the real-time sharing service of global network users due to the development of science and technology. In particular, in the case of YouTube, its propagation power is fast and powerful in that everyone, not limited users, can become potential video providers. As more than 80% of mobile phone users are using YouTube in Korea, YouTube's information means that psychological factors of users are reflected. For example, information such as the number of video views, likes, and comments of a channel with a specific personality shows a measure of the channel's personality interest. This is highly related to the fact that information such as the frequency of keyword search on portal sites is closely related to the stock market economically and psychologically. Therefore, in this study, YouTube information from a representative entertainment company is collected through a crawling algorithm and analyzed for the causal relationship with major variables related to stock prices. This study is considered meaningful in that it conducted research by combining cultural content, IT, and financial fields in accordance with the era of the fourth industry.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.22 no.5
• /
• pp.354-360
• /
• 2021

People hope to live a healthy and happy life achieving satisfaction by striking a good work-life balance. Therefore, there is a growing interest in well-aging which means living happily to a healthy old age without worry. This study identified important factors related to well-aging by analyzing news articles published in Korea. Using Python-based web crawling, 1,199 articles were collected on the news service of portal site Daum till November 2020, and 374 articles were selected which matched the subject of the study. The frequency analysis results of text mining showed keywords such as 'elderly', 'health', 'skin', 'well-aging', 'product', 'person', 'aging', 'female', 'domestic' and 'retirement' as important keywords. Besides, a social network analysis with 45 important keywords revealed strong connections in the order of 'skin-wrinkle', 'skin-aging' and 'old-health'. The result of the CONCOR analysis showed that 45 main keywords were composed of eight clusters of 'life and happiness', 'disease and death', 'nutrition and exercise', 'healing', 'health', and 'elderly services'.

• SUVANNABHUMI
• /
• v.10 no.2
• /
• pp.181-213
• /
• 2018

How to study Southeast Asia (SEA)? The need to explore and identify methodologies for studying SEA are inherent in its multifaceted subject matter. At a minimum, the region's rich cultural diversity inhibits both the articulation of decisive defining characteristics and the training of scholars who can write with confidence beyond their specialisms. Consequently, the challenges of understanding the region remain and a consensus regarding the most effective approaches to studying its history, identity and future seem quite unlikely. Furthermore, "Area Studies" more generally, has proved to be a less attractive frame of reference for burgeoning scholarly trends. This paper will propose a new tool to help address these challenges. Even though the science of artificial intelligence (AI) is in its infancy, it has already yielded new approaches to many commercial, scientific and humanistic questions. At this point, AI has been used to produce news, generate better smart phones, deliver more entertainment choices, analyze earthquakes and write fiction. The time has come to explore the possibility that AI can be put at the service of the study of SEA. The paper intends to lay out what would be required to develop SEABOT. This instrument might exist as a robot on the web which might be called upon to make the study of SEA both broader and more comprehensive. The discussion will explore the financial resources, ownership and timeline needed to make SEABOT go from an idea to a reality. SEABOT would draw upon artificial neural networks (ANNs) to mine the region's "Big Data", while synthesizing the information to form new and useful perspectives on SEA. Overcoming significant language issues, applying multidisciplinary methods and drawing upon new yields of information should produce new questions and ways to conceptualize SEA. SEABOT could lead to findings which might not otherwise be achieved. SEABOT's work might well produce outcomes which could open up solutions to immediate regional problems, provide ASEAN planners with new resources and make it possible to eventually define and capitalize on SEA's "soft power". That is, new findings should provide the basis for ASEAN diplomats and policy-makers to develop new modalities of cultural diplomacy and improved governance. Last, SEABOT might also open up avenues to tell the SEA story in new distinctive ways. SEABOT is seen as a heuristic device to explore the results which this instrument might yield. More important the discussion will also raise the possibility that an AI-driven perspective on SEA may prove to be even more problematic than it is beneficial.

• Management & Information Systems Review
• /
• v.35 no.1
• /
• pp.189-206
• /
• 2016

It was required to attach the documents on the details of external audit including the number of the participants in external audit, audited parts and audit times under the Article 7-2 on the audit report to the accounting audit report from 2014 in accordance with the amendment to the Act on External Audit of Stock Companies. This study aim to calculate the audit fee based on the estimated cost of service calculation of the government contribution agencies by reflecting the implementation of the revised external audit. This study calculated the audit fee for the target company (a listed company assumed to have no internal control risks and relevant audit risks for unqualified opinion in the previous year, 100 billion won of total amount of asset, manufacturing company in the previous year and preliminary client request) by putting together four items of expenditure including employment costs, expenditure, general management expenses and profit in accordance with the calculation system of cost of service under the State Contract Act. Then, it used the data collected from the documents on the details of the revised external audit after requesting estimation on the target company with the estimated cost to Big-4 accounting firms to identify the participants and times of the accounting audit. The employment costs applied 150% of participation rate of the base price of employment costs for the academic research service cost in 2014, the expenditure used the average value of accounting firms of corporate business management analysis of the Bank of Korea (2013), the general management expenses applied 5% of the general management rate of service business under Article 7-1 of the Enforcement Rule of the Act on Contracts to which the State is a Party and the profit applied 10% of profit rate of service business under Article 7-2 of the Enforcement Rule of the Act on Contracts to which the State is a Party. Based on the calculation of the estimated costs by applying the above, the audit fee was estimated at 50,617,769won. Although the result is not the optimal audit fee, it may be used as a basic scale to compare the audit fees of companies without criteria. Also, such amendment to the Act on External Audit of Stock Companies may improve independence of auditors and transparency of the accounting system rather than previous announcing only the total audit times.