• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.23 no.3
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• pp.159-166
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• 2023

This study analyzed what consumer characteristics affect the experience of using overseas direct purchase at a time when the overseas direct purchase market is rapidly increasing and consumers' interest in overseas direct purchase is increasing accordingly. For the study, personal data from the 2022 Korea Media Panel Survey were used, and data from 6,734 people who responded "yes" or "no" to whether or not to use overseas direct purchase among 9,941 total respondents were used for analysis. In addition, three variables (demographic, media utilization status, values and lifestyle) were selected among the items of the Korea Media Panel Survey. First, general characteristics were analyzed fo 6,734 people, then, Chi-square test and t-test were performed for comparative analysis between each variable according to the use of overseas direct purchase. Finally, logistic regression analysis was performed to identify the factors affecting overseas direct purchase. As a result of the analysis, 4 out of 5 demographic variables, 2 out of 3 media utilization variables, and 3 out of 7 values and lifestyle variables were derived as decisive factors for using overseas direct purchase. These results can be used to establish marketing strategies that can increase the use of consumers through domestic shopping malls, such as providing differentiated services for the sale of overseas direct shopping products on domestic shopping sites.

• Convergence Security Journal
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• v.22 no.2
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• pp.115-120
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• 2022

This study analyzed the recent trends in the sports environment to which big data and AI technologies, which are representative technologies of the 4th Industrial Revolution, and approached them from the perspective of convergence of big data and AI technologies in the sports field. And the results are as follows. First, it is being used for player and game data analysis and team strategy establishment and operation. Second, by combining big data collected using GPS, wearable equipment, and IoT with artificial intelligence technology, scientific physical training for each player is possible through user individual motion analysis, which helps to improve performance and efficiently manage injuries. Third, with the introduction of an AI-based judgment system, it is being used for judge judgment. Fourth, it is leading the change in marketing and game broadcasting services. The technology of the 4th Industrial Revolution is bringing innovative changes to all industries, and the sports field is also in the process. The combination of big data and AI is expected to play an important role as a key technology in the rapidly changing future in a sports environment where scientific analysis and training determine victory or defeat.

• Journal of Internet of Things and Convergence
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• v.10 no.1
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• pp.59-68
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• 2024

China's huge population and industrial diversification have driven increased demand for IoT, and in a social environment where IoT technology is changing all aspects of personal and family life, including smart shopping, this study was conducted in Changchun, China. The study aimed to find ways to meet the Fashion needs of female college students living in the country and promote the development of the fashion product industry by improving the service quality of Chinese fashion product live commerce. The analysis results are as follows. First, the service quality characteristics of Chinese fashion product live commerce had a positive effect on customer satisfaction. Second, the service quality characteristics of Chinese fashion product live commerce had a positive effect on reuse intention. Third, customer satisfaction had a positive effect on reuse intention. Based on these results, it can be concluded that improving the service quality of live commerce can directly promote product sales and create direct economic benefits. In addition, based on the results of the study, which show that the service quality of fashion product live commerce affects customer satisfaction and reuse intention, it is judged that it will provide useful information in establishing marketing strategies for live commerce platforms by region and target.

• Information Systems Review
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• v.23 no.3
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• pp.177-200
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• 2021

Recently, the use of prepaid electronic payments such as electronic wallets, digital currency and prepaid points is gradually increasing. Prepaid electronic payments has the characteristic of being used after charging first. This study empirically investigated the factors affecting the intention to use online charging in order to help improve the service that require prepaid recharge by applying transformed TAM. Since there are not many previous studies for the intention to use online charging, we extract factors through preceding researches for electronic cash and mobile easy payment. Also we analyze the intention to use online charging for transportation card users, focusing on the moderating effects. As a result of the study, it was found that 'convenience', 'ubiquity', and 'self-efficacy' among the independent variables had a positive (+) effect on mediation variable 'perceived usefulness'. 'Perceived usefulness' was analyzed to have a significant influence on the dependent variable 'usage intention'. According to users' gender, internet usage time, internet shopping frequency, online charging frequency and transportation card usage type, the moderating effect was significant on 'perceived usefulness' and 'usage intention'. As an implication, it was suggested that service improvement and differentiated marketing are needed in direction of increasing the usefulness of services. Additional research directions were proposed for services such as e-wallets, prepaid points and digital currencies by adding other factors and moderate variables.

• Information Systems Review
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• v.21 no.1
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• pp.135-152
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• 2019

Recently there are many development and support policies for start-up companies because of successful venture companies related to ICT services. However, as these policies have focused on the support for the initial stage of start-up, many start-up companies have difficulties to continuously grow up. The main reason for these difficulties is that they recognize start-up tasks as independent activities. However, many experts or related articles say that start-up tasks are composed of related processes from the initial stage to the stable stage of start-up firms. In this study, we models the start-up processes based on the survey collected by the start-up companies, and analyze the start-up process of ICT service companies with process mining techniques. Through process mining analysis, we can draw a sequential flow of tasks for start-ups and the characteristics of them. The analysis of start-up businessman, idea derivation, creating business model, business diversification processes are resulted as important processes, but marketing activity and managing investment funds are not. This result means that marketing activity and managing investment funds are activities that need ongoing attention. Moreover, we can find temporal and complementary tasks which could not be captured by independent individual-level activity analysis. Our process analysis results are expected to be used in simulation-based web-intelligent system to support start-up business, and more cumulated start-up business cases will be helpful to give more detailed individual-level personalization service. And our proposed process model and analyzing results can be used to solve many difficulties for start-up companies.

• Information Systems Review
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• v.18 no.4
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• pp.69-88
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• 2016

Nowadays, organizations have adopted Smart Work to efficiently manage tasks, such as electronic document approval, customer management, and site inspection, without spatial-temporal constraints. Smartphones, which are commonly used in Smart Work, enable individuals to perform their jobs anytime and anywhere, thus blurring the boundary between work and non-work. To solve the problem of blurred work/non-work boundaries, a construct of self-control and affective factors needs to be considered because business style is changed from command to autonomy in the Smart Work context. Moreover, employees can convey their emotions easily over smartphones. Recent marketing studies have analyzed consumers' behavior based on the combination of cognitive, affective, and behavioral components, and researchers of information systems are also interested in these factors. However, previous research has some limitations, such as not classifying factors into cognitive, affective, and behavioral as well as not covering all three factors. Therefore, we explore the roles of cognitive, affective, and behavioral components in emotional exhaustion and education performance, and conduct a survey on undergraduate and graduate students, who are the major users of smartphones. Findings show that when individuals improve their cognitive capability (self-control) and usage experience (smartphone communication and internet usage), they can decrease emotional exhaustion and increase education performance. In the role of affective capability, increasing education performance is partially accepted. These results imply that organizations should not focus on controlling the usage of smartphones but on promoting appropriate smartphone usage.

• Information Systems Review
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• v.18 no.4
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• pp.141-154
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• 2016

Mobile application is growing explosively with the advent of a new technology: smartphones. Mobile application is a new marketing channel and performs as a start-up platform. This study examines the effect of mobile application on the off-line market. Despite the continuous declining demand for taxi service, paradoxically, the supply of taxi service has increased. The taxi industry can be categorized into general taxi and call taxi. General taxi is accidental and inefficient because it has to search for its own passenger. As call taxi takes the request of a passenger, it is more efficient than general taxi. However, the current defective passenger-taxi driver matching system and insufficient taxi driver management hinder the development of the call taxi market. Differences in differences (DID) is an econometrical methodology that examines whether or not an event has meaningful influence. This research uses DID to investigate the effect of the Kakao taxi application on the call taxi industry. Furthermore, it examines the effect of major companies' reckless diversification, which is considered unethical behavior. The passengers of call taxi data from August 2014 to July 2015 and those of designated driving service data of the same period were collected as the control group.

• Journal of the Korean Applied Science and Technology
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• v.40 no.6
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• pp.1580-1588
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• 2023

The desire for clear and clean skin is universal among both men and women. Women, in particular, seek the help of foundation to achieve beautiful and transparent skin. The choice of foundation is not determined by the race of an individual; instead, it varies based on personal skin color and undertone. Therefore, there is a need to surpass the stereotype of using foundation colors based on racial discrimination. The purpose of this study is to randomly select cosmetics brands from Korea, China, Japan, the United States, France, and the United Kingdom, considering the impact of each photo, environment, and equipment. The objective is to understand the differences in skin tones in foundation advertisement model images on websites. Analyzing the RGB values of foundation colors for each brand revealed that in Korea, the colors were 8.75R, 1.25YR, 2.5YR, 3.75YR, 5YR, and 6.25YR. Chinese brands showed similar colors with 2.5YR, 3.75YR, 5YR, 6.25YR, and 10YR. Japanese brands displayed colors such as 7.5R, 8.75R, 10R, 5YR, 6.25YR, and 7.5YR. American brands presented colors like 6.25R, 8.75R, 10R, 2.5YR, 3.75YR, 5YR, 6.25YR, 7.5YR, and 10YR. French brands featured 10R, 1.25YR, 3.75YR, 5YR. Lastly, British brands displayed 2.5YR, 3.75YR, 7.5YR. As a follow-up study, in-depth research on the reshaping and color changes of foundation over time is recommended. It is hoped that this research will serve as fundamental data for makeup companies' marketing and contribute to the development of both domestic and international color cosmetics markets.

• Journal of Intelligence and Information Systems
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• v.16 no.4
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• pp.195-211
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• 2010

An exhibition is defined as market events for specific duration to present exhibitors' main product range to either business or private visitors, and it also plays a key role as effective marketing channels. Especially, as the effect of the opinions of the visitors after the exhibition impacts directly on sales or the image of companies, exhibition organizers must consider various needs of visitors. To meet needs of visitors, ubiquitous technologies have been applied in some exhibitions. However, despite of the development of the ubiquitous technologies, their services cannot always reflect visitors' preferences as they only generate information when visitors request. As a result, they have reached their limit to meet needs of visitors, which consequently might lead them to loss of marketing opportunity. Recommendation systems can be the right type to overcome these limitations. They can recommend the booths to coincide with visitors' preferences, so that they help visitors who are in difficulty for choices in exhibition environment. One of the most successful and widely used technologies for building recommender systems is called Collaborative Filtering. Traditional recommender systems, however, only use neighbors' evaluations or behaviors for a personalized prediction. Therefore, they can not reflect visitors' dynamic preference, and also lack of accuracy in exhibition environment. Although there is much useful information to infer visitors' preference in ubiquitous environment (e.g., visitors' current location, booth visit path, and so on), they use only limited information for recommendation. In this study, we propose a booth recommendation methodology using Sequential Association Rule which considers the sequence of visiting. Recent studies of Sequential Association Rule use the constraints to improve the performance. However, since traditional Sequential Association Rule considers the whole rules to recommendation, they have a scalability problem when they are adapted to a large exhibition scale. To solve this problem, our methodology composes the confidence database before recommendation process. To compose the confidence database, we first search preceding rules which have the frequency above threshold. Next, we compute the confidences of each preceding rules to each booth which is not contained in preceding rules. Therefore, the confidence database has two kinds of information which are preceding rules and their confidence to each booth. In recommendation process, we just generate preceding rules of the target visitors based on the records of the visits, and recommend booths according to the confidence database. Throughout these steps, we expect reduction of time spent on recommendation process. To evaluate proposed methodology, we use real booth visit records which are collected by RFID technology in IT exhibition. Booth visit records also contain the visit sequence of each visitor. We compare the performance of proposed methodology with traditional Collaborative Filtering system. As a result, our proposed methodology generally shows higher performance than traditional Collaborative Filtering. We can also see some features of it in experimental results. First, it shows the highest performance at one booth recommendation. It detects preceding rules with some portions of visitors. Therefore, if there is a visitor who moved with very a different pattern compared to the whole visitors, it cannot give a correct recommendation for him/her even though we increase the number of recommendation. Trained by the whole visitors, it cannot correctly give recommendation to visitors who have a unique path. Second, the performance of general recommendation systems increase as time expands. However, our methodology shows higher performance with limited information like one or two time periods. Therefore, not only can it recommend even if there is not much information of the target visitors' booth visit records, but also it uses only small amount of information in recommendation process. We expect that it can give real?time recommendations in exhibition environment. Overall, our methodology shows higher performance ability than traditional Collaborative Filtering systems, we expect it could be applied in booth recommendation system to satisfy visitors in exhibition environment.

• Journal of Intelligence and Information Systems
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• v.23 no.3
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• pp.119-138
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• 2017

Recently, SNS has become an important channel for marketing as well as personal communication. However, cybercrime has also evolved with the development of information and communication technology, and illegal advertising is distributed to SNS in large quantity. As a result, personal information is lost and even monetary damages occur more frequently. In this study, we propose a method to analyze which sentences and documents, which have been sent to the SNS, are related to financial fraud. First of all, as a conceptual framework, we developed a matrix of conceptual characteristics of cybercriminality on SNS and emergency management. We also suggested emergency management process which consists of Pre-Cybercriminality (e.g. risk identification) and Post-Cybercriminality steps. Among those we focused on risk identification in this paper. The main process consists of data collection, preprocessing and analysis. First, we selected two words 'daechul(loan)' and 'sachae(private loan)' as seed words and collected data with this word from SNS such as twitter. The collected data are given to the two researchers to decide whether they are related to the cybercriminality, particularly financial fraud, or not. Then we selected some of them as keywords if the vocabularies are related to the nominals and symbols. With the selected keywords, we searched and collected data from web materials such as twitter, news, blog, and more than 820,000 articles collected. The collected articles were refined through preprocessing and made into learning data. The preprocessing process is divided into performing morphological analysis step, removing stop words step, and selecting valid part-of-speech step. In the morphological analysis step, a complex sentence is transformed into some morpheme units to enable mechanical analysis. In the removing stop words step, non-lexical elements such as numbers, punctuation marks, and double spaces are removed from the text. In the step of selecting valid part-of-speech, only two kinds of nouns and symbols are considered. Since nouns could refer to things, the intent of message is expressed better than the other part-of-speech. Moreover, the more illegal the text is, the more frequently symbols are used. The selected data is given 'legal' or 'illegal'. To make the selected data as learning data through the preprocessing process, it is necessary to classify whether each data is legitimate or not. The processed data is then converted into Corpus type and Document-Term Matrix. Finally, the two types of 'legal' and 'illegal' files were mixed and randomly divided into learning data set and test data set. In this study, we set the learning data as 70% and the test data as 30%. SVM was used as the discrimination algorithm. Since SVM requires gamma and cost values as the main parameters, we set gamma as 0.5 and cost as 10, based on the optimal value function. The cost is set higher than general cases. To show the feasibility of the idea proposed in this paper, we compared the proposed method with MLE (Maximum Likelihood Estimation), Term Frequency, and Collective Intelligence method. Overall accuracy and was used as the metric. As a result, the overall accuracy of the proposed method was 92.41% of illegal loan advertisement and 77.75% of illegal visit sales, which is apparently superior to that of the Term Frequency, MLE, etc. Hence, the result suggests that the proposed method is valid and usable practically. In this paper, we propose a framework for crisis management caused by abnormalities of unstructured data sources such as SNS. We hope this study will contribute to the academia by identifying what to consider when applying the SVM-like discrimination algorithm to text analysis. Moreover, the study will also contribute to the practitioners in the field of brand management and opinion mining.