• The Journal of Society for e-Business Studies
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• v.25 no.4
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• pp.61-75
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• 2020

The scope of data for understanding or predicting stock prices has been continuously widened from traditional structured format data to unstructured data. This study investigates whether commentary data collected from SNS may affect future stock prices. From "Stock Discussion Room" in Naver, we collect 20 stocks' commentary data for six months, and test whether this data have prediction power with respect to one-hour ahead price direction and price range. Deep neural network such as LSTM and CNN methods are employed to model the predictive relationship. Among the 20 stocks, we find that future price direction can be predicted with higher than the accuracy of 50% in 13 stocks. Also, the future price range can be predicted with higher than the accuracy of 50% in 16 stocks. This study validate that the investors' sentiment reflected in SNS community such as Naver's "Stock Discussion Room" may affect the demand and supply of stocks, thus driving the stock prices.

• The Journal of Society for e-Business Studies
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• v.15 no.4
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• pp.123-142
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• 2010

Generally speaking, user behavior in the post-adoption period is different from that in the pre-adoption period. Users come to make on their experiences of IT use whether they will continue to use it or not. Most theories about the user behaviors in the pre-adoption period are limited in describing them after adoption since they do not consider user's experiences of using the adopted IT and the beliefs formed by those experiences. Therefore, in this study, we explore user's experiences and beliefs such as familiarity, satisfaction and habits in the post-adoption period and examine how they affect user's intention to continue in using Mobile Social Network Service. Through literature reviews, we proposed the conceptual model to explain the role of users' habits in continuance of IT post-adoption stage. Then, we examine the impact of the constructs to affect the intention to continue using the Mobile SNS. The results show that the intention to continue to use Mobile SNS is strongly influenced by users' habits, satisfaction and familiarity; users' habits is strongly influenced by satisfaction and familiarity; satisfaction is strongly influenced by familiarity.

• The Journal of Society for e-Business Studies
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• v.15 no.1
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• pp.17-37
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• 2010

Currently, online searching sites offer a variety of services such as just sorting by price, manufacturer, sorting by release date for sale as well as the product reviews to help and enjoy online shopping provided consumers with more shopping information. The purpose of this study is to examine the effects of online product reviews attributes (informativeness and usefulness) and familiarity on consumers' loyalty in online product searching site via trust and satisfaction. To identify these relationships, the secondary data or past studies were collected and theoretically arranged. I made the theoretical proposed model to explain the relationships between the constructs, identify the operational definitions and 8 Hypotheses were established, there was executed the survey of 175 customers. As the result of test that make the relations of used variables clear, i can get the conclusion; site familiarity and informativeness, Usefulness of online reviews have the positive effect empirically on trust building and loyalty. From the empirical test, i suggest the strategic advices in online product searching site. To increase the consumers' loyalty, it would be developed that a variety of methods and ways to raise the site familiarity and informativeness, usefulness in online product reviews. It is necessary for sticking the consumers to raise the positive trust building and satisfaction. The results of this study would help companies operating the online product searching site.

• Journal of the Korea Society of Computer and Information
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• v.18 no.11
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• pp.181-190
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• 2013

This study first aims to investigate from the users' perspective what service is the most efficient to users and what service is relatively the most effective in contrast to investment among a variety of services provided by smartphone manufacturers, telecommunication companies, and related corporations. In addition, this research suggests implicatively important elements for making the future model of smartphone services. For this end, this study finds out the factors which generate users' positive or negative attitudes towards smartphone use through a questionnaire of those who are using smartphones at present. In particular, by applying Theory of Planned Behavior, this study analyzes the influence exerted by the user's belief towards the kinds of services by setting up Attitudinal Belief, Subjective Norms, and Control Belief which have an influence on attitude from the perspective of smartphone providing detailed services. The results of this study will eventually help the smartphone manufacturers, telecommunication companies, and related corporations to establish smartphone marketing strategy as well as to select the smartphone services which will have popular appeal to their users.

• The Korean Journal of Archival Studies
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• no.15
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• pp.141-183
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• 2007

It is not an overstatement that nowadays electronic mails are communicated more frequently as well as conveniently than phones and facsimiles, not only in routine life hot also in business transactions. Also, it is evident that emails will be used more and more as a communication method between internal and external organizations. If the information transferred and received via emails takes a role of business records, it is no wonder that emails should be uniformly managed as public records. Currently, however, specific policies or guidelines for the management of email records are not available, nor do most of public employees realize that emails are the actual records of the organization. In fact, the three research methods have been used for this study in the purpose of the establishment of email records management scheme. First of all, bibliographic research has been conducted in an effort to describes the definition and types of email records indicated in the guidelines of each nation, as well as the differences from the transitory email messages. Secondly, email management guidelines and policies of public institutions of England, The United States, Australia, and Canada, so-called the advanced countries of the records management, have been analyzed to examine the advanced examples of email management. In order to manage email records effectively, the functional requirements - capture, classification, storage, access, tracking, disposition, and role and responsibility were categorized in this thesis, based on the ISO 15489. As the designs of these foreign guidelines vary one another, common factors of them were extracted to be included in the realm of the seven stages. Lastly, this thesis has analyzed characteristics of the email system within the Electronic Document Management System of existing administrative institutions. Also, it has examined the overall environment of the email records management of public institutions and sought out its improvement. In essence, focused on the crucial factors on email management drawn out from the email management guidelines of foreign nations and the analysis of the policies, this thesis proposes an email records management scheme for Korean public intuitions, as well as an email management model suitable for forthcoming e-government era.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.11
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• pp.701-710
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• 2018

This study aims to verify the mediating effect of perceived risk of internet shopping mall users' innovativeness on product purchase intention. Research subjects included consumers with prior experience of using internet shopping malls, and 405 respondents were used for final analysis. The major findings of the study are as follows: First, as a result of the investigation on the relationships among users' innovativeness, perceived risk, and product purchase intention, all correlation coefficients were positive. Second, consumer innovativeness was found to significantly influence perceived risk and purchase intention, and perceived risk mediated the relationship between consumer innovativeness and purchase intention. Third, when we performed sub-group analysis on the research model by dividing products into hedonic and utilitarian products, it was found that hedonic products revealed path coefficients which are statistically more significant compared with those for utilitarian products. We can draw the conclusion that in terms of internet shopping mall purchases, hedonic products exert a greater influence on the effect of consumer innovativeness on purchase intention compared to utilitarian products.

• The Journal of Society for e-Business Studies
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• v.24 no.2
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• pp.1-14
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• 2019

Because of the characteristics of game software, it is important to quickly identify and reflect users' needs into game software after its launch. However, most sites such as the Google Play Store, where users can download games and post reviews, provide only very limited and ambiguous classification categories for game reviews. Therefore, in this paper, we develop an automatic classification system for game reviews that categorizes reviews into categories that are clearer and more useful for game providers. The developed system converts words in reviews into vectors using word2vec, which is a representative word embedding model, and classifies reviews into the most relevant categories by measuring the similarity between those vectors and each category. Especially, in order to choose the best similarity measure that directly affects the classification performance of the system, we have compared the performance of three representative similarity measures, the Euclidean similarity, cosine similarity, and the extended Jaccard similarity, in a real environment. Furthermore, to allow a review to be classified into multiple categories, we use a threshold-based multi-category classification method. Through experiments on real reviews collected from Google Play Store, we have confirmed that the system achieved up to 95% accuracy.

• The Journal of Society for e-Business Studies
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• v.24 no.1
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• pp.121-137
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• 2019

De-identification is a method by which the remaining information can not be referred to a specific individual by removing the personal information from the data set. As a result, de-identification can lower the exposure risk of personal information that may occur in the process of collecting, processing, storing and distributing information. Although there have been many studies in de-identification algorithms, protection models, and etc., most of them are limited to structured data, and there are relatively few considerations on de-identification of unstructured data. Especially, in the medical field where the unstructured text is frequently used, many people simply remove all personally identifiable information in order to lower the exposure risk of personal information, while admitting the fact that the data utility is lowered accordingly. This study proposes a new method to perform de-identification by applying the k-anonymity protection model targeting unstructured text in the medical field in which de-identification is mandatory because privacy protection issues are more critical in comparison to other fields. Also, the goal of this study is to propose a new utility metric so that people can comprehend de-identified data set utility intuitively. Therefore, if the result of this research is applied to various industrial fields where unstructured text is used, we expect that we can increase the utility of the unstructured text which contains personal information.

• The Journal of Society for e-Business Studies
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• v.26 no.1
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• pp.93-106
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• 2021

Many recent studies on deep learning-based road crack detection have shown significantly more improved performances than previous works using algorithm-based conventional approaches. However, many deep learning-based studies are still focused on classifying the types of cracks. The classification of crack types is highly anticipated in that it can improve the crack detection process, which is currently relying on manual intervention. However, it is essential to calculate the severity of the cracks as well as identifying the type of cracks in actual pavement maintenance planning, but studies related to road crack detection have not progressed enough to automated calculation of the severity of cracks. In order to calculate the severity of the crack, the type of crack and the area of the crack in the image must be identified together. This study deals with a method of using Mobilenet-SSD that is deep learning-based object detection techniques to effectively automate the simultaneous detection of crack types and crack areas. To improve the accuracy of object-detection for road cracks, several experiments were conducted to combine the U-Net for automatic segmentation of input image and object-detection model, and the results were summarized. As a result, image masking with U-Net is able to maximize object-detection performance with 0.9315 mAP value. While referring the results of this study, it is expected that the automation of the crack detection functionality on pave management system can be further enhanced.

• The Journal of Society for e-Business Studies
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• v.27 no.2
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• pp.205-218
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• 2022

Recently, digital transformation in manufacturing has been accelerating. It results in that the data collection technologies from the shop-floor is becoming important. These approaches focus primarily on obtaining specific manufacturing data using various sensors and communication technologies. In order to expand the channel of field data collection, this study proposes a method to automatically collect manufacturing data based on vision-based artificial intelligence. This is to analyze real-time image information with the object detection and tracking technologies and to obtain manufacturing data. The research team collects object motion information for each frame by applying YOLO (You Only Look Once) and DeepSORT as object detection and tracking algorithms. Thereafter, the motion information is converted into two pieces of manufacturing data (production performance and time) through post-processing. A dynamically moving factory model is created to obtain training data for deep learning. In addition, operating scenarios are proposed to reproduce the shop-floor situation in the real world. The operating scenario assumes a flow-shop consisting of six facilities. As a result of collecting manufacturing data according to the operating scenarios, the accuracy was 96.3%.