• Korean journal of communication and information
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• v.83
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• pp.157-185
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• 2017

The present study examines the dialectic tensions arising within the ICT-mediated labor culture between the dominant power of conglomerates and the precarious labor subjects in the labor practices, as smartphones and tablet PCs have grown in popularity. The present study explores how much young precarious workers named 'Cheongyeon Alba' (young precariat in S Korea) suffers from continually precarious job positions as temporary staff or contract workers, being trapped at the bottom of the pay scale, and also being electronically connected to the workplace in a seamless way. Concretely, this study investigates how the mobile phone becomes deeply entangled with the 'precarious' labor culture in the metropolitan city of Seoul. The mobile precariat has been in a disadvantaged position, in terms not only of the moral issue of exploitation in business but also of social injustice. Labor exploitation of young workers has been reinforced by the mobile labor culture, in which they are remotely monitored by live surveillance mobile apps, and mobile instant messaging from a boss can intrude incessantly into their private life. This study depicts the extension of the business's surveillant power by mobilizing the mobile phone in the working practices.

• The Korean Journal of Franchise Management
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• v.2 no.1
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• pp.134-151
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• 2011

The strategy for brand alliance is a new type of franchise to iron out the problems like the hotel restaurant's structural contradiction and decreasing profits caused by keen competition with external restaurants. This study is purposed to present the decisive factors for the brand alliance throughexamining the correlations between the brand restaurant designation standards and the expected effects from local low- and mid-priced hotel's brand alliance. The questionnaires were distributed to instructors and professors who have experience in teaching the food and beverage sections at college's hotel and tourism departments and 100 specialists at managerial level of a hotel's food and beverage parts.This survey was conducted for 20 days from December 2 to 22, 2004 and analyzed by independent t-test and canonical correlation analysis. The findings of this survey are as follows.Firstly, the service of the expected effect factors of the brand alliance was recognized relatively high by the specialists in hotel industry, while the sales effect factor of restaurant designation standards was recognized higher by the academic experts.The specialists of the hotel industry recognized the factors of menu and corporate culture higher than the academic experts. Secondly, the entire factors of the brand restaurant designation standards showed a correlation with the whole factors of the restaurant designation standards.In particular, the 'menu' factor presented the most influential to the expected effects of brand alliance.The factors of 'risk reduction' and 'synergy effect' exerted the strongest effect on the restaurant designation standards, which indicated the mutual correlation between the expected effect of brand alliance and the restaurant designation standards. Based on this study, the correlation between the expected effect of brand alliance and brand restaurant designation standards may play a primary role to choose a partner for the brand alliance, a decisive factor for the success.The execution of the brand alliance or the method to designate the alliance partner may vary from the hotel's desirable effects when the brand alliance is determined.In other words, the partner designation standards should be corresponding to the expected effects from the brand alliance between hotel and brand restaurant, and the academic and industrial experts' perceived differences in the expected effects of brand alliance and restaurant designation standards should be clarified to display the direction of decision-making and find the potential risks.

• Asia Marketing Journal
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• v.11 no.3
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• pp.57-89
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• 2009

This study aims to shed light on the new insights on the cross-buying intentions in the banking industry and suggests an integrated model of the cross-buying intentions. Recently with globalization in the financial sector, financial companies are trying to retain current customers and attract new one by developing various financial products. In South Korea, this trend is especially apparent in the banking sector. Cross-selling of various financial products such as beneficiary certificates, bankasurance and etc. is becoming more important in retaining competitive advantage in Korean banking industry. However, there are few studies which are trying to find out the factors affecting cross-buying intentions and explain their interrelationships comprehensively. Based upon the previous studies, this study finds out the factors affecting cross-buying intentions and classifies them into two dimensions: affective and instrumental. Affective dimension includes trust, satisfaction and commitment. Instrumental dimension includes the factors such as geological convenience, one-stop convenience, professionality, and direct mail. The results from this study are as follow. All the factors in the affective dimension(trust, satisfaction and commitment) have significant impacts on cross-buying intentions. Also all the factors in the instrumental dimension(geological convenience, one-stop convenience, professionality, and DM) significantly affect cross-buying intentions. Some implications of this dissertation are as follow; First, this study identifies the antecedents of cross-buying intentions comprehensively. Second, this paper provides practical guidelines for the banks attempting to intensify cross-selling activities. Third, banks need to develop sophisticated plans which can consolidate the emotional ties with customers through positive service experiences as the affective dimension is important in influencing cross-buying intentions. Finally, regarding the instrumental dimesnion, the implications are: 1) Developing various new financial products in addition to traditional product such as deposits and installment savings for improving customer convenience, 2) Enhancing the professionality of employees by strengthening education programs on numbers of financial products, 3) Increasing cross-buying intentions through the DM.

• Journal of Service Research and Studies
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• v.13 no.4
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• pp.173-190
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• 2023

In recent times, with the development of virtual convergence technologies, the market for the Metaverse, a digitally virtual space that combines virtuality and reality, is experiencing significant growth. These Metaverses are realizing new value in both reality and virtual spaces through the development of diverse services and content. However, existing research on the Metaverse mostly revolves around its conceptualization and categorization, with limited exploration of intentions to use the Metaverse. Consequently, this study examined the impact of Metaverse service characteristic factors on trust and intention to use within the Metaverse. The results of this study are as follows. First, among the service characteristic factors of the Metaverse, presence, interactivity, and playfulness were found to have a positive impact on Metaverse trust. On the other hand, informativeness was found not to have a significant influence on trust in the Metaverse. Second, Metaverse trust was found to have a positive impact on intention to use the Metaverse. Based on the research results above, this study aims to propose effective communication strategies for activating the Metaverse and developing services within the Metaverse platform.

• The Korean Journal of Applied Statistics
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• v.36 no.6
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• pp.515-528
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• 2023

With the growth of the e-commerce market, consumers increasingly rely on user reviews to make purchasing decisions. Consequently, researchers are actively conducting studies to effectively analyze these reviews. Among the various methods of sentiment analysis, the aspect-based sentiment analysis approach, which examines user reviews from multiple angles rather than solely relying on simple positive or negative sentiments, is gaining widespread attention. Among the various methodologies for aspect-based sentiment analysis, there is an analysis method using a transformer-based model, which is the latest natural language processing technology. In this paper, we conduct an aspect-based sentiment analysis on multilingual user reviews using two real datasets from the latest natural language processing technology model. Specifically, we use restaurant data from the SemEval 2016 public dataset and multilingual user review data from the cosmetic domain. We compare the performance of transformer-based models for aspect-based sentiment analysis and apply various methodologies to improve their performance. Models using multilingual data are expected to be highly useful in that they can analyze multiple languages in one model without building separate models for each language.

• The Journal of the Convergence on Culture Technology
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• v.9 no.6
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• pp.339-345
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• 2023

Design exists behind technology. Design is intertwined with the needs of daily life and market structures, and while dealing with technology, it can become insensitive to its meaning. Unlike other social media platforms, Instagram consists of image-based content. The purpose of this study is to examine the addictive design of Instagram. Furthermore, we discuss the ethical responsibilities that designers must have. A theoretical framework for understanding Instagram design is established through a review of major domestic and international literature that has been previously studied. Understand the history, structure, and functions of Instagram and identify Instagram designs that promote social media addiction. In this study, we introduced the mechanism by which Instagram promotes user addiction through design issues. (1) Pull-to-Refresh (2) Red color in push alarm (3) Profile photo border expression in Instagram Story. This design stimulates users' social desires and FOMO, forming the structure of obsessive Instagram usage habits. Instagram is an example that forces us to reconsider the ethical role of design and designers along with the advancement of technology. In today's world, the intrinsic value of what they create, including our society and life itself.

• Information Systems Review
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• v.22 no.4
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• pp.21-39
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• 2020

Recently, deep learning has shown high performance in various applications such as pattern analysis and image classification. Especially known as a difficult task in the field of machine learning research, stock market forecasting is an area where the effectiveness of deep learning techniques is being verified by many researchers. This study proposed a deep learning Convolutional Neural Network (CNN) model to predict the direction of stock prices. We then used the feature selection method to improve the performance of the model. We compared the performance of machine learning classifiers against CNN. The classifiers used in this study are as follows: Logistic Regression, Decision Tree, Neural Network, Support Vector Machine, Adaboost, Bagging, and Random Forest. The results of this study confirmed that the CNN showed higher performancecompared with other classifiers in the case of feature selection. The results show that the CNN model effectively predicted the stock price direction by analyzing the embedded values of the financial data

• Economic and Environmental Geology
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• v.57 no.1
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• pp.83-91
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• 2024

According to the IEA (2022), global rechargeable battery demand is expected to reach 1.3 TWh in 2040. EV batteries will account for about 80% of this demand, and used EV batteries are expected to be discharged after 30 years. Used EV batteries can be recycled and reused to create new value. They can also resolve one of the most vulnerable parts of the battery supply chain: raw material insecurity. In this study, we analyzed the amount of used batteries generated by EV in Korea and their potential for reuse and recycling. As a result, it was estimated that the annual generation of used batteries for EV began to increase to more than 100,000 in '31 and expanded to 810,000 in '45. In addition, it was found that the market for recycling EV batteries in '45 could be expected to be equivalent to the production of 1 million batteries, and the market for reuse could be expected to be equivalent to the production of 36 Gwh of batteries. On the other hand, according to the plan standard disclosed by the recycling company, domestic used EV batteries can account for 11% of the domestic recycling processing capacity (pre-treatment) ('30). So it will be important to manage the import and export of used batteries in terms of securing raw materials.

• The Journal of the Convergence on Culture Technology
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• v.10 no.1
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• pp.159-164
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• 2024

The domestic petroleum market is highly sensitive to changes in international oil prices. So, it is important to identify and respond to those changes. In particular, it is necessary to clearly understand the factors causing the price fluctuations of gasoline, which exhibits high consumption. International gasoline prices are influenced by global factors such as gasoline supplies, geopolitical events, and fluctuations in the U.S. dollar. However, previous studies have only focused on gasoline supplies. In this study, we explore the causal relationship between economic indicators and international gasoline prices using various machine learning-based regression models. First, we collect data on various global economic indicators. Second, we perform data preprocessing. Third, we model using Multiple linear regression, Ridge regression, and Lasso(Least Absolute Shrinkage and Selection Operator) regression. The multiple linear regression model showed the highest accuracy at 96.73% in test sets. As a result, Our Multiple linear regression model showed the highest accuracy at 96.73% in test sets. We will expect that our proposed model will be helpful for domestic economic stability and energy policy decisions.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.34 no.1
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• pp.129-141
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• 2024

Amidst the acceleration of digital transformation across various sectors, the financial market is increasingly focusing on the development of digital and electronic payment methods, including currency. Among these, Central Bank Digital Currencies (CBDC) are emerging as future digital currencies that could replace physical cash. They are stable, not subject to value fluctuation, and can be exchanged one-to-one with existing physical currencies. Recently, both domestic and international efforts are underway in researching and developing CBDCs. However, current CBDC systems face scalability issues such as delays in processing large transactions, response times, and network congestion. To build a universal CBDC system, it is crucial to resolve these scalability issues, including the low throughput and network overload problems inherent in existing blockchain technologies. Therefore, this study proposes a solution based on reinforcement learning for handling large-scale data in a CBDC environment, aiming to improve throughput and reduce network congestion. The proposed technology can increase throughput by more than 64 times and reduce network congestion by over 20% compared to existing systems.