• Knowledge Management Research
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• v.23 no.3
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• pp.23-44
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• 2022

With the development of blockchain technology, play-to-earn (P2E) games, one of the decentralized applications (dApps), are receiving great social attention. P2E games are positively evaluated as areas with high growth potential based on blockchain technology, and at the same time, they are negatively evaluated as speculative as people can cash P2E game items in the form of cryptocurrency. In this situation, the purpose of this study is to investigate factors affecting the intention to use P2E games. Along with the discussion of hedonic system adoption, we consider the factors with perceived enjoyment, economic incentive, and social influence. In order to verify our research model, data were collected from 350 adults with P2E game experience or recognition, and a structural equation model was carried out. The analysis results find that perceived enjoyment and subjective norm have a significant positive effect on the intention to use P2E games, and economic incentive does not have a significant effect. In addition, peer influence and external influence have a significant positive effect on subjective norm. Drawing on these findings, we present several academic and practical implications for future research.

• Journal of Intelligence and Information Systems
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• v.29 no.1
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• pp.377-396
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• 2023

As the cryptocurrency market continues to grow, it has developed into a new financial market. The need for investment strategy research on the cryptocurrency market is also emerging. This study aims to conduct an empirical analysis on an investment methodology of cryptocurrency that combines short-term trading strategy and deep learning. Daily price data of the Ethereum was collected through the API of Upbit, the Korean cryptocurrency exchange. The investment performance of the experimental model was analyzed by finding the optimal parameters based on past data. The experimental model is a volatility breakout strategy(VBS), a Long Short Term Memory(LSTM) model, moving average cross strategy and a combined model. VBS is a short-term trading strategy that buys when volatility rises significantly on a daily basis and sells at the closing price of the day. LSTM is suitable for time series data among deep learning models, and the predicted closing price obtained through the prediction model was applied to the simple trading rule. The moving average cross strategy determines whether to buy or sell when the moving average crosses. The combined model is a trading rule made by using derived variables of the VBS and LSTM model using AND/OR for the buy conditions. The result shows that combined model is better investment performance than the single model. This study has academic significance in that it goes beyond simple deep learning-based cryptocurrency price prediction and improves investment performance by combining deep learning and short-term trading strategies, and has practical significance in that it shows the applicability in actual investment.

• Journal of Intelligence and Information Systems
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• v.29 no.2
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• pp.129-148
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• 2023

Bitcoin is a blockchain technology-based digital currency that has been recognized as a representative cryptocurrency and a financial investment asset. Due to its highly volatile nature, Bitcoin has gained a lot of attention from investors and the public. Based on this popularity, numerous studies have been conducted on price and trend prediction using machine learning and deep learning. This study employed LSTM (Long Short Term Memory) and CNN (Convolutional Neural Networks), which have shown potential for predictive performance in the finance domain, to enhance the classification accuracy in Bitcoin price trend prediction. XAI(eXplainable Artificial Intelligence) techniques were applied to the predictive model to enhance its explainability and interpretability by providing a comprehensive explanation of the model. In the empirical experiment, CNN was applied to technical indicators and Google trend data to build a Bitcoin price trend prediction model, and the CNN model using both technical indicators and Google trend data clearly outperformed the other models using neural networks, SVM, and LSTM. Then SHAP(Shapley Additive exPlanations) was applied to the predictive model to obtain explanations about the output values. Important prediction drivers in input variables were extracted through global interpretation, and the interpretation of the predictive model's decision process for each instance was suggested through local interpretation. The results show that our proposed research framework demonstrates both improved classification accuracy and explainability by using CNN, Google trend data, and SHAP.

• Knowledge Management Research
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• v.23 no.2
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• pp.37-60
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• 2022

The growth of virtual reality games and the popularization of blockchain technology are bringing significant changes to the formation of the metaverse industry ecosystem. Especially, after Meta acquired Oculus, a VR device and application company, the growth of VR-based metaverse services is accelerating. In this study, the concept that supports leisure activities in the metaverse environment is explored realting to game-like features in VR apps, which differentiates traditional mobile apps based on a smart phone device. Using exploratory text mining methods and network analysis approches, 241 apps registed in the Oculus Quest 2 App Store were analyzed. Analysis results from a quasi-network show that a leisure concept is closely related to various genre features including a game and tourism. Additionally, the anlaysis results of G & F model indicate that the leisure concept is distictive in the view of gateway brokerage role. Those results were also confirmed in LDA topic modeling analysis.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.19 no.5
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• pp.25-32
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• 2019

The purpose of this study is to investigate how to make a proper investment in ICO in the market. Previously, companies used to borrow money from banks or to obtain investments from venture capital (VC) and angel investors, but now ICOs are used as a new type of funding and financing model. The ICO sells the tokens or coins created on the blockchain openly online to raise the necessary funds, and provides the market value by paying the tokens or coins as much as the investment amount. According to this study, the limitations of the ICO market are (1) difficulties in evaluating the company, (2) uncertainties in investments, (3) lack of legal safeguards, and (4) measures to secure corporate stability after recruitment. At present, there is no way to cope with this systematically since the ICO is not protected in the legal framework. Nevertheless, we investigated the ways to make proper investment in the existing ICO market. In investing in ICO, investors should (1) consider investment methods and profitability, and (2) verify and judge investment fraud through various channels (ex. Homepage, composition team profile, etc.) and make investments based on this. This study will contribute to the formation of a healthy ICO market by understanding the newly emerged ICO market and studying the considerations when investing in it, thereby contributing to the right investor training and reducing the mass production of consumer damages caused by fraud. The limitation of this study is that the domestic ICO has not yet been examined in the legal framework, so further research is needed when policy changes occur in the future.

• Journal of Korea Trade
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• v.23 no.3
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• pp.68-83
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• 2019

Purpose - The UNCITRAL Model Law on Electronic Transferable Records (Model Law) is based on the principles of non-discrimination against the use of electronic means, functional equivalence, and technology neutrality underpinning all UNCITRAL texts on electronic commerce. Investigating the disagreements between the Model Law and the Koran Commercial Act (KC Act), including the B/L Regulation, and suggesting the revision of the KC Act including the B/L Regulation, could be a valuable study. The purpose of this paper is to contribute to the harmonization of Korean legislation regarding electronic bill of lading in compliance with the Model Law. Design/methodology - The Model Law is flexible to accommodate the use of all technologies and models, such as registries, tokens, and distributed ledgers: that is, blockchain. In 2007, the KC Act was revised to regulate electronic bills of lading to promote the widespread legal use of electronic bills of lading. In addition, The Regulation on Implementation of the Provisions of the Commercial Act Regarding Electronic Bills of Lading (the B/L Regulation) was enacted to regulate the detailed procedures in using electronic bills of lading in 2008. This paper employs a legal analysis by which this paper does find differences between two rules in light of technology neutrality and global standard of electronic bills of lading model. Findings - The main findings are as follows: i) the Korean registry agency has characteristics of a closed system. ii) The KC Act has no provision regarding control. iii) The KC Act discriminates other electronic bills of lading on the ground that it was issued or used abroad. Moreover, this study does comprehensive analysis of Korean Acts in comparison with the Model Law and, in particular, this study analyzes the differences between the KC Act and the Model Law by comparing article by article in view of the harmonization of the two rules. Originality/value - The subject of previous several studies was draft provisions on Electronic Transferable Records before completion of the Model Law; thus, these studies did not take into consideration the character of the Model Law as the Model Law was chosen at the final stage of legislation. This study is aimed at the final version of the Model Law. So, this study is meaningful by finding the suggestion and directions for the Korean government to revise the KC Act and the B/L Regulation in line with the Model Law.