• Electronics and Telecommunications Trends
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• v.38 no.2
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• pp.56-65
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• 2023

The combination between metaverse and non-fungible token (NFT), which opens up new opportunities for the media industry, raised expectations for a new economic system and business model. Of course, last year, various institutions and researchers continuously introduce representative metaverse services and business strategies such as Roblox, Fortnite, and ZEPETO. However, as 2022 progresses, the reaction to the metaverse is tepid than expected. Search volume on Google has been continuously decreasing. Furthermore, skepticism, regarded as one of the special phenomena caused by the coronavirus disease 2019 pandemic, is expanding since December 2021. Nevertheless, analysis or contemplation of a new business model related to the metaverse, which is still ongoing, is essential for those who must prepare for the future. The reason is that even if without being activated now, advanced preparation can help when various problems arise. In this study, we look at the metaverse and NFT biz models and estimate a picture of the future. In other words, the social and economic problems that may arise when the business model is expanded are summarized, and technical and policy measures are derived as solutions.

• Journal of Forest and Environmental Science
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• v.34 no.1
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• pp.46-49
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• 2018

This study aims to explore the knowledge structure of the leopard cat (Prionailurus bengalensis) research during the period of 1952-2017. Data was collected from Google Scholar and Research Information Service System (RISS), and a total of 482 author keywords from 125 papers from peer-reviewed scholarly journals were retrieved. Co-word analysis was applied to examine patterns and trends in the leopard cat research by measuring the association strengths of the author keywords along with the descriptive analysis of the keywords. The result shows that the most commonly used keywords in leopard cat research were Felidae, Iriomte cat, and camera trap except for its English and scientific name, and camera traps became a frequent keyword since 2005. Co-word analysis also reveals that leopard cat research has been actively conducted in Southeast Asia in conjugation with studying other carnivores using the camera traps. Through the understanding of the patterns and trends, the finding of this study could provide an opportunity for the exploration of neglected areas in the leopard cat research and conservation.

• The Journal of Industrial Distribution & Business
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• v.14 no.9
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• pp.1-11
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• 2023

Purpose: The cosmetics industry is dynamic and constantly evolving. The hair and beauty field is characterized by being very sensitive to social trends. Therefore, it is necessary to understand the intellectual structure of the social function of hair beauty and to analyze the research and industrial trends related to the beauty field. This study is a literature review and presents specific and practical development plans and growth strategies for the hair care market. Research design, data, and methodology: This review study was conducted by searching PubMed, Google Scholar, Riss, Scopus, and Research Gate. We prepared this by referring to keywords such as the beauty care industry, sustainable development, hair care, hair cosmetics, and hair care market. A total of 468 papers were searched, of which 60 were finally included in this study on the PRISMA flowchart. Results: For good consumption and continuous development of hair cosmetics, it will be necessary to clearly understand the beauty and cosmetic needs of various generations. Conclusions: As income level improvement and quality of life become more important, Korea's beauty industry is attracting a lot of attention as a growth industry that transcends gender and age amid social and cultural development, and its importance is expected to grow in the future.

• International Journal of Internet, Broadcasting and Communication
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• v.15 no.4
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• pp.332-341
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• 2023

Notable trends in the restaurant start-up market after the lifting of social distancing include increasing interest in start-ups, emphasizing the importance of food quality and diversity, decreasing the relative importance of delivery services, and increasing interest in certain industries. The data collection period is three years from April 2021 to May 2023, including before and after social distancing, and texts extracted from blogs, news, cafes, web documents, and intellectuals provided by Naver, Daum, and Google were collected. For the collected data, the top 30 words were derived through a refining process. In addition, based on April 2021, the application period of social distancing, data from April 2021 to April 2022, and data from May 2022 to May 2023, Through these changes in trends, founders can capture new opportunities in the market and develop start-up strategies. In conclusion, this paper provides important insights for founders in accurately understanding the changes in food service start-up trends and in developing strategies appropriate to the current market situation.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.37B no.11
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• pp.1058-1071
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• 2012

This paper proposes policies for developing ICT ecosystems so that Korean ICT companies can have a competitive power under the changing ICT industry environment. It analyzes the changes of user characteristics, their consumption trends and ICT industry environment. It also examines problems of domestic ICT industry and overseas ICT policies. In addition, it investigates the ICT ecosystem strategies of Google and Apple based on the theory of business ecosystem. This study suggests government policies for establishing a smart ecosystem, incubating a strategic ecosystem, and maintaining a sustainable ecosystem. The findings of this study provide additional insights and guidelines for policy makers to develop effective ICT ecosystems.

• The Journal of Asian Finance, Economics and Business
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• v.5 no.4
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• pp.45-56
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• 2018

The paper aims to examine relationships between search-based sentiment and stock market reactions in Vietnam. This study constructs an internet search-based measure of sentiment and examines its relationship with Vietnamese stock market returns. The sentiment index is derived from Google Trends' Search Volume Index of financial and economic terms that Vietnamese searched from January 2011 to June 2018. Consistent with prediction from sentiment theories, the study documents significant short-term reversals across three major stock indices. The difference from previous literature is that Vietnam stock market absorbs the contemporaneous decline slower while the subsequent rebound happens within a day. The results of the study suggest that the sentiment-induced effect is mainly driven by pessimism. On the other hand, optimistic investors seem to delay in taking their investment action until the market corrects. The study proposes a unified explanation for our findings based on the overreaction hypothesis of the bearish group and the strategic delay of the optimistic group. The findings of the study contribute to the behavioral finance strand that studies the role of sentiment in emerging financial markets, where noise traders and limits to arbitrage are more obvious. They also encourage the continuous application of search data to explore other investor behaviors in securities markets.

• The Journal of Information Systems
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• v.26 no.3
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• pp.171-185
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• 2017

Purpose Recently, there has been an increase in attempts to analyze social phenomena, consumption trends, and consumption behavior through a vast amount of customer data such as web search traffic information and social buzz information in various fields such as flu prediction and real estate price prediction. Internet portal service providers such as google and naver are disclosing web search traffic information of online users as services such as google trends and naver trends. Academic and industry are paying attention to research on information search behavior and utilization of online users based on the web search traffic information. Although there are many studies predicting social phenomena, consumption trends, political polls, etc. based on web search traffic information, it is hard to find the research to explain and predict tourism demand and establish tourism policy using it. In this study, we try to use web search traffic information to explain the tourism demand for major cities in Gangwon-do, the representative tourist area in Korea, and to develop a nowcasting model for the demand. Design/methodology/approach In the first step, the literature review on travel demand and web search traffic was conducted in parallel in two directions. In the second stage, we conducted a qualitative research to confirm the information retrieval behavior of the traveler. In the next step, we extracted the representative tourist cities of Gangwon-do and confirmed which keywords were used for the search. In the fourth step, we collected tourist demand data to be used as a dependent variable and collected web search traffic information of each keyword to be used as an independent variable. In the fifth step, we set up a time series benchmark model, and added the web search traffic information to this model to confirm whether the prediction model improved. In the last stage, we analyze the prediction models that are finally selected as optimal and confirm whether the influence of the keywords on the prediction of travel demand. Findings This study has developed a tourism demand forecasting model of Gangwon-do, a representative tourist destination in Korea, by expanding and applying web search traffic information to tourism demand forecasting. We compared the existing time series model with the benchmarking model and confirmed the superiority of the proposed model. In addition, this study also confirms that web search traffic information has a positive correlation with travel demand and precedes it by one or two months, thereby asserting its suitability as a prediction model. Furthermore, by deriving search keywords that have a significant effect on tourism demand forecast for each city, representative characteristics of each region can be selected.

• Journal of Intelligence and Information Systems
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• v.28 no.3
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• pp.1-22
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• 2022

We investigate the dynamic relationships among digital piracy activities, TV ratings, and online buzz for Korean TV dramas using a panel vector autoregression model. Our main findings include 1) TV ratings are negatively affected by digital piracy activities but positively affected by google buzz, 2) digital piracy activities are negatively affected by TV ratings and social buzz, and 3) social buzz and google buzz are positively influenced by each other. While many empirical studies were conducted to reveal the effects of music or movie piracy, our understanding of drama piracy is limited. We provide empirical evidence of the dynamic relationships between drama piracy, TV ratings, and online buzz. Our findings show the presence of indirect piracy effects on TV ratings through online buzz. Further, we reveal that social buzz and google trends play different roles in promoting TV ratings and piracy activities. We discuss the implications of our findings for theory and practitioners.

• KIPS Transactions on Software and Data Engineering
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• v.12 no.9
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• pp.387-398
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• 2023

The COVID-19 outbreak has significantly impacted human lifestyles and patterns. It was recommended to avoid face-to-face contact and over-crowded indoor places as much as possible as COVID-19 spreads through air, as well as through droplets or aerosols. Therefore, if a person who has contacted a COVID-19 patient or was at the place where the COVID-19 patient occurred is concerned that he/she may have been infected with COVID-19, it can be fully expected that he/she will search for COVID-19 symptoms on Google. In this study, an exploratory data analysis using deep learning models(DNN & LSTM) was conducted to see if we could predict the number of confirmed COVID-19 cases by summoning Google Trends, which played a major role in surveillance and management of influenza, again and combining it with data on the number of confirmed COVID-19 cases. In particular, search term frequency data used in this study are available publicly and do not invade privacy. When the deep neural network model was applied, Seoul (9.6 million) with the largest population in South Korea and Busan (3.4 million) with the second largest population recorded lower error rates when forecasting including search term frequency data. These analysis results demonstrate that search term frequency data plays an important role in cities with a population above a certain size. We also hope that these predictions can be used as evidentiary materials to decide policies, such as the deregulation or implementation of stronger preventive measures.

• Journal of Satellite, Information and Communications
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• v.11 no.1
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• pp.12-15
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• 2016

Recently, with the pintech fever, competition among global IT companies in the mobile payment market is becoming more competitive. In this paper, we analyze the mobile payment technology trends. We describe the features, technical advantages and disadvantages for each mobile payment type using the short range communication and analyze the characteristics of each mobile payment services of the global market leader Apple, Samsung and Google has launched. Since the rapid growth in the mobile payment market is expected, the study of a variety of short range communication and security technology is considered to be necessary in order to provide more stable service.