• Journal of Distribution Science
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• v.22 no.10
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• pp.79-90
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• 2024

Purpose: This study examines the distribution of skincare products through digital channels, focusing on the role of brand awareness within the field of distribution science. As social media platforms like TikTok revolutionize distribution strategies, this research aims to identify the key factors influencing brand awareness in the distribution of skincare products on TikTok. Specifically, the study explores how influencer marketing, content marketing, and electronic word-of-mouth (E-WOM) affect the distribution process by enhancing brand awareness.. Research design, data, and methodology: Employing an explanatory quantitative method, the study surveyed 400 TikTok users exposed to NPURE skincare promotions. Data was collected via Google Forms using non-probability purposive sampling. The analysis was conducted using SmartPLS and Structural Equation Modeling (SEM) to examine the relationships between distribution factors and brand awareness. Results: The findings reveal significant relationships between (1) influencer marketing and brand awareness, (2) content marketing and brand awareness, and (3) electronic word-of-mouth (E-WOM) and brand awareness in the context of skincare product distribution on TikTok. Conclusion: This research contributes to the field of distribution science by demonstrating how digital marketing strategies on TikTok influence brand awareness, consequently impacting the distribution of skincare products. The findings offer insights for optimizing distribution strategies in the digital age, highlighting the significance of influencer partnerships, content creation, and promoting positive E-WOM in digital distribution channels.

• The Journal of the Korea Contents Association
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• v.7 no.10
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• pp.262-273
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• 2007

The current study sought to acknowledge the female sport fan as a sport marketing segment. In order to do so, female sport consumption motivation was identified and among motivation factors, factors effecting re-attend intention and word of mouth intention according to level of team identification was identified. In order to accomplish the purpose a focus group interview was conducted concerning female sport fans, and a survey took place in Korean Pro-sport event collecting 300 data. Among 300 data 248 data were used for the analysis. Using Lisrel 8.7 and SPSS 15.0 correlation analysis, descriptive analysis, confirmatory factor analysis, and multiple regression analysis was conducted. The results of study are as follows, First, 9 female sport consumption motivation was identified and among these factors, skill, achievement, drama, and escape had a positive effect on re-attend intension. Second, among 9 female sport consumption motivations skill, achievement, and social factor had a positive effect on word of mouth intention. Third, among the motivation factors, factors effecting re-attend intention had difference on the level of team identification. Forth, among the motivation factors, factors effecting word of mouth intention had difference on the level of team identification.

• The Journal of the Korea Contents Association
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• v.21 no.9
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• pp.149-163
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• 2021

The 4th industrial revolution refers to the next-generation industrial revolution led by information and communication technologies such as artificial intelligence (AI), Internet of Things (IoT), robot technology, drones, autonomous driving and virtual reality (VR) and it also has made a significant impact on the development of the advertising industry. However, the world is rapidly changing to a non-contact, non-face-to-face living environment to prevent the spread of COVID 19. Accordingly, the role of the 4th industrial revolution and advertising is changing. Therefore, in this study, text analysis was performed using Big Kinds to examine the 4th industrial revolution and changes in advertising before and after COVID 19. Comparisons were made between 2019 before COVID 19 and 2020 after COVID 19. Main topics and documents were classified through LDA topic model analysis and Word2vec, a deep learning technique. As the result of the study showed that before COVID 19, policies, contents, AI, etc. appeared, but after COVID 19, the field gradually expanded to finance, advertising, and delivery services utilizing data. Further, education appeared as an important issue. In addition, if the use of advertising related to the 4th industrial revolution technology was mainstream before COVID 19, keywords such as participation, cooperation, and daily necessities, were more actively used for education on advanced technology, while talent cultivation appeared prominently. Thus, these research results are meaningful in suggesting a multifaceted strategy that can be applied theoretically and practically, while suggesting the future direction of advertising in the 4th industrial revolution after COVID 19.

• Journal of Korea Entertainment Industry Association
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• v.14 no.7
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• pp.27-41
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• 2020

The purpose of the project is to identify the relationship between the sub-factors of SNS information characteristics (information quality, information reliability) and the impact of immersion on social capital (connected capital) on word of mouth intention, and to present measures to revitalize the tourism industry using the results obtained. The study conducted empirical analysis on 326 adults aged 19 and older. The results of the study are as follows: First, it has been confirmed that the sub-factors of SNS utilization (information quality, information reliability) have a positive effect on immersion. Second, immersion has been verified to have a significant influence relationship on the degree of word of mouth intention. Third, it was analyzed that social capital has positive interrelationships with immersion and word of mouth intention. Based on these findings, social network services (SNS) in the tourism industry can be used as basic data to attract potential tourism customers in the future by providing efficient information on tourism products by utilizing social network services (SNS) in the development of tourism contents and marketing strategies, and it is meaningful in that it is intended to help the tourism industry in practice.

• Journal of Technology Innovation
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• v.24 no.4
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• pp.101-126
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• 2016

This paper identifies core research topics and their relationships by deriving the research topic networks in the technology management field using co-word analysis. Contrary to the conventional approach in which undirected networks are constructed based on normalized co-occurrence frequency, this study analyzes directed networks of keywords by employing the confidence index of association rule mining for pairs of keywords. Author keywords included in 2,456 articles published in nine international journals of technology management in 2011~2014 are extracted and categorized into three types: THEME, METHOD, and FIELD. One-mode networks for each type of keywords are constructed to identify core research keywords and their interrelationships with each type. We then derive the two-mode networks composed of different two types of keywords, THEME-METHOD and THEME-FIELD, to explore which methods or fields are frequently employed or studied for each theme. The findings of this study are expected to be fruitfully referred for researchers in the field of technology management to grasp research trends and set the future research directions.

• Journal of KIISE:Software and Applications
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• v.31 no.9
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• pp.1171-1179
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• 2004

We describe word boundary detection that extracts the boundary between speech and non-speech. The proposed method uses two features. One is the normalized root mean square of speech signal, which is insensitive to white noises and represents temporal information. The other is the normalized met-frequency band energy of voice signal, which is frequency information of the signal. Our method detects word boundaries using a recurrent fuzzy associative memory(RFAM) that extends FAM by adding recurrent nodes. Hebbian learning method is employed to establish the degree of association between an input and output. An error back-propagation algorithm is used for teaming the weights between the consequent layer and the recurrent layer. To confirm the effectiveness, we applied the suggested system to voice data obtained from KAIST.

• The Journal of Information Systems
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• v.29 no.3
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• pp.237-251
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• 2020

Purpose The research was studied the hierarchical Hangul emotion index by organizing all the emotions which SNS users are thinking. As a preliminary study by the researcher, the English-based Plutchick (1980)'s emotional standard was reinterpreted in Korean, and a hashtag with implicit meaning on SNS was studied. To build a multidimensional emotion dictionary and classify three-dimensional emotions, an emotion seed was selected for the composition of seven emotion sets, and an emotion word dictionary was constructed by collecting SNS hashtags derived from each emotion seed. We also want to explore the priority of each Hangul emotion index. Design/methodology/approach In the process of transforming the matrix through the vector process of words constituting the sentence, weights were extracted using TF-IDF (Term Frequency Inverse Document Frequency), and the dimension reduction technique of the matrix in the emotion set was NMF (Nonnegative Matrix Factorization) algorithm. The emotional dimension was solved by using the characteristic value of the emotional word. The cosine distance algorithm was used to measure the distance between vectors by measuring the similarity of emotion words in the emotion set. Findings Customer needs analysis is a force to read changes in emotions, and Korean emotion word research is the customer's needs. In addition, the ranking of the emotion words within the emotion set will be a special criterion for reading the depth of the emotion. The sentiment index study of this research believes that by providing companies with effective information for emotional marketing, new business opportunities will be expanded and valued. In addition, if the emotion dictionary is eventually connected to the emotional DNA of the product, it will be possible to define the "emotional DNA", which is a set of emotions that the product should have.

• The Journal of Information Systems
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• v.27 no.4
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• pp.71-96
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• 2018

Purpose Recently, social interest in the convergence research is at its peak. However, contrary to the keen interest in convergence research, an infrastructure that makes it easier to recruit researchers from other fields is not yet well established, which is why researchers are having considerable difficulty in carrying out real convergence research. In this study, we implemented a researcher recommendation system that helps researchers who want to collaborate easily recruit researchers from other fields, and we expect it to serve as a springboard for growth in the convergence research field. Design/methodology/approach In this study, we implemented a system that recommends proper researchers when users enter keyword in the field of research that they want to collaborate using word embedding techniques, word2vec. In addition, we also implemented function of keyword suggestions by using keywords drawn from LDA Topicmodeling Algorithm. Finally, the UI of the researcher recommendation system was completed by utilizing the collaborative messenger Slack to facilitate immediate exchange of information with the recommended researchers and to accommodate various applications for collaboration. Findings In this study, we validated the completed researcher recommendation system by ensuring that the list of researchers recommended by entering a specific keyword is accurate and that words learned as a similar word with a particular researcher match the researcher's field of research. The results showed 85.89% accuracy in the former, and in the latter case, mostly, the words drawn as similar words were found to match the researcher's field of research, leading to excellent performance of the researcher recommendation system.

• Journal of architectural history
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• v.32 no.1
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• pp.47-60
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• 2023

Due to a mistranslation of Sanskrit to Chinese, East Asian Buddhist community misunderstands the original meaning of the fundamental word, 'sachal(寺刹)'. Sanskrit chattra, a parasol on top of a venerated Indian stupa buried with Buddha's sarira, became the symbol of majesty. The Indian stupa was transformed into a pagoda in China, and the highlighted parasol on the summit was transliterated into chaldara(刹多羅), an abbreviation for chal (刹), and finally designated the whole pagoda(塔). Sachal consists with lying low monastery and high-rise pagoda. Tapsa(塔寺), an archaic word of temple, is exactly the same as sachal, because chal means tap, pagoda. However, during the 7th century a Buddhist monk erroneously double-transliterated the Sanskrit 'kshetra,' meaning of land, into the same word as chal, even despite phonetic disaccord. Thereafter, sutra translators followed and copied the error for long centuries. It was the Japanese pioneer scholars that worsen the situation 100 years ago, to publish Sanskrit dictionaries with the errors insisting on phonetic transliteration, though pronunciation of 'kshe-' which is quite different from 'cha-.' Thereafter, upcoming scholars followed their fallacy without any verification. Fallacy of chal, meaning of land, dominates Buddhist community broadly, falling into conviction of collective fixed dogma in East Asia up to now. In the Buddhist community, it is the most important matter to recognize that the same language has become to refer completely different objects due to translation errors. As a research method, searching for corresponding Sanskrit words in translated sutras and dictionaries of Buddhism is predominant. Then, after analyzing the authenticity, the fallacy toward the truth will be corrected.

• 한국정보교육학회:학술대회논문집
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• 2021.08a
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• pp.59-67
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• 2021

The development of AI technology has brought about a big change in our lives. As AI's influence grows from life to society to the economy, the importance of education on AI and data is also growing. In particular, the OECD Education Research Report and various domestic information and curriculum studies address data literacy and present it as an essential competency. Looking at domestic and international studies, one can see that the definition of data literacy differs in its specific content and scope from researchers to researchers. Thus, the definition of major research related to data literacy was analyzed from various angles and derived from various angles. In key studies, Word2vec natural language processing methods, along with word frequency analysis used to define data literacy, are used to analyze semantic similarities and nominate them based on content elements of curriculum research to derive the definition of 'understanding and using data to process information'. Based on the definition of data literacy derived from this study, we hope that the contents will be revised and supplemented, and more research will be conducted to provide a good foundation for educational research that develops students' future capabilities.