• Journal of the Korea Convergence Society
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• v.10 no.1
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• pp.141-148
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• 2019

In this study, the Technology Acceptance Model (TAM) was applied to explore the process of using card news. Card news users are found to be active in searching and selecting appropriate news for themselves, information seeking style and news literacy were established as antecedent variables that can influence card news usage. A survey of 400 university students with experience of using card news was conducted. For statistical analysis, SEM was conducted. The analysis showed that information seeking style significantly affects perceived ease of use (PEU) and that news literacy influences neither PEU nor PU. PEU was found to have a significant effect on PU, and both PEU and PU had a significant effect on recommendation intention.

• Journal of Intelligence and Information Systems
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• v.23 no.4
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• pp.1-31
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• 2017

Modern businesses are adopting new technologies to serve their markets better as well as to improve efficiency and productivity. The advertising industry has continuously experienced disruptions from the traditional channels (radio, television and print media) to new complex ones including internet, social media and mobile-based advertising. This case study focuses on proposing intelligent advertising business model in Seoul's metro network. Seoul has one of the world's busiest metro network and transports a huge number of travelers on a daily basis. The high number of travelers coupled with a well-planned metro network creates a platform where marketers can initiate engagement and interact with both customers and potential customers. In the current advertising model, advertising is on illuminated and framed posters in the stations and in-car, non-illuminated posters, and digital screens that show scheduled arrivals and departures of metros. Some stations have digital screens that show adverts but they do not have location capability. Most of the current advertising media have one key limitation: space. For posters whether illuminated or not, one space can host only one advert at a time. Empirical literatures show that there is room for improving this advertising model and eliminate the space limitation by replacing the poster adverts with digital advertising platform. This new model will not only be digital, but will also provide intelligent advertising platform that is driven by data. The digital platform will incorporate location sensing, e-commerce, and mobile platform to create new value to all stakeholders. Travel cards used in the metro will be registered and the card scanners will have a capability to capture traveler's data when travelers tap their cards. This data once analyzed will make it possible to identify different customer groups. Advertisers and marketers will then be able to target specific customer groups, customize adverts based on the targeted consumer group, and offer a wide variety of advertising formats. Format includes video, cinemagraphs, moving pictures, and animation. Different advert formats create different emotions in the customer's mind and the goal should be to use format or combination of formats that arouse the expected emotion and lead to an engagement. Combination of different formats will be more effective and this can only work in a digital platform. Adverts will be location based, ensuring that adverts will show more frequently when the metro is near the premises of an advertiser. The advertising platform will automatically detect the next station and screens inside the metro will prioritize adverts in the station where the metro will be stopping. In the mobile platform, customers who opt to receive notifications will receive them when they approach the business premises of advertiser. The mobile platform will have indoor navigation for the underground shopping malls that will allow customers to search for facilities within the mall, products they may want to buy as well as deals going on in the underground mall. To create an end-to-end solution, the mobile solution will have a capability to allow customers purchase products through their phones, get coupons for deals, and review products and shops where they have bought a product. The indoor navigation will host intelligent mobile-based advertisement and a recommendation system. The indoor navigation will have adverts such that when a customer is searching for information, the recommendation system shows adverts that are near the place traveler is searching or in the direction that the traveler is moving. These adverts will be linked to the e-commerce platform such that if a customer clicks on an advert, it leads them to the product description page. The whole system will have multi-language as well as text-to-speech capability such that both locals and tourists have no language barrier. The implications of implementing this model are varied including support for small and medium businesses operating in the underground malls, improved customer experience, new job opportunities, additional revenue to business model operator, and flexibility in advertising. The new value created will benefit all the stakeholders.

• The Research Journal of the Costume Culture
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• v.14 no.5
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• pp.850-863
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• 2006

The purpose of this study was to investigate if star marketing of on-line shopping malls affects consumers' WOM effect, and to compare the differences of consumption behavior between female teenagers and college students. Two hundred five female teenagers and college students who had purchased fashion goods through internet shopping mall participated in this study. For data analysis, descriptive statistics, factor analysis, t-test, and multiple regression were used. As the results, first, recognition of celebrity advertising models was classified into three factors such as 'trust of product', 'attractiveness of product' and 'leading interest of product' factors. Second, the greater exposure to celebrity models, the greater the good feelings about them, showing respondents' positive consumption behavior. Third, results of multiple regression revealed that behavior of pursuing celebrities' style accounted for 37% of the explained variance WOM behavior. Finally, t-test revealed that female college students were affected more by celebrity style and bought fashion items than female teenagers. However, female teenagers conducted more WOM behavior than college students. Based on these results, on-line fashion marketers would use these data for more their efficient fashion marketing strategies.

• The Journal of the Korea Contents Association
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• v.17 no.10
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• pp.548-558
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• 2017

The purpose of this study is to investigate how the types of perceived brand image related to the main concept building brand equity affect 'brand preference' and 'behavioral intentions'. The perceived brand image is set as the brand image type perceived by the consumer from the image pursued by the corporate brand, while in addition to brand preference, behavioral intentions are set as purchase intention and recommendation intention for the result variables. The result shows that the types of perceived brand image were extracted as 'factor 1(challenge spirit)' and factor 2(reliability) and through the cluster analysis 3 groups under each type were identified. Also, a significant difference between the influence of each type of perceived brand image on 'brand preference', 'purchase intention' and 'recommend intention' was indicated. In addition, the differences of perceived brand image types were found to be higher in order of 'challenge spirit type', 'reliability type', 'integrated type'. The empirical implementation of this study lies in the fact that it classifies the concept of brand image not as a broad theoretical model, but as a model directly related to real consumer perception, and that it gives practical suggestion for brand image management related to advertising.

• Journal of the Korea Convergence Society
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• v.10 no.7
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• pp.133-139
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• 2019

This study investigated what type of destination image on Facebook increases positive attitude towards the destination and desire to travel. More specifically, we hypothesized that Facebook user's public self-consciousness will moderate the advertising effect, so we tested the difference in attitude towards the destination and willingness to visit and recommend based on the type of destination image (presence/absence of a tourist in the image) and user's public self-consciousness (high/low). As a result, when a tourist was present in the photo, positive attitude toward destination was higher than when absent. In addition, the high public self-consciousness group showed greater attitude toward destination and higher willingness to visit and recommend than low public self-consciousness group when a tourist was present in the image. However, the low public self-consciousness group showed no difference depending on the presence of a tourist. This research not only is academically significant as it experimentally tested advertising effect depending on type of destination image and Facebook user's public self-consciousness, but also holds practical significance as it suggests advertisement method to consider consumer's predisposition.

• Proceedings of the Korea Information Processing Society Conference
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• 2022.11a
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• pp.878-880
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• 2022

COVID-19 상황 속에서도 전 세계 Twitter K-POP 콘텐츠 관련 트윗 양은 78억 건 이상으로 매년 성장세를 보인다. Twitter 내 K-POP 팬들은 아티스트 관련 해시태그를 포함한 트윗을 작성하여 같은 팬덤끼리 실시간으로 정보를 전달하고 생산한다. 이러한 맛집 트윗들은 K-POP 팬들이 Twitter 내에서 신뢰도 있는 맛집 정보를 얻는 용도로 사용된다. 하지만 팬들이 정보를 얻기 위해서는 여러 맛집 해시태그로 검색하고 리트윗 수가 많은 트윗을 직접 찾아야 한다. 기존의 맛집 추천 시스템은 서비스 제공자 중심의 구조를 띤다. 서비스 제공자가 일방적으로 정보를 전달하거나, 사용자 리뷰 갱신 간격이 길다는 한계가 존재한다. 본 논문에서는 Twitter 내 K-POP 맛집 해시태그가 포함된 트윗을 Twitter API와 Tweepy를 사용하여 크롤링하였다. 수집한 데이터의 좋아요 수와 리트윗 수를 바탕으로 데이터 필터링을 진행하여 bot user와 광고 계정이 제외된 맛집 관련 트윗을 추출한다. 최종적으로는 추출한 트윗의 정보를 마커로 표시하여 웹 사이트를 제작하였다. K-POP 팬들은 맛집 해시태그를 검색하여 일일이 찾을 필요 없이 웹 사이트에 방문하여 맛집 위치를 확인할 수 있다. 웹 사이트 사용자의 위치가 지도상에 표시되어 가까운 맛집을 찾기도 편리하다. 본 논문에서는 맛집의 위치를 서대문구로 한정하여 진행했다.

• Journal of Broadcast Engineering
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• v.15 no.5
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• pp.632-641
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• 2010

This paper shows the research about the personalized broadcasting system. The personalized broadcasting is the service that users only show the programs which they want to watch when they want to watch these. The purpose of the bi-directional broadcasting service is supporting more satisfied and more personalized services by permitting the bi-directional data transformation. This research also develops the user profiling system for the bi-directional and personalized broadcasting service. This system applied the TV-Anytime metadata specifications which is the standard for the personalized broadcasting services, the system supports the various functions for the bi-directionl and personalized broadcasting such as the user profiling, contents metadata and targeting advertisement services. The bi-directional and personalized broadcasting system increases the users' satisfaction with the recommendation and management of the personally favorite broadcasting contents and advertisements, the trial run results show that the services raise the users' satisfaction with the intelligent and discriminating broadcasting services.

• Journal of Convergence for Information Technology
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• v.10 no.4
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• pp.1-10
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• 2020

The performance of the personalized product recommendation system for offline shopping malls is poor compared with the one using online environment information since it is difficult to obtain visitors' characteristic information. In this paper, a mobile robot platform is suggested capable of recommending personalized advertisement using customers' sex and age information provided by Face API of MS Azure Cloud service. The performance of the developed robot is verified through locomotion experiments, and the performance of API used for our robot is tested using sampled images from open Asian FAce Dataset (AFAD). The developed robot could be effective in marketing by providing personalized advertisements at offline shopping malls.

• Journal of Digital Convergence
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• v.15 no.1
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• pp.453-459
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• 2017

This study was conducted to review the usage behavior of mobile social network games that are attracting attention as a new growth engine in the game market. To that end, a survey was conducted on 250 male and female university students. The result of the study showed that first, the relationship formation motivation and seeking fun during leisure times in association with mobile social network games had a positive effect on game attitudes. Second, the relationship formation motivation had a positive effect on the continuous use intention. Third, the relationship formation motivation and the fun-seeking motivation had a positive effect on word-of-mouth recommendation, while the relationship formation motivation and advertisement recommendation motivation had a positive effect on the intention to recommendation online formats. Fourth, the attitude towards mobile social network games had a positive effect on the continuous use intention. Lastly, the attitude towards mobile social network games had a positive effect on only the intention to recommendation through word-of-mouth. This study is expected to provide useful and basic data for the development of quality game content that will cater to users' needs.

• Journal of Digital Convergence
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• v.18 no.12
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• pp.525-531
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• 2020

In today's IT environment where various pieces of information are distributed in large volumes, recommendation systems are in the spotlight capable of figuring out users' needs fast and helping them with their decisions. The current recommendation systems, however, have a couple of problems including that user preference may not be reflected on the systems right away according to their changing tastes or interests and that items with no relations to users' preference may be recommended, being induced by advertising. In an effort to solve these problems, this study set out to propose a Fuzzy-AHP-based movie recommendation system by applying the BRNN(Bidirectional Recurrent Neural Network) language model. Applied to this system was Fuzzy-AHP to reflect users' tastes or interests in clear and objective ways. In addition, the BRNN language model was adopted to analyze movie-related data collected in real time and predict movies preferred by users. The system was assessed for its performance with grid searches to examine the fitness of the learning model for the entire size of word sets. The results show that the learning model of the system recorded a mean cross-validation index of 97.9% according to the entire size of word sets, thus proving its fitness. The model recorded a RMSE of 0.66 and 0.805 against the movie ratings on Naver and LSTM model language model, respectively, demonstrating the system's superior performance in predicting movie ratings.