• Journal of Internet of Things and Convergence
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• v.8 no.4
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• pp.85-96
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• 2022

The purpose of this study was to investigate how the management strategies and organizational culture required in the digital economy have an effect on business performance. It provided basic data on management strategies and organizational culture necessary to approach as a digital leading country. For data collection, a survey was conducted from March 1 to May 30, 2022 for companies located in J province and engaged in industries related to the digital economy. The survey was conducted online and non-face-to-face, and a total of 225 companies participated in the survey. For statistical analysis, frequency analysis, exploratory factor analysis and reliability analysis, cluster analysis, independent sample t-test, and multiple regression analysis were performed. The research results are as follows. First, organizational culture was classified into high and low groups according to preference in innovation oriented, relationship oriented, task oriented, and hierarchical oriented. Second, the 4 types of organizational culture showed differences in prospectors strategy, analyzers strategy, defenders strategy, differentiation strategy, cost leadership strategy, financial performance, and non-financial performance according to preference. Third, management strategies affecting financial performance were found to be analyzers strategy, differentiation strategy, prospectors strategy, and cost leadership strategy. Fourth, management strategies affecting non-financial performance were found to be differentiation strategy, defenders strategy, analysis strategy, offensive strategy, cost leadership strategy, and focus strategy. Fifth, organizational culture affecting financial performance was found to be task oriented. Sixth, organizational culture affecting non-financial performance was found to be innovation oriented and relationship oriented. Through these studies, it is expected that the economy will be revitalized in the domestic market and a growth ecosystem that can take a new leap forward is created in the global market.

• 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.

• Management & Information Systems Review
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• v.31 no.2
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• pp.143-172
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• 2012

While e-commerce market(B2C) grows rapidly, many experts argue that EC(B2C) transactions have not reached its full potential. A notable difference between online and offline consumer markets that is suppressing the growth of EC(B2C) is the decreased presence of human and social elements in the online shopping environments. Generally online shopping lacks human warmth and sociability. In this study, social presence in online shopping mall was proposed as a substitute for face-to-face social interaction in the traditional commerce and author explored what variables affect social presence(human warmth and sociability) on online shopping malls and how human warmth and sociability can influence on online store loyalty. To achieve research objectives, we reviewed literatures related with marketing, psychology and communication research areas. Based on literature review, we proposed a research model on the online shopping mall. To examine the proposed research model, we gathered data by using a self-report questionnaire. Respondents consists of online shoppers with at least five or more times of purchase experience in online shopping malls. Because social presence is a feeling which needs frequent contacts with malls to experience, respondents must have enough purchase experiences. The empirical results are as follows : First, shopping mall's customization efforts influence perceived social presence on the mall significantly. Second, shopping mall's responsiveness influences perceived social presence significantly. Third, perceived activity of community of online shopping mall influences perceived social presence significantly. Mall managers have to activate their customer community to reinforce social presence, resulting in trust building. Finally, perceived social presence influences trust and enjoyment on the mall significantly. And then trust and enjoyment on the mall affect store loyalty significantly. From these findings it can be inferred that perceived social presence appears determinant which is critical to the formation of core variables(trust and loyalty) in existing online shopping papers.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.17 no.5
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• pp.100-116
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• 2018

Recently, with the US protection trade trend, South Korea 's exports to the US have shrunk and the need to pioneer emerging markets in Eurasia has increased. Due to the bankruptcy of Hanjin Shipping, a large Korean national shipping company, the necessity of securing transportation that can replace the Asian - European shipping route has emerged. Moreover, as the international trade and logistics environment is rapidly changing due to the spread of electronic commerce, it is time to prepare for the environment where IT technology is applied to international logistics activities. Therefore, this study was carried out to establish information strategy of Eurasia logistics information sharing platform from Korean perspective through comparison of logistics information level of Eurasian countries. This study aims at standardization direction for supporting logistics activities between Eurasian countries, providing customs clearance and logistics services without interruption, providing information for expanding business of trade companies, and building information linkage infrastructure for expanding cooperation between logistics companies across countries. Through this study, it is expected that logistics activities through information sharing among countries will be actively carried out.

• Spatial Information Research
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• v.21 no.6
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• pp.69-80
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• 2013

Our country has made every efforts to develop Real Estate Transaction culture with emphasis on Licensed Realtors by introducing Real Estate Transaction Law in 1983. Also, MOLIT(Ministry of Land, Infrastructure and Transport) designated several organizations including KAR(Korea Association of Realtors) as Real Estate Transaction Information Network Licensees for data credibility enhancement and transaction transparency. Nevertheless, the level of law abiding spirit and transaction culture are still similar to those of the old 'Bokdeokbang' era. The under-developed transaction behaviors prevent the social capital of people's credibility on Licensed Realtors from advancing, and results in the outcomes of unnecessary social cost. That is, very low credibility on the data on Sales Items in the market and the fear of speculative real estate price uprise and market distortions are continuing on. In this context, the purpose of this study is to propose the model of GIS-based Modernized Real Estate Transaction System and its execution policies to support credible Real Estate Information to the general public for efficient transactions in the market. Accordingly, the study aims at contributing to the modernization of Real Estate Transactions, fostering competitiveness of Realtors in the Real Estate Market.

• Journal of Intelligence and Information Systems
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• v.29 no.1
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• pp.41-63
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• 2023

As the online e-commerce market growing, the need for a recommender system that can provide suitable products or services to customer is emerging. Recently, many studies using the sentiment score of online review have been proposed to improve the limitations of study on recommender systems that utilize only quantitative information. However, this methodology has limitation in extracting specific preference information related to customer within online reviews, making it difficult to improve recommendation performance. To address the limitation of previous studies, this study proposes a novel recommendation methodology that applies deep learning technique and uses various linguistic factors within online reviews to elaborately learn customer preferences. First, the interaction was learned nonlinearly using deep learning technique for the purpose to extract complex interactions between customer and product. And to effectively utilize online review, cognitive contents, affective contents, and linguistic style matching that have an important influence on customer's purchasing decisions among linguistic factors were used. To verify the proposed methodology, an experiment was conducted using online review data in Amazon.com, and the experimental results confirmed the superiority of the proposed model. This study contributed to the theoretical and methodological aspects of recommender system study by proposing a methodology that effectively utilizes characteristics of customer's preferences in online reviews.

• 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.

• Journal of Intelligence and Information Systems
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• v.29 no.4
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• pp.375-393
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• 2023

The demand for Less than Container Load (LCL) has been on the rise due to the growing need for various small-scale production items and the expansion of the e-commerce market. Consequently, more companies in the International Freight Forwarder are now handling LCL. Given the variety in cargo sizes and the diverse interests of stakeholders, there's a growing need for a container loading algorithm that optimizes space efficiency. However, due to the nature of the current situation in which a cargo loading plan is established in advance and delivered to the Container Freight Station (CFS), there is a limitation that variables that can be identified at industrial sites cannot be reflected in the loading plan. Therefore, this study proposes a container loading methodology that makes it easy to modify the loading plan at industrial sites. By allowing the characteristics of cargo and the status of the container to be considered, the requirements of the industrial site were reflected, and the three-dimensional space was manipulated into a two-dimensional planar layer to establish a loading plan to reduce time complexity. Through the methodology presented in this study, it is possible to increase the consistency of the quality of the container loading methodology and contribute to the automation of the loading plan.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.17 no.3
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• pp.121-129
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• 2024

The rapid advancement of digital technology and the COVID-19 pandemic have significantly accelerated the growth of online commerce, highlighting the need for support mechanisms that enable small business owners to effectively respond to these market changes. In response, this paper presents a foundational technology leveraging the Online to Offline (O2O) strategy to automatically capture products displayed on retail shelves and utilize these images to create virtual stores. The essence of this research lies in precisely identifying and recognizing the location and names of displayed products, for which a single-class-targeted, lightweight model based on YOLOv8, named ESD-YOLOv8, is proposed. The detected products are identified by their names through feature-point-based technology, equipped with the capability to swiftly update the system by simply adding photos of new products. Through experiments, product name recognition demonstrated an accuracy of 74.0%, and position detection achieved a performance with an F2-Score of 92.8% using only 0.3M parameters. These results confirm that the proposed method possesses high performance and optimized efficiency.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2000.04a
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• pp.357-364
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• 2000

기존의 웹 디자인은 웹이라는 매체의 특성 상 디자인적인 요소가 매우 중요함에도 불구하고 디자인은 위한 구체적인 방법론이 미약하다. 특히, 많은 소비자들을 유인하고 구매를 촉발시켜야 하는 인터넷 쇼핑몰의 경우에는 더욱 더 그럼하에도 불구하고 이를 위한 전략적인 방법론이 부족하다. 즉, 기존 연구들은 제품의 다양성, 서비스, 촉진, 항해량, 편리성, 사용자 인터페이스 등이 중요하다고 하였지만 실제 인터넷 쇼핑몰을 디자인하는 입장에서는 활용하기가 상당히 애매하다. 그 이유는 이들 요인들은 서로 영향관계를 가지고 있어서 사용자 인터페이스가 복잡하면 항해량이 늘어나 편리성이 감소하고, 제품이 늘어나더라도 검색엔진을 사용하면 상대적으로 항해량이 감소하게 되어 편리성이 증가한다. 따라서, 이들 요인을 활용하여 인터넷 쇼핑몰을 구축하려면 요인간의 영향관계를 면밀히 파악하고 이 영향요인이 소비자의 구매행동에 어떠한 영향을 주는지가 충분히 검토되어야 한다.이에 본 연구에서는 퍼지인식도를 이용하여 인터넷 쇼핑몰 상에서 소비자의 구매행동에 영향을 주는 요인을 추출하고 이들 요인간의 인과관계를 도출하여 보다 구체적이고 전략적으로 인터넷 쇼핑몰을 디자인할 수 있는 방법으로 web-Cogmulator를 제시한다. Web-Cogmulator는 소비자의 쇼핑몰에 대한 암묵지식 형태의 구매행동을 형태지식화하여 지식베이스 형태로 가지고 있기 때문에 인터넷 쇼핑몰의 다양한 요인의 변화에 따른 소비자의 구매행동을 추론 시뮬레이션하는 것이 가능하다. 이에 본 연구에서는 기본적인 인터넷 쇼핑몰 시나리오를 바탕으로 추론 시뮬레이션을 실시하여 Web-Cogmulator의 유용성을 검증하였다.를, 지지도(support), 신뢰도(confidence), 리프트(lift), 컨빅션(conviction)등의 관계를 통해 다양한 방법으로 모색해본다. 이 연구에서 제안하는 이러한 개념계층상의 흥미로운 부분의 탐색은, 전자 상거래에서의 CRM(Customer Relationship Management)나 틈새시장(niche market) 마케팅 등에 적용가능하리라 여겨진다.선의 효과가 나타났다. 표본기업들을 훈련과 시험용으로 구분하여 분석한 결과는 전체적으로 재무/비재무적 지표를 고려한 인공신경망기법의 예측적중률이 높은 것으로 나타났다. 즉, 로지스틱회귀 분석의 재무적 지표모형은 훈련, 시험용이 84.45%, 85.10%인 반면, 재무/비재무적 지표모형은 84.45%, 85.08%로서 거의 동일한 예측적중률을 가졌으나 인공신경망기법 분석에서는 재무적 지표모형이 92.23%, 85.10%인 반면, 재무/비재무적 지표모형에서는 91.12%, 88.06%로서 향상된 예측적중률을 나타내었다.ting LMS according to increasing the step-size parameter $\mu$ in the experimentally computed. learning curve. Also we find that convergence speed of proposed algorithm is increased by (B+1) time proportional to B which B is the number of recycled data buffer without complexity of computati