• The Journal of Economics, Marketing and Management
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• v.3 no.1
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• pp.1-22
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• 2015

High growth and increasing traffic and transport finished vehicles, a significant impact on how organize the flow of parts to auto makers an dagencies have As a result, the automakers to improve its position as a highly responsive, with minimal costs, the out sourcing of their logistics processes. This paperis the result of field research to determine the effectiveness of the logistics industry in Iran and focuses on information technology deals the transport vehicle and parts sales deals, indicators used in the model include: IT focuses, IT Valence, IT Competency, IT Managerial Commitment, IT Resource Commitment and competitive advantage identified. Data collected by questionnaires from managers and experts have been towing companies ISACO and SAIPA trailer hypotheses using structural equation methods and software has been analyzed Amos, Results show, focusing on information technology now has significant impacts on logistics and transport. As a result the impact of, IT valence, IT competency and IT Managerial Commitment analytics to gain competitive advantage was not approved, but the rest of the factors were confirmed.

• Convergence Security Journal
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• v.15 no.2
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• pp.19-24
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• 2015

As the Internet usage such as Web3.0, future internet, and internet of things increases, the big data through information exchange between the users and web servers increases. Analyzing those web data, the commercial web sites use the analytic results for marketing and campaign, and non-commercial web sites also use the results to improve the user's services satisfaction. In this paper, the quantitative index is presented to analyze the web sites, and optimized web site design method is also presented through the correlation analysis of index and significance test. From the results for 138 web sites, it is observed that strong plus(+) correlation for visits-unique visitors and page views-average visit duration exists. We also observe the minus(-) correlation between bounce rate and page views per user(or ratio of new visits). In specific, to reduce the bounce rate for users, the strategy to increase the page views and ratio of new visits rather than visits and unique visitors is needed.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.19 no.6
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• pp.121-134
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• 2009

Consumer-centric marketing business is surely one of the most successful emerging business but it poses a threat to personal privacy. Between the service provider and the user there are many contrary issues to each other. The enterprise asserts that to abuse the privacy data which is anonymous there is not a problem. The individual only will not be able to willingly submit the problem which is latent. Web traffic analysis technology itself doesn't create issues, but this technology when used on data of personal nature might cause concerns. The most criticized ethical issue involving web traffic analysis is the invasion of privacy. So we need to inspect how many and what kind of personal informations being used and if there is any illegal treatment of personal information. In this paper, we inspect the operation of consumer-centric marketing tools such as web log analysis solutions and data gathering services with web browser toolbar. Also we inspect Microsoft explorer-based toolbar application which records and analyzes personal web browsing pattern through reverse engineering technology. Finally, this identified and explored security and privacy requirement issues to develop more reliable solutions. This study is very important for the balanced development with personal privacy protection and web traffic analysis industry.

• Journal of Internet Computing and Services
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• v.21 no.4
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• pp.97-107
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• 2020

This study aims to conduct a comprehensive meta-study from the perspective of content analysis to explore trends in Korean academic research on the sharing economy by using the big data analytics. Comprehensive meta-analysis methodology can examine the entire set of research results historically and wholly to illuminate the tendency or properties of the overall research trend. Academic research related to the sharing economy first appeared in the year in which Professor Lawrence Lessig introduced the concept of the sharing economy to the world in 2008, but research began in earnest in 2013. In particular, between 2006 and 2008, research improved dramatically. In order to grasp the overall flow of domestic academic research of trends, 8 years of papers from 2013 to the present have been selected as target analysis papers, focusing on titles, keywords, and abstracts using database of electronic journals. Big data analysis was performed in the order of cleaning, analysis, and visualization of the collected data to derive research trends and insights by year and type of literature. We used Python3.7 and Textom analysis tools for data preprocessing, text mining, and metrics frequency analysis for key word extraction, and N-gram chart, centrality and social network analysis and CONCOR clustering visualization based on UCINET6/NetDraw, Textom program, the keywords clustered into 8 groups were used to derive the typologies of each research trend. The outcomes of this study will provide useful theoretical insights and guideline to future studies.

• Journal of Information Technology Services
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• v.17 no.1
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• pp.17-32
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• 2018

The city image is considered one of important symbolic and important factors in selecting the travel destination. Many cities are trying to be an attractive and popular city to tourists through the construction of a good brand image by utilizing their representative characteristics. This study measures the city brand image by applying a big data analytic method. In addition, the big data measurement results were rearranged and analyzed to identify further detailed city images by utilizing several previous interpretation methods. Our study has chosen Dubai since this city has the diverse images due to its regional as well as economic characteristics. In particular, nowadays Dubai has been recognized as one of the most important touristic places in the Middle East region for its modern and innovative images in spite of the limitations of location, weather, religion, and even political issues of neighbor countries. Founded on a big data analysis rather than a questionnaire-based survey, the presented interpretation methods are evaluated to improve the understanding of Dubai's diverse city images. In addition, based on the results of this research, it is expected to have a practical impact on establishing the effective marketing strategies to build and implement the valuable city brand image.

• Information Systems Review
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• v.11 no.1
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• pp.133-144
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• 2009

Today, eCRM has been attention as an enterprise information system that systematically manages and utilizes the eCRM customer information visiting Internet home page. On this paper, the case study of N company Korea's leading manufacturers of the dairy industry is been application research in the practices of using optimization tools for analysis of customer information and marketing activities by the introduction of eCRM and doing weblogs analysis. This research is a case study on an introduction and the use of eCRM solutions in dairy industry company. In addition, of the scope and effectiveness for use introduced eCRM explain.

• Knowledge Management Research
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• v.19 no.4
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• pp.59-76
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• 2018

With the arrival of the big data era, customer data and data mining analysis have gradually dominated the process of Customer Relationship Management (CRM). This phenomenon indicates that customer data along with the use of information techniques (IT) have become the basis for building a successful CRM strategy. However, some companies can not discover valuable information through a large amount of customer data, which leads to the failure of making appropriate business strategy. Without suitable strategies, the companies may lose the competitive advantage or probably go bankrupt. The purpose of this study is to propose CRM strategies by segmenting customers into VIPs and Non-VIPs and identifying purchase patterns using the the VIPs' transaction data and data mining techniques (K-means clustering and association rules) of online shopping mall in Korea. The results of this paper indicate that 227 customers were segmented into VIPs among 1866 customers. And according to 51,080 transactions data of VIPs, home product and women wear are frequently associated with food, which means that the purchase of home product or women wears mainly affect the purchase of food. Therefore, marketing managers of shopping mall should consider these shopping patterns when they build CRM strategy.

• The Journal of the Convergence on Culture Technology
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• v.8 no.5
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• pp.507-513
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• 2022

This study developed a model for predicting the number of VOD uses of IPTV, an online market in the film industry. A machine learning-based prediction model was established using the VOD usage data collected by the Korean Film Council from 2017 to 2021. Through literature research and cluster analysis, the difference between the offline market and the online market is revealed, and a new category of VOD usage is proposed. The purpose is to help IPTV companies establish marketing strategies as well as support decision-making by developing a machine learning-based VOD usage prediction model.

• Nuclear Engineering and Technology
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• v.55 no.2
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• pp.603-622
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• 2023

Human error (HE) is an important concern in safety-critical systems such as nuclear power plants (NPPs). HE has played a role in many accidents and outage incidents in NPPs. Despite the increased automation in NPPs, HE remains unavoidable. Hence, the need for HE detection is as important as HE prevention efforts. In NPPs, HE is rather rare. Hence, anomaly detection, a widely used machine learning technique for detecting rare anomalous instances, can be repurposed to detect potential HE. In this study, we develop an unsupervised anomaly detection technique based on generative adversarial networks (GANs) to detect anomalies in manually collected surveillance data in NPPs. More specifically, our GAN is trained to detect mismatches between automatically recorded sensor data and manually collected surveillance data, and hence, identify anomalous instances that can be attributed to HE. We test our GAN on both a real-world dataset and an external dataset obtained from a testbed, and we benchmark our results against state-of-the-art unsupervised anomaly detection algorithms, including one-class support vector machine and isolation forest. Our results show that the proposed GAN provides improved anomaly detection performance. Our study is promising for the future development of artificial intelligence based HE detection systems.

• The Korean Journal of Food & Health Convergence
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• v.10 no.1
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• pp.1-6
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• 2024

Purpose: The research aims to address the intricacies of AI and Big Data application within the food industry. This study explores the strategic implementation of AI and Big Data in the food industry. The study seeks to understand how these technologies can be employed to bolster consumer engagement and contribute to market expansion, while considering ethical implications. Research Method: This research employs a comprehensive approach, analyzing current trends, case studies, and existing academic literature. It focuses on the application of AI and Big Data in areas such as supply chain management, consumer behavior analysis, and personalized marketing strategies. Results: The study finds that AI and Big Data significantly enhance market analytics, consumer personalization, and market trend prediction. It highlights the potential of these technologies in creating more efficient supply chains, improving consumer satisfaction through personalization, and providing valuable market insights. Conclusion and Implications: The paper offers actionable insights and recommendations for the effective implementation of AI and Big Data strategies in the food industry. It emphasizes the need for ethical considerations, particularly in data privacy and the transparency of AI algorithms. The study also explores future trends, suggesting that AI and Big Data will continue to revolutionize the industry, emphasizing sustainability, efficiency, and consumer-centric practices.