• Journal of the Korea Institute of Information and Communication Engineering
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• v.12 no.6
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• pp.1032-1038
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• 2008

RFID is applied in various industry area. The purpose of RFID system is authentication of objects. After Tag's certification, RFID system start to process to be wanted. A RFID electric motor recognizes Tag's action and tails. The application of this system is very wide. For example, a cart in shopping Mall follows customer with a proper Tag. Customer may be very convenient if the cart follows customer autonomously as recognizing the direction of Tag. In this parer, we studied about RFID system that follow objects with a Tag. Finally, we experimented and analysed the proposed system, with Tag, Reader, host computer and electric motion motors.

• Journal of Information Technology Services
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• v.22 no.1
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• pp.43-56
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• 2023

With the development of the e-commerce market, various types of products continue to be released. However, customers face an information overload problem in purchasing decision-making. Therefore, personalized recommendations have become an essential service in providing personalized products to customers. Recently, many studies on GCN-based recommender systems have been actively conducted. Such a methodology can address the limitation in disabling to effectively reflect the interaction between customer and product in the embedding process. However, previous studies mainly use implicit feedback data to conduct experiments. Although implicit feedback data improves the data scarcity problem, it cannot represent customers' preferences for specific products. Therefore, this study proposed a novel model combining explicit and implicit feedback to address such a limitation. This study treats the average ratings of customers and products as the features of customers and products and converts them into a high-dimensional feature vector. Then, this study combines ID embedding vectors and feature vectors in the embedding layer to learn the customer-product interaction effectively. To evaluate recommendation performance, this study used the MovieLens dataset to conduct various experiments. Experimental results showed the proposed model outperforms the state-of-the-art. Therefore, the proposed model in this study can provide an enhanced recommendation service for customers to address the information overload problem.

• The Journal of Industrial Distribution & Business
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• v.15 no.3
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• pp.1-9
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• 2024

Purpose: This research aimed at understanding the dynamics of consumer behavior in the context of nostalgic desserts. The primary objective is to scrutinize how different attributes like quality, health, convenience, and trend influence customer satisfaction and their subsequent decision to repurchase nostalgic desserts. Research Method: The study leverages structural equation modeling, incorporating statistical tools such as SPSS and AMOS for a thorough analysis. It involves collecting data over a specified period, followed by correlation and trend analyses to deduce patterns and relationships. Results: The findings reveal that attributes such as quality, health, convenience, and trend significantly impact customer satisfaction and repurchase intentions. Interestingly, economic factors appeared to have a negligible effect on these decisions. The study offers a comprehensive understanding of the factors that influence consumer decisions in the context of nostalgic desserts, providing valuable implications for both academic research and practical marketing strategies. Conclusions: The insights garnered from this research are pivotal for formulating marketing strategies for nostalgic dessert brands. It underscores the importance of accentuating quality, health, and trend in product offerings to boost customer satisfaction and encourage repurchases. The study also sheds light on the evolving nature of consumer preferences and the integral role of nostalgia in shaping purchasing behaviors.

• Smart Media Journal
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• v.2 no.2
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• pp.20-27
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• 2013

The concept of social media is top of the agenda for many business executives and decision makers, as well as consultants try to identify ways where companies can make profitable use of applications such as Netflix, Flixster. The social media is playing an increasingly important role as the information sources for customers making product choices etc. With the flourish of Web 2.0 technology, customer reviews are becoming more and more useful and important information resources for people to save their time and energy on purchasing products that they want. This paper proposes the Bayesian Probabilistic Classification algorithm to mine the social media review, and evaluates it by different splits and cross validation mechanism from the real data set. The explored study experimental results show the robustness and effectiveness of proposed approach for mining the social media review.

• The Journal of Information Technology and Database
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• v.6 no.1
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• pp.89-101
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• 1999

Increasing number of companies recognize that the understanding of customers and their markets is indispensable for their survival and business success. The companies are rapidly increasing the amount of investments to develop customer databases which is the basis for the database marketing activities. Database marketing is closely related to data mining. Data mining is the non-trivial extraction of implicit, previously unknown and potentially useful knowledge or patterns from large data. Data mining applied to database marketing can make a great contribution to reinforce the company's competitiveness and sustainable competitive advantages. This paper develops the classification model to select the most responsible customers from the customer databases for telemarketing system and evaluates the performance of the developed model using LIFT measure. The model employs the decision tree algorithm, i.e., CHAID which is one of the well-known data mining techniques. This paper also represents the effective database marketing strategy by applying the data mining technique to a credit card company's telemarketing system.

• 한국경영정보학회:학술대회논문집
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• 2007.06a
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• pp.551-556
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• 2007

This paper attempts to develop a framework for interrelationships among human resources information systems (HRIS), outsourcing, and corporate culture. This research investigates impacts of outsourcing HRIS on corporate culture. In this paper, we hypothesize that outsourcing corporate HRIS is less appealing (1) if the quality of product and customer service matters for a firm, (2) if a firm is concerned with a loss of intellectual property, and (3) if a firm requires maintenance of a distinctive human resource service function that is capable of meeting the challenges of fast changing customer demands in a dynamic business environment. In addition, this study argues companies must be aware of the total costs associated with HRIS before outsourcing its human resource functions. Finally, the impact on employee morale and performance must also be considered By outsourcing HRIS, managers will be able to spend more time and resources dedicated to an employee's professional career development.

• Journal of Mechanical Science and Technology
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• v.19 no.3
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• pp.743-755
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• 2005

Human Sensibility Engineering System (HSES) has been applied to product development for customer's satisfaction based on ergonomic technology. The system is composed of three parts such as human sensibility analysis, inference mechanism, and presentation technologies. Inference mechanism translating human sensibility into design elements plays an important role in the HSES. In this paper, we propose a rule-based inference model for HSES. The rule-based inference model is composed of five rules and two inference approaches. Each of these rules reasons the design elements for selected human sensibility words with the decision variables from regression analysis in terms of forward inference. These results are evaluated by means of backward inference. By comparing the evaluation results, the inference model decides on product design elements which are closer to the customer's feeling and emotion. Finally, simulation results are tested statistically in order to ascertain the validity of the model.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2000.04a
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• pp.175-184
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• 2000

Recently Venture business in the area of information and communication industry is booming. Though Technology classification chart helps the potential entrepreneur through Survey paper and Internet Web Page, its service does not meet the customer demand. Hence Technology Classification system, which is proposed in this paper, will solve this problem by using virtual network among venture, technology experts and potential entrepreneurs. This system supports potential entrepreneurs' decision making for choice of venture business items by using dual client technology, and provides better services than existing systems by linking expert client and customer client, .

• The Journal of Information Systems
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• v.18 no.3
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• pp.375-399
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• 2009

In this study, we propose a integrated model of logistic regression, artificial neural networks, support vector machines(SVM), with case-based reasoning(CBR). To predict respondents in the direct marketing is the binary classification problem as like bankruptcy prediction, IDS, churn management and so on. To solve the binary problems, we employed logistic regression, artificial neural networks, SVM. and CBR. CBR is a problem-solving technique and shows significant promise for improving the effectiveness of complex and unstructured decision making, and we can obtain excellent results through CBR in this study. Experimental results show that the classification accuracy of integration model using CBR is superior to logistic regression, artificial neural networks and SVM. When we apply the customer response model to predict respondents in the direct marketing, we have to consider from the view point of profit/cost about the misclassification.

• Proceedings of the Korean Society for Quality Management Conference
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• 2004.04a
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• pp.278-283
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• 2004

CTQ's choice is an important process for quality improvement. It is difficult to get customer satisfaction if CTQ is not chosen properly in view of improvement effort. However, case study or research about the supporting method for CTQ choice is not yet fully accomplished. In this paper, we propose an effective method for CTQ choice considering customer requirement through QFD as well as safety by using AHP which is one of strategic decision-making techniques for product safety. Also we show an empirical study on its application to the auto test robot system of mobile phone development.