• Journal of Intelligence and Information Systems
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• v.10 no.2
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• pp.147-163
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• 2004

For the purchase of a cheaper product on the Internet, many customers have been trying to search online shopping mall sites and visit comparison-pricing shops that compare prices and other criteria of the product. Others have been participating into online auction markets or group-buying markets. However, a lot of online shopping malls, auction markets, and group-buying markets provide the same product with different prices. Since these marketplaces have different price settlement mechanism, it is very difficult for the customers to determine marketplace to purchase, considering different kinds of marketplaces at the same time. To overcome such limitations, decision rules and solution procedures for purchase decision making are necessary, which can cover multiple marketplaces simultaneously. For this purpose, purchase decision making in each market must be conducted to maximize customer's utility, and conflicts with other marketplaces must be resolved. Therefore, we have developed the rules and methods that can negotiate cooperatively the purchase decision making in several marketplaces, and designed an architecture of Intelligent Buyer Agent and a message structure to support the idea.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.44 no.4
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• pp.85-97
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• 2021

Online consumer activities have increased considerably since the COVID-19 outbreak. For the products and services which have an impact on everyday life, online reviews and recommendations can play a significant role in consumer decision-making processes. Thus, to better serve their customers, online firms are required to build online-centric marketing strategies. Especially, it is essential to define core value of customers based on the online customer reviews and to propose these values to their customers. This study discovers specific perceived values of customers in regard to a certain product and service, using online customer reviews and proposes a customer value proposition methodology which enables online firms to develop more effective marketing strategies. In order to discover customers value, the methodology employs a text-mining technology, which combines a sentiment analysis and topic modeling. By the methodology, customer emotions and value factors can be more clearly defined. It is expected that online firms can better identify value elements of their respective customers, provide appropriate value propositions, and thus gain sustainable competitive advantage.

• Journal of Information Technology Services
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• v.17 no.2
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• pp.165-177
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• 2018

Recently, online commerces are becoming more common due to factors such as mobile technology development and smart device dissemination, and online review has a big influence on potential buyer's purchase decision. This study presents a set of analytical methodologies for understanding the meaning of customer reviews of products in online transaction. Using techniques currently developed in deep learning are implemented Hierarchical Attention Network for analyze meaning in online reviews. By using these techniques, we could solve time consuming pre-data analysis time problem and multiple topic problems. To this end, this study analyzes customer reviews of laptops sold in domestic online shopping malls. Our result successfully demonstrates over 90% classification accuracy. Therefore, this study classified the unstructured text data in the semantic analysis and confirmed the practical application possibility of the review analysis process.

• Journal of the Korean Society of Clothing and Textiles
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• v.29 no.6
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• pp.847-858
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• 2005

The purpose of this study was to examine consumer purchasing behavior of the online shoppers particularly using online clothing shopping mall and to analyze the key factors of both satisfaction and dissatisfaction of their purchase and to compare the both group by lifestyle segmentation in order to provide the e-CRM strategies. Focus group interviews and survey were conducted in December, 2003 with 30 online shoppers who have an experience of online clothing purchasing. The data analysis included the content analysis, descriptive statistics, K-means and factor analysis. Key findings of the study were as follows: First, online shoppers spent average 3.5 hours on internet and usually purchased clothing while surfing the web. Second, consumers were satisfied with reasonable price and customized service but dissatisfied with delayed delivery, limited product availability in both size and color and return policy. Third, according to the lifestyle segmentation, online shoppers could be characterized as 'Luxurious', 'Trendy' and 'Prudent' 'Luxury-oriented consumers', who value fashion, diet and social activity, tended to purchase basic yet high quality products. However, 'Trend-oriented consumers', to whom fashion trend was most important, purchased various latest fashion products with reasonable price and showed generally positive response to emails sent by e-retailers. And lastly 'Prudence-oriented consumers', whose buying decision was based solely on practicality, appeared to be reluctant to purchase clothing online while seeking more credible information and competitive price. In conclusion, this study has its significance in that it helps promote relationships between customers and e-retailers by providing differentiated e-CRM strategies through each customer groups 'lifestyle segmentation and consumer purchasing behavior analysis.

• Journal of the Korea Society of Computer and Information
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• v.26 no.1
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• pp.275-281
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• 2021

The purpose of this research is to explore what factors affect consumers' purchasing decisions when purchasing artificial intelligence artworks. The research pointed out that in the real shopping model, customer perceived value includes three dimensions: product perceived value, service perceived value and social perceived value. On this basis, an artificial intelligence work purchase decision-making influence model was constructed, and an online survey was attempted to collect data. Through analysis of the reliability, effectiveness and structural equations of SPSS24.0 and AMOS24.0, and scientific verification and analysis, we found that product cognitive value and service cognitive value have a positive impact on consumers' purchase intentions, but social cognition Value has no positive effect on consumers' purchasing intentions.

• Journal of Wellbeing Management and Applied Psychology
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• v.7 no.2
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• pp.31-37
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• 2024

Purpose: Pop-up stores have emerged in the retail industry in recent years, offering consumers a new shopping experience for a limited time and location, and are used for a variety of purposes, including driving purchase behavior. In particular, they have become an important marketing tool among Gen MZ consumers who are quick to acquire information and sensitive to trends. Therefore, this study aims to analyze the impact of pop-up store characteristics on the purchasing behavior of MZ consumers. Research design, data and methodology: Based on a qualitative research approach, the study analyzed successful pop-up stores in Korea to closely examine how the limited operating period and experience-oriented marketing strategy of pop-up stores affect the perceptual attitudes and purchase decision process of Generation MZ. Results: The results of the case study revealed that selling limited edition items, maximizing customer experience factors, and differentiated concepts are the main factors that positively influence the purchase behavior of Gen MZ consumers. These factors contribute to the enhanced purchasing behavior of Gen MZ, making pop-up stores an effective marketing strategy. Conclusions: Pop-up stores are more than just a sales space, but an important communication channel that can strengthen the emotional connection with Gen MZ and effectively communicate brand values. This study provides useful insights for brands and companies to develop marketing strategies for MZ.

• The Journal of Asian Finance, Economics and Business
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• v.6 no.3
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• pp.247-258
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• 2019

The paper aims to examine the factors affecting brand and student decision in buying fresh milk. Combining qualitative and quantitative research methods, this study used self-completed questionnaires to investigate 520 students in Ho Chi Minh City. The results of the study show that that there are five key determinants affecting the dairy brand and student decision in buying fresh milk, including: (1) product quality, (2) fair price, (3) product promotion and customer services, (4) product convenience, and (5) reference group's attitude to the brand. In addition, it is also found that product brand has a direct and positive impact on the student decision. The finding in this study is quite different from other existing literatures in terms of the importance level of the determinants of the student decision in buying fresh milk; specifically, in deciding to buy their fresh milk, students are often interested in the promotion and customer service, the product convenience, and the reference group for the purchase, more than in the quality and price of the product. From these findings, some managerial implications are proposed for policy-makers and relevant enterprises to have appropriate policies and strategies for their business development.

• CRM연구
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• v.2 no.2
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• pp.71-88
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• 2009

The purpose of this thesis is to examine the effects of e-CRM activities by the internet shopping mall companies on the purchase activities of purchase customers and the potential customers. The internet shopping companies utilize e-CRM to systematically identify customers' varying demands, and to utilize the results as marketing tools, thus producing a significant effect on the potential customers by generating customer feedback through e-Community. Contrary to their intention, however, cognitive dissonance can occur through e-Community, which may lead to customers' complaints. If these complaints are not properly managed and settled in a timely manner, they can be transferred to other potential customers, and the conformity phenomenon could be created by other complaining customers. Findings obtained through this thesis are as follows: If cognitive disharmony is created by customers who purchased products through the internet shopping malls, this can lead to private complaining behaviors, and subsequently, these behaviors are formed through e-Community. If the internet shopping mall companies do not take any timely and proper measures to intervene in the stage of private complaining behaviors in the first place, these behaviors will immediately escalate into the public complaining behaviors. Furthermore, the complaints will be transferred to other potential customers, ultimately resulting in their swift expansion. In other words, contrary to intention of the internet shopping mall companies, e-CRM does not facilitate the potential customers purchase decision, it rather affects them to postpone or withdraw their purchase decision. Accordingly, the internet shopping mall companies are required to manage e-Community with extreme care, and they should promptly respond to the complaining customers so that e-Community can function properly.

• Journal of Internet Computing and Services
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• v.21 no.1
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• pp.111-117
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• 2020

Dessert Ateliers (DA) are small shops that sell high-end homemade desserts such as macaroons, cakes, and cookies, and their popularity is increasing according to the emergence of small luxury trends. Even though each DA sells the same kinds of desserts, they are differentiated by the personality of their pastry chef; thus, there is a need to purchase desserts online that customers cannot see and purchase offline, and thus dessert e-commerce has emerged. However, it is impossible for customers to identify all the information of each DA and clearly understand customers' preferences when buying desserts through the dessert e-commerce. When a dessert e-commerce service provides a DA recommendation service, customers can reduce the time they hesitate before making a decision. Therefore, this paper proposes two kinds of DA recommendation method: a clustering-based recommendation method that calculates the similarity between customers' content and DAs and a dynamic weighting-based recommendation method that trains the importance of decision factors considering customer preferences. Various experiments were conducted using a real-world dataset to evaluate the performance of the proposed methods and it showed satisfactory results.

• Journal of Fashion Business
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• v.6 no.5
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• pp.136-150
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• 2002

In this study, we proposed a method to be standing customers as the supporting system for the improvement of fashion garment industry which was the marginal growth getting into full maturity of market. As for the customer creation method of Fashion garment company is developing a marketing program to be standing customer as customer scoring to estimate a existing customer‘s buying power, and figure out minimum fixed sales of company to use a future purchasing predict. This study was a result of data from total sixty thousands data to be created for the 11 months from september. 2000 to July. 2001. The data is part of which the company leading the Korean fashion garment industry has a lot of a customer purchasing history data. But this study used only 48,845 refined purchased data to discriminate from sixty thousands data and 21,496 customer case with the exception of overlapping purchased data among of those. The software used to handle sixty thousands data was SAS e-miner. As the analysis process is put in to operation the analysis of the purchasing customer’s profile firstly, and the second come into basket analysis to consider the buying associations for Association goods, the third estimate the customer grade of Customer loyalty by 3 ways of logit regression analysis, decision tree, Artificial Neural Network. The result suggested a method to be estimate the customer loyalty as 3 independent variables, 2 coefficients. The 3 independent variables are total purchasing amount, purchasing items per one purchase, payment amount by one purchasing item. The 2 coefficients are royal and normal for customer segmentation. The result was that this model use a logit regression analysis was valid as the method to be estimate the customer loyalty.