• The Research Journal of the Costume Culture
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• v.17 no.1
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• pp.177-188
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• 2009

SCM as the important marketing strategy enhance the firm's efficiency and compatibility in global market environment such as global outsourcing. Firms adopted SCM realized the need to evaluate precisely the performance of SCM. In spite of importance of SCM, there was not much intention and research to measure SCM performance in textile fashion industry. Therefore, the purpose of this case study was to measure performance of supply chain management in textile fashion business using BSC(Balanced Score Card) to measure not only financial perspective but also non-financial perspectives such as customer perspective, internal business perspectives, financial perspective, and innovation & learning perspective. The questionnaire developed by the reviews of the literature was adopted for this study. The results of this study showed that SCM performance was enhanced from the point of customer perspective(cost, quality, time, service), financial perspective(cash cycle time, inventory turn over, inventory obsolescence, return on asset, return on investment, capacity utilization), and innovation & learning perspective(cost for human resource management, service for human resources). But there was same performance level regarding internal business perspective(lead time, cost for manufacturing process, product quality control, productive flexibility for time, quantity, and variety). Therefore, we should keep close relationship and two way communication among supply chain members to promote better SCM performance.

• Journal of Korea Society of Industrial Information Systems
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• v.21 no.2
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• pp.59-72
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• 2016

The main objective of this study is to provide the behavioral model of co-creation by customers and explorer influential factors of participation from the customer's perspective. To achieve the research goals, we employed grounded theory and conducted intensive interviews of 8 customers who had writing product reviews experiences with Beauty Net. The study results indicate that the most important influential factors of participation from the customer's perspective are: (1) Co-creation platform; (2) Co-creation policy; and (3) Individual characteristics.

• Journal of the Korea Society of Computer and Information
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• v.23 no.10
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• pp.103-109
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• 2018

In this paper, we propose a dynamic document classification method which breaks away from existing document classification method with artificial categorization rules focusing on suppliers and has changing categorization rules according to users' needs or social trends. The core of this dynamic document classification method lies in the fact that it creates classification criteria real-time by using topic modeling techniques without standardized category rules, which does not force users to use unnecessary frames. In addition, it can also search the details through the relevance analysis by calculating the relationship between the words that is difficult to grasp by word frequency alone. Rather than for logical and systematic documents, this method proposed can be used more effectively for situation analysis and retrieving information of unstructured data which do not fit the category of existing classification such as VOC (Voice Of Customer), SNS and customer reviews of Internet shopping malls and it can react to users' needs flexibly. In addition, it has no process of selecting the classification rules by the suppliers and in case there is a misclassification, it requires no manual work, which reduces unnecessary workload.

• The Journal of Industrial Distribution & Business
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• v.13 no.8
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• pp.9-18
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• 2022

Purpose: K-beauty products industry trends, estimates and dynamics are examined in this study to discover a potential possibility for growth. There is a thorough examination of the elements that drive and impede the expansion of the K-beauty industry. This study aims to investigate marketing strategy of K beauty product to enhance economic growth in South Korea. Research design, data and methodology: This study used one of the most famous approach for analyzing the current literature which is a PRISMA (Process and Systematic Reviews and Meta-Analyses) method. This method maps out the number of records identified, the included and the excluded ones with the reasons for the exclusion. The technique clearly states the research problem and the appropriate scope. Results: The theoretical findings of prior literature indicates K-beauty companies should retain physical locations despite the trend toward online commerce, in order to guarantee that they meet the demands of different customers and enhance customer experiences to develop trust and loyalty. Conclusions: The findings of this research are of academic importance since they provide light on customer preferences for new K-beauty products. While past research has often ignored certain kinds of influencers, this study emphasized the need of considering influencers and certain product exposure strategies together, which has major academic consequences.

• The Journal of Industrial Distribution & Business
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• v.14 no.1
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• pp.49-56
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• 2023

Purpose: This research aims to explore the impact of fashion marketing on the sales of luxury brand items and to identify the strategies that can be used to market luxury fashion items successfully, addressing the research gap of how fashion marketing can lead to increased sales, customer loyalty, and satisfaction for luxury brand items. Research design, data and methodology: The present study conducted the method of the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) which is a reporting guideline for methodical assessments and meta-analyses. It offers a consistent approach for conducting and reporting these types of studies, which can help to improve their quality and transparency. Results: The findings indicated that fashion marketing can positively impact luxury brand sales. It can significantly increase the number of luxury brand purchases. the presence of the quality label increased the participants' purchase intention and attitude towards the brand, suggesting that the quality label can create a positive perception of the brand and increase the likelihood of purchasing. Conclusions: This research concludes that fashion marketing can have a positive effect on improved customer recognition of the brand. Thus, companies should focus on developing campaigns that capture the attention of potential consumers, creating an emotional connection with them.

• Asia pacific journal of information systems
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• v.33 no.3
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• pp.541-572
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• 2023

Amazon Go is a cashierless convenience store concept, which is seen as a disruption in the grocery retail segment. Although Amazon Go has the ability to disrupt the retail segment, there are speculations on how Amazon Go will be perceived by users. Existing studies have not utilized user-generated content to understand the factors that affect customer behaviour in case of Amazon Go. Additionally, in case of phygital retail, studies have not attempted at understanding the effect of emotions and gratifications on user behaviour. To address the gap of exploring user perspectives based on their experience, we have examined the impact of gratifications and emotions on the acceptance of phygital retail using user-generated-content. A mixed-method approach has been utilized using only user-generated content. Utilizing topic-modelling based content analysis and emotion analysis on 30 articles related to Amazon Go, we found themes like, convenience, technology, experience, personalization, enjoyment and emotions like, bad, good, annoyance, success. In the empirical analysis, we have utilized 522 reviews about Amazon Go from the cognition and emotion theory stance, and found that hedonic gratifications have a positive impact on challenge emotions. We also found a significant impact of emotions on customer's favourite behaviour.

• Journal of Intelligence and Information Systems
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• v.22 no.1
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• pp.187-204
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• 2016

Document classification based on emotional polarity has become a welcomed emerging task owing to the great explosion of data on the Web. In the big data age, there are too many information sources to refer to when making decisions. For example, when considering travel to a city, a person may search reviews from a search engine such as Google or social networking services (SNSs) such as blogs, Twitter, and Facebook. The emotional polarity of positive and negative reviews helps a user decide on whether or not to make a trip. Sentiment analysis of customer reviews has become an important research topic as datamining technology is widely accepted for text mining of the Web. Sentiment analysis has been used to classify documents through machine learning techniques, such as the decision tree, neural networks, and support vector machines (SVMs). is used to determine the attitude, position, and sensibility of people who write articles about various topics that are published on the Web. Regardless of the polarity of customer reviews, emotional reviews are very helpful materials for analyzing the opinions of customers through their reviews. Sentiment analysis helps with understanding what customers really want instantly through the help of automated text mining techniques. Sensitivity analysis utilizes text mining techniques on text on the Web to extract subjective information in the text for text analysis. Sensitivity analysis is utilized to determine the attitudes or positions of the person who wrote the article and presented their opinion about a particular topic. In this study, we developed a model that selects a hot topic from user posts at China's online stock forum by using the k-means algorithm and self-organizing map (SOM). In addition, we developed a detecting model to predict a hot topic by using machine learning techniques such as logit, the decision tree, and SVM. We employed sensitivity analysis to develop our model for the selection and detection of hot topics from China's online stock forum. The sensitivity analysis calculates a sentimental value from a document based on contrast and classification according to the polarity sentimental dictionary (positive or negative). The online stock forum was an attractive site because of its information about stock investment. Users post numerous texts about stock movement by analyzing the market according to government policy announcements, market reports, reports from research institutes on the economy, and even rumors. We divided the online forum's topics into 21 categories to utilize sentiment analysis. One hundred forty-four topics were selected among 21 categories at online forums about stock. The posts were crawled to build a positive and negative text database. We ultimately obtained 21,141 posts on 88 topics by preprocessing the text from March 2013 to February 2015. The interest index was defined to select the hot topics, and the k-means algorithm and SOM presented equivalent results with this data. We developed a decision tree model to detect hot topics with three algorithms: CHAID, CART, and C4.5. The results of CHAID were subpar compared to the others. We also employed SVM to detect the hot topics from negative data. The SVM models were trained with the radial basis function (RBF) kernel function by a grid search to detect the hot topics. The detection of hot topics by using sentiment analysis provides the latest trends and hot topics in the stock forum for investors so that they no longer need to search the vast amounts of information on the Web. Our proposed model is also helpful to rapidly determine customers' signals or attitudes towards government policy and firms' products and services.

• 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.

• Knowledge Management Research
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• v.22 no.3
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• pp.273-293
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• 2021

Due to the COVID-19 pandemic, the size of the e-commerce has been increased rapidly. This pandemic, which made contact-less communication culture in everyday life made the e-commerce market to be opened even to the consumers who would hesitate to purchase and pay by electronic device without any personal contacts and seeing or touching the real products. Consumers who have experienced the easy access and convenience of the online purchase would continue to take those advantages even after the pandemic. During this time of transformation, however, the size of information source for the consumers has become even shrunk into a flat screen and limited to visual only. To provide differentiated and competitive information on products, companies are adopting AR/VR and steaming technologies but the reviews from the honest users need to be recognized as important in that it is regarded as strong as the well refined product information provided by marketing professionals of the company and companies may obtain useful insight for product development, marketing and sales strategies. Then from the consumer's point of view, if the ratings of reviews are widely diverged how consumers would process the review information before purchase? Are non-converged ratings always unreliable and worthless? In this study, we analyzed how consumer's regulatory focus moderate the attitude to process the diverged information. This experiment was designed as a 2x2 factorial study to see how the variance of product review ratings (high vs. low) for cosmetics affects product attitudes by the consumers' regulatory focus (prevention focus vs. improvement focus). As a result of the study, it was found that prevention-focused consumers showed high product attitude when the review variance was low, whereas promotion-focused consumers showed high product attitude when the review variance was high. With such a study, this thesis can explain that even if a product with exactly the same average rating, the converged or diverged review can be interpreted differently by customer's regulatory focus. This paper has a theoretical contribution to elucidate the mechanism of consumer's information process when the information is not converged. In practice, as reviews and sales records of each product are accumulated, as an one of applied knowledge management types with big data, companies may develop and provide even reinforced customer experience by providing personalized and optimized products and review information.

• The Journal of the Korea Contents Association
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• v.12 no.10
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• pp.453-465
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• 2012

The major purpose of this study is to examine the effects of customers' perceptions toward service quality of mobile music service on customer loyalty and switching intention. For this purpose, this study posited three service quality characteristics including interface, service, price quality as key determinants of customer loyalty and switching intention based on relevant literature reviews. A research model and hypotheses concerning the relationship between these variables were constructed. Moreover, this study explored the moderating effects of attractiveness of alternative and switching costs on the relationship between customer loyalty and switching intention. An online survey was administrated on 433 mobile music service users and a simple, multiple, and hierarchical regression analysis were employed. The results indicated that all of interface, service, price quality have significant positive influences on customer loyalty, and both of service quality and attractiveness of alternatives have influences on the switching intention in a positive way. On the other way, it was shown that switching costs have a negative influence on the switching intention. The moderating effect of attractiveness of alternatives on the relationship between customer loyalty and switching intention was also found. The implications of these results are discussed.