• Journal of the Korean Society of Clothing and Textiles
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• v.46 no.5
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• pp.805-826
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• 2022

This study explored the process by which consumers purchase products from small fashion business owners via online and mobile channels. In addition, group types were classified given that the purchasing process depends on the consumers' life cycle. The consumer focus group interview (FGI) was conducted on 18 participants that were divided into six groups by age, work, and children. Results revealed that first, consumer journey comprised four stages. Factors influencing need recognition were "attention to information of social media influencer," "attention to information of affiliated groups," and "repeated advertising of SME products/brands." For information searching, "exploring purchase reviews," "environment for mobile shopping information exploration," and "continuous product tracking" were important factors. Purchasing and shopping stages were affected by "price-free, improvised purchase decision" and "convenient mobile payment system and point benefits." After the purchase, "active sharing and repeated purchase when satisfied" and "blocking relationships when dissatisfied" occurred. Second, six consumer groups based on the fashion life cycle are the "Platform lover," "Influencer follower," "Trust builder," "Novelty seeker," "Convenience seeker," and "New designer supporter." Ultimately, small business owners can develop the process of planning and selling fashion products more efficiently.

• Knowledge Management Research
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• v.24 no.3
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• pp.95-125
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• 2023

As the aviation industry has rapidly been grown, there are more factors for customers to consider when choosing an airline. In response, airlines are trying to increase customer value by providing high-quality services and differentiated experiential value. While early customer value research centered on utilitarian value, which is the trade-off between cost and benefit in terms of utility for products and services, the importance of experiential value has recently been emphasized. However, experiential value needs to be studied in a specific context that fully represents customer preferences because what constitutes customer value changes depending on the product or service context. In addition, customer value has an important influence on customers' decision-making, so it is necessary for airlines to accurately understand what constitutes customer value. In this study, we collected customer reviews and ratings from Skytrax, a website specializing in airlines, and utilized the BERTopic technique to derive factors of customer value. The results revealed nine factors that constitute customer value in airlines, and six of them are related to customer satisfaction. This study proposes a new methodology that enables a granular understanding of customer value and provides airlines with specific directions for improving service quality.

• Journal of Intelligence and Information Systems
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• v.21 no.1
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• pp.161-177
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• 2015

With the rapid evolution of technology, the size, number, and the type of databases has increased concomitantly, so data mining approaches face many challenging applications from databases. One such application is discovery of fraud patterns from agricultural product wholesale transaction instances. The agricultural product wholesale market in Korea is huge, and vast numbers of transactions have been made every day. The demand for agricultural products continues to grow, and the use of electronic auction systems raises the efficiency of operations of wholesale market. Certainly, the number of unusual transactions is also assumed to be increased in proportion to the trading amount, where an unusual transaction is often the first sign of fraud. However, it is very difficult to identify and detect these transactions and the corresponding fraud occurred in agricultural product wholesale market because the types of fraud are more intelligent than ever before. The fraud can be detected by verifying the overall transaction records manually, but it requires significant amount of human resources, and ultimately is not a practical approach. Frauds also can be revealed by victim's report or complaint. But there are usually no victims in the agricultural product wholesale frauds because they are committed by collusion of an auction company and an intermediary wholesaler. Nevertheless, it is required to monitor transaction records continuously and to make an effort to prevent any fraud, because the fraud not only disturbs the fair trade order of the market but also reduces the credibility of the market rapidly. Applying data mining to such an environment is very useful since it can discover unknown fraud patterns or features from a large volume of transaction data properly. The objective of this research is to empirically investigate the factors necessary to detect fraud transactions in an agricultural product wholesale market by developing a data mining based fraud detection model. One of major frauds is the phantom transaction, which is a colluding transaction by the seller(auction company or forwarder) and buyer(intermediary wholesaler) to commit the fraud transaction. They pretend to fulfill the transaction by recording false data in the online transaction processing system without actually selling products, and the seller receives money from the buyer. This leads to the overstatement of sales performance and illegal money transfers, which reduces the credibility of market. This paper reviews the environment of wholesale market such as types of transactions, roles of participants of the market, and various types and characteristics of frauds, and introduces the whole process of developing the phantom transaction detection model. The process consists of the following 4 modules: (1) Data cleaning and standardization (2) Statistical data analysis such as distribution and correlation analysis, (3) Construction of classification model using decision-tree induction approach, (4) Verification of the model in terms of hit ratio. We collected real data from 6 associations of agricultural producers in metropolitan markets. Final model with a decision-tree induction approach revealed that monthly average trading price of item offered by forwarders is a key variable in detecting the phantom transaction. The verification procedure also confirmed the suitability of the results. However, even though the performance of the results of this research is satisfactory, sensitive issues are still remained for improving classification accuracy and conciseness of rules. One such issue is the robustness of data mining model. Data mining is very much data-oriented, so data mining models tend to be very sensitive to changes of data or situations. Thus, it is evident that this non-robustness of data mining model requires continuous remodeling as data or situation changes. We hope that this paper suggest valuable guideline to organizations and companies that consider introducing or constructing a fraud detection model in the future.

• Archives of design research
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• v.17 no.2
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• pp.77-86
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• 2004

User-Centered Design Approach plays important role in dealing with usability issues for developing modern technology products. Yet it is still questionable whether the User-Centered approach is enough for the development of successful consumer contents since the User-Centered Design is originated from the software engineering field where meeting customers' functional requirement is the most critical aspect in developing a software. However, modern consumer market is already saturated and in order to meet ever increasing consumer requirements, the User-Centered Design approach needs to be expanded. As a way of incorporating the User-Centered Approach into the consumer product development, Jordan suggested the 'Pleasure-based Approach' in industrial design field, which usually generates multi-dimensional user requirements: 1)physical, 2)cognitive, 3)identity and 4) social. It is the current tendency that many portal and community service providers focus on fulfilling both functional and emotional needs for users when developing new items, contents and services. Previously fulfilling consumers' emotional needs solely depend on visual designer's graphical sense and capability. However, taking the customer-centered approach on withdrawing consumers' unknown needs is getting critical in the competitive market environment. This paper reviews different types of user research techniques and categorized into 6 ways based on Kano(1992)'s product quality model. Based on his theory, only performance factors, such as suability, can be identified through the user-centered design approach. The user-centered design approach has to be expanded to include factors include personality, sociability, pleasure, and so on. In order to identify performance as well as excellent factors through user research, a user-research framework was established and tested through the case study, which is ' the development of new online service for teens '. The results of the user research were summarized at the end of the paper and the pros and cons of each research techniques were analyzed.

• Journal of Distribution Science
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• v.14 no.7
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• pp.83-90
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• 2016

Purpose - This study focuses on new type distribution channel that named as Omnichannel. Omnichannel is developed from Multichannel which is used in many distribution channels to buy or selling goods. Omnichannel basically needs an Information and Communications Technologies(ICT) to use, so researcher conduct a Technology Acceptance Model(TAM) to research model. Customer-brand relationship was used as dependent variable to focus on the role of Omnichannel. Research design, data, and methodology - The subject of this study is customer who purchase goods or service through omnichannel. Based on the literature from the preceding research analysis of TAM and customer-brand relationship, this study was constructed by the reference to previous studies, final research model design for figure out casual relationship among perceived ease of use, perceived usefulness, omnichannel use and customer-brand relationship. From 2016 February 3 to March 17, questionnaire survey targeted customers who use online and offline channels. 273 questionnaire survey had conducted, then, 252 survey data were available for empirical analysis. Researcher provide descriptive statistics for checking generality. Cronbach's alpha value was used to check the reliability of data. Exploratory factor analysis was used for purification of values and eigenvalue checking. After EFA, Confirmatory factor analysis was used to prepare structural equation modeling with executing structural equation modeling for confirming hypothesis which developed by researcher. Results - The main results of this empirical study are as follows. First, omnichannel's perceived ease of use has positive significant effect on perceived usefulness(estimate: 0.579). Moreover, omnichannel's perceived ease of use and perceived usefulness has positive significant effect on omnichannel use(estimate: 0.325,0.648). Second, using omnichannel has positive significant effect on brand-customer relationship(estimate: 0.521). Every hypothesis adopted as researcher designed. This study found out the intermediate relationship between perceived ease of use and omnichannel use by investigating hypothesis. Conclusions - Base on the empirical result, this study confirmed that TAM theory perceived has relation with omnichannel. First, factors of TAM has positive effect on omnichannel use, so it highlights the important role of customer based interface and usefulness. Especially, perceived usefulness has high indirect influence on ease of use and use of omnichannel. It seems that when customers try to decide use or not use omnichannel, customers focus on percept benefits from omnichannel. Thus, a provider should applicate attractive price table, accurate product or service information and high switching cost strategy to emphasize the usefulness of omnichannel. Second, using omnichannel enhances the relationship between customers and brand, because there are more time and frequency to serve customers. It is important because good relationship between customers can increase the future's financial performance through word of mouse, positive brand image and loyalty to brand or company. Finally, despite of empirical result and implications, this study has limitations. First, there are only a few previous studies about omnicahnnel, so literature reviews are restricted. While set up the factors which can affect the use of omnichannel, next study should be considered with broader theories or models(ex: contingency theory). Second, omnichannel has developed from multichannel, so comparative analysis is needed between these methods because there is a possibility about different forte character of each distribution system on customer's consuming patterns.

• Journal of Intelligence and Information Systems
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• v.25 no.1
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• pp.139-161
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• 2019

The utilization of the e-commerce market has become a common life style in today. It has become important part to know where and how to make reasonable purchases of good quality products for customers. This change in purchase psychology tends to make it difficult for customers to make purchasing decisions in vast amounts of information. In this case, the recommendation system has the effect of reducing the cost of information retrieval and improving the satisfaction by analyzing the purchasing behavior of the customer. Amazon and Netflix are considered to be the well-known examples of sales marketing using the recommendation system. In the case of Amazon, 60% of the recommendation is made by purchasing goods, and 35% of the sales increase was achieved. Netflix, on the other hand, found that 75% of movie recommendations were made using services. This personalization technique is considered to be one of the key strategies for one-to-one marketing that can be useful in online markets where salespeople do not exist. Recommendation techniques that are mainly used in recommendation systems today include collaborative filtering and content-based filtering. Furthermore, hybrid techniques and association rules that use these techniques in combination are also being used in various fields. Of these, collaborative filtering recommendation techniques are the most popular today. Collaborative filtering is a method of recommending products preferred by neighbors who have similar preferences or purchasing behavior, based on the assumption that users who have exhibited similar tendencies in purchasing or evaluating products in the past will have a similar tendency to other products. However, most of the existed systems are recommended only within the same category of products such as books and movies. This is because the recommendation system estimates the purchase satisfaction about new item which have never been bought yet using customer's purchase rating points of a similar commodity based on the transaction data. In addition, there is a problem about the reliability of purchase ratings used in the recommendation system. Reliability of customer purchase ratings is causing serious problems. In particular, 'Compensatory Review' refers to the intentional manipulation of a customer purchase rating by a company intervention. In fact, Amazon has been hard-pressed for these "compassionate reviews" since 2016 and has worked hard to reduce false information and increase credibility. The survey showed that the average rating for products with 'Compensated Review' was higher than those without 'Compensation Review'. And it turns out that 'Compensatory Review' is about 12 times less likely to give the lowest rating, and about 4 times less likely to leave a critical opinion. As such, customer purchase ratings are full of various noises. This problem is directly related to the performance of recommendation systems aimed at maximizing profits by attracting highly satisfied customers in most e-commerce transactions. In this study, we propose the possibility of using new indicators that can objectively substitute existing customer 's purchase ratings by using RFM multi-dimensional analysis technique to solve a series of problems. RFM multi-dimensional analysis technique is the most widely used analytical method in customer relationship management marketing(CRM), and is a data analysis method for selecting customers who are likely to purchase goods. As a result of verifying the actual purchase history data using the relevant index, the accuracy was as high as about 55%. This is a result of recommending a total of 4,386 different types of products that have never been bought before, thus the verification result means relatively high accuracy and utilization value. And this study suggests the possibility of general recommendation system that can be applied to various offline product data. If additional data is acquired in the future, the accuracy of the proposed recommendation system can be improved.

• Journal of Intelligence and Information Systems
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• v.27 no.2
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• pp.55-78
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• 2021

Tosolve complex and diverse social problems and ensure the quality of life of individuals, social robots that can interact with humans are attracting attention. In the past, robots were recognized as beings that provide labor force as they put into industrial sites on behalf of humans. However, the concept of today's robot has been extended to social robots that coexist with humans and enable social interaction with the advent of Smart technology, which is considered an important driver in most industries. Specifically, there are service robots that respond to customers, the robots that have the purpose of edutainment, and the emotionalrobots that can interact with humans intimately. However, popularization of robots is not felt despite the current information environment in the modern ICT service environment and the 4th industrial revolution. Considering social interaction with users which is an important function of social robots, not only the technology of the robots but also other factors should be considered. The design elements of the robot are more important than other factors tomake consumers purchase essentially a social robot. In fact, existing studies on social robots are at the level of proposing "robot development methodology" or testing the effects provided by social robots to users in pieces. On the other hand, consumer emotions felt from the robot's appearance has an important influence in the process of forming user's perception, reasoning, evaluation and expectation. Furthermore, it can affect attitude toward robots and good feeling and performance reasoning, etc. Therefore, this study aims to verify the effect of appearance of social robot and consumer emotions on consumer's attitude toward social robot. At this time, a social robot design evaluation model is constructed by combining heterogeneous data from different sources. Specifically, the three quantitative indicator data for the appearance of social robots from the ABOT Database is included in the model. The consumer emotions of social robot design has been collected through (1) the existing design evaluation literature and (2) online buzzsuch as product reviews and blogs, (3) qualitative interviews for social robot design. Later, we collected the score of consumer emotions and attitudes toward various social robots through a large-scale consumer survey. First, we have derived the six major dimensions of consumer emotions for 23 pieces of detailed emotions through dimension reduction methodology. Then, statistical analysis was performed to verify the effect of derived consumer emotionson attitude toward social robots. Finally, the moderated regression analysis was performed to verify the effect of quantitatively collected indicators of social robot appearance on the relationship between consumer emotions and attitudes toward social robots. Interestingly, several significant moderation effects were identified, these effects are visualized with two-way interaction effect to interpret them from multidisciplinary perspectives. This study has theoretical contributions from the perspective of empirically verifying all stages from technical properties to consumer's emotion and attitudes toward social robots by linking the data from heterogeneous sources. It has practical significance that the result helps to develop the design guidelines based on consumer emotions in the design stage of social robot development.

• Journal of Intelligence and Information Systems
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• v.18 no.4
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• pp.19-42
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• 2012

Consumer consumption patterns are shifting rapidly as buyers migrate from offline markets to e-commerce routes, such as shopping channels on TV and internet shopping malls. In the offline markets consumers go shopping, see the shopping items, and choose from them. Recently consumers tend towards buying at shopping sites free from time and place. However, as e-commerce markets continue to expand, customers are complaining that it is becoming a bigger hassle to shop online. In the online shopping, shoppers have very limited information on the products. The delivered products can be different from what they have wanted. This case results to purchase cancellation. Because these things happen frequently, they are likely to refer to the consumer reviews and companies should be concerned about consumer's voice. E-commerce is a very important marketing tool for suppliers. It can recommend products to customers and connect them directly with suppliers with just a click of a button. The recommender system is being studied in various ways. Some of the more prominent ones include recommendation based on best-seller and demographics, contents filtering, and collaborative filtering. However, these systems all share two weaknesses : they cannot recommend products to consumers on a personal level, and they cannot recommend products to new consumers with no buying history. To fix these problems, we can use the information which has been collected from the questionnaires about their demographics and preference ratings. But, consumers feel these questionnaires are a burden and are unlikely to provide correct information. This study investigates combining collaborative filtering with the centrality of social network analysis. This centrality measure provides the information to infer the preference of new consumers from the shopping history of existing and previous ones. While the past researches had focused on the existing consumers with similar shopping patterns, this study tried to improve the accuracy of recommendation with all shopping information, which included not only similar shopping patterns but also dissimilar ones. Data used in this study, Movie Lens' data, was made by Group Lens research Project Team at University of Minnesota to recommend movies with a collaborative filtering technique. This data was built from the questionnaires of 943 respondents which gave the information on the preference ratings on 1,684 movies. Total data of 100,000 was organized by time, with initial data of 50,000 being existing customers and the latter 50,000 being new customers. The proposed recommender system consists of three systems : [+] group recommender system, [-] group recommender system, and integrated recommender system. [+] group recommender system looks at customers with similar buying patterns as 'neighbors', whereas [-] group recommender system looks at customers with opposite buying patterns as 'contraries'. Integrated recommender system uses both of the aforementioned recommender systems to recommend movies that both recommender systems pick. The study of three systems allows us to find the most suitable recommender system that will optimize accuracy and customer satisfaction. Our analysis showed that integrated recommender system is the best solution among the three systems studied, followed by [-] group recommended system and [+] group recommender system. This result conforms to the intuition that the accuracy of recommendation can be improved using all the relevant information. We provided contour maps and graphs to easily compare the accuracy of each recommender system. Although we saw improvement on accuracy with the integrated recommender system, we must remember that this research is based on static data with no live customers. In other words, consumers did not see the movies actually recommended from the system. Also, this recommendation system may not work well with products other than movies. Thus, it is important to note that recommendation systems need particular calibration for specific product/customer types.