• Journal of Service Research and Studies
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• v.13 no.1
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• pp.127-143
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• 2023

Currently, the domestic traditional market has not escaped the swamp of stagnation that began in the early 2000s despite various projects promoted by many related players such as the central government and local governments. In order to overcome the crisis faced by the traditional market, various R&Ds have recently been conducted on how to build a smart traditional market that combines information and communication technologies such as big data analysis, artificial intelligence, and the Internet of Things. This study analyzes various previous studies, users of traditional markets, and application cases of ICT technology in foreign traditional markets since 2012 and proposes a model to build a smart traditional market using ICT technology based on the analysis. The model proposed in this study includes building a traditional market metaverse that can interact with visitors, certifying visits to traditional markets through digital signage with NFC technology, improving accuracy of fire detection functions using IoT and AI technology, developing smartphone apps for market launch information and event notification, and an e-commerce system. If a smart traditional market platform is implemented and operated based on the smart traditional market platform model presented in this study, it will not only draw interest in the traditional market to MZ generation and foreigners, but also contribute to revitalizing the traditional market in the future.

• The Journal of Bigdata
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• v.6 no.2
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• pp.17-28
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• 2021

Current development of technologies related to 4^th industrial revolution and the pandemic of COVID-19 lead the rapid expansion of e-marketplace. The level of competition among several companies gets increased by introducing different strategies. To cope with the current change in the market and satisfy the customers who request the better delivery service, the new concept, fulfillment, has been introduced. It makes the leadtime of process from order picking to delivery reduced and the efficiency improved. Still, the efficiency of operation in fulfillment centers constrains the service level of the entire delivery process. In order to solve this problem, several different approaches for demand forecasting and coordinating supplies using Bigdata, IoT and AI, which there exists the trivial limitations. Because it requires the most lead time for operation and leads the inefficiency the process from picking to packing the ordered items, the logistics service providers should try to automate this procedure. In this research, it has been proposed to develop the efficient plans to automate the process to move the ordered items from the location where it stores to stage for packing using AGV and AMR. The efficiency of automated devices depends on the number of items and total number of devices based on the demand. Therefore, the result of simulation based on several different scenarios has been analyzed. From the result of simulation, it is possible to identify the several factors which should be concerned for introducing the automated devices in the fulfillment centers. Also, it can be referred to make the optimal decisions based on the efficiency metrics.

• The Journal of Bigdata
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• v.8 no.1
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• pp.131-156
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• 2023

The traditional retail industry significantly changed over the past decade due to the mobile and online technologies. This change has been accompanied by a shift in consumer behavior regarding purchasing patterns. Despite the rise of online shopping, there are still specific categories of products, such as "Processed food" in Mongolia, for which traditional shopping remains the preferred purchase method. To prepare for the inevitable future of retail businesses, firms need to closely analyze the performance of their offline stores to plan their further actions in a new multi-channel environment. Retailers must integrate diverse channels into their operations to stay relevant and adjust to the shifting market. In this research, we have analyzed the performance data such as sales, profit, and amount of sales of offline stores by using clustering approach. From the clustering, we have found the several distinct insights by comparing the circumstances and performance of retail stores. For the certain retail stores, we have proposed three different strategies: a fulfillment hub store between online and offline channels, an experience store to elongate customers' time on the premises, and a merge between two non-related channels that could complement each other to increase traffic based on the store characteristics. With the proposed strategies, it may enhance the user experience and profit at the same time.

• Journal of Intelligence and Information Systems
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• v.28 no.1
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• pp.311-328
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• 2022

Because of the spread of smartphones due to the development of information and communication technology, online shopping mall services can be used on computers and mobile devices. As a result, the number of users using the online shopping mall service increases rapidly, and the types of products traded are also growing. Therefore, to maximize profits, companies need to provide information that may interest users. To this end, the recommendation system presents necessary information or products to the user based on the user's past behavioral data or behavioral purchase records. Representative overseas companies that currently provide recommendation services include Netflix, Amazon, and YouTube. These companies support users' purchase decisions by recommending products to users using ratings, purchase records, and clickstream data that users give to the items. In addition, users refer to the ratings left by other users about the product before buying a product. Most users tend to provide ratings only to products they are satisfied with, and the higher the rating, the higher the purchase intention. And recently, e-commerce sites have provided users with the ability to vote on whether product reviews are helpful. Through this, the user makes a purchase decision by referring to reviews and ratings of products judged to be beneficial. Therefore, in this study, the correlation between the product rating and the helpful information of the review is identified. The valuable data of the evaluation is reflected in the recommendation system to check the recommendation performance. In addition, we want to compare the results of skipping all the ratings in the traditional collaborative filtering technique with the recommended performance results that reflect only the 4 and 5 ratings. For this purpose, electronic product data collected from Amazon was used in this study, and the experimental results confirmed a correlation between ratings and review usefulness information. In addition, as a result of comparing the recommendation performance by reflecting all the ratings and only the 4 and 5 points in the recommendation system, the recommendation performance of remembering only the 4 and 5 points in the recommendation system was higher. In addition, as a result of reflecting review usefulness information in the recommendation system, it was confirmed that the more valuable the review, the higher the recommendation performance. Therefore, these experimental results are expected to improve the performance of personalized recommendation services in the future and provide implications for e-commerce sites.

• Journal of Venture Innovation
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• v.6 no.4
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• pp.99-111
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• 2023

This study aims to confirm that consumers' satisfaction with online shopping websites has changed to a phenomenon different from the past. In other words, in a situation where the use of e-commerce is expanding worldwide after the pandemic and various types of commerce such as mobile commerce and social commerce are formed, the consumer's information processing and decision-making process are meaningful in examining the behavior that has been changed based on the perceived motivation level of consumers by the new environment according to the consumption value and personal characteristics perceived by the consumer. In other words, the purpose of this study was to investigate the effect of consumption value and need for cognition on the satisfaction toward online websites as a mediating role in the trust of the website. As a result of testing Hypothesis 1, not only the hedonic value of the consumer for the website but also the utilitarian value had a positive influence on the satisfaction toward the website, and in particular, the utilitarian value showed a relatively greater influence than the hedonic value. However, the negative relationship between the need for cognition and satisfaction was found to be at a significant level under one-sided verification. In Hypothesis 2, only the utilitarian value among the consumption values of 2-1 showed a positive effect on satisfaction through a mediating role of trust. It was confirmed that the utilitarian value among the consumption values was an important factor in the satisfaction toward the website. The significance of this study is that, unlike previous research results, not only consumption value based on senses and emotions but also utilitarian value has a greater influence. Therefore, utilitarian value and need for cognition have a stronger influence on satisfaction if they play a mediating role based on the trust of the website used by consumers. These findings reflect the current market trend of online consumption, and they are helpful in the management and strategy of online websites based on consumer behavior understanding and major factors.

• Information Systems Review
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• v.23 no.3
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• pp.177-200
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• 2021

Recently, the use of prepaid electronic payments such as electronic wallets, digital currency and prepaid points is gradually increasing. Prepaid electronic payments has the characteristic of being used after charging first. This study empirically investigated the factors affecting the intention to use online charging in order to help improve the service that require prepaid recharge by applying transformed TAM. Since there are not many previous studies for the intention to use online charging, we extract factors through preceding researches for electronic cash and mobile easy payment. Also we analyze the intention to use online charging for transportation card users, focusing on the moderating effects. As a result of the study, it was found that 'convenience', 'ubiquity', and 'self-efficacy' among the independent variables had a positive (+) effect on mediation variable 'perceived usefulness'. 'Perceived usefulness' was analyzed to have a significant influence on the dependent variable 'usage intention'. According to users' gender, internet usage time, internet shopping frequency, online charging frequency and transportation card usage type, the moderating effect was significant on 'perceived usefulness' and 'usage intention'. As an implication, it was suggested that service improvement and differentiated marketing are needed in direction of increasing the usefulness of services. Additional research directions were proposed for services such as e-wallets, prepaid points and digital currencies by adding other factors and moderate variables.

• Journal of Internet Computing and Services
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• v.15 no.6
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• pp.133-144
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• 2014

As the Internet and smartphones prevail, this study investigated the purchasing factors of a new beauty marketing method, the social commerce, and verified the relationship of such purchasing factors to consumer satisfaction and repurchasing intentions. In order to verify the validity of purchasing factors, five sub-factors, such as service, price, interaction, convenience, and interest were classified, while consumer satisfaction and repurchasing intentions are grouped into one factor, using data about 20-39 years old. According to results of this study, purchasing factors in the beauty service markets through social commerce that had effects on the consumer satisfaction were price, service, convenience, and interest, but no relationship was found with interaction. We can predict that consumers buy not based on community activities among buyers or purchasing comments of others, but rather his/her own subjective thoughts and opinions about the services. As the result of repurchasing intention according to purchasing factors, affecting sub-factors were price, service, and convenience. Repurchasing intention is an positive response that reflects satisfactions. Since low price, satisfaction on the service, and convenience for busy modern people should be met, repurchasing intentions are not affected by interest, but rather systematic and professional service. Also, higher satisfaction on service raises repurchasing intention. In this study, it is clear that not only purchasing factors through social-commerce effect the satisfaction and the repurchasing intention, but also consumer satisfaction mediates partly purchasing factors and the repurchasing intention. And as sub-factors of purchasing factors, price, service, or convenience are more important to the consumer satisfaction than community or replies activities. Thus differentiated and professional customer service, the establishment and enhancement of trendy marketing should improve long term repurchasing intentions. This will lead to the increasing revenue of personal-shop and the developments of beauty markets, so strengthening product sourcing and promotion suitable for mobile shoppers are essential.

• Cartoon and Animation Studies
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• s.45
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• pp.197-214
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• 2016

Augmented Reality, virtual reality in recently attracted attention throughout the world. and Mix them mixed reality etc., it has had a significant impact on the overall pop culture beyond the scope of science and technology. The world's leading IT company : Google, Apple, Samsung, Microsoft, Sony, LG is focusing on development of AR, VR technology for the public. The many large and small companies developed VR hardware, VR software, VR content. It does not look that makes a human a human operation in the cognitive experience of certain places or situations or invisible through Specific platforms or program is Encompass a common technique that a realization of the virtual space. In particular, out of the three-dimensional image reveals the limitations of the conventional two-dimensional structure - 180, 360 degree images provided by the subjective and objective symptoms such as vision and sense of time and got participants to select it. VR technology that can significantly induce the commitment and participation is Industry as well as to the general public which leads to the attention of colostrum. It was introduced more than 10 related VR works Year 2015 Sundance Film Festival New Frontier program. The appearance VR content : medical, architecture, shopping, movies, animations. Also, 360 individuals can be produced by the camera / video sharing VR is becoming an interactive tunnel between two possible users. Nevertheless, This confusion of values, moral degeneration and the realization of a virtual space that has been pointed out that the inherent. 4K or HUD, location tracking, motion sensors, processing power, and superior 3D graphics, touch, smell, 4D technology, 3D audio technology - It developed more than ever and possible approaches to reality. Thereafter, This is because the moral degeneration, identity, generational conflict, and escapism concerns. Animation is also seeking costs in this category Reality. Despite the similarities rather it has that image, and may be the reason that the animation is pushed back to the VR content creation. However, it is focused on the game and VR technology and the platform that is entertaining, but also seek new points within the animation staying in the flat Given that eventually consist of visual images is clear that VR sought. Finally, What is the reality created in the virtual space using VR technology could be applied to the animation? So it can be seen that the common interest is research on what methods and means applied.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.16 no.3
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• pp.177-191
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• 2021

Due to the development of mobile devices and streaming technology, many changes in consumption patterns have appeared. In addition, social impact is becoming an era of non-face-to-face consumption due to the panthermic environment of COVID-19. Accordingly, in line with the non-face-to-face consumption trend, we focused on the importance of live commerce, which is emerging as a new distribution channel, and tried to investigate the causal relationship that the characteristics of live commerce and show hosts have on purchase intention. The respondents of this study were 235 general adults of live commerce users. Interaction, economics, entertainment as the characteristics of live commerce and attractiveness, professionality, and awareness as the characteristics of show hosts were set as independent variables. Purchase intention was set as the dependent variable, and content flow was set as the mediating variable. As a result of the study, it was found that the characteristics of live commerce such as Interaction, economics, entertainment, and the characteristics of show hosts such as attractiveness, professionality, and awareness all had a positive (+) significant effect on purchase intention. The impact was shown in the following order: entertainment of live commerce, awareness, attractiveness, professionality of show hosts, economics, interaction of live commerce. In addition, the results of the mediating effect of content flow on purchase intention are as follows. Content flow was found to play a mediating role between interaction, entertainment, attractiveness, professionality, awareness and purchase intention. On the other hand, economics was analyzed to have no mediating effect. The implications of this study are as follows. Companies and show hosts that sell products in live commerce should sell products that can inspire consumers rather than simply sell products. In addition, it is considered that content that provides entertainment and attractions gives pleasure to consumers. If not only a well-recognized show host, but also people with high recognition in various fields such as influencers and creators, become show hosts, consumers' content flow and purchase intentions will increase. And vendors must offer interesting content development and reasonable prices. Show hosts need to focus on active communication with consumers.

• Journal of Intelligence and Information Systems
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• v.24 no.3
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• pp.1-19
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• 2018

Large amount of data is now available for research and business sectors to extract knowledge from it. This data can be in the form of unstructured data such as audio, text, and image data and can be analyzed by deep learning methodology. Deep learning is now widely used for various estimation, classification, and prediction problems. Especially, fashion business adopts deep learning techniques for apparel recognition, apparel search and retrieval engine, and automatic product recommendation. The core model of these applications is the image classification using Convolutional Neural Networks (CNN). CNN is made up of neurons which learn parameters such as weights while inputs come through and reach outputs. CNN has layer structure which is best suited for image classification as it is comprised of convolutional layer for generating feature maps, pooling layer for reducing the dimensionality of feature maps, and fully-connected layer for classifying the extracted features. However, most of the classification models have been trained using online product image, which is taken under controlled situation such as apparel image itself or professional model wearing apparel. This image may not be an effective way to train the classification model considering the situation when one might want to classify street fashion image or walking image, which is taken in uncontrolled situation and involves people's movement and unexpected pose. Therefore, we propose to train the model with runway apparel image dataset which captures mobility. This will allow the classification model to be trained with far more variable data and enhance the adaptation with diverse query image. To achieve both convergence and generalization of the model, we apply Transfer Learning on our training network. As Transfer Learning in CNN is composed of pre-training and fine-tuning stages, we divide the training step into two. First, we pre-train our architecture with large-scale dataset, ImageNet dataset, which consists of 1.2 million images with 1000 categories including animals, plants, activities, materials, instrumentations, scenes, and foods. We use GoogLeNet for our main architecture as it has achieved great accuracy with efficiency in ImageNet Large Scale Visual Recognition Challenge (ILSVRC). Second, we fine-tune the network with our own runway image dataset. For the runway image dataset, we could not find any previously and publicly made dataset, so we collect the dataset from Google Image Search attaining 2426 images of 32 major fashion brands including Anna Molinari, Balenciaga, Balmain, Brioni, Burberry, Celine, Chanel, Chloe, Christian Dior, Cividini, Dolce and Gabbana, Emilio Pucci, Ermenegildo, Fendi, Giuliana Teso, Gucci, Issey Miyake, Kenzo, Leonard, Louis Vuitton, Marc Jacobs, Marni, Max Mara, Missoni, Moschino, Ralph Lauren, Roberto Cavalli, Sonia Rykiel, Stella McCartney, Valentino, Versace, and Yve Saint Laurent. We perform 10-folded experiments to consider the random generation of training data, and our proposed model has achieved accuracy of 67.2% on final test. Our research suggests several advantages over previous related studies as to our best knowledge, there haven't been any previous studies which trained the network for apparel image classification based on runway image dataset. We suggest the idea of training model with image capturing all the possible postures, which is denoted as mobility, by using our own runway apparel image dataset. Moreover, by applying Transfer Learning and using checkpoint and parameters provided by Tensorflow Slim, we could save time spent on training the classification model as taking 6 minutes per experiment to train the classifier. This model can be used in many business applications where the query image can be runway image, product image, or street fashion image. To be specific, runway query image can be used for mobile application service during fashion week to facilitate brand search, street style query image can be classified during fashion editorial task to classify and label the brand or style, and website query image can be processed by e-commerce multi-complex service providing item information or recommending similar item.