• 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.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.

• Knowledge Management Research
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• v.23 no.4
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• pp.91-112
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• 2022

In order to improve the quality of consumption in senior generation, it is necessary to build trust in the digital consumption environment. However, there are limitations from previous researches on consumption in the digital environment that has mainly focused on Millennial and Z generations. Therefore, this study aims to examine the antecedents of digital consumption trust and to explore the market segments of senior generation created by the dynamics of these antecedents. In addition, in order to provide practical implications, we investigate the difference in the level of perceived digital consumption trust using segmentation. This study, therefore, used 2021 Korea's consumer life index data conducted by the Korea Consumer Agency for general consumers, and only consumer data of 60s and older are extracted for the analysis. As a result, we confirm from the main results that the three antecedents, consumer competency, perceived corporate responsibility, and local community's problem-solving environment, are significant in building the digital consumption trust in the senior generation. It is academically significant in this aspect to look at approaches to improve senior customers' trust in digital consumption circumstances by dividing generations because generations exhibit varying levels of understanding of online consumption or digital consumption conditions. It is academically significant in this aspect to look at approaches to improve senior customers' trust in digital consumption circumstances by dividing generations because generations exhibit varying levels of understanding of online consumption or digital consumption conditions. Additionally, it is proposed as a practical implication that it should be managed so that significant improvements of customers, businesses, and regional public institutions are developed in order to allow the senior consumers to prepare trusted digital consumption circumstances.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.1 no.2
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• pp.193-224
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• 2006

When we look into the market economy of our country recently, we learn that the mind of consumption after IMF crisis is very shrunk and the market is led into a serious slump of consumption. For an approach to survive the contraction of the market and the market competition, enterprises command a variety of sales promotion strategy, out of which presentation is a sales promotion strategy to give the same product. The price-discounted strategy through the provision of donation commodity may induce the temporarily-discounted commodity not to be sold to the consumers or make a damage of the images of the brand, or arouse the price war against other companies, or lower the sense of the quality of the commodity. Therefore, it is necessary for a company to meet the end users' demand and also maintain the evaluation of the quality on the consumers' products highly. Therefore, in this study, we have attempted to study and analyze the consumers' satisfaction level and reliability on the donation goods in order to suggest the orientation of the presentation promotion strategy in accordance with the changes of the sales market. In addition, we tried to understand how the recognition, consumers' satisfaction level and reliability on the presentation goods had on the repurchase. With such objectives in this study, we could make an analogy of the following significance and suggestion of study. Firstly, in order to survive a serious competition market, enterprises must execute the product presentation along with diverse events instead of commanding the sales promotion strategy through a simple product presentation. This strategy can be an alternative to lower the danger a person-to-person product presentation may bring about. That is to say, we shall not lower the quality and value of the products but enhance a new image to customers through a product donation occasion together with an event as a new marketing pioneering method. Secondly, during the period of the current economic depression, if a company provides the consumers with an opportunity free of charge through the present special event period and the practical events, it will affect the advertising effect of the goods, the introduction of the customers and customers' repurchase. For this purpose, the company has to heighten customers' preferences by selecting the items customers are liable to prefer and closely analyze the consumers' response and market for such an objective. Thirdly, with the internet age, as the market has a tendency to increase In the number of consumers who do shopping in the internet, the marketing strategy has to build up the strategy of the presentation product instead of a simple offline strategy. For example, a company shall have to draw attention or attraction from end users who intend to do shopping through the online by a product planning expo or a presentation product corner. Fourthly, the excessive sale promotion strategy of presentation products may bring about even a reverse effect on the value of the goods or consumers' attitude as seen above. Therefore, a company has to relay' the value as to the price' to the consumers instead of the sales promotion strategy of donation products just for a temporary sales volume. Conclusively, even if we put the value with a reasonable price through the presentation product strategy in the past, we shall have construct the strategy by providing some plus factors in the price such as the provision of the upgraded products or services instead of just presentation, or the invitation of the events related to diverse events or culture arts from now on.

• 한국벤처창업학회:학술대회논문집
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• 2007.04a
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• pp.375-404
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• 2007

When we look into the market economy of our country recently, we learn that the mind of consumption after IMF crisis is very shrunk and the market is led into a serious slump of consumption. For an approach to survive the contraction of the market and the market competition, enterprises command a variety of sales promotion strategy, out of which presentation is a sales promotion strategy to give the same product. The price-discounted strategy through the provision of donation commodity may induce the temporarily-discounted commodity not to be sold to the consumers or make a damage of the images of the brand, or arouse the price war against other companies, or lower the sense of the quality of the commodity. Therefore, it is necessary for a company to meet the end users' demand and also maintain the evaluation of the quality on the consumers' products highly. Therefore, in this study, we have attempted to study and analyze the consumers' satisfaction level and reliability on the donation goods in order to suggest the orientation of the presentation promotion strategy in accordance with the changes of the sales market. In addition, we tried to understand how the recognition, consumers' satisfaction level and reliability on the presentation goods had on the repurchase. With such objectives in this study, we could make an analogy of the following significance and suggestion of study. Firstly, in order to survive a serious competition market, enterprises must execute the product presentation along with diverse events instead of commanding the sales promotion strategy through a simple product presentation. This strategy can be an alternative to lower the danger a person-to-person product presentation may bring about. That is to say, we shall not lower the quality and value of the products but enhance a new image to customers through a product donation occasion together with an event as a new marketing pioneering method. Secondly, during the period of the current economic depression, if a company provides the consumers with an opportunity free of charge through the present special event period and the practical events, it will affect the advertising effect of the goods, the introduction of the customers and customers' repurchase. For this purpose, the company has to heighten customers' preferences by selecting the items customers are liable to prefer and closely analyze the consumer's response and market for such an objective. Thirdly, with the internet age, as the market has a tendency to increase in the number of consumers who do shopping in the internet, the marketing strategy has to build up the strategy of the presentation product instead of a simple offline strategy. For example, a company shall have to draw attention or attraction from end users who intend to do shopping through the online by a product planning expo or a presentation product corner. Fourthly, the excessive sale promotion strategy of presentation products may bring about even a reverse effect on the value of the goods or consumers' attitude as seen above. Therefore, a company has to relay 'the value as to the price' to the consumers instead of the sales promotion strategy of donation products just for a temporary sales volume. Conclusively, even if we put the value with a reasonable price through the presentation product strategy in the past, we shall have construct the strategy by providing some plus factors in the price such as the provision of the upgraded products or services instead of just presentation, or the invitation of the events related to diverse events or culture arts from now on.

• Journal of Distribution Science
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• v.7 no.4
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• pp.15-26
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• 2009

The internet has dramatically changed a way of business management and competition in the business environment. Especially, it stimulated not only to evolve online floral distribution service but also to change a phase of competition among floral retail stores in industry. And that also led to keen competition among IT service providers as well. This study is to examine how floral retail stores have been evolved and competed with the radical situation of the floral distribution industry through IT service in the aspect of business and information technology. In addition, the Typhoon Positioning Strategy(TPS), a strategy for the IT service positioning, is introduced from IT service provider's perspective. For IT service providers to create high business value and continuous service providing, IT service should be positioned on the customers' "core business" and developed to the level of "solution." The Typhoon Positioning Strategy(TPS) is a strategy for the IT service positioning, indicating that IT service should be positioned according to a Business Process-Service model with the consideration of business development direction, IT service trend, and user's IT capability. That is, IT service providers should find out customers' "core business" area first to provide a right IT service to the company, and the IT service provided should meet to the level of business solution. The capability of the IT solution users is also an important factor to be considered for the advanced IT service. There are four principles of the Typhoon Positioning Strategy(TPS). Principle 1) IT service provided should be an IT solution Map suitable for customer business processes. Principle 2) IT service provided should be able to support customer core business. Principle 3) IT service provided should be a business solution. Principle. 4) IT service provided should be applied differently according to the level of customer's IT capability.

• Journal of Intelligence and Information Systems
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• v.27 no.1
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• pp.23-46
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• 2021

Collaborative filtering, which is often used in personalization recommendations, is recognized as a very useful technique to find similar customers and recommend products to them based on their purchase history. However, the traditional collaborative filtering technique has raised the question of having difficulty calculating the similarity for new customers or products due to the method of calculating similaritiesbased on direct connections and common features among customers. For this reason, a hybrid technique was designed to use content-based filtering techniques together. On the one hand, efforts have been made to solve these problems by applying the structural characteristics of social networks. This applies a method of indirectly calculating similarities through their similar customers placed between them. This means creating a customer's network based on purchasing data and calculating the similarity between the two based on the features of the network that indirectly connects the two customers within this network. Such similarity can be used as a measure to predict whether the target customer accepts recommendations. The centrality metrics of networks can be utilized for the calculation of these similarities. Different centrality metrics have important implications in that they may have different effects on recommended performance. In this study, furthermore, the effect of these centrality metrics on the performance of recommendation may vary depending on recommender algorithms. In addition, recommendation techniques using network analysis can be expected to contribute to increasing recommendation performance even if they apply not only to new customers or products but also to entire customers or products. By considering a customer's purchase of an item as a link generated between the customer and the item on the network, the prediction of user acceptance of recommendation is solved as a prediction of whether a new link will be created between them. As the classification models fit the purpose of solving the binary problem of whether the link is engaged or not, decision tree, k-nearest neighbors (KNN), logistic regression, artificial neural network, and support vector machine (SVM) are selected in the research. The data for performance evaluation used order data collected from an online shopping mall over four years and two months. Among them, the previous three years and eight months constitute social networks composed of and the experiment was conducted by organizing the data collected into the social network. The next four months' records were used to train and evaluate recommender models. Experiments with the centrality metrics applied to each model show that the recommendation acceptance rates of the centrality metrics are different for each algorithm at a meaningful level. In this work, we analyzed only four commonly used centrality metrics: degree centrality, betweenness centrality, closeness centrality, and eigenvector centrality. Eigenvector centrality records the lowest performance in all models except support vector machines. Closeness centrality and betweenness centrality show similar performance across all models. Degree centrality ranking moderate across overall models while betweenness centrality always ranking higher than degree centrality. Finally, closeness centrality is characterized by distinct differences in performance according to the model. It ranks first in logistic regression, artificial neural network, and decision tree withnumerically high performance. However, it only records very low rankings in support vector machine and K-neighborhood with low-performance levels. As the experiment results reveal, in a classification model, network centrality metrics over a subnetwork that connects the two nodes can effectively predict the connectivity between two nodes in a social network. Furthermore, each metric has a different performance depending on the classification model type. This result implies that choosing appropriate metrics for each algorithm can lead to achieving higher recommendation performance. In general, betweenness centrality can guarantee a high level of performance in any model. It would be possible to consider the introduction of proximity centrality to obtain higher performance for certain models.

• Cartoon and Animation Studies
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• s.24
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• pp.223-246
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• 2011

Today, cultural content industry could be defined to service business rather than manufacturing business because of its own trait. Also, it has the realistic restriction that it can't hold the dominant position in the market competition when it can't provide consumers satisfaction regardless of its quality or degree of completion. In other word, it can only expect great success when the business plan and the activities get the perfect balance with its best quality and perfect of completion. As the result, it emphasizes the importance of business competition in the global market. In briefly, there is no doubt that the creativeness of content is very important in the cultural content industry but in the future, making system to maintain the distribution process and share the profits fairly will be taken more important role. Especially, animation genre has the feature, which compares to other genres, such as film or TV drama, would be free from cultural barriers, and it is a great advantage. So to speak, animation can get little influence from cultural discount. However, Korean animation can't use the advantage properly for the foreign distribution because of its poor infrastructure and short of professional human resources. For those reasons, it has been needed to set up the realistic and specific action plan to overcome the situation. Therefore, considering those needs and the situations of Korean animation facing, making B2B online marketplace could be a great solution. The online marketplace stands for taking more efficient and broad distribution channel instead of the passive way, which we have now. If we have the B2B online marketplace, we can share all the information about the Korean animation with the potential customers whom live outside of Korea at real time. It also could be use to the windows of multiple distribution, which can make additional profits and activate the optional markets for the Korean animation. Through the method, Korean animation would be expected to get the higher international competitiveness, and it would be developed in quality and quantity of the business. Finally, it would be a great chance to Korean animation, which can get the unique brand power by improving the backward distribution circumstances.

• Journal of Intelligence and Information Systems
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• v.20 no.2
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• pp.123-136
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• 2014

Recently, online shopping has further developed as the use of the Internet and a variety of smart mobile devices becomes more prevalent. The increase in the scale of such shopping has led to the creation of many Internet shopping malls. Consequently, there is a tendency for increasingly fierce competition among online retailers, and as a result, many Internet shopping malls are making significant attempts to attract online users to their sites. One such attempt is keyword marketing, whereby a retail site pays a fee to expose its link to potential customers when they insert a specific keyword on an Internet portal site. The price related to each keyword is generally estimated by the keyword's frequency of appearance. However, it is widely accepted that the price of keywords cannot be based solely on their frequency because many keywords may appear frequently but have little relationship to shopping. This implies that it is unreasonable for an online shopping mall to spend a great deal on some keywords simply because people frequently use them. Therefore, from the perspective of shopping malls, a specialized process is required to extract meaningful keywords. Further, the demand for automating this extraction process is increasing because of the drive to improve online sales performance. In this study, we propose a methodology that can automatically extract only shopping-related keywords from the entire set of search keywords used on portal sites. We define a shopping-related keyword as a keyword that is used directly before shopping behaviors. In other words, only search keywords that direct the search results page to shopping-related pages are extracted from among the entire set of search keywords. A comparison is then made between the extracted keywords' rankings and the rankings of the entire set of search keywords. Two types of data are used in our study's experiment: web browsing history from July 1, 2012 to June 30, 2013, and site information. The experimental dataset was from a web site ranking site, and the biggest portal site in Korea. The original sample dataset contains 150 million transaction logs. First, portal sites are selected, and search keywords in those sites are extracted. Search keywords can be easily extracted by simple parsing. The extracted keywords are ranked according to their frequency. The experiment uses approximately 3.9 million search results from Korea's largest search portal site. As a result, a total of 344,822 search keywords were extracted. Next, by using web browsing history and site information, the shopping-related keywords were taken from the entire set of search keywords. As a result, we obtained 4,709 shopping-related keywords. For performance evaluation, we compared the hit ratios of all the search keywords with the shopping-related keywords. To achieve this, we extracted 80,298 search keywords from several Internet shopping malls and then chose the top 1,000 keywords as a set of true shopping keywords. We measured precision, recall, and F-scores of the entire amount of keywords and the shopping-related keywords. The F-Score was formulated by calculating the harmonic mean of precision and recall. The precision, recall, and F-score of shopping-related keywords derived by the proposed methodology were revealed to be higher than those of the entire number of keywords. This study proposes a scheme that is able to obtain shopping-related keywords in a relatively simple manner. We could easily extract shopping-related keywords simply by examining transactions whose next visit is a shopping mall. The resultant shopping-related keyword set is expected to be a useful asset for many shopping malls that participate in keyword marketing. Moreover, the proposed methodology can be easily applied to the construction of special area-related keywords as well as shopping-related ones.

• Journal of Intelligence and Information Systems
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• v.24 no.1
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• pp.1-23
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• 2018

From the 21st century, various high-quality services have come up with the growth of the internet or 'Information and Communication Technologies'. Especially, the scale of E-commerce industry in which Amazon and E-bay are standing out is exploding in a large way. As E-commerce grows, Customers could get what they want to buy easily while comparing various products because more products have been registered at online shopping malls. However, a problem has arisen with the growth of E-commerce. As too many products have been registered, it has become difficult for customers to search what they really need in the flood of products. When customers search for desired products with a generalized keyword, too many products have come out as a result. On the contrary, few products have been searched if customers type in details of products because concrete product-attributes have been registered rarely. In this situation, recognizing texts in images automatically with a machine can be a solution. Because bulk of product details are written in catalogs as image format, most of product information are not searched with text inputs in the current text-based searching system. It means if information in images can be converted to text format, customers can search products with product-details, which make them shop more conveniently. There are various existing OCR(Optical Character Recognition) programs which can recognize texts in images. But existing OCR programs are hard to be applied to catalog because they have problems in recognizing texts in certain circumstances, like texts are not big enough or fonts are not consistent. Therefore, this research suggests the way to recognize keywords in catalog with the Deep Learning algorithm which is state of the art in image-recognition area from 2010s. Single Shot Multibox Detector(SSD), which is a credited model for object-detection performance, can be used with structures re-designed to take into account the difference of text from object. But there is an issue that SSD model needs a lot of labeled-train data to be trained, because of the characteristic of deep learning algorithms, that it should be trained by supervised-learning. To collect data, we can try labelling location and classification information to texts in catalog manually. But if data are collected manually, many problems would come up. Some keywords would be missed because human can make mistakes while labelling train data. And it becomes too time-consuming to collect train data considering the scale of data needed or costly if a lot of workers are hired to shorten the time. Furthermore, if some specific keywords are needed to be trained, searching images that have the words would be difficult, as well. To solve the data issue, this research developed a program which create train data automatically. This program can make images which have various keywords and pictures like catalog and save location-information of keywords at the same time. With this program, not only data can be collected efficiently, but also the performance of SSD model becomes better. The SSD model recorded 81.99% of recognition rate with 20,000 data created by the program. Moreover, this research had an efficiency test of SSD model according to data differences to analyze what feature of data exert influence upon the performance of recognizing texts in images. As a result, it is figured out that the number of labeled keywords, the addition of overlapped keyword label, the existence of keywords that is not labeled, the spaces among keywords and the differences of background images are related to the performance of SSD model. This test can lead performance improvement of SSD model or other text-recognizing machine based on deep learning algorithm with high-quality data. SSD model which is re-designed to recognize texts in images and the program developed for creating train data are expected to contribute to improvement of searching system in E-commerce. Suppliers can put less time to register keywords for products and customers can search products with product-details which is written on the catalog.