• 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.

• Journal of Intelligence and Information Systems
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• v.23 no.2
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• pp.39-70
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• 2017

Data-drive analytics techniques have been recently applied to public surveys. Instead of simply gathering survey results or expert opinions to research the preference for a recently launched product, enterprises need a way to collect and analyze various types of online data and then accurately figure out customer preferences. In the main concept of existing data-based survey methods, the sentiment lexicon for a particular domain is first constructed by domain experts who usually judge the positive, neutral, or negative meanings of the frequently used words from the collected text documents. In order to research the preference for a particular product, the existing approach collects (1) review posts, which are related to the product, from several product review web sites; (2) extracts sentences (or phrases) in the collection after the pre-processing step such as stemming and removal of stop words is performed; (3) classifies the polarity (either positive or negative sense) of each sentence (or phrase) based on the sentiment lexicon; and (4) estimates the positive and negative ratios of the product by dividing the total numbers of the positive and negative sentences (or phrases) by the total number of the sentences (or phrases) in the collection. Furthermore, the existing approach automatically finds important sentences (or phrases) including the positive and negative meaning to/against the product. As a motivated example, given a product like Sonata made by Hyundai Motors, customers often want to see the summary note including what positive points are in the 'car design' aspect as well as what negative points are in thesame aspect. They also want to gain more useful information regarding other aspects such as 'car quality', 'car performance', and 'car service.' Such an information will enable customers to make good choice when they attempt to purchase brand-new vehicles. In addition, automobile makers will be able to figure out the preference and positive/negative points for new models on market. In the near future, the weak points of the models will be improved by the sentiment analysis. For this, the existing approach computes the sentiment score of each sentence (or phrase) and then selects top-k sentences (or phrases) with the highest positive and negative scores. However, the existing approach has several shortcomings and is limited to apply to real applications. The main disadvantages of the existing approach is as follows: (1) The main aspects (e.g., car design, quality, performance, and service) to a product (e.g., Hyundai Sonata) are not considered. Through the sentiment analysis without considering aspects, as a result, the summary note including the positive and negative ratios of the product and top-k sentences (or phrases) with the highest sentiment scores in the entire corpus is just reported to customers and car makers. This approach is not enough and main aspects of the target product need to be considered in the sentiment analysis. (2) In general, since the same word has different meanings across different domains, the sentiment lexicon which is proper to each domain needs to be constructed. The efficient way to construct the sentiment lexicon per domain is required because the sentiment lexicon construction is labor intensive and time consuming. To address the above problems, in this article, we propose a novel product reputation mining algorithm that (1) extracts topics hidden in review documents written by customers; (2) mines main aspects based on the extracted topics; (3) measures the positive and negative ratios of the product using the aspects; and (4) presents the digest in which a few important sentences with the positive and negative meanings are listed in each aspect. Unlike the existing approach, using hidden topics makes experts construct the sentimental lexicon easily and quickly. Furthermore, reinforcing topic semantics, we can improve the accuracy of the product reputation mining algorithms more largely than that of the existing approach. In the experiments, we collected large review documents to the domestic vehicles such as K5, SM5, and Avante; measured the positive and negative ratios of the three cars; showed top-k positive and negative summaries per aspect; and conducted statistical analysis. Our experimental results clearly show the effectiveness of the proposed method, compared with the existing method.

• 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.

• Culinary science and hospitality research
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• v.18 no.1
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• pp.156-167
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• 2012

The purpose of this study is to examine the structural elements of HMR in Korea foods and explore the way HMR products using Korean foods can be developed at this time of increased interest. Through an investigation of its importance by attributes and their partial values, hypothetical HMR products using Korean foods were estimated. In order to develop the optimal HMR goods of Korean food, a preference survey was conducted after selecting 9 profiles using conjoint analysis with orthogonal design, and 4 holdout sets were generated and used for cross-validity authorization and reliability of the model. The results of this study showed that customers put cooking levels, menu price, and the location of purchase into importance when selecting HMR products of Korean foods. They preferred to eat the products after sufficiently heating them and buy the products sold online and through home shopping programs, with the price range of 10,000 won and over. It was concluded that more customers can be attracted if a variety of HMR products using Korean foods which can be prepared readily anywhere and at any time are developed.