• The Journal of the Korea Contents Association
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• v.22 no.10
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• pp.658-675
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• 2022

With the advent of the OTT platform, the world has become an era in which the same media content is shared and reacted in real time by being grouped into one culture. This study attempts a producer study of web novel writers, who are producers of the web novel market that is expanding into webtoons, dramas, and movies with IP (intellectual property rights) of the original story at a time when Korean K-content such as "Squid Game" and "Weird Lawyer Woo Young-woo" leads the global market. In this study, web novel writers were viewed as producers of commercial media content, not just 'Novelist', and their identities and characteristics of the labor process were examined. Web novel writers began writing web novels as a side job or two jobs, and cited the fact that they can make profits alone without barriers to entry and without incurring capital or facility costs. Although there is no barrier to entry, most writers experience severe failure in their first work, which is attributed to the misunderstanding that the word "writer" is someone who writes what they want in any genre. Web novels are different, so writers go through the process of realizing that in order to succeed by writing web novels, they must be thoroughly in the audience's shoes and write them according to the trends and codes they want. Web novel writers expressed their identity as "story sellers," "story producers," "people who can produce IP alone," and "people who satisfy fantasies that cannot be achieved in reality," and in common, there was a strong sense of being a person who provides stories and makes profits or sales. Regarding the burden of writing a huge amount of web novels, the writer with a high income expressed a generous position that "the income is higher than the effort," but ordinary writers complained of difficulties in the hard work, saying, "It seems like I am working hard on writing that I have to write constantly.

• The Korean Journal of Franchise Management
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• v.2 no.1
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• pp.173-199
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• 2011

A franchisee has to depend on the overall system, such as knowhow and management support, from a franchisor in the franchise system and the two parties do not start with the same position in economic or information power because the franchisor controls or supports through selling or management styles. For this, unfair trades the franchisor's over controlling and limiting the franchisee might occur and other side effects by the people who give the franchisee scam trades has negatively influenced on the development of franchise industry and national economy. So, the purpose of this study is preventing unfair trade for the franchisee from understanding the causes and problems of dispute between the franchisor and the franchisee focused on the dispute cases submitted the Korea Fair Trade Mediation Agency and seeking ways to secure the transparency of recruitment process and justice of franchise management process. The results of the case analysis are followed; first, affiliation contracts should run on the franchisor's exact public information statement and the surely understanding of the franchisee. Secondly, the franchisor needs to use their past experiences and investigated data for recruiting franchisees. Thirdly, in the case of making a contract with the franchisee, the franchisor has to make sure the business area by checking it with franchisee in person. Fourthly, the contracts are important in affiliation contracts, so enacting the possibility of disputes makes the disputes decreased. Fifthly, lots of investigation and interests are needed for protecting rights and interests between the franchisor and franchisee and preventing the disputes by catching the cause and more practical solutions of the disputes from the government.

• Journal of Intelligence and Information Systems
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• v.24 no.1
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• pp.227-252
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• 2018

The evolution of instant communication has mirrored the development of the Internet and messenger applications are among the most representative manifestations of instant communication technologies. In messenger applications, senders use emoticons to supplement the emotions conveyed in the text of their messages. The fact that communication via messenger applications is not face-to-face makes it difficult for senders to communicate their emotions to message recipients. Emoticons have long been used as symbols that indicate the moods of speakers. However, at present, emoticon-use is evolving into a means of conveying the psychological states of consumers who want to express individual characteristics and personality quirks while communicating their emotions to others. The fact that companies like KakaoTalk, Line, Apple, etc. have begun conducting emoticon business and sales of related content are expected to gradually increase testifies to the significance of this phenomenon. Nevertheless, despite the development of emoticons themselves and the growth of the emoticon market, no suitable emoticon recommendation system has yet been developed. Even KakaoTalk, a messenger application that commands more than 90% of domestic market share in South Korea, just grouped in to popularity, most recent, or brief category. This means consumers face the inconvenience of constantly scrolling around to locate the emoticons they want. The creation of an emoticon recommendation system would improve consumer convenience and satisfaction and increase the sales revenue of companies the sell emoticons. To recommend appropriate emoticons, it is necessary to quantify the emotions that the consumer sees and emotions. Such quantification will enable us to analyze the characteristics and emotions felt by consumers who used similar emoticons, which, in turn, will facilitate our emoticon recommendations for consumers. One way to quantify emoticons use is metadata-ization. Metadata-ization is a means of structuring or organizing unstructured and semi-structured data to extract meaning. By structuring unstructured emoticon data through metadata-ization, we can easily classify emoticons based on the emotions consumers want to express. To determine emoticons' precise emotions, we had to consider sub-detail expressions-not only the seven common emotional adjectives but also the metaphorical expressions that appear only in South Korean proved by previous studies related to emotion focusing on the emoticon's characteristics. We therefore collected the sub-detail expressions of emotion based on the "Shape", "Color" and "Adumbration". Moreover, to design a highly accurate recommendation system, we considered both emotion-technical indexes and emoticon-emotional indexes. We then identified 14 features of emoticon-technical indexes and selected 36 emotional adjectives. The 36 emotional adjectives consisted of contrasting adjectives, which we reduced to 18, and we measured the 18 emotional adjectives using 40 emoticon sets randomly selected from the top-ranked emoticons in the KakaoTalk shop. We surveyed 277 consumers in their mid-twenties who had experience purchasing emoticons; we recruited them online and asked them to evaluate five different emoticon sets. After data acquisition, we conducted a factor analysis of emoticon-emotional factors. We extracted four factors that we named "Comic", Softness", "Modernity" and "Transparency". We analyzed both the relationship between indexes and consumer attitude and the relationship between emoticon-technical indexes and emoticon-emotional factors. Through this process, we confirmed that the emoticon-technical indexes did not directly affect consumer attitudes but had a mediating effect on consumer attitudes through emoticon-emotional factors. The results of the analysis revealed the mechanism consumers use to evaluate emoticons; the results also showed that consumers' emoticon-technical indexes affected emoticon-emotional factors and that the emoticon-emotional factors affected consumer satisfaction. We therefore designed the emoticon recommendation system using only four emoticon-emotional factors; we created a recommendation method to calculate the Euclidean distance from each factors' emotion. In an attempt to increase the accuracy of the emoticon recommendation system, we compared the emotional patterns of selected emoticons with the recommended emoticons. The emotional patterns corresponded in principle. We verified the emoticon recommendation system by testing prediction accuracy; the predictions were 81.02% accurate in the first result, 76.64% accurate in the second, and 81.63% accurate in the third. This study developed a methodology that can be used in various fields academically and practically. We expect that the novel emoticon recommendation system we designed will increase emoticon sales for companies who conduct business in this domain and make consumer experiences more convenient. In addition, this study served as an important first step in the development of an intelligent emoticon recommendation system. The emotional factors proposed in this study could be collected in an emotional library that could serve as an emotion index for evaluation when new emoticons are released. Moreover, by combining the accumulated emotional library with company sales data, sales information, and consumer data, companies could develop hybrid recommendation systems that would bolster convenience for consumers and serve as intellectual assets that companies could strategically deploy.

• Journal of Intelligence and Information Systems
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• v.21 no.4
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• pp.17-35
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• 2015

Demand forecasting is the activity of estimating the quantity of a product or service that consumers will purchase for a certain period of time. Developing precise forecasting models are considered important since corporates can make strategic decisions on new markets based on future demand estimated by the models. Many studies have developed market growth curve models, such as Bass, Logistic, Gompertz models, which estimate future demand when a market is in its early stage. Among the models, Bass model, which explains the demand from two types of adopters, innovators and imitators, has been widely used in forecasting. Such models require sufficient demand observations to ensure qualified results. In the beginning of a new market, however, observations are not sufficient for the models to precisely estimate the market's future demand. For this reason, as an alternative, demands guessed from those of most adjacent markets are often used as references in such cases. Reference markets can be those whose products are developed with the same categorical technologies. A market's demand may be expected to have the similar pattern with that of a reference market in case the adoption pattern of a product in the market is determined mainly by the technology related to the product. However, such processes may not always ensure pleasing results because the similarity between markets depends on intuition and/or experience. There are two major drawbacks that human experts cannot effectively handle in this approach. One is the abundance of candidate reference markets to consider, and the other is the difficulty in calculating the similarity between markets. First, there can be too many markets to consider in selecting reference markets. Mostly, markets in the same category in an industrial hierarchy can be reference markets because they are usually based on the similar technologies. However, markets can be classified into different categories even if they are based on the same generic technologies. Therefore, markets in other categories also need to be considered as potential candidates. Next, even domain experts cannot consistently calculate the similarity between markets with their own qualitative standards. The inconsistency implies missing adjacent reference markets, which may lead to the imprecise estimation of future demand. Even though there are no missing reference markets, the new market's parameters can be hardly estimated from the reference markets without quantitative standards. For this reason, this study proposes a case-based expert system that helps experts overcome the drawbacks in discovering referential markets. First, this study proposes the use of Euclidean distance measure to calculate the similarity between markets. Based on their similarities, markets are grouped into clusters. Then, missing markets with the characteristics of the cluster are searched for. Potential candidate reference markets are extracted and recommended to users. After the iteration of these steps, definite reference markets are determined according to the user's selection among those candidates. Then, finally, the new market's parameters are estimated from the reference markets. For this procedure, two techniques are used in the model. One is clustering data mining technique, and the other content-based filtering of recommender systems. The proposed system implemented with those techniques can determine the most adjacent markets based on whether a user accepts candidate markets. Experiments were conducted to validate the usefulness of the system with five ICT experts involved. In the experiments, the experts were given the list of 16 ICT markets whose parameters to be estimated. For each of the markets, the experts estimated its parameters of growth curve models with intuition at first, and then with the system. The comparison of the experiments results show that the estimated parameters are closer when they use the system in comparison with the results when they guessed them without the system.

• Journal of the Korean Society of Food Science and Nutrition
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• v.34 no.6
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• pp.840-849
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• 2005

In order to investigate the relationship between intake conditions of health food and health-related factors by residence and sex in Tong-Young area, a survey was carried out from 1,303 adults. Health foods were classified 3 groups including vitamin and mineral supplements, toner foods and manufactured health food supplements. Health-related factors were stress, fatigue, smoking and drinking. The $29.5\%$ of the subjects had taken some health food for health. Especially the male took more toner foods habitually than the female did. In take of vitamin and mineral supplements by residence, there was a significant difference $(p\leq0.01)$ as follows. The subjects in island $(20.0\%)$ who took vitamin/mineral supplements were about two times as compared with the subjects in Dong $(10.8\%)$, or Eub-Myeon $(10.0\%)$. The subjects taking supplementary food replied over fair $(82.8\%)$, the subjects taking toner food replied over fair (90.3$\%$) scored higher than who replied bad or very bad in self-perceived health status. Therefore, the better the subjects felt self-perceived health status, the more they took health foods for health themselves. In self-perceived stress status, the subjects who replied a little $(50.0\%,\;45.3\%)$ or little $(19.9\%,\;26.4\%)$, took vitamin and mineral supplements or manufactured health foods a lot. In toner food there was a significant correlation $(p\leq0.05)$ as follows. The less the subjects felt stress, the more they took dietry supplement. No smoker $(12.9\%)$intake rate of vitamin and mineral supplements was higher than smoker $(8.8\%)$. Smokers $(6.5\%)$ intake rate of toner food was higher than no smoker $(4.0\%)$. It was not significant the relationship between intake condition of health food and drinking. The main motivation for taking health food were by self-decision and invitation of friends or neighbors.

• Journal of Intelligence and Information Systems
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• v.26 no.2
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• pp.57-78
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• 2020

With the development of the Internet, consumers have had an opportunity to check product information easily through E-Commerce. Product reviews used in the process of purchasing goods are based on user experience, allowing consumers to engage as producers of information as well as refer to information. This can be a way to increase the efficiency of purchasing decisions from the perspective of consumers, and from the seller's point of view, it can help develop products and strengthen their competitiveness. However, it takes a lot of time and effort to understand the overall assessment and assessment dimensions of the products that I think are important in reading the vast amount of product reviews offered by E-Commerce for the products consumers want to compare. This is because product reviews are unstructured information and it is difficult to read sentiment of reviews and assessment dimension immediately. For example, consumers who want to purchase a laptop would like to check the assessment of comparative products at each dimension, such as performance, weight, delivery, speed, and design. Therefore, in this paper, we would like to propose a method to automatically generate multi-dimensional product assessment scores in product reviews that we would like to compare. The methods presented in this study consist largely of two phases. One is the pre-preparation phase and the second is the individual product scoring phase. In the pre-preparation phase, a dimensioned classification model and a sentiment analysis model are created based on a review of the large category product group review. By combining word embedding and association analysis, the dimensioned classification model complements the limitation that word embedding methods for finding relevance between dimensions and words in existing studies see only the distance of words in sentences. Sentiment analysis models generate CNN models by organizing learning data tagged with positives and negatives on a phrase unit for accurate polarity detection. Through this, the individual product scoring phase applies the models pre-prepared for the phrase unit review. Multi-dimensional assessment scores can be obtained by aggregating them by assessment dimension according to the proportion of reviews organized like this, which are grouped among those that are judged to describe a specific dimension for each phrase. In the experiment of this paper, approximately 260,000 reviews of the large category product group are collected to form a dimensioned classification model and a sentiment analysis model. In addition, reviews of the laptops of S and L companies selling at E-Commerce are collected and used as experimental data, respectively. The dimensioned classification model classified individual product reviews broken down into phrases into six assessment dimensions and combined the existing word embedding method with an association analysis indicating frequency between words and dimensions. As a result of combining word embedding and association analysis, the accuracy of the model increased by 13.7%. The sentiment analysis models could be seen to closely analyze the assessment when they were taught in a phrase unit rather than in sentences. As a result, it was confirmed that the accuracy was 29.4% higher than the sentence-based model. Through this study, both sellers and consumers can expect efficient decision making in purchasing and product development, given that they can make multi-dimensional comparisons of products. In addition, text reviews, which are unstructured data, were transformed into objective values such as frequency and morpheme, and they were analysed together using word embedding and association analysis to improve the objectivity aspects of more precise multi-dimensional analysis and research. This will be an attractive analysis model in terms of not only enabling more effective service deployment during the evolving E-Commerce market and fierce competition, but also satisfying both customers.