• Journal of Intelligence and Information Systems
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• v.24 no.4
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• pp.33-49
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• 2018

The impact of TV Drama success on TV Rating and the channel promotion effectiveness is very high. The cultural and business impact has been also demonstrated through the Korean Wave. Therefore, the early prediction of the blockbuster success of TV Drama is very important from the strategic perspective of the media industry. Previous studies have tried to predict the audience ratings and success of drama based on various methods. However, most of the studies have made simple predictions using intuitive methods such as the main actor and time zone. These studies have limitations in predicting. In this study, we propose a model for predicting the popularity of drama by analyzing the customer's viewing pattern based on various theories. This is not only a theoretical contribution but also has a contribution from the practical point of view that can be used in actual broadcasting companies. In this study, we collected data of 280 TV mini-series dramas, broadcasted over the terrestrial channels for 10 years from 2003 to 2012. From the data, we selected the most highly ranked and the least highly ranked 45 TV drama and analyzed the viewing patterns of them by 11-step. The various assumptions and conditions for modeling are based on existing studies, or by the opinions of actual broadcasters and by data mining techniques. Then, we developed a prediction model by measuring the viewing-time distance (difference) using Euclidean and Correlation method, which is termed in our study similarity (the sum of distance). Through the similarity measure, we predicted the success of dramas from the viewer's initial viewing-time pattern distribution using 1~5 episodes. In order to confirm that the model is shaken according to the measurement method, various distance measurement methods were applied and the model was checked for its dryness. And when the model was established, we could make a more predictive model using a grid search. Furthermore, we classified the viewers who had watched TV drama more than 70% of the total airtime as the "passionate viewer" when a new drama is broadcasted. Then we compared the drama's passionate viewer percentage the most highly ranked and the least highly ranked dramas. So that we can determine the possibility of blockbuster TV mini-series. We find that the initial viewing-time pattern is the key factor for the prediction of blockbuster dramas. From our model, block-buster dramas were correctly classified with the 75.47% accuracy with the initial viewing-time pattern analysis. This paper shows high prediction rate while suggesting audience rating method different from existing ones. Currently, broadcasters rely heavily on some famous actors called so-called star systems, so they are in more severe competition than ever due to rising production costs of broadcasting programs, long-term recession, aggressive investment in comprehensive programming channels and large corporations. Everyone is in a financially difficult situation. The basic revenue model of these broadcasters is advertising, and the execution of advertising is based on audience rating as a basic index. In the drama, there is uncertainty in the drama market that it is difficult to forecast the demand due to the nature of the commodity, while the drama market has a high financial contribution in the success of various contents of the broadcasting company. Therefore, to minimize the risk of failure. Thus, by analyzing the distribution of the first-time viewing time, it can be a practical help to establish a response strategy (organization/ marketing/story change, etc.) of the related company. Also, in this paper, we found that the behavior of the audience is crucial to the success of the program. In this paper, we define TV viewing as a measure of how enthusiastically watching TV is watched. We can predict the success of the program successfully by calculating the loyalty of the customer with the hot blood. This way of calculating loyalty can also be used to calculate loyalty to various platforms. It can also be used for marketing programs such as highlights, script previews, making movies, characters, games, and other marketing projects.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.15 no.4
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• pp.245-261
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• 2020

The aim of this study is to investigate the correlation between the support activities provided by the franchiser and how they affect the intention of the contract renewal and business performances made by franchisees, developing dynamic trust between these transactional partners. Various supportive activities between franchiser and franchisees were divided into financial and non-financial activities and dynamic trust into Transitional-based trust, Calculative-based trust, Relational-based trust, and Balanced-based trust. These trust types, which are variable and adjustable based on the opportunistic behaviors of business parties, were applied to define the impact of the support activities on the contract renewal intention and the performances. This study was developed around domestic franchisees. An investigator visited business owners and manager level-employees, explained the purpose of the survey prior to the response, and the answers were directly written by hands. A total of 348 copies were used for the analysis. As the results of the analysis, first, financial support activities were found to have a positive(+) effect on transitional-based trust, calculative-based trust, and balanced-based trust. On the other hand, non-financial support activities were found to have a positive(+) effect on calculative-based trust, relational-based trust, and balanced-based trust, and there was no significant relationship on transitional-based trust. Second, the dynamic trust had a statistically significant positive(+) effect on inducing the contract renewal. Lastly, in the relationship between the dynamic trust and its impact on business performances, only transitional-based trust, and relational-based trust were found to have a positive(+) effect on the financial performances. In addition, relational-based trust showed a meaningful positive(+) relationship on the non-financial performances, and non-financial performace showed a meaningful positive(+) relationship on the re-contract intention. From the results, it can be concluded that the financial and non-financial activities for a win-win partnership between franchiser and franchisees are essential in not only forming dynamic trust but also boosting business performances as well as maintaining the business relationship. Thus, it suggests that building a win-win partnership can be promoted more efficiently by specifying activities best suitable for a particular relationship. In addition, a specific set of activities could be presented for establishing the level of trust that is formed in situations that vary depending on transaction risks and interdependency arising from having the transactional relationship based on the contract as the franchise industry features. Eventually, it is expected that this study can provide a way to promote the qualitative improvement of the franchise industry by identifying factors essential to establishing a sustainable win-win system and relationships that can improve the business performance of franchisees.

• Journal of Intelligence and Information Systems
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• v.26 no.1
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• pp.1-21
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• 2020

With the rapid development of artificial intelligence technology, various techniques have been developed to extract meaningful information from unstructured text data which constitutes a large portion of big data. Over the past decades, text mining technologies have been utilized in various industries for practical applications. In the field of business intelligence, it has been employed to discover new market and/or technology opportunities and support rational decision making of business participants. The market information such as market size, market growth rate, and market share is essential for setting companies' business strategies. There has been a continuous demand in various fields for specific product level-market information. However, the information has been generally provided at industry level or broad categories based on classification standards, making it difficult to obtain specific and proper information. In this regard, we propose a new methodology that can estimate the market sizes of product groups at more detailed levels than that of previously offered. We applied Word2Vec algorithm, a neural network based semantic word embedding model, to enable automatic market size estimation from individual companies' product information in a bottom-up manner. The overall process is as follows: First, the data related to product information is collected, refined, and restructured into suitable form for applying Word2Vec model. Next, the preprocessed data is embedded into vector space by Word2Vec and then the product groups are derived by extracting similar products names based on cosine similarity calculation. Finally, the sales data on the extracted products is summated to estimate the market size of the product groups. As an experimental data, text data of product names from Statistics Korea's microdata (345,103 cases) were mapped in multidimensional vector space by Word2Vec training. We performed parameters optimization for training and then applied vector dimension of 300 and window size of 15 as optimized parameters for further experiments. We employed index words of Korean Standard Industry Classification (KSIC) as a product name dataset to more efficiently cluster product groups. The product names which are similar to KSIC indexes were extracted based on cosine similarity. The market size of extracted products as one product category was calculated from individual companies' sales data. The market sizes of 11,654 specific product lines were automatically estimated by the proposed model. For the performance verification, the results were compared with actual market size of some items. The Pearson's correlation coefficient was 0.513. Our approach has several advantages differing from the previous studies. First, text mining and machine learning techniques were applied for the first time on market size estimation, overcoming the limitations of traditional sampling based- or multiple assumption required-methods. In addition, the level of market category can be easily and efficiently adjusted according to the purpose of information use by changing cosine similarity threshold. Furthermore, it has a high potential of practical applications since it can resolve unmet needs for detailed market size information in public and private sectors. Specifically, it can be utilized in technology evaluation and technology commercialization support program conducted by governmental institutions, as well as business strategies consulting and market analysis report publishing by private firms. The limitation of our study is that the presented model needs to be improved in terms of accuracy and reliability. The semantic-based word embedding module can be advanced by giving a proper order in the preprocessed dataset or by combining another algorithm such as Jaccard similarity with Word2Vec. Also, the methods of product group clustering can be changed to other types of unsupervised machine learning algorithm. Our group is currently working on subsequent studies and we expect that it can further improve the performance of the conceptually proposed basic model in this study.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.11 no.12
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• pp.4928-4934
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• 2010

Dental health care in modern society has gradually been developing with patients'expectation, desire, and medical knowledge according to the changes in the medical industry. Dental marketing strategy is necessary to construct a better dental health environment in order to cope with customers' changes and desire. The purpose of this study was to evaluate the positive comments by word-of-mouth experience, quality of dental clinic service and customer satisfaction among dental patients. The study surveyed 5oo subjects being treated at 6 dental clinics in Seoul and Gyeonggi province. It was conducted from March 22 to April 9, 2010. The subject groups with a positive experience through word - of - mouth who had a perception about the quality of dental clinic service (p<.001) and customer satisfaction (p<.001) were higher than other groups who had no comments on their dental treatment and clinics. The results showed that there was a correlation(r=. 852, p<.001) between the positive comments by word-of- mouthabout the quality of dental clinics service and customer satisfaction. The perception of dental clinic service and patients' satisfaction with positive comments by word of mouth was highly recognized by older and more experienced patients of dental treatment. Therefore, the results suggest that empirical information on important factors about strategic marketing lead to positive comments by word of mouth experience for patients in dental clinics.

• The Korean Journal of Financial Management
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• v.25 no.1
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• pp.23-49
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• 2008

We test the hypothesis that the gradual diffusion of information across asset markets leads to cross-asset return predictability in Korea. And, the aim of this paper is related to forecast the stock market, business cycle index and industrial production by various indicators of economic activities in Korea. For this, our paper sets models and focuses on empirical test. The stock market on this month correlate with industries in Korea. The stock market doesn't lead to industries. The industries and macroeconomic variables have high correlation. We test that gradual diffusion of industrial information will predict stock market in Korea. For this, we analysis on possibility of Granger cause by VAR models between industries and stock market. As a result, 21 portfolios cause to Kospi statistically significance at 5%. Especially, the Beverage portfolio has bilateral Granger causality to Kospi. In case of Internet and Cosmetics portfolio, Kospi has unilateral Granger causality to it. The predictability of specific industries has a relation to Macroeconomic variables. What industrial portfolios predict to Business Coincidence Index? The only 6 industrial portfolios of 36 portfolios have a statistically significance at 10%. And, 9 portfolios have a statistically significance at 5%.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.3
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• pp.629-637
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• 2016

This study examined the purchasing behavior and satisfaction of people with convenience store PB foods. The factors affecting the purchasing behavior and the overall satisfaction with the convenience store PB foods were examined. This study first found that the purchasing motivation of PB foods is the cheaper price than NB foods. Second, age is a statistically effective variable for the visit frequency at the convenience store and gender and age are effective variables for the purchase frequency of PB foods. Third, there is a statistically positive correlation of convenience store brand preference with the PB food brand preference for 6 food categories. Next, in terms of the attribute-level satisfaction, the most satisfied areas are diverse food groups and diverse food taste and the least satisfied areas is price. This is followed by taste and design/packaging influence overall satisfaction. From this analysis, the following suggestions were drawn. First, because consumers are likely to be matched convenience store brand preference and PB food brand preference, advertising is necessary to ensure that the consumers continue to visit the convenience store. Second, product improvement by increasing the quality should be designed to motivate the major consumer group of people in their 20s and 30s. Finally, to boost PB food satisfaction, the industry should improve the taste and design/packaging.

• The Research Journal of the Costume Culture
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• v.21 no.1
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• pp.57-65
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• 2013

With environmental pollution becoming a serious problem, recently there has been increased interest in the environment and health. In addition, the development of materials for environmentally friendly and functional clothing has increased. Environmentally friendly products that use bark fibers of the mulberry(dak) tree are expressed in terms of dak fiber and Hanji yarn. This research analyzed consumer's perceived images of Hanji and Hanji yarn infant clothing. The research results are as follows. Based on analyzing images of Hanji and Hanji yarn, Hanji was categorized into four images that can be described as natural, pure, decorative, and functional, and Hanji yarn infant clothing was categorized into three images that can be described as natural, decorative, and functional. Based on the analysis of well-being elements in subjects' lifestyles, the following four inclinations were found: environmental friendliness, pursuit of novelty, life care, and environmental protection. On this basis, the subjects were categorized into three groups: the trend-pursuing group, the life-care group, and the social responsibility group. Subjects' perceived images of Hanji and Hanji yarn infant clothing were investigated, and these products were found to have a strong image of being Korean, traditional, and natural, while being weakly perceived as comfortable, functional, and practical. Analyzing the correlation between images of Hanji and Hanji yarn infant clothing and lifestyle groups revealed that these products were not perceived differently according to group. This study showed that Hanji and Hanji yarn are terms that are commonly associated with positive clothing material. However, both Hanji and Hanji yarn are perceived negatively in terms of functional image, which is an important factor in clothing material. In addition, even among consumers who pursue a lifestyle based on well-being, the functional image of Hanji yarn was not perceived highly. Hence, it is necessary to further examine the effect of terms such as Hanji and Hanji yarn on consumer choices. Therefore, caution is considered needed when using such terms in the marketing of related products.

• Journal of the East Asian Society of Dietary Life
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• v.19 no.2
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• pp.157-168
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• 2009

The principal objective of this study was to provide basic data regarding health care services for middle-aged men, via an analysis of the relationships among stress level, food habits and well-being-related attitudes with social-demographic characteristics and health-related factors. The main findings of this study were as follows. The average age of the participants was $48.7{\pm}5.2$. With regard to health status, 18.8% of the participants were diagnosed with digestive diseases within the past year. Stress levels were higher in the participants with lower levels of educational attainment. Furthermore, participants who had become divorced or lived separately from their families, participants who did not have their own houses, and participants who reported low marital satisfaction also had high levels of stress. Our food habits scores revealed meaningful differences among the study subjects, and were shown to vary with marital satisfaction, occupation, and residence type. In terms of both the importance and practice level of well-being-associated food habits, the most frequent response in this study was 'Consume home-made food rather than processed or ready-to-eat food' ($4.30{\pm}0.86$, $3.68{\pm}1.04$). In terms of importance level, the factors most relevant to well-being in terms of food habits were education level, marital satisfaction, residence type, self-assessed health status, smoking, drinking, regular exercises and leisure activities. With regard to practice levels, the degree to which subjects engaged in food habits targeted toward well-being differed meaningfully depending on marital status, marital satisfaction, residence type, family type, self-assessed health status, smoking, drinking, nutritional supplement intake, regular exercises and leisure activities. After analyzing the correlation among stress level, food habit score and the attitude towards well-being-related dietary habits (importance and practice level), we observed a meaningful relationship between the four factors at the level of p<0.001. According to the above result, continuous attention to health, including the appropriate control of smoking and drinking, as well as, stress management, via regular exercises and appropriate food habits is expected to exert a positive influence on the prevention of disease, and is also expected to improve quality of life. For all well-being-associated items, the importance level was shown to be higher than the practice level. Thus, in order to foster health-oriented food habits, we recommend that a new plan be designed, targeted toward ease of active practice for middle-aged men.

• Culinary science and hospitality research
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• v.23 no.2
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• pp.23-34
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• 2017

The current study aims to demonstrate the causal relationships of supervisory support, workplace fun, job satisfaction and customer orientation of Y Generational service provider in the Korean hotel industry. In order to achieve the study purpose, the data were collected from 284 hotel employees who were born from 1981 to 2000, which is considered as Generation Y. To analyze the data, frequency, correlation, regression, PROCESS Macro and confirmatory factor analysis were undertaken using SPSS and AMOS version 18.0. The results show as follows: First, factor of workplace fun had a positive influence on job satisfaction and customer orientation. Second, factor of supervisory support had a positive influence on job satisfaction but its effect on the customer orientation was not significant. Third, job satisfaction had a significantly positive effect on customer orientation. Lastly, job satisfaction was revealed as a significant mediator between dependent variables(supervisory support and workplace fun) and customer orientation. Eventually, this study found that the more workplace fun and job satisfaction employees have, the higher customer orientation they show. Supervisory support was revealed as an indirect factor to enhance customer orientation through job satisfaction.

• Journal of the Korea Concrete Institute
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• v.28 no.3
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• pp.329-340
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• 2016

Increase in demotion and repair works on buildings in the construction market generates a large amount of construction waste. Recycling of construction waste is important for saving of resources, preservation of environment and constant advance of the construction industry. Accordingly, the environmental and economic value of recycled aggregate, which is produced after waste concrete is crushed, is increasingly highlighted. It is generally known that compared to concrete made of ordinary aggregate, concrete made of recycled aggregate has low quality, and the low quality is dependent on the amount of the bonding heterogeneous (cement paste and mortar) as well as the amount of the pores within the bonding heterogeneous. Reports on carbonation mechanism shows that the pores of cement-based materials are filled up by the progress of carbonation. Therefore, this study aims at an estimation of the period for optimum carbonation reforming appropriate for the thickness of the bonding heterogeneous of recycled aggregate, based on carbonation mechanism, with a view to improving the product quality by means of filling up the pores of the bonding heterogeneous of recycled aggregate. This study drew the carbonation depth according to the passage of age by calculating the bonding ratio and bonding thickness of the bonding heterogeneous as against the particle size distribution of recycled aggregate as well as by chemical quantitative analysis according to the age of accelerated carbonation of mock-up samples imitating bonding heterogeneous. Based on the correlation between the age of accelerated carbonation and carbonation depth, this study also proposed the estimated period of carbonation reforming of recycled aggregate appropriate for the thickness of the bonding heterogeneous.