• Journal of Global Scholars of Marketing Science
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• v.20 no.2
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• pp.219-230
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• 2010

Digital content makes big changes to our daily lives while bringing opportunities and challenges for companies. Creative firms integrate pictures, texts, videos, audios, and data by digitalization to develop new products or services and create digital experiences to promote their brands. Most articles on digital content contribute to the basic concept or development of marketing it in literature. Actually, compared with traditional value chains for common products or services, the digital content industry seems to have more potential value. Because quite a bit of digital content is free to the consumer, price is not necessarily perceived as an indicator of the quality or value of information (Rowley 2008). It becomes evident that a current theme in digital content is the issue of "value," and research on customers' perceived value of digital content is a necessity. This article argues that experiential value has an advantage in customers' evaluations of digital content. Two different but related contributions to the understanding of "value" of digital content are made here. First, based on the comparison of digital content with products and services, the article proposes two key characteristics that make experiential strategy available for digital content: intangibility and near-zero reproduction cost. On top of that, based on the discussion of the gap between company's idealized value and customer's perceived value, this article emphasizes that digital content prices and pricing of digital content is different from products and services. As a result of intangibility, prices may not reflect customer value. Moreover, the cost of digital content in the development stage may be very high while reproduction costs shrink dramatically. Moreover, because of the value gap mentioned before, the pricing polices vary for different digital contents. For example, flat price policy is generally used for movies and music (Magiera 2001; Netherby 2002), while for continuous demand, digital content such as online games and anti-virus programs involves a more complicated matter of utility and competitive price levels. Digital content companies have to explore various kinds of strategies to overcome this gap. Rethinking marketing solutions such as advertisements, images, and word-of-mouth and their effect on customers' perceived value becomes essential. China's digital content industry is becoming more and more globalized and drawing special attention from different countries and regions that have respective competitive advantages. The 2008-2009 Annual Report on the Development of China's Digital Content Industry (CCIDConsulting 2009) indicates that, with the driven power of domestic demand and governmental policy support, the country's digital content industry maintained a fast growth of some 30 percent in 2008, obviously indicating the initial stage of industry expansion. In China, anti-virus programs and other software programs which need to be updated use a quarter-based pricing policy. Customers can download a trial version for free and use it for six months or a year. If they want to use it longer, continuous payment is needed. They examine the excellence of the digital content during this trial period and decide whether to pay for continued usage. For China’s music and movie industries, as a result of initial development, experiential strategy has not been much applied, even though firms in other countries find the trial experience and explore important strategies(such as customers listening to music for several seconds for free before downloading it). For the above reasons, anti-virus program may be a representative for digital content industry in China and an exploratory study of the advantage of experiential value in customer's perceived value of digital content is done in the anti-virus market of China. In order to enhance the reliability of the survey data, this study focused on people who were experienced users of anti-virus programs. The empirical results revealed that experiential value has a positive effect on customers' perceived value of digital content. In other words, because digital content is intangible and the reproduction costs are nearly zero, customers' evaluations are based heavily on their experience. Moreover, image and word-of-mouth do not have a positive effect on perceived value, only on experiential value. That is to say, a digital content value chain is different from that of a general product or service. Experiential value has a notable advantage and mediates the effect of image and word-of-mouth on perceived value. The results of this study help provide an understanding of why free digital content downloads exist in developing countries. Customers can perceive the value of digital content only by using and experiencing it. This is also why such governments support the development of digital content. Other developing countries whose digital content business is also in the beginning stage can make use of the suggestions here. Moreover, based on the advantage of experiential strategy, companies should make more of an effort to invest in customers' experience. As a result of the characteristics and value gap of digital content, customers perceive more value in the intangible digital content only by experiencing what they really want. Moreover, because of the near-zero reproduction costs, companies can perhaps use experiential strategy to enhance customer understanding of digital content.

• Journal of Intelligence and Information Systems
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• v.23 no.4
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• pp.127-146
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• 2017

Services using artificial intelligence have begun to emerge in daily life. Artificial intelligence is applied to products in consumer electronics and communications such as artificial intelligence refrigerators and speakers. In the financial sector, using Kensho's artificial intelligence technology, the process of the stock trading system in Goldman Sachs was improved. For example, two stock traders could handle the work of 600 stock traders and the analytical work for 15 people for 4weeks could be processed in 5 minutes. Especially, big data analysis through machine learning among artificial intelligence fields is actively applied throughout the financial industry. The stock market analysis and investment modeling through machine learning theory are also actively studied. The limits of linearity problem existing in financial time series studies are overcome by using machine learning theory such as artificial intelligence prediction model. The study of quantitative financial data based on the past stock market-related numerical data is widely performed using artificial intelligence to forecast future movements of stock price or indices. Various other studies have been conducted to predict the future direction of the market or the stock price of companies by learning based on a large amount of text data such as various news and comments related to the stock market. Investing on commodity asset, one of alternative assets, is usually used for enhancing the stability and safety of traditional stock and bond asset portfolio. There are relatively few researches on the investment model about commodity asset than mainstream assets like equity and bond. Recently machine learning techniques are widely applied on financial world, especially on stock and bond investment model and it makes better trading model on this field and makes the change on the whole financial area. In this study we made investment model using Support Vector Machine among the machine learning models. There are some researches on commodity asset focusing on the price prediction of the specific commodity but it is hard to find the researches about investment model of commodity as asset allocation using machine learning model. We propose a method of forecasting four major commodity indices, portfolio made of commodity futures, and individual commodity futures, using SVM model. The four major commodity indices are Goldman Sachs Commodity Index(GSCI), Dow Jones UBS Commodity Index(DJUI), Thomson Reuters/Core Commodity CRB Index(TRCI), and Rogers International Commodity Index(RI). We selected each two individual futures among three sectors as energy, agriculture, and metals that are actively traded on CME market and have enough liquidity. They are Crude Oil, Natural Gas, Corn, Wheat, Gold and Silver Futures. We made the equally weighted portfolio with six commodity futures for comparing with other commodity indices. We set the 19 macroeconomic indicators including stock market indices, exports & imports trade data, labor market data, and composite leading indicators as the input data of the model because commodity asset is very closely related with the macroeconomic activities. They are 14 US economic indicators, two Chinese economic indicators and two Korean economic indicators. Data period is from January 1990 to May 2017. We set the former 195 monthly data as training data and the latter 125 monthly data as test data. In this study, we verified that the performance of the equally weighted commodity futures portfolio rebalanced by the SVM model is better than that of other commodity indices. The prediction accuracy of the model for the commodity indices does not exceed 50% regardless of the SVM kernel function. On the other hand, the prediction accuracy of equally weighted commodity futures portfolio is 53%. The prediction accuracy of the individual commodity futures model is better than that of commodity indices model especially in agriculture and metal sectors. The individual commodity futures portfolio excluding the energy sector has outperformed the three sectors covered by individual commodity futures portfolio. In order to verify the validity of the model, it is judged that the analysis results should be similar despite variations in data period. So we also examined the odd numbered year data as training data and the even numbered year data as test data and we confirmed that the analysis results are similar. As a result, when we allocate commodity assets to traditional portfolio composed of stock, bond, and cash, we can get more effective investment performance not by investing commodity indices but by investing commodity futures. Especially we can get better performance by rebalanced commodity futures portfolio designed by SVM model.

• Journal of agricultural medicine and community health
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• v.25 no.1
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• pp.65-83
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• 2000

To know the relationship of general characteristics with activities of daily living(ADL) and instrumental activities of daily living(IADL), we carried out the study on the elderies living in Chollabuk-Do area during 6 months, from June to December in 1999. Study subjects were 281, women and men were 195(69.6%) and 85(30.4%) respectively. Mean ages of women and men were 71.9 and 70.8 respectively. 81.1% elderies has disease and 18.9% were disease free. Disease prevalences of movement joint disease, others, circulatory disease, digestive disease, dental disease, respiratory disease were 50.1%, 25.0%, 10.5%, 9.4%, 8.5%, and 6.3% respectively. The percentages to the use of medical institution in recent were 40.0% for hospital, 16.8% for oriental hospital, 14.5% for public health center, 10.9% for drug store, 10.0% for others, and 7.8% for dental service. The percentages to the improvement of symptom after the use of medical institution were 62.3% for normal, 19.4% for improvement, and 18.2% for non-improvement. The percentages to the health situation were 37.1% for bad, 35.7% for good, and 27.1% for normal. Activities of daily living were 67.1% for 6 scores, 27.9% for 5 scores, 2.1% for 4 scores and ADL of women was lower than the men's. Instrumental activities of daily living were 50.4% for 5 scores, 19.3% for 3 scores, 12.1% for 4 scores and IADL of women was lower than the men's. Frequencies of disability in ADL were 28.9% for incontinence, 6.1% for bathing, 2.9% for meal, 2.5% for walking around house, 1.8% for toilet use, 1.4% for dressing and disability frequencies of women in 6 items of ADL were higher than the men's. The percentages of high, intermediate, low ADL in activities of daily living were 67.1%, 32.5%, 0.4% respectively and decrease of high ADL, increase of intermediate ADL were found with the increasing of age. Frequencies of disability in IADL were 42.9% for payment in and out, 31.8% for payment of written claim, 21.1% for shopping, 16.4% for preparation of meal, and 11.8% for use of bus. All items of women in IADL was higher than the men's but preparation of meal. The percentages of high, intermediate, low IADL in instrumental activities of daily living were 50.4%, 42.5%, 7.1% and decrease of high IADL, increase of intermediate IADL were found with the increasing of age. Mean of ADL with the general characteristics was 5.56 and 2 variables of level of education, health situation were statistically significant. Mean of IADL with the general characteristics was 3.76 and 8 variables of age, sex, level of education, occupation, presence of spouse, duty of living cost, health situation, category of ADL were statistically significant. With the result of stepwise regression, ADL was statistically related with religion, health situation and ADL was statistically related with level of education, living together with family, duty of living cost, health situation.

• Journal of Intelligence and Information Systems
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• v.18 no.2
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• pp.143-156
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• 2012

People easily believe that news and stock index are closely related. They think that securing news before anyone else can help them forecast the stock prices and enjoy great profit, or perhaps capture the investment opportunity. However, it is no easy feat to determine to what extent the two are related, come up with the investment decision based on news, or find out such investment information is valid. If the significance of news and its impact on the stock market are analyzed, it will be possible to extract the information that can assist the investment decisions. The reality however is that the world is inundated with a massive wave of news in real time. And news is not patterned text. This study suggests the stock-index invest model based on "News Big Data" opinion mining that systematically collects, categorizes and analyzes the news and creates investment information. To verify the validity of the model, the relationship between the result of news opinion mining and stock-index was empirically analyzed by using statistics. Steps in the mining that converts news into information for investment decision making, are as follows. First, it is indexing information of news after getting a supply of news from news provider that collects news on real-time basis. Not only contents of news but also various information such as media, time, and news type and so on are collected and classified, and then are reworked as variable from which investment decision making can be inferred. Next step is to derive word that can judge polarity by separating text of news contents into morpheme, and to tag positive/negative polarity of each word by comparing this with sentimental dictionary. Third, positive/negative polarity of news is judged by using indexed classification information and scoring rule, and then final investment decision making information is derived according to daily scoring criteria. For this study, KOSPI index and its fluctuation range has been collected for 63 days that stock market was open during 3 months from July 2011 to September in Korea Exchange, and news data was collected by parsing 766 articles of economic news media M company on web page among article carried on stock information>news>main news of portal site Naver.com. In change of the price index of stocks during 3 months, it rose on 33 days and fell on 30 days, and news contents included 197 news articles before opening of stock market, 385 news articles during the session, 184 news articles after closing of market. Results of mining of collected news contents and of comparison with stock price showed that positive/negative opinion of news contents had significant relation with stock price, and change of the price index of stocks could be better explained in case of applying news opinion by deriving in positive/negative ratio instead of judging between simplified positive and negative opinion. And in order to check whether news had an effect on fluctuation of stock price, or at least went ahead of fluctuation of stock price, in the results that change of stock price was compared only with news happening before opening of stock market, it was verified to be statistically significant as well. In addition, because news contained various type and information such as social, economic, and overseas news, and corporate earnings, the present condition of type of industry, market outlook, the present condition of market and so on, it was expected that influence on stock market or significance of the relation would be different according to the type of news, and therefore each type of news was compared with fluctuation of stock price, and the results showed that market condition, outlook, and overseas news was the most useful to explain fluctuation of news. On the contrary, news about individual company was not statistically significant, but opinion mining value showed tendency opposite to stock price, and the reason can be thought to be the appearance of promotional and planned news for preventing stock price from falling. Finally, multiple regression analysis and logistic regression analysis was carried out in order to derive function of investment decision making on the basis of relation between positive/negative opinion of news and stock price, and the results showed that regression equation using variable of market conditions, outlook, and overseas news before opening of stock market was statistically significant, and classification accuracy of logistic regression accuracy results was shown to be 70.0% in rise of stock price, 78.8% in fall of stock price, and 74.6% on average. This study first analyzed relation between news and stock price through analyzing and quantifying sensitivity of atypical news contents by using opinion mining among big data analysis techniques, and furthermore, proposed and verified smart investment decision making model that could systematically carry out opinion mining and derive and support investment information. This shows that news can be used as variable to predict the price index of stocks for investment, and it is expected the model can be used as real investment support system if it is implemented as system and verified in the future.

• Current Research on Agriculture and Life Sciences
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• v.4
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• pp.141-153
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• 1986

This study intends to document the existing cyclical fluctuations of garic and onion price at farm gate level during the period of 1966-1986 in Korea. The existing patterns of such cyclical fluctuations were estimated systematically by removing the seasonal fluctuation and irregular movement as well as secular trend from the original price through the moving average method. It was found that the cyclical fluctuations of garic and onion prices repeated six and seven times respectively during the same period, also the amplitude coefficient of cyclical fluctuations showed speed up in recent years. It was noticed that the cyclical fluctuations of price in onion was higher than that of in garic.

• Asia Marketing Journal
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• v.14 no.2
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• pp.127-156
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• 2012

A lot of interest in the baby-boomer generation, those who were born after World War II, has emerged since their retirement has been accelerated. The retirement of baby-boomers has caused many health, public welfare, social policy and family relationship problems. However, their increased purchasing power has made them more attractive consumers than any other generation, and they have become a fascinating niche market in the depressed economy. This research selected middle-class women of the baby-boomer generation who have had powerful effects on society and have emerged as an attractive niche market, and attempted to understand their lives intensively. Based on research activities, the purpose of this research is to identify baby-boomer generation middle-aged women's life values. Qualitative research methodology was used to achieve research objectives, and this research aimed to suggest marketing implications to connected industries based on the research results. The research objectives are as follows. 1. understanding the lives of baby-boomer middle-class women who have powerful effects on socio-economic phenomena 2. identifying the life values of baby-boomer middle-class women 3. generating marketing implications based on an understanding of baby-boomer middle-class women's lives and life values This research conducted FGIs(focus group interviews), one of the qualitative research methodologies, to figure out baby-boomer middle-class women's life values intensively and selected 10 women living in Seoul for data collection. The qualitative data of collected FGIs were analyzed with spiral data analysis methodology proposed by Creswell(2007). The most effective factors to influence these middle-class women's lives powerfully were 'time' and 'independence'. Their consciousness of the importance of using time affects their life pattern generally, and their independence also impacts greatly on the way they exploit time and on their diverse relationships. They maximized their self-realization and showed long-term partnership with their surrounding circumstances because of those effective factors. Baby-boomer middle-class women's self-realization was divided into two areas. One was their outside activities and another was perfect management of their physical appearance and home interior. Like the results of this research, their need for social entrance will be reinforced more strongly since their internal and external activities aim for the achievement of self-realization. In addition, this research suggests that baby-boomer middle-class women's activities are connected with their management of their physical appearance and home interior decorations, and that such management is caused not only by a simple interest in fashion and beauty but also a profound desire for self-realization. On account of their consciousness, which is different from other generations, Korean baby-boomer middle-class women are able to maintain positive partnerships with their surrounding circumstances; however, they also show ambivalent emotions to retain effective partnerships. To overcome those stressful situations, they make greater efforts to keep up their health and youth, and also engage in diverse activities to maintain their mental health. Finally, they generate positive attitudes toward their economic situation and extra time to develop self-realization and pursue happy, youthful and healthy lives. Based on those results, this study suggests the following implications. First, industries targeting the baby-boomer generation should develop innovative products and services which help the baby-boomer generation maximize their efficiency of time since time is one of the most important factors powerfully impacting the baby-boomer generation. They will engage in various activities to fill up their extra time and consume helpful products and services. Second, such industries should supply the baby-boomer generation with opportunities which propose new ways of self-realization since this generation shows a great desire for self-realization because of their self-efficacy. With customized strategies of satisfying their needs, the baby-boomer generation would discover opportunities to utilize their abilities, relationships and aesthetic senses, and industries would develop a niche market. Third, market segmentations which target the baby-boomer generation's desire to maintain their physical appearance and home interior should be executed since such activities are the main strategies to develop this generation's self-realization. The baby-boomer generation's desire to study those areas would be expanded, and those education systems should produce innovative products and services targeting the baby-boomer generation. This implication also offers to government officials new policies related with the baby-boomer generation. This exploratory study utilized qualitative research methodology to understand baby-boomer middle-class women's lives, and proposed propositions and limitations for further researches. As for the limitations, first, it is hard to generalize the research results so that they may apply to all areas and economic classes of the baby-boomer generation since this research selected only 10 women living in Seoul for the data collection process. To overcome this limitation, extended data collections of subjects from diverse regions and economic classes should be designed. Second, quantitative research should be conducted to supplement the findings with validities. Third, this research focused on only general ideas of the baby-boomer generation's lives since the range of this study was focused on their overall lives. Therefore, intensive research related to specific areas of their lives should be conducted.

• Journal of Intelligence and Information Systems
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• v.23 no.3
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• pp.155-175
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• 2017

As social data become into the spotlight, mainstream web search engines provide data indicate how many people searched specific keyword: Web Search Traffic data. Web search traffic information is collection of each crowd that search for specific keyword. In a various area, web search traffic can be used as one of useful variables that represent the attention of common users on specific interests. A lot of studies uses web search traffic data to nowcast or forecast social phenomenon such as epidemic prediction, consumer pattern analysis, product life cycle, financial invest modeling and so on. Also web search traffic data have begun to be applied to predict tourist inbound. Proper demand prediction is needed because tourism is high value-added industry as increasing employment and foreign exchange. Among those tourists, especially Chinese tourists: Youke is continuously growing nowadays, Youke has been largest tourist inbound of Korea tourism for many years and tourism profits per one Youke as well. It is important that research into proper demand prediction approaches of Youke in both public and private sector. Accurate tourism demands prediction is important to efficient decision making in a limited resource. This study suggests improved model that reflects latest issue of society by presented the attention from group of individual. Trip abroad is generally high-involvement activity so that potential tourists likely deep into searching for information about their own trip. Web search traffic data presents tourists' attention in the process of preparation their journey instantaneous and dynamic way. So that this study attempted select key words that potential Chinese tourists likely searched out internet. Baidu-Chinese biggest web search engine that share over 80%- provides users with accessing to web search traffic data. Qualitative interview with potential tourists helps us to understand the information search behavior before a trip and identify the keywords for this study. Selected key words of web search traffic are categorized by how much directly related to "Korean Tourism" in a three levels. Classifying categories helps to find out which keyword can explain Youke inbound demands from close one to far one as distance of category. Web search traffic data of each key words gathered by web crawler developed to crawling web search data onto Baidu Index. Using automatically gathered variable data, linear model is designed by multiple regression analysis for suitable for operational application of decision and policy making because of easiness to explanation about variables' effective relationship. After regression linear models have composed, comparing with model composed traditional variables and model additional input web search traffic data variables to traditional model has conducted by significance and R squared. after comparing performance of models, final model is composed. Final regression model has improved explanation and advantage of real-time immediacy and convenience than traditional model. Furthermore, this study demonstrates system intuitively visualized to general use -Youke Mining solution has several functions of tourist decision making including embed final regression model. Youke Mining solution has algorithm based on data science and well-designed simple interface. In the end this research suggests three significant meanings on theoretical, practical and political aspects. Theoretically, Youke Mining system and the model in this research are the first step on the Youke inbound prediction using interactive and instant variable: web search traffic information represents tourists' attention while prepare their trip. Baidu web search traffic data has more than 80% of web search engine market. Practically, Baidu data could represent attention of the potential tourists who prepare their own tour as real-time. Finally, in political way, designed Chinese tourist demands prediction model based on web search traffic can be used to tourism decision making for efficient managing of resource and optimizing opportunity for successful policy.