• Asia Marketing Journal
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• v.15 no.4
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• pp.61-101
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• 2014

This study investigates when and how disagreements in online customer ratings prompt more favorable product evaluations. Among the three metrics of volume, valence, and variance that feature in the research on online customer ratings, volume and valence have exhibited consistently positive patterns in their effects on product sales or evaluations (e.g., Dellarocas, Zhang, and Awad 2007; Liu 2006). Ratings variance, or the degree of disagreement among reviewers, however, has shown rather mixed results, with some studies reporting positive effects on product sales (e.g., Clement, Proppe, and Rott 2007) while others finding negative effects on product evaluations (e.g., Zhu and Zhang 2010). This study aims to resolve these contradictory findings by introducing preference heterogeneity as a possible moderator and causal attribution as a mediator to account for the moderating effect. The main proposition of this study is that when preference heterogeneity is perceived as high, a disagreement in ratings is attributed more to reviewers' different preferences than to unreliable product quality, which in turn prompts better quality evaluations of a product. Because disagreements mostly result from differences in reviewers' tastes or the low reliability of a product's quality (Mizerski 1982; Sen and Lerman 2007), a greater level of attribution to reviewer tastes can mitigate the negative effect of disagreement on product evaluations. Specifically, if consumers infer that reviewers' heterogeneous preferences result in subjectively different experiences and thereby highly diverse ratings, they would not disregard the overall quality of a product. However, if consumers infer that reviewers' preferences are quite homogeneous and thus the low reliability of the product quality contributes to such disagreements, they would discount the overall product quality. Therefore, consumers would respond more favorably to disagreements in ratings when preference heterogeneity is perceived as high rather than low. This study furthermore extends this prediction to the various levels of average ratings. The heuristicsystematic processing model so far indicates that the engagement in effortful systematic processing occurs only when sufficient motivation is present (Hann et al. 2007; Maheswaran and Chaiken 1991; Martin and Davies 1998). One of the key factors affecting this motivation is the aspiration level of the decision maker. Only under conditions that meet or exceed his aspiration level does he tend to engage in systematic processing (Patzelt and Shepherd 2008; Stephanous and Sage 1987). Therefore, systematic causal attribution processing regarding ratings variance is likely more activated when the average rating is high enough to meet the aspiration level than when it is too low to meet it. Considering that the interaction between ratings variance and preference heterogeneity occurs through the mediation of causal attribution, this greater activation of causal attribution in high versus low average ratings would lead to more pronounced interaction between ratings variance and preference heterogeneity in high versus low average ratings. Overall, this study proposes that the interaction between ratings variance and preference heterogeneity is more pronounced when the average rating is high as compared to when it is low. Two laboratory studies lend support to these predictions. Study 1 reveals that participants exposed to a high-preference heterogeneity book title (i.e., a novel) attributed disagreement in ratings more to reviewers' tastes, and thereby more favorably evaluated books with such ratings, compared to those exposed to a low-preference heterogeneity title (i.e., an English listening practice book). Study 2 then extended these findings to the various levels of average ratings and found that this greater preference for disagreement options under high preference heterogeneity is more pronounced when the average rating is high compared to when it is low. This study makes an important theoretical contribution to the online customer ratings literature by showing that preference heterogeneity serves as a key moderator of the effect of ratings variance on product evaluations and that causal attribution acts as a mediator of this moderation effect. A more comprehensive picture of the interplay among ratings variance, preference heterogeneity, and average ratings is also provided by revealing that the interaction between ratings variance and preference heterogeneity varies as a function of the average rating. In addition, this work provides some significant managerial implications for marketers in terms of how they manage word of mouth. Because a lack of consensus creates some uncertainty and anxiety over the given information, consumers experience a psychological burden regarding their choice of a product when ratings show disagreement. The results of this study offer a way to address this problem. By explicitly clarifying that there are many more differences in tastes among reviewers than expected, marketers can allow consumers to speculate that differing tastes of reviewers rather than an uncertain or poor product quality contribute to such conflicts in ratings. Thus, when fierce disagreements are observed in the WOM arena, marketers are advised to communicate to consumers that diverse, rather than uniform, tastes govern reviews and evaluations of products.

• Asia Marketing Journal
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• v.15 no.1
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• pp.1-22
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• 2013

This case focuses on Shanghai Tang, the first truly Chinese luxury brand that appeals to both Westerners and, more recently, to Chinese consumers worldwide. A visionary and wealthy businessman Sir David Tang created this company from scratch in 1994 in Hong Kong. Its story, spanned over almost two decades, has been fascinating. It went from what best a Chinese brand could be in the eyes of Westerners who love the Chinese culture, to a nearly-bankrupted company in 1998, before being acquired by Richemont, the second largest luxury group in the world. Since then, its turnaround has been spectacular with a growing appeal among Chinese luxury consumers who represent the core segment of the luxury industry today. The main objective of this case study is to formally examine how Shanghai Tang overcame its downfall and re-emerged as one the very few well- known Chinese luxury brands. More specifically, this case highlights the ways with which Shanghai Tang made a transitional change from a brand for Westerners who love the Chinese culture, to a brand for both, Westerners who love the Chinese culture and Chinese who love luxury. A close examination reveals that Shanghai Tang has followed the brand identity concept that consists of two major components: functional and emotional. The functional component for developing a luxury brand concerns all product characteristics that will make a product 'luxurious' in the eyes of the consumer, such as premium quality of cachemire from Mongolia, Chinese silk, lacquer, finest leather, porcelain, and jade in the case of Shanghai Tang. The emotional component consists of non-functional symbolic meanings of a brand. The symbolic meaning marks the major difference between a premium and a luxury brand. In the case of Shanghai Tang, its symbolic meaning refers to the Chinese culture and the brand aims to represent the best of Chinese traditions and establish itself as "the ambassador of modern Chinese style". It touches the Chinese heritage and emotions. Shanghai Tang has reinvented the modern Chinese chic by drawing back to the stylish decadence of Shanghai in the 1930s, which was then called the "Paris of the East", and this is where the brand finds inspiration to create its own myth. Once the functional and emotional components assured, Shanghai Tang has gone through a four-stage development to become the first global Chinese luxury brand: introduction, deepening, expansion, and revitalization. Introduction: David Tang discovered a market gap and had a vision to launch the first Chinese luxury brand to the world. The key success drivers for the introduction and management of a Chinese luxury brand are a solid brand identity and, above all, a creative mind, an inspired person. This was David Tang then, and this is now Raphael Le Masne de Chermont, the current Executive Chairman. Shanghai Tang combines Chinese and Western elements, which it finds to be the most sustainable platform for drawing consumers. Deepening: A major objective of the next phase is to become recognized as a luxury brand and a fashion or design authority. For this purpose, Shanghai Tang has cooperated with other well-regarded luxury and lifestyle brands such as Puma and Swarovski. It also expanded its product lines from high-end custom-made garments to music CDs and restaurant. Expansion: After the opening of his first store in Hong Kong in 1994, David Tang went on to open his second store in New York City three years later. However this New York retail operation was a financial disaster. Barely nineteen months after the opening, the store was shut down and quietly relocated to a cheaper location of Madison Avenue. Despite this failure, Shanghai Tang products found numerous followers especially among Western tourists and became "souvenir-like" must-haves. However, despite its strong brand DNA, the brand did not generate enough repeated sales and over the years the company cumulated heavy debts and became unprofitable. Revitalizing: After its purchase by Richemont in 1998, Le Masne de Chermont was appointed to lead the company, reposition the brand and undertake some major strategic changes such as revising the "Shanghai Tang" designs to appeal not only to Westerners but also to Chinese consumers, and to open new stores around the world. Since then, Shanghai Tang has become synonymous to a modern Chinese luxury lifestyle brand.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.18 no.2
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• pp.35-52
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• 2023

Recently, as the non-face-to-face environment has developed due to COVID-19 and environmental pollution, the importance of online digital healthcare is increasing, and venture start-ups and activities such as health care, telemedicine, and digital treatments are also actively underway. This study conducted the impact on the acceptability of digital healthcare smartwatches with an integrated approach of the expanded integrated technology acceptance model (UTAUT2) and the behavioral inference model (BRT). The most advanced integrated technology acceptance model for innovative technology acceptance research was used to identify major factors such as utility expectations, social effects, convenience, price barriers, lack of alternatives, and behavioral intentions. For the study, about 410 responses from ordinary people in their teens to 60s across the country were collected, and based on this, the hypothesis was verified using structural equations after testing reliability and validity of the data. SPSS 23 and AMOS 23 were used for research analysis. Studies have shown that personal innovation has a significant impact on the reasons for acceptance (use value, social impact, convenience of use), attitude, and non-use (price barriers, lack of alternatives, and barriers to use). These results are the same as the results of previous studies that confirmed the influence of the main value of innovative ICT on user acceptance intention. In addition, the reason for acceptance had a significant effect on attitude, but the effect of the reason for non-acceptance was not significant. It can be analyzed that consumers are interested in new ICT products and new services, but purchase them more carefully and selectively. This study has evolved from the acceptance analysis of general-purpose consumer innovation technology to the acceptance analysis of consumer value in smartwatch digital healthcare, which is a new and important area in the future. Industrially, it can contribute to the product's purchase and marketing. It is hoped that this study will contribute to increasing research in the digital healthcare sector, which will play an important role in our lives in the future, and that it will develop into in-depth factors that are more suitable for consumer value through integrated approach models and integrated analysis of consumer acceptance and non-acceptance.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.18 no.4
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• pp.1-20
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• 2023

This study attempted to identify the relationship between the investment determinants of accelerators and investment performance through empirical analysis. Through literature review, four dimensions and 12 measurement items were extracted for investment determinants, which are independent variables, and investment performance was adjusted to the cumulative amount of subsequent investment based on previous studies. Performance data from 594 companies selected by TIPS from 2017 to 2019, which are relatively reliable and easy to secure data, were collected, and the subsequent investment cumulative attraction amount, which is a dependent variable, was hypothesized through multiple regression analysis three years after the investment. As a result of the study, 'industrial experience years' in the characteristics of founders, 'market size', 'market growth', 'competitive strength', and 'number of patents' in the characteristics of products and services had a significant positive (+) effect. The impact of independent variables on dependent variables was most influenced by the competitive strength of market characteristics, followed by the number of years of industrial experience, the number of patents, the size of the market, and market growth. This was different from the results of previous studies conducted mainly on qualitative research methods, and in most previous studies, the characteristics of founders were the most important, but the empirical analysis results were market characteristics. As a sub-factor, the intensity of competition, which was the subordinate to the importance of previous studies, had the greatest influence in empirical analysis. The academic significance of this study is that it presented a specific methodology to collect and build 594 empirical samples in the absence of empirical research on accelerator investment determinants, and created an opportunity to expand the theoretical discussion of investment determinants through causal research. In practice, the information asymmetry and uncertainty of startups that accelerators have can help them make effective investment decisions by establishing a systematic model of experience-dependent investment determinants.

• Journal of Intelligence and Information Systems
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• v.23 no.4
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• pp.77-110
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• 2017

Recently, the diversification and individualization of consumption patterns through the web and mobile devices based on the Internet have been rapid. As this happens, the efficient operation of the offline store, which is a traditional distribution channel, has become more important. In order to raise both the sales and profits of stores, stores need to supply and sell the most attractive products to consumers in a timely manner. However, there is a lack of research on which SKUs, out of many products, can increase sales probability and reduce inventory costs. In particular, if a company sells products through multiple in-store stores across multiple locations, it would be helpful to increase sales and profitability of stores if SKUs appealing to customers are recommended. In this study, the recommender system (recommender system such as collaborative filtering and hybrid filtering), which has been used for personalization recommendation, is suggested by SKU recommendation method of a store unit of a distribution company that handles a homogeneous brand through a plurality of sales stores by country and region. We calculated the similarity of each store by using the purchase data of each store's handling items, filtering the collaboration according to the sales history of each store by each SKU, and finally recommending the individual SKU to the store. In addition, the store is classified into four clusters through PCA (Principal Component Analysis) and cluster analysis (Clustering) using the store profile data. The recommendation system is implemented by the hybrid filtering method that applies the collaborative filtering in each cluster and measured the performance of both methods based on actual sales data. Most of the existing recommendation systems have been studied by recommending items such as movies and music to the users. In practice, industrial applications have also become popular. In the meantime, there has been little research on recommending SKUs for each store by applying these recommendation systems, which have been mainly dealt with in the field of personalization services, to the store units of distributors handling similar brands. If the recommendation method of the existing recommendation methodology was 'the individual field', this study expanded the scope of the store beyond the individual domain through a plurality of sales stores by country and region and dealt with the store unit of the distribution company handling the same brand SKU while suggesting a recommendation method. In addition, if the existing recommendation system is limited to online, it is recommended to apply the data mining technique to develop an algorithm suitable for expanding to the store area rather than expanding the utilization range offline and analyzing based on the existing individual. The significance of the results of this study is that the personalization recommendation algorithm is applied to a plurality of sales outlets handling the same brand. A meaningful result is derived and a concrete methodology that can be constructed and used as a system for actual companies is proposed. It is also meaningful that this is the first attempt to expand the research area of the academic field related to the existing recommendation system, which was focused on the personalization domain, to a sales store of a company handling the same brand. From 05 to 03 in 2014, the number of stores' sales volume of the top 100 SKUs are limited to 52 SKUs by collaborative filtering and the hybrid filtering method SKU recommended. We compared the performance of the two recommendation methods by totaling the sales results. The reason for comparing the two recommendation methods is that the recommendation method of this study is defined as the reference model in which offline collaborative filtering is applied to demonstrate higher performance than the existing recommendation method. The results of this model are compared with the Hybrid filtering method, which is a model that reflects the characteristics of the offline store view. The proposed method showed a higher performance than the existing recommendation method. The proposed method was proved by using actual sales data of large Korean apparel companies. In this study, we propose a method to extend the recommendation system of the individual level to the group level and to efficiently approach it. In addition to the theoretical framework, which is of great value.

• Journal of the Korean Society of Food Culture
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• v.14 no.4
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• pp.371-383
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• 1999

This report gave analysis of food demand both in Korea and Japan through introducing the concept of cohort analysis to the conventional demand model. This research was done to clarify the factors which determine food demand of the household. The traits of the new model for demand analysis are to consider and quantify those effects on food demand not only of economic factors such as expenditure and price but also of non-economic factors such as the age and birth cohort of the householder. The results of the analysis can be summarized as follows: 1) The comparison of the item-wise elasticities of food demand demonstrates that the expenditure elasticity is higher in Korea than in Japan and that the expenditure elasticity is -0.1 for cereal and more than 1 for eating-out in both countries. In respect to price elasticity, the absolute values of all the items except alcohol and cooked food are higher in the Korea than in Japan, and especially the price elasticities of beverages, dairy products and fruit are predominantly higher in Japan. In this way, both expenditure and price elasticities of a large number of items are higher in Korea than in Japan, which may be explained from the fact that the level of expenditure is higher in Japan than in Korea. 2) In both of Korea and Japan, as the householder grows older, the expenditure for each item increases and the composition of expenditure changes in such a way that these moves may be regarded as due to the age effect. However, there are both similarities and differences in the details of such moves between Korea and Japan. Those two countries have this trait in common that the young age groups of the householder spend more on dairy products and middle age groups spend more on cake than other age groups. In the Korea, however, there can be seen a certain trend that higher age groups spend more on a large number of items, reflecting the fact that there are more two-generation families in higher age groups. Japan differs from Korea in that expenditure in Japan is diversified, depending upon the age group. For example, in Japan, middle age groups spend more on cake, cereal, high-caloric food like meat and eating-out while older age groups spend more for Japanese-style food like fish/shellfish and vegetable/seaweed, and cooked food. 3) The effect of the birth cohort effect was also demonstrated. The birth cohort effect was introduced under the supposition that the food circumstances under which the householder was born and brought up would determine the current expenditure. Thus, the following was made clear: older generations in both countries placed more emphasis upon stable food in their composition of food consumption; the share of livestock products, oil/fats and externalized food was higher in the food composition of younger generation; differences in food composition among generations were extremely large in Korea while they were relatively small in Japan; and Westernization and externalization of diet made rapid increases simultaneously with generation changes in Korea while they made any gradual increases in Japan during the same time period. 4) The four major factors which impact the long-term change of food demand of the household are expenditure, price, the age of the householder, and the birth cohort of the householder. Investigations were made as to which factor had the largest impact. As a result, it was found that the price effect was the smallest in both countries, and that the relative importance of the factor-by-factor effects differed among the two countries: in Korea the expenditure effect was greater than the effects of age and birth cohort while in Japan the effects of non-economic factors such as the age and birth cohort of householder were greater than those of economic factors such as expenditures.

• 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.21 no.4
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• pp.53-77
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• 2015

Mobile commerce provides a convenient shopping experience in which users can buy products without the constraints of time and space. Mobile commerce has already set off a mega trend in Korea. The market size is estimated at approximately 15 trillion won (KRW) for 2015, thus far. In the Korean market, social commerce and open market are key components. Social commerce has an overwhelming open market in terms of the number of users in the Korean mobile commerce market. From the point of view of the industry, quick market entry, and content curation are considered to be the major success factors, reflecting the rapid growth of social commerce in the market. However, academics' empirical research and analysis to prove the success rate of social commerce is still insufficient. Henceforward, it is to be expected that social commerce and the open market in the Korean mobile commerce will compete intensively. So it is important to conduct an empirical analysis to prove the differences in user experience between social commerce and open market. This paper is an exploratory study that shows a comparative analysis of social commerce and the open market regarding user experience, which is based on the mobile users' reviews. Firstly, this study includes a collection of approximately 10,000 user reviews of social commerce and open market listed Google play. A collection of mobile user reviews were classified into topics, such as perceived usefulness and perceived ease of use through LDA topic modeling. Then, a sentimental analysis and co-occurrence analysis on the topics of perceived usefulness and perceived ease of use was conducted. The study's results demonstrated that social commerce users have a more positive experience in terms of service usefulness and convenience versus open market in the mobile commerce market. Social commerce has provided positive user experiences to mobile users in terms of service areas, like 'delivery,' 'coupon,' and 'discount,' while open market has been faced with user complaints in terms of technical problems and inconveniences like 'login error,' 'view details,' and 'stoppage.' This result has shown that social commerce has a good performance in terms of user service experience, since the aggressive marketing campaign conducted and there have been investments in building logistics infrastructure. However, the open market still has mobile optimization problems, since the open market in mobile commerce still has not resolved user complaints and inconveniences from technical problems. This study presents an exploratory research method used to analyze user experience by utilizing an empirical approach to user reviews. In contrast to previous studies, which conducted surveys to analyze user experience, this study was conducted by using empirical analysis that incorporates user reviews for reflecting users' vivid and actual experiences. Specifically, by using an LDA topic model and TAM this study presents its methodology, which shows an analysis of user reviews that are effective due to the method of dividing user reviews into service areas and technical areas from a new perspective. The methodology of this study has not only proven the differences in user experience between social commerce and open market, but also has provided a deep understanding of user experience in Korean mobile commerce. In addition, the results of this study have important implications on social commerce and open market by proving that user insights can be utilized in establishing competitive and groundbreaking strategies in the market. The limitations and research direction for follow-up studies are as follows. In a follow-up study, it will be required to design a more elaborate technique of the text analysis. This study could not clearly refine the user reviews, even though the ones online have inherent typos and mistakes. This study has proven that the user reviews are an invaluable source to analyze user experience. The methodology of this study can be expected to further expand comparative research of services using user reviews. Even at this moment, users around the world are posting their reviews about service experiences after using the mobile game, commerce, and messenger applications.

• 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 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.