• Journal of Global Scholars of Marketing Science
• /
• v.18 no.1
• /
• pp.59-80
• /
• 2008

Recently, e-sports are growing with potentiality as a new industry with conspicuous profit model. But studies that dealing with e-sports are not enough. Hence, proposes of this paper are both to establish basic model that is for the design of e-sport marketing strategy and to contribute toward future studies which are related to e-sports. Recently, the researches to explain sports-sponsorship through the identification theory have been discovered. Many researches say that somewhat proper identification is a requirement for most sponsors to improve the their images which is essential to sponsorship activity. Consequently, the research for sponsorship associated with identification in the e-sports, not in the physical sports is the core sector of this study. We extracted the variables from online's major characteristics and existing sport sponsorship researches. First, because e-sports mean the tournaments or leagues in the use of online game, the main event of the game is likely to call it online game. Online media's attributes are distinguished from those of offline. Especially, interactivity, anonymity, and expandibility as a e-sport game attributes are able to be mentioned. So, these inherent online attributes are examined on the relationship with flow. Second, in physical sports games, Fisher(1998) revealed that team similarity and team attractivity were positively related to team identification. Wann(1996) said that the result of former game influenced the evaluation of the next game, then in turn has an effect on the identification of team supporters. Considering these results in the e-sports side, e-sports gamer' attractivity, similarity, and match result seem to be important precedent variables of the identification with a gamer. So, these e-sport gamer attributes are examined on the relationship with both flow and identification with a gamer. Csikszentmihalyi(1988) defined the term flow as feeling status for him to be making current positive experience optimally. Hoffman and Novak(1996) also said that if a user experienced the flow he would visit a website without any reward. Therefore flow might be positively associated with user's identification with a gamer. And, Swanson(2003) disclosed that team identification influenced the positive results of sponsorship, which included attitude toward sponsors, sponsor patronage, and satisfaction with sponsors. That is, identification with a gamer expect to be connected with corporation identification significantly. According to the above, we can design the following research model. All variables used in this study(interactivity, anonymity, expandibility, attractivity, similarity, match result, flow, identification with a gamer, and identification with a sponsor) definitely were defined operationally underlying precedent researches. Sample collection was carried out to the person who has an experience to have enjoyed e-sports during June 2006. Much portion of samples is men because much more men than women enjoy e-sports in general. Two-step approach was used to test the hypotheses. First, confirmatory factor analysis was committed to guarantee the validity and reliability of variables. The results showed that all variables had not only intensive and discriminant validity, but also reliability. Then, research model was examined with fully structural equation using LISREL 8.3 version. The fitness of the suggested model mostly was at the acceptable level. Shortly speaking about the results, first of all, in e-sports game attributes, only interactivity which is called a basic feature in online situation affected flow positively. Secondly, in e-sports gamer's attributes, similarity with a gamer and match result influenced flow positively, but there was no significant effect in the relationship between the attractivity of a gamer and flow. And as expected, similarity had an effect on identification with a gamer significantly. But unexpectedly attractivity and match result did not influence identification with a gamer significantly. Just the same as the fact verified in the many precedent researches, flow greatly influenced identification with a gamer, and identification with a gamer continually had an influence on the identification with a sponsor significantly. There are some implications in these results. If the sponsor of e-sports supports the pro-game player who absolutely should have the superior ability to others and is similar to the user enjoying e-sports, many amateur gamers will feel much of the flow and identification with a pro-gamer, and then after all, feel the identification with a sponsor. Such identification with a sponsor leads people enjoying e-sports to have purchasing intention for products produced by the sponsor and to make a positive word-of-mouth for those products or the sponsor. For the future studies, we recommend a few ideas. Based on the results of this study, it is necessary to find new variables relating to the e-sports, which is not mentioned in this study. For this work to be possible, qualitative research seems to be needed to consider the inherent e-sport attributes. Finally, to generalize the results related to e-sports, a wide range of generations not a specific generation should be researched.

• Journal of Intelligence and Information Systems
• /
• v.19 no.2
• /
• pp.73-85
• /
• 2013

Nowadays, in today's information society, the importance of the knowledge service using the information to creative value is getting higher day by day. In addition, depending on the development of IT technology, it is ease to collect and use information. Also, many companies actively use customer information to marketing in a variety of industries. Into the 21st century, companies have been actively using the culture arts to manage corporate image and marketing closely linked to their commercial interests. But, it is difficult that companies attract or maintain consumer's interest through their technology. For that reason, it is trend to perform cultural activities for tool of differentiation over many firms. Many firms used the customer's experience to new marketing strategy in order to effectively respond to competitive market. Accordingly, it is emerging rapidly that the necessity of personalized service to provide a new experience for people based on the personal profile information that contains the characteristics of the individual. Like this, personalized service using customer's individual profile information such as language, symbols, behavior, and emotions is very important today. Through this, we will be able to judge interaction between people and content and to maximize customer's experience and satisfaction. There are various relative works provide customer-centered service. Specially, emotion recognition research is emerging recently. Existing researches experienced emotion recognition using mostly bio-signal. Most of researches are voice and face studies that have great emotional changes. However, there are several difficulties to predict people's emotion caused by limitation of equipment and service environments. So, in this paper, we develop emotion prediction model based on vision-based interface to overcome existing limitations. Emotion recognition research based on people's gesture and posture has been processed by several researchers. This paper developed a model that recognizes people's emotional states through body gesture and posture using difference image method. And we found optimization validation model for four kinds of emotions' prediction. A proposed model purposed to automatically determine and predict 4 human emotions (Sadness, Surprise, Joy, and Disgust). To build up the model, event booth was installed in the KOCCA's lobby and we provided some proper stimulative movie to collect their body gesture and posture as the change of emotions. And then, we extracted body movements using difference image method. And we revised people data to build proposed model through neural network. The proposed model for emotion prediction used 3 type time-frame sets (20 frames, 30 frames, and 40 frames). And then, we adopted the model which has best performance compared with other models.' Before build three kinds of models, the entire 97 data set were divided into three data sets of learning, test, and validation set. The proposed model for emotion prediction was constructed using artificial neural network. In this paper, we used the back-propagation algorithm as a learning method, and set learning rate to 10%, momentum rate to 10%. The sigmoid function was used as the transform function. And we designed a three-layer perceptron neural network with one hidden layer and four output nodes. Based on the test data set, the learning for this research model was stopped when it reaches 50000 after reaching the minimum error in order to explore the point of learning. We finally processed each model's accuracy and found best model to predict each emotions. The result showed prediction accuracy 100% from sadness, and 96% from joy prediction in 20 frames set model. And 88% from surprise, and 98% from disgust in 30 frames set model. The findings of our research are expected to be useful to provide effective algorithm for personalized service in various industries such as advertisement, exhibition, performance, etc.

• Journal of Intelligence and Information Systems
• /
• v.25 no.2
• /
• pp.25-38
• /
• 2019

Selecting high-quality information that meets the interests and needs of users among the overflowing contents is becoming more important as the generation continues. In the flood of information, efforts to reflect the intention of the user in the search result better are being tried, rather than recognizing the information request as a simple string. Also, large IT companies such as Google and Microsoft focus on developing knowledge-based technologies including search engines which provide users with satisfaction and convenience. Especially, the finance is one of the fields expected to have the usefulness and potential of text data analysis because it's constantly generating new information, and the earlier the information is, the more valuable it is. Automatic knowledge extraction can be effective in areas where information flow is vast, such as financial sector, and new information continues to emerge. However, there are several practical difficulties faced by automatic knowledge extraction. First, there are difficulties in making corpus from different fields with same algorithm, and it is difficult to extract good quality triple. Second, it becomes more difficult to produce labeled text data by people if the extent and scope of knowledge increases and patterns are constantly updated. Third, performance evaluation is difficult due to the characteristics of unsupervised learning. Finally, problem definition for automatic knowledge extraction is not easy because of ambiguous conceptual characteristics of knowledge. So, in order to overcome limits described above and improve the semantic performance of stock-related information searching, this study attempts to extract the knowledge entity by using neural tensor network and evaluate the performance of them. Different from other references, the purpose of this study is to extract knowledge entity which is related to individual stock items. Various but relatively simple data processing methods are applied in the presented model to solve the problems of previous researches and to enhance the effectiveness of the model. From these processes, this study has the following three significances. First, A practical and simple automatic knowledge extraction method that can be applied. Second, the possibility of performance evaluation is presented through simple problem definition. Finally, the expressiveness of the knowledge increased by generating input data on a sentence basis without complex morphological analysis. The results of the empirical analysis and objective performance evaluation method are also presented. The empirical study to confirm the usefulness of the presented model, experts' reports about individual 30 stocks which are top 30 items based on frequency of publication from May 30, 2017 to May 21, 2018 are used. the total number of reports are 5,600, and 3,074 reports, which accounts about 55% of the total, is designated as a training set, and other 45% of reports are designated as a testing set. Before constructing the model, all reports of a training set are classified by stocks, and their entities are extracted using named entity recognition tool which is the KKMA. for each stocks, top 100 entities based on appearance frequency are selected, and become vectorized using one-hot encoding. After that, by using neural tensor network, the same number of score functions as stocks are trained. Thus, if a new entity from a testing set appears, we can try to calculate the score by putting it into every single score function, and the stock of the function with the highest score is predicted as the related item with the entity. To evaluate presented models, we confirm prediction power and determining whether the score functions are well constructed by calculating hit ratio for all reports of testing set. As a result of the empirical study, the presented model shows 69.3% hit accuracy for testing set which consists of 2,526 reports. this hit ratio is meaningfully high despite of some constraints for conducting research. Looking at the prediction performance of the model for each stocks, only 3 stocks, which are LG ELECTRONICS, KiaMtr, and Mando, show extremely low performance than average. this result maybe due to the interference effect with other similar items and generation of new knowledge. In this paper, we propose a methodology to find out key entities or their combinations which are necessary to search related information in accordance with the user's investment intention. Graph data is generated by using only the named entity recognition tool and applied to the neural tensor network without learning corpus or word vectors for the field. From the empirical test, we confirm the effectiveness of the presented model as described above. However, there also exist some limits and things to complement. Representatively, the phenomenon that the model performance is especially bad for only some stocks shows the need for further researches. Finally, through the empirical study, we confirmed that the learning method presented in this study can be used for the purpose of matching the new text information semantically with the related stocks.

• Journal of Intelligence and Information Systems
• /
• v.25 no.1
• /
• pp.21-41
• /
• 2019

Thanks to the rapid development of information technologies, the data available on Internet have grown rapidly. In this era of big data, many studies have attempted to offer insights and express the effects of data analysis. In the tourism and hospitality industry, many firms and studies in the era of big data have paid attention to online reviews on social media because of their large influence over customers. As tourism is an information-intensive industry, the effect of these information networks on social media platforms is more remarkable compared to any other types of media. However, there are some limitations to the improvements in service quality that can be made based on opinions on social media platforms. Users on social media platforms represent their opinions as text, images, and so on. Raw data sets from these reviews are unstructured. Moreover, these data sets are too big to extract new information and hidden knowledge by human competences. To use them for business intelligence and analytics applications, proper big data techniques like Natural Language Processing and data mining techniques are needed. This study suggests an analytical approach to directly yield insights from these reviews to improve the service quality of hotels. Our proposed approach consists of topic mining to extract topics contained in the reviews and the decision tree modeling to explain the relationship between topics and ratings. Topic mining refers to a method for finding a group of words from a collection of documents that represents a document. Among several topic mining methods, we adopted the Latent Dirichlet Allocation algorithm, which is considered as the most universal algorithm. However, LDA is not enough to find insights that can improve service quality because it cannot find the relationship between topics and ratings. To overcome this limitation, we also use the Classification and Regression Tree method, which is a kind of decision tree technique. Through the CART method, we can find what topics are related to positive or negative ratings of a hotel and visualize the results. Therefore, this study aims to investigate the representation of an analytical approach for the improvement of hotel service quality from unstructured review data sets. Through experiments for four hotels in Hong Kong, we can find the strengths and weaknesses of services for each hotel and suggest improvements to aid in customer satisfaction. Especially from positive reviews, we find what these hotels should maintain for service quality. For example, compared with the other hotels, a hotel has a good location and room condition which are extracted from positive reviews for it. In contrast, we also find what they should modify in their services from negative reviews. For example, a hotel should improve room condition related to soundproof. These results mean that our approach is useful in finding some insights for the service quality of hotels. That is, from the enormous size of review data, our approach can provide practical suggestions for hotel managers to improve their service quality. In the past, studies for improving service quality relied on surveys or interviews of customers. However, these methods are often costly and time consuming and the results may be biased by biased sampling or untrustworthy answers. The proposed approach directly obtains honest feedback from customers' online reviews and draws some insights through a type of big data analysis. So it will be a more useful tool to overcome the limitations of surveys or interviews. Moreover, our approach easily obtains the service quality information of other hotels or services in the tourism industry because it needs only open online reviews and ratings as input data. Furthermore, the performance of our approach will be better if other structured and unstructured data sources are added.

• Asia Marketing Journal
• /
• v.13 no.2
• /
• pp.99-130
• /
• 2011

In recent years, a new way of differentiating product design has emerged -better known as 'masterpiece marketing,' this is a strategy where famous art pieces are borrowed on to product designs. Because the recent trends of well-being and LOHAS have encouraged the consumers' desires to enjoy culture and live a more opulent lifestyle, famous and notable paintings have grown to be more of "approachable masterpieces" to the public. As a strategy intended to develop a new consumerism, while still prioritizing customers' values and their satisfaction, companies have been drawn to this new type of marketing. The current consumption society has converted renowned art pieces from simply works of 'high culture' to a further way of marketing, aimed to differentiate products and dominate the market. Though many products have had masterpieces applied to their designs and have been noticed for their marketability, there has been less systematic research done on the scientific background behind this marketing approach. This research focused on the art pieces' fundamental nature of inducing emotions in the viewer, and hypothesized about how the evaluation of a product may be influenced by the affect provoked by the art piece used. To be more specific, if art pieces with different levels of pleasure and arousal -the two axis of emotion suggested by existing research on emotion -were used on each product, the goal was to see how the different levels influenced the consumer's assessment of the products, focusing on product's type as well as the evaluation of their attributes. First, a pretest was done to verify the relationship between the emotion provoked by the art piece and the consumer's preference. There were two types of surveys, each with five drawings from the ten that were assumed to differ in levels of the two axis of emotion. The survey was composed of questions asking for positive emotion, negative emotion, level of arousal, and preference. The correlation between the measurements of positive and negative emotions was -0.792, so an integrated entry was used in the analysis by subtracting the measurement of negative emotions from that of positive emotions. The first hypothesis that paintings that provoke positive emotions will be more preferred than paintings that bring out negative emotions was supported; and through this research, paintings that were to be used for the products were selected. The second pretest was conducted to settle on an item that would be used in the research. Items meant to measure utilitarian and hedonic attributes of milk and chocolate, the two products to be used in the research, were extracted. Because milk is a utilitarian product with strong practical attributes while chocolate is a hedonic product with strong hedonic attributes, these two were selected to be used in this research. The first study was executed to see if there is a difference in attitude about products that have different painting on their designs, which either induces positive or negative emotions. It was also to verify whether this difference in attitude was mediated by the viewer's preference for the art piece. This study showed that when positive emotion inducing painting was used, the product was better evaluated compared to the product with a painting that provokes a negative emotion, thus supporting the second hypothesis. It was also supported that the effect of affect on product evaluation was mediated by preference for the art piece. The second study was done to see the influence of the level of arousal on the evaluation of the product's attributes. Art pieces that differ in the level of arousal were selected through the pretest, and later it verified the hypothesis that the level of arousal has an effect on the assessment of the attributes of the product. In the case of milk, a utilitarian product, the fourth hypothesis that a high-arousal painting will better evaluated for its hedonic attributes was supported, as well as the fifth, which hypothesized that a low-arousal painting will receive a higher assessment for its utilitarian attributes. However, for chocolate, a hedonic product, both fourth and fifth hypotheses were not supported. This study is significant for the following basis: first, it verified the importance of the emotion induced by the painting on the evaluation of the product's attributes, by applying a systematic and scientific method. Second, it expanded from the existing research on positive/negative emotions to confirm the additional influence of the state of arousal on product evaluation.

• The Korean Journal of Nuclear Medicine Technology
• /
• v.14 no.2
• /
• pp.3-8
• /
• 2010

Purpose: $^{11}C$-Acetate PET/CT is useful in detecting lesions that are related to livers in the human body and leads to a sensitivity of 87.3%. On the other hand, $^{18}F$-FDG PET/CT has a sensitivity of 47.3% and it has been reported that if both $^{18}F$-FDG and $^{11}C$-Acetate PET/CT are carried out together, their cumulative sensitivity is around 100%. However, the normal intake of the pancreas and the spleen in $^{11}C$-Acetate PET/CT can influence the $^{18}F$-FDG PET/CT leading to an inaccurate diagnosis. This research was aimed at the verification of the usefulness of how much influence these two radioactive medical supplies can cause on the medical images through comparative analysis between the one-day and two-day protocol. Materials and Methods: This research was carried out based on 46 patients who were diagnosed with liver cancer and have gone through the PET/CT (35 male, 11 female participants, average age: $54{\pm}10.6$ years, age range: 29-69 years). The equipment used for this test was the Biograph TruePoint40 PET/CT (Siemens Medical Systems, USA) and 21 participants who went through the one-day protocol test were first given the $^{11}C$-Acetate PET/CT and the $^{18}F$-FDG PET/CT, the latter exactly after one hour. The other 25 participants who went through the two-day protocol test were given the $^{11}C$-Acetate PET/CT on the first day and the $^{18}F$-FDG PET/CT on the next day. These two groups were then graded comparatively by assigning identical areas of interest of the pancreas and the spleen in the $^{18}F$-FDG images and by measuring the Standard Uptake Value (SUV). SPSS Ver.17 (SPSS Inc., USA) was used for statistical analysis, where statistical significance was found through the unpaired t-test. Results: After analyzing the participants' medical images from each of the two different protocol types, the average${\pm}$standard deviation of the SUV of the pancreas carried out under the two-day protocol were as follows: head $1.62{\pm}0.32$ g/mL, body $1.57{\pm}0.37$ g/mL, tail $1.49{\pm}0.33$ g/mL and the spleen $1.53{\pm}0.28$ g/mL. Whereas, the results for participants carried out under the one-day protocol were as follows: head $1.65{\pm}0.35$ g/mL, body $1.58{\pm}0.27$ g/mL, tail $1.49{\pm}0.28$ g/mL and the spleen $1.66{\pm}0.29$ g/mL. Conclusion: It was found that no statistical significant difference existed between the one-day and two-day protocol SUV in the pancreas and the spleen (p<0.05), and nothing which could be misconceived as false positive were found from the PET/CT medical image analysis. From this research, it was also found that no overestimation of the SUV occurred from the influence of $^{11}C$-Acetate on the $^{18}F$-FDG medical images where those two tests were carried out for one day. This result was supported by the statistical significance of the SUV of measurement. If $^{11}C$-Acetate becomes commercialized in the future, the diagnostic ability of liver diseases can be improved by $^{18}F$-FDG and one-day protocol. It is from this result where tests can be accomplished in one day without the interference phenomenon of the two radioactive medical supplies and furthermore, could reduce the waiting time improving customer satisfaction.

• Journal of the Korean Society of Food Science and Nutrition
• /
• v.43 no.9
• /
• pp.1447-1456
• /
• 2014

The purpose of this study was to compare perception of school foodservice facilities and utilities in dietitians and school nutrition teachers in the Gyeongnam area between schools that improved foodservice facilities and utilities versus those who did not. From July 23 to Aug 31, 2012, 391 questionnaires were distributed, and 289 questionnaires were obtained. A total of 275 questionnaires were used for the final analysis, excluding improper ones. The results of this study were as follows. First, among 275 schools, 90 schools (32.7%) improved school foodservice facilities and utilities while 175 schools (67.3%) did not. Second, schools with improved facilities had a more well-equipped receiving room (P<0.01), preparation room (P<0.001), dishwashing room (P<0.001), storage room for supplies (P<0.001), rest-room for school foodservice employees (P<0.05), locker room (P<0.01), shower room (P<0.001), laundry room (P<0.001), boiler room (P<0.05), and room for serving cart (P<0.05) than schools with no improvement. Third, total perception score of school foodservice facilities area from schools with improved facilities (1.71) was significantly higher than that (1.60) of school without improvement (P<0.001). Fourth, total satisfaction (3.32) of school foodservice facilities and utilities in school with improved facilities was significantly higher than that (2.62) of schools without improvement (P<0.01). Fifth, schools with improved facilities had a better equipped of floor (P<0.05), entrance (P<0.001), drain (P<0.001), water supply (P<0.01), lighting (P<0.001), hand washing (P<0.001), foodservice management room (P<0.001), locker room (P<0.001), rest-room and shower room (P<0.001), and preparation room (P<0.001) than schools without improvement. However, there was no significant difference in terms of walls and ceilings, windows, ventilation, and storage. In conclusion, school foodservice facilities and utilities improvement should conducted as soon as possible.

• Journal of Distribution Research
• /
• v.16 no.2
• /
• pp.1-24
• /
• 2011

1. Introduction: Contrast to the offline purchasing environment, online store cannot offer the sense of touch or direct visual information of its product to the consumers. So the builder of the online shopping mall should provide more concrete and detailed product information(Kim 2008), and Alba (1997) also predicted that the quality of the offered information is determined by the post-purchase consumer satisfaction. In practice, many fashion and apparel online shopping malls offer the picture information with the product on the real person model to enhance the usefulness of product information. On the other virtual product experience has been suggested to the ways of overcoming the online consumers' limited perceptual capability (Jiang & Benbasat 2005). However, the adoption and the facilitation of the virtual reality tools requires high investment and technical specialty compared to the text/picture product information offerings (Shaffer 2006). This could make the entry barrier to the online shopping to the small retailers and sometimes it could be demanding high level of consumers' perceptual efforts. So the expensive technological solution could affects negatively to the consumer decision making processes. Nevertheless, most of the previous research on the online product information provision suggests the VR be the more effective tools. 2. Research Model and Hypothesis: Presented in