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

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

• The Korean Journal of Food And Nutrition
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• v.29 no.6
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• pp.835-848
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• 2016

The purpose of this study was to find efficient and customized tools for delivering the benefit of health functional foods (HFFs). Delivery tools which could influence the impact of advertising were images, explanations of ingredients, diagrams of health benefit, patents, and comments from authority. Six advertisements were developed using these tools: "A": relevant image + explanation of ingredients + scientific diagram of efficacy; "B": relevant image + explanation of ingredients; "C": relevant image; "D": irrelevant image; "E": irrelevant image + explanation of ingredient + patent; "F": irrelevant image + explanation of ingredient + comments from authority. To analyze the consumer perceptions on HFFs and advertisement effects, 300 respondents were requested to answer a questionnaire comprising of the following questions: 5 questions of attitudes (necessity of HFFs, trust in HFFs, gathering information, watching advertisements and trust in advertisement claims) and 6 questions on the 6 developed advertisements (attention, understanding, sufficiency of information, sympathy, trust, and purchase). Scoring was done as per the 5 Likert scale. There was a higher proportion of females and the elderly, as compared to males and youngsters. The overall consumer attitudes were positive. Explanation of ingredients, scientific diagram of health benefit, patents and expert comments were helpful factors in increasing the advertisement evaluation by consumer, but the images were not. Advertisement evaluation of consumer did not differ with gender and age. However, differences were observed between some of the consumer attitudes (necessity of HFFs, trust in HFFs, gathering information and trust in advertisements claim) and advertisement evaluations (attention, understanding, sympathy and purchase). Our results indicate that for consumers utilizing the HFFs, advertisements with concrete tools such as diagrams, patent, and expert comments are more helpful. However, for consumers who do not have interest in HFFs, the scientific information was irrelevant. We believe that to maximize the effect of health information in advertisements, consumers should be segmented, and customized tools for each segment needs to be developed.

• Journal of Intelligence and Information Systems
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• v.18 no.3
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• pp.153-169
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• 2012

Exhibitions have played a key role of effective marketing activity which directly informs services and products to current and potential customers. Through participating in exhibitions, exhibitors have got the opportunity to make face-to-face contact so that they can secure the market share and improve their corporate images. According to this economic importance of exhibitions, show organizers try to adopt a new IT technology for improving their performance, and researchers have also studied services which can improve the satisfaction of visitors through analyzing visit patterns of visitors. Especially, as smart technologies make them monitor activities of visitors in real-time, they have considered booth recommender systems which infer preference of visitors and recommender proper service to them like on-line environment. However, while there are many studies which can improve their performance in the side of new technological development, they have not considered the choice factor of visitors for booth recommender systems. That is, studies for factors which can influence the development direction and effective diffusion of these systems are insufficient. Most of prior studies for the acceptance of new technologies and the continuous intention of use have adopted Technology Acceptance Model (TAM) and Extended Technology Acceptance Model (ETAM). Booth recommender systems may not be new technology because they are similar with commercial recommender systems such as book recommender systems, in the smart exhibition environment, they can be considered new technology. However, for considering the smart exhibition environment beyond TAM, measurements for the intention of reuse should focus on how booth recommender systems can provide correct information to visitors. In this study, through literature reviews, we draw factors which can influence the satisfaction and reuse intention of visitors for booth recommender systems, and design a model to forecast adaptation of visitors for booth recommendation in the exhibition environment. For these purposes, we conduct a survey for visitors who attended DMC Culture Open in November 2011 and experienced booth recommender systems using own smart phone, and examine hypothesis by regression analysis. As a result, factors which can influence the satisfaction of visitors for booth recommender systems are the effectiveness, perceived ease of use, argument quality, serendipity, and so on. Moreover, the satisfaction for booth recommender systems has a positive relationship with the development of reuse intention. For these results, we have some insights for booth recommender systems in the smart exhibition environment. First, this study gives shape to important factors which are considered when they establish strategies which induce visitors to consistently use booth recommender systems. Recently, although show organizers try to improve their performances using new IT technologies, their visitors have not felt the satisfaction from these efforts. At this point, this study can help them to provide services which can improve the satisfaction of visitors and make them last relationship with visitors. On the other hands, this study suggests that they managers along the using time of booth recommender systems. For example, in the early stage of the adoption, they should focus on the argument quality, perceived ease of use, and serendipity, so that improve the acceptance of booth recommender systems. After these stages, they should bridge the differences between expectation and perception for booth recommender systems, and lead continuous uses of visitors. However, this study has some limitations. We only use four factors which can influence the satisfaction of visitors. Therefore, we should development our model to consider important additional factors. And the exhibition in our experiments has small number of booths so that visitors may not need to booth recommender systems. In the future study, we will conduct experiments in the exhibition environment which has a larger scale.

• Journal of Global Scholars of Marketing Science
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• v.17 no.2
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• pp.55-73
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• 2007

Festivals are an indispensable feature of cultural tourism(Formica & Uysal, 1998). Cultural tourism festivals are increasingly being used as instruments promoting tourism and boosting the regional economy. So much research related to festivals is undertaken from a variety of perspectives. Plans to revisit a particular festival have been viewed as an important research topic both in academia and the tourism industry. Therefore festivals have frequently been leveled as cultural events. Cultural tourism festivals have become a crucial component in constituting the attractiveness of tourism destinations(Prentice, 2001). As a result, a considerable number of tourist studies have been carried out in diverse cultural tourism festivals(Backman et al., 1995; Crompton & Mckay, 1997; Park, 1998; Clawson & Knetch, 1996). Much of previous literature empirically shows the close linkage between tourist satisfaction and behavioral intention in festivals. The main objective of this study is to investigate the effects of evaluation attributes of cultural tourism festivals on satisfaction and behavioral intention. accomplish the research objective, to find out evaluation items of cultural tourism festivals through the literature study an empirical study. Using a varimax rotation with Kaiser normalization, the research obtained four factors in the 18 evaluation attributes of cultural tourism festivals. Some empirical studies have examined the relationship between behavioral intention and actual behavior. To understand between tourist satisfaction and behavioral intention, this study suggests five hypotheses and hypothesized model. In this study, the analysis is based on primary data collected from visitors who participated in '2006 Gwangju Kimchi Festival'. In total, 700 self-administered questionnaires were distributed and 561 usable questionnaires were obtained. Respondents were presented with the 18 satisfactions item on a scale from 1(strongly disagree) to 7(strongly agree). Dimensionality and stability of the scale were evaluated by a factor analysis with varimax rotation. Four factors emerged with eigenvalues greater than 1, which explained 66.40% of the total variance and Cronbach' alpha raging from 0.876 to 0.774. And four factors named: advertisement and guides, programs, food and souvenirs, and convenient facilities. To test and estimate the hypothesized model, a two-step approach with an initial measurement model and a subsequent structural model for Structural Equation Modeling was used. The AMOS 4.0 analysis package was used to conduct the analysis. In estimating the model, the maximum likelihood procedure was used.In this study Chi-square test is used, which is the most common model goodness-of-fit test. In addition, considering the literature about the Structural Equation Modeling, this study used, besides Chi-square test, more model fit indexes to determine the tangibility of the suggested model: goodness-of-fit index(GFI) and root mean square error of approximation(RMSEA) as absolute fit indexes; normed-fit index(NFI) and non-normed-fit index(NNFI) as incremental fit indexes. The results of T-test and ANOVAs revealed significant differences(0.05 level), therefore H1(Tourist Satisfaction level should be different from Demographic traits) are supported. According to the multiple Regressions analysis and AMOS, H2(Tourist Satisfaction positively influences on revisit intention), H3(Tourist Satisfaction positively influences on word of mouth), H4(Evaluation Attributes of cultural tourism festivals influences on Tourist Satisfaction), and H5(Tourist Satisfaction positively influences on Behavioral Intention) are also supported. As the conclusion of this study are as following: First, there were differences in satisfaction levels in accordance with the demographic information of visitors. Not all visitors had the same degree of satisfaction with their cultural tourism festival experience. Therefore it is necessary to understand the satisfaction of tourists if the experiences that are provided are to meet their expectations. So, in making festival plans, the organizer should consider the demographic variables in explaining and segmenting visitors to cultural tourism festival. Second, satisfaction with attributes of evaluation cultural tourism festivals had a significant direct impact on visitors' intention to revisit such festivals and the word of mouth publicity they shared. The results indicated that visitor satisfaction is a significant antecedent of their intention to revisit such festivals. Festival organizers should strive to forge long-term relationships with the visitors. In addition, it is also necessary to understand how the intention to revisit a festival changes over time and identify the critical satisfaction factors. Third, it is confirmed that behavioral intention was enhanced by satisfaction. The strong link between satisfaction and behavioral intentions of visitors areensured by high quality advertisement and guides, programs, food and souvenirs, and convenient facilities. Thus, examining revisit intention from a time viewpoint may be of a great significance for both practical and theoretical reasons. Additionally, festival organizers should give special attention to visitor satisfaction, as satisfied visitors are more likely to return sooner. The findings of this research have several practical implications for the festivals managers. The promotion of cultural festivals should be based on the understanding of tourist satisfaction for the long- term success of tourism. And this study can help managers carry out this task in a more informed and strategic manner by examining the effects of demographic traits on the level of tourist satisfaction and the behavioral intention. In other words, differentiated marketing strategies should be stressed and executed by relevant parties. The limitations of this study are as follows; the results of this study cannot be generalized to other cultural tourism festivals because we have not explored the many different kinds of festivals. A future study should be a comparative analysis of other festivals of different visitor segments. Also, further efforts should be directed toward developing more comprehensive temporal models that can explain behavioral intentions of tourists.

• Journal of Intelligence and Information Systems
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• v.17 no.4
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• pp.175-191
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• 2011

With the MICE(Meeting, Incentive travel, Convention, Exhibition) industry coming into the spotlight, there has been a growing interest in the domestic exhibition industry. Accordingly, in Korea, various studies of the industry are being conducted to enhance exhibition performance as in the United States or Europe. Some studies are focusing particularly on analyzing visiting patterns of exhibition visitors using intelligent information technology in consideration of the variations in effects of watching exhibitions according to the exhibitory environment or technique, thereby understanding visitors and, furthermore, drawing the correlations between exhibiting businesses and improving exhibition performance. However, previous studies related to booth recommendation systems only discussed the accuracy of recommendation in the aspect of a system rather than determining changes in visitors' behavior or perception by recommendation. A booth recommendation system enables visitors to visit unplanned exhibition booths by recommending visitors suitable ones based on information about visitors' visits. Meanwhile, some visitors may be satisfied with their unplanned visits, while others may consider the recommending process to be cumbersome or obstructive to their free observation. In the latter case, the exhibition is likely to produce worse results compared to when visitors are allowed to freely observe the exhibition. Thus, in order to apply a booth recommendation system to exhibition halls, the factors affecting the performance of the system should be generally examined, and the effects of the system on visitors' unplanned visiting behavior should be carefully studied. As such, this study aims to determine the factors that affect the performance of a booth recommendation system by reviewing theories and literature and to examine the effects of visitors' perceived performance of the system on their satisfaction of unplanned behavior and intention to reuse the system. Toward this end, the unplanned behavior theory was adopted as the theoretical framework. Unplanned behavior can be defined as "behavior that is done by consumers without any prearranged plan". Thus far, consumers' unplanned behavior has been studied in various fields. The field of marketing, in particular, has focused on unplanned purchasing among various types of unplanned behavior, which has been often confused with impulsive purchasing. Nevertheless, the two are different from each other; while impulsive purchasing means strong, continuous urges to purchase things, unplanned purchasing is behavior with purchasing decisions that are made inside a store, not before going into one. In other words, all impulsive purchases are unplanned, but not all unplanned purchases are impulsive. Then why do consumers engage in unplanned behavior? Regarding this question, many scholars have made many suggestions, but there has been a consensus that it is because consumers have enough flexibility to change their plans in the middle instead of developing plans thoroughly. In other words, if unplanned behavior costs much, it will be difficult for consumers to change their prearranged plans. In the case of the exhibition hall examined in this study, visitors learn the programs of the hall and plan which booth to visit in advance. This is because it is practically impossible for visitors to visit all of the various booths that an exhibition operates due to their limited time. Therefore, if the booth recommendation system proposed in this study recommends visitors booths that they may like, they can change their plans and visit the recommended booths. Such visiting behavior can be regarded similarly to consumers' visit to a store or tourists' unplanned behavior in a tourist spot and can be understand in the same context as the recent increase in tourism consumers' unplanned behavior influenced by information devices. Thus, the following research model was established. This research model uses visitors' perceived performance of a booth recommendation system as the parameter, and the factors affecting the performance include trust in the system, exhibition visitors' knowledge levels, expected personalization of the system, and the system's threat to freedom. In addition, the causal relation between visitors' satisfaction of their perceived performance of the system and unplanned behavior and their intention to reuse the system was determined. While doing so, trust in the booth recommendation system consisted of 2nd order factors such as competence, benevolence, and integrity, while the other factors consisted of 1st order factors. In order to verify this model, a booth recommendation system was developed to be tested in 2011 DMC Culture Open, and 101 visitors were empirically studied and analyzed. The results are as follows. First, visitors' trust was the most important factor in the booth recommendation system, and the visitors who used the system perceived its performance as a success based on their trust. Second, visitors' knowledge levels also had significant effects on the performance of the system, which indicates that the performance of a recommendation system requires an advance understanding. In other words, visitors with higher levels of understanding of the exhibition hall learned better the usefulness of the booth recommendation system. Third, expected personalization did not have significant effects, which is a different result from previous studies' results. This is presumably because the booth recommendation system used in this study did not provide enough personalized services. Fourth, the recommendation information provided by the booth recommendation system was not considered to threaten or restrict one's freedom, which means it is valuable in terms of usefulness. Lastly, high performance of the booth recommendation system led to visitors' high satisfaction levels of unplanned behavior and intention to reuse the system. To sum up, in order to analyze the effects of a booth recommendation system on visitors' unplanned visits to a booth, empirical data were examined based on the unplanned behavior theory and, accordingly, useful suggestions for the establishment and design of future booth recommendation systems were made. In the future, further examination should be conducted through elaborate survey questions and survey objects.

• Journal of Distribution Research
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• v.15 no.2
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• pp.1-34
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• 2010

Introduction As consumers' purchase behavior change into a rational and practical direction, the discount store industry came to have keen competition along with rapid external growth. Therefore as a solution, distribution businesses are concentrating on developing PB(Private Brand) which can realize differentiation and profitability at the same time. And as improvement in customer loyalty beyond customer satisfaction is effective in surviving in an environment with keen competition, PB is being used as a strategic tool to improve customer loyalty. To improve loyalty among PB users, it is necessary to develop PB by examining properties of a customer group, first of all, quality level perceived by consumers should be met to obtain customer satisfaction and customer trust and consequently induce customer loyalty. To provide results of systematic analysis on relations between antecedents influenced perceived quality and variables affecting customer loyalty, this study proposed a research model based on causal relations verified in prior researches and set 16 hypotheses about relations among 9 theoretical variables. Data was collected from 400 adult customers residing in Seoul and the Metropolitan area and using large scale discount stores, among them, 375 copies were analyzed using SPSS 15.0 and Amos 7.0. The findings of the present study followed as; We ascertained that the higher company reputation, brand reputation, product experience and brand familiarity, the higher perceived quality. The study also examined the higher perceived quality, the higher customer satisfaction, customer trust and customer loyalty. The findings showed that the higher customer satisfaction and customer trust, the higher customer loyalty. As for moderating effects between PB and NB in terms of influences of perceived quality factors on perceived quality, we can ascertain that PB was higher than NB in the influences of company reputation on perceived quality while NB was higher than PB in the influences of brand reputation and brand familiarity on perceived quality. These results of empirical analysis will be useful for those concerned to do marketing activities based on a clearer understanding of antecedents and consecutive factors influenced perceived quality. At last, discussions about academical and managerial implications in these results, we suggested the limitations of this study and the future research directions. Research Model and Hypotheses Test After analyzing if antecedent variables having influence on perceived quality shows any difference between PB and NB in terms of their influences on them, the relation between variables that have influence on customer loyalty was determined as Figure 1. We established 16 hypotheses to test and hypotheses are as follows; H1-1: Perceived price has a positive effect on perceived quality. H1-2: It is expected that PB and NB would have different influence in terms of perceived price on perceived quality. H2-1: Company reputation has a positive effect on perceived quality. H2-2: It is expected that PB and NB would have different influence in terms of company reputation on perceived quality. H3-1: Brand reputation has a positive effect on perceived quality. H3-2: It is expected that PB and NB would have different influence in terms of brand reputation on perceived quality. H4-1: Product experience has a positive effect on perceived quality. H4-2: It is expected that PB and NB would have different influence in terms of product experience on perceived quality. H5-1: Brand familiarity has a positive effect on perceived quality. H5-2: It is expected that PB and NB would have different influence in terms of brand familiarity on perceived quality. H6: Perceived quality has a positive effect on customer satisfaction. H7: Perceived quality has a positive effect on customer trust. H8: Perceived quality has a positive effect on customer loyalty. H9: Customer satisfaction has a positive effect on customer trust. H10: Customer satisfaction has a positive effect on customer loyalty. H11: Customer trust has a positive effect on customer loyalty. Results from analyzing main effects of research model is shown as