• Journal of Global Scholars of Marketing Science
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• v.20 no.1
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• pp.27-34
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• 2010

During the past 10 years, sales of luxury goods increased significantly to more than US$ 130 billion in 2007. In this industry, more than half of the revenue comes from Asia where the average income has risen significantly, and the demand for luxury products is forecast to grow rapidly. Purchasing luxury brands appears to be an intriguing social phenomenon that is profitable for companies in this region. As a newly developed country, Korea is one of the most attractive luxury markets in Asia. Currently, a total of 120 luxury fashion brands have entered the Korean market, primarily in luxury districts in Seoul where the competition is fierce. The purposes of this study are to: (1) identify antecedents of attitude toward luxury brands, (2) examine the effect of attitudes toward luxury brands on customer equity, (3) determine the impact of attitudes toward luxury brands on customer lifetime value, and (4) investigate the influence of customer equity on customer life time value. Previous studies have examined materialism, social need, experiential need, need for uniqueness, conformity, and fashion involvement as antecedents of attitude toward luxury brands. Richins and Dowson (1992) suggested that that materialism influences consumption behavior relative to quantity of goods purchased. Nueno and Quelch (1998) reported that the ownership of luxury brands conveys information related to the owner's social status, communicates an image of success and prestige, and is a determinant of purchase behavior. Experiential need is recognized as an important aspect of consumption, especially for new products developed to meet consumer demand. Since luxury goods, by definition are relatively scarce, ownership of these types of products may fulfill consumers' need for uniqueness. In this study, value equity, relationship equity, and brand equity are examined as drivers of customer equity. The sample (n = 114) was undergraduate and graduate students at two private women's universities in Seoul, Korea. Data collection was conducted using a self-administered questionnaire survey in March, 2009. Data analysis included descriptive statistics, factor analysis, reliability analysis, and regression analysis using SPSS 15.0 software. Data analysis resulted in a number of conclusions. First, experiential need and fashion involvement positively influence participants' attitude toward luxury brands. Second, attitude toward luxury brands positively influences brand equity, followed by value equity and relationship equity. However, there is no significant relationship between attitude toward luxury brand and customer lifetime value. Finally, relationship equity positively influences customer lifetime value. In conclusion, young consumers are an important potential consumer group that tries different brands to discover the ones most suitable for them. Luxury marketers that use effective marketing strategies to attract and engender loyalty among this potentially lucrative consumer group may increase customer equity and lifetime value.

• Journal of Intelligence and Information Systems
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• v.16 no.3
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• pp.163-179
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• 2010

Recentlythe concept of fast fashion is drawing attention as customer needs are diversified and supply lead time is getting shorter in fashion industry. It is emphasized as one of the critical success factors in the fashion industry how quickly and efficiently to satisfy the customer needs as the competition has intensified. Because the fast fashion is inherently susceptible to trend, it is very important for fashion retailers to make quick decisions regarding items to launch, quantity based on demand prediction, and the time to respond. Also the planning decisions must be executed through the business processes of procurement, production, and logistics in real time. In order to adapt to this trend, the fashion industry urgently needs supports from intelligent quick response(QR) system. However, the traditional functions of QR systems have not been able to completely satisfy such demands of the fast fashion industry. This paper proposes an intelligent quick response system for the fast fashion(IQRS-FF). Presented are models for QR process, QR principles and execution, and QR quantity and timing computation. IQRS-FF models support the decision makers by providing useful information with automated and rule-based algorithms. If the predefined conditions of a rule are satisfied, the actions defined in the rule are automatically taken or informed to the decision makers. In IQRS-FF, QRdecisions are made in two stages: pre-season and in-season. In pre-season, firstly master demand prediction is performed based on the macro level analysis such as local and global economy, fashion trends and competitors. The prediction proceeds to the master production and procurement planning. Checking availability and delivery of materials for production, decision makers must make reservations or request procurements. For the outsourcing materials, they must check the availability and capacity of partners. By the master plans, the performance of the QR during the in-season is greatly enhanced and the decision to select the QR items is made fully considering the availability of materials in warehouse as well as partners' capacity. During in-season, the decision makers must find the right time to QR as the actual sales occur in stores. Then they are to decide items to QRbased not only on the qualitative criteria such as opinions from sales persons but also on the quantitative criteria such as sales volume, the recent sales trend, inventory level, the remaining period, the forecast for the remaining period, and competitors' performance. To calculate QR quantity in IQRS-FF, two calculation methods are designed: QR Index based calculation and attribute similarity based calculation using demographic cluster. In the early period of a new season, the attribute similarity based QR amount calculation is better used because there are not enough historical sales data. By analyzing sales trends of the categories or items that have similar attributes, QR quantity can be computed. On the other hand, in case of having enough information to analyze the sales trends or forecasting, the QR Index based calculation method can be used. Having defined the models for decision making for QR, we design KPIs(Key Performance Indicators) to test the reliability of the models in critical decision makings: the difference of sales volumebetween QR items and non-QR items; the accuracy rate of QR the lead-time spent on QR decision-making. To verify the effectiveness and practicality of the proposed models, a case study has been performed for a representative fashion company which recently developed and launched the IQRS-FF. The case study shows that the average sales rateof QR items increased by 15%, the differences in sales rate between QR items and non-QR items increased by 10%, the QR accuracy was 70%, the lead time for QR dramatically decreased from 120 hours to 8 hours.

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

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

• Journal of Korea Spatial Information System Society
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• v.10 no.4
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• pp.77-88
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• 2008

There has been growing demands to manage the water quality of west coastal region due to the large scale urbanization along the coastal zone, the possibility of application of TMDL(Total Maximum Daily Loadings) to Han river, and the natural disaster such as oil spill incident in Taean, Chungnam. However, no system has been developed for such purposes. In this background, the demand of GIS based effective water quality management has been increased to monitor water quality environment and propose best management alternatives for Han river and Kyeonggi bay. This study mainly focused on the development of integrated water quality management system for Han river bas in and its estuary are a connected to Kyeonggi bay to support integrated water quality management and its plan. Integration was made based on GIS by spatial linking between water quality attributes and location information. A GIS DB was built to estimate the amount of generated and discharged water pollutants according to TMDL technical guide and it included input data to use two different water quality models--W ASP7 for Han river and EFDC for coastal area--to forecast water quality and to suggest BMP(Best management Practices). The results of BOD, TN, and TP from WASP7 were used as the input to run EFDC. Based on the study results, some critical areas which have relatively higher pollutant loadings were identified, and it was also identified that the locations discharging water pollutant loadings to river and seasonal factor affected water quality. And the relationship of water quality between river and its estuary area was quantitatively verified. The results showed that GIS based integrated system could be used as a tool for estimating status-quo of water quality and proposing economically effective BMPs to mitigate water pollution. Further studies need to be made for improving system's capabilities such as adding decision making function as well as cost-benefit analysis, etc. Also, the concrete methodology for water quality management using the system need to be developed.

• Korean Security Journal
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• no.41
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• pp.37-66
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• 2014

In recent years, with the development of computers and electronics, electronics and communication technology in a growing and each part is dependent on the cross-referencing makes all electronic equipment is obsolete due to direct or indirect damage EMP. Korea and the impending standoff North Korea has a considerable level of technologies related to the EMP, EMP weapons you already have or in a few years, the development of EMP weapons will complete. North Korea launched a long-range missile and conducted a nuclear test on several occasions immediately after, when I saw the high-altitude nuclear blackmail has been strengthening the outright offensive nuclear EMP attacks at any time and practical significance for the EMP will need offensive skills would improve. At this point you can predict the damage situation of Korea's security reality that satisfy the need, more than anything else to build a protective system of the EMP. The scale of the damage that unforeseen but significant military damage and socio-economic damage and fatalities when I looked into the situation which started out as a satellite communications systems and equipment to attack military and security systems and transportation, finance, national emergency system, such as the damage elsewhere. In General, there is no direct casualties reported, but EMP medical devices that rely on lethal damage to people who can show up. In addition, the State power system failure due to a power supply interruption would not have thought the damage would bring State highly dependent on domestic power generation of nuclear plants is a serious nuclear power plant accident in the event of a blackout phenomenon can lead to the plant's internal problems should see a forecast. First of all, a special expert Committee of the EMP, the demand for protective facilities and equipment and conduct an investigation, he takes fits into your budget is under strict criteria by configuring the contractors should be sifting through. He then created the Agency for verification of performance EMP protection after you have verified the performance of maintenance, maintenance, safety and security management, design and construction company organized and systematic process Guard facilities or secret communications equipment and perfect for the EMP, such as protective equipment maneuver system should take.

• Journal of Intelligence and Information Systems
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• v.20 no.4
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• pp.1-23
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• 2014

Market forecasting aims to estimate the sales volume of a product or service that is sold to consumers for a specific selling period. From the perspective of the enterprise, accurate market forecasting assists in determining the timing of new product introduction, product design, and establishing production plans and marketing strategies that enable a more efficient decision-making process. Moreover, accurate market forecasting enables governments to efficiently establish a national budget organization. This study aims to generate a market growth curve for ICT (information and communication technology) goods using past time series data; categorize products showing similar growth patterns; understand markets in the industry; and forecast the future outlook of such products. This study suggests the useful and meaningful process (or methodology) to identify the market growth pattern with quantitative growth model and data mining algorithm. The study employs the following methodology. At the first stage, past time series data are collected based on the target products or services of categorized industry. The data, such as the volume of sales and domestic consumption for a specific product or service, are collected from the relevant government ministry, the National Statistical Office, and other relevant government organizations. For collected data that may not be analyzed due to the lack of past data and the alteration of code names, data pre-processing work should be performed. At the second stage of this process, an optimal model for market forecasting should be selected. This model can be varied on the basis of the characteristics of each categorized industry. As this study is focused on the ICT industry, which has more frequent new technology appearances resulting in changes of the market structure, Logistic model, Gompertz model, and Bass model are selected. A hybrid model that combines different models can also be considered. The hybrid model considered for use in this study analyzes the size of the market potential through the Logistic and Gompertz models, and then the figures are used for the Bass model. The third stage of this process is to evaluate which model most accurately explains the data. In order to do this, the parameter should be estimated on the basis of the collected past time series data to generate the models' predictive value and calculate the root-mean squared error (RMSE). The model that shows the lowest average RMSE value for every product type is considered as the best model. At the fourth stage of this process, based on the estimated parameter value generated by the best model, a market growth pattern map is constructed with self-organizing map algorithm. A self-organizing map is learning with market pattern parameters for all products or services as input data, and the products or services are organized into an $N{\times}N$ map. The number of clusters increase from 2 to M, depending on the characteristics of the nodes on the map. The clusters are divided into zones, and the clusters with the ability to provide the most meaningful explanation are selected. Based on the final selection of clusters, the boundaries between the nodes are selected and, ultimately, the market growth pattern map is completed. The last step is to determine the final characteristics of the clusters as well as the market growth curve. The average of the market growth pattern parameters in the clusters is taken to be a representative figure. Using this figure, a growth curve is drawn for each cluster, and their characteristics are analyzed. Also, taking into consideration the product types in each cluster, their characteristics can be qualitatively generated. We expect that the process and system that this paper suggests can be used as a tool for forecasting demand in the ICT and other industries.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.37 no.1
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• pp.125-132
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• 2017

The development project of Busan New Port aims to be Logistics Hub Port but there are too many things to deal with ; enlargement of harbour, interport competition, modernization of harbour loading equipment and so on. At present, 23 berths of North and South container quay are in operation and 22 berths will be constructed on west and south-side by 2020. Namely, Busan New Port will operate 45 berths in 2020. When it comes to port distripark, a large-scale of Port distripark project is underway, such as Ung-Dong district 1,2 phase, West container 1,2phase, North distripark and so on. This study is to deduce traffic system problem of Busan New Port which is caused by the development project through predicting traffic need considering the development project. According to study, there are three main problems of traffic system : 1. traffic congestion caused on main crossroad, connecting second harbour back road. 2. It has been predicted that South-North road and traffic capacity of New Port road would lack compared to traffic volume-to-be-increased. Moreover, the detour volume of traffic is caused because New Port's 1st avenue and route 2 were not connected directly. Thus, this study suggests three kinds of improvement plan for smoother traffic flow. 1st. Operate roundabout on major intersection, for example, second harbour back road, west container wharf's subway corridors(South to North), and permit only right turn on sub-intersection. 2nd. Extend New Port road(North container's port road) by utilizing side walk and median. 3rd. Install exit ramp which utilizes Route 2 connecting New Port's 1st avenue and local road 1042. The method we used to analyze the effect of improvement is Vissim of Mircro Simulation Package.

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

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

• Journal of Intelligence and Information Systems
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• v.25 no.1
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• pp.139-161
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• 2019

The utilization of the e-commerce market has become a common life style in today. It has become important part to know where and how to make reasonable purchases of good quality products for customers. This change in purchase psychology tends to make it difficult for customers to make purchasing decisions in vast amounts of information. In this case, the recommendation system has the effect of reducing the cost of information retrieval and improving the satisfaction by analyzing the purchasing behavior of the customer. Amazon and Netflix are considered to be the well-known examples of sales marketing using the recommendation system. In the case of Amazon, 60% of the recommendation is made by purchasing goods, and 35% of the sales increase was achieved. Netflix, on the other hand, found that 75% of movie recommendations were made using services. This personalization technique is considered to be one of the key strategies for one-to-one marketing that can be useful in online markets where salespeople do not exist. Recommendation techniques that are mainly used in recommendation systems today include collaborative filtering and content-based filtering. Furthermore, hybrid techniques and association rules that use these techniques in combination are also being used in various fields. Of these, collaborative filtering recommendation techniques are the most popular today. Collaborative filtering is a method of recommending products preferred by neighbors who have similar preferences or purchasing behavior, based on the assumption that users who have exhibited similar tendencies in purchasing or evaluating products in the past will have a similar tendency to other products. However, most of the existed systems are recommended only within the same category of products such as books and movies. This is because the recommendation system estimates the purchase satisfaction about new item which have never been bought yet using customer's purchase rating points of a similar commodity based on the transaction data. In addition, there is a problem about the reliability of purchase ratings used in the recommendation system. Reliability of customer purchase ratings is causing serious problems. In particular, 'Compensatory Review' refers to the intentional manipulation of a customer purchase rating by a company intervention. In fact, Amazon has been hard-pressed for these "compassionate reviews" since 2016 and has worked hard to reduce false information and increase credibility. The survey showed that the average rating for products with 'Compensated Review' was higher than those without 'Compensation Review'. And it turns out that 'Compensatory Review' is about 12 times less likely to give the lowest rating, and about 4 times less likely to leave a critical opinion. As such, customer purchase ratings are full of various noises. This problem is directly related to the performance of recommendation systems aimed at maximizing profits by attracting highly satisfied customers in most e-commerce transactions. In this study, we propose the possibility of using new indicators that can objectively substitute existing customer 's purchase ratings by using RFM multi-dimensional analysis technique to solve a series of problems. RFM multi-dimensional analysis technique is the most widely used analytical method in customer relationship management marketing(CRM), and is a data analysis method for selecting customers who are likely to purchase goods. As a result of verifying the actual purchase history data using the relevant index, the accuracy was as high as about 55%. This is a result of recommending a total of 4,386 different types of products that have never been bought before, thus the verification result means relatively high accuracy and utilization value. And this study suggests the possibility of general recommendation system that can be applied to various offline product data. If additional data is acquired in the future, the accuracy of the proposed recommendation system can be improved.

• Journal of Intelligence and Information Systems
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• v.27 no.1
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• pp.177-190
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• 2021

Due to the development of the fourth industrial revolution technology, efforts are being made to improve areas that humans cannot handle by utilizing artificial intelligence techniques such as machine learning. Although on-demand production companies also want to reduce corporate risks such as delays in delivery by predicting total production time for orders, they are having difficulty predicting this because the total production time is all different for each order. The Theory of Constraints (TOC) theory was developed to find the least efficient areas to increase order throughput and reduce order total cost, but failed to provide a forecast of total production time. Order production varies from order to order due to various customer needs, so the total production time of individual orders can be measured postmortem, but it is difficult to predict in advance. The total measured production time of existing orders is also different, which has limitations that cannot be used as standard time. As a result, experienced managers rely on persimmons rather than on the use of the system, while inexperienced managers use simple management indicators (e.g., 60 days total production time for raw materials, 90 days total production time for steel plates, etc.). Too fast work instructions based on imperfections or indicators cause congestion, which leads to productivity degradation, and too late leads to increased production costs or failure to meet delivery dates due to emergency processing. Failure to meet the deadline will result in compensation for delayed compensation or adversely affect business and collection sectors. In this study, to address these problems, an entity that operates an order production system seeks to find a machine learning model that estimates the total production time of new orders. It uses orders, production, and process performance for materials used for machine learning. We compared and analyzed OLS, GLM Gamma, Extra Trees, and Random Forest algorithms as the best algorithms for estimating total production time and present the results.