• Asia Marketing Journal
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• v.13 no.3
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• pp.233-248
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• 2011

This case illustrates a story of passionate and innovative new market development by Pulmuone, a fresh food provider in Korea. The company has been successfully developing a new market by introducing a (non-frying) fresh ramen, 'Jayeonun Masitda - The Nature Is Tasty' in the packaged ramen market dominated by fried ones. In this case, a detailed new market development process by Pulmuone will be investigated including; company overview, a new product development process, marketing strategy formulation, marketing mix implementation, market performance, and future directions. Pulmuone has been making efforts to create a new product category by marketing non-frying ramens since 1995, but with a modest success. In 2011, Pulmuone finally succeeded to develop an innovative product, 'Jayeonun Masitda' that brought more health and nutrition conscious consumers' attention in the ramen market. The company intended to change the current competitive structure in the ramen market, i.e., from the strength of taste and the amount of ingredient to fried/non-fried and the freshness of ingredient. By this new positioning, Pulmuone aimed to reshaping the ramen market into competition between healthy and unhealthy ramens. Pulmuone has been successful in developing a new market. Sales revenue of 'Jayeonun Masitda' has been continuously increasing, and customers are found to be highly satisfied with the product resulting in a high repeat purchase rate. The company's successful new market development can be attributed to a faithful new product development process, innovative technology, an appropriate positioning strategy, and consistent marketing communication. In addition, Pulmuone's eco-friendly corporate image and the organization's passion to grow are also important factors for success of this new market development.

• Smart Media Journal
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• v.13 no.1
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• pp.86-100
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• 2024

Recently, a number of virtual reality exhibition-type content services have been produced using archive resources of visual art records as a means of promoting cultural policy-based public companies. However, it is by no means easy to accumulate 3D works of art as data. Looking at the current state of metadata in public institutions, there was no digitalization of resources when developing digital twins because it was built based on old international standards. It was found that data modeling evolution is inevitable to connect multidimensional data at a capacity and speed that exceeds the functions of existing systems. Therefore, the elements and concepts of data modeling design were first considered among previous studies. When developing virtual reality content, when it is designed for the migration of 3D modeling data, the previously created metadata was analyzed to improve the upper elements that must be added to 3D modeling. Furthermore, this study demonstrated the possibility by directly implementing the process of using newly created metadata in virtual reality content in accordance with the data modeling process. If this study is gradually developed in the future, metadata-based data modeling can become more meaningful in the use of public data than it is today.

• Korean Journal of Construction Engineering and Management
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• v.25 no.2
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• pp.11-22
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• 2024

Power plant engineering industry obtains EPC plan project and delivers results about electricity, measurement, machinery, and piping and so on. Its works are taken by projects. Although power plant engineering composes 2~5% of whole EPC project cost, it's one of the fundamentals because it affects process after planning step a lot. However, domestic power plant engineering companies' project performance ability is insufficient and there's a need for systematic performance. Thus, this study defined related factors of successful performance and analyzed the priority among them through analytical hierarchy process. All respondents recognized experience, knowledge, and communication as important factors. Administrators considered knowledge, experience, and communication. But hands-on workers considered experience, knowledge, human resources. Those who have experience in oversea project considered process, experience, human resources. However those who don't have experience in oversea project considered knowledge, experience, and communication. Recognition of important factors varies by the position and work experience of members.

• The Journal of Bigdata
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• v.8 no.2
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• pp.149-163
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• 2023

Due to the continuous growth of the E-commerce market, the volume of orders that fulfillment centers have to process has increased, and various customer requirements have increased the complexity of order processing. Along with this trend, the operational efficiency of fulfillment centers due to increased labor costs is becoming more important from a corporate management perspective. Using historical performance data as training data, this study focused on real-time box recommendations applicable to packaging areas during fulfillment center shipping. Four types of data, such as product information, order information, packaging information, and delivery information, were applied to the machine learning model through pre-processing and feature-engineering processes. As an input vector, three characteristics were used as product specification information: width, length, and height, the characteristics of the input vector were extracted through a feature engineering process that converts product information from real numbers to an integer system for each section. As a result of comparing the performance of each model, it was confirmed that when the Gradient Boosting model was applied, the prediction was performed with the highest accuracy at 95.2% when the product specification information was converted into integers in 21 sections. This study proposes a machine learning model as a way to reduce the increase in costs and inefficiency of box packaging time caused by incorrect box selection in the fulfillment center, and also proposes a feature engineering method to effectively extract the characteristics of product specification information.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.9 no.4
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• pp.77-85
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• 2014

A goal of Bizcool entrepreneurship education targeting on the youth falls on letting understand the process of starts-up, enhance entrepreneurship will and their business creativities rather than training trivial starts-up skills such as writing business plan for successful starts-up. The effects of education enable Bizcoo students to recognize rightly the concept of starts-up training and lead to spread out demand for entrepreneurship education. The feedback check-up for how entrepreneurship education affects students getting through of it is necessary and possible to bring its' improvement alternatives. Despite of such highlight, not many measuring tools and indexes of evaluating an effectiveness of entrepreneurship education are developed and studied up until. This research suggests for the optimal indexes for them. In specific, this research 49 the first question sets of evaluating an effectiveness of entrepreneurship education classified 3 large categories and 11 following sub categories each of them such as entrepreneurship orientation, creativity, entrepreneurship preparing activities etc,. representing embedding education effects though entrepreneurship education. This research carry out the empirical survey research utilizing driven question sets against 5 different Bizcools sampling 287 students. The survey research delivers the final 3 large categories and 8 following sub categories(Innovativeness, risk-taking, problem-solving potent, cooperative decision-making potent, efficient behavior capacity, data collecting potent, career search, starts-up search and preparation), and 38 measuring indexes by search and confirming factor analysis. This research never drop the confidence test over each indexes and obtain the proper figures. Last but not least, this research confirm the gap between starts-up club members and non members as to an effectiveness of entrepreneurship education and 9 different indexes.

• Proceedings of the Korean DIstribution Association Conference
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• 2005.11a
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• pp.29-51
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• 2005

Brands are important in the consumer market. They are the interface between consumers and the company, consumers may develop loyalty to brands. also, The late development of industrial marketing explains the near absence of research on Brand Equity in business to business. With recent change, industrial companies have shifted from a production focus to a customer focus. industrial brand is fast developing. The basic purpose of this study is to investigate industrial brand trust and loyalty affecting the Result of business relationship between industrial buyers and suppliers. Factors hypothesized to influence trust in a brand include a number of brand characteristics, company characteristics and consumer-brand characteristics. This research presented a comprehensive constructive model consisting of components of industrial brand trust and loyalty, and then propose the research model base on prior researches and studies about relationships among components of industrial brand loyalty. Data were gathered from respondents who work in industrial buying center. For this study, Data were analyzed by SPSS 10.0 and AMOS 4.0. The results of this research analysis were as fallow. Industrial brand trust and loyalty were positively related with a number of industrial brand characteristics, supplier characteristics and buyer-brand characteristics. relationship commitment. This research newly proposed the concept of 'industrial brand trust and loyalty affecting the Result of business relationship between industrial buyers and suppliers'

• Journal of Intelligence and Information Systems
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• v.18 no.3
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• pp.171-183
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• 2012

In the development of technologies, the exhibition industry has received much attention from governments and companies as an important way of marketing activities. Also, the exhibitors have considered the exhibition as new channels of marketing activities. However, the growing size of exhibitions for net square feet and the number of visitors naturally creates the competitive environment for them. Therefore, to make use of the effective marketing tools in these environments, they have planned and implemented many promotion technics. Especially, through smart environment which makes them provide real-time information for visitors, they can implement various kinds of promotion. However, promotions ignoring visitors' various needs and preferences can lose the original purposes and functions of them. That is, as indiscriminate promotions make visitors feel like spam, they can't achieve their purposes. Therefore, they need an approach using STP strategy which segments visitors through right evidences (Segmentation), selects the target visitors (Targeting), and give proper services to them (Positioning). For using STP Strategy in the smart exhibition environment, we consider these characteristics of it. First, an exhibition is defined as market events of a specific duration, which are held at intervals. According to this, exhibitors who plan some promotions should different events and promotions in each exhibition. Therefore, when they adopt traditional STP strategies, a system can provide services using insufficient information and of existing visitors, and should guarantee the performance of it. Second, to segment automatically, cluster analysis which is generally used as data mining technology can be adopted. In the smart exhibition environment, information of visitors can be acquired in real-time. At the same time, services using this information should be also provided in real-time. However, many clustering algorithms have scalability problem which they hardly work on a large database and require for domain knowledge to determine input parameters. Therefore, through selecting a suitable methodology and fitting, it should provide real-time services. Finally, it is needed to make use of data in the smart exhibition environment. As there are useful data such as booth visit records and participation records for events, the STP strategy for the smart exhibition is based on not only demographical segmentation but also behavioral segmentation. Therefore, in this study, we analyze a case of the promotion methodology which exhibitors can provide a differentiated service to segmented visitors in the smart exhibition environment. First, considering characteristics of the smart exhibition environment, we draw evidences of segmentation and fit the clustering methodology for providing real-time services. There are many studies for classify visitors, but we adopt a segmentation methodology based on visitors' behavioral traits. Through the direct observation, Veron and Levasseur classify visitors into four groups to liken visitors' traits to animals (Butterfly, fish, grasshopper, and ant). Especially, because variables of their classification like the number of visits and the average time of a visit can estimate in the smart exhibition environment, it can provide theoretical and practical background for our system. Next, we construct a pilot system which automatically selects suitable visitors along the objectives of promotions and instantly provide promotion messages to them. That is, based on the segmentation of our methodology, our system automatically selects suitable visitors along the characteristics of promotions. We adopt this system to real exhibition environment, and analyze data from results of adaptation. As a result, as we classify visitors into four types through their behavioral pattern in the exhibition, we provide some insights for researchers who build the smart exhibition environment and can gain promotion strategies fitting each cluster. First, visitors of ANT type show high response rate for promotion messages except experience promotion. So they are fascinated by actual profits in exhibition area, and dislike promotions requiring a long time. Contrastively, visitors of GRASSHOPPER type show high response rate only for experience promotion. Second, visitors of FISH type appear favors to coupon and contents promotions. That is, although they don't look in detail, they prefer to obtain further information such as brochure. Especially, exhibitors that want to give much information for limited time should give attention to visitors of this type. Consequently, these promotion strategies are expected to give exhibitors some insights when they plan and organize their activities, and grow the performance of them.

• Journal of Intelligence and Information Systems
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• v.25 no.1
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• pp.179-196
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• 2019

Recently, the "Smart Consumer" has been emerging. He or she is increasingly inclined to search for and purchase products by taking into account personal judgment or expert reviews rather than by relying on information delivered through manufacturers' advertising. This is especially true when purchasing cosmetics. Because cosmetics act directly on the skin, consumers respond seriously to dangerous chemical elements they contain or to skin problems they may cause. Above all, cosmetics should fit well with the purchaser's skin type. In addition, changes in global cosmetics consumer trends make it necessary to study this field. The desire to find one's own individualized cosmetics is being revealed to consumers around the world and is known as "Finding the Holy Grail." Many consumers show a deep interest in customized cosmetics with the cultural boom known as "K-Beauty" (an aspect of "Han-Ryu"), the growth of personal grooming, and the emergence of "self-culture" that includes "self-beauty" and "self-interior." These trends have led to the explosive popularity of cosmetics made in Korea in the Chinese and Southeast Asian markets. In order to meet the customized cosmetics needs of consumers, cosmetics manufacturers and related companies are responding by concentrating on delivering premium services through the convergence of ICT(Information, Communication and Technology). Despite the evolution of companies' responses regarding market trends toward customized cosmetics, there is no "Intelligent Data Platform" that deals holistically with consumers' skin condition experience and thus attaches emotions to products and services. To find the Holy Grail of customized cosmetics, it is important to acquire and analyze consumer data on what they want in order to address their experiences and emotions. The emotions consumers are addressing when purchasing cosmetics varies by their age, sex, skin type, and specific skin issues and influences what price is considered reasonable. Therefore, it is necessary to classify emotions regarding cosmetics by individual consumer. Because of its importance, consumer emotion analysis has been used for both services and products. Given the trends identified above, we judge that consumer emotion analysis can be used in our study. Therefore, we collected and indexed data on consumers' emotions regarding their cosmetics experiences focusing on consumers' language. We crawled the cosmetics emotion data from SNS (blog and Twitter) according to sales ranking ($1^{st}$ to $99^{th}$), focusing on the ample/serum category. A total of 357 emotional adjectives were collected, and we combined and abstracted similar or duplicate emotional adjectives. We conducted a "Consumer Sentiment Journey" workshop to build a "Consumer Sentiment Dictionary," and this resulted in a total of 76 emotional adjectives regarding cosmetics consumer experience. Using these 76 emotional adjectives, we performed clustering with the Self-Organizing Map (SOM) method. As a result of the analysis, we derived eight final clusters of cosmetics consumer sentiments. Using the vector values of each node for each cluster, the characteristics of each cluster were derived based on the top ten most frequently appearing consumer sentiments. Different characteristics were found in consumer sentiments in each cluster. We also developed a cosmetics experience pattern map. The study results confirmed that recommendation and classification systems that consider consumer emotions and sentiments are needed because each consumer differs in what he or she pursues and prefers. Furthermore, this study reaffirms that the application of emotion and sentiment analysis can be extended to various fields other than cosmetics, and it implies that consumer insights can be derived using these methods. They can be used not only to build a specialized sentiment dictionary using scientific processes and "Design Thinking Methodology," but we also expect that these methods can help us to understand consumers' psychological reactions and cognitive behaviors. If this study is further developed, we believe that it will be able to provide solutions based on consumer experience, and therefore that it can be developed as an aspect of marketing intelligence.

• Archives of design research
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• v.18 no.4 s.62
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• pp.85-94
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• 2005

The purpose of this research is the activation proposal about the local design innovation center that was established as a national design policy For this proposal, I have to research about the established process of local design innovation center and then, I analyzed the present condition of local design innovation center for the promoting plan. As a result, we must establish three basic elements to activate the local design center. the first, we have to know the local characteristic. the second, we have to make up the management direction of local design center the third, we have to get the sympathy from the local administration and local people for the positive support. With above conditions, the local design innovation center have to arrange infra elements. (1) design developing facilities for the lending to the local designer, (2) professional designer for the developing of design industry, (3) program development for various activities, (4) the trend research for supply to local company, (5) design one stop service support, (6) the network foundation construction between design administration and design company for the active communication, (7) the innovation of design center for the benefit model, (8) the local design policy establishment with local administration, (9) the independent management of responsibility for the fulfillment For the promotion of the local design innovation center have to make efforts continually with below listed elements. 1. Design supporting for the local industry 2. Various design campaign for the spreading of public recognition about design 3. The supporting for design company and local company with established facilities and expensive equipments. 4. The construction of design information infra for local company 5. The development of new program about the connection between industry and university. 6. The development of local characteristic and local image innovation to make new local where we are.

• 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.