• Asia-Pacific Journal of Business Venturing and Entrepreneurship
• /
• v.7 no.1
• /
• pp.189-206
• /
• 2012

It is urgently requested to innovate the management process of business-service areas in all industry such as financial business, services and manufacturing because of recent business trend - de-manufacturing trend and the weight increment of service in all industries. Many enterprises introduce various management - innovation methodologies in order to meet the rapidly changing business environment. Especially in Korea, it is a vogue to introduce the innovation methodology of the advanced company's. According to this style, the six sigma has been introduced over 10 years since late 1990's and it has become a synonym of innovation indeed. But the result of six sigma introduction has not reached to the level of expectation in its beginning. And the "Lean" have been introduced in Korea in the situation of global financial crisis, economic slump and the pursuit of developing country such as China. Many Korea companies pay attention to the "Lean" innovation activity because the TPS(Toyota Production System) is the matrix of Lean and is the motive power of Toyota growth. In this study, it was analyzed for the evolution course, distinctive features and effects of Lean management and was examined for the difference of Lean management between manufacturing industry and business-service areas. From this results, the characteristics of Lean management in business-service was analyzed. After survey of innovation agent in Korea company, the Lean model of business-service Industry was developed and applied. This study will be worthy to show the right direction to the enterprises which are to apply lean methodologies, or the enterprises which examine lean management for competitive advantages or the peoples who research the same topics.

• Journal of the Korean Crystal Growth and Crystal Technology
• /
• v.21 no.6
• /
• pp.253-258
• /
• 2011

Electro-mechanical coupling performance ($k_t$) of a 1-3 type Piezo-composite was analyzed numerically using FEM. The calculated physical properties of the PZT ceramics were compared with the experimental data and the accuracy of the numerical method was verified. Also the $k_t$ of the composite was analyzed with the vol% and the material properties of the constitutional parts, and the aspect ratio of the PZT rod. As the simulated results, the $k_t$ increased rapidly when the vol% of the PZT ceramics increased up to 30 vol% and saturated the constant value in the above region. And the composite using the soft matrix polymer than the hard one have the superior $k_t$ characteristics. The $k_t$ was greatly influenced by the aspect ratio of the PZT rod up to 30 vol% of PZT ceramics. To improve the $k_t$ characteristics, it is useful that the composite consist of the relatively flexible polymers and the PZT material having the excellent piezoelectric characteristics.

• Information Systems Review
• /
• v.10 no.2
• /
• pp.137-158
• /
• 2008

Rapid development of various information technologies creates new opportunities in online and offline markets. In this changing market environment, customers have various demands on new products and services. Therefore, their power and influence on the markets grow stronger each year. Companies have paid great attention to customer relationship management. Especially, personalized product recommendation systems, which recommend products and services based on customer's private information or purchasing behaviors in stores, is an important asset to most companies. CRM is one of the important business processes where reliable information is mined from customer database. Data mining techniques such as artificial intelligence are popular tools used to extract useful information and knowledge from these customer databases. In this research, we propose a recommendation system that predicts customer's purchase intention. Then, customer's purchasing intention of specific product is predicted by using data mining techniques using receipt data set. The performance of this suggested method is compared with that of other data mining technologies.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
• /
• v.17 no.4
• /
• pp.101-114
• /
• 2022

Recently, various studies on the entrepreneurial ecosystem have been conducted. The entrepreneurial ecosystem is composed of various elements such as entrepreneurs, governments, and infrastructure, and these factors interact to contribute to economic development. The purpose of this study was to analyze differences in importance and performance of the entrepreneurial ecosystem for startups using the importance-performance analysis (IPA) method. Based on this, the importance and current level of the components of the entrepreneurial ecosystem were identified and policy implications were presented. The results of the study are as follows. The importance ranking was in the order of startup support program(4.43), startup funding (4.39), market accessibility(4.30). The ranking of performance was startup support program(3.81), ease of starting a business(3.76), support for startup support institutions(3.66), and startup funding(3.66). All elements of the entrepreneurial ecosystem showed higher importance than performance. This means that the components of the entrepreneurial ecosystem in Korea are recognized as important, but do not play a significant role in terms of performance for startups. In addition, the factors with the highest improvement in the importance-performance matrix were 「safety nets for startup failure」, 「culture of acceptance of failure」, 「ease of market entry」, 「ease of startup survival」, and 「ease of exit」. This study suggested improvement measures such as establishing a social safety net, improving awareness of startup failure culture, matching successful startups, strengthening scale-up support by growth stage, easing regulations in new business fields, and diversifying investment recovery strategies.

• The Journal of Society for e-Business Studies
• /
• v.25 no.3
• /
• pp.131-145
• /
• 2020

The numbers of SNS (Social Network Service) users and usage amounts are increasing every year. The influence of SNS is increasing also. SNS has a wide range of influences from daily decision-making to corporate management activities. Therefore, proper analysis of SNS can be a very meaningful work, and many studies are making a lot of effort to look into various activities and relationships in SNS. In this study, we analyze the SNS following relationships using Twitter, one of the representative SNS services. In other words, unlike the existing SNS analysis, our intention is to analyze the interests of the accounts by extracting and visualizing the accounts that two accounts follow in common. For this, a common following account was extracted using Microsoft Excel macros, and the relationship between the extracted accounts was defined using an adjacency matrix. In addition, to facilitate the analysis of the following relationships, a direction graph was used for visualization, and R programming was used for such visualization.

• Journal of Distribution Science
• /
• v.16 no.12
• /
• pp.57-68
• /
• 2018

Purpose - The development of technologies lead the volume of sale on online market increase but an off-line shopping center is still a core component in the omni-channel strategy. It is generally thought that high-level retailtainment on brick and mortar store affects purchase intentions positively, but some previous studies dispute that and have reported that retailtainment does not affect purchase intentions. So we have studied the additional factors' effect - the nature of purchase and utility - with retailtainment. Research design, data, and methodology - There are 8 treatment groups which were assigned by the method of retailtainment (high vs. low), nature of purchase (essential vs. non-essential), and utility (acquisition vs. transaction). A total of 240 subjects (office workers = 163, 68%; undergraduates = 77, 32%; average age = 30s; female = 39%) were divided into groups and exposed to one of the eight scenarios. Participant's purchase intention was the dependent, and ANOVA and L-matrix were used to analyze for main and interactive effects between factors. Results - First, the main effect and interactive effect between retailtainment and the nature of purchase are significant. We also found that the contrast between essential and non-essential at low-level retailtainment is higher than that of high-level retailtainment. Second, in the case of retailtainment and utility, transaction utility under high-level retailtainment affects purchase intentions positively. Third, between the nature of the purchase and utility, the main effect of the nature of purchase and the interactive effect is significant, but the main effect of utility is not significant. In the case of non-essential goods, the purchase intention was high when transaction utility was provided but in the case of essential goods, acquisition utility increased purchase intentions. Finally, when transaction utility is given, purchase intentions of essential goods increase under low retailtainment, and the purchase intentions of non-essential goods increase under high retailtainment. Conclusions - When customers buy essential goods, discounts decrease purchase intentions. During the season for bargain sales, purchase intentions increase when retailtainment of essential goods is low, and retailtainment of non-essential goods is high.

• Journal of the Korean Society of Costume
• /
• v.65 no.6
• /
• pp.25-35
• /
• 2015

The purpose of this study is to investigate the characteristics of members and communities that have significant influence in the online fashion community through their word-of-mouth activities. In order to identify the influence and the diffusion of word-of-mouth in fashion community, the study selected one online fashion community. Then, the study sorted the online posts and comments made on fashion information and put them into the matrix form to perform social network analysis. The result of the analysis is as follows: First, the fashion community network used in the study has many active members that relay information very quickly. Average time for information diffusion is very short, taking only one or two days in most cases. Second, the influence of word-of-mouth is led by key information produced from only a few members. The number of influential members account for less than 20% of the total number of community members, which indicate high level of degree centrality. The diffusion of word-of-mouth is led by even fewer members, which represent high level of betweenness centrality, compared to the case of degree centrality. Third, component characteristic shares similar information with about 70% of all members being linked to maximize information influence and diffusion. Fourth, a node with high degree centrality and betweenness centrality shares similar interests, presenting strain effect to particular information. Specially, members with high betweenness centrality show similar interests with members of high degree centrality. The members with high betweenness centrality also help expansion of related information by actively commenting on posts. The result of this research emphasizes the necessity of creation and management of network to efficiently convey fashion information by identifying key members with high level of information influence and diffusion to enhance the outcome of online word-of-mouth.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.17 no.7
• /
• pp.263-272
• /
• 2016

Modular product design has contributed to flexible product modification and development, production lead time reduction, and increasing product diversity. Modular product design aims to develop a product architecture that is composed of detachable modules. These modules are constructed by maximizing the similarity of components based on physical and functional interaction analysis among components. Accordingly, a systematic procedure for clustering the components, which is a main activity in modular product design, is proposed in this paper. The first phase in this procedure is to build a component-to-component correlation matrix by analyzing physical and functional interaction relations among the components. In the second phase, network flow modeling is applied to find clusters of components, maximizing their correlations. In the last phase, a network flow model formulated with linear programming is solved to find the clusters and to make them modular. Finally, the proposed procedure in this research and its application are illustrated with an example of modularization for a vacuum cleaner.

• The Journal of Society for e-Business Studies
• /
• v.21 no.4
• /
• pp.1-14
• /
• 2016

Semantic search is known as a series of activities and techniques to improve the search accuracy by clearly understanding users' search intent without big cognitive efforts. Usually, semantic search engines requires ontology and semantic metadata to analyze user queries. However, building a particular ontology and semantic metadata intended for large amounts of data is a very time-consuming and costly task. This is why commercialization practices of semantic search are insufficient. In order to resolve this problem, we propose a novel semantic search method which takes advantage of our previous semantic tensor space model. Since each term is represented as the 2nd-order 'document-by-concept' tensor (i.e., matrix), and each concept as the 2nd-order 'document-by-term' tensor in the model, our proposed semantic search method does not require to build ontology. Nevertheless, through extensive experiments using the OHSUMED document collection and SCOPUS journal abstract data, we show that our proposed method outperforms the vector space model-based search method.

• Information Systems Review
• /
• v.12 no.2
• /
• pp.75-88
• /
• 2010

This paper proposed a method that segmented customers utilizing SOM(Self-organizing Map) and predicted the customers' response of a marketing promotion for each customer's segments. Our proposed method focused on predicting the response of customers dividing into customers' segment whereas most studies have predicted the response of customers all at once. We deployed logistic regression, neural networks, and support vector machines to predict customers' response that is a kind of dichotomous classification while the integrated approach was utilized to improve the performance of the prediction model. Sample data including 45 variables regarding demographic data about 600 customers, transaction data, and promotion activities were applied to the proposed method presenting classification matrix and the comparative analyses of each data mining techniques. We could draw some significant promotion strategies for segmented customers applying our proposed method to sample data.