• Asian Journal of Innovation and Policy
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• v.9 no.3
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• pp.308-338
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• 2020

The entrepreneurial journey is not short of challenges, and about 90% + tech start-ups experience failure (Startup Genome, 2019). The magnitude of the challenges varies across the tech start-up lifecycle stages, namely emergence, stability, and growth. This opens the research question, do the profiles of a start-up and its co-founder impact start-up success or failure across its lifecycle stages? This study aims to understand and identify the profiles of tech start-ups and their co-founders. We gathered primary data from 151 start-ups (Status: 101 failed and 50 successful ones), and they are across different lifecycle stages and represent six major start-up hubs in India. The chi-square test on status and start-up's lifecycle stage indicates a noticeable correlation, and they are not independent. The Kruskal Wallis test was used to distinguish statistically significant profile attributes. The parameters distinguishing success and failure are identified, and the need to deliver customer experience is emphasized by the start-up profile attributes: Product/service, high-tech nature of a start-up, investor fund availed, co-founder experience, and employee count. The importance of entrepreneurial experience is ascertained with entrepreneur profile attributes: Entrepreneurial expertise, the number of prior and current start-ups, their willingness to start again in the event of failure, and age of co-founder, which is a proxy to learning and experience. This study has implications for entrepreneurs, investors, and policymakers.

• The Journal of the Korea Contents Association
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• v.10 no.9
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• pp.363-374
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• 2010

Supply chain (SC) strategies will influence corporate performance and customer satisfaction. However, few empirical research has investigated the impact of the gap between prescriptive and current strategy on customer satisfaction. Based on the profile analysis with data from 31 products, the results of this study show the significant relationship between customer satisfaction and the fit between SC context and speculation-postponement strategies. The results imply that different SC strategies should be applied in accordance with product attributes and supply environments.

• Journal of Digital Convergence
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• v.15 no.12
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• pp.293-301
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• 2017

Most new products not only suddenly disappear in the market but also quickly cannibalize older products. Under such a circumstance, retailers may have too much stock, and customers may be faced with difficulties discovering products suitable to their preferences among short life cycle products. To address these problems, recommender systems are good solutions. However, most previous recommender systems had difficulty in reflecting changes in customer preferences because the systems employ static customer preferences. In this paper, we propose a recommendation methodology that considers dynamic customer preferences. The proposed methodology consists of dynamic customer profile creation, neighborhood formation, and recommendation list generation. For the experiments, we employ a mobile image transaction dataset that has a short product life cycle. Our experimental results demonstrate that the proposed methodology has a higher quality of recommendation than a typical collaborative filtering-based system. From these results, we conclude that the proposed methodology is effective under conditions where most new products have short life cycles. The proposed methodology need to be verified in the physical environment at a future time.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2000.10a
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• pp.349-352
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• 2000

As a lot of people have used electronic commerce, many shopping malls have appeared on the Interne and the shopping information in them has been enormous. So, the need for a system to recommend product to customers is on the increase so as to reduce time and efforts for shopping. In this paper, we suppose a Product Recommender System which is constructed by applying data mining techniques to web for files and analyzing customer's action pattern, customer's profile and product purchase data. This system offers convenience that customers can get their desired information easily, by sending e-mail or mail and recommending web pages when they visit a shopping mall.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.58 no.1
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• pp.1-6
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• 2009

The traditional demand prediction was based on the technique wherein electric power corporations made monthly or seasonal estimation of electric power consumption for each area and subscription type for the next one or two years to consider both seasonally generated and local consumed amounts. Note, however, that techniques such as pricing, power generation plan, or sales strategy establishment were used by corporations without considering the production, comparison, and analysis techniques of the predicted consumption to enable efficient power consumption on the actual demand side. In this paper, to calculate the predicted value of electric power consumption on a short-term basis (15 minutes) according to the amount of electric power actually consumed for 15 minutes on the demand side, we performed comparison and analysis by applying a 15-minute interval prediction technique to the average and that to the time series analysis to show how they were made and what we obtained from the simulations.

• Journal of Information Technology Services
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• v.13 no.3
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• pp.309-321
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• 2014

Recently, due to the advent of ubiquitous computing and the spread of intelligent portable device such as smart phone, iPad and PDA has been amplified, a variety of services and the amount of information has also increased fastly. It is becoming a part of our common life style that the demands for enjoying the wireless internet are increasing anytime or anyplace without any restriction of time and place. And also, the demands for e-commerce and many different items on e-commerce and interesting of associated items are increasing. Existing collaborative filtering (CF), explicit method, can not only reflect exact attributes of item, but also still has the problem of sparsity and scalability, though it has been practically used to improve these defects. In this paper, using a implicit method without onerous question and answer to the users, not used user's profile for rating to reduce customers' searching effort to find out the items with high purchasability, it is necessary for us to analyse the segmentation of customer and item based on customer data and purchase history data, which is able to reflect the attributes of the item in order to improve the accuracy of recommendation. We propose the method of recommendation system using association rule and weighted preference so as to consider many different items on e-commerce and to refect the profit/weight/importance of attributed of a item. To verify improved performance of proposing system, we make experiments with dataset collected in a cosmetic internet shopping mall.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.52 no.12
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• pp.705-712
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• 2003

In this paper, we propose new unified methodologies of reliability and its cost evaluation in power distribution systems. The unified method means that the proposed reliability approaches consider both conventional evaluation factor, i.e. sustained interruptions and additional ones, i.e. momentary interruptions and voltage sags. Because the three voltage quality phenomena generally originate from the outages on distribution systems, the basic and additional reliability indices are summarized considering the fault clearing mechanism. The proposed unified method is divided into the reliability evaluation for calculating the reliability indices and reliability cost evaluation for assessing the damage of customer. The analytic and probabilistic methodologies are presented for each unified reliability and its cost evaluation. The time sequential Monte Carlo technique is used for the probabilistic method. The proposed DVL(Demand Varying Load) model is added to the reliability cost evaluation substituting the average load model. The proposed methods are tested using the modified RBTS(Roy Billinton Test System) form and historical reliability data of KEPCO(Korea Electric Power Corporation) system. The daily load profile of the each customer type in domestic are gathered for the DVL model. Through the case studies, it is verified that the proposed methods can be effectively applied to the distribution systems for more detail reliability assessment than conventional approaches.

• KEPCO Journal on Electric Power and Energy
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• v.2 no.1
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• pp.61-69
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• 2016

Understanding load patterns and customer classification is a basic step in analyzing the behavior of electricity consumers. To achieve that, there have been many researches about clustering customers' daily load data. Nowadays, the deployment of advanced metering infrastructure (AMI) and big-data technologies make it easier to study customers' load data. In this paper, we study load clustering from the view point of yearly and daily load pattern. We compare four clustering methods; K-means clustering, hierarchical clustering (average & Ward's method) and DBSCAN (Density-Based Spatial Clustering of Applications with Noise). We also discuss the relationship between clustering results and Korean Standard Industrial Classification that is one of possible labels for customers' load data. We find that hierarchical clustering with Ward's method is suitable for clustering load data and KSIC can be well characterized by daily load pattern, but not quite well by yearly load pattern.

• Journal of Distribution Science
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• v.10 no.8
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• pp.25-34
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• 2012

Understanding customer' lifestyles important for banks because it will guide in determining marketing policies, such as services, pricing, service delivery and promotion decisions. From the customer' lifestyle, banks will know what kind of customers' attitudes, interests and opinions, so they also will understand what the costumer' needs and what services needed by them. For Islamic banks, customers understanding are important because, nowadays, the competition of the banks is not only with other Islamic banks but also with the well-established conventional banks offering Islamic products or services The aims of this research paper are to describe what factors underline the customer's lifestyle of both Islamic and conventional bank, to segment the bank customers based on their lifestyles and investigate the profile of each segments, to compare the characteristics of the segments, and to identify marketing policies based on the characteristics. The population of the study is banking customers in Indonesia, in which the researchers have used judgment sampling as sample selection. There were 186 customers of Islamic banks and 244 customers of conventional bank as respondents in this study. Statistical methods employed were exploratory factor analysis and cluster analysis. The finding of the study shows that there are twelve factor underlining the customers' lifestyle, namely: factor of fashion conscious, internet usage, sports spectator, financial and technology optimism, price sensitivity, independent, compulsive housekeeper, new brand tryer community activities, opinion leader, credit usage, and homebody. In addition, for Islamic banking, there are two market segments, namely fashionable-independent and innovative-social segment. Based on the lifestyle characteristics, the first segment has higher level in factor of fashion conscious, homebody, independent, optimism and price conscious, which is therefore called fashionable-independent segment. On the other hand, the second cluster has higher level in factor of new brand tryer, community minded, sport spectator, credit user, internet usage, opinion leader, and compulsive housekeeper, which is therefore called the innovative-social segment. Furthermore, for conventional banking, there are also two segments, namely persuasive-optimistic and sensitive-independent segment. The first segment has higher level on some factors, namely: opinion leader, optimism, internet usage rate, credit usage level, sport spectator, and new brand tryer. On the other hand, the second cluster is characterized by higher level in factor of price conscious, confidence, community minded, homebody, fashion conscious, and compulsive housekeeper. Managerial implications for the management of Islamic banks could be identified in this study as follows. Firstly, the twelve lifestyle factors of this study could be an alternative view in observe Islamic banking customers. The domination of both the fashionable conscious and the internet usage factor show that the aspects are quite instrumental in perceiving the customer' lifestyles, in which reflects the importance of these two aspects to customers. Secondly, in serving their customers, Islamic banks need to understand the customer lifestyle, in which the lifestyle segments found in this study provide a guide of how their needs were reflected. Finally, by understanding the segments and the characteristics each segment of the conventional banks, Islamic banks could adjust their marketing strategies differently from the conventional banks.

• Journal of Korea Multimedia Society
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• v.6 no.1
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• pp.97-104
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• 2003

The appearance of various portal web sites that have individual customers, customizing information operate importantly upon a content. But most current portal sites that has a goal for international electronic commerce use information for customer to a simply individual profile and don't create more and new information that customizing. In this paper, we propose a system that generates a new customizing information with classification and analysis in detail and provides automatically to individual customers. The goal of our research is the development of personalizing auto generation agent that composed form of e-mail from preference of each individual user using open rate and mouse event Information for e-mail.