• Journal of Korean Institute of Industrial Engineers
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• v.24 no.3
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• pp.457-473
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• 1998

As a product structure or BOM(bill of material) of products is hierarchically structured, the design based on the concept of relational data base modeling causes low performances in data search or processing. For this reason, an object-oriented approach to designing a product structure is proposed in this paper. Using Rumbaugh's OMT (Object Modeling Technique) method, classes of parts, BOM structure, options, and models are defined and their class-relationship diagrams are proposed. For the representation of the BOM structure suitable for the object-oriented paradigm, a new data architecture called the BOM item class is suggested. It is expected that the proposed data structure ensures better reusability and expandability due to the modularity.

• International conference on construction engineering and project management
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• 2017.10a
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• pp.121-123
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• 2017

The widespread dissemination of the green building certification system has led to the ongoing development of information management technologies with the aim to effectively utilize construction product information. Among them, a data crawling technology enables to collect the data conveniently and to manage large volumes of construction product information in Korea and overseas. However, without a standardized classification system, it is difficult to efficiently utilize information, and problems such as an additional work for classifying information or information-sharing errors. Therefore, this study suggests to present a necessity for the standardization of the information classification system through expert interviews, and to compare construction product classification systems in Korea and overseas. This study is expected to present a necessity for the effective management of construction product information and the standardization of information-sharing with regard to various construction certifications.

• Journal of Distribution Science
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• v.17 no.5
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• pp.85-94
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• 2019

Purpose - The purpose of this study is to analyze the impact of customer's communication on sales performance in the online market. Research design, data, and methodology - This study uses linear regression analysis to examine the effects of product review characteristics which are the result of customer's communication, on sales performance by using product reviews of online marketplace Amazon. Result - The increase in the number of product reviews positively affected sales performance. An increase in extreme opinions in the product review has a positive effect on sales performance. The product review length has a negative effect on sales performance. Conclusions - This study has shown the online marketplace customers' communication can influence sales performance using product review big data. This study contributed to the theoretical completeness by analyzing all the products of the book category in Amazon online market. This research will complement the theories regard to the customer behavior affecting sales performance. We expect the empirical analysis result will provide empirical help to sellers, online marketplace operators, and customers. In particular, the number of letters in the product may negatively affect sales performance, so sellers need to consider this effect carefully when exposing product reviews.

• The Research Journal of the Costume Culture
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• v.23 no.1
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• pp.63-73
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• 2015

The purpose of this study was to investigate the effect of congruency of parent brand image with self-image, perceived risk, and product knowledge on the attitude towards extended product in fashion brand extension. A survey questionnaire was used to collect data from 170 female college students and collected data were subjected to descriptive analysis and regression analysis using SPSS Window program. The results of this study showed that the congruency of parent brand image with self-image and perceived risk had a positive effect on attitude towards extended product, whereas product knowledge had a negative effect on attitude towards extended product in fashion brand extension. Also, the congruency of parent brand image with self-image had a positive effect on the attitude towards extended product in fashion brand extension with positive moderating effect of perceived risk and negative mediating effect of product knowledge.

• The Journal of Economics, Marketing and Management
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• v.12 no.1
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• pp.1-15
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• 2024

Purpose: This study aims to explore the critical factors in the quality control activities of agricultural products in e-commerce companies and analyze how these factors affect customers' perceived value and repurchase intention. Finally, it further reveals the mediating role of perceived value between the factors of quality control activities of agricultural products and customer repurchase intention. Research design, data and methodology: This study identified six independent factors within agricultural product quality control activities: freshness, assurance, diversity, grading, packaging, and timeliness. Subsequently, the impact of these factors on customer repurchase intention was analyzed. Additionally, perceived value was considered as an intermediary variable between the independent and dependent variables. Data was gathered from 269 Chinese consumers who had experience purchasing on agricultural product e-commerce websites. Results: The study results indicate that the relevant factors within agricultural product quality control activities strongly influence customer repurchase intention, with perceived value mediating this relationship. Conclusions: The significance of this study lies in its exploration of the relevant factors within agricultural product e-commerce's product quality control activities. It identifies their impact on customer repurchase intention and confirms the mediating role of perceived value. The results of this research offer valuable insights and practical guidance for academics in related research fields and practitioners in the agricultural product e-commerce industry.

• Journal of IKEEE
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• v.19 no.4
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• pp.541-547
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• 2015

In current semiconductor manufacturing, as the feature size of integrated circuit (IC) devices continuously shrinks, detecting endpoint in plasma etching process is more difficult than before. For endpoint detection, various kinds of sensors are installed in semiconductor manufacturing equipments, and sensor data are gathered with predefined sampling rate. Generally, detecting endpoint is performed using OES data of by-product. In this study, OES data of both by-product and etchant gas are used to improve reliability of endpoint detection. For the OES data pre-processing, a combination of Signal to Noise Ratio (SNR) and Principal Component Analysis (PCA),are used. Polynomial Regression and Expanded Hidden Markov model (eHMM) technique are applied to pre-processed OES data to detect endpoint.

• The Journal of Society for e-Business Studies
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• v.6 no.2
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• pp.127-142
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• 2001

For the B2B e-commerce and SCM (Supply Chain Management), standardization of electronic catalogue that contains product and business data is important. Especially, standardization of hierarchies that is required for categorization, and standardization of product properties are difficult and costly to maintain. The ability of searching for items and data in databases is critical for successful e-commerce system. This paper introduces the data dictionary of PUB (ISO 13584) part 42 which can establish the standard of product data. The method is applied to develop the data dictionary of automotive parts.

• Proceedings of the Korea Society of Design Studies Conference
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• 2004.10a
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• pp.20-21
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• 2004

• Journal of Intelligence and Information Systems
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• v.19 no.2
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• pp.39-54
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• 2013

Recent explosive increase of electronic commerce provides many advantageous purchase opportunities to customers. In this situation, customers who do not have enough knowledge about their purchases, may accept product recommendations. Product recommender systems automatically reflect user's preference and provide recommendation list to the users. Thus, product recommender system in online shopping store has been known as one of the most popular tools for one-to-one marketing. However, recommender systems which do not properly reflect user's preference cause user's disappointment and waste of time. In this study, we propose a novel recommender system which uses data mining and multi-model ensemble techniques to enhance the recommendation performance through reflecting the precise user's preference. The research data is collected from the real-world online shopping store, which deals products from famous art galleries and museums in Korea. The data initially contain 5759 transaction data, but finally remain 3167 transaction data after deletion of null data. In this study, we transform the categorical variables into dummy variables and exclude outlier data. The proposed model consists of two steps. The first step predicts customers who have high likelihood to purchase products in the online shopping store. In this step, we first use logistic regression, decision trees, and artificial neural networks to predict customers who have high likelihood to purchase products in each product group. We perform above data mining techniques using SAS E-Miner software. In this study, we partition datasets into two sets as modeling and validation sets for the logistic regression and decision trees. We also partition datasets into three sets as training, test, and validation sets for the artificial neural network model. The validation dataset is equal for the all experiments. Then we composite the results of each predictor using the multi-model ensemble techniques such as bagging and bumping. Bagging is the abbreviation of "Bootstrap Aggregation" and it composite outputs from several machine learning techniques for raising the performance and stability of prediction or classification. This technique is special form of the averaging method. Bumping is the abbreviation of "Bootstrap Umbrella of Model Parameter," and it only considers the model which has the lowest error value. The results show that bumping outperforms bagging and the other predictors except for "Poster" product group. For the "Poster" product group, artificial neural network model performs better than the other models. In the second step, we use the market basket analysis to extract association rules for co-purchased products. We can extract thirty one association rules according to values of Lift, Support, and Confidence measure. We set the minimum transaction frequency to support associations as 5%, maximum number of items in an association as 4, and minimum confidence for rule generation as 10%. This study also excludes the extracted association rules below 1 of lift value. We finally get fifteen association rules by excluding duplicate rules. Among the fifteen association rules, eleven rules contain association between products in "Office Supplies" product group, one rules include the association between "Office Supplies" and "Fashion" product groups, and other three rules contain association between "Office Supplies" and "Home Decoration" product groups. Finally, the proposed product recommender systems provides list of recommendations to the proper customers. We test the usability of the proposed system by using prototype and real-world transaction and profile data. For this end, we construct the prototype system by using the ASP, Java Script and Microsoft Access. In addition, we survey about user satisfaction for the recommended product list from the proposed system and the randomly selected product lists. The participants for the survey are 173 persons who use MSN Messenger, Daum Caf$\acute{e}$, and P2P services. We evaluate the user satisfaction using five-scale Likert measure. This study also performs "Paired Sample T-test" for the results of the survey. The results show that the proposed model outperforms the random selection model with 1% statistical significance level. It means that the users satisfied the recommended product list significantly. The results also show that the proposed system may be useful in real-world online shopping store.

• The Journal of Industrial Distribution & Business
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• v.10 no.4
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• pp.57-66
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• 2019

Purpose - Present study aimed at investigating the factors that may affect consumers' decision confidence and decision comfort inducing choice commitment taken place at the stage of post-decision/pre-outcome. This study explored whether there are positive effects of dominance and instrumentality of chosen product on the decision confidence, and also identified whether there are positive effects of choice easiness and goal achievement-related affect felt at the chosen product on the decision comfort. Research design, data, and methodology - Portable digital camera as experimental product was used. 260 data were collected from college students. Four were removed from the analyses because they did not complete the questionnaire. Eighteen were removed because they indicated not experiencing the camera in the past. 238 data were used at the analyses to verify hypotheses by structural equation model in AMOS 21.0. Results - First, both of the consumers' decision confidence and decision comfort positively affected choice commitment. Second, the dominance as well as the instrumentality had positive impact upon the decision confidence. Third, the choice easiness and goal achievement-related affect felt at the chosen product had positive impact on the decision comfort. In sum, present study identified the mediation roles of the decision confidence in the effects of both dominance and instrumentality of the chosen product on forming choice commitment, and also found the mediation roles of the decision comfort in the effects of choice easiness and goal achievement-related affect felt at the product on forming choice commitment. Conclusions - Focusing on the stage of post-decision/pre-outcome in decision making process, present study contributes to advancing the choice commitment theories by exploring the positive effects of both dominance and instrumentality of chosen product on the decision confidence, and by finding the positive effects of both choice easiness and goal achievement-related affect felt at the chosen product on the decision comfort. Marketers should promote their products' dominance and instrumentality by showing the superior roles of their product attributes in achieving consumption goal, and should help consumers feel comfort by making choice process easier and giving information that could help feel the goal achievement-related affect at their product chosen.