• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.24 no.2
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• pp.167-175
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• 2024

Due to the Fourth Industrial Revolution, ICT(Information and Communication Technology) industry is becoming more important and sophisticated than ever. In B2B based ICT industry demand forecasting by analyzing the previous customer data is so important. RFM, one of customer relationship management models is a marketing technique that evaluates Recency, Frequency and Monetary value to predict customers behavior. RFM model has been studied focusing on the B2C based industry. On the other hand there is a lack of research on B2B based technology industry. Therefore this study applied it to B2B based high technology industry and considered T(technology collaboration) value, which are identified as important factors in the technology industry. To present an improved model for market performance in B2B technology industry, an empirical study was conducted on comparing the accuracy of the traditional RFM model and the improved RFM-T model. The objective of this study is to contribute to market performance by presenting an improved model in B2B based high technology industry.

• Journal of Animal Science and Technology
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• v.57 no.5
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• pp.17.1-17.6
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• 2015

The present study investigated the nutritional and economic suitability of cashew reject meal (full fat and defatted) as replacement for groundnut cake (GNC) in the diets of laying chickens. A total of eighty four brown shavers at 25 weeks of age were randomly allotted into seven dietary treatments each containing 6 replicates of 2 birds each. The seven diets prepared included diet 1, a control with GNC at $220gkg^{-1}$ as main protein source in the diet. Diets 2, 3 and 4 consist of gradual replacement of GNC with defatted cashew reject meal (DCRM) at 50%, 75% and 100% on weight for weight basis respectively while diets 5, 6 and 7 consist of gradual inclusion of full fat cashew reject meal (FCRM) to replace 25%, 35% and 50% of GNC protein respectively. Each group was allotted a diet in a completely randomized design in a study that lasted eight weeks during which records of the chemical constituent of the test ingredients, performance characteristics, egg quality traits and economic indicators were measured. Results showed that the crude protein were 22.10 and 35.4% for FCRM and DCRM respectively. Gross energy of DCRM was 5035 kcal/kg compared to GNC, 4752 kcal/kg. Result of aflatoxin $B_1$ revealed moderate level between 10 and $17{\mu}g/Kg$ in DCRM and GNC samples respectively. Birds on control gained 10 g, while those on DCRM and FCRM gained about 35 g and 120 g respectively. Feed intake declined (P < 0.05) with increased level of FCRM. Hen day production was highest in birds fed DCRM, followed by control and lowest value (P < 0.05) was recorded for FCRM. No significant change (P > 0.05) was observed for egg weight and shell thickness. Fat deposition and cholesterol content increased (P > 0.05) with increasing level of FCRM. The cost of feed per kilogram decreased gradually with increased inclusion level of CRM. The prediction equation showed the relative worth of DCRM compared to GNC was 92.3% whereas the actual market price of GNC triples that of DCRM. It was recommended that GNC could be completely replaced by DCRM in layer's diets in regions where this by product is abundant. However, FCRM should be cautiously used in diets of laying chickens.

• Analytical Science and Technology
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• v.5 no.4
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• pp.389-399
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• 1992

Liquid Chromatographic behavior of Pd(II) in Isonitrosoethylacetoacetate lmine, $Pd(IEAA-NR)_2$ (R=H, $CH_3$, $C_2H_5$, $n-C_3H_7$, $C_6H_5-CH_2$, $n-C_4H_9$) chelates were investigated by reversed-phase HPLC on Micropak MCH-5 column using methanol/water as mobile phase. The optimum conditions for the separation of $Pd(IEAA-NR)_2$ chelates were examined with respect to the effect of the flow rate, sample solvent, mobile phase strength and column temperature. It wass found that metal chelates were properly eluted in an acceptable range of capacity factor value($0{\leq}log\;k^{\prime}{\leq}1$). The dependence of the logarithm of capacity factor(k') on the volume fraction of water in the binary mobile phase was examined. Also, the dependence of k' on the liquid-liquid extration distribution ratio($D_c$) in methanol-water/n-alkane extration system was investigated. Both kinds of dependence are linear, which susggests that the retention of the electroneutral metal chelate is largely due to the solvophobic effect. Standard adsorption enthalpy changes (${\Delta}H^{\circ}$) and standard adsorption entropy changes (${\Delta}S^{\circ}$) of Pd(II) Isonitrosoethylacetoacetate imine chelates on Micropak MCH-5 column were calculated by measuring capacity factor with changing temperature of the column.

• Journal of KIISE:Software and Applications
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• v.30 no.3_4
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• pp.316-325
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• 2003

A user preference prediction method using an exiting collaborative filtering technique has used the nearest-neighborhood method based on the user preference about items and has sought the user's similarity from the Pearson correlation coefficient. Therefore, it does not reflect any contents about items and also solve the problem of the sparsity. This study suggests the preference prediction system using the similarity weight granted Bayesian estimated value and the associative user clustering to complement problems of an exiting collaborative preference prediction method. This method suggested in this paper groups the user according to the Genre by using Association Rule Hypergraph Partitioning Algorithm and the new user is classified into one of these Genres by Naive Bayes classifier to slove the problem of sparsity in the collaborative filtering system. Besides, for get the similarity between users belonged to the classified genre and new users, this study allows the different estimated value to item which user vote through Naive Bayes learning. If the preference with estimated value is applied to the exiting Pearson correlation coefficient, it is able to promote the precision of the prediction by reducing the error of the prediction because of missing value. To estimate the performance of suggested method, the suggested method is compared with existing collaborative filtering techniques. As a result, the proposed method is efficient for improving the accuracy of prediction through solving problems of existing collaborative filtering techniques.

• Bulletin of the Korean Chemical Society
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• v.35 no.11
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• pp.3289-3293
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• 2014

Femtosecond laser ablation (fs LA) was used in this study to identify pollution by heavy metals and the distribution of elemental nutrients at different rice milling ratios. Polished rice (degrees of milling of 3, 5, 7, 9, and 11) was collected from major Korean supermarkets and one sample thereof was selected. An internal quality control experiment was conducted using a rice flour certified reference material from the Korea Research Institute of Standards and Science (KRISS CRM) for the evaluation of the efficacy. To assess the effectiveness of the analysis method, the reliability was validated using a food analysis performance assessment scheme (FAPAS), with chili powder serving as an external quality control. The results of the analysis of the inorganic elements Ti, Ca, Al, Fe and Mn in white and brown rice with degrees of milling of 3, 5, 7, 9 and 11 using ICP-MS, ICP-OES and AAS revealed contents of 0.40, 49.2, 2.43, 5.36 and 10.3 mg/kg in white rice and 0.59, 78.0, 7.52, 11.0 and 18.5 mg/kg in brown rice, respectively. Among the elements, there were remarkable differences in the measured contents. By comparing the contents of the elements at different degrees of milling, Ti, Co, As, Ca, Al, Cu, Fe, and Mn were determined to be distributed on the surface of the rice grains, whereas the contents of Cd and Pb increased toward the center of the rice grains, and Si was evenly distributed. After the quantitative analysis of rice samples polished to different degrees of milling, Ca and Al, which were contained in large amounts, and Si were analyzed with specificity by fs LA. The results show that Ca and Al were distributed in the rice husk (protective covering of rice) and Si was distributed in all parts of the rice.

• Journal of Distribution Science
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• v.11 no.10
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• pp.5-15
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• 2013

Purpose - With the increase in fervor to purchase luxury brands, new social problems have arisen, such as excessive preoccupation with luxury brands and high preference for forged luxury goods. Therefore, the issues related to luxury brands, termed "Luxury Syndrome," have emerged as an area of great interest for researchers and practitioners. However, previous studies neglect to categorize this preference for luxury brands. Therefore, this study aims to identify the different purchasing behaviors of consumers using the types of luxury brands preferences as parameters. Research design, data, and methodology - This study arranges a causal relationship model assuming that purchase behaviors positively (+) affect typified preference for luxury brands and purchase intentions. We administered a questionnaire survey to the purchasers who bought luxury brands from department stores to secure additional data necessary to verify the hypotheses in this study. We then processed the data using SPSS 19.0. We further analyzed the basic data using frequency and descriptive statistical analysis, and verified the measurement tools through feasibility and reliability analyses. Moreover, this study uses multiple regression analysis to verify the hypotheses. Further, this study tests the path effect between luxury brand purchase attitude and purchase behavior, with non-intrinsic preference and intrinsic preference as the mediating variables. Results - Based on the results, the impact of tendencies of conspicuous consumption and self-monitoring on non-intrinsic preference was significantly positive (+), while the impact of tendencies of pursuit of a reference group, conspicuous consumption, and self-monitoring on intrinsic preference and purchase intentions was significantly positive (+). Further, non-intrinsic and intrinsic preferences positively (+) influence purchase intentions and the impact of non-intrinsic preference took an absolute portion. However, the tendency of dependence on brands negatively (-) impacts purchase intentions. The results showed that self-monitoring and conspicuous consumption tendencies have greater effect on purchase intention, which is mediated by non-intrinsic preference. In contrast, reference group following tendency has a greater effect on purchase intention, which is mediated by intrinsic preference. Conclusions - Based on the results, the study verifies that the consumption of luxury brands in Korea has not yet entered the settling period. The tendency for conspicuous consumption and the tendency for pursuit of the reference group were relatively important aspects for the consumers who prefer luxury brands non-intrinsically and intrinsically, respectively. Especially, it was found that the purchase intentions for forged brands originate from the tendency to depend on brands. Based on these findings, this study suggests the measures to develop and mature the luxury brands market, and reinforce marketing performance at the three levels, that is, government, distributors, and manufacturers. The luxury brands manufacturers should devote themselves to the production and design of products to catch the attention of mature consumers of luxury brands. The luxury brands distributors should then raise the level of Customer Relationship Management (CRM) for opinion leaders. Finally, the Government should prepare effective policies for the development of luxury brands and provide a variety of economic support.

• Journal of Intelligence and Information Systems
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• v.20 no.2
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• pp.137-148
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• 2014

Recommender system has become one of the most important technologies in e-commerce in these days. The ultimate reason to shop online, for many consumers, is to reduce the efforts for information search and purchase. Recommender system is a key technology to serve these needs. Many of the past studies about recommender systems have been devoted to developing and improving recommendation algorithms and collaborative filtering (CF) is known to be the most successful one. Despite its success, however, CF has several shortcomings such as cold-start, sparsity, gray sheep problems. In order to be able to generate recommendations, ordinary CF algorithms require evaluations or preference information directly from users. For new users who do not have any evaluations or preference information, therefore, CF cannot come up with recommendations (Cold-star problem). As the numbers of products and customers increase, the scale of the data increases exponentially and most of the data cells are empty. This sparse dataset makes computation for recommendation extremely hard (Sparsity problem). Since CF is based on the assumption that there are groups of users sharing common preferences or tastes, CF becomes inaccurate if there are many users with rare and unique tastes (Gray sheep problem). This study proposes a new algorithm that utilizes Social Network Analysis (SNA) techniques to resolve the gray sheep problem. We utilize 'degree centrality' in SNA to identify users with unique preferences (gray sheep). Degree centrality in SNA refers to the number of direct links to and from a node. In a network of users who are connected through common preferences or tastes, those with unique tastes have fewer links to other users (nodes) and they are isolated from other users. Therefore, gray sheep can be identified by calculating degree centrality of each node. We divide the dataset into two, gray sheep and others, based on the degree centrality of the users. Then, different similarity measures and recommendation methods are applied to these two datasets. More detail algorithm is as follows: Step 1: Convert the initial data which is a two-mode network (user to item) into an one-mode network (user to user). Step 2: Calculate degree centrality of each node and separate those nodes having degree centrality values lower than the pre-set threshold. The threshold value is determined by simulations such that the accuracy of CF for the remaining dataset is maximized. Step 3: Ordinary CF algorithm is applied to the remaining dataset. Step 4: Since the separated dataset consist of users with unique tastes, an ordinary CF algorithm cannot generate recommendations for them. A 'popular item' method is used to generate recommendations for these users. The F measures of the two datasets are weighted by the numbers of nodes and summed to be used as the final performance metric. In order to test performance improvement by this new algorithm, an empirical study was conducted using a publically available dataset - the MovieLens data by GroupLens research team. We used 100,000 evaluations by 943 users on 1,682 movies. The proposed algorithm was compared with an ordinary CF algorithm utilizing 'Best-N-neighbors' and 'Cosine' similarity method. The empirical results show that F measure was improved about 11% on average when the proposed algorithm was used