• Journal of Food Hygiene and Safety
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• v.34 no.1
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• pp.30-39
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• 2019

An analytical method was developed for the determination of sedaxane in agricultural products using liquid chromatograph-tandem mass spectrometry (LC-MS/MS). The samples were extracted with acetonitrile and partitioned with dichloromethane to remove the interference, and then purified by using silica SPE cartridges to clean up. The analytes were quantified and confirmed by using LC-MS/MS in positive ion mode using multiple reaction monitoring (MRM). The matrix-matched calibration curves were linear over the calibration ranges ($0.001-0.25{\mu}g/mL$) into a blank extract with $r^2$>0.99. For validation, recovery tests were carried out at three different concentration levels (LOQ, 10LOQ, and 50LOQ, n=5) with five replicates performed at each level. The recoveries were ranged between 74.5 to 100.8% with relative standard deviations (RSDs) of less than 12.1% for all analytes. All values were consistent with the criteria ranges requested in the Codex guidelines (CAC/GL 40, 2003) and Food Safety Evaluation Department guidelines (2016). The proposed analytical method was accurate, effective and sensitive for sedaxane determination in agricultural commodities.

• Journal of Distribution Research
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• v.16 no.4
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• pp.1-27
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• 2011

Introduction: Sales of private labels(PU have been growing m recent years. Globally, PLs have already achieved 20% share, although between 25 and 50% share in most of the European markets(AC. Nielson, 2005). These products are aimed to have comparable quality and prices as national brand(NB) products and have been continuously eroding manufacturer's national brand market share. Stores have also started introducing premium PLs that are of higher-quality and more reasonably priced compared to NBs. Worldwide, many retailers already have a multiple-tier private label architecture. Consumers as a consequence are now able to have a more diverse brand choice in store than ever before. Since premium PLs are priced higher than regular PLs and even, in some cases, above NBs, stores can expect to generate higher profits. Brand extensions and private label have been extensively studied in the marketing field. However, less attention has been paid to the private label extension. Therefore, this research focuses on private label extension using the Multi-Attribute Attitude Model(Fishbein and Ajzen, 1975). Especially there are few studies that consider the hierarchical effect of the PL's two parent brands: store brand and the original PL. We assume that the attitude toward each of the two parent brands affects the attitude towards the extended PL. The influence from each parent brand toward extended PL will vary according to the perceived fit between each parent brand and the extended PL. This research focuses on how these two parent brands act as reference points to one another in the consumers' choice consideration. Specifically we seek to understand how store image and attitude towards original PL affect consumer perceptions of extended premium PL. How consumers perceive extended premium PLs could provide strategic suggestions for retailer managers with specific suggestions on whether it is more effective: to position extended premium PL similarly or dissimilarly to original PL especially on the quality dimension and congruency with store image. There is an extensive body of research on branding and brand extensions (e.g. Aaker and Keller, 1990) and more recently on PLs(e.g. Kumar and Steenkamp, 2007). However there are no studies to date that look at the upgrading and influence of original PLs and attitude towards store on the premium PL extension. This research wishes to make a contribution to this gap using the perceived fit difference between parent brands and extended premium PL as the context. In order to meet the above objectives, we investigate which factors heighten consumers' positive attitude toward premium PL extension. Research Model and Hypotheses: When considering the attitude towards the premium PL extension, we expect four factors to have an influence: attitude towards store; attitude towards original PL; perceived congruity between the store image and the premium PL; perceived similarity between the original PL and the premium PL. We expect that all these factors have an influence on consumer attitude towards premium PL extension. Figure 1 gives the research model and hypotheses. Method: Data were collected by an intercept survey conducted on consumers at discount stores. 403 survey responses were attained (total 59.8% female, across all age ranges). Respondents were asked to respond to a series of Questions measured on 7 point likert-type scales. The survey consisted of Questions that measured: the trust towards store and the original PL; the satisfaction towards store and the original PL; the attitudes towards store, the original PL, and the extended premium PL; the perceived similarity of the original PL and the extended premium PL; the perceived congruity between the store image and the extended premium PL. Product images with specific explanations of the features of premium PL, regular PL and NB we reused as the stimuli for the Question response. We developed scales to measure the research constructs. Cronbach's alphaw as measured each construct with the reliability for all constructs exceeding the .70 standard(Nunnally, 1978). Results: To test the hypotheses, path analysis was conducted using LISREL 8.30. The path analysis for verification of the model produced satisfactory results. The validity index shows acceptable results(${\chi}^2=427.00$(P=0.00), GFI= .90, AGFI= .87, NFI= .91, RMSEA= .062, RMR= .047). With the increasing retailer use of premium PLBs, the intention of this research was to examine how consumers use original PL and store image as reference points as to the attitude towards premium PL extension. Results(see table 1 & 2) show that the attitude of each parent brand (attitudes toward store and original pL) influences the attitude towards extended PL and their perceived fit moderates these influences. Attitude toward the extended PL was influenced by the relative level of perceived fit. Discussion of results and future direction: These results suggest that the future strategy for the PL extension needs to consider that positive parent brand attitude is more strongly associated with the attitude toward PL extensions. Specifically, to improve attitude towards PL extension, building and maintaining positive attitude towards original PL is necessary. Positioning premium PL congruently to store image is also important for positive attitude. In order to improve this research, the following alternatives should also be considered. To improve the research model's predictive power, more diverse products should be included in study. Other attributes of product should also be included such as design, brand name since we only considered trust and satisfaction as factors to build consumer attitudes.

• Journal of Intelligence and Information Systems
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• v.26 no.1
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• pp.1-21
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• 2020

With the rapid development of artificial intelligence technology, various techniques have been developed to extract meaningful information from unstructured text data which constitutes a large portion of big data. Over the past decades, text mining technologies have been utilized in various industries for practical applications. In the field of business intelligence, it has been employed to discover new market and/or technology opportunities and support rational decision making of business participants. The market information such as market size, market growth rate, and market share is essential for setting companies' business strategies. There has been a continuous demand in various fields for specific product level-market information. However, the information has been generally provided at industry level or broad categories based on classification standards, making it difficult to obtain specific and proper information. In this regard, we propose a new methodology that can estimate the market sizes of product groups at more detailed levels than that of previously offered. We applied Word2Vec algorithm, a neural network based semantic word embedding model, to enable automatic market size estimation from individual companies' product information in a bottom-up manner. The overall process is as follows: First, the data related to product information is collected, refined, and restructured into suitable form for applying Word2Vec model. Next, the preprocessed data is embedded into vector space by Word2Vec and then the product groups are derived by extracting similar products names based on cosine similarity calculation. Finally, the sales data on the extracted products is summated to estimate the market size of the product groups. As an experimental data, text data of product names from Statistics Korea's microdata (345,103 cases) were mapped in multidimensional vector space by Word2Vec training. We performed parameters optimization for training and then applied vector dimension of 300 and window size of 15 as optimized parameters for further experiments. We employed index words of Korean Standard Industry Classification (KSIC) as a product name dataset to more efficiently cluster product groups. The product names which are similar to KSIC indexes were extracted based on cosine similarity. The market size of extracted products as one product category was calculated from individual companies' sales data. The market sizes of 11,654 specific product lines were automatically estimated by the proposed model. For the performance verification, the results were compared with actual market size of some items. The Pearson's correlation coefficient was 0.513. Our approach has several advantages differing from the previous studies. First, text mining and machine learning techniques were applied for the first time on market size estimation, overcoming the limitations of traditional sampling based- or multiple assumption required-methods. In addition, the level of market category can be easily and efficiently adjusted according to the purpose of information use by changing cosine similarity threshold. Furthermore, it has a high potential of practical applications since it can resolve unmet needs for detailed market size information in public and private sectors. Specifically, it can be utilized in technology evaluation and technology commercialization support program conducted by governmental institutions, as well as business strategies consulting and market analysis report publishing by private firms. The limitation of our study is that the presented model needs to be improved in terms of accuracy and reliability. The semantic-based word embedding module can be advanced by giving a proper order in the preprocessed dataset or by combining another algorithm such as Jaccard similarity with Word2Vec. Also, the methods of product group clustering can be changed to other types of unsupervised machine learning algorithm. Our group is currently working on subsequent studies and we expect that it can further improve the performance of the conceptually proposed basic model in this study.

• Journal of Intelligence and Information Systems
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• v.20 no.1
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• pp.49-65
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• 2014

Word of Mouth (WOM) is a behavior used by consumers to transfer or communicate their product or service experience to other consumers. Due to the popularity of social media such as Facebook, Twitter, blogs, and online communities, electronic WOM (e-WOM) has become important to the success of products or services. As a result, most enterprises pay close attention to e-WOM for their products or services. This is especially important for movies, as these are experiential products. This paper aims to identify the network factors of an online movie community that impact box office revenue using social network analysis. In addition to traditional WOM factors (volume and valence of WOM), network centrality measures of the online community are included as influential factors in box office revenue. Based on previous research results, we develop five hypotheses on the relationships between potential influential factors (WOM volume, WOM valence, degree centrality, betweenness centrality, closeness centrality) and box office revenue. The first hypothesis is that the accumulated volume of WOM in online product communities is positively related to the total revenue of movies. The second hypothesis is that the accumulated valence of WOM in online product communities is positively related to the total revenue of movies. The third hypothesis is that the average of degree centralities of reviewers in online product communities is positively related to the total revenue of movies. The fourth hypothesis is that the average of betweenness centralities of reviewers in online product communities is positively related to the total revenue of movies. The fifth hypothesis is that the average of betweenness centralities of reviewers in online product communities is positively related to the total revenue of movies. To verify our research model, we collect movie review data from the Internet Movie Database (IMDb), which is a representative online movie community, and movie revenue data from the Box-Office-Mojo website. The movies in this analysis include weekly top-10 movies from September 1, 2012, to September 1, 2013, with in total. We collect movie metadata such as screening periods and user ratings; and community data in IMDb including reviewer identification, review content, review times, responder identification, reply content, reply times, and reply relationships. For the same period, the revenue data from Box-Office-Mojo is collected on a weekly basis. Movie community networks are constructed based on reply relationships between reviewers. Using a social network analysis tool, NodeXL, we calculate the averages of three centralities including degree, betweenness, and closeness centrality for each movie. Correlation analysis of focal variables and the dependent variable (final revenue) shows that three centrality measures are highly correlated, prompting us to perform multiple regressions separately with each centrality measure. Consistent with previous research results, our regression analysis results show that the volume and valence of WOM are positively related to the final box office revenue of movies. Moreover, the averages of betweenness centralities from initial community networks impact the final movie revenues. However, both of the averages of degree centralities and closeness centralities do not influence final movie performance. Based on the regression results, three hypotheses, 1, 2, and 4, are accepted, and two hypotheses, 3 and 5, are rejected. This study tries to link the network structure of e-WOM on online product communities with the product's performance. Based on the analysis of a real online movie community, the results show that online community network structures can work as a predictor of movie performance. The results show that the betweenness centralities of the reviewer community are critical for the prediction of movie performance. However, degree centralities and closeness centralities do not influence movie performance. As future research topics, similar analyses are required for other product categories such as electronic goods and online content to generalize the study results.

• Journal of Food Hygiene and Safety
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• v.34 no.3
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• pp.227-234
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• 2019

An analytical method was developed for the determination of oxytetracycline in agricultural products using the QuEChERS (Quick, Easy, Cheap, Effective, Rugged and Safe) method by liquid chromatography-tandem mass spectrometry (LC-MS/MS). After the samples were extracted with methanol, the extracts were adjusted to pH 4 by formic acid and sodium chloride was added to remove water. Dispersive solid phase extraction (d-SPE) cleanup was carried out using $MgSO_4$ (anhydrous magnesium sulfate), PSA (primary secondary amine), $C_{18}$ (octadecyl) and GCB (graphitized carbon black). The analytes were quantified and confirmed with LC-MS/MS using ESI (electrospray ionization) in positive ion MRM (multiple reaction monitoring) mode. The matrix-matched calibration curves were constructed using six levels ($0.001{\sim}0.25{\mu}g/mL$) and coefficient of determination ($r^2$) was above 0.99. Recovery results at three concentrations (LOQ, $10{\times}LOQ$, and $50{\times}LOQ$, n=5) were from 80.0 to 108.2% with relative standard deviations (RSDs) less than of 11.4%. For inter-laboratory validation, the average recovery was in the range of 83.5~103.2% and the coefficient of variation (CV) was below 14.1%. All results satisfied the criteria ranges requested in the Codex guidelines (CAC/GL 40-1993, 2003) and the Food Safety Evaluation Department guidelines (2016). The proposed analytical method was accurate, effective and sensitive for oxytetracycline determination in agricultural commodities. This study could be useful for safety management of oxytetracycline residues in agricultural products.

• Journal of Intelligence and Information Systems
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• v.23 no.4
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• pp.77-110
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• 2017

Recently, the diversification and individualization of consumption patterns through the web and mobile devices based on the Internet have been rapid. As this happens, the efficient operation of the offline store, which is a traditional distribution channel, has become more important. In order to raise both the sales and profits of stores, stores need to supply and sell the most attractive products to consumers in a timely manner. However, there is a lack of research on which SKUs, out of many products, can increase sales probability and reduce inventory costs. In particular, if a company sells products through multiple in-store stores across multiple locations, it would be helpful to increase sales and profitability of stores if SKUs appealing to customers are recommended. In this study, the recommender system (recommender system such as collaborative filtering and hybrid filtering), which has been used for personalization recommendation, is suggested by SKU recommendation method of a store unit of a distribution company that handles a homogeneous brand through a plurality of sales stores by country and region. We calculated the similarity of each store by using the purchase data of each store's handling items, filtering the collaboration according to the sales history of each store by each SKU, and finally recommending the individual SKU to the store. In addition, the store is classified into four clusters through PCA (Principal Component Analysis) and cluster analysis (Clustering) using the store profile data. The recommendation system is implemented by the hybrid filtering method that applies the collaborative filtering in each cluster and measured the performance of both methods based on actual sales data. Most of the existing recommendation systems have been studied by recommending items such as movies and music to the users. In practice, industrial applications have also become popular. In the meantime, there has been little research on recommending SKUs for each store by applying these recommendation systems, which have been mainly dealt with in the field of personalization services, to the store units of distributors handling similar brands. If the recommendation method of the existing recommendation methodology was 'the individual field', this study expanded the scope of the store beyond the individual domain through a plurality of sales stores by country and region and dealt with the store unit of the distribution company handling the same brand SKU while suggesting a recommendation method. In addition, if the existing recommendation system is limited to online, it is recommended to apply the data mining technique to develop an algorithm suitable for expanding to the store area rather than expanding the utilization range offline and analyzing based on the existing individual. The significance of the results of this study is that the personalization recommendation algorithm is applied to a plurality of sales outlets handling the same brand. A meaningful result is derived and a concrete methodology that can be constructed and used as a system for actual companies is proposed. It is also meaningful that this is the first attempt to expand the research area of the academic field related to the existing recommendation system, which was focused on the personalization domain, to a sales store of a company handling the same brand. From 05 to 03 in 2014, the number of stores' sales volume of the top 100 SKUs are limited to 52 SKUs by collaborative filtering and the hybrid filtering method SKU recommended. We compared the performance of the two recommendation methods by totaling the sales results. The reason for comparing the two recommendation methods is that the recommendation method of this study is defined as the reference model in which offline collaborative filtering is applied to demonstrate higher performance than the existing recommendation method. The results of this model are compared with the Hybrid filtering method, which is a model that reflects the characteristics of the offline store view. The proposed method showed a higher performance than the existing recommendation method. The proposed method was proved by using actual sales data of large Korean apparel companies. In this study, we propose a method to extend the recommendation system of the individual level to the group level and to efficiently approach it. In addition to the theoretical framework, which is of great value.

• Korean Journal of Food Science and Technology
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• v.5 no.1
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• pp.16-24
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• 1973

Breads were made from composite flours based on domestic resources and their quality was assessed to obtain the following results. 1) When barley bread was made according to the formula for standard wheat bread, its specific loaf volume (SLV) was quite low (1.3 at 100% addition of water) in comparison with 3.3 for wheat bread. Addition of 10% defatted soy flour increased its SLV to 1.7 (at 100% water). Among various flour-improving additives, the use of 1.5% GMS + 0.5% CSL gave best results (SLV: 2.0 at 100% water). Admixture of wheat flour with the composite flour based on barley was most effective. Replacement of barley flour with 25% wheat flour gave SLV of 2.8 (at 90% water) and that with 50% wheat flour gave SLV of 3.2 (at 90% water), comparable to standard wheat bread with respect to loaf volume, color and texture. 2) Sweet potato bread had the characteristics of turning black-brown on baking. Use of 20% defatted soy flour and GMS + CSL gave higher SLV (1.9 at 100% water). Addition of wheat flour at 25% or 50% level to the composite flour based on sweet potato flour gave SLV of 2.3 and 2.6, respectively, at 90% water and its color and texture were improved 3) Potato flour was different from sweet potato flour in baking, the effect of GMS + CSL being quite low. Bread from corn flour was different from barley flour bread and defatted rice bran was not suitable for bread-making. 4) Bread was made from following composite flours based on naked barley and sweet potato flours along with the use of proper additives: (a) naked barley flour + defatted soy flour + wheat flour (45 : 10 : 45) (b) naked barley flour + defatted soy flour + wheat flour (67 : 10 : 23) (c) naked barley flour + defatted soy flour (90 : 10) (d) sweet potato flour + defatted soy flour + wheat flour (40 : 20 : 40) (e) sweet potato flour + defatted soy flour (60 : 20 : 20) Sensory evaluation of above breads in comparison with standard wheat bread (So) gave the following decreasing order of scores, So>(a)>(b)>(c)>(e)>(d) and Duncan's multiple range test showed that bread (a) was not different from standard wheat bread significantly at 5% level, in overall evaluation including color, texture, taste and flavor.

• Journal of dental hygiene science
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• v.17 no.4
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• pp.333-340
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• 2017

The prevalence of periodontal disease was steadily increased. The best prevention methods for periodontal disease are teeth brushing and scaling. The purpose of this study was to investigate the status of scaling experience and related factors among some workers. Total 455 workers in 5 manufacturing companies in Gwangju were selected using convenience sampling method. General characteristics, work-related characteristics, oral health-related characteristics and scaling experience were collected by self-reported questionnaires. Chi-square tests, t-tests and multiple logistic regression analysis were performed to investigate the factors influencing the scaling experience using SPSS software. Statistical significance was defined as a p-value<0.05. The proportion of scaling experience during the past year was 47.0%. In simple analysis, age, current working position, number of oral disease, interest in oral health, use of secondary oral products, oral health screening use, oral health education experience and awareness of scaling inclusion in the National Health Insurance (NHI) coverage were associated with scaling experience. Finally, the odds ratios (ORs) for scaling experience were significantly higher in younger subjects (adjusted OR [aOR], 3.09; 95% confidence internal [CI], 1.60~5.96), assistant manager (aOR, 2.68; 95% CI, 1.55~4.63), subjects with high interest in oral health (aOR, 2.15; 95% CI, 1.02~4.52), subjects with oral health screening use (aOR, 2.76; 95% CI, 1.50~5.11) and awareness of scaling inclusion in the NHI coverage (aOR; 2.91, 95% CI, 1.80~4.72) in multiple logistic regression analysis. Scaling experience was relatively low (47.0%). The related factors with scaling experience were age, working position, use of screening and awareness of scaling inclusion in the NHI coverage. Considering these factors will increase the utilization rate of scaling.

• Journal of the Korean Society of Food Science and Nutrition
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• v.31 no.4
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• pp.629-635
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• 2002

This study was performed to develop low-fat comminuted sausages (LFSs, < 3%) manufactured with 1% single (Konjac flour, KF; kappa-carrageenan, k-CN and Locust bean gum, LBG) or mixed hydrocolloids and to select the best combination which had similar textural characteristics to those with regular-fat (~25% fat) control. In experiment 1, LFSs were formulated with each 1% hydrocolloid, smoked and cooked to an internal temperature of 71.7$^{\circ}C$. The pH range of LFSs was 6.29 to 6.34 and approximately 23~24% of fat was removed in the final products, resulting in the higher moisture and protein contents (%) in LFSs, as compared to regular-fat control. No differences (p>0.05) in cooking loss (CL, %), expressible moisture (EM, %), and hunter color values (L, a, b) were observed with the addition of each 1% hydrocolloid. However, LFSs containing 1% k-CN had textural hardness values similar to those with low-/regular-fat controls, whereas LFSs having either KF or LBG had similar cohesiveness values to those with regular-fat counterpart. Tn experiment 2, two or three mixed hydrocolloids were added to the low-fat sausage formulation. The addition of mixed KF+LBG (KLL) and KF+CN+LBG(KCL) reduced EM and textural hardness values, as compared to low-fat control. Among the treatments, LFSs containing two or three combinations of CN with KF or/and LBG had similar textural characteristics to those with regular-fat control. These results suggested that multiple addition of CN with other hydrocolloids (KF or LBG) for the replacement of fats in LFSs would be recommended for the proper functional and textural properties.

• Journal of Korean Society of Forest Science
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• v.83 no.2
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• pp.211-220
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• 1994

The critical need of forest road for enchanting the additional values of various forest products, in addition, giving more recreational opportunity to citizen, has been recognized. In this study the present author aimed to ascertain the most effective construction working plan of forest road being tit to Korean geographic condition. To execute this research program, four locations in national forest of Kangweon-do district and other four locations in private forest in Chollabuk-do district both where forest roads have previously been constructed were selected to analyze the effectiveness basing upon the various factors separately or in combination. The results are summarized as follows ; 1. The investment efficiency in forest road construction showed to increase in the area where terrain factors and district social factors rate is high, and to decrease in the area where forest status factors and forest road structure factors rate is high. So in future the Forest Resource Development Model of forest road should take more importance particularly on those area having terrain factor ratio is low. The extractable value of constructed forest road based on forest status factors rate is expected to increase in case of high considerably. 2. To construct of forest road for increasing multiple use of forests, forest road should be construct with priority on area where obtained total score by evaluation factors is high. And these evaluation factors should take possible determine the position of forest road construction. 3. The following five types of forest road basing upon function performance are suggested with regard to the place where road is constructed. (1) Forest Utilization Model ; where forest status factors and forest road structure factors rate are over 60%. (2) Forest Resource Development Model ; where terrain factors, forest status factors, forest road structure factors and district social factors rate are less than 60%. (3) Community Development Model ; where terrain factors, forest road structure factors and district social factors rate are over 60% but forest status factors rate are less than 60%. (4) Recreation and Health Model ; where terrain factors, forest status factors, forest road structure factors and district social factors rate are over 60%. (5) Multiple Use Model ; where both forest status factors and district social factors rate are over 60%.