The purpose of this study was to analyze and evaluate the menu served in government offices foodservice by using Kasavana & Smith's Menu-Engineering. Sales and food costs were collected from the daily sales reports for a year from Jan 2 to Dec 31 in 2007. Calculation for menu analysis and customer's data were done by computer using the MS 2003 Excel spreadsheet program and SPSS 12.0 package program. Menu mix% (MM%) and unit contribution margin were used as variables by Kasavana & Smith. Four possible classifications by Menu-Engineering technique were turned out as 'STAR', 'PLOWHORSE', 'PUZZLE', 'DOG'. The main menus served during a year were 128 dishes and about 141 peoples visited this restaurant daily. The mean age of the men was
I. Introduction: In Chinese electronics industry, the local wholesalers are still dominant but power is rapidly swifting from wholesalers to retailers because in recent foreign big retailers and local mass merchandisers are growing fast. During such transient period, conflicts among channel members emerge important issues. For example, when wholesalers who have more power exercise influence strategies to maintain status, conflicts among manufacturer, wholesaler, and retailer will be intensified. Korean electronics companies in China need differentiated channel strategies by dealing with wholesalers and retailers simultaneously to sell more Korean products in competition with foreign firms. For example, Korean electronics firms should utilize 'guanxi' or relational quality to form long-term relationships with whloesalers instead of power and conflict issues. The major purpose of this study is to investigate the impact of conflict, dependency, and influence strategies between local Korean-products-selling retailers and wholesalers on performance in Chinese electronics distribution channels. In particular, this paper proposes effective distribution strategies for Korean electronics companies in China by analyzing moderating effects of 'Guanxi'. II. Literature Review and Hypotheses: The specific purposes of this study are as follows. First, causes of conflicts between local Korean-products-selling retailers and wholesalers are examined from the perspectives of goal incongruence and role ambiguity and then effects of these causes are found out on perceived conflicts of local retailers. Second, the effects of dependency of local retailers upon wholesalers are investigated on local retailers' perceived conflicts. Third, the effects of non-coercive influence strategies such as information exchange and recommendation and coercive strategies such as threats and legalistic pleas exercised by wholesalers are explored on perceived conflicts by local retailers. Fourth, the effects of level of conflicts perceived by local retailers are verified on local retailers' financial performance and satisfaction. Fifth, moderating effects of relational qualities, say, 'quanxi' between wholesalers and retailers are analyzed on the impact of wholesalers' influence strategies on retailers' performances. Finally, moderating effects of relational qualities are examined on the relationship between conflicts and performance. To accomplish above-mentioned research objectives, Figure 1 and the following research hypotheses are proposed and verified. III. Measurement and Data Analysis: To verify the proposed research model and hypotheses, data were collected from 97 retailers who are selling Korean electronic products located around Central and Southern regions in China. Covariance analysis and moderated regression analysis were employed to validate hypotheses. IV. Conclusion: The following results were drawn using structural equation modeling and hierarchical moderated regression. First, goal incongruence perceived by local retailers significantly affected conflict but role ambiguity did not. Second, consistent with conflict spiral theory, the level of conflict decreased when retailers' dependency increased toward wholesalers. Third, noncoercive influence strategies such as information exchange and recommendation implemented by wholesalers had significant effects on retailers' performance such as sales and satisfaction without conflict. On the other hand, coercive influence strategies such as threat and legalistic plea had insignificant effects on performance in spite of increasing the level of conflict. Fourth, 'guanxi', namely, relational quality between local retailers and wholesalers showed unique effects on performance. In case of noncoercive influence strategies, 'guanxi' did not play a role of moderator. Rather, relational quality and noncoercive influence strategies can serve as independent variables to enhance performance. On the other hand, when 'guanxi' was well built due to mutual trust and commitment, relational quality as a moderator can positively function to improve performance even though hostile, coercive influence strategies were implemented. Fifth, 'guanxi' significantly moderated the effects of conflict on performance. Even if conflict arises, local retailers who form solid relational quality can increase performance by dealing with dysfunctional conflict synergistically compared with low 'quanxi' retailers. In conclusion, this study verified the importance of relational quality via 'quanxi' between local retailers and wholesalers in Chinese electronic industry because relational quality could cross out the adverse effects of coercive influence strategies and conflict on performance.
The purpose of this study is to examine the current status of vascular plants and naturalized plants growing in the Seongeup Folk Village in Jeju and to consider and compare their distribution patterns and the characteristics of emergence of naturalized plants in other folk villages and all parts of Jeju, thereby exploring measures to well manage naturalized plants. The result of this study is as follows.11) The total number of vascular plants growing in Seongeup Folk Village is identified to be 354 taxa which include 93 families, 260 genus, 298 species, 44 varieties and 12 breeds. Among them, the number of naturalized plants is 55 taxa in total including 22 families, 46 genus, 53 species, and 2 varieties, which accounts for 21.7% of the total of 254 taxa identified all over the region of Jeju. The rate of naturalization in Seongeup Folk Village is 15.5%, which is far higher than the rates of plant naturalization in Hahoi Village in Andong, Yangdong Village in Gyeongju, Hangae Village in Seongju, Wanggok Village in Goseong, and Oeam Village in Asan. Among the naturalized plants identified within the targeted villages, the number of those growing in Jeju is 9 taxa including Silene gallica, Modiola caroliniana, Oenothera laciniata, Oenothera stricta, Apium leptophyllum, Gnaphalium purpureum, Gnaphalium calviceps, Paspalum dilatatum and Sisyrinchium angustifolium. It is suggested that appropriate management measures that consider the characteristics of the gateway to import and the birthplace of the naturalized plants are necessary. In the meantime, 3 more taxa that have not been included in the reference list of Jeju have been identified for the first time in Seongeup Folk Village, which include Bromus sterilis, Cannabis sativa and Veronica hederaefolia. The number of naturalized plants identified within the gardens of unit-based cultural properties is 20 taxa, among which the rate of prevalence of Cerastium glomeratum is the highest at 62.5%. On the other hand, the communities of plants that require landscape management are Brassica napus and other naturalized plants, including Cosmos bipinnatus, Trifolium repens, Medicago lupulina, Oenothera stricta, O. laciniata, Lotus corniculatus, Lolium perenne, Silene gallica, Hypochaeris radicata, Plantago virginica, Bromus catharticus and Cerastium glomeratum. As a short-term measure to manage naturalized plants growing in Seongeup Folk Village, it is important to identify the current status of Cosmos bipinnatus and Brassica napus that have been planted for landscape agriculture, and explore how to use flowers during the blooming season. It is suggested that Ambrosia artemisiifolia and Hypochaeris radicata, designated as invasive alien plants by the Ministry of Health and Welfare, should be eradicated initially, followed by regular monitoring in case of further invasion, spread or expansion. As for Hypochaeris radicata, in particular, some physical prevention measures need to be explored, such as for example, identifying the habitat density and eradication of the plant. In addition, it is urgent to remove plants, such as Sonchus oleraceus, Houttuynia cordata, Crassocephalum crepidioides, Erigeron annuus and Lamium purpureum with high index of greenness visually, growing wild at around high Jeongyi town walls. At the same time, as the distribution and dominance value of the naturalized plants growing in deserted or empty houses are high, it is necessary to find measures to preserve and manage them and to use the houses as lodging places.
Overview of Research: Product availability is one of important competences of store to fulfill consumer needs. If stock-outs which means a product what consumer wants to buy is not available occurs, consumer will face decision-making uncertainty that leads to consumer's negative responses such as consumer dissatisfaction on store. Stockouts was much studied in the field of academia as well as practice in other countries. However, stock-outs has not been researched at all in Marketing and/or Distribution area in Korea. The main objectives of this study are to find out determinants of consumer responses such as Substitute, Delay, and Leave(SDL) when consumer encounters out-of-stock situation and then to examine the effects of these factors on consumer responses. Specifically, this study focuses on situational characteristics(e.g., purchase urgency and surprise), store characteristics (e.g., product assortment and store convenience), and consumer characteristics (e.g., brand loyalty and store loyalty). Then, this study empirically investigates relationships these factors with consumers behaviors such as product substitution, purchase delay, and store switching.
As a part of investigation to use the Anatrctic krill, Euphausia superba, more effectively as a food source, processing conditions, utilizations and storage stability of krill paste (intermediate product of krill) were examined and also chemical compositions of krill paste were analyzed. Frozen raw krill was chopped, agitated with
Due to the recent expansion of the Web 2.0 -based services, along with the widespread of smartphones, online social network services are being popularized among users. Online social network services are the online community services which enable users to communicate each other, share information and expand human relationships. In the social network services, each relation between users is represented by a graph consisting of nodes and links. As the users of online social network services are increasing rapidly, the SNS are actively utilized in enterprise marketing, analysis of social phenomenon and so on. Social Network Analysis (SNA) is the systematic way to analyze social relationships among the members of the social network using the network theory. In general social network theory consists of nodes and arcs, and it is often depicted in a social network diagram. In a social network diagram, nodes represent individual actors within the network and arcs represent relationships between the nodes. With SNA, we can measure relationships among the people such as degree of intimacy, intensity of connection and classification of the groups. Ever since Social Networking Services (SNS) have drawn increasing attention from millions of users, numerous researches have made to analyze their user relationships and messages. There are typical representative SNA methods: degree centrality, betweenness centrality and closeness centrality. In the degree of centrality analysis, the shortest path between nodes is not considered. However, it is used as a crucial factor in betweenness centrality, closeness centrality and other SNA methods. In previous researches in SNA, the computation time was not too expensive since the size of social network was small. Unfortunately, most SNA methods require significant time to process relevant data, and it makes difficult to apply the ever increasing SNS data in social network studies. For instance, if the number of nodes in online social network is n, the maximum number of link in social network is n(n-1)/2. It means that it is too expensive to analyze the social network, for example, if the number of nodes is 10,000 the number of links is 49,995,000. Therefore, we propose a heuristic-based method for finding the shortest path among users in the SNS user graph. Through the shortest path finding method, we will show how efficient our proposed approach may be by conducting betweenness centrality analysis and closeness centrality analysis, both of which are widely used in social network studies. Moreover, we devised an enhanced method with addition of best-first-search method and preprocessing step for the reduction of computation time and rapid search of the shortest paths in a huge size of online social network. Best-first-search method finds the shortest path heuristically, which generalizes human experiences. As large number of links is shared by only a few nodes in online social networks, most nods have relatively few connections. As a result, a node with multiple connections functions as a hub node. When searching for a particular node, looking for users with numerous links instead of searching all users indiscriminately has a better chance of finding the desired node more quickly. In this paper, we employ the degree of user node vn as heuristic evaluation function in a graph G = (N, E), where N is a set of vertices, and E is a set of links between two different nodes. As the heuristic evaluation function is used, the worst case could happen when the target node is situated in the bottom of skewed tree. In order to remove such a target node, the preprocessing step is conducted. Next, we find the shortest path between two nodes in social network efficiently and then analyze the social network. For the verification of the proposed method, we crawled 160,000 people from online and then constructed social network. Then we compared with previous methods, which are best-first-search and breath-first-search, in time for searching and analyzing. The suggested method takes 240 seconds to search nodes where breath-first-search based method takes 1,781 seconds (7.4 times faster). Moreover, for social network analysis, the suggested method is 6.8 times and 1.8 times faster than betweenness centrality analysis and closeness centrality analysis, respectively. The proposed method in this paper shows the possibility to analyze a large size of social network with the better performance in time. As a result, our method would improve the efficiency of social network analysis, making it particularly useful in studying social trends or phenomena.
This study focuses on predicting which firms will increase capital by issuing new stocks in the near future. Many stakeholders, including banks, credit rating agencies and investors, performs a variety of analyses for firms' growth, profitability, stability, activity, productivity, etc., and regularly report the firms' financial analysis indices. In the paper, we develop predictive models for rights issues using these financial analysis indices and data mining techniques. This study approaches to building the predictive models from the perspective of two different analyses. The first is the analysis period. We divide the analysis period into before and after the IMF financial crisis, and examine whether there is the difference between the two periods. The second is the prediction time. In order to predict when firms increase capital by issuing new stocks, the prediction time is categorized as one year, two years and three years later. Therefore Total six prediction models are developed and analyzed. In this paper, we employ the decision tree technique to build the prediction models for rights issues. The decision tree is the most widely used prediction method which builds decision trees to label or categorize cases into a set of known classes. In contrast to neural networks, logistic regression and SVM, decision tree techniques are well suited for high-dimensional applications and have strong explanation capabilities. There are well-known decision tree induction algorithms such as CHAID, CART, QUEST, C5.0, etc. Among them, we use C5.0 algorithm which is the most recently developed algorithm and yields performance better than other algorithms. We obtained data for the rights issue and financial analysis from TS2000 of Korea Listed Companies Association. A record of financial analysis data is consisted of 89 variables which include 9 growth indices, 30 profitability indices, 23 stability indices, 6 activity indices and 8 productivity indices. For the model building and test, we used 10,925 financial analysis data of total 658 listed firms. PASW Modeler 13 was used to build C5.0 decision trees for the six prediction models. Total 84 variables among financial analysis data are selected as the input variables of each model, and the rights issue status (issued or not issued) is defined as the output variable. To develop prediction models using C5.0 node (Node Options: Output type = Rule set, Use boosting = false, Cross-validate = false, Mode = Simple, Favor = Generality), we used 60% of data for model building and 40% of data for model test. The results of experimental analysis show that the prediction accuracies of data after the IMF financial crisis (59.04% to 60.43%) are about 10 percent higher than ones before IMF financial crisis (68.78% to 71.41%). These results indicate that since the IMF financial crisis, the reliability of financial analysis indices has increased and the firm intention of rights issue has been more obvious. The experiment results also show that the stability-related indices have a major impact on conducting rights issue in the case of short-term prediction. On the other hand, the long-term prediction of conducting rights issue is affected by financial analysis indices on profitability, stability, activity and productivity. All the prediction models include the industry code as one of significant variables. This means that companies in different types of industries show their different types of patterns for rights issue. We conclude that it is desirable for stakeholders to take into account stability-related indices and more various financial analysis indices for short-term prediction and long-term prediction, respectively. The current study has several limitations. First, we need to compare the differences in accuracy by using different data mining techniques such as neural networks, logistic regression and SVM. Second, we are required to develop and to evaluate new prediction models including variables which research in the theory of capital structure has mentioned about the relevance to rights issue.
This study performs social network analysis based on consumer sentiment related to a location in Seoul using data reflecting consumers' web search activities and emotional evaluations associated with commerce. The study focuses on large commercial districts in Seoul. In addition, to consider their various aspects, social network indexes were combined with the trading area's public data to verify factors affecting the area's sales. According to R square's change, We can see that the model has a little high R square value even though it includes only the district's public data represented by static data. However, the present study confirmed that the R square of the model combined with the network index derived from the social network analysis was even improved much more. A regression analysis of the trading area's public data showed that the five factors of 'number of market district,' 'residential area per person,' 'satisfaction of residential environment,' 'rate of change of trade,' and 'survival rate over 3 years' among twenty two variables. The study confirmed a significant influence on the sales of the trading area. According to the results, 'residential area per person' has the highest standardized beta value. Therefore, 'residential area per person' has the strongest influence on commercial sales. In addition, 'residential area per person,' 'number of market district,' and 'survival rate over 3 years' were found to have positive effects on the sales of all trading area. Thus, as the number of market districts in the trading area increases, residential area per person increases, and as the survival rate over 3 years of each store in the trading area increases, sales increase. On the other hand, 'satisfaction of residential environment' and 'rate of change of trade' were found to have a negative effect on sales. In the case of 'satisfaction of residential environment,' sales increase when the satisfaction level is low. Therefore, as consumer dissatisfaction with the residential environment increases, sales increase. The 'rate of change of trade' shows that sales increase with the decreasing acceleration of transaction frequency. According to the social network analysis, of the 25 regional trading areas in Seoul, Yangcheon-gu has the highest degree of connection. In other words, it has common sentiments with many other trading areas. On the other hand, Nowon-gu and Jungrang-gu have the lowest degree of connection. In other words, they have relatively distinct sentiments from other trading areas. The social network indexes used in the combination model are 'density of ego network,' 'degree centrality,' 'closeness centrality,' 'betweenness centrality,' and 'eigenvector centrality.' The combined model analysis confirmed that the degree centrality and eigenvector centrality of the social network index have a significant influence on sales and the highest influence in the model. 'Degree centrality' has a negative effect on the sales of the districts. This implies that sales decrease when holding various sentiments of other trading area, which conflicts with general social myths. However, this result can be interpreted to mean that if a trading area has low 'degree centrality,' it delivers unique and special sentiments to consumers. The findings of this study can also be interpreted to mean that sales can be increased if the trading area increases consumer recognition by forming a unique sentiment and city atmosphere that distinguish it from other trading areas. On the other hand, 'eigenvector centrality' has the greatest effect on sales in the combined model. In addition, the results confirmed a positive effect on sales. This finding shows that sales increase when a trading area is connected to others with stronger centrality than when it has common sentiments with others. This study can be used as an empirical basis for establishing and implementing a city and trading area strategy plan considering consumers' desired sentiments. In addition, we expect to provide entrepreneurs and potential entrepreneurs entering the trading area with sentiments possessed by those in the trading area and directions into the trading area considering the district-sentiment structure.
Product evaluation criteria is an indicator describing attributes or values of products, which enable users or manufacturers measure and understand the products. When companies analyze their products or compare them with competitors, appropriate criteria must be selected for objective evaluation. The criteria should show the features of products that consumers considered when they purchased, used and evaluated the products. However, current evaluation criteria do not reflect different consumers' opinion from product to product. Previous studies tried to used online reviews from e-commerce sites that reflect consumer opinions to extract the features and topics of products and use them as evaluation criteria. However, there is still a limit that they produce irrelevant criteria to products due to extracted or improper words are not refined. To overcome this limitation, this research suggests LDA-k-NN model which extracts possible criteria words from online reviews by using LDA and refines them with k-nearest neighbor. Proposed approach starts with preparation phase, which is constructed with 6 steps. At first, it collects review data from e-commerce websites. Most e-commerce websites classify their selling items by high-level, middle-level, and low-level categories. Review data for preparation phase are gathered from each middle-level category and collapsed later, which is to present single high-level category. Next, nouns, adjectives, adverbs, and verbs are extracted from reviews by getting part of speech information using morpheme analysis module. After preprocessing, words per each topic from review are shown with LDA and only nouns in topic words are chosen as potential words for criteria. Then, words are tagged based on possibility of criteria for each middle-level category. Next, every tagged word is vectorized by pre-trained word embedding model. Finally, k-nearest neighbor case-based approach is used to classify each word with tags. After setting up preparation phase, criteria extraction phase is conducted with low-level categories. This phase starts with crawling reviews in the corresponding low-level category. Same preprocessing as preparation phase is conducted using morpheme analysis module and LDA. Possible criteria words are extracted by getting nouns from the data and vectorized by pre-trained word embedding model. Finally, evaluation criteria are extracted by refining possible criteria words using k-nearest neighbor approach and reference proportion of each word in the words set. To evaluate the performance of the proposed model, an experiment was conducted with review on '11st', one of the biggest e-commerce companies in Korea. Review data were from 'Electronics/Digital' section, one of high-level categories in 11st. For performance evaluation of suggested model, three other models were used for comparing with the suggested model; actual criteria of 11st, a model that extracts nouns by morpheme analysis module and refines them according to word frequency, and a model that extracts nouns from LDA topics and refines them by word frequency. The performance evaluation was set to predict evaluation criteria of 10 low-level categories with the suggested model and 3 models above. Criteria words extracted from each model were combined into a single words set and it was used for survey questionnaires. In the survey, respondents chose every item they consider as appropriate criteria for each category. Each model got its score when chosen words were extracted from that model. The suggested model had higher scores than other models in 8 out of 10 low-level categories. By conducting paired t-tests on scores of each model, we confirmed that the suggested model shows better performance in 26 tests out of 30. In addition, the suggested model was the best model in terms of accuracy. This research proposes evaluation criteria extracting method that combines topic extraction using LDA and refinement with k-nearest neighbor approach. This method overcomes the limits of previous dictionary-based models and frequency-based refinement models. This study can contribute to improve review analysis for deriving business insights in e-commerce market.
To examine the causal relationship between geomagnetic storm and substorm, we investigate the correlation between dispersionless particle injection rate of proton flux observed from geosynchronous satellites, which is known to be a typical indicator of the substorm expansion activity, and Dst index during magnetic storms. We utilize geomagnetic storms occurred during the period of 1996 ~ 2000 and categorize them into three classes in terms of the minimum value of the Dst index ( shows the research model of this study. To accomplish above-mentioned research objectives, the following ten hypotheses were proposed and verified : is the estimation results of l\1NL model, and
shows the marginal effects for each determinant to consumer's responses(SDL). Significant statistical results were as follows. Purchase urgency, purchase quantities, pre-purchase plan, product assortment, store price image, brand loyalty, and store loyalty were turned out to be significant determinants to influence consumer alternative behaviors in case of out-of-stock situation. Specifically, first, product substitution behavior was triggered by purchase urgency, surprise, purchase quantities, pre-purchase plan, product assortment, store price image, brand loyalty, and store loyalty. Second, purchase delay behavior was led by purchase urgency, purchase quantities, and brand loyalty. Third, store switching behavior was influenced by purchase urgency, purchase quantities, pre-purchase plan, product assortment, store price image, brand loyalty, and store loyalty. Finally, when out-of-stock situation occurs, store convenience and salesperson service did not have significant effects on consumer alternative responses.
Processing of Intermediate Product(Krill Paste) Derived from Krill
(크릴을 원료로 한 식품가공용 중간소재(크릴페이스트) 가공에 관한 연구)
Improved Social Network Analysis Method in SNS
(SNS에서의 개선된 소셜 네트워크 분석 방법)
Development of Predictive Models for Rights Issues Using Financial Analysis Indices and Decision Tree Technique
(경영분석지표와 의사결정나무기법을 이용한 유상증자 예측모형 개발)
Smart Store in Smart City: The Development of Smart Trade Area Analysis System Based on Consumer Sentiments
(Smart Store in Smart City: 소비자 감성기반 상권분석 시스템 개발)
Product Evaluation Criteria Extraction through Online Review Analysis: Using LDA and k-Nearest Neighbor Approach
(온라인 리뷰 분석을 통한 상품 평가 기준 추출: LDA 및 k-최근접 이웃 접근법을 활용하여)
THE RELATIONSHIP BETWEEN PARTICLE INJECTION RATE OBSERVED AT GEOSYNCHRONOUS ORBIT AND DST INDEX DURING GEOMAGNETIC STORMS
(자기폭풍 기간 중 정지궤도 공간에서의 입자 유입률과 Dst 지수 사이의 상관관계)
이메일무단수집거부
이용약관
제 1 장 총칙
제 2 장 이용계약의 체결
제 3 장 계약 당사자의 의무
제 4 장 서비스의 이용
제 5 장 계약 해지 및 이용 제한
제 6 장 손해배상 및 기타사항
Detail Search
Image Search
(β)