• Culinary science and hospitality research
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• v.17 no.3
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• pp.47-64
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• 2011

This research try to find the strategic implication through the importance-performance analysis for the customers who visited local Jeonju-Bibimbap restaurants. It also investigates the effect of the selection attribute of the restaurants on customer satisfaction and behavior intention as well as how customer satisfaction affects behavior intention. The survey was conducted targeting those who visited the local restaurants in Jeonju and had Jeonju-Bibimbap, and 251 copies of the questionnaire were used for the final analysis. As a result, the properties 'Environmental cleanliness' and 'Convenience of reservations' in IPA are probed as 'improvement needed as soon as possible'; the 'food(p<0.01)' and 'service(p<0.05)' factors significantly affect customer satisfaction; the 'physical environment(p<0.01)' and 'service(p<0.05)' factors have significant effect on behavior intention; lastly, it has also been found true that customer satisfaction significantly influences behavior intention(p<0.001).

• Korean journal of food and cookery science
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• v.33 no.2
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• pp.228-236
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• 2017

Purpose: This study investigated beef restaurant's selection attributes using conjoint analysis in order to provide useful information to marketers and managers. Methods: A total of 320 questionnaires were distributed to consumers who visited a beef restaurant in August 2016, and 284 were completed (96.90%). Statistical analyses of data were performed using SPSS/Windows 22.0 for descriptive statistics and conjoint analysis. Results: The results of this study demonstrate the relative importance and level of each attribute for selecting beef restaurants. Price showed the greatest importance (34.86%), followed by origin of beef (27.52%), level of support services (25.72%), and variety of side dishes (11.90%). The optimum attribute combination was various side dishes (0.059), Korean beef (0.385), Very high service level (-0.291), and price of 8,000 won (-0.782). The most preferred beef restaurant gained 37.60% potential market share from choice simulation. There were significant differences in importance of attributes related to age of respondents. For respondents in their 20s and older than 50s, the first consideration was price. Respondents in their 30s considered the level of support services first while those in their 40s considered origin of beef first. Importance of attributes based on companion of respondents revealed that all respondents considered price first. Conclusion: This study contributes to development of marketing plans based on a customer's involvement level focusing on their primary selection criteria when choosing a beef restaurant. Additionally, marketers who manage beef restaurants can estimate the market share of imaginary beef restaurants from these results.

• Journal of KIISE:Software and Applications
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• v.31 no.12
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• pp.1684-1690
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• 2004

This paper presents a method for classifier selection that uses distribution information of the training samples in a small region surrounding a sample. The conventional DCS-LA(Dynamic Classifier Selection - Local Accuracy) selects a classifier dynamically by comparing the local accuracy of each classifier at the test time, which inevitably requires long classification time. On the other hand, in the proposed approach, the best classifier in a local region is stored in the FSA(Feature Space Attribute) table during the training time, and the test is done by just referring to the table. Therefore, this approach enables fast classification because classification is not needed during test. Two feature space attributes are used entropy and density of k training samples around each sample. Each sample in the feature space is mapped into a point in the attribute space made by two attributes. The attribute space is divided into regular rectangular cells in which the local accuracy of each classifier is appended. The cells with associated local accuracy comprise the FSA table. During test, when a test sample is applied, the cell to which the test sample belongs is determined first by calculating the two attributes, and then, the most accurate classifier is chosen from the FSA table. To show the effectiveness of the proposed algorithm, it is compared with the conventional DCS -LA using the Elena database. The experiments show that the accuracy of the proposed algorithm is almost same as DCS-LA, but the classification time is about four times faster than that.

• Proceedings of the KIEE Conference
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• 1995.07b
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• pp.546-549
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• 1995

Recently, electric utility is facing substantially new stream of business environment, such as pressure of business restructuring, competition with private IPPs, diversification of supply-side and demand-side resource options, environmental externalities and uncertainties. Integrated resource planning(IRP) is very useful and powerful approach for solving complex and diversified electricity supply and demand problems. This paper presents a standardized IRP procedure using multi-attribute decision analysis approach. The selection of the most desirable plan is based on multi-attribute trade-off/risk analysis method and score ranking method. As a case study, 50 plans with 12 scenarios are analyzed.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.20 no.43
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• pp.321-331
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• 1997

Measurement of attributes is often highly subjective and imprecise, yet most MADM methods lack provisions for handling imprecise data. Frequently, decision makers must establish a ranking within a finite set of alternatives with respect to multiple attributes which have varying degrees of importance. The problem is more complex if the evaluations of alternatives according to each attribute are not expressed in precise numbers, but rather in fuzzy numbers. Analysis must allow for lack of precision and partial truth. The advantages of a fuzzy approach for MADM are that a decision maker can obtain efficient solutions all at once without trial and error, and that this approach provides better support for judging the interactive improvement of solutions in comparison with o decision making method. The algorithm used in this study is based on the concepts of vague set theory. Linguistic variables and vague values are used to facilitate a decision maker's subjective assessment about attribute weightings and the appropriateness of alternative versus selection attributes in order to obtain final scores which are called vague appropriateness indices. A numerical example is presented to show the practical applicability of this approach.

• The Journal of the Korea Contents Association
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• v.20 no.7
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• pp.585-593
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• 2020

This study was conducted to research the Selection Attributes of tourists in Jeonju Hanok Village. The purpose of this study was to study the importance and satisfaction after visiting the Jeonju Hanok Village using IPA analysis, and to provide results and marketing implications. The survey was conducted from the October to the November in 2018. A total of 300 questionnaires were distributed and 258 responded questionnaires were reliable to be used as a sample. The result of the survey was analyzed by using SPSS 15.0 version for window with Paired t-test and IPA method. Frequency Analysis was also conducted for the characteristic of samples. As a result of the study, first, the cleanliness of tourist attractions was the highest among the selection attributes, and the next ranking was in the order of parking lot facilities, natural scenery, food, and weather. Second, the natural property was the most satisfactory as a selection property item that tourists visiting Hanok Village were satisfied with, followed by climate(weather), regional characteristics, historical and cultural resources, and cleanliness of tourist attractions. Third, depending on the importance-satisfaction value of the selection attribute variable perceived by tourists visiting Hanok Village, it is necessary to develop various programs in Hanok Village and prepare measures to increase tourist satisfaction.

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.668-671
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• 1997

This paper describes a new process-plan selection method using a modified Fuzzy Quantification Theory(FQT). The problem for process-plan selection can be characterized by multiple attributes and used subjective, uncertain information. Fuzzy Quantification Theory is used for handling such informations because it is a useful tool when human judgment or evaluation is quantified via linguistic variables and the proposed method is concerned with the selection of a process plan by derivation of the values of categories for each attribute. In this paper, a modified Fuzzy Quantification Theory(FQT) is described and the procedure of this approach is explained and examples are illustrated.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2001.01a
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• pp.219-223
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• 2001

Within a mechanical system such as an automotive the number of standard machine parts is increasing, so that the parts selection becomes more important than ever before. Selection of appropriate bearings in the preliminary design phase of a machine is also important. In this paper, three decision-making approaches are compared to find out a model that is appropriate to bearing selection problem. An artificial neural network, which is trained with real design cases, is used to select a bearing mechanism at the first step. Then, the subtype of the bearing is selected by the weighting factor method. Finally, types of peripherals such as lubrication methods are determined by a rule-based expert system.

• Journal of KIISE:Software and Applications
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• v.30 no.7_8
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• pp.764-772
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• 2003

The main thrust of our talk will be based on our experience in developing and applying an attribute-based classification technique in the context of an operational answer set driven retrieval system. To alleviate the difficulty and reduce the cost of manually constructing and maintaining answer sets, i.e., knowledge base, we have devised a new method of automating the answer document selection process by using the notion of attribute-based classification, which is in and of itself novel. We attempt to explain through experiments how helpful the proposed method is for the knowledge base construction process.

• Culinary science and hospitality research
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• v.12 no.4 s.31
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• pp.33-45
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• 2006

The purpose of this study is to make special study of the efficient operation of university foodservice. The concrete results through the conjoint analysis can be elicited as follows. First, through the interview in depth we draw out the efficient attribute comparing and analyzing elements of selecting menu and main reasons for selecting either student cafeterias or general cafeterias. Second, we elicit the best attribute based on the results of analysis on preference. Third, we present an improvement program for operating student cafeterias through simulation. As a result of conjoint analysis of the main reason for selecting a cafeteria and the utility of each attribute, the most important factor comes price (34.95%), the time required (33.20%), food taste (30.45%), and various menu (1.42%) in that order. What draws attention in the research is that price (34.93%) is not the only factor which influences students' choice of a cafeteria. Location (33.20%) and food taste (30.45%) are all equally important. These results show that students' expectation for cafeterias is getting various. Basically, all customers look for a nearer restaurant where its food taste is good and menu is various at a low price.