• Culinary science and hospitality research
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• v.17 no.2
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• pp.140-152
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• 2011

The purposes of this study are to analyze the gap between foodservice personnel and customers, to urge foodservice providers to reconsider by identifying the problems in service delivery for customer satisfaction, and to deduce the recognition for foodservice quality improvement. The results of this study can be summarized as follows: the average perception score of personnel(3.78 out of 5) was higher than that of customers(3.79). In particular, the customers' perception of 16 attributes, which included 'overall lighting in the restaurant', 'creativity of the food', 'freshness of the ingredients' and so on, was significantly lower than personnel's. Both service providers and customers perceived that 'meal quality' was the highest and 'table setting' was the lowest among the 4 factors, but there was significant difference on 'interior space(p<.001)', 'meal quality (p<.01)', 'convenience(p<.01)' between the two groups. As a results of the quadrant analysis, 'Q3. dishes and bowls', 'Q6. cleanliness of the tables', 'Q12. display of the food', 'Q17. comfort of the interior space', 'Q18, room temperature', 'Q23. overall lighting in the restaurant', 'Q30. arrangement of the furniture and accessaries' were categorized into Quadrant A, all of which showed dissatisfaction of the customers due to the personnel's lower perception. Therefore, service providers have to perceive the gap between the two viewpoints and grant priority to these attributes to improve foodservice quality.

• KIPS Transactions on Software and Data Engineering
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• v.4 no.12
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• pp.549-560
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• 2015

There is a wide variety of data quality attributes such as the ones proposed by the ISO/IEC organization and also by many other domestic and international institutions. However, it takes considerable time and costs to apply those criteria and guidelines to real environment. Therefore, it needs to define data quality evaluation attributes which are easily applicable and are not influenced by organizational environment limitations. The purpose of this paper is to derive data quality attributes and order of their priorities based on customer requirements for managing the process systematically and evaluating the data quantitatively. This study identifies the customer cognitive constructs of data quality attributes using the RGT(Repertory Grid Technique) based on a Korean quality standard model (DQC-M). Also the correlation analysis on the identified constructs is conducted, and the evaluation attributes is prioritized and ranked using the AHP. As the results of this paper, the consistent system, the accurate data, the efficient environment, the flexible management, and the continuous improvement are derived at the first level of the data quality evaluation attributes. Also, Control Compliance(13%), Regulatory Compliance(10%), Requirement Completeness(9.6%), Accuracy(8.4%), and Traceability(6.8%) are ranked on the top 5 of the 19 attributes in the second level.

• Journal of Digital Convergence
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• v.15 no.6
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• pp.189-196
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• 2017

This study investigates customer satisfaction with full service carriers (FSC) and low cost carriers (LCC) using social media sentiment evaluation. From 2008 to 2016, a total of 77,591 tweets about two FSC and six LCC were aggregated and classified as per airline choice factors. Sentiment evaluation was employed to assess customer satisfaction by three appraisers. The results showed that customer satisfaction with LCC was significantly higher (p<0.001) compared to FSC. Furthermore, overall customer satisfaction with both FSC and LCC has been facing a consistent downward trend since the last seven years. The results also highlighted low customer satisfaction with respect to booking and flight operation factors, and a steep decline in customer satisfaction across booking, onboard services, and marketing factors for FSC. The results of this study have practical implications for the airline industry, which can use this quantitative data to improve customer satisfaction with FSC and LCC.

• Culinary science and hospitality research
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• v.15 no.3
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• pp.15-28
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• 2009

The purpose of this study is to examine customer behavior when choosing event menu at the banquet restaurant of a five-star hotel and analyze the factors of choosing the menu and its customer satisfaction. Based on the results of this study, it is possible for hotel mangers to develop good banquet event menu choices and use them when changing menu. This study adopts the Enter Method, and "t" defines 3 variables such as physiological intent, quality of food, and reasonable price. However, the other factors such as sensory images of food, contents of menu, promotion menu, cleanliness and services prove not to be important variables in this study. Among most important 3 variables, quality of food with the highest figure($\beta$ .416) is the most important variable to customer satisfaction followed by physiological intent ($\beta$ .283) and resonable price($\beta$ .134).

• The Journal of the Korea Contents Association
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• v.13 no.6
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• pp.459-468
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• 2013

This paper is 1) to find out how they recognize medicinal foods, 2) to analyze what make it select for functional restaurants, then which attributes affect on satisfaction and recommendation, 3) and to suggest distinguished guidelines for attracting core customers continuously. The questionnaires are distributed 200 who had only visited in medicinal restaurants and used for data analysis 181 except 19. The findings are summed up as follows; 1. All three factors which are showed 'health managing & prevention', 'functional foods', and 'cuisine presentation' had significant on satisfaction, recommendation, and revisited intention in medicinal restaurant. Among them, 'health managing & prevention' factor had significant highly. 2. Satisfaction variable had affected strongly on recommendation, and also, recommendation had affected on revisited intention.

• The KIPS Transactions:PartD
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• v.11D no.5
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• pp.1063-1072
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• 2004

In the ASP(Application Service Provider) business, applications using database sometimes require some data from clients' databases. These days such data are extracted from client database using manual database operations as an EXCEL file and the ASP, once receiving this file, transfers it into the application's database using manual database operations. This paper describes how to deal with data transmitting between the client database and ASP database on the web without using database manual operations for data extraction and insertion. We propose a framework which enables to transmit client data in a systematical way, to match different attribute names of each database for sharing same attribute values, and to avoid exposing information about the network path of client database to the ASP. This approach consists of two steps of data processing. The first is extracting data from client database as XML format by using a downloaded client program from ASP site, the second is uploading and storing the XML file into the ASP database. The implemented prototype system shows the suggested data integration paradigm is valid and ASP business needing integration of client database can be activated using it.

• Culinary science and hospitality research
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• v.17 no.2
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• pp.1-17
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• 2011

The purpose of this study is to make an evaluation on menu selection and satisfaction levels of the tourists who visit Korean restaurants in Busan. This study aims to propose general, strategic plans for improving the menu quality management of Korean restaurants in the future and find out measures to make restaurant users satisfied and revisit. A survey was conducted on ordinary people who had visited Korean restaurants in tourist hotels in Busan. A total of 310 copies of questionnaire were distributed to them from September 1 to 30, 2008. The SPSS/PC+ for Window 12.0 was used for data processing and analysis. As a result, it was found that selection attributes of Korean restaurant menu had an effect on customer satisfaction and revisit intention, and the most influential factors were health and menu designs. These days, people are getting more interested in keeping in shape with the Well-being trend. Such a modern trend is an important factor when selecting menu items. Therefore, when menu management is considered, health-related factors need to be considered more than anything else. In addition, various and unique menu items need to be designed to draw people's curiosity and make customers revisit.

• Journal of the Korea Society of Computer and Information
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• v.19 no.2
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• pp.193-200
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• 2014

Due to the advent of ubiquitous computing environment, it is becoming a part of our common life style. And tremendous information is cumulated rapidly. In these trends, it is becoming a very important technology to find out exact information in a large data to present users. Collaborative filtering is the method based on other users' preferences, can not only reflect exact attributes of user but also still has the problem of sparsity and scalability, though it has been practically used to improve these defects. In this paper, we propose clustering method by user's features based on SOM for predicting purchase pattern in u-Commerce. it is necessary for us to make the cluster with similarity by user's features to be able to reflect attributes of the customer information in order to find the items with same propensity in the cluster rapidly. The proposed makes the task of clustering to apply the variable of featured vector for the user's information and RFM factors based on purchase history data. To verify improved performance of proposing system, we make experiments with dataset collected in a cosmetic internet shopping mall.

• Proceedings of the KIEE Conference
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• 2008.07a
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• pp.179-180
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• 2008

배전선로의 효과적인 운영을 위해 최근 들어 자동검침 데이터를 활용한 부하분석에 대한 연구가 진행되고 있다. 일반적인 부하분석 방식은 자동검침 고객의 데이터를 이용하여 대표 부하패턴을 생성하고 이를 이용하여 미 검침 고객의 부하패턴을 생성하여, 전체 배전선로의 회선 및 구간에 대한 15분/시간/일/주/월 단위의 최대부하 및 부하패턴 등을 분석하는 방법이다. 기존에는 고객을 분류하기 위해 계약종별 코드만을 사용하였으나, 같은 계약종별 코드를 갖는 고객이라 하더라도 부하패턴이 다른 경우가 많아서 부하분석의 정확도를 떨어뜨렸다. 본 연구에서는 고객의 계약종별 코드뿐 아니라 다양한 고객속성 정보와 15분 단위 자동검침 데이터를 이용하여 k-means 기법을 통해 고객을 분류하는 방식을 제안하였다.

• Journal of the Korea Society of Computer and Information
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• v.27 no.3
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• pp.201-207
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• 2022

In this paper, we propose a movie recommendation model that uses the users' ratings as well as their reviews. To understand the user's preference from multicriteria perspectives, the proposed model is designed to apply attribute-based sentiment analysis to the reviews. For doing this, it divides the reviews left by customers into multicriteria components according to its implicit attributes, and applies BERT-based sentiment analysis to each of them. After that, our model selectively combines the attributes that each user considers important to CF to generate recommendation results. To validate usefulness of the proposed model, we applied it to the real-world movie recommendation case. Experimental results showed that the accuracy of the proposed model was improved compared to the traditional CF. This study has academic and practical significance since it presents a new approach to select and use models in consideration of individual characteristics, and to derive various attributes from a review instead of evaluating each of them.