• Journal of Distribution Science
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• v.21 no.1
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• pp.73-83
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• 2023

Purpose: This study analyzed the influence of restaurant service perception on customer satisfaction and its implications on customers' attitude towards, intention to return to, and word-of-mouth (WOM) regarding a restaurant chain. Research design, data and methodology: Data from 421 respondents were collected using the convenience sampling method. After analyzing the data normality and removing responses with missing data and outliers, 342 responses were selected for further analysis, and the hypotheses were tested using Structural Equation Modeling (SEM). Results: We found that service perception affected customer satisfaction and customer satisfaction affected the customers' attitude toward the restaurant chain, which affected customers' intention to return and WOM about the restaurant chain. Conclusions: This paper provides one of the most important empirical results for managers in the restaurant sector, especially in Indonesia. Restaurant managers should thus provide training to their employees to improve the quality of the interaction with the customers and thereby increase customer satisfaction. The limitations listed in this study include the exclusion of respondents' income. For future research, we suggest investigating models of customer participation or consumer value co-creation for restaurant marketing success. Consumers are generic actors in the service ecosystem engaged in the value co-creation process.

• The Journal of Bigdata
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• v.8 no.1
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• pp.35-47
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• 2023

It is crucial to develop effective and efficient big data analytics methods for problem-solving in the field of business in order to improve the performance of data analytics and reduce costs and risks in the analysis of customer data. In this study, a big data-driven data analysis system using artificial intelligence techniques is designed to increase the accuracy of big data analytics along with the rapid growth of the field of data science. We present a key direction for big data analysis systems through missing value imputation, outlier detection, feature extraction, utilization of explainable artificial intelligence techniques, and exploratory data analysis. Our objective is not only to develop big data analysis techniques with complex structures of business data but also to bridge the gap between the theoretical ideas in artificial intelligence methods and the analysis of real-world data in the field of business.

• Quality Improvement in Health Care
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• v.7 no.2
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• pp.230-242
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• 2000

Background : The measuring instruments for customer satisfaction in hospitals are often composed of some dimensions reflecting the conceptive complexity of them. Then, overall satisfaction would be expected to be equal the 'weighted' sum of scores by dimensions because the importance rated by customers may be different across the dimensions. But the issue of how to weight the dimensions with importance is not yet solved. We examined 3 sets of weighting methods as to make effect on predictive power against overall satisfaction. Methods : We conducted a survey included 483 subjects who had visited or admitted to a university hospital, using the short form questionnaire being developed by The Korean Society of Quality Assurance in Health Care for out-patient and in-patient. By using a multiple linear regression model, we compared among changes of explanatory powers against overall satisfaction as dependent variable after weighting 4 dimensions of the survey questionnaire as independent variables with importance scores of dimensions perceived by consumers. And we compared the feasibility of each weighting, methods by checking missing cases. Results : There were no weighting methods increasing the explanatory power after applying them. The method of absolute scoring was found higher explanatory-power than others, but this finding had no statistical significance. Regarding the number of missing value, method of absolutely scoring had the least cases. Conclusion : Our findings suggested that weighting the dimensions with importance might have little significance in the cases of scales having items highly correlated, such as consumers' satisfaction. Though asking with items to be answered absolutely, customers might be rating relatively in some degree and this method produced least missing cases. Considering these points, in the cases when weighting the dimensions with importance would be required, we suggest that weighting method by absolute scoring might be better than others.

• The Journal of the Korea Contents Association
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• v.21 no.1
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• pp.277-283
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• 2021

Traffic data by vehicle classification are important data used as basic data in various fields such as road and traffic design. Traffic data is collected through permanent and temporary surveys and is provided as an annual average daily traffic (AATD) in the statistical yearbook of road traffic. permanent surveys are collected through traffic collection equipment (AVC), and the AVC consists of a loop sensor that detects traffic volume and a piezo sensor that detects the number of axes. Due to the nature of the buried type of traffic collection equipment, missing data is generated due to failure of detection equipment. In the existing method, it is corrected through historical data and the trend of traffic around the point. However, this method has a disadvantage in that it does not reflect temporal and spatial characteristics and that the existing data used for correction may also be a correction value. In this study, we proposed a method to correct the missing traffic volume by calculating the axis correction coefficient through the accumulated number of axes acquired by using a piezo sensor that can detect the axis of the vehicle. This has the advantage of being able to reflect temporal and spatial characteristics, which are the limitations of the existing methods, and as a result of comparative evaluation, the error rate was derived lower than that of the existing methods. The traffic volume correction system using axis count is judged as a correction method applicable to the field system with a simple algorithm.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.4 no.1
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• pp.119-124
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• 2004

Real data as web log file tend to be incomplete. But we have to find useful knowledge from these for optimal decision. In web log data, many useful things which are hyperlink information and web usages of connected users may be found. The size of web data is too huge to use for effective knowledge discovery. To make matters worse, they are very sparse. We overcome this sparse problem using Markov Chain Monte Carlo method as multiple imputations. This missing value imputation changes spare web data to complete. Our study may be a useful tool for discovering knowledge from data set with sparseness. The more sparseness of data in increased, the better performance of MCMC imputation is good. We verified our work by experiments using UCI machine learning repository data.

• Journal of the Korean Society for Precision Engineering
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• v.25 no.2
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• pp.88-95
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• 2008

With society focusing more and more on environmental issues, the recycling of materials of all types has become an important concern. In this paper, optimization method is developed for reducing cost and improving quality in mill scale recycling. An experimental investigation into the process parameter effects is presented to determine the optimum configuration of parameters for performance, quality and cost. Taguchi's optimization approach was used to obtain the optimal parameters. The significant parameters were identified and their effects on mill scale recycling were studied. As a results, a confirmation experiment with the optimal levels of process parameters was carried out in order to demonstrate the effectiveness of the Taguchi method.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.35 no.1
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• pp.47-56
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• 2012

Service Parts Management is defined as a supply management associated with service parts from the part suppliers to the final customer. A series of process to improve the customer service level by forecasting the demand and to minimize cost by maintaining the inventory level is included. Uniqueness such as missing value correction, the data pattern analysis and planned order system is designed and implemented. Main feature of order management system is to calculate order amount and order time based on selection of optimal forecasting algorithm.

• Journal of Korean Society for Quality Management
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• v.36 no.4
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• pp.29-36
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• 2008

최근 자동차 경량화를 위한 부단한 노력이 진행되고 있다. 이 목적에서, HSS (high strength steel)는 전통적인 연강 (mild steel)의 대안으로 널리 사용되고 있다. 본 연구의 목적은 판금의 형단조에 있어서의 공구와 공정설계를 위하여 HSS의 스프링백(springback)을 정확히 예측하기 위한 성공적인 방법론을 추구하고자 함이다. 연구를 위하여 먼저 스프링백의 개념과 그의 측정치들을 설명했으며 U-draw bending 시험을 수행하였다. 시험 결과 및 선정된 파라메터들 중심의 수행평가기준에 근거하여, 주어진 파라메터 조합들을 중심으로 유한요소 해석을 수행하였다. 직교배열을 통하여 스프링백에 대한 인자 효과들을 포괄적으로 분석하였으며 최적 인자 조합들을 도출하였다. 이 과정에서 직교배열상의 한 조합 전체의 데이터가 가용하지 않는 문제가 수반되었으며, 반복적으로 signal-to-noise 비(ratio)를 개선해가는 기법을 적용하여 해결하였다.

• Journal of the Korean Data and Information Science Society
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• v.23 no.6
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• pp.1183-1194
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• 2012

When it comes to a survey, no answer would occur frequently. Therefore various methods for handling nonresponse have been applied to analyse the survey. In this paper, the ratio of occurrence of two type of nonresponse cases - unit nonresponse and item nonresponse - is presented using previous real survey data, and we compared complete data and data with nonresponse. We suggest the reason of happening of nonresponse and the ratio of nonresponse using data collected through group interviews.

• Journal of Institute of Control, Robotics and Systems
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• v.1 no.2
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• pp.105-111
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• 1995

This paper proposes a segmentation algorithm using gray-level discontinuity and surface reflectance ratio of input images obtained under different illumination conditions. Each image is divided by a certain number of subregions based on the thresholds. The thresholds are determined using the histogram of fusion image which is obtained by ANDing the multiple input images. The subregions of images are projected on the eigenspace where their bases are the major eigenvectors of image matrix. Points in the eigenspace are classified into two clusters. Images associated with the bigger cluster are fused by revised ANDing to form a combined edge image. Missing edges are detected using surface reflectance ration and chain code. The proposed algorithm obtains more accurate edge information and allows to more efficiently recognize the environment under various illumination conditions.