• Journal of Industrial Technology
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• v.22 no.B
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• pp.89-93
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• 2002

When testing products before shipment to the customer, quality characteristics are measured to decide whether or not their values are between the specification limits. Unfortunately, this testing procedure can lead to incorrect decisions because of gauge error. That is, good products can erroneously be qualified as bad, and bad products as good, and this has consequences for producer's and consumer's risk. In cases of such as this, the compressed gauge limit can be used to achieve the desired product quality level dictated by the manufacturer or the customer. A compressed gauge limit is a limit set by the manufacturer on a test gauge that is tighter than the specification limit established by the customer. The compressed gauge limits should be set at levels to achieve the defect levels desired by the customer and simultaneously minimize the loss of good product that is rejected due to errors in the gauges. In this article, the models for determining the defect levels and the losses obtained by adding compressed gauge limits will be developed. A response surface model approach is utilized which allows an optimal operating condition to be generated relatively easily.

• Information Systems Review
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• v.12 no.2
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• pp.75-88
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• 2010

This paper proposed a method that segmented customers utilizing SOM(Self-organizing Map) and predicted the customers' response of a marketing promotion for each customer's segments. Our proposed method focused on predicting the response of customers dividing into customers' segment whereas most studies have predicted the response of customers all at once. We deployed logistic regression, neural networks, and support vector machines to predict customers' response that is a kind of dichotomous classification while the integrated approach was utilized to improve the performance of the prediction model. Sample data including 45 variables regarding demographic data about 600 customers, transaction data, and promotion activities were applied to the proposed method presenting classification matrix and the comparative analyses of each data mining techniques. We could draw some significant promotion strategies for segmented customers applying our proposed method to sample data.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.43 no.4
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• pp.93-106
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• 2020

This research explores how imported automobile companies can develop their strategies to improve the outcome of their recalls. For this, the researchers analyzed patterns of recall demand, classified recall types based on the demand patterns and examined response strategies, considering plans on how to procure parts and induce customers to visit workshops, recall execution capacity and costs. As a result, recalls are classified into four types: U-type, reverse U-type, L- type and reverse L-type. Also, as determinants of the types, the following factors are further categorized into four types and 12 sub-types of recalls: the height of maximum demand, which indicates the volatility of recall demand; the number of peaks, which are the patterns of demand variations; and the tail length of the demand curve, which indicates the speed of recalls. The classification resulted in the following: L-type, or customer-driven recall, is the most common type of recalls, taking up 25 out of the total 36 cases, followed by five U-type, four reverse L-type, and two reverse U-type cases. Prior studies show that the types of recalls are determined by factors influencing recall execution rates: severity, the number of cars to be recalled, recall execution rate, government policies, time since model launch, and recall costs, etc. As a component demand forecast model for automobile recalls, this study estimated the ARIMA model. ARIMA models were shown in three models: ARIMA (1,0,0), ARIMA (0,0,1) and ARIMA (0,0,0). These all three ARIMA models appear to be significant for all recall patterns, indicating that the ARIMA model is very valid as a predictive model for car recall patterns. Based on the classification of recall types, we drew some strategic implications for recall response according to types of recalls. The conclusion section of this research suggests the implications for several aspects: how to improve the recall outcome (execution rate), customer satisfaction, brand image, recall costs, and response to the regulatory authority.

• Journal of Intelligence and Information Systems
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• v.21 no.1
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• pp.29-45
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• 2015

Response modeling is a well-known research issue for those who have tried to get more superior performance in the capability of predicting the customers' response for the marketing promotion. The response model for customers would reduce the marketing cost by identifying prospective customers from very large customer database and predicting the purchasing intention of the selected customers while the promotion which is derived from an undifferentiated marketing strategy results in unnecessary cost. In addition, the big data environment has accelerated developing the response model with data mining techniques such as CBR, neural networks and support vector machines. And CBR is one of the most major tools in business because it is known as simple and robust to apply to the response model. However, CBR is an attractive data mining technique for data mining applications in business even though it hasn't shown high performance compared to other machine learning techniques. Thus many studies have tried to improve CBR and utilized in business data mining with the enhanced algorithms or the support of other techniques such as genetic algorithm, decision tree and AHP (Analytic Process Hierarchy). Ahn and Kim(2008) utilized logit, neural networks, CBR to predict that which customers would purchase the items promoted by marketing department and tried to optimized the number of k for k-nearest neighbor with genetic algorithm for the purpose of improving the performance of the integrated model. Hong and Park(2009) noted that the integrated approach with CBR for logit, neural networks, and Support Vector Machine (SVM) showed more improved prediction ability for response of customers to marketing promotion than each data mining models such as logit, neural networks, and SVM. This paper presented an approach to predict customers' response of marketing promotion with Case Based Reasoning. The proposed model was developed by applying different weights to each feature. We deployed logit model with a database including the promotion and the purchasing data of bath soap. After that, the coefficients were used to give different weights of CBR. We analyzed the performance of proposed weighted CBR based model compared to neural networks and pure CBR based model empirically and found that the proposed weighted CBR based model showed more superior performance than pure CBR model. Imbalanced data is a common problem to build data mining model to classify a class with real data such as bankruptcy prediction, intrusion detection, fraud detection, churn management, and response modeling. Imbalanced data means that the number of instance in one class is remarkably small or large compared to the number of instance in other classes. The classification model such as response modeling has a lot of trouble to recognize the pattern from data through learning because the model tends to ignore a small number of classes while classifying a large number of classes correctly. To resolve the problem caused from imbalanced data distribution, sampling method is one of the most representative approach. The sampling method could be categorized to under sampling and over sampling. However, CBR is not sensitive to data distribution because it doesn't learn from data unlike machine learning algorithm. In this study, we investigated the robustness of our proposed model while changing the ratio of response customers and nonresponse customers to the promotion program because the response customers for the suggested promotion is always a small part of nonresponse customers in the real world. We simulated the proposed model 100 times to validate the robustness with different ratio of response customers to response customers under the imbalanced data distribution. Finally, we found that our proposed CBR based model showed superior performance than compared models under the imbalanced data sets. Our study is expected to improve the performance of response model for the promotion program with CBR under imbalanced data distribution in the real world.

• Journal of the Korean Institute of Navigation
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• v.23 no.3
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• pp.75-89
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• 1999

The importance of service quality in any service industry cannot be disputed. Shippers have increased expectations concerning the quality of shipping service they receive and carriers are struggling to meet these expectations. This struggle between shipper and carriers would suggest that there is room to improve much more understandings of how shipper define shipping service quality in the carriers' perspectives. This is an empirical investigation and study on the measurement of customer response and service quality as perceived by customer in the international transportation logistics system. The purpose of this study is to clarify factors of shipping service quality on the basis of service marketing concept. In order to fulfill the objectives, this paper combined research tools that include both empirical study and documentary research. Data was gathered from 132 freight forwarder by the use of questionnaire. In this study, the established hypotheses were generated on the basis of the service quality evaluation model(SERVQUAL, SERVPERF, and EP) and Gap model.

• Management & Information Systems Review
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• v.15
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• pp.147-164
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• 2004

The purpose of this paper is on employees as internal customers and the critical role this group plays in the delivery of quality results. The set up of research model for verification was as follows. The research model was drawn as internal service quality level $\Rightarrow$ internal customer satisfaction $\Rightarrow$ enterprise outcome. Then, two hypotheses were established to the research model. Through the factor analysis and multiple regression analysis, the results are as follows. First, internal service quality level turned out to be affected indirectly through internal customers' satisfaction rather than a direct factor to affect the enterprise outcome. Second, internal customers' satisfaction was proved to be the most important factor for the enterprise outcome as ti was the intimate factor precedent to the enterprise outcome. However, there could be a variation of response according to the personal circumstances of respondents since the respondents were from different enterprises and consisted various job positions and age group. Namely it included a limitation of rather unaccurate resulting values because the transverse methods were performed for convenience though it needed a longitudinal research to accomplish the general purpose of this study.

• Journal of Korean Society for Quality Management
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• v.47 no.3
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• pp.615-630
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• 2019

Purpose: Based on the theory of "Stimulus-Organism-Response" (S-O-R), this thesis takes customer forgiveness as a medium variable to explore the impact of merchant service remedy on customer behavior intention in the context of online shopping service failure. This thesis divides the merchant service remedy into two dimensions: spiritual recovery and material recovery, and reveals the influence difference of different merchant service remedy methods on customer behavior intention and the mediating role of customer forgiveness. Methods : 325 questionnaires were distributed and 307 valid questionnaires were collected for data analysis. The relationship between potential variables is proposed by using Structural Equations Modeling. Results : The two dimensions of service recovery have significant positive impact on customer forgiveness, and physical recovery has greater impact on customer forgiveness. In the influence of physical recovery on customer behavior intention, customer forgiveness is a partial mediating effect. However, in the influence of spiritual recovery on customer behavior intention, customer forgiveness is a complete mediating effect. Conclusion : In case of service faults, merchants should take the initiative to provide appropriate physical recovery and provide spiritual recovery sincerely and patiently. Only in this way can they regain good impression in the hearts of consumers and promote them to improve the quality of service recovery, so as to increase their willingness to repurchase Intention and positive word of mouth.

• The Korean Journal of Community Living Science
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• v.11 no.1
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• pp.9-18
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• 2000

The purposed of the study was to assess customer satisfaction concerning foodservice quality characteristics by using developed DINESERV model for university employee foodservices. Specially, it was intended to develop the tool which assesses the differences between customer importance and perceptions of customer with actual foodservice delivery by university employee foodservices. Questionnaires were distributed to 300 un9iversity employees. Total 230 university employees responded with a usable response rate of 67.7%. Statistical data analysis was completed using SAS programs for descriptive analysis and t-test. The results of the study are as follows: 1) Employees´first choice was distance when they select foodserveices. They answered their preference as the first factor when they order menu in the foodservices. The first complain factor concerning university foodservices was the taste of food. 2) Customers did not satisfied with the foodservice quality of university employee foodservices. Importance mean score of service quality was 3.81 out of 5 but percption mean score of service quality was 3.10. Importance mean score of food quality was 4.11 out of 5 but perception mean score of food quality was 2.96. 3) Customers´satisfaction of service quality by dimensions were as following order: assurance > reliability > responsiveness > empathy > tangibles. And customers´satisfaction of food quality by dimensions were as following order: nutrition > food > price > sanitation. There were no significant difference about customer satisfaction between contracted management and self-operated.

• Journal of Distribution Science
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• v.21 no.3
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• pp.83-92
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• 2023

Purpose: An omnichannel strategy creates a consistent brand image and customer experience across all channels, making it easier for customers to interact with a business and share information. This research aimed to investigated the relationship between consumers' information-sharing intention and their omnichannel experiences. Research design, data, and methodology: Through an online survey conducted in Vietnam, the study obtained 915 responses. The study used Partial Least Square Structural Equation Modeling (PLS-SEM) to analyze research data and confirm proposed research hypotheses. Results: Research results indicated that information-sharing intention is affected by both online and offline customer experience, and at the same time, the study also confirmed that omnichannel's three characteristics (integration, individualization, interaction) positively impact on customer experience. Conclusions: From the research result, businesses may boost consumer trust and loyalty with the help of an omnichannel approach, which in turn increases customers' propensity to provide personally identifying information to the firm. One way to do this is to facilitate information exchange by delivering customized and relevant offers. Furthermore, companies show consumers the benefit of providing their data by utilizing it to enhance the customer experience.

• Journal of Korea Society of Digital Industry and Information Management
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• v.19 no.1
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• pp.57-76
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• 2023

This study analyzes the factors and effects on the users' intention to switch from contact center-oriented to AI chatbot-oriented customer services by combining Push-Pull-Mooring Model and provides insights for companies considering the adoption of AI chatbots. To test the model, we surveyed users with experience using chatbots at least once across different age groups. Finally, we analyzed 176 cases for the analysis using IBM SPSS Statistics and SmartPLS 4.0. The results of hypotheses testing rejected the hypotheses for variables of inconsistent quality and low availability of push factors and low switching cost of mooring factor while accepting the hypotheses for the tardy response of push factors and all pull factors. Therefore, these findings provide important implications for researchers and practitioners who wish to conduct research or adopt AI chatbots. In conclusion, users do not feel inconvenienced by the contact center-oriented service but also perceive high trust and convenience with AI chatbot-oriented service. However, despite low switching costs, users consider chatbots a complementary tool rather than an alternative. So, companies adopting AI chatbots should consider what features the users expect from AI chatbots and facilitate these features when implementing AI chatbots.