• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2008.11a
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• pp.387-388
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• 2008

Finding reasonable flexural modes from the full vehicle modal testing has always been a difficult job to N&V engineers due to FRF inconsistency, nonlinearity, heavy damping and, in many cases, interactions between global body structural modes and massive isolate/non-isolated subsystem modes. This paper provides a brier overview of the mode map validation using single-run FRF measurement with highly sensitive accelerometers fur the full vehicle modal analysis and then it can be used to characterize the vehicle's global/local vibration performances, especially customer perceived "structural feel" typically below 40Hz.

• Smart Media Journal
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• v.2 no.2
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• pp.20-27
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• 2013

The concept of social media is top of the agenda for many business executives and decision makers, as well as consultants try to identify ways where companies can make profitable use of applications such as Netflix, Flixster. The social media is playing an increasingly important role as the information sources for customers making product choices etc. With the flourish of Web 2.0 technology, customer reviews are becoming more and more useful and important information resources for people to save their time and energy on purchasing products that they want. This paper proposes the Bayesian Probabilistic Classification algorithm to mine the social media review, and evaluates it by different splits and cross validation mechanism from the real data set. The explored study experimental results show the robustness and effectiveness of proposed approach for mining the social media review.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.35 no.1
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• pp.93-100
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• 2012

The purpose of this study is to present the green management requisites effect on corporate performance. Corporate performance consists of customer and green performance, work satisfaction and financial performance. In order to carry out this research we obtained 175 survey sheets, inspect the twelve assumptions focused on reliability, appropriateness of the model. Validation results of hypothesis are summarized as follows. First, the green management components : system, resource/energy affect significantly to the customer and green performance. Second, the green management components : system, resource/energy influence significantly to work satisfaction. Finally, the strategy has on effect to the financial performance. This paper shows that the green management requisites have an effect on the corporate performance directly and significantly.

• Journal of Information Technology Applications and Management
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• v.16 no.4
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• pp.185-203
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• 2009

The literature reveals that service quality is critical determinants affecting customer satisfaction in the retail industry. Recent online B2C studies also show that service quality significantly influences online customer satisfaction. However, research on analyzing antecedents to determine the service quality level of online shopping has not been sufficiently reported. Moreover, the offline synergy effect on the Internet shopping mall has been presented mostly at a conceptual level. The purpose of this study is to investigate key managerial variables exploiting offline synergy, including pure online attributes, that impact on service quality of the click and mortar Internet shopping mall. A survey was conducted for empirical validation of the hypotheses. The results indicate that core offline synergy factors significantly enhance the perceived service quality level of the Internet shopping mall. Implications, limitations, and future research directions are also discussed.

• Journal of Korea Society of Industrial Information Systems
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• v.23 no.1
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• pp.53-63
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• 2018

In this paper, we analyze the waiting time of a queueing system with D-BMAP (discrete-time batch Markovian arrival process) and D-policy. Customer group or packets arrives at the system according to discrete-time Markovian arrival process, and an idle single server becomes busy when the total service time of waiting customer group exceeds the predetermined workload threshold D. Once the server starts busy period, the server provides service until there is no customer in the system. The steady-state waiting time distribution is derived in the form of a generating function. Mean waiting time is derived as a performance measure. Simulation is also performed for the purpose of verification and validation. Two simple numerical examples are shown.

• Culinary science and hospitality research
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• v.24 no.2
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• pp.112-118
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• 2018

The purpose of this study is to develop and test a model that explains the effect of experiential marketing factors on: 1) brand image, 2) satisfaction, and 3) loyalty in context of restaurants located in complex shopping mall. In addition, the study clarified how these variables relate to each other. Survey were distributed to customers who have visiting experience(s) in a restaurants in complex shopping mall. Total 360 participants were distributed and 344 questionnaires were used for analysing. The confirmatory factor analysis and structural equation modeling(SEM) have been employed research methods for frequency analysis, reliability analysis and measurement model validation. The findings of this study identified that relation factor of experiential marketing elements was only significant factor on brand image Furthermore, sense and recognition were critical components of customer satisfaction. Last, present study also identified the significant relationship between satisfaction and customer loyalty. These findings may contribute to provide valuable marketing strategic for this business segmentation, and it can be utilized as a fundamental study to establish an efficient business plan to increase revenue especially for restaurant business located in complex shopping mall.

• Journal of Information Technology Applications and Management
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• v.21 no.4
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• pp.203-223
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• 2014

This study proposes a novel context-aware recommender system, which is designed to recommend the items according to the customer's responses to the previously recommended item. In specific, our proposed system predicts the user's emotional state from his or her responses (such as facial expressions and movements) to the previous recommended item, and then it recommends the items that are similar to the previous one when his or her emotional state is estimated as positive. If the customer's emotional state on the previously recommended item is regarded as negative, the system recommends the items that have characteristics opposite to the previous item. Our proposed system consists of two sub modules-(1) emotion prediction module, and (2) responsive recommendation module. Emotion prediction module contains the emotion prediction model that predicts a customer's arousal level-a physiological and psychological state of being awake or reactive to stimuli-using the customer's reaction data including facial expressions and body movements, which can be measured using Microsoft's Kinect Sensor. Responsive recommendation module generates a recommendation list by using the results from the first module-emotion prediction module. If a customer shows a high level of arousal on the previously recommended item, the module recommends the items that are most similar to the previous item. Otherwise, it recommends the items that are most dissimilar to the previous one. In order to validate the performance and usefulness of the proposed recommender system, we conducted empirical validation. In total, 30 undergraduate students participated in the experiment. We used 100 trailers of Korean movies that had been released from 2009 to 2012 as the items for recommendation. For the experiment, we manually constructed Korean movie trailer DB which contains the fields such as release date, genre, director, writer, and actors. In order to check if the recommendation using customers' responses outperforms the recommendation using their demographic information, we compared them. The performance of the recommendation was measured using two metrics-satisfaction and arousal levels. Experimental results showed that the recommendation using customers' responses (i.e. our proposed system) outperformed the recommendation using their demographic information with statistical significance.

• Information Systems Review
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• v.10 no.2
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• pp.137-158
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• 2008

Rapid development of various information technologies creates new opportunities in online and offline markets. In this changing market environment, customers have various demands on new products and services. Therefore, their power and influence on the markets grow stronger each year. Companies have paid great attention to customer relationship management. Especially, personalized product recommendation systems, which recommend products and services based on customer's private information or purchasing behaviors in stores, is an important asset to most companies. CRM is one of the important business processes where reliable information is mined from customer database. Data mining techniques such as artificial intelligence are popular tools used to extract useful information and knowledge from these customer databases. In this research, we propose a recommendation system that predicts customer's purchase intention. Then, customer's purchasing intention of specific product is predicted by using data mining techniques using receipt data set. The performance of this suggested method is compared with that of other data mining technologies.

• Journal of Digital Convergence
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• v.11 no.3
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• pp.263-272
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• 2013

This study was conducted to look into consulting areas that can secure loyalty customers by clarifying relations between perceived quality factors, brand image and customer loyalty in terms of a multi-function printer. To accomplish the study purpose, this study carried out a validation analysis, a reliability analysis and a structural equation model analysis by setting up a study model and a hypothesis based on precedent studies and collecting data from purchase decision-makers. The findings of the study are summarized as follows. First, effects of perceived quality components of a multi-function printer on brand image were product quality, sales man's service and service quality in order of majority. Second, brand image of a multi-function printer has a positive impact on customer loyalty. This study is meaningful as it expanded the scope of study related to a multi-function printer up to the administrative field. This study is expected to help industries, who have similar business structure of a multi-function printer, establish a business strategy and support practical work and administrative consulting.

• Culinary science and hospitality research
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• v.15 no.1
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• pp.105-119
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• 2009

The elevated standard of living and increased interest in health and well-being have caused customers to expect a role. Consumers are more and more interested in food service merchandise development. The purpose of this paper is to perform a discriminant analysis of merchandise development policies based on customer participation strategies. Statistical techniques employed included the reliability analysis and the discriminant analysis. The current thesis is based on the 350-questionnaire-survey conducted from July 1 to July 31, 2008 at five food service companies in Seoul and Gwangju. The data collected for this study was analyzed with frequency analysis, reliability analysis, validation analysis, factor analysis, and discriminant analysis using the SPSS 12.0 package program. The results of the test of the hypotheses can be summarized as follows: the analysis shows that there is significant difference between the active groups and the passive groups in all the merchandise development related factors, market directivity, and customer satisfaction directivity. Thus, food service management needs to apply customer participation strategies aggressively.