• Journal of Distribution Science
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• v.15 no.6
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• pp.77-96
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• 2017

Purpose - This study aims to develop correspondence strategies to the environment change in domestic retail store types. Recently, new types of retails have emerged in retail industries. Therefore, trade area platform has developed focusing on the speed of data, no longer trade area from district border. Besides, 'trade area smart' brings about change in retail types with the development of giga internet. Thus, context shopping is changing the way of consumers' purchase pattern through data capture, technology capability, and algorithm development. For these reasons, the sales estimation model has been shown to be flawed using the notion of former scale and time, and it is necessary to construct a new model. Research design, data, and methodology - This study focuses on measuring retail change in large multi-shopping mall for the outlook for retail industry and competition for trade area with the theoretical background understanding of retail store types and overall domestic retail conditions. The competition among retail store types are strong, whereas the borders among them are fading. There is a greater need to analyze on a new model because sales expectation can be hard to get with business area competition. For comprehensive research, therefore, the research method based on the statistical analysis was excluded, and field survey and literature investigation method were used to identify problems and propose an alternative. In research material, research fidelity has improved with complementing research data related with retail specialists' as well as department stores. Results - This study analyzed trade area survival and its pattern through sales estimation and empirical studies on trade areas. The sales estimation, based on Huff model system, counts the number of households shopping absorption expectation from trade areas. Based on the results, this paper estimated sales scale, and then deducted modified probability model. Conclusions - In times of retail store chain destruction and off-line store reorganization, modified Huff model has problems in estimating sales. Transformation probability model, supplemented by the existing problems, was analyzed to be more effective in competitiveness business condition. This study offers a viable alternative to figure out related trade areas' sale estimation by reconstructing new-modified probability model. As a result, the future task is to enlarge the borders from IT infrastructure with data and evidence based business into DT infrastructure.

• The Journal of Information Systems
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• v.25 no.1
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• pp.49-73
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• 2016

Purpose Though many information systems researchers have made various attempts to investigate the relationship between information technology capability and firm performance from diverse perspectives, we have not come to a conclusion yet with some mixed results. In this research, focusing on the adoption of Enterprise Resource Planning (ERP) systems by firms as a proxy measure for information technology capability, we reexamine whether the association is significantly positive. Design/methodology/approach Previous research on this topic had some limitations to the samples and analysis method. Some research focused only on the 1990s or early 2000s, and other studies failed to adequately compare the impact of ERP adoption on firm performance between the treatment group and the control group. In this research, extending previous analysis approaches with the matched sample comparison of IT leaders and the control group, we attempt to apply propensity score matching in combination with difference-in-difference analysis with a sample of Korean firms that adopted ERP systems in the late 2000s. We match ERP adopters and non-adopters with propensity score matching and compare their financial performance with difference-in-difference estimation between the pre- and post-adoption periods. Findings According to our analysis, we find no positive and significant relationship between ERP adoption and firm performance in profit ratios. This research shows that, contrary to the era of proprietary information systems, standardized information systems today have no additional competitive advantages over competitors.

• Journal of the Korean Institute of Gas
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• v.12 no.2
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• pp.69-76
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• 2008

Three failure cases of CNG composite vessels were reported since after January 2005. The 1st and 2nd accidents were indebted to vessel defect and installation mistake. The 3rd was caused by gas leak at pipe connections. In this paper various aspects were studied based on information of the three failure analysis, which must be improved for better safety of the CNG bus system. Overpressure region caused by vessel explosion was theoretically predicted and also assessed by PHAST program. Explosion of 120 l vessel under 20 MPa is equivalent to 1.2 kg TNT explosion. The predicted value by PHAST was more serious than theoretical one. However, actual consequence of explosion was much less than both of the predicted consequences. Since the CNG vessel was designed by the performance based design methodology, it is difficult to verify whether the required process and tests were properly conducted or not after production. If material toughness is not enough, the vessel should be weak in brittle fracture at early in the morning of winter season since the metal temperature can be lower than the transition temperature. If autofrettage pressure is not correct, fatigue failure due to tensile stress during repeated charging is possible. One positive aspect is that fire did not ocurred after vessel failure. This may be indebted to fast diffusion of natural gas which hindered starting fire.

• Journal of Industrial Convergence
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• v.20 no.5
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• pp.39-59
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• 2022

We analyze the perceptions and requirements of early childhood teachers for artificial intelligence(AI) education to develop an AI education program for 5-year-olds. As for the research methodology, we conducted a survey and an in-depth interview to extract the AI educational elements centering on the analysis stage, the first stage of the ADDIE model. The research result is that first, it is necessary to design a curriculum that combines the contents of early childhood education and AI education to be naturally accepted as AI education for 5-year-olds. Second, an evaluation tool for AI education that can showcase the teacher's reflection should be developed systematically. Third, it is necessary to support a play-centered AI education support and environment for early childhood teachers. Lastly, it is essential to establish a system that can be continuously operated in the field of early childhood education in consideration of AI education in the non-curricular curriculum. It is expected that in the future, a play-oriented AI education program for 5-year-olds will be developed to spread awareness of AI education for infants and present an AI education approach for each age and stage of learners.

• The Korean Journal of Franchise Management
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• v.9 no.4
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• pp.7-19
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• 2018

Purpose - Recently, global management in Korea franchise industry is becoming an important keyword. As an important branch market, Chinese market plays a major role not only by making experience of the competitiveness among global brands which offers a foothold to become a top global brand, but also by actualizing an economies of scale in production, sales, etc. Therefore, it is necessary to identify key successful factor influencing customer evaluation and responses of Korean franchise restaurant targeting Chinese consumers in China context. The purpose of this study is to examine the effects for Korean franchise restaurant selection attributes on perceived value, customer satisfaction and customer loyalty in Chinese context with SmartPLS 3 and Artifical Neural Network(ANN). Research design, data, and methodology - For these purposes, the authors developed several hypotheses. A questionnaire survey was conducted on the panel of online survey companies for Chinese consumers who have visited Korean franchise restaurants. A total of 404 data were analyzed using structural equation modeling(SEM) and artifical neural network(ANN) with SPSS 22.0 and SmartPLS 3.0. Result - The findings of this study are as follows: First, the alternative model findings show that facilities & atmosphere, employee service, and menu influenced on utilitarian value, customer satisfaction, and customer loyalty directly. Second, employee service influenced on customer satisfaction. Third, menu influenced on hedonic value. Fourth, brand reputation influenced on utilitarian value. Fifth, hedonic value increase customer satisfaction and customer loyalty. Sixth, hedonic value increase customer loyalty. Seventh, customer increase customer loyalty. And, the ANN analysis shows that utilitarian value is the first most important factor influencing customer satisfaction, followed by hedonic value, facilities & atmosphere, menu and employee service. However, the ANN analysis shows that customer satisfaction is the first most important factor influencing customer loyalty, followed by utilitarian value, hedonic value, brand reputation, menu, and employee service. Conclusions - This study provides practical implications for enhancing customer satisfaction and customer loyalty by applying the ANN technique that complements the limitations of the linear structural relationship analysis using the proposed model and the alternative model. In other words, the SEM-ANN model provides guidelines on how Korean franchise restaurants should formulate facilities & atmosphere, employee service, and menu mix strategies in China. In addition, ANN 's analysis shows that restaurant brand reputation plays a pivotal role in increasing customer loyalty. The fact suggests that Korean franchise companies should establish their domestic brand reputation prior to their entry into overseas markets such as China.

• Trans-
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• v.14
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• pp.35-63
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• 2023

Parkinson's disease is a degenerative neurological disease that affects even basic daily life movements due to impairment of body function caused by a lack of dopamine, which is charge of the body movement. Presently, it is hard to cure Parkinson's disease entirely with medical technology, so movement therapy as a solution to delay and prevent disease is getting more attention. Therefore, this study aims at desiging and disseminating a body movement program that concentrates on individual self-care and balacing the state of body and mind by applying the Feldenkrais Method® to patients with Parkinson's disease. The Feldenkrais Method® is a mind-body perceptual learning method using body movements. It is a methodology that re-educates the nervous system by connecting the brain and behavior as a function of neuroplasticity. In this study, the body movement program developed and verified by the researcher was modified and supplemented with a focus on the self-awareness of the Feldenkrais Method®. A 24-session physical exercise program was composed of 5 stages to improve the self-management ability of patients with Parkinson's disease. The stages include self-awareness, self-observation, self-organization, self-control, and self-care. The overall changes recognize one's condition and improve one's ability to detect modifications in the internal sense and external environment. In conclusion, the body movement program improves the body movement program improves mental and physical functions and self-care for Parkinson's disease patients through the Feldenkrais method. The availability of the program's on-site applicability remains a follow-up task. Furthermore, it is necessary to establish a systematic structure to spread it more widely through convergent cooperation with the scientific field applied with metaverse as a reference for the wellness of the elderly.

• Korean Chemical Engineering Research
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• v.61 no.4
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• pp.542-549
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• 2023

Since the growing interest in surrogate modeling, there has been continuous research aimed at simulating nonlinear chemical processes using data-driven machine learning. However, the opaque nature of machine learning models, which limits their interpretability, poses a challenge for their practical application in industry. Therefore, this study aims to analyze chemical processes using Explainable Artificial Intelligence (XAI), a concept that improves interpretability while ensuring model accuracy. While conventional sensitivity analysis of chemical processes has been limited to calculating and ranking the sensitivity indices of variables, we propose a methodology that utilizes XAI to not only perform global and local sensitivity analysis, but also examine the interactions among variables to gain physical insights from the data. For the ammonia synthesis process, which is the target process of the case study, we set the temperature of the preheater leading to the first reactor and the split ratio of the cold shot to the three reactors as process variables. By integrating Matlab and Aspen Plus, we obtained data on ammonia production and the maximum temperatures of the three reactors while systematically varying the process variables. We then trained tree-based models and performed sensitivity analysis using the SHAP technique, one of the XAI methods, on the most accurate model. The global sensitivity analysis showed that the preheater temperature had the greatest effect, and the local sensitivity analysis provided insights for defining the ranges of process variables to improve productivity and prevent overheating. By constructing alternative models for chemical processes and using XAI for sensitivity analysis, this work contributes to providing both quantitative and qualitative feedback for process optimization.

• The Transactions of the Korea Information Processing Society
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• v.13 no.3
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• pp.111-121
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• 2024

Recently, thanks to the development of artificial intelligence technologies such as image recognition, research on unmanned aerial vehicles is being actively conducted. In particular, related research is increasing in the field of military drones, which costs a lot to foster professional pilot personnel, and one of them is the study of an intelligent framework for autonomous mission performance of reconnaissance drones. In this study, we tried to design an intelligent framework for unmanned aerial vehicles using the methodology of designing an intelligent framework for service robots. For the autonomous mission performance of unmanned aerial vehicles, the intelligent framework and unmanned aerial vehicle module must be smoothly linked. However, it was difficult to provide interworking for drones using periodic message protocols with model-based interfaces of intelligent frameworks for existing service robots. First, the message model lacked expressive power for periodic message protocols, followed by the problem that interoperability of asynchronous data exchange methods of periodic message protocols and intelligent frameworks was not provided. To solve this problem, this paper proposes a message model extension method for message periodic description to secure the model's expressive power for the periodic message model, and proposes periodic and asynchronous data exchange methods using the extended model to provide interoperability of different data exchange methods.

• Journal of the Korean Institute of Intelligent Systems
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• v.17 no.2
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• pp.244-251
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• 2007

Intention reading technique is essential to provide personalized services toward more convenient and human-friendly services in complex ubiquitous environment such as a smart home. If a system has knowledge about an user's intention of his/her behavioral pattern, the system can provide mote qualified and satisfactory services automatically in advance to the user's explicit command. In this sense, learning capability is considered as a key function for the intention reading technique in view of knowledge discovery. In this paper, ore introduce a personalized media control method for a possible application iii a smart home. Note that data pattern such as human behavior contains lots of inconsistent data due to limitation of feature extraction and insufficiently available features, where separable data groups are intermingled with inseparable data groups. To deal with such a data pattern, we introduce an effective engineering approach with the combination of fuzzy logic and probabilistic reasoning. The proposed learning system, which is based on IFCS (Iterative Fuzzy Clustering with Supervision) algorithm, extract probabilistic fuzzy rules effectively from the given numerical training data pattern. Furthermore, an extended architectural design methodology of the learning system incorporating with the IFCS algorithm are introduced. Finally, experimental results of the media contents recommendation system are given to show the effectiveness of the proposed system.

• Journal of Distribution Science
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• v.15 no.8
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• pp.47-54
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• 2017

Purpose - According to agency theory, managers have incentives to adjust firm revenues to meet earnings expectations or delay bad news disclosure because of performance-based compensation and their reputation in the market. When the bad news accumulates, stock prices fail to reflect all available information. Thus, market prices of stocks are higher than their intrinsic value. After all, bad news crosses the tipping point, it comes out all at once. That results in stock crashes. Auditors can decrease stock crash risk by reducing agency costs through their informational role. Especially, stock price crash risk is expected to be lower for firms adopting high-quality audits. We focus on distribution and service industry to examine the relation between audit quality and stock price crash risk. Industry specialization and auditor size are used as proxies for auditor quality. Research design, data and methodology - Our sample contains distribution and service industry firms listed in KOSPI and KOSDAQ during a period of 2004-2011. We use a logistic regression to test whether auditor quality influences crash risk. Auditor quality was measured by industry specialist auditor and Big4 / non-Big4 dichotomy. Following the approach in prior researches, we use firm-specific weekly returns to measure crash risk. Firms experiencing at least one stock price crash in a specific week during year are classified as the high risk group. Results - The result of analyzing 429 companies in distribution and service industry is summarized as follows: Above all, it is shown that higher audit quality has a significant negative(-) effect on the crash risk. Crash risk is alleviated for firms audited by industry specialist auditors and Big 4 audit firms. Therefore, our results show that hypotheses are supported. Conclusions - This study is very meaningful as the first study which investigated the effects of high audit quality on stock price crash risk. We provide evidence that high-quality auditors reduce stock price crash risk. Our finding implies that the risk of extreme losses can be reduced through screening of high-quality auditors. Therefore investors and regulators may utilize our findings in their investment and rule making decisions.