• 한국IT서비스학회지
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• 제9권2호
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• pp.129-149
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• 2010

Although many organizations have implemented and used ERP systems for years, industry reports point out that the usage level does not reach their expectation. As a result, they do not enjoy the benefits from the investment into ERP systems. This study attempts to develop a research model and test the model empirically for identifying factors that affect the usage of an ERP system at a public organization. This study has classified potential factors into three groups of technical system characteristics (usability, usefulness), organizational support characteristics (operational support, education and training, evaluation and measurement), and user characteristics (organizational citizenship, self-efficacy). Then, a structural equation model has been established on the basis of previous literature and tested with empirical data. In summary, this study has found that self-efficacy, usefulness, and operational support have an effect on the ERP usage. Self-efficacy is the strongest factor; Usefulness is the second; and the third is operational support. On the contrary to the previous literature, this study has not found a significant effect of organizational citizenship on the usage. The result confirms that an organization can increase the ERP usage by improving the usefulness of an ERP system to some extent. However, to boost the usage further, the result implies that organizations need to strengthen the self-efficacy of their members by reinforcing operational support, providing education and training steadily, and establishing an evaluation mechanism in relation with the ERP usage.

• 보건행정학회지
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• 제33권3호
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• pp.295-310
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• 2023

Background: The long-term care (LTC) group has higher rates of chronic disease and disability registration compared to the general older people population. There is a need to provide integrated medical services and care for LTC group. Consequently, this study aimed to identify medical usage patterns based on the ratings of LTC and the characteristics of benefits usage in the LTC group. Methods: This study employed the National Health Insurance Service Database to analyze the effects of demographic and LTC-related characteristics on medical usage from 2015 to 2019 using a repeated measures analysis. A longitudinal logit model was applied to binary data, while a linear mixed model was utilized for continuous data. Results: In the case of LTC ratings, a positive correlation was observed with overall medical usage. In terms of LTC benefit usage characteristics, a higher overall level of medical usage was found in the group using home care benefits. Detailed analysis by medical institution classification revealed a maintained correlation between care ratings and the volume of medical usage. However, medical usage by classification varied based on the characteristics of LTC benefit usage. Conclusion: This study identified a complex interaction between LTC characteristics and medical usage. Predicting the requisite medical services based on the LTC rating presented a challenge. Consequently, it becomes essential for the LTC group to continuously monitor medical and care needs, even after admission into the LTC system. To facilitate this, it is crucial to devise an LTC rating system that accurately reflects medical needs and to broaden the implementation of integrated medical-care policies.

• 한국국방경영분석학회지
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• 제17권2호
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• pp.100-112
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• 1991

Software system is a hierarchical structure with collection of program units. Software system can import external packages globally or locally depending on the usage within a system. If the imported package is used globally, the soft-ware system can be influenced globally by any change of package and programmer's debugging time for the program maintenance will be greater. To solve these problems, it is desirable to use the imported package locally right on the usage point within the system. The model presented in this paper analyzed entity usage of package in structure of program, identified the usage level to obtain localization and provided information for restructure of the program to localize package usage. To obtain localization, it identified declared entities inside the imported package and analyzed the specification and body part of program unit to identify entities referenced from the imported package. The proposed model can be used to improve the maintainability of software system and contributed to reduction of programmer's debugging time in program maintenance.

• 통상정보연구
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• 제9권1호
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• pp.119-148
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• 2007

The purpose of this research is to study whether environments of participating firms of electronic marketplace(e-MP) have an influence on usage factors which are supplying from e-MP and usage factors affect usage performances. Based on the existing researches we found variables and executed a empirical study of environment-usage-performance model. Through this research we suggested useful factors for the performance of trade enterprises. Usage factors of e-MP will overcome geographical limitation and acquire new sales areas and simultaneously widen the relationships of global enterprises. Those firms could be accessible into global market promptly. To enhance usages of e-MP, service quality which can satisfy both buyer and seller at the same time should be strengtened. Finally, the usage of e-trade information should also heighten through usage factors.

• 정보화정책
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• 제18권3호
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• pp.50-68
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• 2011

본 연구의 목적은 최근 주목받고 있는 스마트폰의 사용 행동 중에서 사용 중독에 영향을 미치는 요인들의 효과를 분석하고, 스마트폰 사용 중독이 사용자의 사회적 위축을 가져 오는지를 실증 분석을 통해 규명하는 데 있다. 구체적으로, 스마트폰 사용자의 자기 감시, 외로움, 자아존중감, 스마트폰 특성을 중심으로 한 변수들이 스마트폰 사용 중독에 어떠한 영향을 미치는지, 그리고 스마트폰 사용 중독이 사회적 위축에 미치는 효과를 실증적으로 분석하고자 하였다. 이를 위해 문헌 연구와 더불어 실증 조사를 실시하였으며, 스마트폰 사용자 315명을 대상으로 수집된 자료는 구조방정식 모형(Structural Equation Model) 기법의 한 통계 프로그램인 PLS(Partial Least Square)를 이용하여 측정 모형 및 가설들에 대한 검증을 실시하였다. 자료 분석 결과, 스마트폰 사용자의 자기 감시, 외로움 및 스마트폰의 특성은 스마트폰 사용 중독에 유의한 영향을 미치며, 스마트폰 사용 중독은 다시 사용자의 사회적 위축에 영향을 미치는 것으로 나타났다. 이러한 연구 결과를 토대로 연구의 의의와 시사점을 논의하였으며, 아울러 연구의 한계점과 향후 연구 방향을 기술하였다.

• 인터넷정보학회논문지
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• 제24권2호
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• pp.57-66
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• 2023

현재, 에너지 효율 향상으로 소비감축을 시행하는 새로운 에너지 시스템이 대두되고 있다. 이에 스마트그리드가 확산되면서 계시별 요금제가 확대되고 있다. 계시별 요금제는 계절별 / 시간별로 요금을 다르게 적용해 사용량에 따라 요금을 내는 요금제이다. 본 연구에서는 에너지 전력 사용량 데이터를 예측하기 위해, 온도/요일/시간/계절 등 외부 요인을 고려하고 시계열 예측 모델인 LSTM을 활용한다. 이러한 에너지 사용량 예측 모델을 기반으로 기기별 사용패턴을 분석하여 전력 에너지를 최대부하시간대에서 경부하시간대로 수요이전 함으로써 에너지 사용요금을 절감한다. 기기별 사용패턴을 분석하기 위해서는 시간대별로 기기의 사용량 패턴을 학습 및 분류하는 clustering 기법을 사용한다. 정리하자면, 본 연구에서는 사용자의 전력 데이터 사용량을 기반으로 사용량과 사용 요금을 예측 및 기기별 사용패턴을 분석하고 분석 기반의 맞춤형 수요이전 서비스를 제공함으로써 사용자에게 요금 절감 효과를 가져다 준다.

• 설비공학논문집
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• 제27권6호
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• pp.283-292
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• 2015

A model of apartment heating load in a district heating system could be useful in the management and utilization of energy resources, since it could predict energy usage and so could assist in the efficient use of energy resources. The heating load in a district heating system varies in a highly nonlinear manner and is subject to many different factors, such as heating area, number of people living in that complex, and ambient temperature. Thus there are few published papers with accurate models of heating load, especially in domestic literature. This work is concerned with the modeling of apartment heating load in a district heating system in winter, using the reduced least square support vector machine (LS-SVM), and with the purpose of using the model to predict heating energy usage in domestic city area. We collected 23,856 pieces of data on heating energy usage over a 12-week period in winter, from 12 heat exchangers in five apartments. Half of the collected data were used to construct the heating load model, and the other half were used to test the model's accuracy. The model was able to predict the heating energy usage pattern rather accurately. It could also estimate the usage of heating energy within of mean absolute percentage error. This implies that the model prediction accuracy needs to be improved further, but it still could be considered as an acceptable model if we consider the nonlinearity and uncertainty of apartment heating energy usage in a district heating system.

• 한국수자원학회논문집
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• 제54권spc1호
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• pp.1083-1093
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• 2021

물 사용량 예측은 최적의 용수 공급 운영 방안을 수립하고 전력 소비량 절감을 위하여 꼭 필요한 과정이라고 할 수 있다. 그러나 수용가 단위의 물 사용량은 용도, 사용자의 패턴, 날씨 등의 다양한 요인으로 인해 변화하는 비선형적 특성을 지니고 있다. 따라서 본 연구에서는 비선형적인 수용가 단위의 물 사용량을 예측하기 위하여 다양한 기법들을 연계한 KWD 프레임워크를 제안하고자 하였다. 즉, 먼저 개별 수용가 마다 용도에 따른 유사한 패턴을 파악하기 위해 K-means (K) 군집분석을 수행하였고, 잡음성분을 제거함으로써 핵심적인 주기패턴을 파악하기 위해 Wavelet (W) 방법을 적용하였다. 또한 비선형적 특성을 학습시키기 위해 Deep learning (D) 알고리즘을 적용하였다. 그리고 기존의 선형 시계열 모형인 ARMA 모형과 비교하여 KWD 프레임워크의 성능을 분석하였다. 그 결과 제안된 모형의 상관성은 92%, ARMA 모형은 약 39%로 KWD 프레임워크가 2배 이상의 성능을 가지는 것으로 분석되었다. 따라서 본 연구에서 제안한 방법을 활용할 경우 정확한 물 사용량 예측이 가능해질 것이며, 상황에 따른 최적의 공급 방안을 수립할 수 있을 것이다.

• 한국정보시스템학회지:정보시스템연구
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• 제16권3호
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• pp.133-159
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• 2007

The primary purpose of this study is to examine the effects of perceived characteristics on user satisfaction and continuous usage intention in personal communities. We developed a research model based on the literature reviews of personal communities, TAM, perceived risks, and satisfaction. The research model includes perceived playfulness, perceived ease of use, perceived usefulness, and perceived risk as perceived characteristics in personal communities. For validation of this theoretical model, we survey the users of 'Mini-hompy', one of the most popular personal communities in Korea. The research model was empirically verified by structural equation model analysis with data collected from 407 samples. Analysis of the results indicates that perceived ease of use is positively related perceived playfulness and perceived usefulness. Perceived playfulness, perceived ease of use, and perceived risks are significantly related to satisfaction. User's satisfaction has positive relationship with continuous usage intention in personal communities.

• Journal of information and communication convergence engineering
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• 제19권3호
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• pp.142-147
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• 2021

In this paper, we propose an intelligent research fund management system that applies artificial intelligence technology to an integrated research fund management system. By defining research fund management rules as work rules, a detection model learned using deep learning is designed, through which the disapproval status is presented for each research fund usage history. The disapproval detection system of the RCMS implemented in this study predicts whether the newly registered usage details are recognized or disapproved using an artificial intelligence model designed based on the use of an 8.87 million research fund registered in the RCMS. In addition, the item-detail recommendation system described herein presents the usage details according to the usage history item newly registered by the artificial intelligence model through a correlation between the research cost usage details and the item itself. The accuracy of the recommendation was shown to be 97.21%.