• Proceedings of the Technology Innovation Conference
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• 2000.06a
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• pp.65-83
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• 2000

In this study, we propose an expert system aided model necessary for extracting the rules, which can diagnose the success level of new product development project. For this purpose, first, we design a classification model to analyze the characteristics of $\ulcorner$parts development projects$\lrcorner$. Second, in order to select success factors, we explore literatures and interview with project managers working at large sized companies of electronic parts. And we conduct the enquete survey and statistical analysis to extract key success factors. Third, in order to decide the level of project success, we use the concept of 'Recearchitivity' as a framework of judgement criteria. Fourth, in order to extract diagnosis rules, we use expert system development tool.

• Proceedings of the Korea Society of Design Studies Conference
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• 2001.10a
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• pp.64.1-64
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• 2001

• The Journal of Asian Finance, Economics and Business
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• v.7 no.10
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• pp.1007-1013
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• 2020

This study aims to understand the determinant of the employee's intention to use information system within the framework of the Technology Acceptance Model (TAM) and Information System Success Model (ISSM). This paper also aims to examine the influence of ISSM dimension on the perceived usefulness and the perceived ease of use. The research respondents were 248 employees of Universitas Negeri Semarang (UNNES) who are users of the Financial Information System (SIKEU). Data was obtained using a questionnaire that was distributed online via Google form. The data analysis method used is Structural Equation Model (SEM) analysis using the Warp-PLS software. The results showed that the dimensions of TAM (perceived ease of use and perceived usefulness) had a positive and significant influence on the employee's intention to use SIKEU. The ISSM dimension (system quality and information quality) also had a significant influence, although other ISSM dimensions (service quality) had not been proven to have a significant influence on the employee's intention to use SIKEU. Moreover, the results showed that the employee's intention to use is a determinant of SIKEU's actual usage. Perceived ease of use was significantly determined by system quality, information quality, and service quality. In addition, the perceived usefulness was significantly determined by system quality and information quality.

• 한국사회복지학회:학술대회논문집
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• 2003.10a
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• pp.185-216
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• 2003

This study was aimed at investigating important factors influencing career success among regular workers. The current researcher scrutinized the degree to which variables and factors affect the career success and occupational turnover rates of the research participants. At the same tune, two hypothetical path models established by the researcher were examined using linear multiple regression methods and the LISREL. After examining the differences among the factors of career success, a comparison was made between the disabled worker group and the non-disabled worker group. A questionnaire using the 5-point Likert scale was distributed to a group of 374 workers with disabilities and 463 workers without disabilities. For the data analysis purpose, the structural equation model, factor analysis, correlation analysis, and multiple regression analysis were carried out. The results of this study ran be summarized as follows. First, the results of factor analysis showed important categories of conceptual themes of career success. The initial conceptual factor model did not accord with the empirical one. A three-factorial model revealed categories of personal, family, and organizational factor respectively. The personal factor was composed of the self-esteem and self-efficiency. The family factor was consisted of the multi-roles stress and the number of children. Finally, the organizational factor was composed of the capacity for utilizing resources, networking, and the frequency of mentoring. In addition, the total 10 sub areas of career success were divided by two important aspects; the subjective career success and the objective career success. Second, both research participant groups seemed to be influenced by their occupational types. However, all predictive variables excluding the wage rate and the average length of work years had significant impact on job success for the disabled work group, while all the variables excluding the frequency of advice and length of working years had significant impact on job success for the non-disabled worker group. Third, the turnover rate was significantly influenced by the age and the experience of turnover of the research participants. However, the number of co-workers was the strongest predictive variable for the worker group with disabilities, but the occupation choice variable for the worker group without disabilities. For the disabled worker group, the turnover rate was differently influenced by the type of occupation, the length of working years, while multi-role stress and the average working years at the time of turnover for the worker group without disabilities. Fifth, as a result of verifying the hypothetical path model, it showed that the first model was somewhat proper and could predict the career success on both research participant groups. In the second model, the Chi-square, the degree of freedom (($x^2=64.950$, df=61, P=0.341), and the adjusted Goodness of Fit Index (AGFI) were .964, and the Comparative Fit Index (CFI) were .997, and the Root Mean Squared Residual (RMR) was respectively. .038. The model was best fitted and could predict the career success more highly because the goodness of fit index in the whole models was within the allowed range. In conclusion, the following research implications can be suggested. First, the occupational type of research participants was one of the most important variables to predict the career success for both research participant groups. It means that people with disabilities require human development services including education. They need to improve themselves in this knowledge-based society. Furthermore, for maintaining the career success, people with disabilities should be approached by considering the subjective career success aspects including wages and the promotion opportunities than the objective career success aspects.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2000.10a
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• pp.52-55
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• 2000

The purpose of this study is to develope a stage management model for R&D projects using expert system in order to evaluate the successful accomplishment and diagnose the success level of projects at each development stage. Constructing this model, we classified the development projects for electronic parts, and designed the basic system structure through causation analysis between the factors of success influence and R＆D process. We gathered the input data of expert system by enquete survey, and used ID3 algorithm in order to extract the reasoning rule.

• Journal of Digital Convergence
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• v.12 no.12
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• pp.203-215
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• 2014

Although many studies about start-up existed, It is almost never shown study about ICT convergence start-up enterprise. This study tries to analyze success factor to start-up enterprise in rapidly growing ICT convergence area. Based on 10 entrepreneurial success cases, We figure out the total 29 units success factors utilizing ERIS model. We classify ICT commercialization with 4 types which are software-R&D centric, software marketing centric, software plus hardware R&D centric, software plus hardware marketing centric. Among success factors the most success factor is related entrepreneur;s ability and include major and professional knowledge about ICT's filed, insight through sensitivity about technical paradigm etc. It was derived the success factors indicated in the course of commercialization of ICT Covergence Unlike other forms of the existing start-up entrepreneurs and contributed to typified in four forms startups for ICT Covergence start-up entrepreneurs.

• Journal of Korea Society of Industrial Information Systems
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• v.25 no.1
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• pp.71-87
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• 2020

As startup companies are recognized as the important source of job creation and economic growth, the study is trying to find out the current situation of startup companies and their success factors in South Korea. The study analyzes the success factor of Woowa Brothers recognized as a unicorn start-up company in 2018 by using ERIS model and Start-up Developmental Stages. The ERIS model emphasizes Entrepreneurs, Resources, Industry Environment and Strategy for company performance and three developmental stages are divided into initiation, commercial and growth stages. The results indicate that the Woowa Brothers' success factor was to catch the appropriate timing in ERIS and to combine unique methods by differentiating itself from existing competitors at each stage. With analyses of success factor of Woowa Brothers as a case model, the study reveals limitations and recommends the direction of future research for producing more unicorn companies in South Korea.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.41 no.9
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• pp.1051-1059
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• 1992

In this paper, two general methods using time-series analysis in the functional separation of the myoelectric signal of human arm movements are developed. Autocorrelation, covariance method and sequential least squares algorithm were used to determine the model parameters and the order of signal model to describe six arm movement patterns` the forearm flexion and extension, the wrist pronation and supination, rotation-in and rotation-out. The confidence interval to classify the functions of arm movement was defined by the mean and standard deviation of total squared error. With the error signals of autoregressive(AR) model, the result showed that the highest success rate was obtained in the case of 4th order, and success rate was decreased with increase of order. Autocorrelation was the method of choice for better success rate. This technique might be applied to biomedical and rehabilitation engineering.

• The Journal of Korea Robotics Society
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• v.15 no.4
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• pp.398-403
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• 2020

Robot's throwing control is difficult to accurately calculate because of air resistance and rotational inertia, etc. This complexity can be solved by using machine learning. Reinforcement learning using reward function puts limit on adapting to new environment for robots. Therefore, this paper applied deep reinforcement learning using neural network without reward function. Throwing is evaluated as a success or failure. AI network learns by taking the target position and control policy as input and yielding the evaluation as output. Then, the task is carried out by predicting the success probability according to the target location and control policy and searching the policy with the highest probability. Repeating this task can result in performance improvements as data accumulates. And this model can even predict tasks that were not previously attempted which means it is an universally applicable learning model for any new environment. According to the data results from 520 experiments, this learning model guarantees 75% success rate.

• Journal of Information Technology Services
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• v.6 no.3
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• pp.251-272
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• 2007

Although Enterprise Systems (ES) have promised major strategic benefits and process improvements from business and technology integration, their implementation has been plagued by a high failure rate and difficulty in realizing the promised benefits. For the purpose of understanding implementation failures, previous studies have focused on identifying critical success factors (CSFs) for information systems implementation. However, there has been little research on how these CSFs actually lead to successful results. In this study, based on process theory, we examined the process of ES implementation by explaining how the factors of ES implementation influence each other and how interaction among them produces results. Based on a failure case, we then developed a process model of ES implementation thus allowing us to explain the process of ES implementation. The proposed model facilitated an understanding of how repeating patterns of ES failure can be reversed. This model can be used for guiding new ES implementation projects.