• Korean small business review
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• v.42 no.4
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• pp.69-102
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• 2020

Shedding new light on the research trend on entrepreneurial ecosystems in the 40-year history of the Asia Pacific Journal of Small Business, this study aims at exploring a potential measurement framework of ecological inputs and outputs in an entrepreneurial ecosystem that promotes entrepreneurship at geographical and spatial levels. As a result of the analysis of research on the entrepreneurial ecosystem in the journal, we found that prior studies emphasized the managerial importance of various ecological factors on the premise of possible causalities between the factors and entrepreneurship. However, empirical research to verify the premised causality has been underexplored yet. This literature gap may lead to unbalanced development of conceptual and case studies that identify requirements for successful entrepreneurial ecosystems based on experiential facts, thereby hindering the generalization of the research results for practical implications. In that there is a growing interest in creating and operating productive entrepreneurial ecosystems as an innovation engine that drives national and regional economic growth, it is necessary to explore and develop the measurement framework for ecological factors that can be used in future empirical research. Hereupon, we apply a conceptual model of 'input-output-outcome-impact' to categorize individual environmental factors identified in prior studies. Based on the model. We operationalize ecological input factors as the financial, intellectual, institutional, and social capitals, and ecological output factors as the establishment-based, innovation-based, and performance-based entrepreneurship. Also, we propose several longitudinal databases that future empirical research can use in analyzing the potential causality between the ecological input and output factors. The proposed framework of entrepreneurial ecosystems, which focuses on measuring ecological input and output factors, has a high application value for future research that analyzes the causality.

• Journal of Welding and Joining
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• v.26 no.3
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• pp.23-29
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• 2008

The purpose of this study is to propose the proper fillet welding process for preventing the buckling distortion in thin panel welded structure. In order to do it, a heat input model for laser hybrid welding process was developed using FEA and experiment. The principal factors controlling the angular distortion and longitudinal shrinkage force caused by FCA and laser hybrid welding were identified as the welding heat input and weld rigidity using FEA. The predictive equations of angular distortion and longitudinal shrinkage force for each welding process were formulated as a function of the principal factors proposed. With the predictive equations, the buckling distortion at the thin panel welded structure with welding process was evaluated and compared using nonlinear buckling analysis and STEM(simplified thermo elastic method). Based on the results, the best way to prevent the buckling distortion at the given welded panel structures was identified as an intermittent FCA welding.

• IE interfaces
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• v.11 no.3
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• pp.23-40
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• 1998

In this paper, a method to determine the optimal configuration of operating policies in an integrated-automated manufacturing system using the Taguchi method and computer simulation experiments is presented. An integrated-automated manufacturing system called direct-input-output manufacturing system(DIOMS) is described. We only consider the operational aspect of the DIOMS. Four operating policies including input sequencing control, dispatching rule for the storage/retrieval(S/R) machine, machine center-based part type selection rule, and storage assignment policy are treated as design factors. The number of machine centers, the number of part types, demand rate, processing time and the rate of each part type, vertical and horizontal speed of the S/R machine, and the size of a local buffer in the machine centers are considered as noise factors in generating various manufacturing system environment. For the performance characteristics, mean flow time and throughput are adopted. A robust design experiment with inner and outer orthogonal arrays are conducted by computer simulation, and an optimal configuration of operating policies is presented which consists of a combination of the level of each design factor. The validity of the optimal configurations is investigated by comparing their signal-to-noise ratios with those obtained with full factorial designs.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1997.10a
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• pp.454-457
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• 1997

The purpose of this study is to develop an optimal model, using existing models, that is able to estimate the amount of spatter utilizing artificial neural network in the short circuit transfer mode of gas metal arc (GMA) welding. The amount of spatter generated during welding can become a barometer which represents the process stability of metal transfer in GMA welding, and it depends on some factors which constitute a periodic waveforms of welding current and arc voltage in short circuit GMA welding. So, the 12 factors, which could express the characteristics for the waveforms, and the amount of spatter are used as input and output variables of the neural network, respectively. Two neural network models to estimate the amount of spatter are proposed: A neural network model, where arc extinction is not considered, and a combined neural network model where it is considered. In order to reduce the calculation time it take to produce an output, the input vector and hidden layers for each model are optimized using the correlation coefficients between each factor and the amount of spattcr. The est~mation performance of each optimized model to the amount of spatter IS assessed and compared to the est~mation performance of the model proposed by Kang. Also, through the evaluation for the estimation performance of each optimized model, it is shown that the combined neural network model can almost perfectly predict the amount of spatter.

• Journal of Distribution Science
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• v.14 no.7
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• pp.53-61
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• 2016

Purpose - The production efficiency of agricultural crops has been the subject of numerous studies in the field of agricultural economics. This study examines the production efficiency of emerging vineyards in the 14 northern U.S. states and aims to understand raw input and managerial factors affecting the grape production with focusing on the effect of farm size. Research design, data, and methodology - Using a unique survey dataset that was collected from 176 vineyards in 2012, we employed data envelopment analysis (DEA) for estimation of production efficiency in individual vineyards. Production efficiency is regressed on various input and managerial covariates to understand factors influencing the productivity. Results - Although there exists positive correlation between the farm size and productivity of vineyards in Northern U.S. states, we find negative relationship when the farm size is instrumented by the additional farm size expansion indicator. The negative effect is more pronounced for the recently established vineyards. Conclusions - This study suggests that there needs to be adequate managerial improvements for emerging vineyards in northern states for the achievement of increased productivity.

• The Korean Journal of Health Service Management
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• v.5 no.1
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• pp.1-13
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• 2011

The purpose of this paper was to evaluate performance of information system for one national university hospital in order to identify the factors influencing performance of information system. KPIs were collected for 181 users of information system (41 doctors, 104 nurses, and 11 medical supporting staffs, and 25 administrative staffs) from August 10 to 24, 2010. The results were as follows: Average performance score for input layer was 3.16; average performance score for process layer was 3.35; and average performance score for business layer was 3.57. Scores for input layer was lowest for nurses and scores for process and business layer were lowest for doctors. Results from the path analysis showed that system quality, demographic characteristics, and security significantly influenced management process but these factors except demographic characteristics influenced user satisfaction; and management process also significantly influenced user satisfaction.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.52 no.7
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• pp.424-433
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• 2003

Evolutionary design related to the optimal design of Polynomial Neural Networks (PNNs) structure for model identification of complex and nonlinear system is studied in this paper. The PNN structure is consisted of layers and nodes like conventional neural networks but is not fixed and can be changable according to the system environments. three types of polynomials such as linear, quadratic, and modified quadratic is used in each node that is connected with various kinds of multi-variable inputs. Inputs and order of polynomials in each node are very important element for the performance of model. In most cases these factors are decided by the background information and trial and error of designer. For the high reliability and good performance of the PNN, the factors must be decided according to a logical and systematic way. In the paper evolutionary algorithm is applied to choose the optimal input variables and order. Evolutionary (genetic) algorithm is a random search optimization technique. The evolved PNN with optimally chosen input variables and order is not fixed in advance but becomes fully optimized automatically during the identification process. Gas furnace and pH neutralization processes are used in conventional PNN version are modeled. It shows that the designed PNN architecture with evolutionary structure optimization can produce the model with higher accuracy than previous PNN and other works.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.36 no.3
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• pp.126-132
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• 2013

In this paper, efficiencies of core technology development projects, conducted by Ministry of Trade, Industry and Energy, are compared. In the process, DEA (Data Envelopment Analysis) is utilized as a main technique for comparing efficiencies. For DEA, input oriented BCC Model is adopted with government grant, recipient expenditure, the number of participating institutions, and project duration as input factors, and the number of patents, the number of papers, and occurred sales as output factors. As a result, next generation mobile communication project turns out to be the most efficient project of all. Therefore, next generation mobile communication project should be benchmarked for the other projects to follow. However, these results should be used only for reference data since every project has a different objective and, of course, is run under a different environment.

• Journal of Korean Institute of Industrial Engineers
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• v.41 no.5
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• pp.425-438
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• 2015

This study proposes a three-stage model of R&BD performance which captures commercialization outcomes as well as conventional R&D performance. The model is composed of three factors : inputs (R&D budgets and researchers), outputs (patents and papers), and outcomes (technical fees, products sales, and cost savings). Three stages are defined for each transformation process between the three factors : efficiency stage from input to output (stage 1), effectiveness stage from output to outcome (stage 2), and productivity stage from input to outcome (stage 3). The performance of each stage is measured by data envelopment analysis (DEA). DEA is a non-parametric efficiency measurement technique that has widely been used in R&D performance measurement. We measure the performance of 171 projects of 6 public R&BD programs managed by Seoul Business Agency using the proposed three-stage model. In order to provide a balanced and holistic view of R&BD performance, the R&BD performance map is also constructed based on performance of efficiency and productivity stages.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.138-140
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• 2003

The aim of this study is to evaluate the effect of spatial resolutions on the accuracy to landslide susceptibility mapping. For this, landslide locations were identified in the Boun, Korea from interpretation of aerial photographs and field surveys. The topographic, soil, forest, geologic, linearment and land use data were collected, processed and constructed into a spatial database using GIS and remote sensing data. The 15 factors that influence landslide occurrence were extracted and calculated from the spatial database with 5m, 10m, 30m, 100m and 200m spatial resolutions. Landslide hazardous area were analysed and mapped using the landslide-occurrence factors by probability model, likelihood ratio, for the five cases spatial resolutions. The results of the analysis were verified using the landslide location data. In the cases of spatial resolution 5m, 10m and 30m, the verification results was similar, but in the cases of 100m and 200m the results worse than the others. Because the scale of input data was 1:5,000 ? 1:50,000, so the cases of 5m, 10m and 30m have similar accuracy but the cases of 100m and 200m have the lower accuracy. From this, there is an effect of spatial resolutions on accuracy and landslide susceptibility mapping the result is dependent on input map.