• 한국벤처창업학회:학술대회논문집
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• 2021.04a
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• pp.33-42
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• 2021

Based on the findings of Lee et al.(2020) and Lee & Oh(2021), this paper aims to fill the gap in our knowledge regarding the relationship between strategic choices and corporate growth by utilizing a novel dataset of 'Unicorn' and 'Hyper-growing' companies. Two previous studies provide coherent findings that the relationship between firms' strategies and their performance should be explored under a more comprehensive framework with consideration of both internal and external factors. Therefore, in this study, we apply a single conceptual framework to two different datasets, which considers the strategy factors as independent variables, and the industry(market) and the firm age as moderating variables. For our dependent variables, valuations for unicorn companies and revenue CAGR for hyper-growing companies are used after categorizing them into three uniform groups. The strategy variables include 'Generic (Cost-leadership, Differentiation, focus) strategies', 'Growth(Organic, M&A) strategies', 'Leading(Pioneer, Fast-follower) strategies', 'Target market(B2B, B2C, B2G, C2C) strategies', 'Global(Global, Local) strategies', 'Digital(Online, Offline) strategies.' For industry(market) factors, it consists of historical growth rate for industries and economic, demographic, and regulatory aspects of states and countries. To overcome the differences in their units, they are also uniformly categorized into multiple groups. Before we conduct a regression analysis, we analyze the industry distribution of the 'Unicorn' and the 'Hyper-growing' companies with descriptive statistics at the integrated and individual levels. Next, we employ hierarchical regression models on Study A('Unicorn' companies in 2019) and Study B('Hyper-growing' companies in 2019) under the same comprehensive framework. We then analyze the relationship between the 'strategy' and the 'performance' factors with two different approaches: 1) an integrated regression model with both the sample of Study A and B and 2) respective regression models on Study A and B. This empirical study aims to provide a complete understanding and a reference to which strategy factors should be considered to promote firms' scale-up and growth.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.12
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• pp.1926-1933
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• 2022

Parameterized multichannel Wiener filter (PMWF) is a linear filter that can control the trade-off between residual noise and signal distortion using the embedded parameter. To apply the PMWF to noisy inputs, accurate noise estimation is important and multichannel minima-controlled recursive averaging (MMCRA) is widely used. However, in the case of the MMCRA, the accuracy of noise estimation decreases when a directional interference is involved into the array inputs. Consequently, the performance of the PMWF is degraded. Therefore, we propose a noise power spectral density (PSD) estimation method for the PMWF in this paper. The proposed method is based on a consecutive process of eigenvalue decomposition on noisy input PSD, estimation of the target component contribution using directional information, and exponential weighting for improved estimation of the target contribution. For evaluation, four objective measures were compared with the MMCRA and we verify that the PMWF with the proposed noise estimation method can improve performance in environments where directional interfereces exist.

• The Journal of Economics, Marketing and Management
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• v.3 no.3
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• pp.7-23
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• 2015

Women are more disadvantaged than men in many fronts and this confines them to informal sector livelihood activities. Any attempt to improve women's economic status will require information on the organization, cost and returns to investment in the livelihood activities in which they predominate. This is the issue for this study which compared yield performance in artisanal fishing and fresh fish marketing. Primary data collected through multi-stage sampling method were analyzed using inferential statistics, budgeting and regression models. Empirical findings revealed that about 75.0% of fisher folks either had no formal education or acquired only primary school education while 50.0% of marketers had secondary school education. The budgeting model revealed fisher-folks' and marketers' annual net profit to be N2,882,626.00 and N640,227.00, respectively. Profit from fishing was significantly higher than that of fish marketing. At 53.2% for fishing and 40.3% for marketing, returns to investment was better in fishing. Regression model results showed the significant factors influencing returns to each livelihood strategy to include fishing ground, distance covered and years of experience. The major constraint faced by operators of both livelihoods groups was insufficient credit. Despite this, the livelihood strategies were shown to be profitable ventures which contributed to households' consumption expenditure. Organizing women informal sector operators into groups to enhance access to government support and formal credit are recommended for improving livelihood strategy performance.

• Journal of Distribution Science
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• v.14 no.1
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• pp.67-74
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• 2016

Purpose - In this paper, the degree of similarity and dissimilarity between pairs of 50 major cities in China can be shown on the basis of three evaluation variables(internet businessman index, internet shopping index and e-commerce development index). Dissimilarity distance matrix is used to analyze both similarity and dissimilarity between each fifty city in China by calculating dissimilarity as distance. Higher value signifies higher degree of dissimilarity between two cities. Cluster analysis is exploited to classify 50 cities into a number of different groups such that similar cities are placed in the same group. In addition, multidimensional scaling(MDS) technique can obtain visual representation for exploring the pattern of proximities among 50 major cities in China based on three development performance attributes. Research design, data, and methodology - This research is performed by the 2013 report provided with AliResearch in China(1/1/2013~11/30/2013) and utilized multivariate methods such as dissimilarity distance matrix, cluster analysis and MDS by using CLUSTER, KMEANS, PROXIMITIES and ALSCAL procedures in SPSS 21.0. Results - This research applies two types of cluster analysis and MDS on three development performances based on the 2013 report of Aliresearch. As a result, it is confirmed that grouping is possible by categorizing the types into four clusters which share similar characteristics. MDS is exploited to carry out positioning of both grouped locations of cluster and 50 major cities belonging to each cluster. Since all the values corresponding to Shenzhen, Guangzhou and Hangzhou(which belong to cluster 1 among 50 major cities) are very large, these cities are superior to other cities in all three evaluation attributes. Twelve cities(Beijing, ShangHai, Jinghua, ZhuHai, XiaMen, SuZhou, NanJing, DongWan, ZhangShan, JiaXing, NingBo and FoShan), which belong to cluster 3, are inferior to those of cluster 1 in terms of all three attributes, but they can be expected to be the next e-commerce revolution. The rest of major cities, in particular, which belong to cluster 4 are relatively inferior in all three attributes, so that this automatically evokes creative innovation, which leads to e-commerce development as a whole in China. In terms of internet businessman index, on the other hand, Tainan, Taizhong, and Gaoxiong(which belong to cluster 2) are situated superior to others. However, these three cities are inferior to others in an internet shopping index sense. The rest of major cities, in particular, which belong to cluster 4 are relatively inferior in all three evaluation attributes, so that this automatically evokes innovation and entrepreneurship, which leads to e-commerce development as a whole in China. Conclusions - This study suggests the implications to help e-governmental officers and companies make strategies in both Korea and China. This is expected to give some useful information in understanding the recent situation of e-commerce in China, by looking over development performances of 50 major cities. Therefore, we should develop marketing, branding and communication relevant to online Chinese consumers. One of these efforts will be incentives like loyalty points and coupons that can encourage consumers and building in-house logistics networks.

• Journal of Korea Technology Innovation Society
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• v.19 no.1
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• pp.48-79
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• 2016

Recently, to overcome the global economic recession and stimulate the sluggish economy, Korea government has adopted various policies designed to strengthen the innovative capabilities of its small or medium sized enterprises (SMEs). With our focus on ICT technology companies, this study empirically analyzes both the potentials and the limitations of the R&D information support programs which are part of these governmental efforts. Our goal is to generate insights and help develop policies based on evidence. In this study, we used statistics from 2014 on small or medium business technologies to analyze the effect that the government's R&D information support policy has had on the technological or economic performance of small or medium businesses. According to these research results, R&D support programs (assistance with R&D planning and provision of technological information) made available to small or medium businesses did have a significant correlation to technological investment. By contrast, R&D information supporting programs were found to have no direct, significant correlation to technological or economic performance. One exception was that programs that gained the benefits of R&D planning support did have a significant correlation to technological performance in the case of companies engaged in ICT research. The results of this study will provide various insights for policymakers designing policies to support technologically-driven small or medium businesses, including ICT-based companies. We anticipate that this study will be a particularly helpful guide to policy development for corporations or researchers that provide supportive information to small or medium businesses.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.5
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• pp.577-584
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• 2020

This study examined the effects of management consultant competency (ability, professionalism) and consulting service quality (reliability, assurance) on organizational creativity and innovation. The research target was companies with experience in consulting, and sample data from 62 surveyed companies (80 persons) were analyzed using SPSS 25.0 and Smart PLS 2.0 based on statistics to perform frequency analysis, reliability, and feasibility analysis. The summary of the research results is as follows. First, it has been verified that consultant competency (ability, professionalism) and consulting service quality (reliability, assurance) had positive impacts. Second, service reliability and assurance, which are components of consulting service quality, significantly affected consulting performance (organizational creativity, innovation). Taken together, management consulting has a positive effect on creativity and innovation in an organization and ultimately contributes to improvement of the business performance of the company, depending on the competency of the consultant and quality of the services provided. Based on the results of this study, we intend to improve the quality of SME consulting by providing theoretical and practical implications as well as contribute to the growth of SMEs requiring innovation in the era of the 4th Industrial Revolution.

• Journal of Intelligence and Information Systems
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• v.18 no.2
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• pp.29-45
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• 2012

Bond rating is regarded as an important event for measuring financial risk of companies and for determining the investment returns of investors. As a result, it has been a popular research topic for researchers to predict companies' credit ratings by applying statistical and machine learning techniques. The statistical techniques, including multiple regression, multiple discriminant analysis (MDA), logistic models (LOGIT), and probit analysis, have been traditionally used in bond rating. However, one major drawback is that it should be based on strict assumptions. Such strict assumptions include linearity, normality, independence among predictor variables and pre-existing functional forms relating the criterion variablesand the predictor variables. Those strict assumptions of traditional statistics have limited their application to the real world. Machine learning techniques also used in bond rating prediction models include decision trees (DT), neural networks (NN), and Support Vector Machine (SVM). Especially, SVM is recognized as a new and promising classification and regression analysis method. SVM learns a separating hyperplane that can maximize the margin between two categories. SVM is simple enough to be analyzed mathematical, and leads to high performance in practical applications. SVM implements the structuralrisk minimization principle and searches to minimize an upper bound of the generalization error. In addition, the solution of SVM may be a global optimum and thus, overfitting is unlikely to occur with SVM. In addition, SVM does not require too many data sample for training since it builds prediction models by only using some representative sample near the boundaries called support vectors. A number of experimental researches have indicated that SVM has been successfully applied in a variety of pattern recognition fields. However, there are three major drawbacks that can be potential causes for degrading SVM's performance. First, SVM is originally proposed for solving binary-class classification problems. Methods for combining SVMs for multi-class classification such as One-Against-One, One-Against-All have been proposed, but they do not improve the performance in multi-class classification problem as much as SVM for binary-class classification. Second, approximation algorithms (e.g. decomposition methods, sequential minimal optimization algorithm) could be used for effective multi-class computation to reduce computation time, but it could deteriorate classification performance. Third, the difficulty in multi-class prediction problems is in data imbalance problem that can occur when the number of instances in one class greatly outnumbers the number of instances in the other class. Such data sets often cause a default classifier to be built due to skewed boundary and thus the reduction in the classification accuracy of such a classifier. SVM ensemble learning is one of machine learning methods to cope with the above drawbacks. Ensemble learning is a method for improving the performance of classification and prediction algorithms. AdaBoost is one of the widely used ensemble learning techniques. It constructs a composite classifier by sequentially training classifiers while increasing weight on the misclassified observations through iterations. The observations that are incorrectly predicted by previous classifiers are chosen more often than examples that are correctly predicted. Thus Boosting attempts to produce new classifiers that are better able to predict examples for which the current ensemble's performance is poor. In this way, it can reinforce the training of the misclassified observations of the minority class. This paper proposes a multiclass Geometric Mean-based Boosting (MGM-Boost) to resolve multiclass prediction problem. Since MGM-Boost introduces the notion of geometric mean into AdaBoost, it can perform learning process considering the geometric mean-based accuracy and errors of multiclass. This study applies MGM-Boost to the real-world bond rating case for Korean companies to examine the feasibility of MGM-Boost. 10-fold cross validations for threetimes with different random seeds are performed in order to ensure that the comparison among three different classifiers does not happen by chance. For each of 10-fold cross validation, the entire data set is first partitioned into tenequal-sized sets, and then each set is in turn used as the test set while the classifier trains on the other nine sets. That is, cross-validated folds have been tested independently of each algorithm. Through these steps, we have obtained the results for classifiers on each of the 30 experiments. In the comparison of arithmetic mean-based prediction accuracy between individual classifiers, MGM-Boost (52.95%) shows higher prediction accuracy than both AdaBoost (51.69%) and SVM (49.47%). MGM-Boost (28.12%) also shows the higher prediction accuracy than AdaBoost (24.65%) and SVM (15.42%)in terms of geometric mean-based prediction accuracy. T-test is used to examine whether the performance of each classifiers for 30 folds is significantly different. The results indicate that performance of MGM-Boost is significantly different from AdaBoost and SVM classifiers at 1% level. These results mean that MGM-Boost can provide robust and stable solutions to multi-classproblems such as bond rating.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.47 no.4
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• pp.90-96
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• 2010

The focusing system is an important factor to determine the imaging quality of a digital imaging system. The focusing system consist of measuring the focusing index with high frequency energy of an image and controlling the movement of the focusing lens based on the computed focusing index. The computation of the focusing index is a key aspect in implementing the focusing system and the noise of the image cause the error in the sharpness evaluation of the image. To reduce this error, the noise under the low illumination condition is considered. A noise insensitive focusing index using adaptive weights is proposed in this paper. This measure determines the sharpness of an image using the spatially adaptive weights based on the local statistics of the image and noise. Experimental results under the condition without and with the noise verify the performance of the proposed method.

• Journal of Korean Society of Archives and Records Management
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• v.17 no.1
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• pp.163-192
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• 2017

The purpose of this study is to clarify the work of a local government records manager, provide job analysis using importance-performance analysis (IPA), and define the differences and characteristics in the perception of the work by employment and career type with the aim of providing basic data on effective business operation. To this end, this study conducted statistics analysis and an IPA matrix after analyzing the work of records managers in the local government and conducting surveys. Based on the analysis results, this study suggests installing a local archives management institution, securing the necessary budget for performing works, training staff, calling for assistant staff and collaborative work between other departments within the organization, and retraining of jobs differently based on the difference in perception of work by employment type and career as solutions.

• Proceedings of the Korean Society of Near Infrared Spectroscopy Conference
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• 2001.06a
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• pp.1154-1154
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• 2001

Near infra-red (NIR) spectroscopy has been used for the non-invasive assessment of intact fruit for eating quality attributes such as total soluble solids (TSS) content. However, little information is available in the literature with respect to the robustness of such calibration models validated against independent populations (however, see Peiris et al. 1998 and Guthrie et al. 1998). Many studies report ‘prediction’ statistics in which the calibration and prediction sets are subsets of the same population (e. g. a three year calibration validated against a set from the same population, Peiris et al. 1998; calibration and validation subsets of the same initial population, Guthrie and Walsh 1997 and McGlone and Kawano 1998). In this study, a calibration was developed across 84 melon fruit (R$^2$= 0.86$^{\circ}$Brix, SECV = 0.38$^{\circ}$Brix), which predicted well on fruit excluded from the calibration set but taken from the same population (n = 24, SEP = 0.38$^{\circ}$Brix with 0.1$^{\circ}$Brix bias), relative to an independent group (same variety and farm but different harvest date) (n = 24, SEP= 0.66$^{\circ}$ Brix with 0.1$^{\circ}$Brix bias). Prediction on a different variety, different growing district and time was worse (n = 24, SEP = 1.2$^{\circ}$Brix with 0.9$^{\circ}$Brix bias). Using an ‘in-line’ unit based on a silicon diode array spectrometer, as described in Walsh et al. (2000), we collected spectra from fruit populations covering different varieties, growing districts and time. The calibration procedure was optimized in terms of spectral window, derivative function and scatter correction. Performance of a calibration across new populations of fruit (different varieties, growing districts and harvest date) is reported. Various calibration sample selection techniques (primarily based on Mahalanobis distances), were trialled to structure the calibration population to improve robustness of prediction on independent sets. Optimization of calibration population structure (using the ISI protocols of neighbourhood and global distances) resulted in the elimination of over 50% of the initial data set. The use of the ISI Local Calibration routine was also investigated.