• Management & Information Systems Review
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• v.32 no.3
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• pp.153-172
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• 2013

Small and medium-sized enterprises(SMEs) have limited human and material resources than large firms. SMEs are characterized by high risk and dynamic working environments. Thus human resources having knowledge and technologies are very important factor for survival and performance in SMEs. Recently, as core employee group is a key asset to firm competitiveness, many SMEs attend to set up core employee policy and system. The purposes of this research are to examine the effect of core employee policy and core employee management system on competitiveness of human resource and organizational commitment in SMEs. The major findings of the research are core employee policy have non-significant influence core employee management system, core employee policy have positive influence competitiveness of human resource, core employee management system have non-significant influence competitiveness of human resource, and competitiveness of human resource have positive influence organizational commitment in SMEs.

• Korean Journal of Construction Engineering and Management
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• v.13 no.5
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• pp.64-75
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• 2012

Today organizations are making considerable efforts in order to maintain excellent talent, and in particular, they are focusing on understanding their intentions of changing jobs which are most highly correlated with job turnover. In the case of architectural design firms, its intensity of work is very high unlike industrial settings, and it not only takes much time to cultivate new men of talent and but also is characteristic that employees can change livery easily because of the flexible labor market. The turnover rated by National Statistical Office indicates that specialized, scientific and technical service industry including the architectural design firm has a relatively high turnover rate compared to the average of the turnover rate of the overall industries. However, studies on intentions of changing jobs until now were conducted focused on employees engaged in other industrial areas, and it is true that studies regarding intentions of changing jobs of practitioners of architectural design firms are very insufficient. In this context, the present study aimed to draw determinants affecting intentions of changing jobs of practitioners of architectural design firms, to objectively understand the practitioners' intentions of changing jobs through importance analysis by each factor based on this and to make a comparative analysis of differences between the large scale architectural design firms and the small and medium sized architectural design firms.

• Journal of Korea Technology Innovation Society
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• v.20 no.1
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• pp.62-80
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• 2017

The purpose of this paper is to explore the relationship between industrial regulations and the R&D activities of firms by analysing the case of manufacturing enterprises in Korea. The sample is gathered from the 2012 Korean Innovation Survey data of Korean Institute of Science & Technology Evaluation and Planning and merged with Korean Regulation Index data of Korean Institute of Public Administration. The Ordinary Least Square (OLS) as well as 2 Stage Least Square (2SLS) regression results show that the impact of the level of the manufacturing field's regulation on firms' R&D activities or inputs may be both positive and negative, depending on the size of the firms. The analysis results suggest that regulatory policy makers need to formulate and implement R&D programs that consider the different effects of industrial regulations on large enterprises or Small and Medium sized Enterprises (SMEs).

• KDI Journal of Economic Policy
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• v.33 no.2
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• pp.111-143
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• 2011

As the 'jobless growth' is developing into a worldwide phenomenon, many countries try to recover a virtuous relationship between the growth and employment using various wage subsidy programs. This study focuses on wage subsidy to employers, labor demand-side wage subsidy for which one can think of two types-a tax credit(a flat wage subsidy) and a social insurance premium exemption(a proportional wage subsidy). For job creation, Korean government reintroduced a tax credit to small and medium-sized enterprises(SMEs) which have increased their employment level in 2010. But many experts has continuously insisted that it should be replaced with a social insurance premium exemption arguing only a few SMEs benefit from the tax credit as most of them are actually not paying any corporate or general income tax bills. However, as the insurance premium exemption accompanies an increase in the amount of budget with the coverage widen, one cannot confirm its cost effectiveness over the tax credit. This paper aims to provide a theoretical analysis to derive some formal conditions under which a social insurance premium exemption creates more jobs than a tax credit does given a budget constraint. We show that the former's dominance over the latter depends on whether there exists a dead zone of social insurance or not. If there does not exist a dead zone, a social insurance premium exemption is more desirable in many cases, whereas one cannot guarantees its dominance over a tax credit if there exists a dead zone. Therefore in order to realize its dominance, the government should minimize a dead zone so that most SMEs effectively benefit from the insurance premium exemption. In addition, applying discriminative exemption rates which reflect each firm's job conditions such as wage level and labor demand/supply sensitivity, the government try to enhance job creation effect.

• Journal of Intelligence and Information Systems
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• v.21 no.4
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• pp.111-131
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• 2015

There are only a handful number of research conducted on pattern analysis of corporate distress as compared with research for bankruptcy prediction. The few that exists mainly focus on audited firms because financial data collection is easier for these firms. But in reality, corporate financial distress is a far more common and critical phenomenon for non-audited firms which are mainly comprised of small and medium sized firms. The purpose of this paper is to classify non-audited firms under distress according to their financial ratio using data mining; Self-Organizing Map (SOM). SOM is a type of artificial neural network that is trained using unsupervised learning to produce a lower dimensional discretized representation of the input space of the training samples, called a map. SOM is different from other artificial neural networks as it applies competitive learning as opposed to error-correction learning such as backpropagation with gradient descent, and in the sense that it uses a neighborhood function to preserve the topological properties of the input space. It is one of the popular and successful clustering algorithm. In this study, we classify types of financial distress firms, specially, non-audited firms. In the empirical test, we collect 10 financial ratios of 100 non-audited firms under distress in 2004 for the previous two years (2002 and 2003). Using these financial ratios and the SOM algorithm, five distinct patterns were distinguished. In pattern 1, financial distress was very serious in almost all financial ratios. 12% of the firms are included in these patterns. In pattern 2, financial distress was weak in almost financial ratios. 14% of the firms are included in pattern 2. In pattern 3, growth ratio was the worst among all patterns. It is speculated that the firms of this pattern may be under distress due to severe competition in their industries. Approximately 30% of the firms fell into this group. In pattern 4, the growth ratio was higher than any other pattern but the cash ratio and profitability ratio were not at the level of the growth ratio. It is concluded that the firms of this pattern were under distress in pursuit of expanding their business. About 25% of the firms were in this pattern. Last, pattern 5 encompassed very solvent firms. Perhaps firms of this pattern were distressed due to a bad short-term strategic decision or due to problems with the enterpriser of the firms. Approximately 18% of the firms were under this pattern. This study has the academic and empirical contribution. In the perspectives of the academic contribution, non-audited companies that tend to be easily bankrupt and have the unstructured or easily manipulated financial data are classified by the data mining technology (Self-Organizing Map) rather than big sized audited firms that have the well prepared and reliable financial data. In the perspectives of the empirical one, even though the financial data of the non-audited firms are conducted to analyze, it is useful for find out the first order symptom of financial distress, which makes us to forecast the prediction of bankruptcy of the firms and to manage the early warning and alert signal. These are the academic and empirical contribution of this study. The limitation of this research is to analyze only 100 corporates due to the difficulty of collecting the financial data of the non-audited firms, which make us to be hard to proceed to the analysis by the category or size difference. Also, non-financial qualitative data is crucial for the analysis of bankruptcy. Thus, the non-financial qualitative factor is taken into account for the next study. This study sheds some light on the non-audited small and medium sized firms' distress prediction in the future.

• Journal of Navigation and Port Research
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• v.39 no.6
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• pp.545-552
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• 2015

This study One of the key elements of corporate competitiveness in the modern world of unlimited competition is human resource management. The reason that the world's leading companies are devoting a lot of investment and effort for good human resource development and management is that human resource can impact firm survival. In particular, there is little research on the internal and external environmental stimuli and job stress in the employee of small business which are often led to turnover, while they have suffered from chronic shortage of manpower. The purpose of this study is to determine the turnover factors in the small logistics companies and contribute to stable maintenance of workforce, facilitating human resource management and minimizing turnover. This study empirically analyzed the factors of the turnover in the organization of logistics companies from Busan Port, South Korea, which became one of the national infrastructure and the fifth world largest harbor. The conclusion proposed the development and direction of the human resource management which could promote the job environment improving the turnover factors and creating sustainable work condition through conducting preventive measures. The results indicated that the highest turnover rates was found in the category of field work, and the highest turnover group was from the 'less than one year', which implies that high turnover rates after and during job training might be greater cost to the companies than early turnover. The most common reasons for the high employee turnover were 'excessive workload' and 'dissatisfaction with wages'. Followed reasons including 'troubles with managers' and 'failure in organizational adaptation' can be understood in line with worse working conditions of the small logistic companies. It turned out that the preventive programs of the logistic enterprises had little effect through 'incentives system' and 'improving wage system' which are mainly conducted. The human resource managers appreciated the importance of 'wage raise' and 'benefits improvement'. This study is aimed at contributing to efficient human resource management through understanding of the turnover causes and human resource managers applying preventive measures. In particular, this can benefit small port logistics companies securing competitiveness and promoting persistent growth and development.

• Journal of Technology Innovation
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• v.19 no.1
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• pp.111-144
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• 2011

This study is to confirm the core factors of innovative capabilities and technological entrepreneurship affecting the performance of technology management and business management of small and medium-sized enterprises (SMEs). Through the consideration about the complex natures of technological innovation affecting by multidimensional factors, this study designs the research model that innovative capabilities, the performances of technology and business management are arranged in accordance with the innovation process; input-output-outcome. To meet this research purpose, the hypothesis are set up based on the previous research studies and the research samples are selected from members of the Innovative Business (INNO-BIZ) Association, located in Seoul and Geyonggi province. As a result of regression analysis to the responses gathered from 360 firms, the performance of business management is influenced positively by the technology superiority, market growth and business profitability which are the dominant factors of performance of technology management. In addition, three sub-variables of innovative capabilities such as R&D, strategic planning and learning capability, have positive effects on both the managerial performances. Innovativeness and progressiveness of technological entrepreneurship affect both the performances positively. Moreover, the co-relation between technological entrepreneurship of an innovation leader and innovative capabilities of organizational members are identified. Lastly, technological entrepreneurship has the mediating effect on the path of leading innovative capabilities to the managerial performances. In conclusion, the research results imply that technological innovation-type firms should periodically evaluate the performance of technology management which are the output of technological innovations and the reinvestment for ultimate business success. And improving and developing innovative capabilities and technological entrepreneurship is required to continuously and consistently investing and supporting resources on technological innovations at the firm-and government-level. It is considered that these are the crucial methods for securing the technologically competitive advantage of SMEs with less resources and narrow innovation range.

• Journal of Intelligence and Information Systems
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• v.22 no.2
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• pp.33-56
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• 2016

Many researchers have focused on developing bankruptcy prediction models using modeling techniques, such as statistical methods including multiple discriminant analysis (MDA) and logit analysis or artificial intelligence techniques containing artificial neural networks (ANN), decision trees, and support vector machines (SVM), to secure enhanced performance. Most of the bankruptcy prediction models in academic studies have used financial ratios as main input variables. The bankruptcy of firms is associated with firm's financial states and the external economic situation. However, the inclusion of qualitative information, such as the economic atmosphere, has not been actively discussed despite the fact that exploiting only financial ratios has some drawbacks. Accounting information, such as financial ratios, is based on past data, and it is usually determined one year before bankruptcy. Thus, a time lag exists between the point of closing financial statements and the point of credit evaluation. In addition, financial ratios do not contain environmental factors, such as external economic situations. Therefore, using only financial ratios may be insufficient in constructing a bankruptcy prediction model, because they essentially reflect past corporate internal accounting information while neglecting recent information. Thus, qualitative information must be added to the conventional bankruptcy prediction model to supplement accounting information. Due to the lack of an analytic mechanism for obtaining and processing qualitative information from various information sources, previous studies have only used qualitative information. However, recently, big data analytics, such as text mining techniques, have been drawing much attention in academia and industry, with an increasing amount of unstructured text data available on the web. A few previous studies have sought to adopt big data analytics in business prediction modeling. Nevertheless, the use of qualitative information on the web for business prediction modeling is still deemed to be in the primary stage, restricted to limited applications, such as stock prediction and movie revenue prediction applications. Thus, it is necessary to apply big data analytics techniques, such as text mining, to various business prediction problems, including credit risk evaluation. Analytic methods are required for processing qualitative information represented in unstructured text form due to the complexity of managing and processing unstructured text data. This study proposes a bankruptcy prediction model for Korean small- and medium-sized construction firms using both quantitative information, such as financial ratios, and qualitative information acquired from economic news articles. The performance of the proposed method depends on how well information types are transformed from qualitative into quantitative information that is suitable for incorporating into the bankruptcy prediction model. We employ big data analytics techniques, especially text mining, as a mechanism for processing qualitative information. The sentiment index is provided at the industry level by extracting from a large amount of text data to quantify the external economic atmosphere represented in the media. The proposed method involves keyword-based sentiment analysis using a domain-specific sentiment lexicon to extract sentiment from economic news articles. The generated sentiment lexicon is designed to represent sentiment for the construction business by considering the relationship between the occurring term and the actual situation with respect to the economic condition of the industry rather than the inherent semantics of the term. The experimental results proved that incorporating qualitative information based on big data analytics into the traditional bankruptcy prediction model based on accounting information is effective for enhancing the predictive performance. The sentiment variable extracted from economic news articles had an impact on corporate bankruptcy. In particular, a negative sentiment variable improved the accuracy of corporate bankruptcy prediction because the corporate bankruptcy of construction firms is sensitive to poor economic conditions. The bankruptcy prediction model using qualitative information based on big data analytics contributes to the field, in that it reflects not only relatively recent information but also environmental factors, such as external economic conditions.