• International Commerce and Information Review
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• v.16 no.5
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• pp.151-178
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• 2014

The objective of the present case study is to analysis how effectively Hilti, which is a former family firm owned and managed by a family in Liechtenstein as a tiny european country, a land sandwiched between Switzerland and Austria, has made a global market success. Liechtenstein has $160km^2$ land and about 36,000 residents. Despite its small size of country, however, Hilti Corporation doesn't view its location as a liability in its business strategy. Hilti is a global leading provider of professional power tools in building, mining, civil engineering etc. Also, Hilti is a firm with a clear vision to become the leading industry partner for construction professionals and building installations through customer focus, high quality equipment, and tools and systems specially designed for specific jobs. This study considered Hilti as a good case, which verifies that born-conditions, endogenous factors according to Michael Porters diamond model does not decisive role more for international competitiveness of firms. Lessons from Hilti are that in order to obtain and sustain the global competitiveness of small and medium-sized firms in Korean manufacturing sector under high production cost, they have to do actively innovative. Also they can give to customers newer and higher customer-values than competitors in abroad give. The case summarizes that the strategy of Hilti for the global market success is comprised of several factors: Technological and organizational innovation, and a clear customer-value oriented business strategy and its implementation. Innovation and its integration into marketing for the customers value creation is central to Hilti's Success. The present case study is expected to provide insights and implication for many firms in Korea that are seeking to secure global presence and market success.

• 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.

• The Journal of the Korea Contents Association
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• v.21 no.4
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• pp.77-87
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• 2021

Smart Factory refers to a factory that can be controlled by itself with an intelligent factory that improves productivity, quality and customer satisfaction by combining the entire process of manufacturing and production with digital automation solutions. The manufacturing industry around the world is rapidly changing, with Germany, Europe, and the United States at the center. In order to cope with such changes, the Korean government is also implementing a policy to spread the supply of smart factories for small and medium-sized companies, and related ministries and agencies such as the Ministry of Commerce, Industry and Energy, the Ministry of SMEs and Venture Business, the Korea Institute of Technology and Information Promotion, and local technoparks, as well as large companies such as Samsung, SK and LG are actively investing in smart manufacturing projects to support smart factories[1]. Factory Automation (FA) construction has many issues regarding the connection of heterogeneous equipment. The most difficult aspect of configuring various communications from various equipment is the reason. Although it may not be known if there are standards or products made up of the same company, it is not easy to build equipment that is old, up-to-date, and different use environments through a series of communications. To solve this problem, we would like to propose a method of communication using Modbus, one of FieldBus, which is one of the many industrial devices of PLC, a representative facility control system, and is used as a communication standard.

• 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.

• Journal of Korea Society of Industrial Information Systems
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• v.22 no.1
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• pp.127-138
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• 2017

The Purpose of this Study is to Examine the Effects of Organizational Commitment on Safety Behavior. This Study Built a Exploratory Model that there is Causal Relationship of Organizational Commitment to Employee's Performance that Safety Behavior. Additionally this Study Examine the Moderating Effect of Employee's Job Insecurity on the Effect of Employee Safety Behavior. For the Verification of this Study Model, the Moderating Effects Regression Analysis was Applied to the Surveys of 240 Members of Small and Medium -Sized Construction Industry Employee in Busan. As a Result of the Verification, the First of Organizational Commitment on Organizational Performance has Found that all Three Organizational Commitment of Affective Commitment, Continuance Commitment and Normative Commitment have Impacts Safety Behavior. Second the Study Verifies the Moderating Effect of Employee's Job Insecurity. the Moderating Effect of Employee's Safety Behavior Between the Independent Variable(organizational commitment) and the Outcome Variables has been Analyzed. As a Result, High level of Job Insecurity Employee has been shown to have a Moderating Effect Between the Independent Variable and Safety Behavior.

• KDI Journal of Economic Policy
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• v.14 no.3
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• pp.97-124
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• 1992

The recent sharp increase in wages has driven many Korean manufacturing firms to move into technology-intensive fields. The task of industrial restructuring is, however, rather difficult for small and medium-sized enterprises (hereafter, "SMEs") which suffer from limited R&D resources. If the R&D activities of SMEs are left unattended, industrial restructuring process may be retarded. Hence, the government-sponsored programs can be justified when used to promote the technological level of SMEs. Because of the limited internal R&D resources of SMEs, in particular human resources, the government-sponsored programs that depend on financial subsidies to stimulate the R&D activities of SMEs may not be recommended. Rather, a more desirable policy is programs to subsidize outside sourcing of SMEs. Basic principles of the program are; (i) that the government should establish R&D laboratories which are specialized in joint researches with SMEs in each industry; (ii) research projects of the laboratories should be funded by SMEs; the government's support covers only fixed costs such as construction costs in order to avoid moral hazard problem. (iii) technology adviser programs sponsored by the government should be improved; geographical distribution is to be expanded and the activities are to be monitored by local governments. Also foreign networks need be strengthened.

• Journal of Intelligence and Information Systems
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• v.21 no.3
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• pp.79-99
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• 2015

The prediction of bankruptcy has been extensively studied in the accounting and finance field. It can have an important impact on lending decisions and the profitability of financial institutions in terms of risk management. Many researchers have focused on constructing a more robust bankruptcy prediction model. Early studies primarily used statistical techniques such as multiple discriminant analysis (MDA) and logit analysis for bankruptcy prediction. However, many studies have demonstrated that artificial intelligence (AI) approaches, such as artificial neural networks (ANN), decision trees, case-based reasoning (CBR), and support vector machine (SVM), have been outperforming statistical techniques since 1990s for business classification problems because statistical methods have some rigid assumptions in their application. In previous studies on corporate bankruptcy, many researchers have focused on developing a bankruptcy prediction model using financial ratios. However, there are few studies that suggest the specific types of bankruptcy. Previous bankruptcy prediction models have generally been interested in predicting whether or not firms will become bankrupt. Most of the studies on bankruptcy types have focused on reviewing the previous literature or performing a case study. Thus, this study develops a model using data mining techniques for predicting the specific types of bankruptcy as well as the occurrence of bankruptcy in Korean small- and medium-sized construction firms in terms of profitability, stability, and activity index. Thus, firms will be able to prevent it from occurring in advance. We propose a hybrid approach using two artificial neural networks (ANNs) for the prediction of bankruptcy types. The first is a back-propagation neural network (BPN) model using supervised learning for bankruptcy prediction and the second is a self-organizing map (SOM) model using unsupervised learning to classify bankruptcy data into several types. Based on the constructed model, we predict the bankruptcy of companies by applying the BPN model to a validation set that was not utilized in the development of the model. This allows for identifying the specific types of bankruptcy by using bankruptcy data predicted by the BPN model. We calculated the average of selected input variables through statistical test for each cluster to interpret characteristics of the derived clusters in the SOM model. Each cluster represents bankruptcy type classified through data of bankruptcy firms, and input variables indicate financial ratios in interpreting the meaning of each cluster. The experimental result shows that each of five bankruptcy types has different characteristics according to financial ratios. Type 1 (severe bankruptcy) has inferior financial statements except for EBITDA (earnings before interest, taxes, depreciation, and amortization) to sales based on the clustering results. Type 2 (lack of stability) has a low quick ratio, low stockholder's equity to total assets, and high total borrowings to total assets. Type 3 (lack of activity) has a slightly low total asset turnover and fixed asset turnover. Type 4 (lack of profitability) has low retained earnings to total assets and EBITDA to sales which represent the indices of profitability. Type 5 (recoverable bankruptcy) includes firms that have a relatively good financial condition as compared to other bankruptcy types even though they are bankrupt. Based on the findings, researchers and practitioners engaged in the credit evaluation field can obtain more useful information about the types of corporate bankruptcy. In this paper, we utilized the financial ratios of firms to classify bankruptcy types. It is important to select the input variables that correctly predict bankruptcy and meaningfully classify the type of bankruptcy. In a further study, we will include non-financial factors such as size, industry, and age of the firms. Thus, we can obtain realistic clustering results for bankruptcy types by combining qualitative factors and reflecting the domain knowledge of experts.