• Journal of Distribution Science
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• v.9 no.3
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• pp.25-36
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• 2011

Owing to the rapid growth of hypermarket/discount store formats since 1996, Korean retailing has suddenly attracted the significant attention from researchers. Before the emergence of large scale retailers such as E-Mart, Lotte Mart and Tesco Korea, there were the two retail formats who led the Korean retailing in the modern retailing history: department store and supermarket formats. Nevertheless, there has been little literature concerned about the two retail formats as a case study, while some authors have paid their attention to hypermarket/discount store formats. In addition, when mentioning the development process of retailing history, it is less likely that authors have made an effort to illustrate supermarket retailing history. In order to regard supermarket retailing as part of the Korean retailing, it is interesting to look at a representative supermarket retailer, Haitai, who was one of the subsidiaries of Haitai chaebol. Based on supermarket retailing, the company which was established as a joint venture in 1974 led a supermarket retailing in the Korean modernised retailing history. Before analysing whether Haitai failed or not, the definition of failure should be illustrated. With regard to the term, failure, in the academic world, authors have interchangeably used the following terms: failure, divestment, closure, organisational restructuring, and exit. To collect research data as a case study, the author adopted an in-depth interview method. The research is based on research interviews with 13 ex-staff who left after Haitai went bankruptcy, from store management department to merchandise department. By investigating Haitai's experiences through field interviews, the research found that Haitai restructured organisational decision-making process at the early stage when companies started to modernise organisational charts, benchmarking sophisticated retailing knowledge through the strategic alliance with a Japanese retailer. In respect of buying system, the company established firmly buying functions by adopting central buying system, and further, outstandingly allocated considerable marketing resources to the development of retailer brands with the dedicated team of retailer brand development. In the grocery retailing, abandoning a 'no-frill' packaging concept, the introduction of retailer brand packaging equal to, or better than national brand packaging design, encouraged other retailers to change their retailer brand development strategies. In product sourcing ways, Haitai organised for the first time the overseas sourcing team with the aim of improving the profit margins of foreign products and providing exotic products for customers, followed by other retailers. Regarding distribution system, the company introduced the innovative idea which delivered products ordered by stores directly to each store withboth its own vehicles and its own warehouse in which could deal with dry foods, chilly foods, frozen food, and non-foods, and even, process produce. In addition, Haitai developed many promotional methods to attract more customers like 'the guarantee of the lowest price', and expanded its own business to US in 1996, although withdrew, because of bankruptcy in 1997. Together with POS introduction in 1994, Haitai made a significant contribution to the development of the Korean retailing, influencing other retailers in many aspects. As a case study, the study has provided a number of lessons from Haitai's experiences for academicians and practitioners, suggesting that its history should be involved in the Korean modernised retailing.

• Journal of the Korean association of regional geographers
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• v.7 no.2
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• pp.17-34
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• 2001

This paper is to analyze how firms in a large firm-led industrial city have carried out the restructuring in the face of radical shifts, with focus on the strategy and the restructuring of firms in Ulsan, a typical industrial district in Korea that is specialized in heavy & chemical industry. It has been well known that the local economy has been led by a small number of large firms, including affiliates of chaebol, and its industrial structure has also been characterised as a clear dichotomy between large firms as a customer and small and medium-size firms as a supplier, which can be called not horizontal but vertical relations. It can identify some tendencies, however, that local companies have been rather dynamically changing in response to increasingly turbulent environment since the Asian crisis. Some are radical, but some incremental. These can be summarized in four distinctive but interlinked ways. First, more than half of local companies surveyed have attempted to change their production systems, mainly from the fordist mass production towards the flexible mass production, seeking both economies of scale and scope. Second, local firms have vigorously continued to reorganize the boundary of the production and the organization, by specializing products and focusing on the core competence in order to save costs and cope with radically changing customer demands in a flexible way. Third, there have been various strategies for the organizational innovation such as the introduction of team organization, the boundary blurring between the managerial and production workers and the intra-firm spin-offs, so as to improve managerial efficiency and competence in the use of internal labour market. Finally, they have tried to be more sensitive to the market and customers. These tendencies seem to be increasingly critical to sustain their competitiveness. To do so, they tend to focus increasingly not only on the competing via the product quality rather than through price, but also to seek to diversify the market and customer firms beyond national boundary.

• The Korean Journal of Financial Management
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• v.27 no.1
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• pp.181-208
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• 2010

In July 2007, Korean government has passed "The Capital Market and Financial Investment Services Act" to further develop the capital markets and the Act was to become effective in February 2009. Using a large sample of Korean firms, we have examined (i) the effect of underwriting activities on the firm value (bond spread) comparing commercial bank and investment bank, and (ii) the determinants of the firm value changes following underwriting activities of bank. To test our goal, we collected a wide range of samples of data for bond issuing activities executed by Korean firms listed on the Korea Stock Exchange (KSE) between 2000 and 2003. Our paper is distinguished from previous studies on this subject in a way that we analyzed the effect of corporate bond underwriting activities with regard to commercial banking and investment banking. Initially, we set up a hypothesis that "Certification View" and "Conflict-of-interest View" are major driving forces behind cross-firm differences in performance following bond issuance. We find that, in general, underwriting by investment bank (securities company) brings a positive effect on the firm value (spread between bench mark rate and bond issuing rate). This result indicates that firm value has been negatively affected by the bank underwriting and provides the evidence for "Conflict-of-interest View" in Korea. Our studies have also revealed that any change in firm value following bond issuance is positively related with the firm size (total asset), operating performance, liquidity (cashflow), and equity ownership by foreign investors. Overall, our results support the view that bank underwriting activities can play an important role in determining firm value and financial strategies under "The Capital Market and Financial Investment Services Act" of 2007.

• Journal of Intelligence and Information Systems
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• v.24 no.4
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• pp.1-32
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• 2018

In addition to stakeholders including managers, employees, creditors, and investors of bankrupt companies, corporate defaults have a ripple effect on the local and national economy. Before the Asian financial crisis, the Korean government only analyzed SMEs and tried to improve the forecasting power of a default prediction model, rather than developing various corporate default models. As a result, even large corporations called 'chaebol enterprises' become bankrupt. Even after that, the analysis of past corporate defaults has been focused on specific variables, and when the government restructured immediately after the global financial crisis, they only focused on certain main variables such as 'debt ratio'. A multifaceted study of corporate default prediction models is essential to ensure diverse interests, to avoid situations like the 'Lehman Brothers Case' of the global financial crisis, to avoid total collapse in a single moment. The key variables used in corporate defaults vary over time. This is confirmed by Beaver (1967, 1968) and Altman's (1968) analysis that Deakins'(1972) study shows that the major factors affecting corporate failure have changed. In Grice's (2001) study, the importance of predictive variables was also found through Zmijewski's (1984) and Ohlson's (1980) models. However, the studies that have been carried out in the past use static models. Most of them do not consider the changes that occur in the course of time. Therefore, in order to construct consistent prediction models, it is necessary to compensate the time-dependent bias by means of a time series analysis algorithm reflecting dynamic change. Based on the global financial crisis, which has had a significant impact on Korea, this study is conducted using 10 years of annual corporate data from 2000 to 2009. Data are divided into training data, validation data, and test data respectively, and are divided into 7, 2, and 1 years respectively. In order to construct a consistent bankruptcy model in the flow of time change, we first train a time series deep learning algorithm model using the data before the financial crisis (2000~2006). The parameter tuning of the existing model and the deep learning time series algorithm is conducted with validation data including the financial crisis period (2007~2008). As a result, we construct a model that shows similar pattern to the results of the learning data and shows excellent prediction power. After that, each bankruptcy prediction model is restructured by integrating the learning data and validation data again (2000 ~ 2008), applying the optimal parameters as in the previous validation. Finally, each corporate default prediction model is evaluated and compared using test data (2009) based on the trained models over nine years. Then, the usefulness of the corporate default prediction model based on the deep learning time series algorithm is proved. In addition, by adding the Lasso regression analysis to the existing methods (multiple discriminant analysis, logit model) which select the variables, it is proved that the deep learning time series algorithm model based on the three bundles of variables is useful for robust corporate default prediction. The definition of bankruptcy used is the same as that of Lee (2015). Independent variables include financial information such as financial ratios used in previous studies. Multivariate discriminant analysis, logit model, and Lasso regression model are used to select the optimal variable group. The influence of the Multivariate discriminant analysis model proposed by Altman (1968), the Logit model proposed by Ohlson (1980), the non-time series machine learning algorithms, and the deep learning time series algorithms are compared. In the case of corporate data, there are limitations of 'nonlinear variables', 'multi-collinearity' of variables, and 'lack of data'. While the logit model is nonlinear, the Lasso regression model solves the multi-collinearity problem, and the deep learning time series algorithm using the variable data generation method complements the lack of data. Big Data Technology, a leading technology in the future, is moving from simple human analysis, to automated AI analysis, and finally towards future intertwined AI applications. Although the study of the corporate default prediction model using the time series algorithm is still in its early stages, deep learning algorithm is much faster than regression analysis at corporate default prediction modeling. Also, it is more effective on prediction power. Through the Fourth Industrial Revolution, the current government and other overseas governments are working hard to integrate the system in everyday life of their nation and society. Yet the field of deep learning time series research for the financial industry is still insufficient. This is an initial study on deep learning time series algorithm analysis of corporate defaults. Therefore it is hoped that it will be used as a comparative analysis data for non-specialists who start a study combining financial data and deep learning time series algorithm.