• Journal of the Korea Institute of Building Construction
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• v.23 no.3
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• pp.305-313
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• 2023

With an increasing interest in the commercialization of research results in the present societal climate, especially in the construction industry, preliminary product analysis plays a critical role when introducing a new product to the market. It significantly influences the product's success or failure. In this context, this study aims to investigate the utility of Importance-Performance Analysis (IPA) as a management strategy tool for preliminary analysis in the commercialization of new architectural technologies. The study specifically assesses a smart ball product engineered for pipeline inspection. The evaluation is carried out based on product quality, convenience, and usability categories. Seventeen factors are recognized as sub-items, and a survey is conducted among relevant experts and consumer groups. From the survey, four key items are chosen: "Keep up the good work," "Concentrate here," "Low priority," and "Possible overkill." Suitable strategic measures are derived for each item. By conducting a correlation analysis between product importance and performance, this study offers a method to establish priority directions for future development. This analysis assists in identifying areas that necessitate improvement or additional focus to increase the product's commercial potential. On the whole, this study contributes to understanding and applying Importance-Performance Analysis as a valuable tool in the preliminary analysis and commercialization of novel technologies in the field of architecture.

• 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.

• Asian Pacific Journal of Cancer Prevention
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• v.14 no.12
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• pp.7459-7466
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• 2013

Background: Some 60 years after introduction of the Papanicolaou smear worldwide, cervical cancer remains a burden in developing countries where >85% of world new cases and deaths occur, suggesting a failure to establish comprehensive cervical-cancer control programs. Effective interventions are available to control cervical cancer but are not all affordable in low-income settings. Disease awareness saves lives by risk-reduction as witnessed in reducing mortality of HIV/AIDS and smoking-related cancers. Subjects and Methods: We initiated a community-based awareness program on cervical cancer in two low-income Muslim Uyghur townships in Kashi (Kashgar) Prefecture, Xinjiang, China in 2008. The education involved more than 5,000 women from two rural townships and awareness was then evaluated in 2010 and 2011, respectively, using a questionnaire with 10 basic knowledge questions on cervical cancer. Demographic information was also collected and included in an EpiData database. A 10-point scoring system was used to score the awareness. Results: The effectiveness and feasibility of the program were evaluated among 4,475 women aged 19-70 years, of whom >92% lived on/below US$1.00/day. Women without prior education showed a poor average awareness rate of 6.4% (164/2,559). A onetime education intervention, however, sharply raised the awareness rate by 4-fold to 25.5% (493/1,916). Importantly, low income and illiteracy were two reliable factors affecting awareness before or after education intervention. Conclusions: Education intervention can significantly raise the awareness of cervical cancer in low-income women. Economic development and compulsory education are two important solutions in raising general disease awareness. We propose that implementing community-based awareness programs against cervical cancer is realistic, locally affordable and sustainable in low-income countries, which may save many lives over time and, importantly, will facilitate the integration of comprehensive programs when feasible. In this context, adopting this strategy may provide one good example of how to achieve "good health at low cost".

• Management & Information Systems Review
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• v.36 no.3
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• pp.219-237
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• 2017

Looking into the top-five innovative sectors in Korea's manufacturing and service industries, this study empirically analyzes the effect of innovation performance on R&D investment, which is one of the most important strategic decisions for corporate management. In the midst of an uncertain business environment, R&D investment has been regarded as the most important strategic decision making in corporate management related to innovation. Corporate management, however, tend to be reluctant to make sufficient R&D investment due to the risk of an investment failure. Therefore, having R&D investment by offsetting this risk has been deemed as a key task for corporate management. However, prior studies have failed to identify which factors affect companies' strategic decision making on R&D investment. This study is to remedy this weakness of prior study. Relying on path dependency theory at organization-level and dominant logic at individual-level, this study empirically examines the multiple regression model, which sees entrepreneurial orientation as a positive mediator between innovation performance and R&D investment. The results found in the analysis of 242 local companies in the manufacturing and service sectors represent that innovation performance has a direct and positive effect on R&D investment, while it indirectly affects R&D investment through the mediating roles of entrepreneurial orientation. They also revealed that innovation performance had a meaningful impact on entrepreneurial orientation, which is an inclination to seek innovation, led to R&D investment. The founding of this study imply that innovation performance in the past affects innovation strategies in the future, and such a relationship could be strengthened by entrepreneurial orientation as the dominant logic of corporate management.

• Korean small business review
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• v.31 no.1
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• pp.73-88
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• 2009

This study examines the characteristics of 24 Korean hidden champions such as key success factors, core competences, strategic problems, and desirable future directions. The study categorized them into 8 types with Danny Miller's four trajectories and top manager's decision making style(rationality and passion). Danny Miller argued in his book, Icarus paradox, that outstanding firms will extend their orientations until they reach dangerous extremes and their momentum will result in common trajectories of decline. He suggested four very common success types: Craftsmen, Builders, Pioneers, Salesmen. He also suggested common trajectories of decline:Focusing(from Craftsmen to Tinkers), Venturing(from Builders to Imperialists), Inventing(from Pioneers to Escapists), Decoupling(from Salesmen to Drifts). In Korea, successful startups appear to possess three kinds of drive: Technology-drive, Vision-drive, Market-drive. Successful technology-driven firms tend to grow as craftsmen or pioneers. Successful vision-driven and market-driven ones tend to grow as builders and salesmen respectively. Korean top managers or founders seem to have two kinds of decision making style: Passion-based and Rationality-bases. Passion-based(passionate) entrepreneurs are biased towards action or proactiveness in competing and getting things done. Rationality- based ones tend to emphasis the effort devoted to scanning and analysing information to better understand a company's threats, opportunities and options. Consequently this study suggested 4*2 types of Korean hidden champions: (1) passionate craftsmen, (2) rational craftsmen, (3) passionate builders, (4) rational builders, (5) passionate pioneers, (6) rational pioneers, (7) passionate salesmen, (8) rational salesmen. These 8 type firms showed different success stories and appeared to possess different trajectories of decline. These hidden champions have acquired competitive advantage within domestic or globally niche markets in spite of the weak market power and lack of internal resources. They have maintained their sustainable competitiveness by utilizing three types of growth strategy; (1) penetrating into the global market, (2) exploring new service market, (3) occupying the domestic market. According to the types of growth strategy, these firms showed different financial outcomes and possessed different issues for maintaining their competitiveness. This study found that Korean hidden champions were facing serious challenges from the transforming economic structure these days and possessed the decline potential from their success momentum or self-complacence. It argues that they need to take a new growth engine not to decline in the turbulent environment. It also discusses how firms overcome the economic crisis and find a new business area in promising industries for the future. It summarized the recent policy of Korean government called as "Green Growth" and discussed how small firms utilize such benefits and supports from the government. Other implications for firm strategies and governmental policies were discussed.

• 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.