• Journal of Digital Convergence
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• v.19 no.3
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• pp.127-135
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• 2021

We analyze the information asymmetry in the capital market by examining the long-term performance by the insider's trading behavior in the companies that made investment announcements for the construction of the new office building. The results are summarized as follows. On average, the long-term abnormal returns on share prices of sample firms represent a significant positive value. The regression analysis confirmed that there is a statistically significant positive correlation between the factor of the change in equity of large shareholders and the long-term performance. On the other hand, negative correlation was observed between change in equity of small individual investors and long-term performance. These results mean that an insider can determine the authenticity of a manager's private intention. In other words, it supports that the insider is in a position of information superiority. In addition, it is expected to provide practical usefulness to investors in that the change in equity can be used as a predictor of long-term performance.

• Journal of Intelligence and Information Systems
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• v.25 no.2
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• pp.99-122
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• 2019

According to the recent IDC (International Data Corporation) report, as from 2025, the total volume of data is estimated to reach ten times higher than that of 2016, corresponding to 163 zettabytes. then the main body of generating information is moving more toward corporations than consumers. So-called "the wave of Big-data" is arriving, and the following aftermath affects entire industries and firms, respectively and collectively. Therefore, effective management of vast amounts of data is more important than ever in terms of the firm. However, there have been no previous studies that measure the effects of big data investment, even though there are number of previous studies that quantitatively the effects of IT investment. Therefore, we quantitatively analyze the Big-data investment effects, which assists firm's investment decision making. This study applied the Event Study Methodology, which is based on the efficient market hypothesis as the theoretical basis, to measure the effect of the big data investment of firms on the response of market investors. In addition, five sub-variables were set to analyze this effect in more depth: the contents are firm size classification, industry classification (finance and ICT), investment completion classification, and vendor existence classification. To measure the impact of Big data investment announcements, Data from 91 announcements from 2010 to 2017 were used as data, and the effect of investment was more empirically observed by observing changes in corporate value immediately after the disclosure. This study collected data on Big Data Investment related to Naver 's' News' category, the largest portal site in Korea. In addition, when selecting the target companies, we extracted the disclosures of listed companies in the KOSPI and KOSDAQ market. During the collection process, the search keywords were searched through the keywords 'Big data construction', 'Big data introduction', 'Big data investment', 'Big data order', and 'Big data development'. The results of the empirically proved analysis are as follows. First, we found that the market value of 91 publicly listed firms, who announced Big-data investment, increased by 0.92%. In particular, we can see that the market value of finance firms, non-ICT firms, small-cap firms are significantly increased. This result can be interpreted as the market investors perceive positively the big data investment of the enterprise, allowing market investors to better understand the company's big data investment. Second, statistical demonstration that the market value of financial firms and non - ICT firms increases after Big data investment announcement is proved statistically. Third, this study measured the effect of big data investment by dividing by company size and classified it into the top 30% and the bottom 30% of company size standard (market capitalization) without measuring the median value. To maximize the difference. The analysis showed that the investment effect of small sample companies was greater, and the difference between the two groups was also clear. Fourth, one of the most significant features of this study is that the Big Data Investment announcements are classified and structured according to vendor status. We have shown that the investment effect of a group with vendor involvement (with or without a vendor) is very large, indicating that market investors are very positive about the involvement of big data specialist vendors. Lastly but not least, it is also interesting that market investors are evaluating investment more positively at the time of the Big data Investment announcement, which is scheduled to be built rather than completed. Applying this to the industry, it would be effective for a company to make a disclosure when it decided to invest in big data in terms of increasing the market value. Our study has an academic implication, as prior research looked for the impact of Big-data investment has been nonexistent. This study also has a practical implication in that it can be a practical reference material for business decision makers considering big data investment.

• Journal of Navigation and Port Research
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• v.34 no.10
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• pp.823-831
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• 2010

The shipping business industries which are the transport and logistics intensive in Busan, have been small sized and scattered in many areas, that is why they could not create a synergy effect utterly. Because of the need to develop the shipping business cluster complex in Busan in order to concentrate those industries and attract high value added firms, this study tries to suggest an approach to build the cluster. Firstly, how various shipping business related firms in Busan and capital area demand the cluster complex are searched through questioning survey. Secondly, the gradual scheme to integrate lots of business companies, governmental authorities and educational institutes and global strategy to invite domestic and foreign organizations in Myeong Ji area near to Busan New Port. Thirdly, the expected economic benefits of the cluster construction are calculated quantitatively.

• Korean Journal of Construction Engineering and Management
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• v.10 no.5
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• pp.135-147
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• 2009

The world has witnessed the dramatic expansion of international construction markets during the last decades, particularly around the developing economies and energy resource-rich countries. However, despite the booming markets, the risks of emerging regions have also increased under the rapidly changing environments confronting the global contractors. Most of all, success in overseas business mainly depends on selecting the right market to enter. Accordingly, the right market selection requires global firms to carefully carry out the scientific market entry decision by evaluating country risks, market prospects, firm's capability, level of competition, and among others. This study aims at developing a market entry model by the use of real option analysis (ROA) and scenario planning, which addresses the corporate strategic flexibility against the uncertainties encompassing the overseas construction markets. Based on the suggested approach, global contractors are expected to make a better decision rather than a typically static approach in pursuing, postponing, or abandoning a prospective market to their capacity with a concurrent consideration of uncertainties as well as its option value.

• 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.

• Asia pacific journal of information systems
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• v.19 no.3
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• pp.151-179
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• 2009

The purpose of this study is to make an empirical analysis on the factors affecting the intention for the continuous use of the BSC system, which has been recently spread in the public sector. Because the object of acceptance is the performance management system based on BSC (Balanced Scorecard) implemented in the form of information systems, this study proposes a research model by applying TAM (Technology Acceptance Model). Independent variables are factors affecting the acceptance of BSC system such as training, communication, IS support, CEO support and personal innovativeness, and we examine their effects on the dependent variable, namely, intention on continuous use via mediating variables: perceived usefulness and perceived ease of use. A questionnaire survey was conducted with public institutions(firms) that had introduced and were operating the BSC system; 264 valid questionnaires are adopted. Collected data are analyzed using SPSS 16.0 and AMOS 7.0. Results of reliability test show that all analyzed data are reliable. In validity test, one item regarding communication was excluded; 9 latent variables and 34 observed variables are used in the final analysis. Based on the results of the hypothesis test through path analysis using a structural equation model, 10 out of 16 hypotheses are accepted. Factors affecting perceived usefulness are training and IS(Information System) support. The analysis results indicate that perceived ease of use is mainly affected by IS support, CEO support, and personal innovativeness among the factors related to the acceptance of the BSC system. This suggests that, contrary to the expectation that the BSC system may be used without difficulty, the management's active support is required in order to attain expected improvement in productivity and work efficiency. This was also pointed out in case studies on the construction of the BSC system in public sectors. On the other hand, perceived ease of use is found to affect perceived usefulness. This supports the results of previous researches on TAM. Perceived ease of use and perceived usefulness are found to affect the attitude towards the use of the system. The intention on continuous use is affected more by perceived usefulness than by the attitude towards the use of system. This result supports the results of previous researches on TAM, showing that the BSC system is utilized substantially in worksites. This study is considered meaningful in that it was actually performed on users at public institutions(firms) that had introduced the BSC system and that it empirically tested hypotheses on the acceptance of the BSC system by applying TAM to the research model.

• Korean Journal of Construction Engineering and Management
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• v.24 no.5
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• pp.52-62
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• 2023

The objective of this study is to propose an automated algorithm for precise cutting length of slab rebar complying with regulations such as anchorage length, standard hooks, and lapping length. This algorithm aims to improve the traditional manual quantity take-off process typically outsourced by external contractors. By providing accurate rebar quantity data at BBS(Bar Bending Schedule) level from the bidding phase, uncertainty in quantity take-off can be eliminated and reliance on out-sourcing reduced. In addition, the algorithm allows for early determination of precise quantities, enabling construction firms to preapre competitive and optimized bids, leading to increased profit margins during contract negotiations. The proposed algorithm not only streamlines redundant tasks across various processes, including estimating, budgeting, and BBS generation but also offers flexibility in handling post-contract structural drawing changes. In particular, the proposed algorithm, when combined with BIM, can solve the technical problems of using BIM in the early phases of construction, and the algorithm's formulas and shape codes that built as REVIT-based family files, can help saving time and manpower.

• Journal of Information Technology Applications and Management
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• v.15 no.4
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• pp.159-188
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• 2008

This study suggests strategies which can enable to creation of new opportunities of competitive advantages while operating a long lasting and consistent business with major trading partners, based on interorganizational information systems (IOISs) specially established and installed for interorganizational transactions. Nowadays, IOISs based mechanism having been widely expanded as a conventional business infrastructure for the interorganizational transactions and/or exchanges, it is customary difficult to obtain any strongly sound advantage over the competitors who have adopted even the simplest deployment of the IOIS mechanisms. In this connection, this study intends to investigate the interorganizational collaborative activities conducted by under the auspicious of IOISs, focused on the prospect of the exploitation of IOISs rather than the implementation of the IOISs. In this study, we, firstly, suggest the concept of Electronic Collaboration which can be defined by the collaborative activities conducted by IOISs, compared to the ones conducted on off-line. In addition, we suggest the Electronic Collaboration as a multi-dimensional concept, constituted by three sub-constructs, the Electronic Information Sharing (EIS), the Electronic Joint Activity (EJA), and the construction of the Electronic Relational Knowledge Store (ERKS). Secondly, we empirically verify the effects of relational and environmental determinants on the Electronic Collaboration. In this study, the relational determinants relate to the variables created in interorganizational relationship like Trust, Influence, Relational Specific Asset-asset invested for the transaction-, and Continuity of the relationship. On the other hand, the environmental determinants relate to the variables surrounding the relationship which are difficult to control. We consider Product Complexity, Technological Uncertainty, and Market Variability as the domain of the environmental determinants. To test our hypotheses, we conducted both paper-based survey and online-based survey. After refining the data with missing responses, a total of 150 data was used for analysis. The results were as follows : Firstly, it is statistically significant that the Electronic Collaboration is composed of EIS, EJA, and ERKS. In particular, the results imply that the firms are able to accumulate relational knowledge base as well as to exchange information or knowledge, and to conduct joint activities through effort to further expand the Electronic Collaboration. Secondly, we have verified the individual effects of the relational and the environmental determinants on the Electronic Collaboration. Product Complexity has been revealed as the most influential variable affecting the Electronic Collaboration. Next, Interorganizational Trust and Technological Uncertainty, in that order, have been seen to have significant effects on the Electronic Collaboration. In other words, when products or services seem to be difficult to standardize, and the core technologies seem to rapidly change, the need for the Electronic Collaboration increase. In addition, the observation dictates that the interorganizational trust turns out to be a critical variable in building a relationship and in seeking further collaboration. The results, further, illustrate that the environmental determinants are relatively more effective than the relational determinants, which is not consistent with a few prior researches relational determinants emphasized. It is because this study doesn't consider the size of the firm. A few researchers have given an emphasis on the relational determinants like trust and influence, especially from the perspective of small firms in interorganizational relationship. However, in our study, where all the sizes of the firms are contained, electronic collaboration is considerably affected by the environmental determinants.

• The Journal of Asian Finance, Economics and Business
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• v.7 no.3
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• pp.311-322
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• 2020

This study aims to examine curvilinearly moderating effects of task interdependence on the relationship between demographic faultlines and group performance. It posits that the degree of task interdependence has an impact on the effects of demographic faultlines. It was conducted in six organizations in Korea, their industries including heavy industries, hospital, construction, petrochemical, fine chemicals, and system integration. The survey was distributed to 1330 individuals in 162 teams and 1082 individuals in 137 teams responded to the questionnaire. To test the hypotheses including nonlinear interactions, we conducted a hierarchical regression analysis to the survey data from 82 groups within six firms in Korea. The results show that for groups that experience a high level of task interdependence, the slope for the regression of demographic faultlines on group performance is comparatively low and, at the low level of task interdependence, insignificant. However, at intermediate levels of task interdependence, the association was strongly negative and significant. This study finds that the negative relationship between demographic faultlines and group performance is stronger when task interdependence is moderate than when task interdependence is high or low. Therefore, managers should pay attention to optimal group design by carefully assigning tasks in diverse and divided groups.

• Knowledge Management Research
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• v.18 no.3
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• pp.1-35
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• 2017

This study conducted comparative study on autonomous car's industry policy and corporate strategy of US, China, Germany, Japan and Korea. By analyzing core technologies and industry paradigm shifts of autonomous car industry, I was able to figure out autonomous car has high potential to be dominant transportation in the future and it is important to construct core competency in technology area. The meaningful findings by analyzing various primary and secondary data are as followings: First, in case of US, Google was leading autonomous car industry by developing its own OS and Platform. US government has been actively supporting and interacting with private firms and Universities for stimulating industry/technology convergence and establishing standard. Second, in case of Germany, autonomous car development was leading by several auto makers such as Mercedes, BMW in Hardware and manufacturing area, and German government was focusing on deregulations for private company. Third, in case of Japan which quite similar with German situation, they were both independently developing technology and expanding alliances with MNCs. And Japanese government was supporting triple helix system construction between local companies and universities. Fourth, in case of China, autonomous car industry was leading by IT companies, and various cooperations between IT companies and automakers were established. Chinese government was regulating foreign companies and supporting domestic companies both in market and technologies Last, in Korean case, the active and extensive alliances were lacking in Korean companies while strategic and strong government supports were missing in public sector. For competing with other countries and players, more active collaboration between different countries and strong policy supports are needed in Korean auto industry.