• Journal of the Korea Institute of Building Construction
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• v.18 no.2
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• pp.151-159
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• 2018

Accident rate in the construction industry of South Korea is increasing every year, and it represents the highest percentage among industries. This shows that activities performed to prevent safety accidents in the country are not efficient when it comes to reduce the accident rate. In order to resolve this issue, a model for the prediction of human accidents should be established. In addition, it is required a quantification study based on pattern of human accidents. Therefore, the objective of this study is to quantify uncertainty of human accidents risk and predict how to change in various circumstances by using Monte Carlo Simulation. To achieve the objective, first, pattern of human accidents was defined. Second, insurance claim payout and information of human accidents during 14 years in construction site were collected. Third, descriptive analysis is conducted to determine the characteristics of the accident pattern. Fourth, to quantitatively analyze the pattern of the human accidents, the population of each accident occurrence and payout were estimated. Finally, estimated populations was analyzed according to characteristics of distribution by using Monte carlo simulation. In the future, this study can be used as a reference for developing the safety management checklist in construction site and development of prediction models of human accident.

• Journal of the Korea Institute of Building Construction
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• v.20 no.1
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• pp.83-91
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• 2020

The recent bridge construction projects is demanded more sophisticated risk management measures and loss forecasts to brace for risk losses from an increase in the trend of bridge construction. This study aims to analyze the risk factors that caused the loss of material in actual bridge construction and to develop a quantified predictive loss model, based on the past record of insurance payment by major domestic insurance companies for bridge construction projects. For the development of quantitative bridge construction loss model, the dependent variable was selected as the loss ratio, i.e., the ratio of insurance payout divided by the total project cost, while the independent variable adopted 1) Technical factors: superstructure type, foundation type, construction method, and bridge length 2) Natural hazards: typhoon and flood 3) Project information: construction period and total project cost. Among the selected independent variables, superstructure type, construction method, and project period were shown to affect the ratio of bridge construction losses. The results of this study can provide government agencies, bridge construction design and construction and insurance companies with the quantitative damage prediction and risk assessment services, using risk indicators and loss prediction functions derived from the findings of this study and can be used as a guideline for future basic bridge risk assessment development research.

• Korean Journal of Construction Engineering and Management
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• v.15 no.4
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• pp.128-138
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• 2014

Development of infrastructure component libraries is a critical requirement for the accelerated adoption of BIM in the civil engineering sector. Libraries reduce the time for BIM model creation, allows accurate quantity take offs, and shared use of standard models in a project. However, such libraries are currently in very short supply in the domestic infrastructure domain. This research introduces library components for retaining walls and box culverts generated from 2D standard drawings made publicly available by MOLIT. Commercial BIM software was used to create the concrete geometry and rebar, and dimensional/volumetric parameters were defined to maximize the reuse and generality of the libraries. Use of the these libraries in a project context demonstrates that they allow accurate and quick quantity take offs, and easier management of geometric information through the use of a single library as to numerous 2D drawings. It also demonstrates the easy modification of the geometries of the components if and when they need to changed. However, the application also showed that some of the rebar components (stirrups and length wise rebars) do not get properly updated when concrete geometries are changed, demonstrating the limits of current software applications. The research provides evidence of the many advantages of using BIM libraries in the civil engineering, thus providing the incentive for further development of standard libraries and promoting the use of BIM in infrastructure projects.

• Asia pacific journal of information systems
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• v.24 no.1
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• pp.31-50
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• 2014

The ubiquitous accessibility of information through mobile devices has led to an increased mobility of workers from their fixed workplaces. Market researchers estimate that by 2016, 350 million workers will be using their smartphones for business purposes, and the use of smartphones will offer new business benefits. Enterprises are now adopting mobile technologies for numerous applications to increase their operational efficiency, improve their responsiveness and competitiveness, and cultivate their innovativeness. For these reasons, various organizational aspects concerning "mobile work" have received a great deal of recent attention. Moreover, many CIOs plan to allocate a considerable amount of their budgets mobile work environments. In particular, with the consumerization of information technology, enterprise mobile applications (EMA) have played a significant role in the explosive growth of mobile computing in the workplace, and even in improving sales for firms in this field. EMA can be defined as mobile technologies and role-based applications, as companies design them for specific roles and functions in organizations. Technically, EMA can be defined as business enterprise systems, including critical business functions that enable users to access enterprise systems via wireless mobile devices, such as smartphones or tablets. Specifically, EMA enables employees to have greater access to real-time information, and provides them with simple features and functionalities that are easy for them to complete specific tasks. While the impact of EMA on organizational workers' productivity has been given considerable attention in various literatures, relatively little research effort has been made to examine how EMA actually lead to users' job performance. In particular, we have a limited understanding of what the key antecedents are of such an EMA usage outcome. In this paper, we focus on employees' perceived job performance as the outcome of EMA use, which indicates the successful role of EMA with regard to employees' tasks. Thus, to develop a deeper understanding of the relationship among EMA, its environment, and employees' perceived job performance, we develop a comprehensive model that considers the perceived-fit between EMA and employees' tasks, satisfaction on EMA, and the organizational environment. With this model, we try to examine EMA to explain how job performance through EMA is revealed from both the task-technology fit for EMA and satisfaction on EMA, while also considering the antecedent factors for these constructs. The objectives of this study are to address the following research questions: (1) How can employees successfully manage EMA in order to enhance their perceived job performance? (2) What internal and/or external factors are important antecedents in increasing EMA users' satisfaction on MES and task-technology fit for EMA? (3) What are the impacts of organizational (e.g. organizational agility), and task-related antecedents (e.g., task mobility) on task-technology fit for EMA? (4) What are the impacts of internal (e.g., self-efficacy) and external antecedents (e.g., system reputation) for the habitual use of EMA? Based on a survey from 254 actual employees who use EMA in their workplace across industries, our results indicate that task-technology fit for EMA and satisfaction on EMA are positively associated with job performance. We also identify task mobility, organizational agility, and system accessibility that are found to be positively associated with task-technology fit for EMA. Further, we find that external factor, such as the reputation of EMA, and internal factor, such as self-efficacy for EMA that are found to be positively associated with the satisfaction of EMA. The present findings enable researchers and practitioners to understand the role of EMA, which facilitates organizational workers' efficient work processes, as well as the importance of task-technology fit for EMA. Our model provides a new set of antecedents and consequence variables for a TAM involving mobile applications. The research model also provides empirical evidence that EMA are important mobile services that positively influence individuals' performance. Our findings suggest that perceived organizational agility and task mobility do have a significant influence on task-technology fit for EMA usage through positive beliefs about EMA, that self-efficacy and system reputation can also influence individuals' satisfaction on EMA, and that these factors are important contingent factors for the impact of system satisfaction and perceived job performance. Our findings can help managers gauge the impact of EMA in terms of its contribution to job performance. Our results provide an explanation as to why many firms have recently adopted EMA for efficient business processes and productivity support. Our findings additionally suggest that the cognitive fit between task and technology can be an important requirement for the productivity support of EMA. Further, our study findings can help managers in formulating their strategies and building organizational culture that can affect employees perceived job performance. Managers, thus, can tailor their dependence on EMA as high or low, depending on their task's characteristics, to maximize the job performance in the workplace. Overall, this study strengthens our knowledge regarding the impact of mobile applications in organizational contexts, technology acceptance and the role of task characteristics. To conclude, we hope that our research inspires future studies exploring digital productivity in the workplace and/or taking the role of EMA into account for employee job performance.

• Asia pacific journal of information systems
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• v.19 no.1
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• pp.147-165
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• 2009

Virtual community(VC) will increasingly be organized as commercial enterprises, with the objective of earning an attractive financial return by providing members with valuable resources and environment. For example, Cyworld.com in Korea uses several community services to enable customers of Cyworld to take control of their own value as potential purchasers of products and services. Although initial adoption is important for online network service success, it does not necessarily result in the desired managerial performance unless the initial usage is continuously related to the continuous usage and purchase. Particularly, the customer who receives relevant online services and is well equipped with online network services, will trust the online service provider and perceive less risk and experience more activities such as continuous usage and purchase. Thus, how to promote continued online service usage or, alternatively, how to prevent discontinuance is a critical issue for VC service providers to consider. By aggregating a wide range of information and online environments for customers and providing trust to its members, the service providers of virtual communities help to reduce the perceived risk of continuous usage and purchase. Drill down, online service managers realize that achieving strong and sustained customers who continuously use online service and purchase on it is crucial. Therefore, the research into this online service continuance will identify the relationship between the initial usage and the continuous usage and purchase. The research of continuous usage or post adoption has recently emerged as an important issue in the IS literature. Individuals' information systems(IS) continuous usage decisions are congruent with consumers' repeat purchase decisions. The TAM(Technology Acceptance Model) paradigm has been strongly confirmed across a wide range from product purchase on EC to online service usage contexts. The analysis of IS usage based on TAM has proven to be successful across almost online service contexts. However, most of previous studies have focused on only an area (i.e., VC or EC). Just little research has tried to analyze the relationship between VC and EC. The effect of some factors on user intention, captured through several theories such as TAM, has been demonstrated. Yet, few studies have explored the salient relationships of VC users' EC acceptance. To fill this gap between VC and EC research, this paper attempts to develop a research model that extends the TAM perspective in view of the additional contributions of trust in the service provider and trust in members on some factors that affect EC and VC adoption. In this extension, we applied the TAM-to-TAM(T2T) model, and analyzed the transfer effect of trust between these two TAMs. The research model was empirically tested on the context of a social network service. The model was to extend TAM with the trust concept for the virtual community environment from the perspective of tasks. By building an extended model of TAM and examining the relationships between trust and the existing variables of TAM, it is aimed to explain a user's continuous intention to use VC and purchase on EC. The unit of analysis in this paper is an individual user of a virtual community. The population of interest is the individual with the experiences in virtual community. The data for this paper was made available via a Web survey of VC users. In total, 281 cases were gathered for about one week, but there were some missing values in the sample and there were some inappropriate cases. Thus, only 248 cases were finally analyzed. We chose the structural equation analysis to test the hypotheses and it is better suited for explaining complex relationships than the other methods. In this test, AMOS was used to test the Structural Equation Model (SEM). Noticeable results have been found in the T2T model regarding the factors affecting the intention to use of virtual community and loyalty. Our result showed that trust transfer plays a key role in forming the two adoption beliefs. Overall, this study preliminarily confirms the salience of trust transfer in online service.

• Industry Promotion Research
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• v.8 no.3
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• pp.181-199
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• 2023

E-commerce is gradually upgrading from traditional text and image formats to short video and livestreaming formats. Livestreaming e-commerce enriches the content and forms of information dissemination and product display, enhances the consumer's shopping experience, and gradually becomes the mainstream new consumer scene. However, there are many negative phenomena in the development of livestreaming e-commerce, such as false propaganda, counterfeit goods, and various negative events, which seriously affect the level of consumer trust in livestreaming e-commerce. Trust is the core competitive factor of livestreaming e-commerce. Based on previous research on trust theory and combined with the characteristic elements of "people, goods, and scenes" of livestreaming e-commerce, this article constructs a trust model for livestreaming e-commerce, proposes hypotheses, and proves through empirical research that factors such as store characteristics, livestream host characteristics, brand image, product information, platform reputation, livestreaming situation, and trust tendency have a significant positive impact on consumer trust. Based on the research conclusions, this article provides insights and management suggestions, such as emphasizing the construction of store characteristic indicators, creating desirable livestream host characteristics, focusing on product brand building and selection, maintaining the display of product information, selecting suitable livestreaming platforms, and creating rich content for livestreaming situations.

• Management & Information Systems Review
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• v.33 no.1
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• pp.45-63
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• 2014

A trading system is a computer trading program that automatically submits trades to an exchange. Mechanical a trading system to execute trade is spreading in the stock market. However, a trading system to trade a single asset might occur instability of the profit because payoff of this system is determined a asset movement. Therefore, it is necessary to develop a trading system that is trade two assets such as a pair trading that is to sell overvalued assets and buy the undervalued ones. The aim of this study is to propose a relative value based trading system designed to yield stable and profitable profits regardless of market conditions. In fact, we propose a procedure for building a trading system that is based on the rough set analysis of indicators derived from a price ratio between two assets. KOSPI 200 index futures and S&P 500 index futures are used as a data for evaluation of the proposed trading system. We intend to examine the usefulness of this model through an empirical study.

• Journal of Intelligence and Information Systems
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• v.24 no.2
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• pp.243-264
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• 2018

Although the worldwide battery market is recently spurring the development of lithium secondary battery, lead acid batteries (rechargeable batteries) which have good-performance and can be reused are consumed in a wide range of industry fields. However, lead-acid batteries have a serious problem in that deterioration of a battery makes progress quickly in the presence of that degradation of only one cell among several cells which is packed in a battery begins. To overcome this problem, previous researches have attempted to identify the mechanism of deterioration of a battery in many ways. However, most of previous researches have used data obtained in a laboratory to analyze the mechanism of deterioration of a battery but not used data obtained in a real world. The usage of real data can increase the feasibility and the applicability of the findings of a research. Therefore, this study aims to develop a model which predicts the battery deterioration using data obtained in real world. To this end, we collected data which presents change of battery state by attaching sensors enabling to monitor the battery condition in real time to dozens of golf carts operated in the real golf field. As a result, total 16,883 samples were obtained. And then, we developed a model which predicts a precursor phenomenon representing deterioration of a battery by analyzing the data collected from the sensors using machine learning techniques. As initial independent variables, we used 1) inbound time of a cart, 2) outbound time of a cart, 3) duration(from outbound time to charge time), 4) charge amount, 5) used amount, 6) charge efficiency, 7) lowest temperature of battery cell 1 to 6, 8) lowest voltage of battery cell 1 to 6, 9) highest voltage of battery cell 1 to 6, 10) voltage of battery cell 1 to 6 at the beginning of operation, 11) voltage of battery cell 1 to 6 at the end of charge, 12) used amount of battery cell 1 to 6 during operation, 13) used amount of battery during operation(Max-Min), 14) duration of battery use, and 15) highest current during operation. Since the values of the independent variables, lowest temperature of battery cell 1 to 6, lowest voltage of battery cell 1 to 6, highest voltage of battery cell 1 to 6, voltage of battery cell 1 to 6 at the beginning of operation, voltage of battery cell 1 to 6 at the end of charge, and used amount of battery cell 1 to 6 during operation are similar to that of each battery cell, we conducted principal component analysis using verimax orthogonal rotation in order to mitigate the multiple collinearity problem. According to the results, we made new variables by averaging the values of independent variables clustered together, and used them as final independent variables instead of origin variables, thereby reducing the dimension. We used decision tree, logistic regression, Bayesian network as algorithms for building prediction models. And also, we built prediction models using the bagging of each of them, the boosting of each of them, and RandomForest. Experimental results show that the prediction model using the bagging of decision tree yields the best accuracy of 89.3923%. This study has some limitations in that the additional variables which affect the deterioration of battery such as weather (temperature, humidity) and driving habits, did not considered, therefore, we would like to consider the them in the future research. However, the battery deterioration prediction model proposed in the present study is expected to enable effective and efficient management of battery used in the real filed by dramatically and to reduce the cost caused by not detecting battery deterioration accordingly.

• Journal of Intelligence and Information Systems
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• v.19 no.2
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• pp.55-71
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• 2013

The prediction model is the main factor affecting the performance of a knowledge-based system for bankruptcy prediction. Earlier studies on prediction modeling have focused on the building of a single best model using statistical and artificial intelligence techniques. However, since the mid-1980s, integration of multiple techniques (hybrid techniques) and, by extension, combinations of the outputs of several models (ensemble techniques) have, according to the experimental results, generally outperformed individual models. An ensemble is a technique that constructs a set of multiple models, combines their outputs, and produces one final prediction. The way in which the outputs of ensemble members are combined is one of the important issues affecting prediction accuracy. A variety of combination schemes have been proposed in order to improve prediction performance in ensembles. Each combination scheme has advantages and limitations, and can be influenced by domain and circumstance. Accordingly, decisions on the most appropriate combination scheme in a given domain and contingency are very difficult. This paper proposes a confidence-based selection approach as part of an ensemble bankruptcy-prediction scheme that can measure unified confidence, even if ensemble members produce different types of continuous-valued outputs. The present experimental results show that when varying the number of models to combine, according to the creation type of ensemble members, the proposed combination method offers the best performance in the ensemble having the largest number of models, even when compared with the methods most often employed in bankruptcy prediction.

• Journal of Intelligence and Information Systems
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• v.18 no.4
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• pp.59-77
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• 2012

This study focuses on predicting which firms will increase capital by issuing new stocks in the near future. Many stakeholders, including banks, credit rating agencies and investors, performs a variety of analyses for firms' growth, profitability, stability, activity, productivity, etc., and regularly report the firms' financial analysis indices. In the paper, we develop predictive models for rights issues using these financial analysis indices and data mining techniques. This study approaches to building the predictive models from the perspective of two different analyses. The first is the analysis period. We divide the analysis period into before and after the IMF financial crisis, and examine whether there is the difference between the two periods. The second is the prediction time. In order to predict when firms increase capital by issuing new stocks, the prediction time is categorized as one year, two years and three years later. Therefore Total six prediction models are developed and analyzed. In this paper, we employ the decision tree technique to build the prediction models for rights issues. The decision tree is the most widely used prediction method which builds decision trees to label or categorize cases into a set of known classes. In contrast to neural networks, logistic regression and SVM, decision tree techniques are well suited for high-dimensional applications and have strong explanation capabilities. There are well-known decision tree induction algorithms such as CHAID, CART, QUEST, C5.0, etc. Among them, we use C5.0 algorithm which is the most recently developed algorithm and yields performance better than other algorithms. We obtained data for the rights issue and financial analysis from TS2000 of Korea Listed Companies Association. A record of financial analysis data is consisted of 89 variables which include 9 growth indices, 30 profitability indices, 23 stability indices, 6 activity indices and 8 productivity indices. For the model building and test, we used 10,925 financial analysis data of total 658 listed firms. PASW Modeler 13 was used to build C5.0 decision trees for the six prediction models. Total 84 variables among financial analysis data are selected as the input variables of each model, and the rights issue status (issued or not issued) is defined as the output variable. To develop prediction models using C5.0 node (Node Options: Output type = Rule set, Use boosting = false, Cross-validate = false, Mode = Simple, Favor = Generality), we used 60% of data for model building and 40% of data for model test. The results of experimental analysis show that the prediction accuracies of data after the IMF financial crisis (59.04% to 60.43%) are about 10 percent higher than ones before IMF financial crisis (68.78% to 71.41%). These results indicate that since the IMF financial crisis, the reliability of financial analysis indices has increased and the firm intention of rights issue has been more obvious. The experiment results also show that the stability-related indices have a major impact on conducting rights issue in the case of short-term prediction. On the other hand, the long-term prediction of conducting rights issue is affected by financial analysis indices on profitability, stability, activity and productivity. All the prediction models include the industry code as one of significant variables. This means that companies in different types of industries show their different types of patterns for rights issue. We conclude that it is desirable for stakeholders to take into account stability-related indices and more various financial analysis indices for short-term prediction and long-term prediction, respectively. The current study has several limitations. First, we need to compare the differences in accuracy by using different data mining techniques such as neural networks, logistic regression and SVM. Second, we are required to develop and to evaluate new prediction models including variables which research in the theory of capital structure has mentioned about the relevance to rights issue.