• Journal of Digital Convergence
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• v.20 no.5
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• pp.129-138
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• 2022

After the spread of COVID-19 in China, Chinese medical workers bear the high-intensity work, these problems reduced psychological well-being. Psychological well-being will affect the members'behavior and job performance. Authentic leadership can improve the members'psychological well-being. This study focuses on the psychological well-being and explores how authentic leadership in the process of affecting the psychological well-being, to find out the mediating role of the leaders' relational conflict and the job stress. This study found that authentic leadership in the process of improving the members'psychological well-being, job stress will play a negative mediating effect. Under the background of COVID-19 era, this study helps to improve the psychological well-being level of medical staff.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.10 no.4
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• pp.113-131
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• 2015

Decision effectiveness depends on type of knowledge within team members generated by decision making process. Thus, organization in accordance with teams' experience and capability ultimately achieve their desired outcome. However, previous research has not addressed a mediating role between different knowledge type in decision making and product competitive advantages(pioneering advantage and product quality superiority). Based on the knowledge-based view, we model how different knowledge characteristics in decision making affect to acquire each of knowledge in decision making effectively and then to apply acquired knowledge in decision making. Anchored in a source-position-performance (SPP) framework (Day and Wensley's, 1988), we shed light on the effects of three knowledge characteristics dimensions in decision making process on knowledge management competences in decision making for a new product project. We also examine the relationship between two dimensions of NPD knowledge management competences, and product competitive advantages which consist of market pioneering advantage and product quality superiority. To test the relationships, the empirical analyses are conducted using a sample of team managers who participated in NPD projects. This study suggest that managers should increase their acquirability and applicability of knowledge by integrating complexity of diverse and new knowledge, developing codifiability of well-documented knowledge, and creating the sharing common knowledge among NPD team members. Thus, they are able to outrun major competitors in terms of pioneering advantage and product quality superiority perspective.

• The Journal of Society for e-Business Studies
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• v.26 no.3
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• pp.97-117
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• 2021

Predicting term deposit subscriptions is one of representative financial marketing in banks, and banks can build a prediction model using various customer information. In order to improve the classification accuracy for term deposit subscriptions, many studies have been conducted based on machine learning techniques. However, even if these models can achieve satisfactory performance, utilizing them is not an easy task in the industry when their decision-making process is not adequately explained. To address this issue, this paper proposes an explainable scheme for term deposit subscription forecasting. For this, we first construct several classification models using decision tree-based ensemble learning methods, which yield excellent performance in tabular data, such as random forest, gradient boosting machine (GBM), extreme gradient boosting (XGB), and light gradient boosting machine (LightGBM). We then analyze their classification performance in depth through 10-fold cross-validation. After that, we provide the rationale for interpreting the influence of customer information and the decision-making process by applying Shapley additive explanation (SHAP), an explainable artificial intelligence technique, to the best classification model. To verify the practicality and validity of our scheme, experiments were conducted with the bank marketing dataset provided by Kaggle; we applied the SHAP to the GBM and LightGBM models, respectively, according to different dataset configurations and then performed their analysis and visualization for explainable term deposit subscriptions.

• Journal of Korea Technology Innovation Society
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• v.20 no.1
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• pp.1-33
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• 2017

In the last decades, technology-oriented small firms, i.e. venture businesses, have been increasingly engaged in R&D collaborations with external parties as strategic means for technological innovation. Despite ample evidence on the benefit of such collaborations for the firms, there has been less attention to examining whether and how the firms' social interactions with cooperating partners and their managerial characteristics contribute to that benefit. Drawing on the theories of social capital and entrepreneurial orientation, this study is to remedy this gap. The theory of social capital, referring to a sum of the value and potential resources embedded in social relationships of collectives, provides an integrated view of social factors among cooperating partners, e.g. strong ties, network stability, trust, reciprocity, shared vision and value. It categorizes these factors into structural, relational, and cognitive dimensions of social capital. Entrepreneurial orientation theory captures firms' managerial characteristics as a combination of innovativeness, proactiveness, and risk-taking. This addresses firms' managerial process to utilize and combine internal and external resources for wealth creation and opportunity realization. Against this background, this study investigates what roles social capital among cooperating R&D partners and entrepreneurial orientation of the collaborating firms play for collective performance improvement in R&D collaborations. In terms of the collective performance, this study adopts two indicators: technological competitiveness and business performance. Technological competitiveness refers to the contribution of a technology developed by a cooperative R&D project to competitive advantage of a firm while business performance is defined as the financial and economic outcome of a collaboration. Using a sample of 218 Korean ventures engaging in R&D collaboration with external parties, the author finds the significant effects of social capital (i.e. structural, relational, and cognitive dimensions) and entrepreneurial orientation (i.e. innovativeness, proactiveness, and risk-taking) on both of the technological competitiveness and the business performance. Further, the higher the social capital among R&D partners, the more likely it is to foster the entrepreneurial orientation at firm-level. Most importantly, the entrepreneurial orientation at firm-level is an significant mediator of the relationship between social capital and collective performance. Beyond these novel empirical findings, this study contributes to the literature on R&D collaboration. The findings' implications for management and policy are deeply discussed in the conclusion.

• Information Systems Review
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• v.12 no.1
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• pp.43-58
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• 2010

In order to improve total competitive advantages in domestic shipbuilding industry, shipyards as well as marine-parts companies achieve managerial efficiency through ERP system. But, successful ERP system implementation cases of marine-parts companies are seldom reported by characteristics of small and build-to-order industry. Therefore, this study attempts to analyze a case for ERP system implementation in marin-parts company, K Inc. Especially, this study divides ERP system implementation process into preparation stage, implement stage and settle-down and stabilization stage, and then derives critical success factors in each stage. At present, the implemented ERP system is successfully operating, ERP system improve qualitative and quantitative performance. The result of study suggests some practical implications to the manager of marine-parts companies and the manager of Informatization Programs for Small and Medium Enterprises.

• Journal of KIISE:Databases
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• v.30 no.3
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• pp.310-319
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• 2003

Workflow is defined as the automation of a business process, and consists of interrelated workflow tasks. Because many modem business processes nay involve activities that are geographically distributed between different departments or organizations, workflow inherently has the characteristics of distribution. In distributed workflow systems, each workflow task performs its assigned role by utilizing information resources placed at some hosts, and then transmits workflow execution control to the next tasks in a workflow definition. Hence, it is very important to appropriately allocate workflow tasks to hosts for high performance workflow processing. In this paper, we propose a multilevel workflow graph partitioning scheme for efficient placement of workflow tasks. This method can improve the performance of workflow processing by minimizing the remote communication costs occurred during workflow execution.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.14 no.11
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• pp.5993-5999
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• 2013

This study presents the information management item and the business process improvement plan managed all construction technique services of Design, Construction Supervision and Construction Management which is ordered and constracted by the public institution in the field of construction and business steps. In addition, it develped the Management System of Construction Technique Service Performances based on improved processes for the Public Agencies and Construction Service Director to register, review and approve the Service Performance Information. As a result, it would be help transparently managed Technical Engineer's work duplication status of Constuction Company, and checked online without having to submit Company performance data by providing objective and reliable information.

• Journal of Information Technology Services
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• v.16 no.3
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• pp.167-183
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• 2017

The rapid growth of information technology and mobile service platforms, i.e., internet, google, and facebook, etc. has led the abundance of data. Due to this environment, the world is now facing a revolution in the process that data is searched, collected, stored, and shared. Abundance of data gives us several opportunities to knowledge discovery and data mining techniques. In recent years, data mining methods as a solution to discovery and extraction of available knowledge in database has been more popular in e-commerce service fields such as, in particular, movie recommendation. However, most of the classification approaches for predicting the movie popularity have used only several types of information of the movie such as actor, director, rating score, language and countries etc. In this study, we propose a classification-based support vector machine (SVM) model for predicting the movie popularity based on movie's genre data and social network data. Social network analysis (SNA) is used for improving the classification accuracy. This study builds the movies' network (one mode network) based on initial data which is a two mode network as user-to-movie network. For the proposed method we computed degree centrality, betweenness centrality, closeness centrality, and eigenvector centrality as centrality measures in movie's network. Those four centrality values and movies' genre data were used to classify the movie popularity in this study. The logistic regression, neural network, $na{\ddot{i}}ve$ Bayes classifier, and decision tree as benchmarking models for movie popularity classification were also used for comparison with the performance of our proposed model. To assess the classifier's performance accuracy this study used MovieLens data as an open database. Our empirical results indicate that our proposed model with movie's genre and centrality data has by approximately 0% higher accuracy than other classification models with only movie's genre data. The implications of our results show that our proposed model can be used for improving movie popularity classification accuracy.

• International Journal of Computer Science & Network Security
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• v.21 no.8
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• pp.353-359
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• 2021

Information technology and the need for timely and effective communication during the Covid-19 have made most governments adopt technological approaches to provide their services. E-government services have been adopted by most governments especially in developed countries to quickly and effectively share information. This study discusses the reasons why governments in the Gulf region should develop a new model for information technology knowledge management practices. To achieve this, the author identified possible benefits of adopting information technology knowledge management practices and why most governments in the Gulf find it hard to adopt them. Knowledge management allows for learning, transfer as well as sharing of information between government organizations and citizens and with the development of technology, the effectiveness of electronic services can easily be achieved. Also, effective adoption of information technology can improve knowledge management with the help of techniques that enhance capture, storage, retrieval as well as sharing of information. The author used systematic literature review to select 28 journals and articles published post 2019. IEEE, Google Scholar and Science Direct were used to select potential studies from which 722 journals and articles were selected. Through screening and eligibility assessment, 21 articles were retained while the back and forward search had 7 more articles which were also included in the study. Using information gathered from these articles and journals a new conceptual model was developed to help improve information technology knowledge management for governments in the Gulf region to effectively deliver e-services during Covid-19. This model was developed based on the process of KM, Theory of Planned Behavior and Unified Theory of Acceptance and Use of Technology. Based on the developed model. From UTAUT model, performance expectancy, effort expectancy as well as social influence had a great impact.

• Journal of the Korean Society of Industry Convergence
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• v.26 no.5
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• pp.855-866
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• 2023

With the rapid advancement and sophistication of defense weapon systems, the government, military, and the defense industry have conducted various innovative attempts to improve the efficiency of post-logistics support(PLS). The Ministry of Defense has mandated RAM-C(Reliability, Availability, and Maintainability-Cost) analysis as a requirement according to revised Total Life Cycle System Management Code of Practice in May 2022. Especially, for the project budget forecast of new PBL(Performance Based Logistics) business contacts, RAM-C is recognized as an obligatory factor. However, relevant entities have not officially provided guidelines or manuals for RAM-C analysis, and each defense contractor conducts RAM-C analysis with different standards and methods to win PBL-related business contract. Hence, this study aims to contribute to the generalization of the analysis procedure by presenting a cost analysis case based on RAM-C for the supply of military depot maintenance PBL project. This study presents formulas and procedures to determine requirements of military depot maintenance PBL project for repair parts supply. Moreover, a sensitivity analysis was conducted to find the optimal cost/utilization ratio. During the process, a correlation was found between supply delay and total cost of ownership as well as between cost variability and utilization rate. The analysis results are expected to provide an important basis for the conceptualization of the cost analysis for the supply of military depot maintenance PBL project and are capable of proposing the optimal utilization rate in relation to cost.