• Asia-Pacific Journal of Business
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• v.15 no.1
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• pp.131-143
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• 2024

Purpose - The purpose of this study was to analyze cases of big data-driven business in the financial industry, focusing on organizational structure and business processes using big data in banking industry. Design/methodology/approach - This study used a case study approach. To this end, cases of two banks implementing big data-driven business were collected and analyzed. Findings - There are two things in common between the two cases. One is that the central tasks for big data-driven business are performed by a centralized organization. The other is that the role distribution and work collaboration between the headquarters and business departments are well established. On the other hand, there are two differences between the two banks. One marketing campaign is led by the headquarters and the other marketing campaign is led by the business departments. The two banks differ in how they carry out marketing campaigns and how they carry out big data-related tasks. Research implications or Originality - When banks plan and implement big data-driven business, the common aspects of the two banks analyzed through this case study can be fully referenced when creating an organization and process. In addition, it will be necessary to create an organizational structure and work process that best fit the special situation considering the company's environment or capabilities.

• Journal of Astronomy and Space Sciences
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• v.33 no.4
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• pp.265-271
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• 2016

Interplanetary scintillation-driven (IPS-driven) ENLIL model was jointly developed by University of California, San Diego (UCSD) and National Aeronaucics and Space Administration/Goddard Space Flight Center (NASA/GSFC). The model has been in operation by Korean Space Weather Cetner (KSWC) since 2014. IPS-driven ENLIL model has a variety of ambient solar wind parameters and the results of the model depend on the combination of these parameters. We have conducted researches to determine the best combination of parameters to improve the performance of the IPS-driven ENLIL model. The model results with input of 1,440 combinations of parameters are compared with the Advanced Composition Explorer (ACE) observation data. In this way, the top 10 parameter sets showing best performance were determined. Finally, the characteristics of the parameter sets were analyzed and application of the results to IPS-driven ENLIL model was discussed.

• Journal of Korean Academy of Nursing Administration
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• v.21 no.3
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• pp.308-316
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• 2015

Purpose: The purpose of this study was to identify the level of project perception for those nurses from research-driven hospitals and to analyze the effect of research-relevant performance in the health care field focusing on the mediated effect of research capacity and job satisfaction. Methods: Data were collected from June, 2014 to July, 2014, and participants were 106 research nurses in Research-driven hospitals. Descriptive statistics, Independent t-test, One-way ANOVA, structural equation modeling (SEM). Results: As a result, Research-relevant performance according to project perception of research nurses from Research-driven Hospitals was not statistically significant, but research capacity and job satisfaction had a mediating role. Evaluation System Perception was significantly different from Research Capacity (p<.001), Research Capacity was significantly different from Job Satisfaction (p<.001), Job Satisfaction was significantly different from Research Performance (p<.001) Conclusion: The results indicate that research capacity building and job security research nurses are able to contribute to improving research performance of research-driven hospitals.

• Proceedings of the KAIS Fall Conference
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• 2003.11a
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• pp.99-104
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• 2003

In this research, we proposed the mechanism to develop self evolving expert systems (SEES) based on data mining (DM), fuzzy neural networks (FNN), and relational database (RDB)-driven forward/backward inference engine. Most former researchers tried to develop a text-oriented knowledge base (KB) and inference engine (IE). However, thy have some limitations such as 1) automatic rule extraction, 2) manipulation of ambiguousness in knowledge, 3) expandability of knowledge base, and 4) speed of inference. To overcome these limitations, many of researchers had tried to develop an automatic knowledge extraction and refining mechanisms. As a result, the adaptability of the expert systems was improved. Nonetheless, they didn't suggest a hybrid and generalized solution to develop self-evolving expert systems. To this purpose, in this study, we propose an automatic knowledge acquisition and composite inference mechanism based on DM, FNN, and RDB-driven inference. Our proposed mechanism has five advantages empirically. First, it could extract and reduce the specific domain knowledge from incomplete database by using data mining algorithm. Second, our proposed mechanism could manipulate the ambiguousness in knowledge by using fuzzy membership functions. Third, it could construct the relational knowledge base and expand the knowledge base unlimitedly with RDBMS (relational database management systems). Fourth, our proposed hybrid data mining mechanism can reflect both association rule-based logical inference and complicate fuzzy logic. Fifth, RDB-driven forward and backward inference is faster than the traditional text-oriented inference.

• Asia-Pacific Journal of Business
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• v.13 no.4
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• pp.123-134
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• 2022

Purpose - The purpose of this study is to investigate the effects of entrepreneurial motivation, self-efficacy, and entrepreneurial orientation on the performance of Mongolian entrepreneurs. Design/methodology/approach This study collected data from a survey on 236 entrepreneurs in Mongolia and investigate research hypotheses by empirical analysis. Findings It was found that entrepreneurial motivation (independence, opportunity-driven, achievement motivation) had a positive effect on the startups' performances, and necessity-driven motivation did not have a significant effect on the startups' performances. Entrepreneurial self-efficacy and entrepreneurial orientation had a positive effect on performance of startups. There are differences by gender on the relationships between entrepreneurial motivations and startup performances. Research implications or Originality This paper investigates the effects of entrepreneurial motivation, self-efficacy, and entrepreneurial orientation on the performance of startups in Mongolian.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.16 no.1
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• pp.44-51
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• 2016

Recently, artificial intelligence has reached the level of top information technologies that will have significant influence over many aspects of our future lifestyles. In particular, in the fields of machine learning technologies for classification and decision-making, there have been a lot of research efforts for solving estimation and control problems that appear in the various kinds of portfolio management problems via data-driven approaches. Note that these modern data-driven approaches, which try to find solutions to the problems based on relevant empirical data rather than mathematical analyses, are useful particularly in practical application domains. In this paper, we consider some applications of modern data-driven machine learning methods for portfolio management problems. More precisely, we apply a simplified version of the sparse Gaussian process (GP) classification method for classifying users' sensitivity with respect to financial risk, and then present two portfolio management issues in which the GP application results can be useful. Experimental results show that the GP applications work well in handling simulated data sets.

• Journal of Intelligence and Information Systems
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• v.9 no.2
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• pp.19-38
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• 2003

In this research, we propose the mechanism to develop self-evolving expert systems (SEES) based on data mining (DM), fuzzy neural networks (FNN), and relational database (RDB)-driven forward/backward inference engine. Most researchers had tried to develop a text-oriented knowledge base (KB) and inference engine (IE). However, this approach had some limitations such as 1) automatic rule extraction, 2) manipulation of ambiguousness in knowledge, 3) expandability of knowledge base, and 4) speed of inference. To overcome these limitations, knowledge engineers had tried to develop an automatic knowledge extraction mechanism. As a result, the adaptability of the expert systems was improved. Nonetheless, they didn't suggest a hybrid and generalized solution to develop self-evolving expert systems. To this purpose, we propose an automatic knowledge acquisition and composite inference mechanism based on DM, FNN, and RDB-driven inference engine. Our proposed mechanism has five advantages. First, it can extract and reduce the specific domain knowledge from incomplete database by using data mining technology. Second, our proposed mechanism can manipulate the ambiguousness in knowledge by using fuzzy membership functions. Third, it can construct the relational knowledge base and expand the knowledge base unlimitedly with RDBMS (relational database management systems) module. Fourth, our proposed hybrid data mining mechanism can reflect both association rule-based logical inference and complicate fuzzy relationships. Fifth, RDB-driven forward and backward inference time is shorter than the traditional text-oriented inference time.

• The Journal of Industrial Distribution & Business
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• v.11 no.4
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• pp.19-30
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• 2020

Purpose: The purpose of this study examines the mediating effect of clan leadership in the relationship between hierarchy culture and organizational commitment. Most previous research focused on the relationship between organizational culture and organizational performance or organizational culture and job satisfaction. There are few empirical studies that focus on organizational commitment data because it is difficult to collect in many cases of export-driven small and medium sized enterprises. However, this research measures affective commitment, continuance commitment, and normative commitment differently than previous research, which is mostly focused on the hierarchy culture, clan leadership, and organizational commitment measurements. Research design, data, methodology: Conceptual research model is based on the studies of Cameron and Quinn (2011), and Gungor and Sahin (2018). The model is designed with three constructs such as hierarchy culture, organizational commitment, and clan leadership. The monitor culture and coordinator culture are as proxy for the hierarchy culture. The affective commitment, continuance commitment, and normative commitment are as proxy for the organizational commitment. And also the facilitator leadership and mentor leadership are as proxy for the clan leadership. Based on three hundred cases such as export-driven small and medium sized enterprises (SMEs), this study verify the hypothesis. Hypothesis was analyzed with the structural equation modeling. Results: In case of export-driven small and medium sized enterprises (SMEs), clan leadership acts as a mediator in the relationship between hierarchy culture and organizational commitment. In case of export-driven small and medium sized enterprises (SMEs) with high organizational commitment, clan leadership acts as a mediator in the relationship between hierarchy culture and organizational commitment. In case of export-driven small and medium sized enterprises (SMEs) with low organizational commitment, clan leadership did not act as a mediator in the relationship between hierarchy culture and organizational commitment. Conclusions: By controlling for the mediating effect of clan culture, this study have improved the academic contributions as well as policy and practical implications through empirical study of clan leadership that affect organizational commitment in the fields of hierarchy culture. In addition, this study means that the mediating effects on the variables of clan leadership were examined.

• Korean Journal of Agricultural Science
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• v.49 no.4
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• pp.749-760
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• 2022

Omics-driven synthetic biology is a multidisciplinary research field that creates new artificial life by employing genetic components, biological devices, and engineering technique based on genetic knowledge and technological expertise. It is also utilized to make valuable biomaterials with limited production via current organisms faster, more efficient, and in huge quantities. As the bioeconomic age begins, and the global synthetic biology market becomes more competitive, investment in research and development (R&D) and associated sectors has grown considerably. By overcoming the constraints of present biotechnologies through the merging of big data and artificial intelligence technologies, huge ripple effects are envisaged in the pharmaceutical, chemical, and energy industries. In agriculture, synthetic biology is being used to solve current agricultural problems and develop sustainable agricultural systems by increasing crop productivity, implementing low-carbon agriculture, and developing plant-based, high-value-added bio-materials such as vaccines for diagnosing and preventing livestock diseases. As international regulatory debates on synthetic biology are now underway, discussions should also take place in our country for the growth of bioindustries and the dissemination of research findings. Furthermore, the system must be improved to facilitate practical application and to enhance the risk evaluation technology and management system.

• The Journal of Asian Finance, Economics and Business
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• v.7 no.6
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• pp.457-464
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• 2020

This study aims to recognize customers' real sentiment and then discover the data-driven insights for strategic decision-making in the financial sector of Saudi Arabia. The data was collected from the social media (Facebook and Twitter) from start till October 2018 in financial companies (NCB, Al Rajhi, and Bupa) selected in the Kingdom of Saudi Arabia according to criteria. Then, it was analyzed using a sentiment analysis, one of data mining techniques. All three companies have similar likes and followers as they serve customers as B2B and B2C companies. In addition, for Al Rajhi no negative sentiment was detected in English posts, while it can be seen that Internet penetration of both banks are higher than BUPA, rarely mentioned in few hours. This study helps to predict the overall popularity as well as the perception or real mood of people by identifying the positive and negative feelings or emotions behind customers' social media posts or messages. This research presents meaningful insights in data-driven approaches using a specific data mining technique as a tool for corporate decision-making and forecasting. Understanding what the key issues are from customers' perspective, it becomes possible to develop a better data-based global strategies to create a sustainable competitive advantage.