• Korean Journal of Artificial Intelligence
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• v.5 no.1
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• pp.18-28
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• 2017

Recently, there are many companies that use systems based on artificial intelligence. The accuracy of artificial intelligence depends on the amount of learning data and the appropriate algorithm. However, it is not easy to obtain learning data with a large number of entity. Less data set have large generalization errors due to overfitting. In order to minimize this generalization error, this study proposed DGA which can expect relatively high accuracy even though data with a less data set is applied to machine learning based genetic algorithm to deep learning based dropout. The idea of this paper is to determine the active state of the nodes. Using Gradient about loss function, A new fitness function is defined. Proposed Algorithm DGA is supplementing stochastic inconsistency about Dropout. Also DGA solved problem by the complexity of the fitness function and expression range of the model about Genetic Algorithm As a result of experiments using MNIST data proposed algorithm accuracy is 75.3%. Using only Dropout algorithm accuracy is 41.4%. It is shown that DGA is better than using only dropout.

• Management & Information Systems Review
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• v.29 no.2
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• pp.119-151
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• 2010

This study focuses on the workplaces of two distinct nations, the United States and Korea, to ascertain the impact of culture on emotional intelligence (EI). This paper examines if EI is dependant on culture by finding significant variances of emotional responses under a given situation. The results suggest that EI is significantly impacted by national culture. In addition, this study investigates the relationship between cultural distance and EI by using the secondary data of 19,402 participants across 13 nations. The results demonstrate that only power distance among Hofstede's dimensions has significant effect on EI.

• Journal of Korea Multimedia Society
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• v.23 no.2
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• pp.317-327
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• 2020

R&D(Research and Development) efficiency analysis is a very important issue in academia and industry. Although many studies have been conducted to analyze R&D(Research and Development) efficiency since the past, studies that analyzed R&D(Research and Development) efficiency considering both patentability and patent quality efficiency according to the financial performance of a company do not seem to have been actively conducted. In this study, measuring the patent application and patent quality efficiency according to financial performance, patent quality efficiency according to patent application were applied to corporate groups related to artificial intelligence hardware technology defined as GPU(Graphics Processing Unit), FPGA(Field Programmable Gate Array), ASIC(Application Specific Integrated Circuit) and Neuromorphic. We analyze the efficiency empirically and use Data Envelopment Analysis as a measure of efficiency. This study examines which companies group has high R&D(Research and Development) efficiency about artificial intelligence hardware technology.

• Asian Journal of Business Environment
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• v.5 no.4
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• pp.13-18
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• 2015

Purpose - Based on previous studies, this study extends current research and investigates whether the sub-factors of emotional intelligence increase job satisfaction or employee intrinsic and extrinsic motivation and perceived organizational support. Research design, data, and methodology - This study categorizes service employees' (consultants) emotional intelligence into four sub-factors: regulation of emotion, appraisal of emotion, utilization of emotion, and expression of emotion. The study then investigates the sub-factor effects on job satisfaction. A total of 353 valid questionnaires were collected. Results - The results of the path analysis showed that appraisal, utilization, and expression of emotion had a positive effect on intrinsic motivation, and utilization of emotion had a positive effect on extrinsic motivation. Extrinsic motivation had a positive effect on perceived organizational support and job satisfaction, and perceived organizational support had a positive effect on job satisfaction. Conclusion - As consultants' utilization of emotion is rendered as the ability to use emotion to improve performance, the conclusion is that such factors as monetary performance incentives are important in order to boost job satisfaction of the consultants.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.5
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• pp.1690-1707
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• 2021

Precipitation prediction during flood season has been a key task of climate prediction for a long time. This type of prediction is linked with the national economy and people's livelihood, and is also one of the difficult problems in climatology. At present, there are some precipitation forecast models for the flood season, but there are also some deviations from these models, which makes it difficult to forecast accurately. In this paper, based on the measured precipitation data from the flood season from 1993 to 2019 and the precipitation return data of CWRF, ANN cycle modeling and a weighted integration method is used to correct the CWRF used in today's operational systems. The MAE and TCC of the precipitation forecast in the flood season are used to check the prediction performance of the proposed algorithm model. The results demonstrate a good correction effect for the proposed algorithm. In particular, the MAE error of the new algorithm is reduced by about 50%, while the time correlation TCC is improved by about 40%. Therefore, both the generalization of the correction results and the prediction performance are improved.

• Journal of Intelligence and Information Systems
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• v.27 no.3
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• pp.231-252
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• 2021

Artificial intelligence (AI) is a key technology that will change the future the most. It affects the industry as a whole and daily life in various ways. As data availability increases, artificial intelligence finds an optimal solution and infers/predicts through self-learning. Research and investment related to automation that discovers and solves problems on its own are ongoing continuously. Automation of artificial intelligence has benefits such as cost reduction, minimization of human intervention and the difference of human capability. However, there are side effects, such as limiting the artificial intelligence's autonomy and erroneous results due to algorithmic bias. In the labor market, it raises the fear of job replacement. Prior studies on the utilization of artificial intelligence have shown that individuals do not necessarily use the information (or advice) it provides. Algorithm error is more sensitive than human error; so, people avoid algorithms after seeing errors, which is called "algorithm aversion." Recently, artificial intelligence has begun to be understood from the perspective of the augmentation of human intelligence. We have started to be interested in Human-AI collaboration rather than AI alone without human. A study of 1500 companies in various industries found that human-AI collaboration outperformed AI alone. In the medicine area, pathologist-deep learning collaboration dropped the pathologist cancer diagnosis error rate by 85%. Leading AI companies, such as IBM and Microsoft, are starting to adopt the direction of AI as augmented intelligence. Human-AI collaboration is emphasized in the decision-making process, because artificial intelligence is superior in analysis ability based on information. Intuition is a unique human capability so that human-AI collaboration can make optimal decisions. In an environment where change is getting faster and uncertainty increases, the need for artificial intelligence in decision-making will increase. In addition, active discussions are expected on approaches that utilize artificial intelligence for rational decision-making. This study investigates the impact of artificial intelligence on decision-making focuses on human-AI collaboration and the interaction between the decision maker personal traits and advisor type. The advisors were classified into three types: human, artificial intelligence, and human-AI collaboration. We investigated perceived usefulness of advice and the utilization of advice in decision making and whether the decision-maker's personal traits are influencing factors. Three hundred and eleven adult male and female experimenters conducted a task that predicts the age of faces in photos and the results showed that the advisor type does not directly affect the utilization of advice. The decision-maker utilizes it only when they believed advice can improve prediction performance. In the case of human-AI collaboration, decision-makers higher evaluated the perceived usefulness of advice, regardless of the decision maker's personal traits and the advice was more actively utilized. If the type of advisor was artificial intelligence alone, decision-makers who scored high in conscientiousness, high in extroversion, or low in neuroticism, high evaluated the perceived usefulness of the advice so they utilized advice actively. This study has academic significance in that it focuses on human-AI collaboration that the recent growing interest in artificial intelligence roles. It has expanded the relevant research area by considering the role of artificial intelligence as an advisor of decision-making and judgment research, and in aspects of practical significance, suggested views that companies should consider in order to enhance AI capability. To improve the effectiveness of AI-based systems, companies not only must introduce high-performance systems, but also need employees who properly understand digital information presented by AI, and can add non-digital information to make decisions. Moreover, to increase utilization in AI-based systems, task-oriented competencies, such as analytical skills and information technology capabilities, are important. in addition, it is expected that greater performance will be achieved if employee's personal traits are considered.

• Journal of Intelligence and Information Systems
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• v.11 no.3
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• pp.103-115
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• 2005

For today's enterprises to survive in the current rapidly changing business environments, it is imperative to make quick and successful decisions to various challenges. In making important business decisions, if enterprises utilize business rules and knowledge, properly and promptly, they may effectively reduce the chance of failures. However, in most of today's information systems, these rules and knowledge are not managed in centralized and systemic manner. They disperse over entire enterprises' information systems, and sometimes reside in the heads or memos of enterprises' employees. BRE (Business Rule Engines) is a solution that systematically and centrally manages these business knowledge and rules of an enterprise. With BREs, any business user is able to store, edit, retrieve and utilize business rules and knowledge in centralized repository, without IT development skills. And with BRE, enterprises could improve business intelligence and attain strategic advantages over other enterprises. However, since there is no clear criteria for BRE introductions, it is not easy to decide whether or not to introduce the expensive BRE solution to an enterprise. In this research we propose a decision model for BRE introduction. Using this model, business analysts considering BRE introduction, readily make decisions on BRE introduction.

• Journal of Intelligence and Information Systems
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• v.26 no.3
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• pp.71-90
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• 2020

Recently, the global insurance industry is rapidly developing digital transformation through the use of artificial intelligence technologies such as machine learning, natural language processing, and deep learning. As a result, more and more foreign insurers have achieved the success of artificial intelligence technology-based InsurTech and platform business, and Ping An Insurance Group Ltd., China's largest private company, is leading China's global fourth industrial revolution with remarkable achievements in InsurTech and Digital Platform as a result of its constant innovation, using 'finance and technology' and 'finance and ecosystem' as keywords for companies. In response, this study analyzed the InsurTech and platform business activities of Ping An Insurance Group Ltd. through the ser-M analysis model to provide strategic implications for revitalizing AI technology-based businesses of domestic insurers. The ser-M analysis model has been studied so that the vision and leadership of the CEO, the historical environment of the enterprise, the utilization of various resources, and the unique mechanism relationships can be interpreted in an integrated manner as a frame that can be interpreted in terms of the subject, environment, resource and mechanism. As a result of the case analysis, Ping An Insurance Group Ltd. has achieved cost reduction and customer service development by digitally innovating its entire business area such as sales, underwriting, claims, and loan service by utilizing core artificial intelligence technologies such as facial, voice, and facial expression recognition. In addition, "online data in China" and "the vast offline data and insights accumulated by the company" were combined with new technologies such as artificial intelligence and big data analysis to build a digital platform that integrates financial services and digital service businesses. Ping An Insurance Group Ltd. challenged constant innovation, and as of 2019, sales reached $155 billion, ranking seventh among all companies in the Global 2000 rankings selected by Forbes Magazine. Analyzing the background of the success of Ping An Insurance Group Ltd. from the perspective of ser-M, founder Mammingz quickly captured the development of digital technology, market competition and changes in population structure in the era of the fourth industrial revolution, and established a new vision and displayed an agile leadership of digital technology-focused. Based on the strong leadership led by the founder in response to environmental changes, the company has successfully led InsurTech and Platform Business through innovation of internal resources such as investment in artificial intelligence technology, securing excellent professionals, and strengthening big data capabilities, combining external absorption capabilities, and strategic alliances among various industries. Through this success story analysis of Ping An Insurance Group Ltd., the following implications can be given to domestic insurance companies that are preparing for digital transformation. First, CEOs of domestic companies also need to recognize the paradigm shift in industry due to the change in digital technology and quickly arm themselves with digital technology-oriented leadership to spearhead the digital transformation of enterprises. Second, the Korean government should urgently overhaul related laws and systems to further promote the use of data between different industries and provide drastic support such as deregulation, tax benefits and platform provision to help the domestic insurance industry secure global competitiveness. Third, Korean companies also need to make bolder investments in the development of artificial intelligence technology so that systematic securing of internal and external data, training of technical personnel, and patent applications can be expanded, and digital platforms should be quickly established so that diverse customer experiences can be integrated through learned artificial intelligence technology. Finally, since there may be limitations to generalization through a single case of an overseas insurance company, I hope that in the future, more extensive research will be conducted on various management strategies related to artificial intelligence technology by analyzing cases of multiple industries or multiple companies or conducting empirical research.

• Management & Information Systems Review
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• v.38 no.4
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• pp.113-136
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• 2019

Business performance is the ultimate output of company's effort. So in all academic area of business, scholars have tried to identify influencing factors on the business performance. This study also try to identify the factors and relati on among factors focusing on market orientation and relationship specific investment. Based on the literature review, we developed hypotheses. To examine the hypotheses we gathered survey data from B2B companies located in the Ulsan and 288 survey data was used to the analysis. Research results are as follows. As sub-factor of market orientation intelligence generation and responsiveness to intelligence affected on the relationship specific investment significantly and relationship specific investment also affected performance significantly. But all sub-factors of market orientation didn't influenced performance significantly meaning the mediating effect of relationship specific investment between market orientation and performance. Finally, the relation between intelligence generation and relationship specific investment was moderated negatively by dependence. The theoretical and practical implications of this study and limitations were discussed.

• Asia-Pacific Journal of Business
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• v.10 no.3
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• pp.69-93
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• 2019

There is a growing interest in researching new leadership and efficient organizational management due to the changes and spread of Information Communications Technologies (ICT) related organizations. Therefore, the purpose of this study is to examine the effect of leadership on organization in ICT R & D, and to follow the relationship between coaching leadership, leader member exchange (LMX), emotional intelligence of leaders, and organizational effectiveness. In order to verify this research problem, a total of 310 people were surveyed from employees working in the ICT R & D field. Reliability analysis of the measuring tool was tested on 80 pilot surveys in advance, and the final 285 data were used for empirical analysis excluding 25 unsatisfactory responses. For analysis, SPSS 22.0 program and AMOS were used to analyze the reliability, validity, path analysis, and control effects of the measurement tools. The findings demonstrate empirically the positive relationship between LMX and organizational effectiveness on coaching leadership in ICT R & D. In addition, by empirically verifying the effect that the emotional intelligence of the leader regulates the relationship between LMX, organizational effectiveness, and coaching leadership, then emotional intelligence of the leader can be usefully applied in ICT R & D. Furthermore, the demand for more in-depth research on coaching leadership, LMX, and emotional intelligence of leaders, which positively affects organizational effectiveness, was identified as demand for ICT R & D is increasing and horizontal and interactive leadership is needed.