• The Journal of Bigdata
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• v.8 no.1
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• pp.35-47
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• 2023

It is crucial to develop effective and efficient big data analytics methods for problem-solving in the field of business in order to improve the performance of data analytics and reduce costs and risks in the analysis of customer data. In this study, a big data-driven data analysis system using artificial intelligence techniques is designed to increase the accuracy of big data analytics along with the rapid growth of the field of data science. We present a key direction for big data analysis systems through missing value imputation, outlier detection, feature extraction, utilization of explainable artificial intelligence techniques, and exploratory data analysis. Our objective is not only to develop big data analysis techniques with complex structures of business data but also to bridge the gap between the theoretical ideas in artificial intelligence methods and the analysis of real-world data in the field of business.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.12 no.9
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• pp.1527-1538
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• 2008

There are growing needs of introducing Next Generation Business Intelligence into firm to strengthen competencies. Recently, Next Generation Business Intelligence appeared in the spotlight because it could support to connect the strategies establishment with enforcing the strategies. In this parer, we analyze the factors and build the theoretical model of influencing the utilization of Next Generation Business Intelligence. The practical implications were also discussed.

• Asia pacific journal of information systems
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• v.32 no.4
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• pp.916-944
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• 2022

Artificial Intelligence has been a conceptual area for several decades. It has been studied extensively through experiments by the Information Systems community. When Information Systems supported with Information Technology became all pervasive in business and other allied areas, gradually the advancements in Artificial Intelligence also emerged as innovations across domains. Artificial Intelligence by definition is expected to substitute Human Intelligence, thereby making a huge space for innovation. In fact, all processes effected by human intelligence are liable to be replaced by AI which in itself is a massive innovation space. This paper will study the publication's repository (Scopus and Google Scholar from 1983 till 2021) in the area of Artificial Intelligence and innovation, then analyze the trend to gain insight into the evolution of AI as a vehicle for innovation.

• Journal of Distribution Science
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• v.20 no.9
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• pp.109-126
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• 2022

Purpose: The role of salespersons' emotions in effective selling behavior garners attention among scholars and practitioners. Previous studies have investigated the effects of emotional intelligence and emotional labor on sales success separately. However, to understand the whole process, the relationships among salespersons' cognition, emotions, and behaviors should be considered simultaneously. Accordingly, we uniquely examined how salespersons' emotional intelligence (emotional antecedent) and customer orientation (cognitive antecedent) influence their emotional labor (deep acting vs. surface acting), adaptive selling behavior, and the selling results in the retail environment. Research design, data, and methodology: To improve methodological rigor, we used the dyadic approach. We measured 182 salespersons' emotional intelligence, customer orientation, and emotional labor, and 364 customers assessed the salespersons' adaptive selling behavior and selling results in the insurance and duty-free department retailing sectors. Result: The findings suggest that salespersons' customer orientation and emotional intelligence relate to deep-acting of emotional labor, affecting their adaptive selling behavior and relationship quality with customers. Conclusions: As for managerial implications, sales managers may well consider emotional intelligence levels when selecting salespersons in the retail industry. Additionally, practical training programs are required to cultivate customer orientation, emotional intelligence, and deep acting while performing emotional labor.

• The Korean Journal of Food & Health Convergence
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• v.8 no.6
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• pp.19-29
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• 2022

Business intelligence (BI) is a process for turning data into insights that inform an organization's strategic and tactical decisions. BI aims to give decision-makers the information they need to make better decisions Patient safety analysis, illness surveillance, and fraud identification are just a few healthcare decision-making processes that can be supported by data mining. Thus, the purpose of the current research is to outline the need if BI as an essential factor in the healthcare sector by reviewing various scholarly materials and the findings. The present author conducted one of the most famous qualitative literature approach which has been called as PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analysis) statement. The selecting criteria for eligible prior studies were estimated by whether studies are suitable for the current research, identifying they are peer-reviewed and issued by notable publishers between 2017 and 2022. According to the result based on the PRISMA analysis, BI plays a vital role in the healthcare sector and there are four business intelligence factors (Data, Analytic, Reporting, and Visualization) that will ensure that the healthcare sector provides the right healthcare services to the customers to be addressed in this section include; data, analytics, reporting, and visualization.

• Journal of KIISE:Computing Practices and Letters
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• v.8 no.5
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• pp.489-500
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• 2002

As the interest in business interest is increased, data mining is increasingly used in BI as the core technique. To support Business Intelligence in e-business environment, the integrated data mining system which included in various mining operations should be able to flexibly integrate with database system and also it must provide the easy and efficient interface to implement the marketing process in various business applications. In this paper, we have implemented the EC-DaMiner system to support business intelligence in e-business area. The implemented system can be integrated with the conventional database system with the standard interface. Business applications can use MQL mining query language to discover the rules and mining result is modeled in marketing database, and the EC-DaMiner system make the implementation of business marketing process more easy.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.36 no.12B
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• pp.1509-1521
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• 2011

WfMC, which is one of the international standardization organizations leading the business process and workflow technologies, has been officially released the BPAF1.0 that is a standard format to record process instances' event logs according as the business process intelligence mining technologies have recently issued in the business process and workflow literature. The business process mining technologies consist of two groups of algorithms and their analysis techniques; one is to rediscover flow-oriented process-intelligence, such as control-flow, data-flow, role-flow, and actor-flow intelligence, from process instances' event logs, and the other has something to do with rediscovering relation-oriented process-intelligence like process-driven social networks and process-driven affiliation networks from the event logs. The current standardized format of BPAF1.0 aims at only supporting the control-flow oriented process-intelligence mining techniques, and so it is unable to properly support the relation-oriented process-intelligence mining techniques. Therefore, this paper tries to extend the BPAF1.0 so as to reasonably support the relation-oriented process-intelligence mining techniques, and the extended BPAF is termed BPAF2.0. Particularly, we have a plan to standardize the extended BPAF2.0 as not only the national standard specifications through the e-Business project group of TTA, but also the international standard specifications of WfMC.

• Journal of Intelligence and Information Systems
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• v.1 no.1
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• pp.23-41
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• 1995

Business researchers have traditionally used statistical techniques for classification. In late 1980's, inductive learning started to be used for business classification. Recently, neural network began to be a, pp.ied for business classification. This study reviews the business classification studies, identifies a neural network a, pp.oach as the most powerful classification tool, and discusses the problems and issues in neural network a, pp.ications.

• The Journal of Asian Finance, Economics and Business
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• v.9 no.5
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• pp.225-237
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• 2022

The term "artificial intelligence" is considered a component of sophisticated technological developments, and several intelligent tools have been developed to assist organizations and entrepreneurs in making business decisions. Artificial intelligence (AI) is defined as the concept of transforming inanimate objects into intelligent beings that can reason in the same way that humans do. Computer systems can imitate a variety of human intelligence activities, including learning, reasoning, problem-solving, speech recognition, and planning. This study's objective is to provide responses to the questions: Which factors should be taken into account while deciding whether or not to use AI applications? What role do these elements have in AI application adoption? However, this study proposes a framework to explore the significance and relation of success factors to AI adoption based on the technology-organization-environment model. Ten critical factors related to AI adoption are identified. The framework is empirically tested with data collected by mail surveying organizations in Vietnam. Structural Equation Modeling is applied to analyze the data. The results indicate that Technical compatibility, Relative advantage, Technical complexity, Technical capability, Managerial capability, Organizational readiness, Government involvement, Market uncertainty, and Vendor partnership are significantly related to AI applications adoption.

• Informatization Policy
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• v.29 no.4
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• pp.43-66
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• 2022

With the development of artificial intelligence technology, competition for artificial intelligence technology patents around the world is intensifying. During the period 2000 ~ 2021, artificial intelligence technology patent applications at the US Patent and Trademark Office have been steadily increasing, and the growth rate has been steeper since the 2010s. As a result of analyzing Korea's artificial intelligence technology competitiveness through patent indices, it is evaluated that patent activity, impact, and marketability are superior in areas such as auditory intelligence and visual intelligence. However, compared to other countries, overall Korea's artificial intelligence technology patents are good in terms of activity and marketability, but somewhat inferior in technological impact. While noise canceling and voice recognition have recently decreased as topics for artificial intelligence, growth is expected in areas such as model learning optimization, smart sensors, and autonomous driving. In the case of Korea, efforts are required as there is a slight lack of patent applications in areas such as fraud detection/security and medical vision learning.