• International Journal of Internet, Broadcasting and Communication
• /
• v.15 no.3
• /
• pp.58-65
• /
• 2023

Bankruptcy is a significant risk for start-up companies, but with the help of cutting-edge artificial intelligence technology, we can now predict bankruptcy with detailed explanations. In this paper, we implemented the Category Boosting algorithm following data cleaning and editing using OpenRefine. We further explained our model using the Shapash library, incorporating domain knowledge. By leveraging the 5C's credit domain knowledge, financial analysts in banks or investors can utilize the detailed results provided by our model to enhance their decision-making processes, even without extensive knowledge about AI. This empowers investors to identify potential bankruptcy risks in their business models, enabling them to make necessary improvements or reconsider their ventures before proceeding. As a result, our model serves as a "glass-box" model, allowing end-users to understand which specific financial indicators contribute to the prediction of bankruptcy. This transparency enhances trust and provides valuable insights for decision-makers in mitigating bankruptcy risks.

• Asia pacific journal of information systems
• /
• v.32 no.3
• /
• pp.496-513
• /
• 2022

Artificial intelligence or big data technologies can benefit finance companies such as those in the insurance sector. With artificial intelligence, companies can develop better customer segmentation methods and eventually improve the quality of customer relationship management. However, the application of AI-based customer segmentation in the insurance industry seems to have been unsuccessful. Findings from our interviews with sales agents and customer service managers indicate that current customer segmentation in the Korean insurance company relies upon individual agents' heuristic decisions rather than a generalizable data-based method. We propose guidelines for AI-based customer segmentation for the insurance industry, based on the CRISP-DM standard data mining project framework. Our proposed guideline provides new insights for studies on AI-based technology implementation and has practical implications for companies that deploy algorithm-based customer relationship management systems.

• The Journal of Asian Finance, Economics and Business
• /
• v.7 no.1
• /
• pp.183-193
• /
• 2020

This study presents a more comprehensive approach to investigate how Emotional Intelligence (EI) is associated with performance. Previous research has revealed that EI is related to positive outcomes such as better performance, life satisfaction and career success. However, in most research of EI, this factor has been investigated independently from the effect of contextual factors and other individual characteristics. This study extended the research on the impact of EI on student performance by simultaneously examining the impact of EI, individual factors - (1) Perseverance of Effort, (2) Goal Consistency and (3) Goal Clarity and learning environment factors - Program Fit in one study. The results based on the data collected from 1,117 students in Vietnam largely support the hypothesis that that EI was an important but not the only factor affecting the student performance. MANOVA and post-hoc analysis indicated that both individual and environmental factors better predicted performance than EI could. These results suggest that the impact of EI on individual performance is more complicated than what we have already known. Future study is encouraged to investigate both the mechanism through which EI impact individual performance and the interaction between EI and other factors that moderate the EI - performance relationship.

• Journal of Information Technology Applications and Management
• /
• v.24 no.3
• /
• pp.1-21
• /
• 2017

Business Intelligence (BI) system is a strategic tool that presents an analytical perspective about business and external environments. Even though its strategic value was well known, users often avoid using it or adopt it ceremonially. In fact, over 50 per cent of BI projects worldwide are reported to end in failure. Such an unexpectedly lower success rate has been a key issue in BI studies. In order to enhance a proper use of information systems, MIS field provided a number of theoretical constructs. One example is Goodhue & Thompson's Task-Technology Fit (TTF). In addition, internalization, the degree to which people make their own effort to modify behavior, was recently suggested as another important determinant of use. Though in MIS community both TTF and internalization proved to be a key determinant of system use, there has been not much study aiming to discover antecedents influencing these constructs. In this study we assert that user participation should be highlighted in BI projects. Especially, we emphasize user participation at the phase of feasibility study that is mainly conducted to determine whether a BI system is essentially necessary and practicable. Our research model employs participative feasibility study as a major antecedent for TTF and internalization that consequently will lead to user satisfaction and actual use. This model was empirically tested on 121 BI system users. The result shows that user participation in feasibility study is positively associated with TTF and internalization, each being related to user satisfaction and system use. It implies that, if an organization has BI users get involved in strategic feasibility study phase, the BI system would turn out to fit users' tasks and, furthermore, users would put more efforts spontaneously in order to use it properly.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2013.10a
• /
• pp.211-213
• /
• 2013

In these days, the delusions of Big Data and apprehension about them are coming into the picture in many business fields. General techniques for preservation, analysis, and utilization of Big Data are falling short of useful techniques for the volume of fast-increasing data. However, there are some assertions that the power of analysis and prediction of Artificial Intelligence would intensify the power of Big Data analysis. This paper studies on business cases to try to graft the Artificial Intelligence technique onto Big Data analysis. We first research on various techniques of Artificial Intelligence and relations between Artificial Intelligence and Big Data. And then, we perform case studies of Big Data with using Artificial Intelligence and propose some roles of Big Data in the future.

• Journal of Digital Convergence
• /
• v.19 no.10
• /
• pp.203-208
• /
• 2021

This study is to investigate the impact of emotional intelligence of online shopping mall call center counselor on customer orientation. The SPSS 25.0 ststistics program was applied for the empirical analysis 148(87.05%)of 170 copies are used for final data analysis. The implications of results are as follows. First, use of emotion, a subfactor of emotional Intelligence, has shown to have a significant positive effect on customer orientation. Second, other's emotional appraisal, a subfactor of emotional Intelligence, has shown to have a significant positive effect on customer orientation. Third, self emotional appraisal, a subfactor of emotional Intelligence, has shown to have a significant positive effect on customer orientation. Fourth, regulation of emotion, a subfactor of emotional Intelligence, has shown to have a significant positive effect on customer orientation.

• Asian Journal of Business Environment
• /
• v.8 no.4
• /
• pp.37-44
• /
• 2018

Purpose - The artificial intelligence industry plays an increasingly significant role in stimulating the development of United States of America's economy. On account of this background, this paper attempts to explore the impact of artificial intelligence industry on United States of America's macroeconomy. Research design, data, and methodology - This paper mainly focuses on the impact of artificial intelligence industry on GDP, employment, real income, import, export and foreign direct investment. Furthermore, the Phillips-Perron test and Canonical cointegrating regression will be employed to examine the impact of artificial intelligence industry on United States of America's macroeconomy with a sample form 2010-Q1 to 2017-Q4. Results - Via the empirical analysis, the results reveal that the artificial intelligence industry has a positive effect on United States of America's GDP, employment, real income, export and foreign direct investment. Conversely, the artificial intelligence industry has a negative effect on United States of America's import. Conclusions - In summary, the impact of artificial intelligence industry on United States of America's macroeconomy is positive and significant in statistics. Therefore, the government of United States of America should put more input into artificial intelligence industry.

• Journal of Information Technology Services
• /
• v.15 no.1
• /
• pp.179-195
• /
• 2016

Popular business magazines continue to highlight the employee's emotional intelligence in the organization. The purpose of this study is to explore a mediation mechanism of the relationship between emotional intelligence and job satisfaction in China IT corporation. For this, this study focused on examining the influential relationship between employee's emotional intelligence, Awareness of Self-Emotion, Awareness of other-Emotion, Emotion management, Emotion Control and job satisfaction and the mediating effect of organization culture awareness upon the relationship. Study findings are as follows : First, China IT corporation employee emotional intelligence has a significant positive(+) effect on job satisfaction and is partially supported organization culture awareness. Second, organization culture awareness has partially mediated effect on relationship between emotional intelligence and job satisfaction. In this study, the more employee's emotional intelligence have a positive impact on job satisfaction and organizational culture. We can designing a support program that emotional intelligence and organization culture awareness for China IT corporation employees will have direct influence upon job satisfaction. Employee's great talent to make good use of elements of emotional intelligence that promote the work environment and their's condition and the relationship between employee and employee.

• Korean Journal of Artificial Intelligence
• /
• v.10 no.2
• /
• pp.13-18
• /
• 2022

The role of artificial medical intelligence through medical big data has been focused on data-based medical device business and medical service technology development in the field of diagnostic examination of the patient's current condition, clinical decision support, and patient monitoring and management. Recently, with the 4th Industrial Revolution, the medical field changed the medical treatment paradigm from the method of treatment based on the knowledge and experience of doctors in the past to the form of receiving the help of high-precision medical intelligence based on medical data. In addition, due to the spread of non-face-to-face treatment due to the COVID-19 pandemic, it is expected that the era of telemedicine, in which patients will be treated by doctors at home rather than hospitals, will soon come. It can be said that artificial medical intelligence plays a big role at the center of this paradigm shift in prevention-centered treatment rather than treatment. Based on big data, this paper analyzes the current status of artificial intelligence technology for chronic disease patients, market trends, and domestic and foreign company trends to predict the expected effect and future development direction of artificial intelligence technology for chronic disease patients. In addition, it is intended to present the necessity of developing digital therapeutics that can provide various medical services to chronically ill patients and serve as medical support to clinicians.

• Journal of Arbitration Studies
• /
• v.30 no.3
• /
• pp.95-113
• /
• 2020

The worldwide diffusion of COVID-19 contributes to electronic commerce all over the world. The proliferation of high volume and small value electronic commerce naturally has combined artificial intelligence with online dispute resolution (ODR). This paper investigates the age effect on Artificial Intelligence acceptability in online dispute resolution and its empirical findings are as follows. First, seven measures out of the nine employed in this case study shows a coherent dynamic pattern over the age spectrum. In other words, the total samples are a heterogenous group rather than a homogeneous one. Second, medium answer occupies a non-negligible portion across answers from nine research questions. It seems to indicate that a considerable portion of Korean respondents are hesitant to make a choice on artificial intelligence at this juncture. Third, all of the respondents agree that the introduction of AI to the dispute resolution could contribute to the hastening of the dispute resolution process. Fourth, most of the respondents agree that artificial intelligence might have the cognitive ability but not the sympathetic or affective ability to handle the electronic commerce disputes.