• Journal of Information Technology Services
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• v.8 no.3
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• pp.1-14
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• 2009

Due to economic turbulence and fierce competition in the IT service industry, companies have been seeking breakthrough of offerings by investing in research and development (R&D). This paper aims to examine the impact of R&D expenditure size on financial performance focusing on Korean IT service companies. The expected growth rate of revenue and net profit in the upcoming two years were analyzed based on three groups according to different R&D expenditure rates using collected data from 100 of IT service companies. Unlike our presumptions, our finding presents a non-significant relationship between the R&D expenditure size and companies' financial performance. An interesting result among others is that all companies invested in R&D strongly believe there will be an increase of their financial performance in the future.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.05a
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• pp.232-234
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• 2022

Big data is widely used in customer relationship management, relationship marketing, financial business improvement, credit information and risk management. Moreover, as non-face-to-face financial transactions have become more active recently due to the COVID-19 virus, the use of financial big data is more demanded in terms of relationships with customers. In terms of customer relationship, financial big data has arrived at a time that requires an emotional rather than a technical approach. In relational marketing, it was necessary to emphasize the emotional aspect rather than the cognitive, rational, and rational aspects. Existing traditional financial data was collected and utilized through text-type customer transaction data, corporate financial information, and questionnaires. In this study, the customer's emotional image data, that is, atypical data based on the customer's cultural and leisure activities, is acquired through SNS and the customer's activity image is analyzed with an artificial intelligence CNN algorithm. Activity analysis is again applied to the annotated AI, and the AI big data model is designed to analyze the behavior model shown in the annotation.

• Journal of Digital Convergence
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• v.14 no.8
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• pp.163-175
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• 2016

The purpose of this study is to provide an efficient internal control system formation incentives for company and to confirm empirically usefulness of the internal accounting control system for financial institutions by analyzing whether the internal control vulnerabilities of companies related significantly to the classification and assessment of soundness of financial institutions. Empirical analysis covered KOSPI, KOSDAQ listed companies and unlisted companies with more than 100 billion won of assets which have trading performance with "K" financial institution from 2008 until 2013. Whereas non-internal control vulnerability reporting companies by the internal control of financial reporting received average credit rating of BBB on average, reporting companies received CCC rating. And statistically significantly, non-reporting companies are classified as "normal" and reporting companies are classified as "precautionary loan" when it comes to asset quality classification rating. Therefore, reported information of internal control vulnerability reduced the credibility of the financial data, which causes low credit ratings for companies and suggests financial institutions save additional allowance for asset insolvency prevention and require high interest rates. It is a major contribution of this study that vulnerability reporting of internal control in accordance with the internal control of financial reporting can be used as information significant for the evaluation of financial institutions on corporate soundness.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.13 no.5
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• pp.63-72
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• 2018

The purpose of this study was to analyze the survival rate and survival characteristics of young start-up entrepreneurs supported with public financing, by using non-parametric statistic of Kaplanr-Meier Analysis on non-financial data. Average survival periods of different survival characteristics have been estimated by dividing the age groups into 20s and 30s. After then, the main variables affecting the survival period have been analyzed. 3,825 firms guaranteed by Credit Guarantee Institutions in Korea were used as database for the analysis. 3,242 firms have survived while 583 firms have gone insolvent. The study period was from January 1, 2011 to December 31, 2017. Age-based breakdown of the business founders show that 3 variables in the 20s and 5 variables in the 30s are derived as the significant variables, resulting in the significant differences of each age group. In other words, the start-up support agencies and financial institutions need to develop a credit evaluation system that distinguishes the criteria of age range and find information that reflect the characteristics of entrepreneurs in their 20s as well as developing tailor-made financial products. Also, step-by-step support measures are required for the start-ups of high survival times and make them grow into promising SMEs. Meanwhile, non-financial support plans shall be invigorated along with the financial ones to help the start-ups of low survival times. This study is meaningful in that the survival analysis has been conducted by using the non-financial data of young start-up entrepreneurs. It is expected that the results of this analysis contribute to the enhancement of survival rate of start-ups by providing start-up support agencies and start-up business owners with the unique information of the survival characteristics.

• International Journal of Human Ecology
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• v.12 no.2
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• pp.77-93
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• 2011

This study investigates the association between chronic health conditions (CHD) and depression with a focus on the mediating effect of financial strain. We tested if age makes any difference in the effect of CHD and financial strain on depression. The data comes from the 2006 Korea Longitudinal Study of Aging (KLoSA) collected by the Institute of Korean Labor Research. The sample consisted of information from 8,961 individuals ages 45-79. Separate analyses were done for middle-age (45-64) and older-adult groups (65-79). There were significant financial portfolio differences among CHD patients and non-CHD, for both age groups, that may constitute the impact of a health event on financial wellbeing; in addition, the associations of CHD on depressive symptoms were different by age groups. The mediating effect of financial wellbeing on the association between CHD and depressive symptoms was verified; in addition, the role of financial wellbeing on the association was especially strong for the older-adult group. The effect of CHD on depression was contingent on the amount of net assets and annual personal income. Implications are discussed based on the findings.

• Journal of Information Technology Applications and Management
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• v.16 no.4
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• pp.135-165
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• 2009

The major purpose of this study is to identify the factors influencing the implementation performance of ERP Systems from an integrated viewpoint. For this purpose, a research model is developed based on the literature reviews of ERP systems, contingency theory, and change management theory. The research model proposed fifteen variables as the factors influencing the implementation performance in the ERP systems. The data have been collected from the 164 enterprises which implemented ERP systems at least one year ago. The respondents were person in charge of ERP system of each corporation. The results of hypothesis testing through multiple regression analysis are summarized as follows. Firstly, standardization of work, concentration of decision making, top management concern and support, real user participation, project support goodness, ease of use, and system usefulness have positive influence upon non-financial performance. Secondly, market uncertainty, industrial competition, project support goodness, and customization minimization have positive influence upon financial performance. From the analysis, this research have identified important characteristics for the successful implementation of ERP systems. Consequently, this research ends with managerial and theoretical implications of the study results, as well as limitations and future research directions.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.2
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• pp.387-399
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• 2021

This study mainly aims to examine the effect of intangible assets on the value relevance of the Gulf Cooperation Council (GCC)-listed non-financial firms. This study tested three types of models by using a large sample of non-financial firms listed in GCC countries as emerging markets from 2008 to 2016. The types of models are accounting information (earnings per share and book value of share) without intangible assets model, intangible assets model, and accounting information (earnings per share and book value of share) with intangible assets model. Ordinary least square (OLS) shows mixed results as intangible assets improve the value relevance of accounting information positively in UAE and negatively in Kuwait but not in other countries. The study documents a robust positive relationship between intangible assets and earnings quality in terms of value relevance in KSA and Qatar. The findings provide implications for policymakers, investors, and managers. The results suggest that intangible assets can improve the value relevance in emerging markets, such as GCC, as the need to organize the requirements of information disclosures on intangible assets and provide great transparency and additional disclosure of information about intangible assets and their components.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.30 no.6
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• pp.1291-1300
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• 2020

Recently, a deep learning-based non-profiling side-channel analysis was proposed. The deep learning-based non-profiling analysis is a technique that trains a neural network model for all guessed keys and then finds the correct secret key through the difference in the training metrics. As the performance of non-profiling analysis varies greatly depending on the neural network training model design, a correct model design criterion is required. This paper describes the two types of loss functions and eight labeling methods used in the training model design. It predicts the analysis performance of each labeling method in terms of non-profiling analysis and power consumption model. Considering the characteristics of non-profiling analysis and the HW (Hamming Weight) power consumption model is assumed, we predict that the learning model applying the HW label without One-hot encoding and the Correlation Optimization (CO) loss will have the best analysis performance. And we performed actual analysis on three data sets that are Subbytes operation part of AES-128 1 round. We verified our prediction by non-profiling analyzing two data sets with a total 16 of MLP-based model, which we describe.

• International Journal of Advanced Culture Technology
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• v.8 no.4
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• pp.271-278
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• 2020

Modern financial services go beyond the stage of internet banking, and new concepts of financial transactions such as Internet of Things, mobile banking, electronic payments, and fintech have emerged. As a result, banks are less influential in financial transactions, and changes are being demanded. In the present era, the basic business of banks has decreased, and it is transforming into a space where both consumer finance work and reside. The bank office stands for the brand image of the bank, and it is represented by trust with customers in the basic business of financial transactions, and the rise in real estate value is a natural social phenomenon due to the nature of the location and location of real estate owned by the bank. The business method and space of the bank office that meets the new paradigm of the modern society is an inefficient space only for the convenience and rest of consumers, but it must be used as a variety of spaces suitable for the region to increase the functional value of the bank office. Through this study, as a convenience space for consumers, various service facilities should be introduced to understand the characteristics of the region as a convenience space for consumers, and various service facilities should be introduced to meet the needs of consumers, and the bank office should be improved as a complex service space for local residents.

• Journal of Distribution Science
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• v.13 no.10
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• pp.73-82
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• 2015

Purpose - This paper aims to investigate whether information asymmetry could explain capital structures in Korean corporations. According to Myers (1984), firms prefer internal funding to external financing due to the costs associated with information asymmetry. When external financing is necessary, firms prefer to issue debt rather than equity by the same reasoning. Since Shyam-Sunder and Myers (1999), numerous studies continue to debate the validity of the theory. In this paper, we show how the theory depends on assumptions and incorporated variables. We hope our investigation can provide helpful implications regarding capital structure, information asymmetry, and other firm characteristics. Specifically, our empirical results are complementary to the analysis of Son and Lee's (2015), a recent study that examines the pecking order theory prediction for Korean retail firms. Research design, data, and methodology - We test empirical models that are some variants of model used in Shyam-Sunder and Myers (1999). The financial and accounting data are provided by WISEfn for the firms listed on the KOSPI during 1990 to 2013. Bond ratings are supplied by the Korea Investor Service (KIS). We take into account the heterogeneity in debt capacity; a firm's debt capacity is measured by using the method of Lemmon and Zender (2010) based on its bond ratings. Finally, we estimate empirical models suggested by Shyam-Sunder and Myers (1999), Frank and Goyal (2003), and Lemmon and Zender (2010). Results - First, we find that Shyam-Sunder and Myers' (1999) prediction fails to explain total debt changes of Korean firms. Second, we find a non-monotonic relationship between total debt changes and financial deficits with respect to debt capacity. This contradicts the prediction of Lemmon and Zender (2010) that argues the pecking order theory survives with a monotonically increasing relationship. Third, we estimate a negative correlation coefficient between financial deficit and current debt changes. The result is the complete opposite of the prediction of Lemmon and Zender (2010). Finally, we also confirm the non-monotonic relationship between non-current debt changes and financial deficits with respect to debt capacity. Yet, the slope of coefficient is smaller than that of total debt change case. Indeed, the results are, to some extent, consistent with the prediction of pecking order theory, if we exclude the mid-debt capacity firms. Conclusions - Our empirical results complementary to the analysis of Son and Lee (2015), a recent study focusing on capital structure in Korean retail firms; their paper suggests interesting topics regarding capital structure, information asymmetry, and other firm characteristics in Korean corporations. Contrary to Son and Lee (2015), our results show that total debt changes and current debt changes are inconsistent with the prediction of Shyam-Sunder and Myers (1999). However, similar to Son and Lee (2015), non-current debt changes are consistent with the pecking order prediction, in the case of excluding the mid-level debt capacity firms. This contrast allows us to infer that industry characteristics significantly affect the validity of the pecking order prediction. Further studies are needed to analyze the economics behind this phenomenon, which is beyond the scope of our paper. In addition, the estimation bias potentially matters regarding the firm-level debt capacity calculation. We also reserve this topic for future research.