• The Journal of Asian Finance, Economics and Business
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• v.10 no.2
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• pp.301-311
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• 2023

This research aims to identify the role that small and medium enterprises (SMEs) can play in achieving the economic goals of the vision of Saudi Arabia 2030. The study relied on descriptive analysis, designing a standard model, and analyzing it using the Eviews9 program. The study also adopted the questionnaire as a tool for data collection. The study area covered Alkharj and Hawtat Bani Tamim governorates. The sample size of the study was 142 participants. The study's results confirmed the existence of a significant impact of changes in independent variables (X1, X2, X3, X4), which are (GDP, non-oil exports, number of employees, and public revenues), respectively. The dependent variable (Y) represents the number of small and medium-sized businesses in the Kingdom of Saudi Arabia. Additionally, it was found that 61.3% of small and medium-sized enterprises in the governorates of Al-Kharj and Hawtat Bani Tamim operate in the commercial sector. Most study participants concur that SMEs significantly lowered the unemployment rate and helped boost the GDP rate in the Kingdom of Saudi Arabia. The obstacles and difficulties facing the establishment of these enterprises were financial problems, marketing problems, and corporate monopoly. Furthermore, most of the small and medium l enterprises faced financing problems.

• Asia Marketing Journal
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• v.15 no.4
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• pp.223-241
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• 2014

In May 2012, the collaboration of Hana Bank, top financial service company, and SK Planet, top telecommunication service provider, introduced a new credit card that was filled with all-in-one benefits into the market. Leveraging strong infrastructure of two companies, each top in its own industries, the awareness and preference of 'Club SK Card' brand rapidly increased to about 25% in less than one year. Moreover, this new card was enthroned in the most sold credit card of year 2012, accounting for a market share of 7.2% in the credit card market and more than 80% in the mobile credit card market. To make these results possible, 'Club SK Card' marketing team developed an effective marketing communication strategy which followed the 6M model. The mission of the marketing communication strategy was simple and clear. It was to deliver the card's inherent strengths on consumer benefits that come from the support of subsidiary and affiliated companies of SK Planet. According to OK Cashbag data, the marketing communication team selected the appropriate target consumers and approached them directly, inducing actual purchase behavior. The target consumers received straightforward messages about 'Club SK Card' and were led to join in the new membership at their most frequently visited supermarket or franchise restaurant. The straightforward communication message embedded in an eye-catching commercial ad with a hook song accompanied with a dance was delivered via public media. The ad became so popular that many other television programs quoted or made parodies of the ad. Courtesy of the commercial ad, the brand name disseminated rapidly and widely among the public. In October 2012, an ingenious planning and persistent implementation of the communication strategy results 'Club SK Card' to be ranked top in brand awareness as well as advertising preference tests.

• Journal of the Korean Society of Environmental Restoration Technology
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• v.27 no.1
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• pp.55-70
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• 2024

The loss of biodiversity poses a significant threat not only to business sustainability and investment risk but also to societal well-being. Nature serves as a crucial driver for long-term business viability and economic prosperity. The Task Force on Nature-related Financial Disclosures (TNFD), established in September 2023, mandates that companies assess and disclose their impacts on nature. Despite this, many businesses lack a full understanding of their reliance on and impact upon natural capital and ecosystem services, leading to insufficient disclosures. This study evaluates the applicability of TNFD's assessment methodologies and indicators within a domestic context, highlighting the condition of nature and ecosystem services, and exploring potential synergies with national biodiversity policies. Our analysis suggests that TNFD necessitates a unique approach to the spatial and temporal data and methodologies traditionally employed in environmental impact assessments. This includes assessing the reciprocal influences of corporate activities on natural capital and ecosystem services via the LEAP framework. Moreover, in industries where the choice of specific indicators depends on unique sectoral traits, developing a standardized strategy for data and assessment indicators-adapted to local conditions-is crucial due to the variability in the availability of assessment tools and data. The proactive engagement of the private sector in ecosystem restoration projects is particularly promising for contributing towards national biodiversity objectives. Although TNFD is in its nascent phase, its global adoption by numerous companies signifies its potential impact. Successful implementation of TNFD is anticipated to deepen businesses' and financial institutions' understanding of natural capital and ecosystem services, thereby reinforcing their commitment to sustainable development.

• Management & Information Systems Review
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• v.29 no.4
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• pp.1-22
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• 2010

There have been arguments in Korea that fair value accounting system improves quality of accounting information through the asset revaluation. These arguments are based on the fact that investors prefer fair value to cost value information. Others argue that cost principles may offer more proper information to the investors because financial statements applied the cost principles are more objective and thus more reliable. Prior researches focused mainly on the motives of asset revaluation but I examined the effects of the tangible asset revaluation on the stock prices. The empirical findings indicate that : (1) the gains on the tangible asset revaluation are positively correlated with the stock prices; (2) the net book values applied the cost principles explain stock prices better than the net book values applied fair values. My findings suggest that the gains on the tangible asset revaluation constitute a part of the firms' values but the accounting informations measured fair value are not always useful to the investors in the capital market.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.18 no.5
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• pp.543-548
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• 2017

This paper examines the effects of strong corporate governance for listed companies in accessing capital markets from the point of view of the weighted average cost of capital. Results found that corporate governance had a significant negative(-) relation to the weighted average cost of capital. This finding is consistent with previous research and implies that the higher shareholder ownership and foreign ownership have confidence in the financial information of the company, and therefore, risk is reduced for investors. This results in lower expected rates of return and companies will pay a lower cost of capital. Second, tax evasion had a positive effect(+) on the weighted average cost of capital. The low quality of corporate accounting information is expected to increase tax avoidance. Accordingly, this results in increased risk. If the required rate of return is high in its impact,it leads to increased capital costs. In addition, corporate governance and tax avoidance factors showed a negative affect (-) on the weighted average cost of capital. Corporate governance plays an important role in tax avoidance and the weighted average cost of capital, and strong corporate governance reducesthe impact on tax avoidance. In addition, the weighted average cost of capital in capital markets showed the reducing effect.

• Journal of Digital Convergence
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• v.18 no.9
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• pp.103-112
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• 2020

The purpose of this study is to examine the current status and components of Korean National Debt and to analyze the effects of each component on National Debt. In the Korean Statistical Information Service (KOSIS), we searched for data such as General Accounting Deficit Conservation, For Foreign Exchange Market Stabilization, For Common Housing Stability, Local Government Net Debt Public Funds, etc that constitute National Debt. The analysis period used a total of 23 annual data from 1997 to 2019. The data collected in this study use the rate of change compared to the previous year for each component. Using this, this study attempted index analysis, numerical analysis, and model analysis. Correlation analysis result, the National Debt has a high relationship with the For Common Housing Stability. For Foreign Exchange Market Stabilization, Public Funds, etc., but has a low relationship with the Local Government Net Debt. Since 1997, National Debt has been increasing similarly to the For Foreign Exchange Market Stabilization, For Common Housing Stability and Public Funds etc. Since 2020, Korea is expected to increase significantly in terms of For Common Housing Stability and Public Funds, etc due to Corona19. At a time when the global economic situation is difficult, Korea's National Debt is expected to increase significantly due to the use of national disaster subsidies. However, if possible, the government expects to operate efficiently for economic growth and financial market stability.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.2
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• pp.602-609
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• 2021

Under accrual basic accounting, financial statements may be less reliable compared to cash basis accounting. The purpose of this study is to conduct an empirical analysis to determine the possibility of profit adjustment through the increase and decrease of deferred tax accounts. For our empirical analysis, a dummy variable of '1' was used as a dependent variable when the deferred tax net assets increased from the previous year and '0' when the deferred tax net assets decreased. Meanwhile, the variables of interest were discretionary accruals and ROA variation compared to the previous year. Logistic regression analysis was performed to establish the relevance between variables. Results found larger discretionary accruals related to lower net deferred tax assets compared to the previous year. In addition, there was a correlation between ROA and net deferred tax assets only if the ROA increased and net profit was greater than '0'. Study results will enable deferred tax information to be used in investment decision-making, and supervisory institutions can establish policies to prevent profit adjustments and enhance reporting standards.

• Journal of Intelligence and Information Systems
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• v.23 no.3
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• pp.139-153
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• 2017

Corporate bankruptcy can cause great losses not only to stakeholders but also to many related sectors in society. Through the economic crises, bankruptcy have increased and bankruptcy prediction models have become more and more important. Therefore, corporate bankruptcy has been regarded as one of the major topics of research in business management. Also, many studies in the industry are in progress and important. Previous studies attempted to utilize various methodologies to improve the bankruptcy prediction accuracy and to resolve the overfitting problem, such as Multivariate Discriminant Analysis (MDA), Generalized Linear Model (GLM). These methods are based on statistics. Recently, researchers have used machine learning methodologies such as Support Vector Machine (SVM), Artificial Neural Network (ANN). Furthermore, fuzzy theory and genetic algorithms were used. Because of this change, many of bankruptcy models are developed. Also, performance has been improved. In general, the company's financial and accounting information will change over time. Likewise, the market situation also changes, so there are many difficulties in predicting bankruptcy only with information at a certain point in time. However, even though traditional research has problems that don't take into account the time effect, dynamic model has not been studied much. When we ignore the time effect, we get the biased results. So the static model may not be suitable for predicting bankruptcy. Thus, using the dynamic model, there is a possibility that bankruptcy prediction model is improved. In this paper, we propose RNN (Recurrent Neural Network) which is one of the deep learning methodologies. The RNN learns time series data and the performance is known to be good. Prior to experiment, we selected non-financial firms listed on the KOSPI, KOSDAQ and KONEX markets from 2010 to 2016 for the estimation of the bankruptcy prediction model and the comparison of forecasting performance. In order to prevent a mistake of predicting bankruptcy by using the financial information already reflected in the deterioration of the financial condition of the company, the financial information was collected with a lag of two years, and the default period was defined from January to December of the year. Then we defined the bankruptcy. The bankruptcy we defined is the abolition of the listing due to sluggish earnings. We confirmed abolition of the list at KIND that is corporate stock information website. Then we selected variables at previous papers. The first set of variables are Z-score variables. These variables have become traditional variables in predicting bankruptcy. The second set of variables are dynamic variable set. Finally we selected 240 normal companies and 226 bankrupt companies at the first variable set. Likewise, we selected 229 normal companies and 226 bankrupt companies at the second variable set. We created a model that reflects dynamic changes in time-series financial data and by comparing the suggested model with the analysis of existing bankruptcy predictive models, we found that the suggested model could help to improve the accuracy of bankruptcy predictions. We used financial data in KIS Value (Financial database) and selected Multivariate Discriminant Analysis (MDA), Generalized Linear Model called logistic regression (GLM), Support Vector Machine (SVM), Artificial Neural Network (ANN) model as benchmark. The result of the experiment proved that RNN's performance was better than comparative model. The accuracy of RNN was high in both sets of variables and the Area Under the Curve (AUC) value was also high. Also when we saw the hit-ratio table, the ratio of RNNs that predicted a poor company to be bankrupt was higher than that of other comparative models. However the limitation of this paper is that an overfitting problem occurs during RNN learning. But we expect to be able to solve the overfitting problem by selecting more learning data and appropriate variables. From these result, it is expected that this research will contribute to the development of a bankruptcy prediction by proposing a new dynamic model.

• Journal of Intelligence and Information Systems
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• v.21 no.3
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• pp.79-99
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• 2015

The prediction of bankruptcy has been extensively studied in the accounting and finance field. It can have an important impact on lending decisions and the profitability of financial institutions in terms of risk management. Many researchers have focused on constructing a more robust bankruptcy prediction model. Early studies primarily used statistical techniques such as multiple discriminant analysis (MDA) and logit analysis for bankruptcy prediction. However, many studies have demonstrated that artificial intelligence (AI) approaches, such as artificial neural networks (ANN), decision trees, case-based reasoning (CBR), and support vector machine (SVM), have been outperforming statistical techniques since 1990s for business classification problems because statistical methods have some rigid assumptions in their application. In previous studies on corporate bankruptcy, many researchers have focused on developing a bankruptcy prediction model using financial ratios. However, there are few studies that suggest the specific types of bankruptcy. Previous bankruptcy prediction models have generally been interested in predicting whether or not firms will become bankrupt. Most of the studies on bankruptcy types have focused on reviewing the previous literature or performing a case study. Thus, this study develops a model using data mining techniques for predicting the specific types of bankruptcy as well as the occurrence of bankruptcy in Korean small- and medium-sized construction firms in terms of profitability, stability, and activity index. Thus, firms will be able to prevent it from occurring in advance. We propose a hybrid approach using two artificial neural networks (ANNs) for the prediction of bankruptcy types. The first is a back-propagation neural network (BPN) model using supervised learning for bankruptcy prediction and the second is a self-organizing map (SOM) model using unsupervised learning to classify bankruptcy data into several types. Based on the constructed model, we predict the bankruptcy of companies by applying the BPN model to a validation set that was not utilized in the development of the model. This allows for identifying the specific types of bankruptcy by using bankruptcy data predicted by the BPN model. We calculated the average of selected input variables through statistical test for each cluster to interpret characteristics of the derived clusters in the SOM model. Each cluster represents bankruptcy type classified through data of bankruptcy firms, and input variables indicate financial ratios in interpreting the meaning of each cluster. The experimental result shows that each of five bankruptcy types has different characteristics according to financial ratios. Type 1 (severe bankruptcy) has inferior financial statements except for EBITDA (earnings before interest, taxes, depreciation, and amortization) to sales based on the clustering results. Type 2 (lack of stability) has a low quick ratio, low stockholder's equity to total assets, and high total borrowings to total assets. Type 3 (lack of activity) has a slightly low total asset turnover and fixed asset turnover. Type 4 (lack of profitability) has low retained earnings to total assets and EBITDA to sales which represent the indices of profitability. Type 5 (recoverable bankruptcy) includes firms that have a relatively good financial condition as compared to other bankruptcy types even though they are bankrupt. Based on the findings, researchers and practitioners engaged in the credit evaluation field can obtain more useful information about the types of corporate bankruptcy. In this paper, we utilized the financial ratios of firms to classify bankruptcy types. It is important to select the input variables that correctly predict bankruptcy and meaningfully classify the type of bankruptcy. In a further study, we will include non-financial factors such as size, industry, and age of the firms. Thus, we can obtain realistic clustering results for bankruptcy types by combining qualitative factors and reflecting the domain knowledge of experts.

• The Journal of Asian Finance, Economics and Business
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• v.7 no.12
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• pp.1175-1184
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• 2020

This research examines the personal (i.e., Machiavellianism) and situational factors (i.e., tax climate) that are believed to be psychologically salient aspects in tax compliance. To the best of our knowledge, no research has been carried out to investigate the interaction effect of the two factors. This study uses a paper-and-pencil laboratory experiment 2x2 between-subject factorial design that involved 158 participants. The results indicate that a taxpayer who has a low Machiavellianism score or who is in a high synergistic tax climate reports a higher level of income. In the high synergistic tax climate, where tax norms apply, personal ethics do not play a significant role in tax compliance decisions. Where the synergistic relationship between taxpayer and authorities is low, personal ethics play an important role, i.e., low Machiavellians report a higher reported income than high Machiavellians do. This research contributes to the literature that deviates from the traditional model of tax compliance. Taxpayers are not always rational, but they might pay tax for reasons other than financial motives (Alm, 1991, 2018), that is, personal ethics in this study. This research implies the need for policymakers to consider other approaches rather than only relying on audits and fines.