• Management & Information Systems Review
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• v.33 no.3
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• pp.167-187
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• 2014

While Korean GAAP had detailed regulations for the measurement and disclosure of operating income in the past, K-IFRS did not provide specific rules for operating income until 2011. Some firms that adopted K-IFRS before 2011 did not disclose or calculated operating income in an inconsistent manner although operating income is usually considered as one of the core information items to assess firm valuation. Inconsistency in firms' treatment of operating income invoked much criticism from diverse users of financial statement. The Korean Accounting Institute (KAI hereafter) revised the K-IFRS rules relevant to operating income in September 2010 in response to the voices raised by the business community, whereby the operating income number is allowed to be calculated in conformity with the previous K-GAAP. This study was motivated by the revision of K-IFRS and aims to provide a clue on the validity of such policy decision. To achieve the research objective, we test the relative value relevance of the alternative operating income numbers under K-IFRS versus K-GAAP. Our main findings are as follows. The value relevance of operating income reported before K-IFRS is proved to be higher than after K-IFRS. K-IFRS operating income adjusted to the previous K-GAAP has greater explanatory power for market values relative to one calculated under the K-IFRS regime. In an additional analysis, the sample was decomposed according to whether the operating income under K-IFRS is greater than under K-GAAP. The difference in the value relevance of K-IFRS versus K-GAAP operating income is significant only in the subsample consisting of firms which reports higher operating income under K-IFRS compared to K-GAAP. Also, the firms which would have reported negative operating income on a consecutive basis are more likely to have chosen K-IFRS, resulting in higher numbers than otherwise. It is likely that firms facing the threat of delisting due to consecutive operating loss reporting are more likely to have adopted K-IFRS disclosure rules by which they could report higher operating income numbers. To sum up, these results corroborate the limitation inherent in the K-IFRS regarding operating income disclosures. This paper suggests that the recent revision of K-IFRS implemented by KAI is likely to mitigate some of afore-mentioned limitations effectively.

• Journal of Intelligence and Information Systems
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• v.24 no.2
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• pp.243-264
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• 2018

Although the worldwide battery market is recently spurring the development of lithium secondary battery, lead acid batteries (rechargeable batteries) which have good-performance and can be reused are consumed in a wide range of industry fields. However, lead-acid batteries have a serious problem in that deterioration of a battery makes progress quickly in the presence of that degradation of only one cell among several cells which is packed in a battery begins. To overcome this problem, previous researches have attempted to identify the mechanism of deterioration of a battery in many ways. However, most of previous researches have used data obtained in a laboratory to analyze the mechanism of deterioration of a battery but not used data obtained in a real world. The usage of real data can increase the feasibility and the applicability of the findings of a research. Therefore, this study aims to develop a model which predicts the battery deterioration using data obtained in real world. To this end, we collected data which presents change of battery state by attaching sensors enabling to monitor the battery condition in real time to dozens of golf carts operated in the real golf field. As a result, total 16,883 samples were obtained. And then, we developed a model which predicts a precursor phenomenon representing deterioration of a battery by analyzing the data collected from the sensors using machine learning techniques. As initial independent variables, we used 1) inbound time of a cart, 2) outbound time of a cart, 3) duration(from outbound time to charge time), 4) charge amount, 5) used amount, 6) charge efficiency, 7) lowest temperature of battery cell 1 to 6, 8) lowest voltage of battery cell 1 to 6, 9) highest voltage of battery cell 1 to 6, 10) voltage of battery cell 1 to 6 at the beginning of operation, 11) voltage of battery cell 1 to 6 at the end of charge, 12) used amount of battery cell 1 to 6 during operation, 13) used amount of battery during operation(Max-Min), 14) duration of battery use, and 15) highest current during operation. Since the values of the independent variables, lowest temperature of battery cell 1 to 6, lowest voltage of battery cell 1 to 6, highest voltage of battery cell 1 to 6, voltage of battery cell 1 to 6 at the beginning of operation, voltage of battery cell 1 to 6 at the end of charge, and used amount of battery cell 1 to 6 during operation are similar to that of each battery cell, we conducted principal component analysis using verimax orthogonal rotation in order to mitigate the multiple collinearity problem. According to the results, we made new variables by averaging the values of independent variables clustered together, and used them as final independent variables instead of origin variables, thereby reducing the dimension. We used decision tree, logistic regression, Bayesian network as algorithms for building prediction models. And also, we built prediction models using the bagging of each of them, the boosting of each of them, and RandomForest. Experimental results show that the prediction model using the bagging of decision tree yields the best accuracy of 89.3923%. This study has some limitations in that the additional variables which affect the deterioration of battery such as weather (temperature, humidity) and driving habits, did not considered, therefore, we would like to consider the them in the future research. However, the battery deterioration prediction model proposed in the present study is expected to enable effective and efficient management of battery used in the real filed by dramatically and to reduce the cost caused by not detecting battery deterioration accordingly.

• Management & Information Systems Review
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• v.28 no.2
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• pp.41-67
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• 2009

Organizational interpersonal supervisory trust, organizational justice(distributive and procedural justice) and job satisfaction have been lately more spotlighted as generally concluded by many previous studies. The purpose of this study is to find out possible effects of these three factors upon organizational commitment. The results of this study can be outlined as follows: First, it was found that organizational trust, a preceding variable, had significant positive effects on distributive and procedural justice, as well as on organizational commitment as a dependent variable. Second, it was found that two independent variables, i.e. distributive and procedural justice had significant positive effects upon job satisfaction, and procedural justice had significant positive effects on organizational commitment as a dependent variable, but distributive fairness had no significant effects on organizational commitment. Third, it was found that job satisfaction, an independent variable, had significant positive effects on organizational commitment. Fourth, it was found that organizational trust had significant positive secondhand associations with organizational commitment by way of distributive and procedural justice and job satisfaction, and also had overall significant positive effects on organizational commitment. Thus, it is concluded that the higher organizational trust is an index of higher organizational commitment. Fifth, it was found that distributive justice had just significant secondhand effects on organizational commitment by way of job satisfaction, but it had no significant effects overall upon organizational commitment, since such secondhand effects were considerably set off due to negative firsthand effects of distributive justice upon organizational commitment. But procedural justice and job satisfaction had significant firsthand and overall effects on organizational commitment, so it is concluded that the higher procedural justice and the higher job satisfaction are good indices of the higher organizational commitment. Hence, it is concluded that organizational supervisory trust has positive effects on distributive and procedural justice and organizational commitment; distributive justice has positive effects on job satisfaction; procedural justice has positive effects on job satisfaction and organizational commitment; and job satisfaction has positive effects on organizational commitment, so these empirical findings hereof are consistent with general results of previous studies.

• Management & Information Systems Review
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• v.35 no.3
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• pp.1-22
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• 2016

We analyzed the international comovements and structural changes in the quarterly real GDP by the Markov-switching vector autoregressive model (MS-VAR) from 1971(1) to 2016(1). The main results of this study were as follows. First, the business cycle phenomenon that occurs in the models or individual time series in real GDP has been grasped through the MS-VAR models. Unlike previous studies, this study showed the significant comovements, asymmetry and structural changes in the MS-VAR model using a real GDP across countries. Second, even if there was a partial difference, there were remarkable structural changes in the economy contraction regime(recession), such as 1988(2) ending the global oil shock crisis and 2007(3) starting the global financial crisis by the MS-VAR model. Third, large-scale structural changes were generated in the economic expansion and/or contraction regime simultaneously among countries. We found that the second world oil shocks that occurred after the first global oil shocks of 1973 and 1974 were the main reasons that caused the large-scale comovements of the international real GDP among countries. In addition, the spillover between Korea and 5 countries has been weak during the Asian currency crisis from 1997 to 1999, but there was strong transmission between Korea and 5 countries at the end of 2007 including the period of the global financial crisis. Fourth, it showed characteristics that simultaneous correlation appeared to be high due to the country-specific shocks generated for each country with the regime switching using real GDP since 1973. Thus, we confirmed that conclusions were consistent with a number of theoretical and empirical evidence available, and the macro-economic changes were mainly caused by the global shocks for the past 30 years. This study found that the global business cycles were due to large-scale asymmetric shocks in addition to the general changes, and then showed the main international comovements and/or structural changes through country-specific shocks.

• Management & Information Systems Review
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• v.33 no.1
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• pp.139-155
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• 2014

This study is to examine the linkage of volatility between changes in the stock market of India and other countries through the integration of the world economy. The results were as follows: First, autocorrelation or serial correlation did not exist in the classic RS model, but long-term memory was present in the modified RS model. Second, unit root did not exist in the unit root test for all periods, and the series were a stable explanatory power and a long-term memory with the normal conditions in the ARFIMA model. Third, in the multivariate asymmetric BEKK and VAR model before the financial crisis, it showed that there was a strong influence of the own market of Taiwan and UK in the conditional mean equation, and a strong spillover effect from Japan to India, from Taiwan to China(Korea, US), from US(Japan) to UK in one direction. In the conditional variance equation, GARCH showed a strong spillover effect that indicated the same direction as the result of ARCH coefficient of the market itself. Asymmetric effects in three home markets and between markets existed. Fourth, after the financial crisis, in the conditional mean equation, only the domestic market in Taiwan showed strong influences, and strong spillover effects existed from India to US, from Taiwan to Japan, from Korea to Germany in one direction. In the conditional variance equation, strong spillover effects were the same as the result of the pre-crisis and asymmetric effect in the domestic market in UK was present, and one-way asymmetric effect existed in Germany from Taiwan. Therefore, the results of this study presented the linkage between the volatilities of the stock market of India and other countries through the integration of the world economy, observing and confirming the asymmetric reactions and return(volatility) spillover effects between the stock market of India and other countries.

• Management & Information Systems Review
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• v.34 no.3
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• pp.17-39
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• 2015

This study examined the structural changes and volatility in the global stock markets using a Markov Regime Switching ARCH model developed by the Hamilton and Susmel (1994). Firstly, the US, Italy and Ireland showed that variance in the high volatility regime was more than five times that in the low volatility, while Korea, Russia, India, and Greece exhibited that variance in the high volatility regime was increased more than eight times that in the low. On average, a jump from regime 1 to regime 2 implied roughly three times increased in risk, while the risk during regime 3 was up to almost thirteen times than during regime 1 over the study period. And Korea, the US, India, Italy showed ARCH(1) and ARCH(2) effects, leverage and asymmetric effects. Secondly, 278 days were estimated in the persistence of low volatility regime, indicating that the mean transition probability between volatilities exhibited the highest long-term persistence in Korea. Thirdly, the coefficients appeared to be unstable structural changes and volatility for the stock markets in Chow tests during the Asian, Global and European financial crisis. In addition, 1-Step prediction error tests showed that stock markets were unstable during the Asian crisis of 1997-1998 except for Russia, and the Global crisis of 2007-2008 except for Korea and the European crisis of 2010-2011 except for Korea, the US, Russia and India. N-Step tests exhibited that most of stock markets were unstable during the Asian and Global crisis. There was little change in the Asian crisis in CUSUM tests, while stock markets were stable until the late 2000s except for some countries. Also there were stable and unstable stock markets mixed across countries in CUSUMSQ test during the crises. Fourthly, I confirmed a close relevance of the volatility between Korea and other countries in the stock markets through the likelihood ratio tests. Accordingly, I have identified the episode or events that generated the high volatility in the stock markets for the financial crisis, and for all seven stock markets the significant switch between the volatility regimes implied a considerable change in the market risk. It appeared that the high stock market volatility was related with business recession at the beginning in 1990s. By closely examining the history of political and economical events in the global countries, I found that the results of Lamoureux and Lastrapes (1990) were consistent with those of this paper, indicating there were the structural changes and volatility during the crises and specificly every high volatility regime in SWARCH-L(3,2) student t-model was accompanied by some important policy changes or financial crises in countries or other critical events in the international economy. The sophisticated nonlinear models are needed to further analysis.

• Management & Information Systems Review
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• v.38 no.4
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• pp.61-78
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• 2019

When calculating gains from transfers of assets inherited or donated, the value recognized at the market price as of the date of inheritance or acquisition is recognized as the actual transaction value at the time of acquisition. However, Precedents for the appeal for review by the NTS, the request for adjudgment by the Tax Tribunal(TT) and the request of examination by the Board of Audit and Inspection of Korea(BAI) and the cases of the lawsuit have not shown a consistent results on how much such a the actual transaction value will be measured. This study investigates the operating state of the current tax appeal system using the statistical data of the TT, NTS, and BAI and cases of the lawsuit from 2008 to 2017, and suggests the Improvement of Capital Gains Tax Act on the transfer of inherited and donated property. As a result, total number of requested cases has diminished because cases of the pre-assessment review and the reconsideration appeal by the NTS have decreased steadily over the past decade, while the cases of the lawsuit and the administrative trials(the request for adjudgment by the TT, the appeal for review by the NTS, and the request of examination by the BAI) have been steadily increasing. Also This study found that more than 40% of the complainants proceeded with the cases of the lawsuit proceedings in disagreement with the disposition of tax dissatisfaction under the administrative trials. In addition, Even though the retrospective appraisal price is not recognized as the market price due to the strict interpretation of the tax regulations, it can be seen that it is interpreted as a more expanded concept in the application of the market price than the government office or the tax judge. Therefore, according to the precedents of the cases lawsuit, it is necessary to establish a regulation on the recognition of retroactive appraisal value.

• Management & Information Systems Review
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• v.35 no.2
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• pp.67-81
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• 2016

The purpose of this study was to investigate lots of problems that the current tax appeal system has, which are becoming serious issues as tax appeal cases are recently increasing. Those problems include the unreasonable procedure and period of deliberation on tax appeal cases, permission of a same tax appeal by more than one governmental agencies and the compulsory transposition system of tax appeal cases. All of these problems should be rectified in order to ensure that the currently tax appeal system protect taxpayers' rights and interests effectively. According to the current tax appeal system, the period from the receipt of tax appeal cases to ruling on them is up to 90 days. This is unrealistic, so that period should be allowed to be extended if those cases about more complicated taxation or if they are even harder to be treated for any reason. At present, chief of Tax Tribunal has to unconditionally accept resolution from the meeting of tax judges and make a ruling accordingly because he has no right to reject that resolution. But now, it's time to establish legal grounds based on which the chief suggests the tax judges to reconsider their resolution if it is undoubtedly wrong. Currently, there's a relatively little acceptance of tax appeals from people who can't financially afford to designate a proxy for them. To solve this problem, lots of efforts to make socially recognized the necessity to relive those people's rights and interests and make widely known the Public Proxy of Tax Appeal System. The current tax appeal system allows the Board of Audit and Inspection to be an appealer. This means taxation may be deliberated on by more than one governmental agencies. It is so inefficient. Therefore, tax appeal by the board should be only about taxation that they found unacceptable by audit and inspection. Except for this, it is not allowed that the Board of Audit and Inspection file tax appeals that are, in turn, necessarily transported to the National Taxation. Esecially, the transposition should be a procedure that is occasionally taken. In sum, this study investigated problems with the current tax appeal system, and made suggestions about solutions that are not theoretical but practical.

• Information Systems Review
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• v.17 no.2
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• pp.77-100
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• 2015

This study examined the factors that influence the smartphone user's decision to accept the personalized mobile advertisement. As a theoretical basis, we applied the privacy calculus model (PCM) that illustrates how consumers are engaged in a dynamic adjustment process in which privacy risks are weighted against benefits of information disclosure. In particular, we investigated how smartphone users make a risk-benefit assessment under which personalized service as benefit-side factor and information privacy risks as a risk-side factor accompanying their acceptance of advertisements. Further, we extend the current PCM by considering advertisement fatigue as a new factor that may influence the user's acceptance. The research model with five (5) hypotheses was tested using data gathered from 215 respondents through a quasi-experimental survey method. During the survey, each participant was asked to navigate the website where the experimental simulation of a mobile advertisement service was provided. The results showed that three (3) out of five (5) hypotheses were supported. First, we found that the intention to accept advertisements is positively and significantly influenced by the perceived value of personalization. Second, perceived advertisement fatigue was also found to be a strong predictor of the intention to accept advertisements. However, we did not find any evidence of direct influence of privacy risks. Finally, we found that the significant moderating effect between the perceived value of personalization and advertisement fatigue. This suggests that the firms should provide effective tailored advertisement that can increase the perceived value of personalization to mitigate the negative impacts of advertisement fatigue.

• 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.