• Journal of Distribution Research
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• v.12 no.3
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• pp.23-48
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• 2007

Category management has been implemented to enhance competitiveness in the food distribution industry since 2000 in Korea. This study helps to understand why suppliers achieve better or worse performance than competitors in a category. The major objective of this article is to explore which category tactics are effective to have influence on category performance when suppliers as a category captain implement category management with variety enhancer categories like shampoo, toothpaste, and detergent. The Nielsen data were analyzed using regression and Chow test. The empirical results that were varied upon the store type and market position found out which specific actions on product assortments, pricing, shelving, and product replenishment can increase category sales. Specifically, in the case of market leader in large supermarket, the significant indicators of category sales with respect to category tactics are the out-of-stock rate, the variance across brand shares, the forward inventory, and the days supply of a product. However, in the case of follower in large supermarket, the significant indicators of category sales are the variance across brand shares, the forward inventory, and the days supply of a product. On the other hand, in the case of small supermarket, the significant factors on category sales for both market leader and follower are the retail distribution rate, the variance across brand shares, the forward inventory, and the days supply of a product category. In sum, regardless of the store type and market position, dominant brands in a category, the forward inventory, and short days supply of a product improved performance in all categories. Critical difference is that the out-of-stock rate acted as a key ingredient for the market leader between large and small supermarket and the retail distribution rate for the follower between large and small supermarket. This article presents some theoretical and managerial implications of the empirical results and finalizes the paper by addressing limitations and future research directions.

• Management & Information Systems Review
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• v.36 no.3
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• pp.151-165
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• 2017

This study investigates the effect of revenue recognition by percentage of completion method on financial analysts' earnings forecasting information in order industry. Specifically, we examines how the analysts' earnings forecast errors and biases differ according to whether or not to report the unbilled revenue account balance and the level of unbilled revenue account balance. The sample consists of 453 firm-years listed in Korea Stock Exchange during the period from 2010 to 2014 since the information on unbilled revenue accounts can be obtained after the adoption of K-IFRS. The results are as follows. First, we find that the firms with unbilled revenue account balances have lower analysts' earnings forecast accuracy than the firms who do not report unbilled revue account balances. In addition, we find that the accuracy of analysts' earnings forecasts decreases as the amount of unbilled revenue increases. Unbilled revenue account balances occur when the revenue recognition of the contractor is faster than the client. There is a possibility that managerial discretionary judgment and estimation may intervene when the contractor calculates the progress rate. The difference between the actual progress of the construction and the progress recognized by the company lowers the predictive value of financial statements. Our results suggest that the analysts' earnings forecasts may be more difficult for the firms that report unbilled revenue balances as applying the revenue recognition method based on the progress criteria. Second, we find that the firms reporting unbilled revenue account balances tend to have higher the optimistic biases in analysts' earnings forecast than the firms who do not report unbilled revenue account balances. And we find that the analysts' earnings forecast biases are increases as the amount of unbilled revenue increases. This study suggests an effort to reduce the arbitrary adjustment and estimation in the measurement of the progress as well as the introduction of the progress measurement method which can reflect the actual progress. Investors are encouraged to invest and analyze the characteristics of the order-based industry accounting standards. In addition, the results of this study empower the accounting transparency enhancement plan for order industry proposed by the policy authorities.

• Journal of Intelligence and Information Systems
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• v.22 no.2
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• pp.33-56
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• 2016

Many researchers have focused on developing bankruptcy prediction models using modeling techniques, such as statistical methods including multiple discriminant analysis (MDA) and logit analysis or artificial intelligence techniques containing artificial neural networks (ANN), decision trees, and support vector machines (SVM), to secure enhanced performance. Most of the bankruptcy prediction models in academic studies have used financial ratios as main input variables. The bankruptcy of firms is associated with firm's financial states and the external economic situation. However, the inclusion of qualitative information, such as the economic atmosphere, has not been actively discussed despite the fact that exploiting only financial ratios has some drawbacks. Accounting information, such as financial ratios, is based on past data, and it is usually determined one year before bankruptcy. Thus, a time lag exists between the point of closing financial statements and the point of credit evaluation. In addition, financial ratios do not contain environmental factors, such as external economic situations. Therefore, using only financial ratios may be insufficient in constructing a bankruptcy prediction model, because they essentially reflect past corporate internal accounting information while neglecting recent information. Thus, qualitative information must be added to the conventional bankruptcy prediction model to supplement accounting information. Due to the lack of an analytic mechanism for obtaining and processing qualitative information from various information sources, previous studies have only used qualitative information. However, recently, big data analytics, such as text mining techniques, have been drawing much attention in academia and industry, with an increasing amount of unstructured text data available on the web. A few previous studies have sought to adopt big data analytics in business prediction modeling. Nevertheless, the use of qualitative information on the web for business prediction modeling is still deemed to be in the primary stage, restricted to limited applications, such as stock prediction and movie revenue prediction applications. Thus, it is necessary to apply big data analytics techniques, such as text mining, to various business prediction problems, including credit risk evaluation. Analytic methods are required for processing qualitative information represented in unstructured text form due to the complexity of managing and processing unstructured text data. This study proposes a bankruptcy prediction model for Korean small- and medium-sized construction firms using both quantitative information, such as financial ratios, and qualitative information acquired from economic news articles. The performance of the proposed method depends on how well information types are transformed from qualitative into quantitative information that is suitable for incorporating into the bankruptcy prediction model. We employ big data analytics techniques, especially text mining, as a mechanism for processing qualitative information. The sentiment index is provided at the industry level by extracting from a large amount of text data to quantify the external economic atmosphere represented in the media. The proposed method involves keyword-based sentiment analysis using a domain-specific sentiment lexicon to extract sentiment from economic news articles. The generated sentiment lexicon is designed to represent sentiment for the construction business by considering the relationship between the occurring term and the actual situation with respect to the economic condition of the industry rather than the inherent semantics of the term. The experimental results proved that incorporating qualitative information based on big data analytics into the traditional bankruptcy prediction model based on accounting information is effective for enhancing the predictive performance. The sentiment variable extracted from economic news articles had an impact on corporate bankruptcy. In particular, a negative sentiment variable improved the accuracy of corporate bankruptcy prediction because the corporate bankruptcy of construction firms is sensitive to poor economic conditions. The bankruptcy prediction model using qualitative information based on big data analytics contributes to the field, in that it reflects not only relatively recent information but also environmental factors, such as external economic conditions.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.16 no.6
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• pp.31-42
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• 2021

The importance of startups and ventures in the Korean economy is growing. This study measured whether the start-up and venture ecosystem is growing, including the growth of startups and ventures. The startup and venture ecosystem consists of startups and ventures, investors, and government, which are the main actors of the 'ecosystem', and their movements were measured with 25 quantitative indicators. Based on the original data of the time series from 2010 to 2020, the startup and venture ecosystem index was calculated by applying weights through the comprehensive stock index method and AHP. In 2020, the startup and venture ecosystem grew 2.9 times compared to 2010, and the increase in the government index had a significant impact on growth. Also, the individual indicators that make up each index in 2020, the corporate index had the greatest impact on the growth of the number of 100-billion ventures, while the investment index had a recovery amount and the government index had a significant impact. Based on the original data, the startup and venture ecosystem index was analyzed by dividing it into ecosystems (startup ecosystem and venture ecosystem), industry by industry (all industries and manufacturing industry), and region (Korea and Busan). As a result, the growth of the startup ecosystem over the past decade has been slightly larger than that of the venture ecosystem. The manufacturing was lower than that of all industries, and Busan was lower than that of the nation. This study was intended to use it for the establishment and implementation of support policies by developing, measuring, and monitoring the startup and venture ecosystem index. This index has the advantage of being able to research the interrelationships between major actors, and anyone can calculate the index using the results of official statistical surveys. In the future, it is necessary to continuously update this content to understand how economic and social events or policy support have affected the startup and venture ecosystem.

• International Commerce and Information Review
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• v.15 no.3
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• pp.263-287
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• 2013

This study aims to analyse whether outward foreign direct investment(FDI) by Korean manufacturers has a positive or negative effect on the nation's exports and imports. It provides a comprehensive analysis using both micro and macro approaches to overcome the limitations of the previous studies. In its micro-analysis, this study analyzed the impact of the outstanding outward FDI stock and other related factors on net export/import creation using panel data of 589 overseas affiliates of Korean manufacturers during the period of 2006 to 2011. And in the macro-analysis, the study analyzed the impact of outward FDI on exports using panel data of 23 manufacturing sectors during the period of 2000 to 2011. As a result of empirical study, contrary to the results of most previous studies, Korea's export can be negatively affected when it's manufacturing companies increase their outward FDI and localize their overseas businesses.

• Asian-Australasian Journal of Animal Sciences
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• v.27 no.5
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• pp.616-627
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• 2014

The Lanyu is a miniature pig breed indigenous to Lanyu Island, Taiwan. It is distantly related to Asian and European pig breeds. It has been inbred to generate two breeds and crossed with Landrace and Duroc to produce two hybrids for laboratory use. Selecting sets of informative genetic markers to track the genetic qualities of laboratory animals and stud stock is an important function of genetic databases. For more than two decades, Lanyu derived breeds of common ancestry and crossbreeds have been used to examine the effectiveness of genetic marker selection and optimal approaches for individual assignment. In this paper, these pigs and the following breeds: Berkshire, Duroc, Landrace and Yorkshire, Meishan and Taoyuan, TLRI Black Pig No. 1, and Kaohsiung Animal Propagation Station Black pig are studied to build a genetic reference database. Nineteen microsatellite markers (loci) provide information on genetic variation and differentiation among studied breeds. High differentiation index ($F_{ST}$) and Cavalli-Sforza chord distances give genetic differentiation among breeds, including Lanyu's inbred populations. Inbreeding values ($F_{IS}$) show that Lanyu and its derived inbred breeds have significant loss of heterozygosity. Individual assignment testing of 352 animals was done with different numbers of microsatellite markers in this study. The testing assigned 99% of the animals successfully into their correct reference populations based on 9 to 14 markers ranking D-scores, allelic number, expected heterozygosity ($H_E$) or $F_{ST}$, respectively. All miss-assigned individuals came from close lineage Lanyu breeds. To improve individual assignment among close lineage breeds, microsatellite markers selected from Lanyu populations with high polymorphic, heterozygosity, $F_{ST}$ and D-scores were used. Only 6 to 8 markers ranking $H_E$, $F_{ST}$ or allelic number were required to obtain 99% assignment accuracy. This result suggests empirical examination of assignment-error rates is required if discernible levels of co-ancestry exist. In the reference group, optimum assignment accuracy was achievable achieved through a combination of different markers by ranking the heterozygosity, $F_{ST}$ and allelic number of close lineage populations.

• Journal of Distribution Science
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• v.13 no.9
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• pp.47-57
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• 2015

Purpose - The aim of this paper is to investigate the explanatory power of the Pecking-order theory (the cost of financing increases with asymmetric information) among Korean retail firms from the perspective of debt capacity. According to the Pecking-order theory, a firm's first preference is to use internal funds for its capital needs, its next preference is the issuance of debt, and its last preference is the issuance of equity; this is due to the information asymmetry problem between existing shareholders and investors. However, prior empirical studies, such as Lemmon and Zender (2010), argue that the entire sample test for the Pecking-order theory could be misleading due to the different levels of debt issuance capability of each of the individual firms; in fact, they confirm that the explanatory power of the Pecking-order theory improves after taking into account the differences in debt capacity of the U.S. firms they examined. This paper implements a case study approach among Korean retail firms to examine the relationship between debt capacity and the explanatory power of the Pecking-order theory in Korea. Research design, data, and methodology - This study uses the sample of public retail firms on the Korea Composite Stock Price Index (KOSPI) from the time period of 1990 to 2013. We gather related financial and accounting statements from the financial information firm WISEfn. Credit rating information is provided by the Korea Investor Service. We employ the models of Lemmon and Zender (2010) and Son and Kim (2013) to measure a firm's debt capacity. Their logit models use the rating dummy variable as a dependent variable and incorporate other firm characteristics as independent variables to estimate debt capacity. To test the Pecking-order theory, we adopt variants of the financing deficit model of Shyam-Sunder and Myers (1999). In the test of the Pecking-order theory, we consider all of the changes in total debt obligations, current debt obligations, and long-term debt obligations. Results - Our main contribution to the literature is our confirmation of the predicted relationship between debt capacity and the explanatory power of the Pecking-order theory among Korean retail firms. The coefficients on financing deficits become greater as a firm's debt capacity improves. This is consistent with the results of Lemmon and Zender (2010). The coefficients on the square of the financing deficits are also negative for the firms in the largest debt capacity group, which is also consistent with the predictions in prior literature. Conclusions - This study takes a case study approach by examining Korean retail firms. We confirm that the Pecking-order theory explains the capital structure of retail firms more appropriately, after taking into account the debt capacity of each firm. This result suggests the importance of debt capacity consideration in the testing of the Pecking-order theory. Our result also implies that there has been a potential underestimation of the explanatory power of the Pecking-order theory in existing studies.

• Korean Journal of Construction Engineering and Management
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• v.6 no.3 s.25
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• pp.144-155
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• 2005

Since 1950s, the Korean housing market has continually experienced the chronicle lack of housing stock because of lower housing investment in comparison with a population explosion, prompt urbanization and rapid restructuring of family. The Korean housing market have thus been driven not by the pricing model by housing demand-supply chain but by the Korean housing policies focusing on the increase of housing supply and the living stability of the middle or low-income bracket. After all, repetitive economic vicious circle of housing price and the increase of unsold apartments aggravated the malfunction of the Korean housing market. Meanwhile, the Korean construction firms have exacerbated their profitability. Such terrible situations are mainly triggered by the Korean construction firms that weighed on the short-term profits and quick response of the government policy alterations rather than the prospect of housing market Therefore, this research focusing on the dynamics of housing market identified and classified the demand and supply elements that consist not only of housing system structures but also of the environmental elements that affect the structures. Based on the system thinking and traditional theory of consumer's choice, the interactions of these elements were constructed as a causal loop diagram that explains the mutual influences among housing subsystems with feedback loops. This paper describes and discusses about the causes of the dynamic changes in the Korean housing market. This study would help housing suppliers, including housing developers, construction firms, etc., to form a more comprehensive understanding on the fundamental issues that constitute the Korean housing market and thereby increasing their long term as well as minimizing the risk involved in the housing supply businesses.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.18 no.7
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• pp.149-161
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• 2017

This paper analyzed the impact of alliances on the market value of the 106 bio-pharmaceutical companies listed on the KOSPI or KOSDAQ in Korea by using the 'Event study methodology'. Although general alliances did not impact the corporate value significantly, in the analysis corresponding to the alliance type, R&D alliances created positive value, as technology acts as an important factor for the alliance. Among the R&D alliances, 'Technology Transfer alliances', in particular 'Development Technology Transfer alliances', had a positive influence on the corporate value. We interpret these differentiated results as market tends to screen for types of alliances. Meanwhile, we confirmed that the possibility of a stock price increase before the alliance announcement is high by analyzing the impact of the timing of corporate alliance announcements on the company value. It can be inferred that the possibility of information leakage is high. This paper analyzes the impact of alliances for managers and practitioners seeking to create value for domestic bio-pharmaceutical companies, and suggests the need to prevent information leakages by establishing a suitable policy.

• Journal of Distribution Science
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• v.10 no.2
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• pp.7-17
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• 2012

The aim of this study is to empirically explore micro and macroeconomic factors affecting the Pakistani sugar industries and searching the energy potential of this industry, through the survey of literature. The empirical part has been explored by employing Vector Autoregression (VAR), Granger Causality tests and simultaneous equation models through quarterly data for the period of 1991q2-2008q4. The study also aims to devise policies for the development of sugar industries and identify its growing importance for the energy sector of Pakistan. Empirical tests applied on the domestic prices of sugar, domestic interest rates, and exchange rate, productive capacities of sugar mills, per capita income, world sugar prices on cultivable area and sugar production reveal very useful results. Results reveal an improvement of productive capacity of the sugar mills of Pakistan on account of increasing crushing capacity of this sector. Negative effect of rising wholesale prices on the harvesting area was also observed. Profit earnings of the sugar mills significantly increase with the rise of sugar prices but the system does not exist for the farming community to share the rising prices of sugar. The models indicate positive and significant effect of local prices of sugar on its volume of import. Another of the findings of this study positively relates the local sugar markets with the international prices of sugar. Additionally, the causality tests results reveal exchange rate, harvesting area and overall output of sugarcane to have significant effects on the local prices of sugar. Similarly, import of sugar, interest rate, per capita consumption of sugar, per capita national income and the international prices of sugar also significantly affect currency exchange rate of Pakistani rupee in terms of US$. The study also finds sugar as an essential and basic necessity of the Pakistani consumers. That is why there are no significant income and price effects on the per capita consumption of sugar in Pakistan. All the empirical methods reiterate the relationship of variables. Economic policy makers are recommended to improve governance and management in the production, stock taking, internal and external trading and distribution of sugar in Pakistan using bumper crop policies. Macroeconomic variables such as interest rate, exchange rate per capita income and consumption are closely connected with the production and distribution of sugar in Pakistan. The cartelized role of the sugar industries should also be examined by further studies. There is need to further explore sugar sector of Pakistan with the perspective of energy generation through this sector; cartelized sugar markets in Pakistan and many more other dimensions of this sector. Exact appraisal of sugar industries for energy generation can be done appropriately by the experts from applied sciences.