• Fashion & Textile Research Journal
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• v.23 no.3
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• pp.358-369
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• 2021

This study aims to classify the dimensions of the sales information of secondhand clothing goods on the C2C secondhand trading platform and to systematically analyze the components of each dimension. To this end, the NVivo 12.0 qualitative data analysis software was used. The content analysis showed that the sales information of secondhand clothing goods was classified into four dimensions: detailed information of the sale goods, information specific to secondhand clothing goods, seller opinion information, and service information. The components of each dimension were as follows. The detailed information of the sale goods included size, sale price, item, design, brand name, material, color, wearing season, fit, gender, etc. The information specific to secondhand clothing goods included the number of times the item was worn, its purchase history, and product condition. Seller opinion information included product review, sales motivation, notes for the transaction, coordination proposal, and usage proposal. The service information included the transaction mode, exchange·return·refund, and promotion. The frequency analysis showed that the highest frequencies were sale goods(37.47%), information specific to secondhand clothing goods(24.63%), seller opinion information(20.54%), and service information(17.37%). This study will help C2C secondhand trading platform managers or sellers establish clear standards for presenting sales information and developing ideas toward constructing differentiated platform contents.

• The Journal of Asian Finance, Economics and Business
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• v.7 no.10
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• pp.43-50
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• 2020

Disclosure level in interim financial reporting is important for information users to make business decisions. This has received much attention from the information users. The article is aimed at determining the factors of financial ratios, which impact on the disclosure level in interim financial reporting. The authors use the ordinary least squares to test. The sample consists of 418 VN100 over a 6-year period from 2014 to 2019. The results show that there are four factors that positively impact on the disclosure level in interim financial reporting: Enterprise size (SIZE); Liquidity (LIQI); Sales growth (GROW) and Profitability (ROE). The article proposes some policy recommendations to contribute to improving disclosure level in interim financial reporting. Accordingly, State Securities Commission of Vietnam should strengthen the regular inspection of VN100's disclosure level in interim financial reporting and also should enforce strict sanctions or may consider delisting in cases of listed enterprises with incomplete disclosure. The managers of VN100 need to raise the sense of responsibility of information providers to ensure adequate information in interim financial reporting. Investors should also pay attention to the financial ratios of VN100 such as firm size, return-on-equity, liquidity, and sales growth to get useful information and ensure sound business decisions.

• Culinary science and hospitality research
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• v.20 no.6
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• pp.175-189
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• 2014

This study was intended to provide fundamental data concerning franchising companies' characteristics and performance in foodservice business by employing financial data from the firms' IDS(Information Disclosure Statements). Multiple regression analysis method was used to identify any correlations among franchising size, marketing competency, operational characteristics, and performance according to technique based upon 169 IDS data as of 2013. In terms of franchisor size and performance, the number of company-operated stores had statistically significant corelation with sales, net income, the total number of stores, and the number of franchisees. With respect to marketing competency and performance, advertising expenses showed statistically significant correlation with sales, the total number of stores, promotion expenses with sales, net income, and the total number of stores. On the other hand, there was no statistically significant correlation with current year's net income. At last, present study found significant correlations among business years, sales, current year's net income, and the total number of stores by regarding operational characteristics and performance, but there was no significant correlation between brands and performance. This study is cross-sectional study which is a limitation to be overcome in further studies. In addition, it is required to review the possibility for franchise management style to contribute to expanding the Korean traditional foods.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.13 no.2
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• pp.1-13
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• 2018

This study examines the relationship between the organizational general characteristics (industry, size, location, development stage, and company age) and success factors of Korean venture firms using secondary data. Among the industries with the highest sales figures in 2016 are food / fiber / (non) metals, and the smallest category was software development. The sectors with the highest net profit were computer / semiconductor / electronic components, and the smallest category was telecommunication equipment / broadcasting equipment. The industries with the largest sales growth rate are IT / broadcasting services and software development. The industries with the highest net profit margin of sales are energy / medical / precision, and the smallest is telecommunication equipment / broadcasting equipment. In terms of the number of employees, venture firms with more than 100 employees have the largest sales and net profit, with employees between 1 and 9 have the smallest. However, these results are predictable. In general, the number of employees is highly correlated with sales and net profit. Rather, the sales growth rate and the net profit margin of sales may be meaningful. In particular, with employees between 50 ~ 99, the growth rate of sales and the net profit margin of sales were high. In terms of location, Seoul / Incheon / Gyeonggi were the regions with the highest sales and Daejeon / Sejong / Chungcheong / Gangwon were the least regions. Gwangju / Jeolla / Jeju and Seoul / Incheon / Gyeonggi were almost similar in the areas with the largest net profit. However, Daejeon / Sejong / Chungcheong / Gangwon had the lowest net profit. Unusually, the areas with the highest sales growth rate and the highest net profit margin of sales were Gwangju / Jeolla / Jeju, and the smallest areas were Busan / Jeonnam / Ulsan In the relationship between the stage of development and the performance of the company, the sales of maturity and decline stages were the highest and establishing stage was the lowest. Net profit was also the highest in mature stage and the smallest in establishing stage. The sales growth rate shows a typical pattern in the order of establishing stage, early growth stage, high growth stage, maturity stage, and decline stage. In terms of business performance, sales and net profit are the highest with 21 years or more of company age, and the smallest is less than 3 years. In addition, the sales growth rate was the highest in three years or less, and the net profit margin of sales was the highest in 4 to 10 years. This study can present lots of useful implications by suggesting integrated research model and examining the success factors of Korean venture firms and presenting the application methods of secondary data in analyzing the current status of venture industry in Korea.

• Asia pacific journal of information systems
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• v.18 no.2
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• pp.95-114
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• 2008

Recent interests in data mining result from the expansion of the amount of business data and the growing business needs for extracting valuable knowledge from the data and then utilizing it for decision making process. In particular, recent advances in association rule mining techniques enable us to acquire knowledge concerning sales patterns among individual items from the voluminous transactional data. Certainly, one of the major purposes of association rule mining is to utilize acquired knowledge in providing marketing strategies such as cross-selling, sales promotion, and shelf-space allocation. In spite of the potential applicability of association rule mining, unfortunately, it is not often the case that the marketing mix acquired from data mining leads to the realized profit. The main difficulty of mining-based profit realization can be found in the fact that tremendous numbers of patterns are discovered by the association rule mining. Due to the many patterns, data mining experts should perform additional mining of the results of initial mining in order to extract only actionable and profitable knowledge, which exhausts much time and costs. In the literature, a number of interestingness measures have been devised for estimating discovered patterns. Most of the measures can be directly calculated from what is known as a contingency table, which summarizes the sales frequencies of exclusive items or itemsets. A contingency table can provide brief insights into the relationship between two or more itemsets of concern. However, it is important to note that some useful information concerning sales transactions may be lost when a contingency table is constructed. For instance, information regarding the size of each market basket(i.e., the number of items in each transaction) cannot be described in a contingency table. It is natural that a larger basket has a tendency to consist of more sales patterns. Therefore, if two itemsets are sold together in a very large basket, it can be expected that the basket contains two or more patterns and that the two itemsets belong to mutually different patterns. Therefore, we should classify frequent itemset into two categories, inter-pattern co-occurrence and intra-pattern co-occurrence, and investigate the effect of the market basket size on the two categories. This notion implies that any interestingness measures for association rules should consider not only the total frequency of target itemsets but also the size of each basket. There have been many attempts on analyzing various interestingness measures in the literature. Most of them have conducted qualitative comparison among various measures. The studies proposed desirable properties of interestingness measures and then surveyed how many properties are obeyed by each measure. However, relatively few attentions have been made on evaluating how well the patterns discovered by each measure are regarded to be valuable in the real world. In this paper, attempts are made to propose two notions regarding association rule measures. First, a quantitative criterion for estimating accuracy of association rule measures is presented. According to this criterion, a measure can be considered to be accurate if it assigns high scores to meaningful patterns that actually exist and low scores to arbitrary patterns that co-occur by coincidence. Next, complementary measures are presented to improve the accuracy of traditional association rule measures. By adopting the factor of market basket size, the devised measures attempt to discriminate the co-occurrence of itemsets in a small basket from another co-occurrence in a large basket. Intensive computer simulations under various workloads were performed in order to analyze the accuracy of various interestingness measures including traditional measures and the proposed measures.

• Journal of the Korean housing association
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• v.11 no.1
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• pp.37-44
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• 2000

The purpose of this research is to examine the evolution of low-cost sales housing and give suggestions for the future housing for the 21st century. This paper investigates the trends of low-cost suburban housing since world War II, as examplified by the Levitt housing in the Northeastern regions of the States. This research analyzes the trends of 14 variables including total floor area. lot size, living room size, kitchen size, mean bedroom size, master bedroom size, number of bedrooms, number of bathrooms, family room, garage, number of story, appliances and features, values, and values per sq ft. Nine entry-level houses were chosen as cases for comparative purpose at different points in time from 1940's to 1990's. This research finds that house sizes have been grown until 1960s, but this trend has reversed with shrinking economy and changing family lifestyles. The trend for smaller housing equipped with several features and convenient equipments will be continued for the next decades to come.

• East Asian Economic Review
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• v.16 no.2
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• pp.205-225
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• 2012

I use panel data of sales by the foreign subsidiaries of the U.S. MNCs to examine whether trading blocs create more or less FDI and the impacts on FDI of the extended market size created by forming blocs. By employing a region-fixed effects model, I find that countries forming trading blocs attract more FDI, particularly from non-member countries, but that FDI does not always increase with the market size of the blocs. As the market size increases, FDI increases only for large blocs. However, these findings are sensitive to model specifications. A policy implication is that a country considering forming or joining a trading bloc with a view to attract FDI may want to form a trading bloc with a country or countries with a large market size.

• Journal of Korean Society for Quality Management
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• v.42 no.3
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• pp.477-496
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• 2014

Purpose: This paper has examined the impact of open innovation business model in technology commercialization with the data from 30 companies of manufacturing firms in South Korea. Methods: The findings provide support for distinguishing five hypotheses relating to development time, IP management, sales, firm size and R&D intensity. To test the hypotheses, data were collected using via e-mail and fax. Small and medium-sized (less than 300 employees) and large industrial firms were chosen for this study. Results: The result shows that openness in its business model is positively associated with successful technology commercialization. Conclusion: The major findings and the implications are: First, as the business model gets more open, development period of technology will be more favorable which gets benefit from rising costs of innovation. Second, as the business model gets more open, large portion of sales are created from new products. Thus, the problem of shorter product life in the market which affects large portion of market revenue can be solved through an open business model. Third, in general, R&D intensity, firm size and the level of IP management affect determination of business model types. The findings also suggest that companies need to increasingly address their external technology exploitation process instead of focusing on their internal innovation processes.

• The Journal of Asian Finance, Economics and Business
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• v.7 no.11
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• pp.177-184
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• 2020

This paper analyzes factors affecting corporate investment decisions in economic groups listed on the Vietnam stock market. The panel data of the research sample includes 39 economic groups listed on the Vietnam stock market from 2009 to 2019. The Generalized Least Square (GLS) is employed to address econometric issues and to improve the accuracy of the regression coefficients. In this research, the investment rate is a dependent variable. Cash-flow (CF), Investment opportunities (ROA), Fixed capital intensity (FCI), Leverage (LEV), Sales growth (GR), Size (SZ), Business risk (RISK) are independent variables in the study. The model results show that cash flow and sales growth have the same impact on investment decisions of economic groups in Vietnam. In addition, investment opportunities have a negative impact on the capital investment decisions of economic groups. The remaining factors include fixed capital intensity, leverage, firm size, and business risks that have a weak and insignificant impact on capital investment decisions of economic groups in Vietnam. The findings of this article are useful for business administrators, and helping business managers make the right financial decisions. Besides, the research results are also meaningful to money management agencies. The authors recommend that the State Bank of Vietnam should maintain a sustainable monetary policy.

• The Journal of Bigdata
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• v.5 no.1
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• pp.147-154
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• 2020

Today, research is actively being conducted to derive meaningful results from big data. In this paper, we propose a partition-based big data analysis algorithm that can analyze the correlation between variables by setting the data areas of big data as partitions and calculating the representative values of each partition. In this paper, the analyzed visualization results are compared according to the partition size of a proposed partition-based big data analysis (PBDA) algorithm that can control the size of the partition. In order to verify the proposed PBDA algorithm, the big data of 'A' is analyzed, and meaningful results are obtained through the analysis of changes in sales volume of products according to changes in temperature and sales price.