In the long economic downturn, workers’ expectations from corporate employment are lower, and therefore the movement to small business owners through start-ups may continue each year, increasing the number of small businessmen and businesses. Howeve...
In the long economic downturn, workers’ expectations from corporate employment are lower, and therefore the movement to small business owners through start-ups may continue each year, increasing the number of small businessmen and businesses. However, according to the Korea Institute of Small and Medium Business in 2016, 40 percent of businesses closed down in the first year and the actual success rate was only 30 percent. Local governments have various policies to improve the low survival rate of small businesses and to provide stable self-employment activities. However, the local governments that provide various support such as consulting and business market analysis are still in the process of trial and error due to the limited period of service. Under these socio-economic circumstances, a study of small businesses' survival rate and the effort to boost their business activities covering the street businesses, especially studies about the sales volume that can judge the growth of businesses are important.
So far, commercial sector related studies have mainly analyzed the factors influencing the selection of locations for small businesses, the factors influencing sales by individual sectors, and formation of a new distribution of areas and sectors. However, the analysis of traditional market sales is limited to presenting direction of public policy as it is analyzed as the influence of micro location analysis and space detail analysis. Therefore, this study went beyond the analysis at the specific store level and analyzed the physical characteristics, demographic characteristics of the areas with relatively macroscopic variables in the analysis of the single market unit.
This study analyzed the factors that affect the change in sales by dividing the physical characteristics of commercial businesses and the demographic characteristics of the alley. Physical characteristics consist of the number of residential facilities, the number of stores, the variety of businesses, the density of stores, and the price difference of apartments per pyeong, and the demographic characteristics are divided by the resident population and the floating population of the market. Also, second term variables, consisting of the square of changes in store density, the square of the floating population, and the square of the variety of business variables are included to analyze the relationship between the linear relationship of the primary paragraph and the dependent variables of sales. As a result of analyzing the above explanatory variables and dependency with a polynomial regression model, changes in industry diversity have an positive effect on the amount of revenue and a result on the amount of store density changes. It was also analyzed that the second term of the diversity in the business and the second term of the current population were derived indisputably, but the second term of store density had negative effects on the change in sales. These results suggest that sales increases as business diversity increases and store density increases, but in the higher state than the threshold levels of store density sales start to decrease. These results provide a more interpretable commercial segment to analyze the cost of discovery to consumers of goods and services and to provide a more volume-effective buyer with a pattern of buying multipurpose shopping.
This study analyzes the commercial districts from various perspectives as the public institution's support for small business owners has shifted from financial support to the provision of practical business analysis services. This has a policy implication that the government and local governments have proposed a new direction for establishing support policies for small businesses and would-be start-ups to operate their businesses in a stable manner. However, this study failed to analyze food, service, and retail sectors in the section, so the results were interpreted only by the merchant companies. In addition, due to the characteristics of the renewal cycle of the supply and demand data, the analysis result is a two year difference in regression due to the lack of monthly data, which limits the panel data. These limitations could lead to further studies that could suggest new implications, more descriptive, with improvements in the data updating cycle and the development of research through model revisions.