ABSTRACT

This study analyzes the relative efficiency of chain stores according to regional characteristics and identifies its factors. In this study, the efficiency of 321 chain stores is measured by DEA model and its factors are identified by Tobit regression model. Three inputs(store size, number of items, and number of employees) and two outputs(sales and number of customers) are used for the DEA model. As a result, only 3 ∼6% of the stores are efficient and the average technical efficiency score is 0.5968 and the average pure technical efficiency score is 0.6457. The efficiency of metropolitan stores is higher than that of small town stores and the efficiency of residential and commercial area stores is higher than that of residential area stores. In Tobit regression model, the DEA efficiency scores are used as dependent variables and five variables, namely store age, number of items per store size, number of items per employee, trade area index, and number of competitors are used as independent variables. The results show that store age, number of items per employee, trade area index, and number of competitors play a significant role in influencing stores’ efficiency. All of five variables are statistically significant in metropolitan stores, but in small town stores so are two variables, namely store age and number of items per store size. In residential area stores number of items per store size, number of items per employee, and trade area index are statistically significant, but number of items per store size is not statistically significant in residential and commercial area stores. Managerial implications of the study are discussed.

KEYWORD

Retail Shop, Efficiency, Efficiency Factors, DEA, Tobit Regression

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