• 한국산림과학회지
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• 제102권3호
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• pp.390-397
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• 2013

본 연구는 비모수추정 방법인 자료포락 기법을 이용하여 청양, 공주, 부여 등 충남지역의 밤나무 재배임가를 대상으로 경영효율성을 평가하였다. 분석자료는 2012년 3월부터 10월까지 실시한 '|밤나무 경영에 관한 실태조사'결과 중에서 20개 임가의 투입요소(관리비, 수확비, 재료비, 간접비, 재배면적)와 산출요소(조수입액)를 대상으로 하였다. 그리고 DEA 기법중 CCR 모형과 BCC 모형을 이용하여 기술효율성, 순수기술효율성, 규모효율성을 분석하였고 비효율성 임가의 개선방안도 제시하였다. 추가적으로 DEA 분석결과의 검증을 위하여 기술효율성과 표준진단표 결과를 비교하였다. 연구결과, 기술효율성은 평균값이 0.667로 전체적으로 비효율적으로 분석되었고 그 원인은 순수기술효율성이 평균 0.944, 규모효율성이 평균 0.703으로 나타나 재배기술적인 측면에서는 순수기술효율성이 높았지만 규모의 효율성에서는 비효율성이 존재하였다. 효율성이 1인 임가는 기술효율성 6개, 순수기술효율성 13개, 규모효율성이 6개 임가로 나타났고, 3개 모두 1인 임가는 6개로 나타났다. 표준진단표 점수와 비교한 결과 80점이상 고득점을 기록한 임가는 5개로 나타났고 이 중 기술효율성이 1로 분석된 임가는 3개로 나타났다. 또한 기술효율성과 표준진단표의 최저점수와 최고점수를 기록한 임가도 동일하게 나타났다. 이것은 DEA를 이용한 경영 효율성 평가가 표준진단표와 함께 현장에 적용 가능하다는 것을 나타낸다.

• 유통과학연구
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• 제12권4호
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• pp.23-30
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• 2014

Purpose - The purpose of this study is to analyze the efficiency of retail businesses by dividing domestic retailers into discount stores, super supermarkets (SSMs), and department stores. It suggests retail-business investment strategies by using data environment analysis (DEA) to analyze how input elements such as store area, parking lot area, number of employees, and sales management expenses for the convenience of customers positively affect business performance measurements such as sales and visiting customers per day. Research Design, Data, and Methodology - The DEA model calculates a ratio of the weighted mean of various inputs to the weighted mean of various outputs and measures the efficiency of a specific decision making unit (DMU). The study included 19 companies (five discount store DMUs, ten SSM DMUs, and four department store DMUs). Because the business elements and sizes of retail store DMUs used in this analysis are different, average per-store input and output variables were used. Data were collected from "The Yearbook of Retail Industry in Korea (2012)." DEA analysis was used to determine differences in efficiency among discount stores, SSMs, and department stores in terms of the business elements of each retail business. It was also used to determine what business elements were excessively invested in by comparing and analyzing efficiency by business elements using SPSS software's ANOVA (Analysis of Variance). Results - The CCR and BCC efficiency analysis found that the efficiency of discount stores is low. We believe that the saturation state of discount stores is a major factor. The ANOVA analysis confirms the VRS hypothesis with a statistically significant difference among the three groups, based on an analysis confidence interval of 95%. CRS and SE were not found to be significantly different among the three groups. As for the post hoc test, which concretely shows differences by group, the Scheffe's multiple comparison analysis test found the average differences between group 1 (discount stores) and group 2 (SSM) to be statistically significant. Conclusions - The DEA efficiency analysis implies that investment in input elements, including store area, parking lot area, and sales management expenses, were excessive in the case of discount stores, while SSMs need to invest more in promotion activities such as gifts, events, and coupons for customer management. Department stores have found that small companies invest excessively in input elements. Department stores need to invest in differentiated shopping mall complexes. This study was limited in acquiring statistical data; various input variables which might have shown more secure customer management and promotional expenses could not be applied. As the study was limited in various aspects of the efficiency analyses because financial analyses of the companies and of causal relationships, including satisfaction and loyalty of visiting customers, were not done, these aspects will be examined in the next study.