Journal of Korean Society for Quality Management (품질경영학회지)

Korean Society for Quality Management (한국품질경영학회)
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The Analysis of the Management Efficiency and Impact Factors of Smart Greenhouse Business Entities - Focusing on the Business Entities of Strawberry Cultivation in Jeolla-do -

스마트온실 경영체의 경영 효율성 및 영향요인 분석 - 전라권 딸기 재배 경영체를 중심으로-

  • 하지영 ((주)지앤비G&B 연구개발팀) ;
  • 이승현 ((주)지앤비G&B) ;
  • 나명환 (전남대학교 수학/통계학부) ;
  • 김덕현 (전라남도농업기술원 경영자원과) ;
  • 이혜림 (농촌진흥청 빅데이터일자리팀) ;
  • 이용건 (한국표준협회)
  • Received : 2021.05.09
  • Accepted : 2021.06.10
  • Published : 2021.06.30
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Abstract

Purpose: This study intends to provide decision-making information to improve efficiency by analyzing the management efficiency of smart greenhouse business entities and identifying factors that affect the efficiency based on input and output. Methods: The subjects of analysis were business entities for cultivating strawberries in smart greenhouses in Jeolla region (northern and southern Jeolla provinces), and the analysis focused on the management performance of the 2019-2020 crop period (year). Data Envelopment Analysis(DEA) was applied as an analysis method for efficiency analysis, Quantile Regression(QR) analysis was applied as a factor affecting the efficiency. Results: The reason for the efficiency gap between business entities was that there were many business entities that did not minimize the input cost at the current level of output, and the area where the variance among business entities was large was the fixed cost per 10a. In the results of the affecting factor analysis, it was found that the seed-seedlings cost, fertilizer cost, other material cost, and employment and labor cost had a negative (-) effect on the efficiency, and that the repair and maintenance cost had a positive (+) effect. Conclusion: Therefore, to achieve the efficiency of scale, it is necessary to reduce the input scale to an appropriate level. In the case of business entities with low efficiency by quartile, the seed-seedlings, fertilizer, and other material costs reduce expenditures, and repair maintenance costs can improve efficiency by increasing expenditures.

Keywords

Acknowledgement

본 논문은 농촌진흥청 사업(과제번호: : PJ01536109)의 지원에 의해 연구되었습니다.

References

  1. Banker, R., Charnes A., and Cooper, W. 1984. Some Models for Estimating Technical and Scale Inefficiencies in Data Envelopment Analysis. Management Science 30(9):1078-1092. https://doi.org/10.1287/mnsc.30.9.1078
  2. Brian, S. C., and Barry. R. N. 2003. A Gentle Introduction to Quantile Regression for Ecologists. Frontiers in Ecology and the Environment 1(8):412-20. https://doi.org/10.1890/1540-9295(2003)001[0412:AGITQR]2.0.CO;2
  3. Choi, Don-woo, and Lim. Cheongryong. 2018.Statistical Analysis of Production Efficiency on the Strawberry Farms Using Smart Farming. Korean Soc Qual Manag 46(3):707-716.
  4. Farrell, M. J. 1957. The Measurement of Productive Efficiency. Journal of the Royal Statistical Society 120:253-290. https://doi.org/10.2307/2343100
  5. Ha, Jiyoung, Lee, Seunghyun, Kim, Deokhyeon, and Lee, Hyerim. 2019. Impact Relationship between Smart Greenhouse Productivity Improvement Indicators and the Production Volume of Management: Focusing on the Paprika Management in Jeollanam-do. The Korean Society for Quality Management. Autumn Conference 2019(0):164.
  6. Kim, HyunJin, and Park,Sunyoung. 2020. The Effects of Intangible Asset Expenditure on Firm Performance: Qqantile Regression Analysis. Journal of Business Research 35(1):63-88. https://doi.org/10.22903/JBR.2020.35.1.63
  7. Kim, Juncheol, Kwon Sookhee, Ha, Ildo, and Na, Myunghwan. 2021. Survival Analysis for Tomato Big Data in Smart Farming. Journal of the Korean Data & Information Science Society 32(2):361-374. https://doi.org/10.7465/jkdi.2021.32.2.361
  8. Kim, Kunwee, and Choi, Hojin. 2005. A Critical Review on the Measurement of Local Governments: Relative Efficiency Using the DEA Model. The Korea Local Administration Review 19(3):213-244.
  9. Ko, Kilkon. 2017. Theory of Efficiency Analysis. Moonwoosa(Goyang City).
  10. Lee, Seunghyun, Ha, Jiyoung, Park, Hyangja, Kim, Deokhyeon, and Lee, Hyerim. 2018. ICT-Based Smart Farm Export Paprika Management Performance of Agricultural Management. The Korean Society for Quality Management. Autumn Conference 2018(0):72.
  11. Lee, Seunghyun, Kim, Deokhyeon, Ha, Jiyoung, Park, Hyangja, and Lee, Hyerim. 2017. Comparative Analysis of Management Performance of ICT-Based Smart Farm Tomato Agricultural Management: Targeting Agricultural Management Companies Introduced and Non-introduced Agricultural Management Companies in the Jeonnam-do. The Korea Society of Management information Systems, Spring Integrated Conference(June), p.439-442.
  12. Ministry of Agricultutr, Food and Rural Affairs. Website, https://www.mafra.go.kr.
  13. Policy Briefing of Korea. 2020. Smart Farm (Intelligent Farm). 2020.2.25., https://www.korea.kr.
  14. Rural Development Administration. 2012. Manual on How to Use Agricultural Product Income Data. Agricultural Management Information 2012-7.
  15. Rural Development Administration. 2018. Research on Management Performance of Farming Introducing Smart Farms for Facility Vegetables.
  16. Simar and Wilson. 2007. Estimation and Inference in Two-stage, Semi-parametric Models of Production Processes. Journal of Ecomometrics 136(1):31-64. https://doi.org/10.1016/j.jeconom.2005.07.009
  17. The Farmers Newspaper. 2021. Why Farmers Hesitate to Adopt Smart Farms. Column: 2021.1.13.
  18. Tone, K. 2001. A Slacks-based Measure of Efficiency in Data Envelopment Analysis. European Journal of Operational Research 130(3):498-509. https://doi.org/10.1016/S0377-2217(99)00407-5
  19. Yum, Dongki, and Shin, Hyeondae. 2013. Relative Efficiency of Research and Development Business Foundations through Data Envelopment Analysis. Korean Journal of Public Administration 51(1):293-319.