Input-Output Performance Evaluation of Science and Technology Enterprises Based on DEA Model

Guoliang Sun; Bayi Guan

doi:10.4108/ew.4131

Authors

Guoliang Sun Zhengzhou Tourism College
Bayi Guan Zhengzhou Tourism College

DOI:

https://doi.org/10.4108/ew.4131

Keywords:

performance evaluation, input-output ratio, technical efficiency

Abstract

INTRODUCTION: For enterprises, the results of performance evaluation will be affected by the selected input and output factors, so the indicators of the evaluation system should be representative and characteristic, and the evaluation method should be reasonable.

OBJECTIVES: According to the characteristics of science and technology industry, this paper establishes the performance evaluation index system, and the evaluation model of the input-output performance for the science and technology enterprises.

METHODS: With the evaluation model, this paper empirically analyses the evaluation index data of 30 science and technology enterprises in recent 3 years, and verifies the effectiveness of the selection of management performance evaluation index of science and technology enterprises.

RESULTS: Finally, according to the empirical analysis results of the evaluation model.

CONCLUSION: This paper puts forward some countermeasures to improve the business performance of science and technology enterprises.

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References

Apigian, Charles H, Bhanu S. Internet Technology: The Stratrgic Imperative[J]. Journal of Electronic Commerce Research, 2005,6(2):123-145.

Teck-Yong Eng. The Effects of the Internet on Network Structures and Business Performance[J]. Electronic Markets, 2008,18(1):91-101.

Fang-Mei Tseng, Yu-Jing Chiu. Measuring Business performance in the high-tech manufacturing industry: A case study of Taiwan’s large-size TFT-LCD panel companies[J]. The International Journal of Management Science, 2009(37):686-697.

Kallunki J P, Laitinen E K..Impact of Enterprise Resource Planning Systems on Management Control Systems and Firm Performance[J]. International Journal of Accounting Information Systems, 2011,12(1):20-39.

Zheng Cailing. Research on Enterprise Performance Evaluation and Ranking based on an improved DEA method[D]. Tianjin University of Commerce. 2015.

Meza L A. Evaluation of Brazilian airlines efficiencies and targets using an alternative DEA approach[J]. International Transactions in Operational Research, 2016,23(4):669-689. DOI: https://doi.org/10.1111/itor.12162

Jang H, Lee S, Suh E. A comparative analysis of the change in R&D efficiency: a case of R&D leaders in the technology industry[J]. Technology Analysis & Strategic Management, 2016,28(8):1-15.

Xue Zhuozhi. E-government performance evaluation based on super-efficiency DEA-AHP[D]. Nanjing University of Technology. 2017

Xue Jun, Yu Hongyan. Business performance and evaluation of Internet Enterprises based on Principal Component analysis[J]. Economic Research Guide, 2018(2):27-29.

Zhang Fengzheng. Research on Internet Enterprise Evaluation Model based on Financial information[D]. Harbin Institute of Technology. 2018.

Bai Nannan, Fontaine. Research on Corporate Social Responsibility Investment and Corporate Performance based on DEA-Tobit Analysis[J]. Journal of Liaoning Shihua University,2018,38(05):95-100.

Ma Hongchao. Research on the relationship between Social Responsibility and Financial Performance of Internet Enterprises in China[D]. Hebei University of Engineering,2018.

Devaraj S, Krajewski L. Impact of eBusiness technologies on operational performance: The role of production information integration in the supply chain[J]. Journal of Operations Management,2018, 25(6):1199-1216. DOI: https://doi.org/10.1016/j.jom.2007.01.002

Li Yancheng. Research on scale economy measurement and Optimization Strategy of tea processing Enterprises based on DEA[D]. Zhejiang University of Finance and Economics,2018.

Jang H, Lee S, Suh E. A comparative analysis of the change in R&D efficiency: a case of R&D leaders in the technology industry[J]. Technology Analysis & Strategic Management, 2018,28(8):1-15. DOI: https://doi.org/10.1080/09537325.2016.1180354

Majid Z A L, Mustafa A. Ranking efficient decision-making units in data envelopment analysis using fuzzy concept[J]. Computers & Industrial Engineering,2018,59(4):712-719. DOI: https://doi.org/10.1016/j.cie.2010.07.025

Eng T Y. The Effects of the Internet on Network Structures and Business Performance[J]. Electronic Markets,2018,18(1):91-101. DOI: https://doi.org/10.1080/10196780701797706

Fang-Mei Tseng, Yu-Jing Chiu. Measuring Business performance in the high-tech manufacturing industry: A case study of Taiwan’s large-size TFT-LCD panel companies[J]. The International Journal of Management Science, 2019(37):686-697. DOI: https://doi.org/10.1016/j.omega.2007.07.004

Kallunki J P, Laitinen E K. Impact of Enterprise Resource Planning Systems on Management Control Systems and Firm Performance. International Journal of Accounting Information Systems,2020,12(1):20-39. DOI: https://doi.org/10.1016/j.accinf.2010.02.001

Li Shuang. Government support and technological innovation efficiency of new energy enterprises[J]. Soft Science, 2020,30(3):11-14.

Liu Jiachen. Research on the Value evaluation of Internet E-commerce enterprises[D]. Capital University of Economics and Business,2021.

Du Xiaoyan. Research on the impact of Working Capital Management on Corporate Performance of Real Estate Listed Companies[D]. Xi’an University of Science and Technology,2021.

Chen Xili, Zhang Chang. Research on the impact of R&D expenditure and social responsibility on corporate financial Performance. Journal of Finance and Accounting, 2022(27):111-117.

Suhartono SuhartonoA, Sulastiningsih Sulastiningsih. The Relationship of Leadership, Discipline, Satisfaction, and Performance: A Case Study of Steel Manufacture in Indonesia[J]. International journal of Professional Business Review, 2023,8(2):66-70. DOI: https://doi.org/10.26668/businessreview/2023.v8i2.1146