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Evaluating and Optimizing Technological Innovation Efficiency of Industrial Enterprises Based on Both Data and Judgments

Author

Listed:
  • Wei Gu

    (Donlinks School of Economics and Management, University of Science and Technology Beijing, Beijing 100083, P. R. China)

  • Thomas L. Saaty

    (Joseph M. Katz Graduate School of Business, University of Pittsburgh, Pittsburgh, 15260 PA, USA)

  • Lirong Wei

    (Department of Statistics, University of Pittsburgh, Pittsburgh, 15260 PA, USA)

Abstract

Technological innovation as one of the most important competitive strategies for companies has attracted the attentions of companies and governments. In this paper, we present an evaluation method based on data and judgments to rank the technological innovation capability and technological innovation efficiency of enterprises of various sizes in China. Furthermore, based on the efficiency measures, we design a model for the government to optimally allocate innovation resource to businesses, i.e. prioritize public expenditures dedicated to innovation. In evaluating the efficiency of industrial enterprises, we employ the “input-process-output” perspective to identify multiple criteria. We also take into account the cost of technological innovation in efficiency assessment. The optimization model proposed for government is to maximize the overall efficiency of resources utilization. We adopt the genetic algorithm as the solution methodology to solve the optimization model. Simulation is conducted to validate the model and the algorithm. The research framework proposed in paper can be adapted for government in many countries to better distribute resources for technological innovation and development.

Suggested Citation

  • Wei Gu & Thomas L. Saaty & Lirong Wei, 2018. "Evaluating and Optimizing Technological Innovation Efficiency of Industrial Enterprises Based on Both Data and Judgments," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 17(01), pages 9-43, January.
    • Handle: RePEc:wsi:ijitdm:v:17:y:2018:i:01:n:s0219622017500390
    DOI: 10.1142/S0219622017500390
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    Citations

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    Cited by:

    1. Yaliu Yang & Yuan Wang & Cui Wang & Yingyan Zhang & Cuixia Zhang, 2022. "Temporal and Spatial Evolution of the Science and Technology Innovative Efficiency of Regional Industrial Enterprises: A Data-Driven Perspective," Sustainability, MDPI, vol. 14(17), pages 1-21, August.
    2. Skrynkovskyy, Ruslan & Pavlenchyk, Nataliia & Tsyuh, Svyatoslav & Zanevskyy, Ihor & Pavlenchyk, Anatoliі, 2022. "Economic-mathematical model of enterprise profit maximization in the system of sustainable development values," Agricultural and Resource Economics: International Scientific E-Journal, Agricultural and Resource Economics: International Scientific E-Journal, vol. 8(4), December.
    3. He Huang & Liwei Zhong & Ting Shen & Huixin Wang, 2022. "Performance prediction and optimization for healthcare enterprises in the context of the COVID-19 pandemic: an intelligent DEA-SVM model," Journal of Combinatorial Optimization, Springer, vol. 44(5), pages 3778-3791, December.
    4. Xiongfeng Pan & Cuicui Han & Xiaowei Lu & Zhiming Jiao & Yang Ming, 2020. "Green innovation ability evaluation of manufacturing enterprises based on AHP–OVP model," Annals of Operations Research, Springer, vol. 290(1), pages 409-419, July.
    5. Chen Wang & Qingyan Yang & Shufen Dai, 2019. "Supplier Selection and Order Allocation under a Carbon Emission Trading Scheme: A Case Study from China," IJERPH, MDPI, vol. 17(1), pages 1-19, December.

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