Green innovation ability evaluation of manufacturing enterprises based on AHP–OVP model

Abstract

Existing evaluation methods of green innovation ability have strong subjectivity and can not deal with the problem of information overlapping among the indexes. To overcome these shortcomings, this paper combine analytic hierarchy process (AHP) and osculating value process (OVP) to construct the AHP–OVP evaluation model. Then this model is introduced into the green innovation ability evaluation of manufacturing enterprises, and the validity of the evaluation method is verified by a case. The results show that AHP–OVP evaluation model can effectively evaluate green innovation ability of manufacturing enterprises. And this model also fully solve the problems of green innovation ability evaluation methods of manufacturing enterprises built by previous scholars.

This is a preview of subscription content, log in to check access.

Fig. 1

References

  1. Abdollahzadeh, G. (2016). Selecting strategies for rice stem borer management using the analytic hierarchy process (ahp). Crop Protection,84, 27–36.

    Article  Google Scholar 

  2. Albort, M. G., Leal-Rodriguez, A. L., & De Marchi, V. (2018). Absorptive capacity and relationship learning mechanisms as complementary drivers of green innovation performance. Journal of Knowledge Management.,22(2), 432–452.

    Article  Google Scholar 

  3. Arfi, W. B., Hikkerova, L., & Sahut, J.-M. (2018). External knowledge sources, green innovation and performance. Technological Forecasting and Social Change,129, 210–220.

    Article  Google Scholar 

  4. Chiou, Tzu-Yun, Chan, Hing Kai, Lettice, Fiona, & Chung, Sai Ho. (2011). The influence of greening the suppliers and green innovation on environmental performance and competitive advantage in Taiwan. Transportation Research Part E,47(6), 822–836.

    Article  Google Scholar 

  5. Dong, Y., Zhang, G., Hong, W. C., & Xu, Y. (2010). Consensus models for ahp group decision making under row geometric mean prioritization method. Decision Support Systems, 49(3), 281–289.

    Article  Google Scholar 

  6. El-Kassar, A. N., & Singh, S. K. (2017). Green innovation and organizational performance: The influence of big data and the moderating role of management commitment and HR practices. Technological Forecasting and Social Change. https://doi.org/10.1016/j.techfore.2017.12.016.

    Article  Google Scholar 

  7. Gu, W., Saaty, T. L., & Wei, L. R. (2018). Evaluation and optimizing technological innovation efficiency of industrial enterprises based on both data and judgments. International Journal of Information Technology & Decision Making.,17(1), 9–43.

    Article  Google Scholar 

  8. Harker, P. T., & Vargas, L. G. (1988). Erratum: “the theory of ratio scale estimation: Saaty’s analytic hierarchy process”. Management Science,34(12), 1511.

    Article  Google Scholar 

  9. Hong, Y. Y., & He, Q. (2009). The evaluation analysis of green technological innovation. In: Comprehensive Evaluation of Economy and Society with Statistical Science (pp. 547–550).

  10. Ishizaka, A., Balkenborg, D., & Kaplan, T. (2011). Influence of aggregation and measurement scale on ranking a compromise alternative in ahp. Journal of the Operational Research Society,62(4), 700–710.

    Article  Google Scholar 

  11. Ishizaka, A., Siraj, S., & Nemery, P. (2016). Which energy mix for the uk (united kingdom)? an evolutive descriptive mapping with the integrated gaia (graphical analysis for interactive aid)–ahp (analytic hierarchy process) visualization tool. Energy,95(9), 602–611.

    Article  Google Scholar 

  12. Jabbour, C. J. C., Jugend, D., Abls, J., Govindan, K., Kannan, D., & Leal, F. W. (2018). “There is no carnival without samba”: revealing barriers hampering biodiversity-based R&D and eco-design in brazil. Journal of Environmental Management,206, 236–245.

    Article  Google Scholar 

  13. Jabbour, C. J. C., Mauricio, A. L., & Jabbour, A. (2017). Critical success factors and green supply Chain management proactivity: Shedding light on the human aspects of this relationship based on cases from the Brazilian industry. Production Planning & Control,28, 671–683.

    Article  Google Scholar 

  14. Küçükoğlu, Mübeyyen Tepe, & İbrahim Pınar, R. (2015). Positive influences of green innovation on company performance. Procedia-Social and Behavioral Sciences,195, 1232–1237.

    Article  Google Scholar 

  15. Leal-Rodríguez, Antonio L., Ariza-Montes, Antonio J., Fernández, Emilio Morales-, & Albort-Morant, Gema. (2018). Green innovation, indeed a cornerstone in linking market requests and business performance. Evidence from the Spanish automotive components industry. Technological Forecasting and Social Change,129, 158–193.

    Article  Google Scholar 

  16. Lin, S. F., Sun, D., & Zhao, M. D. (2018). Evaluation of the green technology innovation efficiency of China’s manufacturing industries: DEA window analysis with ideal window width. Technology Analysis & Strategic Management,30(10), 1166–1181.

    Article  Google Scholar 

  17. Lootsma, F. A. (1989). Conflict resolution via pairwise comparison of concessions. European Journal of Operational Research,40(1), 109–116.

    Article  Google Scholar 

  18. Nath, P., & Ramanathan, R. (2016). Environmental management practices,environmental technology portfolio, and environmental commitment: A content analytic approach for UK manufacturing firms. International Journal of Production Economics, 109(1–3), 65–72.

    Google Scholar 

  19. Pan, X., Zhang, J., Song, M., & Ai, B. (2018). Innovation resources integration pattern in high-tech entrepreneurial enterprises. International Entrepreneurship & Management Journal,14(3), 1–16.

    Google Scholar 

  20. Saaty, T. L. (1980). The analytic hierarchy process: Planning, priority setting, resource Allocation. In The analytic hierarchy process: Planning, priority setting, resource allocation (p. 287). New York: McGraw-Hill.

  21. Saaty, R. W. (1987). The analytic hierarchy process—What it is and how it is used. Mathematical Modelling,9(3), 161–176.

    Article  Google Scholar 

  22. Saaty, T. L. (2001). The seven pillars of the analytic hierarchy process. In International conference on multiple criteria decision making (pp. 15–37).

  23. Singh, S. P., Akhter, Z., & Akhtar, M. J. (2015). Calibration independent estimation of optical constants using terahertz time-domain spectroscopy. Microwave & Optical Technology Letters,57(8), 1861–1864.

    Article  Google Scholar 

  24. Singh, R. P., & Nachtnebel, H. P. (2016). Analytical hierarchy process (ahp) application for reinforcement of hydropower strategy in nepal. Renewable and Sustainable Energy Reviews,55, 43–58.

    Article  Google Scholar 

  25. Song, Y. (2018). Improving primary students’ collaborative problem solving competency in project-based science learning with productive failure instructional design in a seamless learning environment. Educational Technology Research & Development, 66(4), 1–30.

    Article  Google Scholar 

  26. Sun, L. Y., Miao, C. L., & Yang, L. (2017). Ecological-economic efficiency evaluation of green technology innovation in strategic emerging industries based on entropy weighted TOPSOS method. Ecological Indicators,73, 554–558.

    Article  Google Scholar 

  27. Tang, Xie J. M., Tang, X. W., & Shao, Y. F. (2012). Research on stratified cluster evaluation of enterprise green technology innovation based on the rough set. Technology and Investment,3, 68–73.

    Article  Google Scholar 

  28. Tong, L., Zhong, S., & Zhang, X. (2017). Evaluation on green development ability of Chinese automobile manufacturing enterprises. In International conference on industrial economics system and industrial security engineering (1–8).

  29. Wang, Y. R., & Xu, Q. L. (2011). Evaluation of green technology innovation capacity of automobile manufacture industry. Frontiers of Manufacturing and Design Science.,44, 206–212.

    Google Scholar 

  30. Wang, W. X., Yu, B., Yan, X., et al. (2017). Estimation of innovation’s green performance: A range-adjusted measure approach to assess the unified efficiency of China’s manufacturing industry. Journal of Cleaner Production,149, 919–924.

    Article  Google Scholar 

  31. Yin, Y. (2015). On the evaluation of small and micro enterprises' green innovation capability. In Proceedings of the 11th Euro-Asia conference on environment and CSR: Tourism, Society and Education Session (pp. 242–248).

  32. Zeng, Z. (2017). Model for evaluating the technological innovation capability in high-tech enterprises with fuzzy number intuitionistic fuzzy information. Journal of Intelligent & Fuzzy Systems.,33(4), 2085–2094.

    Article  Google Scholar 

  33. Zhang, Z., Liu, X., & Yang, S. (2009). A note on the 1–9 scale and index scale. In AHP  Cutting-edge research topics on multiple criteria decision making (pp. 630–634). Berlin: Springer.

  34. Zhou, Z. F. (2014). On evaluation model of green technology innovation capability of pulp and paper enterprise based on support vector machines. Advanced Materials Research, 886, 285–288.

    Article  Google Scholar 

  35. Zhu, W. G., & He, Y. J. (2017). Green product design in supply chains under competition. European Journal of Operational Research, 258(1), 165–180.

    Article  Google Scholar 

Download references

Acknowledgements

This paper is the stage achievement of National Nature Science Fundation Project (71303029), National Social Science Fundation Project (17BGL266), Liaoning Provincial Economic and Social Development Project (2019lslktyb-011) and Fundamental Research Funds of Dalian University of Technology (DUT18RW210) in China. The authors thank for the support of National Nature Science Fundation, National Social Science Fundation, Liaoning Provincial Federation Social Science Circles and Fundamental Research Funds of Dalian University of Technology in China. Meanwhile, The authors would like to thank Bowei Ai for the help in the use of the osculating value process model.

Author information

Affiliations

Authors

Corresponding author

Correspondence to Xiaowei Lu.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Pan, X., Han, C., Lu, X. et al. Green innovation ability evaluation of manufacturing enterprises based on AHP–OVP model. Ann Oper Res 290, 409–419 (2020). https://doi.org/10.1007/s10479-018-3094-6

Download citation

Keywords

  • Green innovation ability
  • Manufacturing enterprises
  • Analytic hierarchy process
  • Osculating value process