IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v10y2022i20p3755-d940384.html
   My bibliography  Save this article

Analysis of Innovation Drivers of New and Old Kinetic Energy Conversion Using a Hybrid Multiple-Criteria Decision-Making Model in the Post-COVID-19 Era: A Chinese Case

Author

Listed:
  • Chun-Chieh Tseng

    (School of Internet Economics and Business, Fujian University of Technology, Fuzhou 350118, China)

  • Jun-Yi Zeng

    (Institute of Industrial Engineering, College of Transportation, Fujian University of Technology, Fuzhou 350118, China)

  • Min-Liang Hsieh

    (School of Economics and Management, Weifang University of Science and Technology, Weifang 262700, China)

  • Chih-Hung Hsu

    (Institute of Industrial Engineering, College of Transportation, Fujian University of Technology, Fuzhou 350118, China)

Abstract

To overcome the continuous decline in its gross domestic product growth rate, China has advocated new and old kinetic energy conversion (NOKEC) as a policy for sustainable economic development in the post-COVID-19 era. The innovation drivers of NOKEC are the key to promoting sustainable economic development. However, the innovation drivers have various orientations, and their selection requires multiple-criteria decision-making (MCDM). This study proposes a modified Delphi method combined with the best–worst method (BWM) as a research framework for selecting and ranking innovation drivers. Our results show the validity of this integrated research framework on a case based in China in the post-COVID-19 era. The results reveal 21 innovation-driven factors of NOKEC with varying levels of relative importance. These results may provide a basis for policymakers and researchers with a useful further understanding of the importance and prioritizing of innovation drivers. In this study, BWM uses 4% fewer pairwise comparisons than AHP, and the consistency ratio is in the range of 0.00 to 0.24.

Suggested Citation

  • Chun-Chieh Tseng & Jun-Yi Zeng & Min-Liang Hsieh & Chih-Hung Hsu, 2022. "Analysis of Innovation Drivers of New and Old Kinetic Energy Conversion Using a Hybrid Multiple-Criteria Decision-Making Model in the Post-COVID-19 Era: A Chinese Case," Mathematics, MDPI, vol. 10(20), pages 1-25, October.
  • Handle: RePEc:gam:jmathe:v:10:y:2022:i:20:p:3755-:d:940384
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/10/20/3755/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2227-7390/10/20/3755/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Wanlei Xue & Bingkang Li & Yongqi Yang & Huiru Zhao & Nan Xu, 2019. "Evaluating the Effectiveness of New and Old Kinetic Energy Conversion from an Electric Power Economics Perspective: Evidence on the Shandong Province of China," Energies, MDPI, vol. 12(6), pages 1-17, March.
    2. Ramani, Vinay & Ghosh, Debabrata & Sodhi, ManMohan S., 2022. "Understanding systemic disruption from the Covid-19-induced semiconductor shortage for the auto industry," Omega, Elsevier, vol. 113(C).
    3. Luthra, Sunil & Mangla, Sachin Kumar & Xu, Lei & Diabat, Ali, 2016. "Using AHP to evaluate barriers in adopting sustainable consumption and production initiatives in a supply chain," International Journal of Production Economics, Elsevier, vol. 181(PB), pages 342-349.
    4. Saaty, Thomas L., 1990. "How to make a decision: The analytic hierarchy process," European Journal of Operational Research, Elsevier, vol. 48(1), pages 9-26, September.
    5. Meesapawong, Pawadee & Rezgui, Yacine & Li, Haijiang, 2014. "Planning innovation orientation in public research and development organizations: Using a combined Delphi and Analytic Hierarchy Process approach," Technological Forecasting and Social Change, Elsevier, vol. 87(C), pages 245-256.
    6. Pätäri, Satu & Tuppura, Anni & Toppinen, Anne & Korhonen, Jaana, 2016. "Global sustainability megaforces in shaping the future of the European pulp and paper industry towards a bioeconomy," Forest Policy and Economics, Elsevier, vol. 66(C), pages 38-46.
    7. Bouzon, Marina & Govindan, Kannan & Rodriguez, Carlos M.Taboada & Campos, Lucila M.S., 2016. "Identification and analysis of reverse logistics barriers using fuzzy Delphi method and AHP," Resources, Conservation & Recycling, Elsevier, vol. 108(C), pages 182-197.
    8. Delbari, Seyyed Ali & Ng, Siew Imm & Aziz, Yuhanis Abdul & Ho, Jo Ann, 2016. "An investigation of key competitiveness indicators and drivers of full-service airlines using Delphi and AHP techniques," Journal of Air Transport Management, Elsevier, vol. 52(C), pages 23-34.
    9. Justin Yifu Lin, 2012. "New Structural Economics : A Framework for Rethinking Development and Policy," World Bank Publications - Books, The World Bank Group, number 2232, December.
    10. Tang, Yong & Sun, Honghang & Yao, Qiang & Wang, Yibo, 2014. "The selection of key technologies by the silicon photovoltaic industry based on the Delphi method and AHP (analytic hierarchy process): Case study of China," Energy, Elsevier, vol. 75(C), pages 474-482.
    11. Rowe, Gene & Wright, George, 1999. "The Delphi technique as a forecasting tool: issues and analysis," International Journal of Forecasting, Elsevier, vol. 15(4), pages 353-375, October.
    12. Orji, Ifeyinwa Juliet & Kusi-Sarpong, Simonov & Gupta, Himanshu & Okwu, Modestus, 2019. "Evaluating challenges to implementing eco-innovation for freight logistics sustainability in Nigeria," Transportation Research Part A: Policy and Practice, Elsevier, vol. 129(C), pages 288-305.
    13. Boulomytis, V.T.G. & Zuffo, A.C. & Imteaz, M.A., 2019. "Detection of flood influence criteria in ungauged basins on a combined Delphi-AHP approach," Operations Research Perspectives, Elsevier, vol. 6(C).
    14. Haoran Zhao & Sen Guo & Huiru Zhao, 2018. "Comprehensive benefit evaluation of eco-industrial parks by employing the best-worst method based on circular economy and sustainability," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 20(3), pages 1229-1253, June.
    15. Billig, Eric & Thrän, Daniela, 2016. "Evaluation of biomethane technologies in Europe – Technical concepts under the scope of a Delphi-Survey embedded in a multi-criteria analysis," Energy, Elsevier, vol. 114(C), pages 1176-1186.
    16. Williamson, Peter, 2021. "De-Globalisation and Decoupling: Post-COVID-19 Myths versus Realities," Management and Organization Review, Cambridge University Press, vol. 17(1), pages 29-34, February.
    17. Lisanne I. Lier & Judith E. Bosmans & Hein P. J. Hout & Lidwine B. Mokkink & Wilbert B. Hout & G. Ardine Wit & Carmen D. Dirksen & Henk L. G. R. Nies & Cees M. P. M. Hertogh & Henriëtte G. Roest, 2018. "Consensus-based cross-European recommendations for the identification, measurement and valuation of costs in health economic evaluations: a European Delphi study," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 19(7), pages 993-1008, September.
    18. Rezaei, Jafar, 2016. "Best-worst multi-criteria decision-making method: Some properties and a linear model," Omega, Elsevier, vol. 64(C), pages 126-130.
    19. Rezaei, Jafar, 2015. "Best-worst multi-criteria decision-making method," Omega, Elsevier, vol. 53(C), pages 49-57.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Besharati Fard, Moein & Moradian, Parisa & Emarati, Mohammadreza & Ebadi, Mehdi & Gholamzadeh Chofreh, Abdoulmohammad & Klemeŝ, Jiří Jaromír, 2022. "Ground-mounted photovoltaic power station site selection and economic analysis based on a hybrid fuzzy best-worst method and geographic information system: A case study Guilan province," Renewable and Sustainable Energy Reviews, Elsevier, vol. 169(C).
    2. Milad Kolagar & Seyed Mohammad Hassan Hosseini & Ramin Felegari & Parviz Fattahi, 2020. "Policy-making for renewable energy sources in search of sustainable development: a hybrid DEA-FBWM approach," Environment Systems and Decisions, Springer, vol. 40(4), pages 485-509, December.
    3. Nan Li & Haining Zhang & Xiangcheng Zhang & Xue Ma & Sen Guo, 2020. "How to Select the Optimal Electrochemical Energy Storage Planning Program? A Hybrid MCDM Method," Energies, MDPI, vol. 13(4), pages 1-20, February.
    4. Amelia Bilbao-Terol & Mar Arenas-Parra & Raquel Quiroga-García & Celia Bilbao-Terol, 2022. "An extended best–worst multiple reference point method: application in the assessment of non-life insurance companies," Operational Research, Springer, vol. 22(5), pages 5323-5362, November.
    5. Kusi-Sarpong, Simonov & Orji, Ifeyinwa Juliet & Gupta, Himanshu & Kunc, Martin, 2021. "Risks associated with the implementation of big data analytics in sustainable supply chains," Omega, Elsevier, vol. 105(C).
    6. Gholamreza Haseli & Reza Sheikh & Jianqiang Wang & Hana Tomaskova & Erfan Babaee Tirkolaee, 2021. "A Novel Approach for Group Decision Making Based on the Best–Worst Method (G-BWM): Application to Supply Chain Management," Mathematics, MDPI, vol. 9(16), pages 1-20, August.
    7. Sen Guo & Wenyue Zhang & Xiao Gao, 2020. "Business Risk Evaluation of Electricity Retail Company in China Using a Hybrid MCDM Method," Sustainability, MDPI, vol. 12(5), pages 1-21, March.
    8. Kumar, Anish & Mangla, Sachin Kumar & Kumar, Pradeep & Song, Malin, 2021. "Mitigate risks in perishable food supply chains: Learning from COVID-19," Technological Forecasting and Social Change, Elsevier, vol. 166(C).
    9. Mi, Xiaomei & Tang, Ming & Liao, Huchang & Shen, Wenjing & Lev, Benjamin, 2019. "The state-of-the-art survey on integrations and applications of the best worst method in decision making: Why, what, what for and what's next?," Omega, Elsevier, vol. 87(C), pages 205-225.
    10. Chand, Pushpendu & Thakkar, Jitesh J. & Ghosh, Kunal Kanti, 2020. "Analysis of supply chain performance metrics for Indian mining & earthmoving equipment manufacturing companies using hybrid MCDM model," Resources Policy, Elsevier, vol. 68(C).
    11. Mustafa Hamurcu & Tamer Eren, 2020. "Strategic Planning Based on Sustainability for Urban Transportation: An Application to Decision-Making," Sustainability, MDPI, vol. 12(9), pages 1-24, April.
    12. Jahangoshai Rezaee, Mustafa & Yousefi, Samuel, 2018. "An intelligent decision making approach for identifying and analyzing airport risks," Journal of Air Transport Management, Elsevier, vol. 68(C), pages 14-27.
    13. Roya Ghamari & Mohammad Mahdavi-Mazdeh & Seyed Farid Ghannadpour, 2022. "Resilient and sustainable supplier selection via a new framework: a case study from the steel industry," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 24(8), pages 10403-10441, August.
    14. James J. H. Liou & Perry C. Y. Liu & Huai-Wei Lo, 2020. "A Failure Mode Assessment Model Based on Neutrosophic Logic for Switched-Mode Power Supply Risk Analysis," Mathematics, MDPI, vol. 8(12), pages 1-19, December.
    15. Junnan Wu & Xin Liu & Dianqi Pan & Yichen Zhang & Jiquan Zhang & Kai Ke, 2023. "Research on Safety Evaluation of Municipal Sewage Treatment Plant Based on Improved Best-Worst Method and Fuzzy Comprehensive Method," Sustainability, MDPI, vol. 15(11), pages 1-15, May.
    16. Liang, Fuqi & Brunelli, Matteo & Rezaei, Jafar, 2020. "Consistency issues in the best worst method: Measurements and thresholds," Omega, Elsevier, vol. 96(C).
    17. Pushparenu Bhattacharjee & Syed Abou Iltaf Hussain & V. Dey & U. K. Mandal, 2023. "Failure mode and effects analysis for submersible pump component using proportionate risk assessment model: a case study in the power plant of Agartala," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 14(5), pages 1778-1798, October.
    18. Heiskanen, Aleksi & Hurmekoski, Elias & Toppinen, Anne & Näyhä, Annukka, 2022. "Exploring the unknowns – State of the art in qualitative forest-based sector foresight research," Forest Policy and Economics, Elsevier, vol. 135(C).
    19. Salimi, Negin & Rezaei, Jafar, 2018. "Evaluating firms’ R&D performance using best worst method," Evaluation and Program Planning, Elsevier, vol. 66(C), pages 147-155.
    20. Yuanxin Liu & FengYun Li & Yi Wang & Xinhua Yu & Jiahai Yuan & Yuwei Wang, 2018. "Assessing the Environmental Impact Caused by Power Grid Projects in High Altitude Areas Based on BWM and Vague Sets Techniques," Sustainability, MDPI, vol. 10(6), pages 1-20, May.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jmathe:v:10:y:2022:i:20:p:3755-:d:940384. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.