IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v14y2022i19p12709-d934810.html
   My bibliography  Save this article

Cohesion of Agricultural Crowdfunding Risk Prevention under Sustainable Development Based on Gray–Rough Set and FAHP-TOPSIS

Author

Listed:
  • Ye Xue

    (College of Economics and Management, Taiyuan University of Technology, Taiyuan 030024, China)

  • Ying Li

    (College of Economics and Management, Taiyuan University of Technology, Taiyuan 030024, China)

Abstract

Agricultural crowdfunding has promoted the development of China’s agriculture and rural economy. Ensuring the sustainable development of agricultural crowdfunding is a key issue that needs attention against the current background. The concept of cohesion is introduced into the study of agricultural crowdfunding risk prevention, and the cohesion evaluation index system is determined with the help of the gray-rough set method, weights of which are determined by using triangular fuzzy hierarchy analysis. The TOPSIS method is used to evaluate it, four crowdfunding projects are selected for case studies, and the models are compared and analyzed. Finally, the influencing factors are comprehensively analyzed. The results show that: (1) The case evaluation results are consistent with its actual situation, and the comparison with the model presents the accuracy of the selected model, both of which verify the feasibility of the evaluation model. (2) Collaboration, organizational leadership, and the degree of assurance of the quantity and quality of agricultural products are important factors affecting the improvement of the cohesion in agricultural crowdfunding risk prevention. (3) The most significant factors in enhancing the cohesiveness of agricultural crowdfunding risk prevention are “responsiveness” and “safety of agricultural products”. Finally, the targeted countermeasures and suggestions are expected to provide the decision-making basis for the risk management of agricultural crowdfunding and realize the sustainable development of agricultural crowdfunding.

Suggested Citation

  • Ye Xue & Ying Li, 2022. "Cohesion of Agricultural Crowdfunding Risk Prevention under Sustainable Development Based on Gray–Rough Set and FAHP-TOPSIS," Sustainability, MDPI, vol. 14(19), pages 1-22, October.
  • Handle: RePEc:gam:jsusta:v:14:y:2022:i:19:p:12709-:d:934810
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/14/19/12709/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2071-1050/14/19/12709/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Fei Wang & Zhi Dong & Jichang Dong, 2023. "Assessment of the Drivers and Effects of International Science and Technology Cooperation in Xinjiang in the Context of the Belt and Road Initiative," Sustainability, MDPI, vol. 15(2), pages 1-20, January.
    2. Yuanzhong Li & Xinbang Cao & Shaojian Qu & Ying Ji & Zilong Xia, 2022. "Cost Sharing in Insurance Communities: A Hybrid Approach Based on Multiple-Choice Objective Programming and Cooperative Games," Sustainability, MDPI, vol. 14(24), pages 1-18, December.

    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. Alptekin Ulutaş & Ayşe Topal & Dragan Pamučar & Željko Stević & Darjan Karabašević & Gabrijela Popović, 2022. "A New Integrated Multi-Criteria Decision-Making Model for Sustainable Supplier Selection Based on a Novel Grey WISP and Grey BWM Methods," Sustainability, MDPI, vol. 14(24), pages 1-20, December.
    2. 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.
    3. 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.
    4. Zarei, Esmaeil & Khan, Faisal & Abbassi, Rouzbeh, 2021. "Importance of human reliability in process operation: A critical analysis," Reliability Engineering and System Safety, Elsevier, vol. 211(C).
    5. Sarfaraz Hashemkhani Zolfani & Ramin Bazrafshan & Fatih Ecer & Çağlar Karamaşa, 2022. "The Suitability-Feasibility-Acceptability Strategy Integrated with Bayesian BWM-MARCOS Methods to Determine the Optimal Lithium Battery Plant Located in South America," Mathematics, MDPI, vol. 10(14), pages 1-18, July.
    6. Paul, Ananna & Shukla, Nagesh & Trianni, Andrea, 2023. "Modelling supply chain sustainability challenges in the food processing sector amid the COVID-19 outbreak," Socio-Economic Planning Sciences, Elsevier, vol. 87(PA).
    7. Liang, Fuqi & Brunelli, Matteo & Rezaei, Jafar, 2020. "Consistency issues in the best worst method: Measurements and thresholds," Omega, Elsevier, vol. 96(C).
    8. 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.
    9. Dilupa Nakandala & Yung Po Tsang & Henry Lau & Carman Ka Man Lee, 2022. "An Industrial Blockchain-Based Multi-Criteria Decision Framework for Global Freight Management in Agricultural Supply Chains," Mathematics, MDPI, vol. 10(19), pages 1-23, September.
    10. Zeng, Shouzhen & Zhou, Jiamin & Zhang, Chonghui & Merigó, José M., 2022. "Intuitionistic fuzzy social network hybrid MCDM model for an assessment of digital reforms of manufacturing industry in China," Technological Forecasting and Social Change, Elsevier, vol. 176(C).
    11. 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.
    12. Željko Stević & Irena Đalić & Dragan Pamučar & Zdravko Nunić & Slavko Vesković & Marko Vasiljević & Ilija Tanackov, 2019. "A new hybrid model for quality assessment of scientific conferences based on Rough BWM and SERVQUAL," Scientometrics, Springer;Akadémiai Kiadó, vol. 119(1), pages 1-30, April.
    13. Wu, Xingli & Liao, Huchang, 2021. "Modeling personalized cognition of customers in online shopping," Omega, Elsevier, vol. 104(C).
    14. 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.
    15. Maghsoodi, Abtin Ijadi, 2023. "Cryptocurrency portfolio allocation using a novel hybrid and predictive big data decision support system," Omega, Elsevier, vol. 115(C).
    16. Kik, M.C. & Claassen, G.D.H. & Meuwissen, M.P.M. & Smit, A.B. & Saatkamp, H.W., 2021. "Actor analysis for sustainable soil management – A case study from the Netherlands," Land Use Policy, Elsevier, vol. 107(C).
    17. Ravindra Singh Saluja & Varinder Singh, 2023. "Attribute-based characterization, coding, and selection of joining processes using a novel MADM approach," OPSEARCH, Springer;Operational Research Society of India, vol. 60(2), pages 616-655, June.
    18. Huang, Beijia & Zhang, Long & Ma, Linmao & Bai, Wuliyasu & Ren, Jingzheng, 2021. "Multi-criteria decision analysis of China’s energy security from 2008 to 2017 based on Fuzzy BWM-DEA-AR model and Malmquist Productivity Index," Energy, Elsevier, vol. 228(C).
    19. Zheng Yuan & Baohua Wen & Cheng He & Jin Zhou & Zhonghua Zhou & Feng Xu, 2022. "Application of Multi-Criteria Decision-Making Analysis to Rural Spatial Sustainability Evaluation: A Systematic Review," IJERPH, MDPI, vol. 19(11), pages 1-31, May.
    20. Ghadimi, Pezhman & Donnelly, Oisin & Sar, Kubra & Wang, Chao & Azadnia, Amir Hossein, 2022. "The successful implementation of industry 4.0 in manufacturing: An analysis and prioritization of risks in Irish industry," Technological Forecasting and Social Change, Elsevier, vol. 175(C).

    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:jsusta:v:14:y:2022:i:19:p:12709-:d:934810. 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.