IDEAS home Printed from https://ideas.repec.org/a/eee/ejores/v293y2021i1p276-289.html
   My bibliography  Save this article

Towards understanding socially influenced vaccination decision making: An integrated model of multiple criteria belief modelling and social network analysis

Author

Listed:
  • Ni, Lei
  • Chen, Yu-wang
  • de Brujin, Oscar

Abstract

Understanding the socially influenced decision-making process that determines voluntary vaccination is essential for developing strategies and interventions of vaccine-preventable diseases. Both theoretical and experimental studies have suggested that a variety of factors, such as safety of vaccines, severity of diseases, information and advice from healthcare professionals, influence an individual's intention to vaccinate. However, limited research has been conducted on analysing systematically how individuals’ vaccine acceptance decisions are made from their beliefs and judgements on the influential factors. In particular, there is lack of quantitative analysis on how individuals’ beliefs and judgements may evolve from the spreading of vaccination-related information in a social network, which further affects their decision making. In this paper, an integrated model is first proposed to characterise the socially influenced vaccination decision-making process, in which each individual's beliefs and subjective judgements on the decision criteria are formulated as belief distributions in the framework of multiple criteria decision analysis (MCDA). The spreading of social influence in the network environment is further incorporated into the information aggregation process for supporting informed vaccination decision analysis. A series of simulation-based analyses on a real-world social network is conducted to demonstrate that the overall vaccination coverage is determined primarily by individuals’ beliefs and judgements on the decision criteria, and is also affected sensitively by the characteristics of influence spreading (including the content and amount of vaccination-related information) in the social network.

Suggested Citation

  • Ni, Lei & Chen, Yu-wang & de Brujin, Oscar, 2021. "Towards understanding socially influenced vaccination decision making: An integrated model of multiple criteria belief modelling and social network analysis," European Journal of Operational Research, Elsevier, vol. 293(1), pages 276-289.
  • Handle: RePEc:eee:ejores:v:293:y:2021:i:1:p:276-289
    DOI: 10.1016/j.ejor.2020.12.011
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0377221720310213
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.ejor.2020.12.011?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. de Bekker-Grob, E.W. & Donkers, B. & Bliemer, M.C.J. & Veldwijk, J. & Swait, J.D., 2020. "Can healthcare choice be predicted using stated preference data?," Social Science & Medicine, Elsevier, vol. 246(C).
    2. Edmundas Kazimieras Zavadskas & Valentinas Podvezko, 2016. "Integrated Determination of Objective Criteria Weights in MCDM," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 15(02), pages 267-283, March.
    3. Wang, Ying-Ming & Yang, Jian-Bo & Xu, Dong-Ling & Chin, Kwai-Sang, 2006. "The evidential reasoning approach for multiple attribute decision analysis using interval belief degrees," European Journal of Operational Research, Elsevier, vol. 175(1), pages 35-66, November.
    4. Ling, Mathew & Kothe, Emily J. & Mullan, Barbara A., 2019. "Predicting intention to receive a seasonal influenza vaccination using Protection Motivation Theory," Social Science & Medicine, Elsevier, vol. 233(C), pages 87-92.
    5. Markus M. Mobius & Neel Rao & Tanya Rosenblat, 2007. "Social networks and vaccination decisions," Working Papers 07-12, Federal Reserve Bank of Boston.
    6. Ivlev, Ilya & Vacek, Jakub & Kneppo, Peter, 2015. "Multi-criteria decision analysis for supporting the selection of medical devices under uncertainty," European Journal of Operational Research, Elsevier, vol. 247(1), pages 216-228.
    7. Enayati, Shakiba & Özaltın, Osman Y., 2020. "Optimal influenza vaccine distribution with equity," European Journal of Operational Research, Elsevier, vol. 283(2), pages 714-725.
    8. Markus Schöbel & Jörg Rieskamp & Rafael Huber, 2016. "Social Influences in Sequential Decision Making," PLOS ONE, Public Library of Science, vol. 11(1), pages 1-23, January.
    9. Mats Danielson & Love Ekenberg & Ying He, 2014. "Augmenting Ordinal Methods of Attribute Weight Approximation," Decision Analysis, INFORMS, vol. 11(1), pages 21-26, March.
    10. Xu, Dong-Ling & Yang, Jian-Bo & Wang, Ying-Ming, 2006. "The evidential reasoning approach for multi-attribute decision analysis under interval uncertainty," European Journal of Operational Research, Elsevier, vol. 174(3), pages 1914-1943, November.
    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. Li, Siran & Xiao, Fuyuan, 2023. "Normal distribution based on maximum Deng entropy," Chaos, Solitons & Fractals, Elsevier, vol. 167(C).

    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. Gao, Bin & Ni, Ming-Fang, 2009. "A note on article "The evidential reasoning approach for multiple attribute decision analysis using interval belief degrees"," European Journal of Operational Research, Elsevier, vol. 197(2), pages 809-812, September.
    2. Fu, Chao & Yang, Shanlin, 2012. "An evidential reasoning based consensus model for multiple attribute group decision analysis problems with interval-valued group consensus requirements," European Journal of Operational Research, Elsevier, vol. 223(1), pages 167-176.
    3. Guo, Min & Yang, Jian-Bo & Chin, Kwai-Sang & Wang, Hongwei, 2007. "Evidential reasoning based preference programming for multiple attribute decision analysis under uncertainty," European Journal of Operational Research, Elsevier, vol. 182(3), pages 1294-1312, November.
    4. S. Nodoust & A. Mirzazadeh & G.-W. Weber, 2020. "An evidential reasoning approach for production modeling with deteriorating and ameliorating items," Operational Research, Springer, vol. 20(1), pages 1-19, March.
    5. Wang, Ying-Ming, 2009. "Reply to the note on article "The evidential reasoning approach for multiple attribute decision analysis using interval belief degrees"," European Journal of Operational Research, Elsevier, vol. 197(2), pages 813-817, September.
    6. Durbach, Ian N. & Stewart, Theodor J., 2012. "Modeling uncertainty in multi-criteria decision analysis," European Journal of Operational Research, Elsevier, vol. 223(1), pages 1-14.
    7. Maddulapalli, Anil Kumar & Yang, Jian-Bo & Xu, Dong-Ling, 2012. "Estimation, modeling, and aggregation of missing survey data for prioritizing customer voices," European Journal of Operational Research, Elsevier, vol. 220(3), pages 762-776.
    8. Deng, Xinyang & Hu, Yong & Chan, Felix T.S. & Mahadevan, Sankaran & Deng, Yong, 2015. "Parameter estimation based on interval-valued belief structures," European Journal of Operational Research, Elsevier, vol. 241(2), pages 579-582.
    9. Hua Zhu & Jianbin Zhao & Yang Xu & Limin Du, 2016. "Interval-Valued Belief Rule Inference Methodology Based on Evidential Reasoning-IRIMER," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 15(06), pages 1345-1366, November.
    10. Dong-Ling Xu, 2012. "An introduction and survey of the evidential reasoning approach for multiple criteria decision analysis," Annals of Operations Research, Springer, vol. 195(1), pages 163-187, May.
    11. Fu, Chao & Yang, Shanlin, 2011. "An attribute weight based feedback model for multiple attributive group decision analysis problems with group consensus requirements in evidential reasoning context," European Journal of Operational Research, Elsevier, vol. 212(1), pages 179-189, July.
    12. Yang, Guo-liang & Yang, Jian-bo & Liu, Wen-bin & Li, Xiao-xuan, 2013. "Cross-efficiency aggregation in DEA models using the evidential-reasoning approach," European Journal of Operational Research, Elsevier, vol. 231(2), pages 393-404.
    13. Cui, Huizi & Zhou, Lingge & Li, Yan & Kang, Bingyi, 2022. "Belief entropy-of-entropy and its application in the cardiac interbeat interval time series analysis," Chaos, Solitons & Fractals, Elsevier, vol. 155(C).
    14. Fu, Chao & Yang, Shan-Lin, 2010. "The group consensus based evidential reasoning approach for multiple attributive group decision analysis," European Journal of Operational Research, Elsevier, vol. 206(3), pages 601-608, November.
    15. Zhang, Mei-Jing & Wang, Ying-Ming & Li, Ling-Hui & Chen, Sheng-Qun, 2017. "A general evidential reasoning algorithm for multi-attribute decision analysis under interval uncertainty," European Journal of Operational Research, Elsevier, vol. 257(3), pages 1005-1015.
    16. Karaaslan, Abdulkerim & Gezen, Mesliha, 2022. "The evaluation of renewable energy resources in Turkey by integer multi-objective selection problem with interval coefficient," Renewable Energy, Elsevier, vol. 182(C), pages 842-854.
    17. Voola, Persis & A., Vinaya Babu, 2017. "Study of aggregation algorithms for aggregating imprecise software requirements’ priorities," European Journal of Operational Research, Elsevier, vol. 259(3), pages 1191-1199.
    18. Shiva Mehrabi-Kandsar & Abolfazl Mirzazadeh & Aref Gholami-Qadikolaei, 2017. "The quality function deployment method under uncertain environment using evidential reasoning: a case study of compressor manufacturing," 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. 8(2), pages 1867-1884, November.
    19. Alene Sze Jing Yong & Yi Heng Lim & Mark Wing Loong Cheong & Ednin Hamzah & Siew Li Teoh, 2022. "Willingness-to-pay for cancer treatment and outcome: a systematic review," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 23(6), pages 1037-1057, August.
    20. Edmundas Kazimieras Zavadskas & Fausto Cavallaro & Valentinas Podvezko & Ieva Ubarte & Arturas Kaklauskas, 2017. "MCDM Assessment of a Healthy and Safe Built Environment According to Sustainable Development Principles: A Practical Neighborhood Approach in Vilnius," Sustainability, MDPI, vol. 9(5), pages 1-30, April.

    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:eee:ejores:v:293:y:2021:i:1:p:276-289. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/eor .

    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.