IDEAS home Printed from https://ideas.repec.org/a/spr/annopr/v341y2024i1d10.1007_s10479-022-04827-9.html
   My bibliography  Save this article

Physician recommendation via online and offline social network group decision making with cross-network uncertain trust propagation

Author

Listed:
  • Mingwei Wang

    (University of Electronic Science and Technology of China)

  • Decui Liang

    (University of Electronic Science and Technology of China)

  • Wen Cao

    (University of Electronic Science and Technology of China)

  • Yuanyuan Fu

    (University of Electronic Science and Technology of China)

Abstract

Online and offline integration is an increasingly popular method of performing modern medical services. To provide suggestions for physician selection by patients with full use of previous online and offline patient evaluations, this paper investigates online and offline social network group decision making (OAOSNGDM) in depth. With the aim of inferring the indirect trust relationships of online and offline patients, we first construct a q-rung orthopair fuzzy dual trust propagation operator based on the q-rung orthopair fuzzy trust function, which can effectively deal with inconsistency in trust functions among patients. Considering patient inconsistency in online and offline scenarios, which can increase the uncertainty of the trust relationship in cross-network propagation, we propose a q-rung orthopair fuzzy dual trust cross-network propagation operator by introducing cross-network propagation efficiency. Considering the signal-to-noise ratio, we calculate the trust propagation efficiency and introduce it into the trust propagation operators. To aggregate the trust information of multiple trust paths among patients, we introduce the Dempster rule from evidence theory which can handle the uncertainty of trust functions. In addition, to accurately determine the patient weights according to online and offline social networks, we integrate the ranking results of patients in terms of degree centrality, neighbor importance and betweenness centrality by developing an improved linear assignment method. We then propose a novel decision-making method for OAOSNGDM and design a complete decision-making process for the evaluation of physicians. Finally, we verify the effectiveness of our proposed method for the evaluation of physicians in an online and offline scenario.

Suggested Citation

  • Mingwei Wang & Decui Liang & Wen Cao & Yuanyuan Fu, 2024. "Physician recommendation via online and offline social network group decision making with cross-network uncertain trust propagation," Annals of Operations Research, Springer, vol. 341(1), pages 583-619, October.
    • Handle: RePEc:spr:annopr:v:341:y:2024:i:1:d:10.1007_s10479-022-04827-9
    DOI: 10.1007/s10479-022-04827-9
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10479-022-04827-9
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s10479-022-04827-9?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. Bernardo, John J & Blin, Jean-Marie, 1977. "A Programming Model of Consumer Choice among Multi-Attributed Brands," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 4(2), pages 111-118, Se.
    2. Tsuen-Ho Hsu & Li-Chu Hung & Jia-Wei Tang, 2012. "An analytical model for building brand equity in hospitality firms," Annals of Operations Research, Springer, vol. 195(1), pages 355-378, May.
    3. Yuxuan Gao & Yueping Du & Haiming Liang & Bingzhen Sun, 2018. "Large Group Decision-Making Approach Based on Stochastic MULTIMOORA: An Application of Doctor Evaluation in Healthcare Service," Complexity, Hindawi, vol. 2018, pages 1-13, November.
    4. Qingxian An & Fanyong Meng & Beibei Xiong, 2018. "Interval cross efficiency for fully ranking decision making units using DEA/AHP approach," Annals of Operations Research, Springer, vol. 271(2), pages 297-317, December.
    5. Liu, Bingsheng & Zhou, Qi & Ding, Ru-Xi & Palomares, Iván & Herrera, Francisco, 2019. "Large-scale group decision making model based on social network analysis: Trust relationship-based conflict detection and elimination," European Journal of Operational Research, Elsevier, vol. 275(2), pages 737-754.
    6. Parand, Fereshteh-Azadi & Rahimi, Hossein & Gorzin, Mohsen, 2016. "Combining fuzzy logic and eigenvector centrality measure in social network analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 459(C), pages 24-31.
    7. Chong (Alex) Wang & Xiaoquan (Michael) Zhang & Il-Horn Hann, 2018. "Socially Nudged: A Quasi-Experimental Study of Friends’ Social Influence in Online Product Ratings," Information Systems Research, INFORMS, vol. 29(3), pages 641-655, September.
    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. Wang, Le & Luo, Xin (Robert) & Li, Han, 2022. "Envy or conformity? An empirical investigation of peer influence on the purchase of non-functional items in mobile free-to-play games," Journal of Business Research, Elsevier, vol. 147(C), pages 308-324.
    2. Ryan M. Hynes & Bernardo S. Buarque & Ronald B. Davies & Dieter F. Kogler, 2020. "Hops, Skip & a Jump - The Regional Uniqueness of Beer Styles," Working Papers 202013, Geary Institute, University College Dublin.
    3. Xunjie Gou & Zeshui Xu & Huchang Liao, 2019. "Hesitant Fuzzy Linguistic Possibility Degree-Based Linear Assignment Method for Multiple Criteria Decision-Making," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 18(01), pages 35-63, January.
    4. Gong, Zaiwu & Guo, Weiwei & Słowiński, Roman, 2021. "Transaction and interaction behavior-based consensus model and its application to optimal carbon emission reduction," Omega, Elsevier, vol. 104(C).
    5. Liangfei Qiu & Arunima Chhikara & Asoo Vakharia, 2021. "Multidimensional Observational Learning in Social Networks: Theory and Experimental Evidence," Information Systems Research, INFORMS, vol. 32(3), pages 876-894, September.
    6. Chen, Lei & Kou, Yingxin & Li, Zhanwu & Xu, An & Wu, Cheng, 2018. "Empirical research on complex networks modeling of combat SoS based on data from real war-game, Part I: Statistical characteristics," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 490(C), pages 754-773.
    7. Richard Thomas Watson & Kirk Plangger & Leyland Pitt & Amrit Tiwana, 2023. "A Theory of Information Compression: When Judgments Are Costly," Information Systems Research, INFORMS, vol. 34(3), pages 1089-1108, September.
    8. Lin, Shoufu & Lin, Ruoyun & Sun, Ji & Wang, Fei & Wu, Weixiang, 2021. "Dynamically evaluating technological innovation efficiency of high-tech industry in China: Provincial, regional and industrial perspective," Socio-Economic Planning Sciences, Elsevier, vol. 74(C).
    9. Haris Krijestorac & Rajiv Garg & Vijay Mahajan, 2020. "Cross-Platform Spillover Effects in Consumption of Viral Content: A Quasi-Experimental Analysis Using Synthetic Controls," Information Systems Research, INFORMS, vol. 31(2), pages 449-472, June.
    10. Tata, Sai Vijay & Prashar, Sanjeev & Gupta, Sumeet, 2020. "An examination of the role of review valence and review source in varying consumption contexts on purchase decision," Journal of Retailing and Consumer Services, Elsevier, vol. 52(C).
    11. Weerasinghe, Kasuni & Scahill, Shane L. & Pauleen, David J. & Taskin, Nazim, 2022. "Big data analytics for clinical decision-making: Understanding health sector perceptions of policy and practice," Technological Forecasting and Social Change, Elsevier, vol. 174(C).
    12. Liu, Jia-Bao & Zheng, Ya-Qian & Lee, Chien-Chiang, 2024. "Statistical analysis of the regional air quality index of Yangtze River Delta based on complex network theory," Applied Energy, Elsevier, vol. 357(C).
    13. Zhiying Zhang & Huchang Liao, 2024. "A stochastic cross-efficiency DEA approach based on the prospect theory and its application in winner determination in public procurement tenders," Annals of Operations Research, Springer, vol. 341(1), pages 509-537, October.
    14. Zhuo (June) Cheng & Arun Rai & Feng Tian & Sean Xin Xu, 2021. "Social Learning in Information Technology Investment: The Role of Board Interlocks," Management Science, INFORMS, vol. 67(1), pages 547-576, January.
    15. Choi, Tsan-Ming & Chen, Yue, 2021. "Circular supply chain management with large scale group decision making in the big data era: The macro-micro model," Technological Forecasting and Social Change, Elsevier, vol. 169(C).
    16. Xiangrui Chao & Yucheng Dong & Gang Kou & Yi Peng, 2022. "How to determine the consensus threshold in group decision making: a method based on efficiency benchmark using benefit and cost insight," Annals of Operations Research, Springer, vol. 316(1), pages 143-177, September.
    17. Pradhan, Priodyuti & C.U., Angeliya & Jalan, Sarika, 2020. "Principal eigenvector localization and centrality in networks: Revisited," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 554(C).
    18. Shan, Wei & Wang, Jiaxuan & Shi, Xiaoxiao & David Evans, Richard, 2024. "The impact of electronic word-of-mouth on patients’ choices in online health communities: A cross-media perspective," Journal of Business Research, Elsevier, vol. 173(C).
    19. Juan Antonio Morente-Molinera & Francisco Javier Cabrerizo & Sergio Alonso & Ignacio Javier Pérez & Enrique Herrera-Viedma, 2020. "Assisting Users in Decisions Using Fuzzy Ontologies: Application in the Wine Market," Mathematics, MDPI, vol. 8(10), pages 1-18, October.
    20. Michael Kummer & Olga Slivko & Xiaoquan (Michael) Zhang, 2020. "Unemployment and Digital Public Goods Contribution," Information Systems Research, INFORMS, vol. 31(3), pages 801-819, September.

    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:spr:annopr:v:341:y:2024:i:1:d:10.1007_s10479-022-04827-9. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.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.