IDEAS home Printed from https://ideas.repec.org/a/eee/jomega/v100y2021ics0305048319307285.html
   My bibliography  Save this article

From conventional group decision making to large-scale group decision making: What are the challenges and how to meet them in big data era? A state-of-the-art survey

Author

Listed:
  • Tang, Ming
  • Liao, Huchang

Abstract

The arrival of Big Data era has brought large, complex, and growing data generated from numerous sources. Due to the power in felicitous decision making based on diverse and large data, Big Data can be used in distinct disciplines, especially in social Big Data such as e-commerce, e-marketplaces and social media platforms. As a result, the large-scale group decision making, in which a large number of decision-makers take part in the decision-making process, has become a much-talked-about topic in decision science. Because of the characteristics of social Big Data, much more information in large-scale group decision making will arise than conventional group decision making. Information is a key factor that influences the performance of decision-makers. Therefore, how to manage the challenges from conventional group decision making to large-scale group decision making is a critical and interesting research topic. Up to now, many studies have been published to tackle these challenges. The objective of this study is to summarize the challenges and present a state-of-the-art survey of main achievements in this field. We also provide existing research gaps and future directions that require further consideration. It is hoped that our study could give insights for scholars and practitioners along the developments and promising research of large-scale group decision making.

Suggested Citation

  • Tang, Ming & Liao, Huchang, 2021. "From conventional group decision making to large-scale group decision making: What are the challenges and how to meet them in big data era? A state-of-the-art survey," Omega, Elsevier, vol. 100(C).
  • Handle: RePEc:eee:jomega:v:100:y:2021:i:c:s0305048319307285
    DOI: 10.1016/j.omega.2019.102141
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0305048319307285
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.omega.2019.102141?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. Pei Wang & Xuanhua Xu & Shuai Huang, 2019. "An Improved Consensus-Based Model for Large Group Decision Making Problems Considering Experts with Linguistic Weighted Information," Group Decision and Negotiation, Springer, vol. 28(3), pages 619-640, June.
    2. Wu, Zhibin & Xu, Jiuping, 2016. "Managing consistency and consensus in group decision making with hesitant fuzzy linguistic preference relations," Omega, Elsevier, vol. 65(C), pages 28-40.
    3. Bingsheng Liu & Tengfei Huo & Pinchao Liao & Jie Gong & Bin Xue, 2015. "A Group Decision-Making Aggregation Model for Contractor Selection in Large Scale Construction Projects Based on Two-Stage Partial Least Squares (PLS) Path Modeling," Group Decision and Negotiation, Springer, vol. 24(5), pages 855-883, September.
    4. Dong, Qingxing & Cooper, Orrin, 2016. "A peer-to-peer dynamic adaptive consensus reaching model for the group AHP decision making," European Journal of Operational Research, Elsevier, vol. 250(2), pages 521-530.
    5. Zahir, Sajjad, 1999. "Geometry of decision making and the vector space formulation of the analytic hierarchy process," European Journal of Operational Research, Elsevier, vol. 112(2), pages 373-396, January.
    6. Xuanhua Xu & Yanxia Huang & Ke Chen, 2019. "Method for large group emergency decision making with complex preferences based on emergency similarity and interval consistency," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 97(1), pages 45-64, May.
    7. Liang, Gin-Shuh & Chou, Tsung-Yu & Han, Tzeu-Chen, 2005. "Cluster analysis based on fuzzy equivalence relation," European Journal of Operational Research, Elsevier, vol. 166(1), pages 160-171, October.
    8. Janssen, Marijn & van der Voort, Haiko & Wahyudi, Agung, 2017. "Factors influencing big data decision-making quality," Journal of Business Research, Elsevier, vol. 70(C), pages 338-345.
    9. Cheng, Dong & Zhou, Zhili & Cheng, Faxin & Zhou, Yanfang & Xie, Yujing, 2018. "Modeling the minimum cost consensus problem in an asymmetric costs context," European Journal of Operational Research, Elsevier, vol. 270(3), pages 1122-1137.
    10. Boujelben, Mohamed Ayman, 2017. "A unicriterion analysis based on the PROMETHEE principles for multicriteria ordered clustering," Omega, Elsevier, vol. 69(C), pages 126-140.
    11. Liu, Hu-Chen & Li, Zhaojun & Zhang, Jian-Qing & You, Xiao-Yue, 2018. "A large group decision making approach for dependence assessment in human reliability analysis," Reliability Engineering and System Safety, Elsevier, vol. 176(C), pages 135-144.
    12. Jinbaek Kim, 2008. "A model and case for supporting participatory public decision making in e-democracy," Group Decision and Negotiation, Springer, vol. 17(3), pages 179-193, May.
    13. Pang, Jifang & Liang, Jiye, 2012. "Evaluation of the results of multi-attribute group decision-making with linguistic information," Omega, Elsevier, vol. 40(3), pages 294-301.
    14. Sueyoshi, Toshiyuki, 1999. "DEA-discriminant analysis in the view of goal programming," European Journal of Operational Research, Elsevier, vol. 115(3), pages 564-582, June.
    15. Liu, Bingsheng & Shen, Yinghua & Zhang, Wei & Chen, Xiaohong & Wang, Xueqing, 2015. "An interval-valued intuitionistic fuzzy principal component analysis model-based method for complex multi-attribute large-group decision-making," European Journal of Operational Research, Elsevier, vol. 245(1), pages 209-225.
    16. Gong, Zaiwu & Xu, Xiaoxia & Zhang, Huanhuan & Aytun Ozturk, U. & Herrera-Viedma, Enrique & Xu, Chao, 2015. "The consensus models with interval preference opinions and their economic interpretation," Omega, Elsevier, vol. 55(C), pages 81-90.
    17. Zahir, Sajjad, 1999. "Clusters in a group: Decision making in the vector space formulation of the analytic hierarchy process," European Journal of Operational Research, Elsevier, vol. 112(3), pages 620-634, February.
    18. Jianjun Zhu & Shitao Zhang & Ye Chen & Lili Zhang, 2016. "A Hierarchical Clustering Approach Based on Three-Dimensional Gray Relational Analysis for Clustering a Large Group of Decision Makers with Double Information," Group Decision and Negotiation, Springer, vol. 25(2), pages 325-354, March.
    19. Vivien Marx, 2013. "The big challenges of big data," Nature, Nature, vol. 498(7453), pages 255-260, June.
    20. Fernandez, Eduardo & Navarro, Jorge & Bernal, Sergio, 2010. "Handling multicriteria preferences in cluster analysis," European Journal of Operational Research, Elsevier, vol. 202(3), pages 819-827, May.
    21. Benjamin T. Hazen & Joseph B. Skipper & Christopher A. Boone & Raymond R. Hill, 2018. "Back in business: operations research in support of big data analytics for operations and supply chain management," Annals of Operations Research, Springer, vol. 270(1), pages 201-211, November.
    22. Sarrazin, R. & De Smet, Y. & Rosenfeld, J., 2018. "An extension of PROMETHEE to interval clustering," Omega, Elsevier, vol. 80(C), pages 12-21.
    23. Zhang, Huanhuan & Kou, Gang & Peng, Yi, 2019. "Soft consensus cost models for group decision making and economic interpretations," European Journal of Operational Research, Elsevier, vol. 277(3), pages 964-980.
    24. 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.
    25. Wu, Xingli & Liao, Huchang, 2019. "A consensus-based probabilistic linguistic gained and lost dominance score method," European Journal of Operational Research, Elsevier, vol. 272(3), pages 1017-1027.
    26. Liu, Jiapeng & Liao, Xiuwu & Zhao, Wenhong & Yang, Na, 2016. "A classification approach based on the outranking model for multiple criteria ABC analysis," Omega, Elsevier, vol. 61(C), pages 19-34.
    27. Liao, Huchang & Tang, Ming & Li, Zongmin & Lev, Benjamin, 2019. "Bibliometric analysis for highly cited papers in operations research and management science from 2008 to 2017 based on Essential Science Indicators," Omega, Elsevier, vol. 88(C), pages 223-236.
    28. Yongming Song & Guangxu Li, 2019. "A large-scale group decision-making with incomplete multi-granular probabilistic linguistic term sets and its application in sustainable supplier selection," Journal of the Operational Research Society, Taylor & Francis Journals, vol. 70(5), pages 827-841, May.
    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. Meng, Fanyong & Tang, Jie & An, Qingxian, 2023. "Cooperative game based two-stage consensus adjustment mechanism for large-scale group decision making," Omega, Elsevier, vol. 117(C).
    2. Lu Gan & Yuanyuan Wang & Yusheng Wang & Benjamin Lev & Wenjing Shen & Wen Jiang, 2021. "Coupling coordination analysis with data-driven technology for disaster–economy–ecology system: an empirical study in China," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 107(3), pages 2123-2153, July.
    3. Wu, Xingli & Liao, Huchang, 2023. "Value-driven preference disaggregation analysis for uncertain preference information," Omega, Elsevier, vol. 115(C).
    4. Dominika Krol-Smetak & Ireneusz Miciula & Apoloniusz Kurylczyk & Malgorzata Chojnacka & Karolina Rogowska & Monika Rozycka, 2023. "Analysis of the Impact of Implemented IT Systems on the Economic Efficiency of Enterprises in the Construction Industry in the Context of Sustainable Development in Poland," European Research Studies Journal, European Research Studies Journal, vol. 0(3), pages 901-915.
    5. Tang, Ming & Liao, Huchang, 2021. "Multi-attribute large-scale group decision making with data mining and subgroup leaders: An application to the development of the circular economy," Technological Forecasting and Social Change, Elsevier, vol. 167(C).
    6. Ming Tang & Huchang Liao, 2023. "Group Structure and Information Distribution on the Emergence of Collective Intelligence," Decision Analysis, INFORMS, vol. 20(2), pages 133-150, June.
    7. Tang, Ming & Liao, Huchang, 2021. "Failure mode and effect analysis considering the fairness-oriented consensus of a large group with core-periphery structure," Reliability Engineering and System Safety, Elsevier, vol. 215(C).
    8. Lu Chen & Ayad Hendalianpour & Mohammad Reza Feylizadeh & Haiyan Xu, 2023. "Factors Affecting the Use of Blockchain Technology in Humanitarian Supply Chain: A Novel Fuzzy Large-Scale Group-DEMATEL," Group Decision and Negotiation, Springer, vol. 32(2), pages 359-394, April.
    9. Yuanming Li & Ying Ji & Shaojian Qu, 2022. "Consensus Building for Uncertain Large-Scale Group Decision-Making Based on the Clustering Algorithm and Robust Discrete Optimization," Group Decision and Negotiation, Springer, vol. 31(2), pages 453-489, April.
    10. Kao, Chiang & Liu, Shiang-Tai, 2022. "Group decision making in data envelopment analysis: A robot selection application," European Journal of Operational Research, Elsevier, vol. 297(2), pages 592-599.
    11. Jahangir Wasim & Vijay Vyas & Pietro Amenta & Antonio Lucadamo & Gabriella Marcarelli & Alessio Ishizaka, 2023. "Deriving the weights for aggregating judgments in a multi-group problem: an application to curriculum development in entrepreneurship," Annals of Operations Research, Springer, vol. 326(2), pages 853-877, July.
    12. Long, Yilu & Tang, Ming & Liao, Huchang, 2022. "Renewable energy source technology selection considering the empathetic preferences of experts in a cognitive fuzzy social participatory allocation network," Technological Forecasting and Social Change, Elsevier, vol. 175(C).
    13. Feifei Jin & Jinpei Liu & Ligang Zhou & Luis Martínez, 2021. "Consensus-Based Linguistic Distribution Large-Scale Group Decision Making Using Statistical Inference and Regret Theory," Group Decision and Negotiation, Springer, vol. 30(4), pages 813-845, August.

    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. 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).
    2. Zhang, Hengjie & Dong, Yucheng & Chiclana, Francisco & Yu, Shui, 2019. "Consensus efficiency in group decision making: A comprehensive comparative study and its optimal design," European Journal of Operational Research, Elsevier, vol. 275(2), pages 580-598.
    3. Tang, Ming & Liao, Huchang & Xu, Jiuping & Streimikiene, Dalia & Zheng, Xiaosong, 2020. "Adaptive consensus reaching process with hybrid strategies for large-scale group decision making," European Journal of Operational Research, Elsevier, vol. 282(3), pages 957-971.
    4. Zhang, Bowen & Dong, Yucheng & Zhang, Hengjie & Pedrycz, Witold, 2020. "Consensus mechanism with maximum-return modifications and minimum-cost feedback: A perspective of game theory," European Journal of Operational Research, Elsevier, vol. 287(2), pages 546-559.
    5. Li, Yanhong & Kou, Gang & Li, Guangxu & Peng, Yi, 2022. "Consensus reaching process in large-scale group decision making based on bounded confidence and social network," European Journal of Operational Research, Elsevier, vol. 303(2), pages 790-802.
    6. Zhang, Huanhuan & Kou, Gang & Peng, Yi, 2019. "Soft consensus cost models for group decision making and economic interpretations," European Journal of Operational Research, Elsevier, vol. 277(3), pages 964-980.
    7. Gong, Zaiwu & Guo, Weiwei & Herrera-Viedma, Enrique & Gong, Zejun & Wei, Guo, 2020. "Consistency and consensus modeling of linear uncertain preference relations," European Journal of Operational Research, Elsevier, vol. 283(1), pages 290-307.
    8. Chao, Xiangrui & Kou, Gang & Peng, Yi & Viedma, Enrique Herrera, 2021. "Large-scale group decision-making with non-cooperative behaviors and heterogeneous preferences: An application in financial inclusion," European Journal of Operational Research, Elsevier, vol. 288(1), pages 271-293.
    9. Qifeng Wan & Xuanhua Xu & Xiaohong Chen & Jun Zhuang, 2020. "A Two-Stage Optimization Model for Large-Scale Group Decision-Making in Disaster Management: Minimizing Group Conflict and Maximizing Individual Satisfaction," Group Decision and Negotiation, Springer, vol. 29(5), pages 901-921, October.
    10. Yong Liu & Ting Zhou & Wei-xue Diao & Jinhong Yi, 2022. "A multivariate minimum cost consensus approach for two-level group decision making," OPSEARCH, Springer;Operational Research Society of India, vol. 59(3), pages 839-861, September.
    11. Sumin Yu & Zhijiao Du & Xuanhua Xu, 2021. "Hierarchical Punishment-Driven Consensus Model for Probabilistic Linguistic Large-Group Decision Making with Application to Global Supplier Selection," Group Decision and Negotiation, Springer, vol. 30(6), pages 1343-1372, December.
    12. Triantaphyllou, Evangelos & Yanase, Juri & Hou, Fujun, 2020. "Post-consensus analysis of group decision making processes by means of a graph theoretic and an association rules mining approach," Omega, Elsevier, vol. 94(C).
    13. 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).
    14. Labella, Álvaro & Liu, Hongbin & Rodríguez, Rosa M. & Martínez, Luis, 2020. "A Cost Consensus Metric for Consensus Reaching Processes based on a comprehensive minimum cost model," European Journal of Operational Research, Elsevier, vol. 281(2), pages 316-331.
    15. 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.
    16. Yong Liu & Ting Zhou & Jeffrey Yi-Lin Forrest, 2020. "A Multivariate Minimum Cost Consensus Model for Negotiations of Holdout Demolition," Group Decision and Negotiation, Springer, vol. 29(5), pages 871-899, October.
    17. Yin, Xuanpeng & Xu, Xuanhua & Pan, Bin, 2021. "Selection of Strategy for Large Group Emergency Decision-making based on Risk Measurement," Reliability Engineering and System Safety, Elsevier, vol. 208(C).
    18. Wenjun Chang & Chao Fu & Nanping Feng & Shanlin Yang, 2021. "Multi-criteria Group Decision Making with Various Ordinal Assessments," Group Decision and Negotiation, Springer, vol. 30(6), pages 1285-1314, December.
    19. Cheng, Dong & Yuan, Yuxiang & Wu, Yong & Hao, Tiantian & Cheng, Faxin, 2022. "Maximum satisfaction consensus with budget constraints considering individual tolerance and compromise limit behaviors," European Journal of Operational Research, Elsevier, vol. 297(1), pages 221-238.
    20. Heidary-Dahooie, Jalil & Rafiee, Mostafa & Mohammadi, Mehdi & Meidute-Kavaliauskienė, Ieva, 2022. "Proposing a new LSGDM framework based on BWM with hesitant fuzzy information for prioritizing blockchain adoption barriers in supply chain," Technology in Society, Elsevier, vol. 71(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:eee:jomega:v:100:y:2021:i:c:s0305048319307285. 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/wps/find/journaldescription.cws_home/375/description#description .

    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.