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
作者：
TangMing;LiaoHuchang
通讯作者：
Liao, H.
作者机构：
Sichuan Univ, Business Sch, Chengdu 610064, Peoples R China.
通讯机构：
Business School, Sichuan University, Chengdu, China
语种：
英文
关键词：
Big data;Challenges;Future directions;Large-scale group decision making;Review
期刊：
OMEGA-INTERNATIONAL JOURNAL OF MANAGEMENT SCIENCE
ISSN：
03050483
年：
2021
卷：
100
页码：
102141-
基金类别：
National Natural Science Foundation of ChinaNational Natural Science Foundation of China (NSFC) [71771156, 71971145]
摘要：
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. (c) 2019 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license. (http://creativecommons.org/licenses/by/4.0/
