Consensus group decision‐making, quality of decision, and group satisfaction: An attempt to sort “fact” from “fiction”

1982 ◽  
Vol 33 (2) ◽  
pp. 407-415 ◽  
Author(s):  
Randy Y. Hirokawa
Keyword(s):  
Decision Making ◽  
Group Decision Making ◽  
Group Decision ◽  
Consensus Group ◽  
Group Satisfaction

Similarity–trust network for clustering‐based consensus group decision‐making model

2021 ◽  
Author(s):  
Wan Syahimi Afiq Wan Ahlim ◽  
Nor Hanimah Kamis ◽  
Sharifah Aniza Sayed Ahmad ◽  
Francisco Chiclana
Keyword(s):  
Decision Making ◽  
Group Decision Making ◽  
Group Decision ◽  
Trust Network ◽  
Consensus Group ◽  
Decision Making Model

A self-testing cloud model for multi-criteria group decision making

2016 ◽  
Vol 33 (6) ◽  
pp. 1767-1783 ◽  
Author(s):  
Ting-Cheng Chang ◽  
Hui Wang
Keyword(s):  
Decision Making ◽  
Group Decision Making ◽  
Group Decision ◽  
Cloud Model ◽  
New Approach ◽  
Content Type ◽  
Qualitative And Quantitative ◽  
Composite Scale ◽  
Self Testing

Purpose – The purpose of this paper is to select the best scaling coefficient during the quantitative-qualitative conversion. Design/methodology/approach – Cloud model can describe the qualitative concept of randomness and fuzziness, achieve uncertain transition between qualitative and quantitative in the field of multi-criteria group decision and has been receiving widespread attention. This paper discusses scale conversion issues of the cloud model when evaluating qualitative information. In order to improve the accuracy of the evaluation on multi-attribute decision problems based on uncertainty of natural linguistic information, this paper proposes a method of self-testing cloud model based on a composite scale (with the exponential scale and the scale as a basis). Findings – Through experimental verification results show that under composite scale, the best suitable selection of can effectively improve the accuracy and reliability of decision results. Originality/value – This research presents a new approach to determine the suitable value for coefficient based on uncertain knowledge of natural multi-criteria group decision making, and gives concrete steps and examples. This method has positive significance to improve the quality of qualitative and quantitative conversion based on cloud model.

scholarly journals Group decision making and Quality-of-Information in e-Health systems

2010 ◽  
Vol 19 (2) ◽  
pp. 315-332 ◽  
Author(s):  
L. Lima ◽  
P. Novais ◽  
R. Costa ◽  
J. Bulas Cruz ◽  
J. Neves
Keyword(s):  
Decision Making ◽  
Health Systems ◽  
Group Decision Making ◽  
Group Decision ◽  
Quality Of Information

IJOSpontaneous Group Decision Making in Distributed Collaborative Learning

2013 ◽  
Vol 3 (2) ◽  
pp. 40-58
Author(s):  
Geoffrey Z. Liu
Keyword(s):  
Decision Making ◽  
Collaborative Learning ◽  
Group Decision Making ◽  
Information Science ◽  
Group Performance ◽  
Group Decision ◽  
Decision Quality ◽  
Library And Information Science ◽  
Poor Group

The paper reports on an exploratory study of student spontaneous group decision making (GDM) in distributed collaborative learning environments. Recordings of group meetings were collected from graduate students working on a database design project (in a library and information science program in California), from which group decision instances were extracted and formally coded for quantitative analysis. A follow-up survey was conducted to gather more information. The study finds that students are generally in favor of an unfacilitated and semi-structured GDM process, with group decisions typically made by consensus. A rigidly structured GDM process tends to be associated with poor group performance. GDM efficiency is an important predictor of the quality of final group products, and too much brainstorming may lead to difficulties. Students relying exclusively on text chatting tend to be unsure if their opinion was given equal attention, and those in underperforming groups are more doubtful about decision quality.

scholarly journals GROUP DECISION-MAKING MODELS FOR VENTURE CAPITALISTS: THE PROMETHEE WITH HESITANT FUZZY LINGUISTIC INFORMATION

2019 ◽  
Vol 25 (5) ◽  
pp. 743-773 ◽  
Author(s):  
Xiaoli Tian ◽  
Zeshui Xu ◽  
Jing Gu
Keyword(s):  
Decision Making ◽  
Group Decision Making ◽  
Group Decision ◽  
Behavioral Characteristics ◽  
Venture Capitalists ◽  
Linguistic Information ◽  
Start Up ◽  
The Common ◽  
Fuzzy Linguistic

Venture capitalists (VCs) have long been preoccupied by the issue of selecting a promising start-up firm, whereas, ranking the available start-up firms is an effective way to solve this issue. In this paper, the PROMETHEE is chosen to be the fundamental ranking method. Also, the hesitant fuzzy linguistic term set is a suitable tool to simulate VCs’ evaluation information. Additionally, as the deepening of social division of labor and specialization of individuals, group decision making is famous for improving decision-making quality. Moreover, in the decision-making process, VCs exhibit behavioral characteristics which is depicted well by prospect theory that VCs are risk averse for gains and risk seeking for losses and rely on the transformed probability to make their decisions rather than unidimensional probability. Thus, a group prospect PROMETHEE with hesitant fuzzy linguistic information is constructed for VCs to make a better decision. Then, the proposed method is applied to rank start-up firms and the comparative analyses are made as well. It confirms that the group prospect PROMETHEE is better in describing the common behavioral characteristics of VCs and in enhancing the quality of evaluation.

scholarly journals PREFERENCE SIMILARITY NETWORK CLUSTERING CONSENSUS GROUP DECISION MAKING MODEL IN ANALYSING CONSUMERS’ REVIEWS AND SELECTING SAMPLES OF PRODUCT

2020 ◽  
Vol 5 (2) ◽  
pp. 635
Author(s):  
Nor Hanimah Kamis ◽  
Nur Syahera Ishak
Keyword(s):  
Decision Making ◽  
Group Decision Making ◽  
Group Decision ◽  
Real Life ◽  
Network Clustering ◽  
Essential Information ◽  
Similarity Network ◽  
Consensus Group ◽  
Real Life Situation ◽  
Decision Making Model

In recent years, the integration of notions from Social Network Analysis (SNA) into decision making context is rapidly increased. One of the feasible procedures is Preference Similarity Network Clustering Consensus Group Decision Making model, where it is capable to improve the effectiveness and efficiency of decision making process. We utilize this approach in analysing consumers’ reviews and selecting the best sample of laboratory products. This is the first effort of applying this model in real life situation. The referred approach is capable of  measuring the similarity of consumers’ reviews, visualize their similarities in the form of network structure, partition them into subgroups, measure their group consensus level and select the best sample of product. The obtained results provide essential information to the laboratory, manufacturer or a company to improve the quality of product and further plan on the marketing strategy, advertisement and research development. Generally, this model can be used as an alternative tool in solving decision making problems, especially in analysing reviews and selection of alternatives.

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