In green supply chain initiatives, small suppliers often struggle to achieve collaborative adjustment goals due to limited budgets, even when overall resources are sufficient. This reflects a common challenge in group decision making (GDM) under individual budget constraints. To address this, a novel framework is proposed for achieving group consensus in social networks with limited individual budgets. First, the minimum cost consensus model (MCCM) is extended by introducing a budget relaxation method to explore the potential of Pareto improvement when strict budget constraints hinder consensus. To realize this potential, an asymmetric Nash bargaining cost allocation model with compensation factors is developed to fairly distribute consensus costs within individual budgets. On this basis, an asymmetric Nash bargaining compensation mechanism in ego-networks is designed, reflecting the reality that in decentralized decision-making scenarios, compensation typically occurs among directly connected individuals due to stronger trust, more frequent interactions, and shared interests. This mechanism enables DM to negotiate and reallocate resources within their local social circles, balancing individual and collective benefits and enhancing the feasibility of cooperation. The effectiveness of the proposed framework is demonstrated through an illustrative example.

Supply chain finance (SCF) can provide innovative financing approaches for cash-constrained supply chain partners, which has been utilized for industries to optimize capital, reduce costs and alleviate financing pressure. Despite there are promising results in SCF, there are still many challenges in the development of SCF practice, such as the information asymmetry problem and credit data transmission barriers. Blockchain has been widely adopted in the finance industry, it can provide solutions to these challenges. However, the capabilities and performance of different blockchain platforms (BPs) vary greatly, thus, it is necessary to select an appropriate BP according to the specific circumstances of SCF. The main purpose of this article is to develop a decision-making framework to select a suitable BP for the blockchain implementation of SCF. Since SCF business involves multiple entities such as core enterprises, suppliers, and financial institutions, each entity has different requirements for BP, so conflicts of opinion are prone to occur in the decision making process. To this end, we develop a bilateral negotiation group multiattribute decision making (BN-GMADM) method with personalized individual semantics (PIS) to coordinate opinions and promote consensus. In addition, the distributed linguistic scoring rule (DLSR) is proposed for BP alternatives scoring and ranking. A case study and several analyses are provided to illustrate and validate the proposed approach.

In social network group decision making (SN-GDM), overlapping communities are special community structures that can assist opinion interaction to reach group consensus. However, the specific mechanisms of how overlapping structures facilitate community interaction need to be further explored. In addition, the compromise behavior of decision makers (DMs) is conducive to group consensus, but it is usually fixed at the same value, and then it need further research the characteristic of the dynamics compromise limits. To this end, the overlapping community structures under DMs’ trust network is detected. Then, the effect of community overlap in social networks on community interaction is explored. Meanwhile, a limited compromise function is built based on prospect theory to describe the dynamic compromise behavior of communities. Hence, a dynamic compromise behavior driven bidirectional feedback mechanism with overlapping communities is proposed in the context of SN-GDM, and an illustrative example with comparative analysis is provided to testify the advantages of proposed method. It is proved that overlapping communities can improve the compromise willingness compared to nonoverlapping communities, indicating that overlapping communities can serve as a bridge to facilitate interaction, and the dynamic compromise behavior can more realistically describe the real behavior of DMs. In general terms, the proposed method provides a solution to the consensus reaching issue of SN-GDM from a new perspective. Specifically, it can be applied to real-life application scenarios, such as group recommendation to recommend acceptable solutions for social network group users.

In this article, a novel framework to prevent manipulation behavior in consensus reaching process under social network group decision making is proposed, which is based on a theoretically sound optimal feedback model. The manipulation behavior classification is twofold: first, “individual manipulation” where each expert manipulates his/her own behavior to achieve higher importance degree (weight); and second, “group manipulation” where a group of experts force inconsistent experts to adopt specific recommendation advices obtained via the use of a fixed feedback parameter. To counteract “individual manipulation,” a behavioral weights assignment method modeling sequential attitude ranging from “dictatorship” to “democracy” is developed, and then a reasonable policy for group minimum adjustment cost is established to assign appropriate weights to experts. To prevent “group manipulation,” an optimal feedback model is investigated where objective function is the individual adjustments cost and constraints related to the group threshold of consensus. This approach allows the inconsistent experts to balance group consensus and adjustment cost, which enhances their willingness to adopt the recommendation advices and consequently the group reaching consensus on the decision-making problem at hand. A numerical example is presented to illustrate and verify the proposed optimal feedback model.

This article proposes a framework of personalized feedback mechanism to help multiple inconsistent experts to reach consensus in group decision making by allowing to select different feedback parameters according to individual consensus degree. The general harmony degree (GHD) is defined to determine the before/after feedback difference between the original and revised opinions. It is proved that the GHD index is monotonically decreasing with respect to the feedback parameter, which means that higher parameter values will result in higher changes of opinions. An optimization model is built with the GHD as the objective function and the consensus thresholds as constraints, with the solution being personalized feedback advices to the inconsistent experts that keep a balance between consensus (group aim) and independence (individual aim). This approach is, therefore, more reasonable than the unpersonalized feedback mechanisms in which the inconsistent experts are forced to adopt feedback generated with only consensus target without considering the extent of the changes acceptable by individual experts. Furthermore, the following interesting theoretical results are also proved: 1) the personalized feedback mechanism guarantees that the increase of consensus level after feedback advices are implemented; 2) the GHD by the personalized feedback mechanism is higher than that of the unpersonalized one; and 3) the personalized feedback mechanism generalizes the unpersonalized one as it is proved the latter is a particular type of the former. Finally, a numerical example is provided to model the feedback process and to corroborates these results when comparing both feedback mechanism approaches.