Neural Responses to Mandatory and Voluntary Donation Impact Charitable Giving Decisions: An Event-Related Potentials Study

The present study aimed to explore the influence of donation amounts on donation decisions in different donation contexts and to reveal the psychological mechanisms. Furthermore, we focused on how to enhance individuals’ intention to donate voluntarily. We designed an experiment on donation decisions, employing event-related potentials (ERPs) to probe the effect of psychological mechanisms on donation decisions by detecting the neural basis of donation decision-making. Based on S-O-R (stimulus-organism-response) theory, we used donation contexts and donation amounts (stimuli) to induce psychological activity in the participants (organism) and then influence individual donation decision behaviors (response). Moreover, we applied psychological reactance (PR) theory to discuss the effect of donation context on decisions and the corresponding psychological process. The behavioral results showed that donation contexts (mandatory vs. voluntary) were significantly related to the donation amounts (i.e., less vs. more money that the charity received than money that the participants donated). At the ERP level, compared with mandatory donation, voluntary donation evoked a larger P2 amplitude when the charity received less money. In addition, a larger mean amplitude of LPP was elicited by voluntary donation compared to mandatory donation. This study provides practical implications for charity organizers to guide people to donate voluntarily.

Assessing the Uncertainty of Hydropower-Environmental Conflict-Resolution Management under Climate Change

To balance the water demands of different departments and produce a win–win result for reservoir operation, a series of conflict-resolution methods have been developed to define the socio-optimal operation strategy for specific conflict problems. However, given the inherent uncertainty of reservoir operation brought by climate change, the compromised strategies selected by conflict-resolution methods can vary. Therefore, quantifying the impacts of climate change on the decision characteristics of conflict-resolution methods can help to address questions about whether conflict-resolution decisions are sustainable given unforeseen changes. In this study, the Yangtze River is regarded as study area. As a world-class hydropower project located on the midstream of Yangtze River, Three Gorges Hydroelectric Power Station can transfer plenty of water energy into electricity. To alleviate the ecological water shortage caused by hydropower operation, sustainable and balanced operation strategies considering the water demands of two departments needs to be studied. In the context of hydropower-environmental conflict-resolution management, the decision behaviors of two fuzzy social choice methods and four game-theoretical bargaining methods under 25 kinds of future climate scenarios are analyzed. Comparing the strategy selection results of different methods for a future period (2021–2082) shows that in all proposed climate scenarios, the decisions of the Nash bargaining method, alternating offer method, and unanimity fallback bargaining method in game-theoretical bargaining methods are more stable than other studied methods, which means that climate change affects the decision behaviors of these three methods slightly. In addition, balanced strategies selected by these three methods could formulate adaptable reservoir operation policies that would satisfy the interests of hydropower and environmental stakeholders equally, and avoid a very low satisfaction level of individual stakeholder and whole stakeholders in the water-conflict year. Therefore, against the background of an increasing demand for environmental protection, these three methods can provide socio-optimal strategies considering social and economic benefits for water resource management.

Feeder Ship Size Optimization in Maritime Markets with Uncertainty

This paper considers the ship size optimization problem for a liner shipping company that provides feeder service between one hub port and one feeder port. In the maritime market with uncertainty, this problem becomes more challenging. This research first analyzes the decision behaviors of the shipping company. Then, a stochastic dynamic programming method is proposed to calculate the expected total volume of containers transported within the planning horizon. Using the calculated volumes as input parameters calculate the profit of each ship sizes and then determine the suitable ship size for the feeder route. Numerical experiments are performed to validate the effectiveness of the proposed method and the efficiency of the proposed algorithm.

Comparative study on e-bikes’ decision-making behaviors under flashing green and green countdown

Green flash light (FG) and green countdown (GC) are the two most common signal formats applied in green-red transition that provides drivers additional alert before termination of green phase. Due to their importance and function in stop-pass decision-making process, proper use of them has become a critical issue to greatly improve the safety and efficiency of signalized intersections. Gradually e-bike riders have become more important commuters in China, however, the influence of FG or GC on them is not clear yet and need pay more attention to it. This study chooses two almost identical intersections to obtain highly accurate trajectory data of e-bike riders to study their decision-making behaviors under FG or GC. The e-bike riders’ behavior is classified into four categories and is to identify their stop-pass decision points using the acceleration trend. Two binary-logit models were built to predict the stop–pass decision behaviors for the different e-bike rider groups, explaining that the potential time to the stop-line is the dominant independent factor of the different behaviors of GC and FG. Furthermore empirical analysis of decision points indicated that GC provides the earlier stop-pass decision point and longer decision making duration on the one side while results in more complexity of decision making and greater risk of stop-line crossing than FG on the other side.

An analysis of sustainability and channel coordination in a three-echelon supply chain

PurposeThe purpose of this paper is to investigate the decision behaviors and channel coordination of a sustainable three-echelon supply chain with retailer-led game setting.Design/methodology/approachThe authors develop game-theoretic models to examine the decision behaviors of channel members in a three-echelon supply chain consisting of one supplier, one distributor and one dominated retailer. The authors first formulate two models for centralized decision and decentralized decision. And then this paper proposes sub-supply chain coordination and a two-tariff contract to coordinate the full supply chain. Finally, some management insights are obtained with a case study.FindingsThe authors find that when faced with environmentally conscious consumers, the channel members can benefit from higher greening level; however, higher cost of green technologies would have negative impacts on manufacturer's effort. The analysis also shows that cooperation among players can improve the supply chain performance and help with environmental improvement.Research limitations/implicationsThe findings provide important managerial insights for the three-echelon supply chain to achieve sustainable goals where the retailer bears the environmental responsibility. However, this paper also has some limitations with assuming risk-neutral channel members and symmetric information.Originality/valueThe findings of the study contribute to coordination and collaboration in sustainability of supply chains. The results provide important managerial insights for the three-echelon supply chain to achieve sustainable goals.

Agent-based modeling and simulation of the decision behaviors of e-retailers

Purpose Social media facilitates consumer exchanges on product opinions and provides comprehensive knowledge of online products. The interaction between consumers and e-retailers evolves into a collective set of dynamics within a complex system. Agent-based modeling is well suited to stimulate such complex systems. The purpose of this paper is to integrate agent-based model and technique for order performance by similarity to ideal solution (TOPSIS) to simulate decision behaviors of e-retailers in competitive online markets. Design/methodology/approach An agent-based network model using the TOPSIS driven by actual price data is developed. The authors ran an experimental model to simulate interactions between online consumers and e-retailers and to record simulation data. A nonparametric test is used to conduct data analysis and evaluate the sensibility of parameters. Findings Simulation results showed that different profits could be obtained for various brands under different social network structures. E-retailers could achieve more profits through cross-selling than single-selling; however, the highest profits can be achieved when some adopt cross-selling, whereas others use single-selling. From a game perspective, the equilibrium for price-adjustment frequency can be determined from the simulation data. Thus, price adjustment differences significantly affect e-retailer profit. Originality/value This study provides new insights into the evolutionary dynamics of online markets. This work also indicates how to build an integrated simulation model with an agent-based model and TOPSIS and how to use an integrated simulation model and interpret its results.

The Analysis of Driver's Behavior in Non-Signalized Intersection Based on the Game

In order to study the driver's decision-making behaviors of the conflict vehicles in non-signalized intersection, according to time refinement, the driver's personality factors and the relative potential factors in the different strategies which affect the driver to make decisions, Based on the dynamic reduplicate game theory, the utility function of the driver's behaviors was built up. As the decision-making behavior by the driver in the process of cross-road, analyzing the different combination of the utility of the driver's decision-making behavior, Nash equilibrium was existed in a single game process, and the driver's optimal decision behaviors in a dynamic game was obtained. The illustration shows that impulse drivers in the decision-making period of time are more willing to choose to accelerate the first strategy; mild drivers prefer to choose acceleration strategy or uniform strategy; cautious drivers prefer to choose to uniform or deceleration strategy.