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This study examines the effects of supply reliability, risk aversion, and wealth on the optimal order strategy of retailers in the case of uncertain demand by measuring the degree of risk aversion. A more practical model of optimal ordering strategy is proposed, considering supply reliability, demand uncertainty, risk aversion, and retailer wealth, in which two random variables, supply reliability factors and demand, are introduced into the retailer's function of expected utility. To avoid nonconvergence at both ends, the demand follows a triangular rather than a normal distribution. It is found that the optimal order quantity will increase with the improvement of supply reliability when the risk-averse degree is fixed. The results also show that the optimal order quantity of risk-averse retailers is smaller than that of risk-neutral retailers. Additionally, the optimal order quantity for the risk-averse retailer decreases as the degree of risk aversion increases, when supply reliability is fixed. Further research shows that the retailer is a constant absolute risk aversion (CARA). That means retailer's wealth has nothing to do with the risk aversion and changes in the retailer's wealth will not affect the retailer's optimal order quantity. This study provides valuable insights for sustainable supply chain management and marketing. [ABSTRACT FROM AUTHOR]

In the face of increasingly complex market environment, supply chain members pay attention to the possibility of obtaining expected profits and various risks while considering profit maximization. The behaviors of decision makers are often influenced by their psychological preferences, and loss aversion is one of the basic characteristics of human economic behavior. We research the supply chain coordination problem with capital constraint and loss-averse retailer in the case of stochastic production and demand. In order to address the loss aversion, a novel utility function is structured according to the theory of mental account. Under wholesale price contract, the optimal expected utility of supply chain under decentralized decision is smaller than that under centralized decision due to the retailer's constrained capital. Based on that, a model is constructed by making revenue sharing contract act as the dual role of supply chain coordination and delayed payment strategy without seeking external financing or internal financing, and the contract conditions and optimal decisions of the coordinated supply chain are solved. Theoretical analysis indicates that when the contract parameters are properly selected, even though the retailer's capital is constrained, the revenue sharing contract can not only coordinate the supply chain but also realize the arbitrary distribution of supply chain profits within a certain range. Finally, the change of decision variables and expected utility or profit with contract parameters is analyzed by several numerical examples. [ABSTRACT FROM AUTHOR]

By utilizing the mobile terminals' sensing and computing capabilities, mobile crowdsourcing network is considered to be a promising technology to support the various large-scale sensing applications. However, considering the limited resources and security issue, mobile users may be unwilling to participate in crowdsourcing without any incentive. In this work, by combining reputation and contract theory, a dynamic long-term incentive mechanism is proposed to attract the mobile users to participate in mobile crowdsourcing networks. A two-period dynamic contract is first investigated to deal with the asymmetric information problem in the crowdsourcing tasks. Reputation strategy is then introduced to further attract the mobile users to complete the long-term crowdsourcing tasks. The optimal contracts are designed to obtain the maximum expected utility of service provider with reputation strategy and without reputation strategy, respectively. Simulation results demonstrate that the long-term crowdsourcing tasks can be guaranteed by combining the contract's explicit incentive with the reputation's implicit incentive. The incentive mechanism can gain a higher expected utility, the more implicit reputation effect factor. [ABSTRACT FROM AUTHOR]

From the perspective of D-S evidence theory and area measurement, a risk-based comprehensive decision-making method that considers both the expected utility and the uncertainty of the scheme is proposed under the interval uncertainty environment of attribute values. The upper and lower bounds of the synthetic probability distribution of attributes values in different natural states are constructed based on the belief measure and plausibility measure. Based on the area measurement, a method for calculating the expected utility of each scheme is proposed. To reflect the influence of the uncertainty in the evaluation value of each scheme attribute on the final decision result, two indexes are defined: the evaluation uncertainty of attributes (EUA) and the uncertainty of the expected utility of scheme (UEU). Finally, considering the expected value of the expected utility and its uncertainty, three decision methods, namely, risk-neutral, risk-averse, and risk-preference, are constructed. An example is considered to show that the proposed method is effective and practical, and the uncertainty of the expected utility has a significant impact on the result of risky decisions. The new method can solve the problems of existing methods that overlook the impact of epistemic uncertainty on the decision-making process. [ABSTRACT FROM AUTHOR]