The trust concept has become more and more popular by paving its way gradually into the modern field. Thus, it becomes a more alluring and attractive solution to secure and protect information sharing against attackers. Indeed, malicious entities can launch intentionally or unintentionally very harmful attacks against the information-sharing process causing network paralysis. In this paper, we propose composite trust-based schemes from the perspective of direct experience and entities’ recommendations to enhance the shared threat information in the public and private communities. Therefore, we introduce a first trust-based model defined by several specific dimensions to improve the security of private/targeted communities. Furthermore, a second trust-based scheme is proposed for the public community where the concept of zero-trust is introduced to enhance the shared critical data inside this open environment. Thus, our proposal reveals a new level of defense against the misbehaving entities by introducing a penalty scheme to punish malicious and suspicious users and to exempt the attackers with continuous misbehaving from participating in the information-sharing process. Extensive simulations demonstrate that the proposed trust-based model provides high stability and resistance in a heavily hostile environment for the public and private communities.

In the Two-Echelon Vehicle Cooperation Problem (2ECOP), different vehicles operate on primary and secondary echelons to optimize package delivery, resulting in cost savings and efficient services. Our work focuses on the cooperation of two-echelon vehicles and their optimal scheduling and routing strategies, as well as their numerous applications across industry. We examine the literature of the 2ECOP across various dimensions, including various cooperated modes, distinct optimization methodologies, and practical settings. Our work identifies various effective algorithms and heuristic solution procedures for the 2ECOP. We also propose a unified approach for delivery that is designed for reducing cost and improving service efficiency. We believe that academic research and industry would both benefit from a unified classification scheme that extends to new two-echelon operated logistics models that may emerge in response to future technological advances.

Recent technological advances enable firms to engage more effectively with customers who reveal their intent to churn. When consumers disclose such intentions, firms can offer targeted discounts, a form of personalized pricing, to encourage retention. Prior literature, however, shows that broad use of personalized pricing for all returning customers can intensify competition and erode profits in competitive markets. We show that these adverse effects are substantially mitigated when personalized prices are offered selectively, i.e., only after firms identify consumers who are likely to churn. Analyzing a retention-focused personalized pricing strategy, we find three main results. First, personalized pricing increases firm profits only when the proportion of disclosers is relatively low; although it raises margins on retained customers, it also intensifies first-period competition. Second, consumer surplus and social welfare improve when the share of disclosers is high, because higher disclosure induces stronger price reductions and greater retention. Third, firms often have incentives to invest in personalized pricing capabilities, but mutual investment can generate a Prisoner’s Dilemma: Even with low investment costs, simultaneous adoption may strengthen competition and reduce industry profits. These results emphasize the importance of selective targeting and timing when deploying personalized pricing for retention and offer guidance for firms and policymakers considering such investments.

The increasing popularity of information technology has generated many innovative product marketing strategies. To facilitate the sustainable development of the probabilistic selling strategy, we developed a game-theoretic model incorporating a manufacturer and a retailer to explore whether and when they should implement a probabilistic product and examine its effect on supply chain performance in vertically differentiated markets. First, the marketing strategy preference is closely related to product quality differentiation and the demand cannibalization effect. While a retailer benefits from adopting probabilistic selling (PS) regardless of quality differentiation, the manufacturer prefers traditional selling (TS) under certain market conditions. Second, the PS strategy increases the retailer’s profit from low-quality products by adjusting prices, though the manufacturer benefits only under certain conditions. Third, demand cannibalization intensifies with weak quality differentiation and low wholesale price discount, and a “win-win” situation is achieved only when product heterogeneity is limited. Furthermore, we provide managerial implications for supply chain members when PS is employed as a marketing strategy.

To improve the quality of fresh agricultural products, the Chinese government provides financial subsidies to farmers for constructing refrigerated storage facilities. Our study examines a dual-channel fresh agricultural product (FAP) supply chain composed of a farmer and a retailer. Within this framework, we have developed decision models for decentralized and centralized scenarios, compared the optimal solutions with and without government subsidies, and proposed a contract mechanism to coordinate the decentralized supply chain. Our findings indicate that in the decentralized scenario if the cost coefficient for maintaining freshness exceeds a certain threshold, the retailer’s online and offline prices may surpass those in centralized scenarios. Government subsidies not only enhance product freshness but also increase retail prices and overall supply chain profitability. Additionally, the revenue and cost-sharing contract could effectively coordinate the supply chain to achieve optimal performance as seen in centralized scenarios. These insights provide valuable guidance for farmers and retailers making optimal decisions with or without government support.