This paper investigates a closed loop supply chain (CLSC) encompassing a manufacturer, a retailer, and consumers operating within the carbon trading scheme. Employing the focus theory of choice, we analyze the decision-making processes of the retailer, considering various personality traits. A Stackelberg game is formulated, wherein the manufacturer assumes responsibility for recycling activities. The research explores the impact of the retailer’s optimism and confidence levels on optimal decision-making within a positive evaluation system. Numerical examples are employed to elucidate equilibrium solutions, illustrating the correlation between the retailer’s personality traits and the manufacturer’s optimal decisions. Furthermore, a sensitivity analysis is conducted on the carbon trading price and the manufacturer’s carbon emission quota allocation within a single cycle under the carbon trading scheme. The investigation concludes with an examination of the influence of recycling prices on the manufacturer’s optimal revenue. The findings indicate that retailers with distinct personality traits adopt varied pricing strategies. Decreases in optimism and self-confidence levels prompt the retailer to opt for relatively lower retail profit pricing. Simultaneously, the manufacturer demonstrates a preference for collaborating with a retailer characterized by optimism and lower confidence levels, thereby enhancing overall manufacturing revenue. Notably, under the carbon trading scheme, fluctuations in carbon trading and recycling prices distinctly influence the manufacturer’s decisions.

This paper considers a sales mode selection problem under revenue sharing contracts between resale and agency modes for risk-averse manufacturers with traditional retail channel, direct selling channel, and e-commerce platform channel. By considering the factors including price competition intensity, market share, revenue sharing ratio, commission rate, and degree of risk aversion, we construct leader-follower game models with manufacturers as leaders and traditional retailers and e-commerce platforms as followers. To obtain optimal solutions, we discuss conditions to ensure the upper and lower models to be convex and then give the optimal strategies for all members in the network. Through numerical experiments, we analyze the involved parameters’ impact on sales mode selection strategy and the changing trends of each member's optimal pricing and profit under different sales modes. The numerical results reveal the following revelations: The manufacturer should choose the agency mode when the commission rate is low and the direct selling channel has a large market share. If both the commission rate and degree of risk aversion are high, direct selling channels have a low market share, and price competition intensity is weak, the manufacturer should choose the resale mode. The degree of risk aversion has an effect on each member’s optimal decision. Regardless of which sales mode the manufacturer chooses, the optimal price of each member decreases as the degree of risk aversion increases. Under certain conditions, the manufacturer’s choice of agency mode can create win-win situations with supply chain members.

The growing popularity of aggregation platforms has attracted widespread attention in the ride-hailing market in recent years. In order to obtain additional orders by charging commissions and slotting fees, many ride-hailing platforms choose to bundle with aggregation platforms. Unlike traditional reseller electronic channels, the bundle channels may affect pricing of platforms, service levels of drivers, market demands and they may further impact on profits. These different attitudes raise an interesting and key question about the influence of bundle channels in ride-hailing platforms. In this paper, we propose an analytical framework for pricing strategies of ride-hailing and aggregation platforms under bundle mode and analyze their pricing process from the perspective of Nash and Stackelberg games, where the platforms serve as leaders to determine optimal prices through Nash equilibrium and the drivers serve as followers to provide optimal service levels. Through sensitivity analysis of service levels and costs, we capture the distribution trends of profits between the platforms. Based on some numerical examples and results analysis, some interesting managerial insights on pricing of ride-hailing and aggregation platforms are gained.

As a useful strategy to improve the flexibility of the system to manage uncertainty in supply and demand and to improve the sustainability of the supply chain, vendor-managed inventory (VMI) programs have attracted widespread attention in the field of supply chain management. However, a growing body of empirical literature has shown that participants’ decisions deviate significantly from the standard theoretical predictions. Under a VMI program, the supplier bears not only the production cost, but also the risk of leftover inventory. Moreover, the inequality among participants and different personalities of decision-makers in VMI programs may lead to the divergence of decision-making. To understand the supplier’s replenishment decision in view of the behavioral pattern, we propose a new inventory model for the supplier with the focus theory of choice. The proposed model conceives that the retailer evaluates each replenishment quantity based on the most salient demand for him/her instead of calculating the expected utility. By employing this inventory model, we construct a two-tier supply chain model with revenue-sharing contract and theoretically derive the optimal sharing percentage of the revenue and replenishment quantity. Results analysis gains managerial insights into the strategic selection of the retailer who faces suppliers with different personalities. Comparisons between the classic revenue-sharing contract model and the proposed model are also carried out by illustrative examples. This research provides a new perspective to analyze individual supplier’s behavior in a VMI program with revenue-sharing contracts.