Commission and traffic support under demand uncertainty in a dual-channel supply chain
This study analyzes a dual-channel supply chain where a platform jointly optimizes commission rates and traffic support under demand uncertainty, revealing that these instruments are complementary when direct monetization outweighs pricing-induced volume reductions, and demonstrating that welfare-maximizing policies may diverge from platform-optimal strategies due to complex spillover effects and non-monotonic responses to uncertainty.
Original paper licensed under CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/). This is an AI-generated explanation of the paper below. It is not written or endorsed by the authors. For technical accuracy, refer to the original paper. Read full disclaimer
In the modern economy, digital platforms act as the great intermediaries, standing between the people who make goods and the people who buy them. These platforms do more than just host a store; they decide how much of the money from a sale they keep as a fee, and they decide how much attention or "traffic" to direct toward a seller. This attention is not free; it requires investment in search rankings, recommendations, and promotional campaigns. The central puzzle for economists and business leaders is how these two levers—the fee and the attention—work together. If a platform charges a higher fee, does it have more money to spend on getting customers? Or does the higher fee make the seller so expensive that customers leave? Furthermore, because the future demand for products is never perfectly known, these decisions must often be made before the market state is clear, adding a layer of risk to the mix. Understanding this balance is crucial because it determines not just the profits of the companies involved, but the prices consumers pay and the overall health of the marketplace.
A new study by Chen Li from the Wuhan Institute of Technology explores this complex interaction within a dual-channel supply chain, where a manufacturer sells products both through an online agency platform and through a traditional offline retailer. The research builds a detailed model to simulate how a platform, a manufacturer, and a retailer make decisions when they face uncertainty about future demand. The platform moves first, committing to a specific commission rate and a specific level of traffic support before the market conditions are known. The manufacturer then decides whether to join the platform, and once the actual demand state is revealed, the manufacturer and the retailer adjust their prices accordingly. By solving this sequence of events mathematically, the study reveals a subtle feedback loop that governs how these digital markets function.
The core discovery is that the relationship between the commission fee and the traffic support is not automatically positive. It is possible to imagine that a higher fee gives the platform more revenue per sale, encouraging it to invest more in getting customers. However, the study shows that a higher fee also squeezes the manufacturer's profit margin, which often forces the manufacturer to raise prices. Higher prices can drive customers away, reducing the total volume of sales. The platform's decision to invest in traffic support depends on which of these two forces is stronger. If the direct gain from the fee outweighs the loss from customers leaving due to higher prices, the two tools work together as complements. But if the price hike drives too many customers away, they become substitutes, and the platform may actually reduce its support when it raises its fee. The researchers found that in many realistic scenarios, these tools do work together, but the strength of that partnership changes depending on how sensitive customers are to price changes and how much the online and offline channels compete with each other.
The study also uncovers how uncertainty about the future affects these decisions. When the platform is locked into a fixed fee, the mere existence of unpredictable demand does not directly change how much traffic support it provides. However, because the platform is free to adjust its fee in response to uncertainty, the uncertainty indirectly changes the support level. The platform might choose a different fee to protect itself against bad market conditions, and that new fee then triggers a different level of investment in traffic. This creates a non-linear response where small changes in market uncertainty can lead to surprisingly large shifts in strategy, particularly near the boundaries where the platform might stop offering support or where the manufacturer might leave the deal entirely.
Perhaps most surprisingly, the research shows that what is best for the platform is not always what is best for society. The study calculates that the fee the platform would naturally choose to maximize its own profit is significantly higher than the fee that would maximize overall social welfare, which includes consumer happiness and the profits of all businesses. When a regulator tries to lower the fee to help consumers, the platform reacts by cutting back on its traffic support. This reduction in support can hurt the market, meaning that a simple fee cap does not always improve the situation. The study suggests that a more effective approach might involve cost-sharing, where the manufacturer helps pay for the traffic support. In specific scenarios, this arrangement allows the platform to invest more in attracting customers while keeping fees reasonable, leading to better outcomes for the platform, the manufacturer, the retailer, and the consumer simultaneously.
The researchers also mapped out exactly how traffic support affects different sales channels. They identified specific thresholds that determine whether a boost in online visibility helps both the online and offline stores, or if it simply steals customers from the offline store to the online one. If the support creates a strong brand awareness that spills over, both channels can grow. But if the support is purely about driving online clicks, it can cannibalize offline sales. The study provides clear mathematical boundaries for these outcomes, showing that the direct effect of the traffic on brand awareness is distinct from the price competition between the two channels.
Through extensive simulations using a wide range of possible market conditions, the study confirms that these mechanisms are robust. The results indicate that while joint optimization of fees and support generally leads to better outcomes than using only one tool, the benefits are not uniform. The gap between what the platform wants and what society needs remains significant, highlighting the need for careful policy design that accounts for how platforms will react to regulation. The findings offer a clear explanation of how value is extracted, how demand is generated, and how conflicts between channels are managed in the digital age, providing a roadmap for understanding the invisible forces that shape our online shopping experiences.
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