Strategic Interactions Between Manufacturers’ Blockchain Traceability Services and Platforms’ Demand Information Sharing
This paper employs a multi-stage dynamic game model to analyze how e-commerce platforms' demand information sharing strategies interact with manufacturers' blockchain traceability adoption under resale and agency models, revealing that manufacturers are inherently incentivized to adopt blockchain while platforms should conceal information under resale but share it under agency to achieve Pareto-optimal outcomes.
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 vast, invisible ecosystem of modern commerce, two powerful forces constantly shape what we buy and how much we pay: the flow of information and the trust we place in the products we hold. On one side stands the online marketplace, a digital hub that sees the pulse of consumer demand before anyone else, armed with data on what people are searching for and buying. On the other side stands the manufacturer, the creator of the goods, who often operates in the dark regarding these shifting market tides. For years, these two parties have navigated a complex relationship defined by how they share—or hide—this crucial information. Simultaneously, a new layer of trust has been introduced through blockchain technology, a digital ledger that acts as an unchangeable record, allowing anyone to trace a product's journey from its origin to the shelf. This technology promises to solve the age-old problem of counterfeit goods and uncertainty about quality, but it comes with a cost for the manufacturer to implement. The central question facing the digital economy is how these two dynamics interact: does the marketplace's decision to share its secret market data encourage or discourage the manufacturer from investing in this high-tech traceability, and does the answer change depending on how the marketplace chooses to sell the goods?
A researcher set out to map this intricate relationship by building a mathematical model of the strategic game played between an online platform and a manufacturer. They focused on the two dominant ways platforms operate: the resale model, where the platform buys products in bulk, takes ownership, and sets the final price, bearing the risk of unsold inventory; and the agency model, where the manufacturer sets the price and the platform simply facilitates the sale, taking a commission while the manufacturer retains ownership. The researcher simulated a series of scenarios where the platform decided whether to reveal its demand forecasts to the manufacturer, and the manufacturer decided whether to invest in blockchain traceability services. They then calculated the profits for both parties under every possible combination of these choices to find the most stable and profitable strategies.
The study revealed a clear and consistent pattern: manufacturers almost always have a strong incentive to adopt blockchain traceability services. When a manufacturer invests in this technology, it boosts consumer confidence, which expands the total market demand. This growth allows the manufacturer to charge higher prices, whether they are selling to the platform or directly to the consumer, ultimately increasing their own profits. However, the platform's reaction to this investment is more nuanced and reveals a distinct behavior known as "free-riding." In both sales models, the platform benefits significantly when the manufacturer pays for the traceability service. The manufacturer bears the full cost of the technology, yet the platform reaps the rewards of increased sales and higher prices without spending a dime on the service itself. This dynamic suggests that platforms often rely on the manufacturer's investment to drive their own revenue growth.
The most striking finding, however, concerns the platform's decision to share information, which shifts dramatically depending on the sales model. Under the resale model, where the platform controls the pricing and holds the inventory risk, the optimal strategy is to keep demand information secret. By withholding this data, the platform maintains its informational advantage and pricing power, allowing it to maximize its own profits while the manufacturer operates with less certainty. In contrast, under the agency model, where the manufacturer sets the price, the platform's best move is to share its demand information. In this arrangement, the platform benefits more from the manufacturer making precise pricing decisions based on accurate market data than from hoarding that data for itself. The researcher found that this sharing of information, combined with the manufacturer's investment in traceability, creates specific conditions where both parties can achieve a "win-win" outcome. These scenarios, identified as Pareto-optimal regions, show that when information accuracy is low or when the efficiency of the traceability service is high, cooperation through information sharing and service investment leads to the highest combined profits for the entire supply chain.
The study also explored how the uncertainty of the market influences these choices. As the unpredictability of consumer demand increases, the platform becomes more protective of its information. In highly uncertain environments, the platform is more likely to choose the resale model and refuse to share data, leveraging its superior ability to predict trends to secure higher profits. Conversely, when the market is more stable or the platform's ability to predict demand is less precise, it becomes more willing to share information and adopt the agency model. The researcher confirmed these theoretical findings through numerical simulations, showing that the balance between the cost of traceability, the accuracy of information, and the commission rates determines the final strategy. While the model assumes that all parties are risk-neutral and focuses on a single manufacturer, the results offer a clear roadmap for understanding the digital economy. They suggest that the future of supply chain coordination lies in recognizing these strategic interdependencies: manufacturers should invest in traceability to build trust and demand, while platforms must carefully choose whether to share their data based on their specific role in the sales process to ensure mutual success.
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