With the rise of online channel selling models in the supply chain, numerous scholars have conducted research on the dual-channel supply chain, focusing on pricing strategies (13, 14), coordination strategies (15), and the impact of consumer behavior on dual-channel supply chains (16, 17). Regarding pricing strategies for dual-channel supply chains, Liu et al. (18) investigated optimal pricing strategies for manufacturers and retailers with overconfident consumers. Yu et al. (19) compared the impact of consumer strategies on the choice of different sales models and pricing. Huang et al. (20) incorporated production costs into their study and analyzed pricing issues that arise when production costs fluctuate within a dual-channel supply chain. In terms of coordination issues in dual-channel supply chains, Chen et al. (21) constructed a Stackelberg game with the manufacturer as the leader to study coordination schemes in dual channels. Xu et al. (22) examined the impact of coordination contracts on supply chain performance using a mean–variance model, when agents exhibit risk-averse behavior. Mu et al. (23) investigated operational decisions and coordination issues in dual-channel supply chains under credit sales transactions. In studying the impact of consumer behavior on dual-channel supply chains, Sun et al. (24) explored optimal digital showroom strategies for dual-channel supply chains in the presence of online shopping behavior. He et al. (25) focused on closed-loop supply chains and they evaluated the impact of consumer free-riding behavior on product lifecycle carbon emissions in dual-channel closed-loop supply chains. Hu et al. (26) analyzed the influence of consumer delivery time preferences on retailer channel selection and pricing strategies. However, current literature on dual-channel supply chains mostly focuses on ordinary products, with limited research on products with significant social attributes, such as pharmaceuticals.

Research on pharmaceutical supply chains primarily focuses on operations management (27), pricing strategies (28), and innovative models (29). Within hospital supply chain operations management, Marucheck et al. (30) discuss novel perspectives in tackling drug safety challenges, while Dobrzykowski et al. (31) examine the impact of governmental regulations on pharmaceutical supply chain coordination. Regarding pricing strategies, Chen et al. (32) explore the effects of price ceilings on pharmaceutical pricing decisions, highlighting the negative economic effects of unilateral price ceilings on regulated companies. Building on this research, Chen et al. (33) consider quality regulation factors, finding that appropriate minimum quality standards can enhance the economic performance of the entire drug supply chain. Additionally, Zhang et al. (34) analyze the influence of pharmaceutical enterprise sales channels and various factors on drug quality and pricing using evolutionary game theory, proposing strategies for quality control across different distribution channels. In the realm of pharmaceutical supply chain innovation, Erokhin et al. (35) propose distributed ledger technology to track medical product journeys, improving supply chain models and preventing counterfeit drugs from entering the market. Furthermore, Papalexi et al. (36) evaluate innovation levels in service provision by hospitals and community pharmacies, offering guidance for developing more efficient supply chain strategies for pharmacies and healthcare organizations. While these studies form a solid foundation for understanding pharmaceutical supply chains, they have yet to incorporate medical insurance policies. Gao et al. (37) utilize game theory models to analyze how different healthcare insurance reimbursement strategies, considering reference price effects, impact consumer choices and the medical supply chain online, finding a positive correlation between medical insurance policy and consumer reference price effects. Wen et al. (38), in the context of centralized drug procurement policies, investigate the effects of medical insurance policy on supply chain performance. These studies underscore the importance of integrating medical insurance policies into pharmaceutical supply chain research, particularly within the context of dual-channel supply chains, to reveal the impact of online sales models on pharmaceutical supply chains.

To study the decision-making processes regarding OTC drug supply chain selling models under medical insurance policy, we examine a dual-channel system led by a pharmaceutical manufacturer. Within this system, the manufacturer develops OTC drugs eligible for medical insurance coverage, distributing them through both traditional and online sales channels. Similar to the study by Liang et al. (39), the interactions between the pharmaceutical manufacturer and the retailers are modeled using the Stackelberg game theory, where the pharmaceutical manufacturer is the Stackelberg leader. This is common in the supply chain literature and in practice. We outline three distinct dual-channel selling models: online direct selling, online agency selling, and online reselling, as shown in Figure 1.

This model consists of a pharmaceutical manufacturer and an offline retailer. In the offline mode, the pharmaceutical manufacturer wholesales OTC drugs to the offline retailer at wholesale prices, allowing the offline retailer to independently set the retail price. In this online mode, the pharmaceutical manufacturer establishes its own platform to sell OTC drugs directly to consumers through the internet.

In this model, alongside the traditional sales approach, pharmaceutical manufacturers collaborate with third-party platforms. These platforms act as intermediaries for drug sales, with the pharmaceutical manufacturers paying a commission γ 0 < γ < 1 . In this paper, the commission is treated as an exogenous variable, and it is assumed that the e-commerce platforms do not participate in supply chain decision-making processes.

In this model, there is a pharmaceutical manufacturer, an offline retailer, and an online retailer. Both the offline retailer and the online retailer act as distributors for the pharmaceutical manufacturer. The pharmaceutical manufacturer wholesales the products to the offline retailer and the online retailer at different wholesale prices, and both retailers independently determine their retail prices.

Based on the supply chain modeling and problem description, the following assumptions are derived from existing research:

Assumption 1: Pharmaceutical manufacturers research and develop health-insured over-the-counter (OTC) drugs, such as cold medicine, cough syrup, etc., with pricing determined independently by manufacturers and retailers.

Assumption 2: To simplify the model, this paper does not consider costs related to pharmaceutical manufacturers’ self-built platform expenses, drug research, production, logistics, and other aspects.

Assumption 3: Consumers are fully rational and will only choose one channel to purchase the drug. Referring to the assumptions of Liu et al. (40), the total market demand for drugs is normalized to 1, and consumers value the drug at V 0 ≤ V ≤ 1 .

Assumption 4: Due to restrictions imposed by medical insurance policy, consumers can only use medical insurance in offline channels. Assuming the percentage of consumers’ out-of-pocket expense for drugs is λ 0 < λ < 1 , the medical insurance reimbursement ratio is 1 − λ .

Assumption 5: Similar to the study by Kevin et al. (41), consumer preferences for different channels are converted into their trust levels in these channels. Consumers’ offline acceptance is assumed to be 1, and their online acceptance is θ 0 < θ < 1 .

The relevant variables used in this paper are annotated as shown in Table 1.

Based on the model proposed by Ryan et al. (42), we assume that consumers value both online and offline pharmaceuticals equally at a value V, where V is a random variable uniformly distributed between 0 and 1. When purchasing through offline channels, consumers buy the drug at a price p r and benefit from a medical insurance reimbursement rate of 1 − λ . Thus, the utility for consumers using the offline channel can be expressed as follows:

For online channels, consumers’ acceptance level is denoted by θ . They purchase the drug online at a price p m . Therefore, the utility for consumers using the online channel can be expressed as follows:

U r 、 U m measures how much value the consumer gains after accounting for the cost they pay out of pocket. This equation shows the trade-off between the perceived value of the drug and its actual cost to the consumer.

Consumers will demand medication only when the utility from a channel is greater than zero, specifically when U r > 0 or U m > 0 . If both utilities are positive, consumers will choose the channel with the higher utility, represented by max U r U m . When U r = U m , indicating equal utility from both channels, consumers’ valuation of the medication V ∗ is calculated as V ∗ = λ p r − p m 1 − θ .

This paper focuses exclusively on the second scenario, where the drug competes in both offline and online channels within the market.

In the dual-channel model for online direct selling, the decision sequence among supply chain members is as follows: the pharmaceutical manufacturer first determines the offline wholesale price ω r and the online retail price p m . The offline retailer, as a follower in the supply chain, subsequently decides the offline retail price p r . The profit functions for the pharmaceutical manufacturer and the offline retailer are in Equations 1, 2:

The profit formula for pharmaceutical manufacturers consists of two parts: the first part represents the profit from online sales through the manufacturers’ self-built platforms, and the second part represents the profit from supplying drugs to offline retailers.

Using backward induction to solve the model, we begin by addressing the two-stage decision-making problem. Backward induction is a method commonly used in dynamic optimization to solve problems by first analyzing the final stage of the decision process and then working backward to determine the optimal strategies for earlier stages. Specifically, we first analyze the second stage, where consumers make their purchasing decisions based on given prices and other parameters. Once the optimal outcomes for the second stage are established, we move to the first stage, where firms or decision-makers set strategies such as pricing or reimbursement rates, taking into account the expected behavior of consumers in the subsequent stage. This approach ensures that the solutions are both consistent and optimal across the entire decision-making framework.

In the online agency selling model, the decision sequence is as follows: pharmaceutical manufacturers first determine the offline wholesale price ω r and the online retail price p m . Based on these decisions, offline retailers determine the offline retail price p r . The online retailer does not participate in the decision-making process. The profit functions for the pharmaceutical manufacturer, offline retailer, and online retailer are given in Equations 3–5:

The profit formula for pharmaceutical manufacturers is divided into two parts: the first part represents the profit from online sales through third-party platforms, and the second part represents the profit from supplying drugs to offline retailers.

In this model, the decision sequence among supply chain members is as follows: pharmaceutical manufacturers first determine the offline wholesale price ω r and the online wholesale price ω m . Subsequently, offline retailer and online retailer independently decide on the offline retail price p r and the online retail price p m . The profit functions for the pharmaceutical manufacturer, offline retailer, and online retailer are are given in Equations 6–8:

The profit formula for pharmaceutical manufacturers consists of two parts: the first part represents the profit from supplying drugs to online retailers, and the second part represents the profit from supplying drugs to offline retailers.

In this section, numerical analysis is employed to validate the aforementioned properties and explore the impact of varying percentages of consumers’ out-of-pocket expenses on drug pricing decisions, channel demands, and supply chain member profits under the D, A, and R models. The parameters used are set as θ = 0.5 and γ = 0.05 . These parameter settings are verified to satisfy the constraint conditions of the model’s optimal solutions. Additionally, λ is assumed to range between 0.6 and 1, within which the optimal solution conditions are also met. The software used for simulation is MATLAB 2018a. Detailed simulation results are presented in Figures 2–4.

From Figure 2, it is evident that under the D, A, R sales models, offline wholesale prices, offline retail prices, and online retail prices of drugs all decrease with increasing percentage of consumers’ out-of-pocket expenses. This trend aligns with the earlier discussions suggesting that higher percentage of consumers’ out-of-pocket expenses may increase the financial burden on consumer for purchasing drugs offline, thereby reducing demand for offline drug purchases. The decline in channel demand directly affects the purchasing needs of drug retailers, forming a closely interconnected chain. Specifically, as the percentage of consumers’ out-of-pocket expenses increases, offline retailers reduce drug retail prices to maintain their profits and attract consumers. Despite this reduction, consumer demand for the offline channel still decreases. In response, pharmaceutical manufacturers lower offline wholesale prices to maintain market share. This adjustment cascades through the market, prompting offline retailers to further reduce prices and necessitating promotional pricing strategies for online wholesale drug prices. This phenomenon reflects the competitive market environment, where offline retailers lowering prices triggers adjustments in prices across other channels. These findings underscore the profound impact of medical insurance policies on drug market prices, offering insights into the mechanisms shaping drug pricing.

Figure 3 illustrates the impact of varying percentages of consumers’ out-of-pocket expenses on the demand for different types of channels. Specifically, it shows that offline channel demand decreases as the percentage of consumers’ out-of-pocket expenses increases, while online channel demand increases with an increase in the percentage of consumers’ out-of-pocket expenses. This observation is consistent with the earlier conclusions. As the percentage of consumers’ out-of-pocket expenses rises, consumers face greater financial burdens when purchasing drugs offline. Consequently, more consumers are shifting toward online channels to purchase drugs, resulting in decreased offline channel demand and increased online channel demand. This shift underscores the significant influence of medical insurance policies on consumer purchasing behavior and channel preferences within the pharmaceutical market.

Based on Figure 4, it is clear that as the percentage of consumers’ out-of-pocket expenses increases, both pharmaceutical manufacturers and offline retailers experience declining profits. Pharmaceutical manufacturers are directly affected by the decreasing prices in both offline and online retail markets. Even though there is a rise in online channel demand, it does not fully offset the income loss from reduced online retail prices, creating a significant profitability challenge. Offline retailers also face profitability issues as the medical insurance out-of-pocket ratio rises. Higher consumer costs result in increased price sensitivity, leading to decreased offline channel demand. To maintain market share, pharmaceutical manufacturers may further reduce retail prices, adversely affecting offline retailers’ profits. Consequently, offline retailers are compelled to explore alternative sales models to sustain their profitability. In conclusion, reducing the percentage of consumers’ out-of-pocket expenses and increasing drug reimbursement rates positively impact both pharmaceutical manufacturers and offline retailers. Therefore, governments should develop suitable medical insurance policies to ensure the sustainability of the healthcare system and enhance citizens’ medical security.

In this section, we investigate the impact of online channel trustworthiness on drug pricing decisions, channel demands, and supply chain member profits under the D, A, and R models. The parameters are set as λ = 0.6 , γ = 0.05 , and θ ranges from 0.1 to 0.5. The simulation results are presented in Figures 5–7.

In Figure 5, it shows that consumer acceptance of online purchases has minimal impact on drug wholesale prices, which initially decrease and then increase. However, offline retail prices of drugs decline with rising trust in online channels, while online retail prices increase. This occurs because as more consumers prefer purchasing drugs through online channels, offline retailers face heightened market competition. To maintain their market share and profitability, offline retailers implement strategies like promotions and discounts, reducing offline retail prices. This approach attracts price-sensitive consumers back to physical stores, enhancing the competitiveness and viability of offline retailers in a competitive market. The increase in online retail prices is driven by businesses aiming to maximize profits. Specifically, as online channels gain popularity and consumer trust grows, the preference for purchasing drugs through online channels grows. Consumers are willing to pay slightly higher prices for the convenience and rapid access to drugs. Observing this trend, businesses gradually adjust their pricing strategies to capitalize on the opportunity to increase profits by raising online retail prices. This strategy leverages changes in market supply and demand dynamics, optimizing revenue by catering to consumers’ increasing preference for convenience and their acceptance of higher prices. Figure 6 illustrates that as consumer acceptance of online purchases increases, offline channel demand rises while online channel demand gradually decreases. Although this may seem counterintuitive, it can be explained by businesses’ profit-maximization strategies. For online channel demand, the key factor is businesses’ drive to maximize profits. As consumer trust in online channels grows, more consumers prefer purchasing drugs online. To capitalize on this, businesses raise online retail prices. However, when prices exceed consumers’ expectations, overall online channel demand declines. In contrast, for offline channel demand, as acceptance of online channels increases and online retail prices of drugs significantly rise, many consumers opt to purchase drugs offline to avoid higher prices. This highlights the importance of price considerations in consumers’ drug-purchasing decisions. Even if consumers trust and prefer online channels, they shift to more economical offline options when online prices become too high.

Figure 7 indicates that as consumer acceptance of online purchases increases, pharmaceutical manufacturers’ profits rise while offline retailers’ profits gradually decrease. The increase in pharmaceutical manufacturers’ profits can be attributed to several factors. As consumer acceptance of online purchases improves, their preference for online purchases grows. Despite an overall reduction in online demand due to higher online retail prices, the price increases partly offset the decline in demand. This allows pharmaceutical manufacturers to leverage higher consumer acceptance by strategically raising online drug prices, thereby enhancing overall profitability. This demonstrates that pharmaceutical manufacturers can effectively optimize profits by strategically adjusting prices in response to growing trust in online channels. Conversely, offline retailers face a different scenario. As consumer online acceptance increases, more consumers opt to purchase drugs online, leading to a potential loss of market share for offline retailers. To counter this, offline retailers may reduce prices and implement other measures to maintain their market presence. While these strategies might temporarily boost offline channel demand, they typically fail to fully compensate for the losses from price reductions, resulting in an overall decline in offline retailers’ profitability. This highlights the adverse effect of increasing consumer trust in online channels on offline retailers’ profitability. Therefore, offline retailers need to closely monitor consumer preferences and adjust their market strategies accordingly to sustain their profitability.

Figure 8 shows that in the model R, channel demand is highest offline and lowest online, whereas in the model D, channel demand is lowest offline and highest online. This phenomenon can be explained by pricing disparities in these selling models. In all three selling models, offline drug prices are relatively similar, but online retail prices vary significantly. In the model D, drug prices are lowest online, which encourages consumers purchasing behavior, making the online channel more attractive. In contrast, in the model R, higher online prices reduce consumers’ willingness to purchase online, prompting them to prefer buying drugs through physical stores instead. Therefore, in the model R, consumers are less inclined to make online purchases, leading to a greater preference for offline purchasing channels. This distinction in consumer behavior across different sales models highlights the significant impact of pricing strategies on consumer channel preferences and the overall demand distribution between online and offline channels.

According to Figure 9A, in the model D, pharmaceutical manufacturers achieve the highest profit, whereas in the model R, they achieve the lowest profit. This indicates that the choice of sales model significantly impacts pharmaceutical manufacturers’ profit levels. In the model D, pharmaceutical manufacturers engage directly with customers and control the online retail prices of their products. This direct selling approach reduces intermediaries, giving manufacturers greater flexibility in pricing decisions. Additionally, direct selling allows manufacturers to capture sales revenue without incurring additional distribution costs or commission fees, potentially leading to higher profit margins. Conversely, in the model R, pharmaceutical manufacturers wholesale their products to online retailers who set the online retail prices. This indirect sales model limits the manufacturers’ control over pricing and exposes them to competitive pressures from online retailers, squeezing profit margins. Although online retailers attract more traffic and maintain product competitiveness, manufacturers’ overall profit margins are lower. In the model A, manufacturers sell their products online through platforms, paying a commission to the platform. While manufacturers retain some pricing control in this model, resulting in higher profits compared to the model R, their profits are still affected by commission fees. When the percentage of consumers’ out-of-pocket expense is low and consumer trust in online channels is high, manufacturers’ profits in the model R are the lowest. This analysis highlights how different sales models influence pharmaceutical manufacturers’ profitability, emphasizing the trade-offs between direct control over pricing and the benefits of broader market reach through indirect distribution channels.

According to Figure 9B, in the model R, offline retailers achieve the highest profit, whereas in the model D, they achieve the lowest profit. This difference is due to the dynamics within each sales model. In the model R, pharmaceutical manufacturers wholesale drugs to both offline retailers and online retailers. Online retailers typically mark up prices after purchasing drugs at a lower wholesale price to gain additional profit. This price markup reduces the price advantage of online drugs, prompting price-sensitive consumers to prefer offline purchases. Consequently, this enhances the competitiveness and profitability of offline retailers. Conversely, in the model D, pharmaceutical manufacturers sell drugs directly to consumers through online channels and control the online retail prices. As a result, online retail prices are relatively lower, making consumers more inclined to purchase drugs online. This leads to reduced profitability for physical retail stores in the model D. These observations underscore how different sales models impact the profitability of offline retailers, highlighting the trade-offs between pricing control, consumer preferences, and competitive dynamics in the pharmaceutical market.

This paper examines three distinct selling models for online drug distribution: the dual-channel model for online direct selling, the dual-channel model for online agency selling, and the dual-channel model for online reselling. It investigates optimal pricing decisions under these models, considering medical insurance policy and consumer channel preferences. This paper evaluates how these factors impact pricing, channel demand dynamics, and profitability, providing insights to help pharmaceutical manufacturers select optimal sales channel strategies. Through numerical analysis, this study derives the following conclusions:

These findings offer valuable insights for pharmaceutical manufacturers in selecting optimal sales channel strategies, highlighting the trade-offs between direct control over pricing and the benefits of broader market reach through indirect distribution channels. Expanding beyond the context of China, these insights are also applicable to global discussions on dual-channel pharmaceutical supply chains, particularly in countries such as the United States and the United Kingdom. In the U.S., where private healthcare providers play a dominant role, dual-channel models may need to address complexities related to diverse insurance coverages and a highly competitive retail market. In contrast, the U.K.’s National Health Service (NHS) provides a more centralized approach, potentially influencing the acceptance and implementation of dual-channel strategies. By examining these differences, this study offers a foundation for adapting dual-channel approaches under varying regulatory frameworks, providing practical guidance for policymakers and pharmaceutical companies in different healthcare systems.

Based on the above conclusions, we offer the following strategies and managerial insights:

Governments must develop and implement comprehensive, multi-tiered medical insurance policies that balance the interests of pharmaceutical manufacturers, retailers, and consumers. These policies should safeguard the profitability of pharmaceutical manufacturers and retail entities while preventing excessive drug pricing, ensuring that the public’s medication costs remain manageable. The goal is to achieve both economic and social benefits and ensuring the sustainable development of the pharmaceutical industry.

To increase demand for pharmaceuticals through online channels, it is essential to enhance the consumer acceptance of online purchases and establish reasonable pricing strategies. This ensures that consumers perceive genuine price advantages and enjoy a positive shopping experience when purchasing pharmaceuticals online.

Large and small-to-medium-sized pharmaceutical companies (SMEs) should adopt customized strategies when expanding into dual-channel markets based on their resources and market position.

These strategies aim to optimize the benefits of medical insurance policies, enhance consumer experiences, and tailor channel strategies to the unique needs of different-sized pharmaceutical manufacturers, promoting overall industry sustainability and growth.

There are several limitations in this paper. Firstly, the study focuses solely on medical insurance policy and consumer channel preferences, without considering the costs associated with channel establishment and operations. Subsequent studies could delve into these costs and assess their impact on the choice of sales models.

Secondly, this paper confines its examination of medical insurance policy to reimbursement policies, without incorporating factors such as price regulation. Subsequent research could include these elements to provide a more comprehensive analysis. By addressing these limitations, future research can offer a deeper and more nuanced understanding of the dynamics between pharmaceutical manufacturers, retailers, and consumers, thereby enhancing the strategic insights for optimizing sales models and policies in the pharmaceutical industry.