Blockchain adoption strategies under the framework of a dual-channel supply chain

Abstract

This paper investigates the adoption strategies of blockchain technology within the framework of a dual-channel supply chain. Applying game theory, we demonstrate that the adoption decision of supply chain members depends on consumer privacy concern. Furthermore, we derive an equilibrium in which one chain member adopts blockchain technology while the other does not, depending on consumer acceptance of the online channel and transportation costs. Additionally, we identify an equilibrium where both chain members adopt blockchain technology, which may lead to either a win–win or a win–lose outcome for the manufacturer and retailer, respectively. Our findings also suggest that consumer surplus increases as consumer privacy concern grows. Moreover, three extensions analytically confirm the robustness of our results.

1 Introduction

With the rapid advancement of internet technologies and the growing prominence of the token economy, a substantial body of research has examined how blockchain technology (BCT) enhances supply chain management from various perspectives. Several studies highlight the increasing maturity and adoption of BCT in supply chain applications, particularly within dual-channel systems in industries such as food and luxury goods (Babich and Hilary, 2020; Chod et al., 2020; Wang et al., 2021). In the food industry, for example, upstream manufacturers like Nestlé have adopted the IBM Food Trust blockchain technology platform to improve transparency. Nestlé has extended this technology to its Zoégas coffee brand,¹ which sources 100,100 Rainforest Alliance certified Arabica beans from Rwanda, Colombia, and Brazil.

By leveraging blockchain-recorded big data, consumers can trace the origin of their coffee, thereby enhancing trust and product transparency. Similarly, downstream retail giants such as Walmart and Carrefour have joined the Food IBM Trust initiative.² Carrefour, in particular, utilizes BCT to provide consumers with greater visibility into its supply chain operations. The trend is also evident in the luxury goods industry, where both upstream producers and downstream retailers have simultaneously adopted BCT within dual-channel frameworks. For instance, LVMH has partnered with other luxury brands—including Prada, Cartier, Bulgari, and Hublot—to develop a blockchain-based platform aimed at ensuring product authenticity, enhancing traceability, and fostering consumer trust.³

Although these applications highlight the transparency and traceability benefits of blockchain technology, they also point to a more fundamental managerial question: how does blockchain adoption change channel competition and operational decision making in a dual-channel supply chain? In particular, blockchain does not alter the physical product itself, but it changes the information environment in which the product is evaluated and purchased. By making provenance, transaction records, and traceability information more verifiable and more visible to consumers, blockchain adoption can reshape perceived product value, channel preference, and pricing incentives. As a result, the adoption of blockchain technology is not merely a technological upgrade, but also a strategic operational decision for both manufacturers and retailers.

BCT not only facilitates transparency but also enhances consumers’ perceived value of products by reinforcing trust between brands and buyers (Niu et al., 2021; Song et al., 2023). However, despite its growing application, supply chain participants exhibit varied levels of enthusiasm toward BCT adoption under dual-channel structures. To date, the literature has not clearly explained what drives these heterogeneous adoption incentives in dual-channel supply chains.

Furthermore, the application of blockchain technology (BCT) can not only enhance the transparency of product information but also intensify consumer privacy concern. For instance, an important issue that cannot be overlooked is that BCT may heighten consumers’ anxiety regarding privacy (Pun et al., 2021). Companies implementing BCT solutions—such as IBM Food Trust and Curvegrid—often require users to register with their digital identity information to access certain features. These firms assert that such private data will be used solely for legitimate purposes to strengthen consumer trust and engagement. However, survey results indicate that approximately 50 percent of respondents would reduce their online activity due to privacy concern, and over 90 percent expressed fears about potential data breaches (Zhang et al., 2022). It is also important to note that BCT does not offer true anonymity but rather pseudonymity; once a transaction is linked to a specific individual or entity, the user’s real identity and associated wallet can be traced (Zhang et al., 2020). Although the implications of BCT adoption within dual-channel supply chain frameworks remain underexplored, investigating this area is crucial, as consumer privacy concern may significantly influence firms’ operational decisions (Zhang et al., 2022). Notably, existing literature has yet to examine how privacy concern shape the preference structures of supply chain members or impact consumer surplus—an important research gap that this study seeks to address. Importantly, we do not argue that every blockchain application necessarily creates privacy concern in all settings. Rather, we focus on consumer-facing blockchain-enabled retail environments in which adoption may involve traceable digital interaction, such as app registration, account login, wallet linkage, or identity-related data submission. In such settings, consumers may associate blockchain-enabled transactions with privacy exposure, which can in turn affect their channel choice and willingness to pay. This tension between transparency gains and privacy concern makes blockchain adoption a nontrivial strategic problem in dual-channel supply chains. Therefore, the key issue is not whether blockchain technology is beneficial on average, but whether and how it should be adopted by different channel members when transparency gains coexist with consumer privacy concern. This issue is particularly important in dual-channel supply chains, because blockchain adoption by one channel member may alter not only its own demand and pricing decisions, but also the strategic responses of the other channel member.

To address these research questions, we model a dual-channel supply chain comprising an upstream manufacturer and a downstream retailer, each of whom independently decides whether to adopt blockchain technology (BCT). The manufacturer sells products through both a traditional wholesale-retail channel and a direct-to-consumer channel, while the retailer operates via a brick-and-mortar platform. Using a game-theoretic framework, we analyze the equilibrium outcomes under various BCT adoption scenarios. By doing so, this paper contributes to the literature by integrating blockchain adoption, consumer privacy concern, and channel competition into a unified dual-channel supply chain framework. Our main findings are as follows:

1.1 Impact on pricing strategies

The optimal pricing strategies of supply chain members are significantly influenced by consumer privacy concern in the context of BCT adoption. When the manufacturer adopts BCT but the retailer does not, the manufacturer’s optimal price decreases as consumer privacy concern rises, indicating a compensatory pricing strategy to offset privacy-related disutility. Conversely, when the retailer adopts BCT but the manufacturer does not, both the retailer and manufacturer reduce their prices in response to increasing privacy concern. This is because the manufacturer, whose profits stem from both channels, anticipates that the retailer will retain the retail channel and reduce the wholesale price accordingly (Yoo and Lee, 2011). When both parties adopt BCT, the manufacturer’s profit declines with higher privacy concern, while the retailer’s profit remains stable. 1n this case, the increased transparency and logistics inefficiencies associated with online channels (e.g., shipping costs and product mismatch) partially mitigate the negative impact of privacy concern.

1.2 Effects on consumer surplus

BCT adoption by both the manufacturer and retailer increases consumer surplus when privacy concern exceed a certain threshold. When privacy concern is low, channel members are incentivized to raise prices, thereby reducing consumer surplus. However, when privacy concern is high, firms tend to lower prices to counteract the perceived risks of BCT, thus enhancing consumer surplus. In essence, heightened consumer privacy concern coupled with greater product transparency prompts price reductions and may ultimately increase consumer utility and surplus.

1.3 Profitability and equilibrium strategies

BCT adoption does not universally enhance the profitability of supply chain members. Specifically, when consumer privacy concern is high, neither the manufacturer nor the retailer finds it optimal to adopt BCT. Instead, BCT adoption becomes favorable only when consumer privacy concern is relatively low. Interestingly, under moderate levels of privacy concern and information transparency, asymmetric BCT adoption—where only one party adopts—can constitute an equilibrium. In equilibrium, BCT adoption can yield either win–win or win–lose outcomes for the manufacturer and retailer, depending on the specific configuration of privacy concern and transparency effects.

To keep the analysis analytically tractable while isolating the strategic effect of privacy concern, we model privacy concern as a reduced-form disutility associated with blockchain-enabled transactions. Conceptually, this paper contributes in three respects. First, it reframes blockchain adoption in a dual-channel supply chain as a strategic choice under a transparency–privacy trade-off, rather than treating blockchain as a purely value-enhancing technology. Second, it introduces consumer privacy concern as a demand-side mechanism that directly reshapes channel competition, pricing incentives, and equilibrium adoption patterns. Third, it shows that blockchain adoption may generate asymmetric strategic outcomes across channel members, implying that the value of adoption depends not only on transparency gains, but also on how privacy concern redistributes demand and profit across channels. Accordingly, the privacy parameter in our model should not be interpreted as a conventional operational cost, but as a perception-based demand-side disutility that changes channel choice and adoption incentives. The remainder of this paper is organized as follows. Section 2 provides a brief review of the relevant literature. In Section 3, we develop a game-theoretic model involving a manufacturer and a retailer. Section 4 analyzes the optimal pricing strategies under different BCT adoption scenarios. Section 5 presents the equilibrium strategies and examines their impact on consumer surplus. Section 6 further explores the implications for consumer surplus. Section 7 discusses several model extensions, while Section 8 offers managerial insights derived from the findings. Finally, Section 9 concludes the paper. All mathematical proofs are provided in the Supplementary Appendix.

2 Literature review

This study is mainly related to two strands of literature: research on blockchain technology in operations and supply chains, and research on privacy concern in blockchain-enabled environments.

A growing number of studies have examined blockchain applications in operations and supply chains. For example, Hastig and Sodhi (2020) explore the impact of BCT traceability system implementation on supply chain stakeholders, while Choi et al. (2022) discuss the application value of different BCT functions in the Industry 4.0 era. Regarding the role of BCT in information transparency, Chod et al. (2020) show that blockchain can improve supply chain information transparency and thereby enhance firms’ financing capacity. More recent studies further examine whether blockchain can effectively eliminate information asymmetry and under what market conditions such transparency gains create value for supply chain members. For example, Liu et al. (2025b) show that the value of blockchain-driven information asymmetry elimination depends on consumer trust in product quality and the magnitude of blockchain benefits. Moreover, Wu and Yu (2023) show that BCT can reduce transaction frictions by making asymmetric information more transparent.

Recent research has also begun to examine blockchain adoption under channel competition and consumer-side heterogeneity. For instance, Liu et al. (2025a) investigate blockchain adoption in competing retail channels and show that its value depends on consumer trust, channel acceptance, and mismatch risk. Related work further suggests that blockchain adoption may reshape competitive interaction asymmetrically rather than benefit all firms equally (Liu et al., 2023). In addition, consumer-side heterogeneity has also been incorporated into blockchain adoption models. For example, Ma et al. (2025) analyze how consumer quality preference affects blockchain adoption decisions in supply chains. Other studies also show that BCT can affect consumer risk perception, product authentication, and platform pricing decisions (Choi, 2019; Choi et al., 2020; Ji et al., 2022; Shen et al., 2022). Saleh (2021) provide an economic analysis of the proof-of-stake consensus design, thereby offering developers more design options. Subsequently, the application of BCT in the field of digital currency has been widely studied (Chod et al., 2020; Griffin et al., 2020; Huberman et al., 2021). At the same time, a growing number of studies have examined blockchain applications in operations management. Some recent studies have also begun to analyze blockchain adoption under channel competition. For instance, Liu et al. (2025a) investigate blockchain adoption in competing retail channels and show that its value depends on consumer trust, channel acceptance, and mismatch risk. Related work further suggests that blockchain adoption may reshape competitive interaction asymmetrically rather than benefit all firms equally (Liu et al., 2023). Hastig and Sodhi (2020) explore the impact of BCT traceability system implementation on supply chain stakeholders, and Choi et al. (2022) point out the application value in the industry 4.0 era of different BCT functions. Regarding the role of BCT in information transparency, Chod et al. (2020) show that blockchain can improve supply chain information transparency and thereby enhance firms’ financing capacity. More recent studies further examine whether blockchain can effectively eliminate information asymmetry and under what market conditions such transparency gains create value for supply chain members. For example, Liu et al. (2025b) show that the value of blockchain-driven information asymmetry elimination depends on consumer trust in product quality and the magnitude of blockchain benefits. Moreover, Wu and Yu (2023) discuss that asymmetric information was made transparent by BCT and transaction costs were eliminated, resulting in a win–win–win outcome. In particular, a considerable amount of literature has studied the contribution of information transparency to operations management in supply chains (Yang, 2019; Luo et al., 2024). Choi (2019) study how to use BCT to identify diamonds. Choi et al. (2020) propose that BCT can help sales platforms differentiate the types and proportions of consumer risk to provide customized services with optimal pricing strategies. Ji et al. (2022) examine the sensitivity of consumers to BCT in the market and the impact of the composition of consumers on the performance of supply chain stakeholders. In addition, consumer-side heterogeneity has also been incorporated into blockchain adoption models. For example, Ma et al. (2025) analyze how consumer quality preference affects blockchain adoption decisions in supply chains. Shen et al. (2022) point out that BCT platforms can help consumers distinguish between counterfeits and genuine products, and retailers can use the platform to help brand name companies combat copycats.

However, blockchain adoption is not without risks. Technical vulnerabilities, imperfect system design, and cyberattacks may all create privacy-related concerns for users. In particular, Pun et al. (2021) examine the cost implications of consumer privacy concern in blockchain-enabled settings. Zheng et al. (2020) point out that blockchain systems remain vulnerable to a range of security threats, including smart contract exploits and routing attacks. Dong et al. (2025) show that privacy-aware design is an important issue in blockchain-enabled systems. Their study highlights a fundamental trade-off between blockchain-enabled transparency and user privacy protection, even though the application context is UAV communications rather than supply chain management. This broader insight supports our focus on privacy concern as a relevant demand-side consideration in blockchain-enabled transaction environments. However, the present study differs from the existing literature in several important ways. First, unlike studies that primarily emphasize whether blockchain improves transparency or reduces information asymmetry (Liu et al., 2025b), we focus on the strategic trade-off between transparency gains and consumer privacy concern. Second, unlike studies of blockchain adoption in competing channels or under product competition (Liu et al., 2025a; Liu et al., 2023), we analyze blockchain adoption in a dual-channel supply chain with endogenous adoption choices by both the manufacturer and the retailer. Third, unlike studies that examine how consumer preferences such as quality preference shape blockchain adoption (Ma et al., 2025), we specifically identify consumer privacy concern as a demand-side mechanism that affects pricing, equilibrium adoption, profit allocation, and consumer surplus. Accordingly, this paper connects three strands of literature that have largely been studied separately: blockchain adoption, dual-channel competition, and consumer privacy concern.

3 Basic model

3.1 The manufacturer and retailer

A dual-channel supply chain is considered, in which the manufacturer sells its product through both direct and traditional retail channels. In the traditional channel, the product is sold by the brick-and-mortar retailer to the consumer. Simultaneously, the manufacturer’s own products are sold in a direct channel. The manufacturer decides the wholesale and direct prices, and the retailer decides the retail prices. The sequence of events is shown in Figure 1. The manufacturer and retailer determine whether to apply BCT on their own product sales channel. Hence, there are four cases: (1) neither the manufacturer nor retailer applies BCT (Case NN); (2) The manufacturer uses BCT but retailer does not (Case AN); (3) The manufacturer does not adopt BCT but the retailer does (Case NA); and (4) both choose BCT (Case AA). Following Zhang et al. (2022), we assume that the cost for the manufacturer and retailer to apply BCT is 0. In order to prove the robustness of our basic model, we add the cost of applying the BCT in an extension in Section 6 discussed below. See Table 1 for notation.

FIGURE 1

3.2 The consumers

Consumers obtain valuation from the product after purchase. We assume that follows a uniform distribution (Bernstein et al., 2009). Each consumer purchases at most one unit and chooses the channel that yields the highest utility, provided that this utility is nonnegative. Following the standard dual-channel setting, the direct channel is subject to a valuation discount. Specifically, when purchasing online, consumers value the product at , where captures consumers’ acceptance of the online channel and the potential mismatch between the online product presentation and actual consumer needs.

Following Balasubramanian (1998), consumers visiting the brick-and-mortar retailer incur transportation cost. Let consumers be uniformly located on a Hotelling line of unit length (d’Aspremont et al., 1979). A consumer located at incurs transportation cost when purchasing from the retail channel, where is the unit transportation cost. By contrast, the transportation cost in the direct channel is normalized to zero, consistent with Chiang et al. (2003).

When neither the manufacturer nor the retailer adopts blockchain technology, consumer utilities are given by:

In this paper, blockchain technology is modeled as a traceability and verification technology that enhances product information transparency. Operationally, blockchain adoption does not change the physical product itself; rather, it changes the information environment of the supply chain by making product provenance, transaction history, and traceability records more verifiable and more visible to consumers. We capture this transparency effect by parameter , where a larger indicates a higher level of blockchain-enabled information transparency (Zhang et al., 2022; Xu et al., 2021). In Section 7, we further consider the case to examine the possibility that excessive information disclosure may discourage consumers. At the same time, blockchain-enabled purchasing environments may also give rise to consumer privacy concern. We do not claim that every blockchain application necessarily creates privacy concern in all settings. Rather, we focus on consumer-facing retail settings in which blockchain use may involve traceable digital interaction, such as app registration, account login, wallet linkage, or identity-related data submission. In such settings, consumers may perceive privacy exposure once blockchain technology is introduced into the transaction environment. To preserve analytical tractability, we model this privacy concern as a reduced-form additive utility loss , where represents the average disutility associated with perceived privacy exposure rather than the actual probability of data leakage. Unlike standard disutility or transaction-cost parameters in the OM literature, in our model does not represent a physical shopping cost, search cost, or operational friction. Instead, it captures consumers’ perceived privacy-related disutility associated with blockchain-enabled transactions. In this sense, is demand-side and perception-based, rather than a technological or logistical cost parameter.

Accordingly, consumer utilities under the four adoption scenarios are given by:

To ensure that blockchain adoption remains a meaningful strategic option, we assume that is not prohibitively large, so that the transparency effect of BCT is not always dominated by privacy concern.

4 Equilibrium

4.1 Case NN

In this subsection, we consider the benchmark case in which neither the manufacturer nor the retailer adopts BCT. Let denote the marginal consumer under Case NN, that is, the consumer who is indifferent between purchasing from the retail channel and the direct channel. If , consumers located in purchase from the retail channel, whereas those in purchase from the direct channel. If , the retail channel covers the entire market. We then apply backward induction to derive the corresponding equilibrium prices and profits. We first solve the retailer’s pricing problem with the following objective function indicated by Equation 1:from which we obtain Given the retailer’s pricing decision, the manufacturer, as the Stackelberg leader, determines its wholesale and direct-channel prices indicated by Equation 2:

The manufacturer’s optimal responses are given by The results are shown in Table 2.

In our model setting, we assume that the product will be purchased by consumers only if their utility . Hence, under equilibrium conditions, it is worth noting that the maximum consumer valuation will be squeezed by charging a price at from the manufacturer in the online channel. This means that all consumers from the direct channel will purchase the product when the price is , even if they acquire utility . In Guo and Wu (2018), similar conclusions and assumptions are made. Consumers still buy products when their expected value is , so manufacturers can use this price to extract the ceiling amount of consumer valuation. Moreover, our analysis shows that the conclusion of this paper is still valid even though consumers do not purchase the products when the price is .

Proposition 1 indicates that the location of the marginal consumer affects the pricing decisions and total profits of the manufacturer and retailer when neither party adopts BCT. If , consumers purchase from the brick-and-mortar retailer because the retail channel covers the entire market. Conversely, if , the consumers will purchase products from the direct marketing channel and retailer, respectively, according to their own utility. The consumers will buy products from the physical retailer even if its price is higher than that of the direct channel. The reason for this is that the retailer can let the consumer touch the actual product, reducing the problem of product information mismatch. This is common in online channels.

4.2 Case AN

In this subsection, we consider the case in which the manufacturer adopts BCT and the retailer does not. When the manufacturer adopts BCT, consumers benefit from greater product information transparency in the direct channel, so the perceived valuation of the product increases. At the same time, consumers also bear privacy concern costs associated with BCT-enabled transactions. Let denote the marginal consumer under Case AN, that is, the consumer who is indifferent between the retail channel and the BCT-enabled direct channel. If , the retail channel covers the entire market. We then apply backward induction to derive the corresponding equilibrium prices and profits in Case AN. We first solve the retailer’s pricing problem with the following objective function indicated by Equation 3:from which we obtain the retail price In line with Case indicated by Equation 4,

The manufacturer’s optimal responses are given by , . The corresponding equilibrium results are given in Table 3.

Proposition 2 shows that equilibrium prices and profits in Case AN are sensitive to consumer privacy concern. When is low, the direct-channel price is only mildly affected, and the gap between the retail price and the wholesale price remains relatively small. As increases, however, the feasible region of the interior solution may cease to be economically meaningful, implying that the corresponding equilibrium is no longer relevant in practice. Moreover, the manufacturer’s profit decreases with , whereas the retailer’s profit increases. The intuition is that stronger privacy concern reduces consumers’ willingness to purchase through the BCT-enabled direct channel and shifts demand toward the retail channel.

Corollary 1 indicates that, when the direct channel adopts BCT and the retail channel does not, consumer privacy concern affects equilibrium prices and profits through the BCT-enabled direct channel. From Figures 2, 3 we find that the manufacturer's profit declines as consumer privacy concern increases. When β = (k − 1)v, the profit from the direct channel becomes zero, indicating that the direct channel is driven out of the market. By contrast, for the retailer, retail prices will also decrease as consumers’ privacy concern rise, but the profit from the retailer will rise as consumers’ privacy concern grows. The reason is that, with the sharp drop in the wholesale price, the retailer will be affected by this and will lower its own price, so as to avoid consumer aversion to unfair prices. In addition, when consumers place greater weight on privacy protection, the direct channel must reduce its price to offset the negative utility associated with BCT adoption. As a result, some consumers may switch to the retail channel, even though the retailer itself does not adopt BCT.

FIGURE 2

FIGURE 3

4.3 Case NA

In this subsection, we consider the case in which the retailer adopts BCT and the manufacturer does not. When the retailer adopts BCT, consumers benefit from greater product information transparency in the retail channel, but they also bear privacy concern costs associated with BCT-enabled transactions. Let denote the marginal consumer under Case NA, that is, the consumer who is indifferent between the BCT-enabled retail channel and the direct channel. If , the retail channel covers the entire market; if , consumers located in purchase from the retail channel, whereas the remaining consumers purchase from the direct channel. We then apply backward induction to derive the corresponding equilibrium prices and profits in Case NA. We first solve the retailer’s pricing problem with the following objective function indicated by Equation 5:

From that we obtain the retail price , like in Case NN indicated by Equation 6,

The manufacturer’s optimal responses are given by . The equilibrium results are shown in Table 4.

Proposition 3 indicates that consumer privacy concern affects both equilibrium prices and profits in Case NA (see Figure 4). When the retailer adopts BCT, the retail price decreases as consumer privacy concern rises. When privacy concern becomes sufficiently strong, the retailer’s incentive to adopt BCT weakens substantially, and non-adoption becomes the more attractive strategy. By contrast, when privacy concern is relatively low, the retail channel benefits from greater information transparency and may therefore attract more consumers.

FIGURE 4

Corollary 2 further shows that stronger privacy concern reduces both the retail price and the wholesale price in Case NA (see Figure 5). As increases, the profitability of BCT adoption by the retailer weakens. When privacy concern is sufficiently high, the retailer’s profit may fall substantially, which makes BCT adoption less attractive from the retailer’s perspective. Therefore, non-adoption becomes the retailer’s more attractive strategy.

FIGURE 5

4.4 Case AA

In this subsection, we consider the case in which both the manufacturer and the retailer adopt BCT. In this scenario, both channels benefit from enhanced product information transparency, while consumers also bear privacy concern costs in the BCT-enabled transaction environment. Let denote the marginal consumer under Case AA, that is, the consumer who is indifferent between the BCT-enabled retail channel and the BCT-enabled direct channel. If , the retail channel covers the entire market; if , consumers located in purchase from the retail channel, whereas the remaining consumers purchase from the direct channel. We first solve the retailer’s pricing problem with the following objective function indicated by Equation 7:

From that we obtain the retail price .

Similar to Case indicated by Equation 8,

The manufacturer’s optimal responses are given by , . The equilibrium results are shown in Table 5.

Proposition 4 shows that, in Case AA, the manufacturer’s equilibrium outcome is more sensitive to consumer privacy concern than the retailer’s (see Figure 6). When privacy concern is low, both channels benefit from enhanced information transparency, and the direct channel remains attractive to consumers. As privacy concern increases, however, the manufacturer must reduce the direct-channel price to offset the negative utility associated with BCT adoption. If privacy concern exceeds a sufficiently high threshold, the direct channel may be driven out of the market. This result suggests that stronger transparency does not necessarily offset the negative effect of privacy concern in a dual-adoption scenario.

FIGURE 6

Corollary 3 indicates that, in Case AA, both the direct-channel price and the retail price decline as consumer privacy concern increases, while the manufacturer’s profit also decreases (see Figure 7). When privacy concern is low, the manufacturer still has an incentive to adopt BCT because the transparency effect dominates. However, when privacy concern becomes sufficiently strong, the manufacturer’s profitability is substantially weakened, and the attractiveness of the direct channel declines accordingly.

FIGURE 7

5 The optimal strategy

In this section, we compare the results above in the four cases and analyze the impact of applying BCT. We also discuss how the profits of chain members are affected. In addition, we investigate the equilibrium strategies for the manufacturer and the retailer.

5.1 The manufacturer’s strategic selection

We analyze the manufacturer’s selection strategy when faced with whether the retailer chooses BCT or not, as presented in the following proposition.

For the manufacturer, result (i) in Proposition 5 indicates that the manufacturer prefers to adopt BCT when consumers are highly accepting of direct channel products and have lower privacy concern. Because the manufacturer can obtain more profit, as consumers become more receptive to online products, the manufacturer will use BCT in smaller areas, as shown in Figure 8b. By contrast, if consumer privacy concern are higher, then the manufacturer will lose a portion of its profits. The factor is that when concerns are large, consumers will attach great importance to their privacy security. The disadvantages brought by BCT will be magnified in consumers’ consciousness, exceeding the benefits bought by BCT. When consumers have lower acceptance of online products and privacy concern, it is beneficial for the manufacturer to use BCT. At this time, consumers have low recognition of products and are reluctant to buy products from this channel. Similarly, if consumer privacy concern are higher, the manufacturer will not apply BCT because consumers are concerned about privacy, and the increased transparency of product information is not enough to eliminate this suspicion, as illustrated in Figure 8a.

FIGURE 8

As indicted by result (ii) in Proposition 5, the manufacturer will adopt BCT when transportation costs and consumer privacy concern are lower, and information transparency is lower and higher, as shown in Figure 9a. But as concerns increase, the manufacturer will abandon BCT because consumers are fully aware that the negative impact of this technology is far greater than products’ information promotion. Thus, the manufacturer will realize that the benefits are not enough to recoup consumer privacy concern, and it is unwise to use BCT. When consumers’ transportation costs are higher, the region using BCT is getting smaller, as shown in Figure 9b. Conversely, if consumer privacy concern increase, then the manufacturer will not choose to use BCT even if the retailer uses it. The reason is that while using BCT will increase the transparency of product information, but it is not enough to offset consumer privacy concern, and will even have the opposite effect.

FIGURE 9

5.2 The retailer’s strategic selection

In this section, we obtain the optimal selection strategy of the retailer if the manufacturer uses BCT.

According to result (i) in Proposition 6, when consumer concerns involving privacy are relatively low, the retailer has incentives to apply BCT to obtain greater profits. In contrast, it is better for the retailer not to use BCT when consumers are highly sensitive about the disclosure of their private data in Figure 10. The reason is that consumers are convinced that the additional product information given by BCT is higher than the deep concern when consumer privacy concern are lower. However, it is smarter for the retailer not to apply BCT because more profits will be gained if consumers are concerned about privacy and are more worried about damage to their property than the benefits of product information transparency.

FIGURE 10

Result (ii) in Proposition 6 shows that this situation is not only related to consumer privacy concern but also conditional on the consumer acceptance of online products and product information transparency. From Figure 11a, when consumers’ acceptance of online products is lower and the products’ information transparency is larger, if consumer privacy concern are lower, the retailer will adopt BCT. The reason for this is that consumers are slightly worried about their privacy. This indicates that retailers are more profitable. Therefore, consumers are less receptive to online products and more inclined to purchase products in the retailer. By contrast, if consumers are particularly concerned about data privacy, then it is the most sensible choice for the retailer not to apply BCT. When consumers have a higher acceptance of products through direct online channels and consumer privacy concern are lower, the retailer will choose to apply BCT to maximize profits, as shown in Figure 11b. But as concerns increase, even increased product information promotion fails to reverse the fact that retailer does not opt for BCT.

FIGURE 11

5.3 Equilibrium of the retailer and the manufacturer on BCT

In this section, we examine which cases are the optimal strategic choice. We analyze this by comparing the profits in different situations. A detailed analysis is given as Proposition 7.

Part (i) in proposition 7 shows that both the manufacturer and retailer should adopt BCT only when consumer privacy concern and acceptance of online products are lower, as illustrated in Figure 12a. Consumers are almost oblivious to their privacy concern and they have access to more information about the product, so the manufacturer and the retailer will apply BCT. The reason for this is that consumers will believe in the authenticity of the information, and this will increase their purchase demand, without arousing their aversion. Thus, the manufacturer and the retailer can obtain more profits without considering the negative effects from BCT. If consumer concerns about privacy disclosure and quality information about product are moderate, strategies Case AN and Case NA will become the alternative choices of the manufacturer and the retailer. The manufacturer is a relative disadvantage to the retailer, which forces the manufacturer to use BCT when the retailer does not, in order to maintain more profits. If consumer concerns about privacy disclosure and product information promotion are higher, neither the manufacturer nor retailer uses BCT. The reason for this is that consumers are very worried that their privacy will be leaked, and this risk will reduce the use of BCT-related applications.

FIGURE 12

Part (ii) in Proposition 7 shows that the retailer uses BCT and the manufacturer does not as an equilibrium condition because consumer acceptance of online products is higher, so the retailer must use BCT to keep its profits. This area of use has increased significantly, as shown in Figure 12b. Because consumers are more accepting of products online, while retailers are more affected, the retailer must apply BCT to attract consumers. When consumer privacy concern are lower and product information transparency is higher, both the manufacturer and retailer will use BCT. The reason for this is that consumers’ recognition of online products is higher and product information is more transparent, which will induce them to buy products, and the manufacturer will obtain more profits. If consumer concerns about privacy disclosure are higher, neither the manufacturer nor retailer uses BCT. The reason for this is that consumers are very worried that their privacy will be leaked. The profit from increased information transparency is not enough to compensate for the loss of consumer privacy concern.

5.4 Profit implications of BCT

In this section, we analyze whether the manufacturer and retailer reach a win–win situation when using BCT. In reality, we have Proposition 8, which shows that there appears to be a win–win situation and win–lose situation in this sequential game when equilibrium strategy AA is adopted by the supply chain members.

The manufacturer and retailer both get more profits when equilibrium strategy Case AA is adopted by them, , and , as shown in Propositions 6, 5. Therefore, it is not the optimal strategy for the manufacturer and the retailer to adopt BCT at the same time. As indicated by Proposition 8, if both the manufacturer and retailer adopt BCT and if neither of them uses BCT, the retailer’s profit will be constant and greater than zero, but the manufacturer’s profit will change as consumer concerns about privacy data change. Specifically, when is small , the profit of the manufacturer and retailer increases, which leads to a win–win situation. However, when is large , the manufacturer’s profits decrease and the retailer’s profits increase, and this is a win–lose situation, as shown in Figure 13. A deeper implication of our results is that the benefits of blockchain adoption are not transmitted symmetrically across channels. While transparency gains may increase perceived product value, privacy concern reshapes demand reallocation across channels, so that one channel member may benefit at the expense of the other. The win–win versus win–lose outcome arises because blockchain adoption affects the two channel members through different mechanisms. The adopting party benefits from enhanced transparency only insofar as this gain is not offset by privacy-induced demand loss, whereas the non-adopting or less exposed party may gain indirectly from redirected demand or strategic price adjustment. Importantly, consumer privacy concern does not merely reduce demand in a uniform way; it changes the structure of equilibrium adoption by altering whether blockchain adoption is profitable for one party, both parties, or neither party.

FIGURE 13

6 Consumer surplus

The consumer surplus is defined as the difference between what consumers are willing to purchase and the actual purchase price. Therefore, the formula for calculating consumer surplus in Case is given as follows indicated by Equation 9:where , and embody the equalization marginal spot, product information transparency promotion, the retailer price, consumer concerns about privacy, the direct online price, and consumer acceptance of online products, respectively. Note that and never exist when the manufacturer and retailer do not use BCT in their channels. Table 6 shows the consumer surplus in each case.

From Proposition 9, we find that consumer surplus is affected by consumer privacy concern. Part (i) shows that the retailer using BCT will increase consumer surplus when the manufacturer does not use BCT. We show that the manufacturer and retailer applying BCT increases consumer surplus when consumer privacy concern are above a certain threshold. Specifically, if BCT is applied by chain members to increase consumers’ personal information concerns, then they will decrease the price to mitigate the negative influence from BCT and thus improve consumer surplus. In contrast, if privacy concern is low when using BCT, channel members have an incentive to drive up prices, and then consumer surplus will decrease. In other words, if concerns about privacy are small, product information will be more transparent when the chain members adopt BCT and consumers know more information, which can promote consumer utility and increase the chain members’ profits.

7 Extensions

In this section, in order to improve the rigor of the research content, we examine whether our findings are robust to parameter changes in the basic model. The results of the analysis are presented in the following propositions.

7.1 Power structure

We start by looking at when the structure power varies between the manufacturer and retailer, when there is a retailer-led in the market who has more decision-making power. We consider a situation where the retailer is the leader that makes the decision first, and the manufacturer is the follower.

Result (i) in Proposition 10 shows that the results of equilibrium strategies of the manufacturer and retailer in different market power structures are generally efficient and robust. Additionally, there are a smaller number of different regions of equilibrium strategy compared to the previous conclusion. Case AA is the equilibrium strategy when consumer concerns about privacy disclosure are small and the promotion of product quality information is large. By contrast, Case NN is the equilibrium strategy when consumer concerns about privacy are large and the advancement of product quality information is small. However, when transportation costs are lower, we find that Case AN is the equilibrium when consumer privacy concern are small and the promotion of product quality information is small. The retailer will not use BCT because of the low consumer acceptance of online products, as shown in Figure 14a. Result (ii) in Proposition 10 indicates that when transportation costs are higher, this strategy is the best when consumer privacy concern are moderate and product quality information transparency is high. Because consumers are more receptive to online products, the manufacturer only needs to use BCT when the promotion of product information transparency is enough to stimulate consumers to buy. Case NA is the equilibrium strategy in other regions, as shown in Figure 14b. The reason for this is that the concerns on the direct channel decreases as consumers gain acceptance for products purchased online, and the manufacturer, as a follower, has less capacity to run the venture of BCT than the retailer. Thus, not using BCT is smart for the manufacturer.

FIGURE 14

7.2 Cost of using BCT

We did not consider the cost of using BCT in the basic model, but we here analyze the impact of this factor on decision makers. Because of the fee for recording information in the process of adopting BCT, as well as the cost of each product identifier being scanned by consumers, such as a QR code, to obtain traceability information and ensure the authenticity of the product. Based on this, we hypothesize that the cost of using BCT is and we conduct the following analysis to derive the equilibrium strategies.

Part (i) in Proposition 11 indicates that when consumer concerns involving privacy are relatively low and product quality information promotion is relatively large, both the manufacturer and retailer will adopt BCT, as shown in Figure 15a. Conversely, neither the manufacturer nor the retailer will adopt BCT when consumer privacy concern are higher. When product information promotion is moderate and consumer privacy concern are lower, Case AN is the equilibrium strategy. Part (ii) in Proposition 11 shows that consumer privacy concern and information promotion are moderate, either the manufacturer and retailer will choose to use BCT. As consumer acceptance of online products increases, the area for the manufacturer to use BCT will increase. The reason for this is that BCT costs increase the retailer’s burden and reduces its profit, so the manufacturer will increase the application of BCT in Figure 15b. When consumer privacy concern are lower and information transparency is higher, both the manufacturer and the retailer will use BCT. However, neither the manufacturer or retailer adopts BCT when consumer privacy concern are higher, because these concerns could lead to consumer backlash.

FIGURE 15

7.3 Adverse impact of improving the transparency of product quality information

Although BCT can improve product information transparency and increase the chance of a comprehensive understanding of consumer products so that consumers can make better purchasing decisions, some studies have concluded that if the product information is too transparent, it will have the opposite effect on consumers, thus lowering consumers’ expectations. In certain cases, consumers begin to balk. In what follows, we stipulate in this section.

From Proposition 12, we find that the manufacturer and retailer will voluntarily choose not to adopt BCT when product information transparency has a negative effect. The reason for this is that not all product information can guide consumers to have an interest in buying. Some consumers reject the culture of the origin of products or have antipathy to the high cost of information, which will reduce consumer purchases. In other words, the manufacturer and retailer will obtain more consumer favor and higher profits when neither the manufacturer and retailer adopting equilibrium strategy (Case ) than the other three equilibrium strategies. This proves the rigor of our basic model.

8 Managerial insights

This paper explored how BCT affects optimal pricing and consumer surplus of chain members in a dual-channel framework. Some interesting managerial insights into supply chain practices are here derived. First, managers should not interpret blockchain adoption as universally beneficial. When consumer-facing blockchain applications require traceable digital interaction and privacy concern is salient, adopting blockchain without a corresponding privacy-management strategy may undermine demand and weaken profitability. Second, blockchain adoption should be accompanied by privacy-governance measures. Firms should reduce perceived privacy exposure through interface design, data minimization, access control, and clearer communication regarding the use of consumer information. In practice, privacy-sensitive implementation may be as important as transparency enhancement itself. Third, manufacturers and retailers should evaluate blockchain adoption strategically rather than independently. In a dual-channel setting, one party’s adoption decision may alter not only its own demand and pricing incentives, but also the other party’s optimal response. Therefore, adoption decisions should be coordinated with channel structure, pricing strategy, and anticipated consumer privacy concern. Finally, platform operators and retailers should recognize that transparency-oriented technologies may require differentiated implementation across channels. A uniform blockchain deployment strategy may be suboptimal when consumer privacy concern differs across transaction environments.

9 Conclusion

BCT can improve the transparency of product information, but it also threatens to disclose private consumer data in the process of its application. In this paper, we considered a supply chain in which one manufacturer sells products through a direct channel and one retailer sells products at a physical store. We determined the impact of BCT on the optimal pricing of supply chain members. In addition, we analyzed the impact of the use of BCT by supply chain members on consumer surplus. After obtaining equilibrium results, we also derived the strategic choices of the manufacturer and retailer regarding whether they adopt BCT.

Despite the managerial insights into whether manufacturers and retails should adopt BCT, our paper has a few limitations. First, we did not consider service investments by retailers seeking to improve their service level in the market. Retailers can provide technical shopping assistance, advertising displays in physical stores, and return services, which will have a significant impact on consumers’ channel choice and loyalty. Second, we only considered the dual channel of one manufacturer and one retailer, whereas in reality there are several members in the supply chain, such as raw material providers and third-party logistics suppliers. Third, we did not assume that the manufacturer and retailer have sufficient knowledge of consumer behavior, such as whether consumers are sensitive to BCT. Information asymmetry can also be considered as a research direction. Finally, consumers can be divided into traditional consumers and online consumers according to different channel preferences. The influence of consumer behavior regarding such preferences on the application of BCT is also worth studying in the future.

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