Adoption of Blockchain Technology for Weak and Strong Brand Companies

Abstract

As e-commerce competition intensifies, brand companies face challenges such as product homogenization, consumer trust erosion, and price wars. Blockchain technology (BCT) has emerged as a solution to enhance transparency and optimize competitive strategies. In this study, the adoption of BCT and pricing decisions between weak and strong brand companies is analyzed in an asymmetric competitive environment. A game-theoretic framework is developed to analyze whether each company should adopt BCT. The results show that when the difference in brand awareness is small, BCT adoption allows pricing advantages to be shifted between weak and strong brands. As the difference in brand awareness increases, both types of brands reduce their BCT investments. Moreover, when one brand adopts BCT, the other often follows suit to maintain competitiveness. Three equilibrium scenarios are identified based on consumer privacy concerns and differences in brand awareness. Interestingly, the strategy where only the weak brand adopts BCT does not constitute an equilibrium in any scenario. Finally, when the difference in brand awareness is significant and price competition is high, either both brands adopt BCT or neither does. This paper provides strategic insights into BCT adoption and its implications for competitive behavior in asymmetric markets.

1. Introduction

Companies engage in asymmetric competition in many industries. Typically, those with larger market shares and higher brand awareness are referred to as strong brands, while those with smaller market shares and lower brand awareness are considered weak brands [1]. As the e-commerce market expands, the demand for product transparency and trust grows, intensifying asymmetric competition among companies. This dynamic exacerbates the core issue of the traditional e-commerce supply chain: information opacity and the resulting lack of trust. These challenges affect the competitive landscape of weak and strong brand companies. BCT, with its decentralized, tamper-proof, and traceable characteristics, has become a key technology for addressing these problems, showing great potential for improving supply chain management [2,3,4,5]. Motivated by this potential, companies across industries are exploring the adoption of BCT. For instance, weak brands such as small agricultural cooperatives use BCT for traceability to demonstrate product authenticity, while strong brands such as Walmart and Amazon employ BCT to optimize their supply chain and enhance transparency [6,7]. These cases reflect significant differences in the motives behind BCT adoption, technology investment levels, and market impacts between weak and strong brand companies, highlighting the need to study this asymmetric competitive landscape.

Scholars have confirmed the effectiveness of BCT in curbing false advertising in e-commerce [8]. Studies have shown that BCT reduces counterfeiting in manufacturing by up to 87% and in pharmaceuticals by up to 75% [9]. Major retailers such as Walmart and Carrefour have implemented BCT for food traceability, reducing the time needed to investigate foodborne illnesses from days to seconds while increasing the level of consumer trust by 35% [10]. However, the implementation of BCT faces practical challenges: product traceability labeling increases operating costs [11,12], and widespread BCT use in commodity traceability raises security risks regarding personal consumer data [13].

Driven by these trade-offs, our study examines BCT adoption decisions under asymmetric competition. We address the following research questions:

First, how does asymmetry between a weak brand company and a strong brand company affect their pricing, BCT adoption, and profits? Second, how does the investment of one of these companies into BCT affect the investment of the other company into BCT? Third, how do consumer privacy concerns and price competition affect companies’ choices about BCT?

To study these questions, we develop a game framework where a weak brand company and a strong brand company decide whether to adopt BCT. We discuss four different cases, including neither adopting BCT (NN), only the weak brand company adopting BCT (BN), only the strong brand company adopting BCT (NB), and both adopting BCT (BB). We derive optimal pricing, BCT investment levels, and profits for each scenario, and compare outcomes to identify adoption strategies and equilibria.

We make three main contributions in this paper. First, we analyze how differences in brand awareness between weak and strong companies influence competitive strategies and outcomes in the four contexts of BCT adoption. Second, we compare BCT levels in scenarios where only one company adopts BCT and where both companies adopt BCT to explore how the adoption of BCT by competitors affects companies’ BCT investment. Finally, we analyze the optimal strategy for weak and strong brand companies, as well as their equilibrium strategies, to explore how consumer privacy concerns, company asymmetry, and price competition influence companies’ BCT choices.

While prior research has extensively documented the role of BCT in supply chain management, few studies have explored how differences in brand awareness influence the motivations and outcomes of its adoption. Our study fills this gap, providing a nuanced understanding of how asymmetric competition influences BCT strategies.

This paper is structured as follows: The literature review is presented in Section 2. The model setting is introduced in Section 3. The optimal prices, the BCT level, and profits for the weak and strong brand companies are analyzed in Section 4. BCT investment decisions and equilibrium strategies for the two companies are analyzed in Section 5. The model assumptions are replaced in Section 6 to discuss the stability of the model. Finally, the paper’s conclusions and managerial insights are summarized in Section 7.

2. Literature Review

This paper lies at the intersection of research on sales competition and the application of BCT. A common research approach in the literature is to utilize game theory to describe competitor interactions. The studies that are most closely related to the work of this paper are related to the areas of (1) competitive operations for weak and strong brand companies and (2) BCT adoption decisions in competitive scenarios.

2.1. Competitive Operations for Weak and Strong Brand Companies

Weak brands are typically at a disadvantage in terms of market awareness, consumer trust, and resource allocation [14]. In contrast, strong brands usually enjoy higher brand awareness, stronger brand loyalty, and significant market share [15]. These differences between weak and strong brands lead to substantial variations in their market behavior and competitive interactions. These differences are reflected not only in brand performance, resource allocation, and competitive strategies, but also in consumer behavior and market structure. Game theory is frequently applied to analyze the competitive dynamics between brands. In a competitive environment, brand companies face a series of strategic decisions, such as pricing, advertising, and market segmentation, all of which directly impact their performance. In the competition between weak and strong brands, the latter can often exert pressure through non-cooperative game strategies, such as price wars, while weaker brands may adopt more flexible market strategies to compete [16]. Game models, such as the Cournot and Bertrand models, have been widely used to study brand competition, as brand decisions shape the competitive landscape and pricing structure of the market [17]. For example, Wu et al. [18] consider a scenario where a strong brand manufacturer and a weak brand retailer produce products through the same contract manufacturer. They develop a cooperative game model between the manufacturer and the retailer to study the retailer’s brand spillover effect and the manufacturer’s channel strategy. Vaidyanathan and Aggarwal [1] examine the impact of chain promotions between strong and weak brands on consumer brand evaluations.

Weak brands typically rely on strategies such as cost leadership, market segmentation, and technological empowerment. For example, cost leadership can be achieved through lean production methods or localized sourcing to reduce costs [14]. In addition, weak brands focus on unmet niche market needs, which enables them to establish competitive advantages through specialized brand development [19]. Technological empowerment, including the use of BCT to combat counterfeit products or leveraging shared platforms (e.g., IBM Food Trust) to reduce traceability costs, also helps to enhance the competitive edge of weak brands [20,21]. In contrast, strong brands often adopt strategies centered around innovation and R&D, brand management, and supply chain optimization. For instance, leading companies such as Tesla maintain technological barriers through cutting-edge technologies including autonomous driving, while companies such as Google strengthen their competitive advantage through strategic acquisitions (e.g., DeepMind) [22].

Different from existing studies, in this study the impact of brand differences on BCT adoption decisions and the influence of technology adoption on the competitive dynamics between brands are explored. Specifically, this paper explores how weak and strong brand companies attempt to increase consumers’ purchase intentions by adopting BCT while balancing its pros and cons. Traditional asymmetric brand competition research rarely explores this perspective because these studies often focus on operational strategies or pricing.

2.2. BCT Adoption Decisions in Competitive Scenarios

Many scholars have studied BCT adoption decisions in competitive situations, focusing primarily on the impact of BCT on optimal product pricing and quality decisions [23] and how it can be used to combat counterfeiting [24,25]. For example, Tao, Wang, and Zhu [23] investigate how BCT impacts optimal pricing strategies and quality decisions in two different supply chain structures. Geng and Maskulka [26] explore anti-counterfeiting in a supply chain consisting of genuine manufacturers, counterfeit manufacturers, and online retailers. Pun, Swaminathan, and Hou [24] consider a marketplace with a genuine manufacturer and a counterfeit manufacturer, and explore the impact of counterfeit product quality, privacy risks, and government subsidies on BCT adoption by the genuine manufacturer. Shen, Dong, and Minner [25] investigate how retailers can utilize BCT to address the problem of counterfeit goods in a scenario where brands rely on retailers for product sales. They categorize consumers into two groups, novice and professional, based on their experience level and find that the adoption of BCT can only effectively curb the circulation of counterfeit products when the number of novice consumers reaches a certain threshold.

Recently, researchers took a deeper look at the decisions to adopt BCT in supply chain systems consisting of two competing manufacturers, retail platforms, or retailers. For example, Ji et al. [27] distinguish between blockchain-sensitive and average consumers and find that a manufacturer should only adopt BCT if the consumers’ sensitivity to BCT exceeds a certain level, and the manufacturer that introduces BCT first is likely to be more profitable. Niu et al. [28] find that a retailer will only adopt BCT when there is mild manufacturer competition and low demand variation in the market. Li et al. [29] focus on information sharing and leakage in the supply chain and find that the introduction of BCT has little impact on manufacturers’ production decisions. The above studies focus on the adoption of BCT in supply chain scenarios. Regarding the application of BCT in competitive retail platforms, Hsieh et al. [30] examine the cross-channel impact of applying BCT to green supply chains on competitive retail platforms with information asymmetry. Zhang et al. [31] study BCT adoption decisions under competition between an initial retailer and a newcomer retailer and analyze the impact of privacy leakage risk on BCT adoption. They find that the two retailers only apply BCT when the level of consumer privacy concerns is low and the level of information transparency is high; when the level of consumer privacy concerns is high and the promotion of information transparency is low, neither of them will adopt BCT; and when both consumer privacy concerns and the promotion of information transparency are at a medium level, the initial retailer will adopt BCT, while the newcomer will not. Zhang, Ren, Lan, and Yang [31] study firm-level BCT adoption decisions and analyze the impact of privacy leakage risk in the context of competition between initial and newcomer retailers. They find that both retailers adopt BCT when there are low privacy concerns and high information transparency; neither does when there are high privacy concerns and low transparency; and when there are medium privacy concerns and transparency, only the initial retailer adopts BCT and the newcomer does not.

3. Model Setting and Methods

We assume that there are two competing companies operating in the e-commerce market: a weak brand company (denoted as $F_1$) and a strong brand company (denoted as $F_2$). $F_1$ has low awareness, while $F_2$ has high awareness [1]. Both companies sell similar products online and simultaneously decide whether to adopt BCT. BCT is a verifiable and tamper-proof information technology that discloses true information about online products, thereby increasing consumer trust and expanding the market [32]. To ground our model in realistic contexts, we consider illustrative examples from different industries. For instance, Vietnam launched the NDA Trace system through the national blockchain platform NDAChain to provide GS1 international standard traceability certification for agricultural products [33]. In this context, strong brands (multinational agricultural groups) and weak brands (local small-and medium-sized farms) compete in the global market. In addition, the auto parts market has long been plagued by counterfeiting problems, with fierce competition between strong brands (such as international first-tier parts manufacturers) and weak brands (such as local small-and medium-sized manufacturers) [34,35]. BCT uses unique identification codes to achieve full-link traceability of accessories, addressing information asymmetry problems. These cases serve as relevant examples to demonstrate that the assumptions of our model, such as asymmetric brand power and competition based on quality/authenticity, may hold true. However, it is important to note that these cases are isolated examples.

To ensure the clarity and validity of the game-theoretic model, we explicitly state the key methodological assumptions underlying our analysis:

• The weak brand company F₁ and the strong brand company F₂ are rational entities that make simultaneous decisions to maximize their own profits.
• The unit production costs of both companies are normalized to zero. This simplification allows us to focus on the competitive effects of pricing and BCT adoption strategies, avoiding potential cost asymmetries that are irrelevant to our research questions [18,31].
• Linear demand functions are used to capture the negative effects of the brand’s own prices and the positive effects of competitors’ prices (cross-price effects). Brand awareness and BCT effects are incorporated into these functions, following established practice in the literature [31,36], to simulate market expansion, trust enhancement, and privacy issues.
• The model assumes perfect information. Both companies have common knowledge of all parameters and each other’s payoff functions, allowing the derivation of Nash equilibria in adoption and pricing decisions.

We analyze four possible cases based on whether the two companies adopt BCT: neither adopts BCT (Case NN); $F_1$ adopts BCT and $F_2$ does not (Case BN); $F_1$ does not adopt BCT but $F_2$ does (Case NB); and both adopt BCT (Case BB).

We also assume that brand awareness is a key differentiator between weak and strong brand companies. To model the competition between weak and strong brands, we assume that $F_1$ has lower brand awareness compared to $F_2$ [31]. This assumption reflects real-world scenarios where the smaller brand often struggles with consumer recognition and market share compared to the larger brand. The market demand for each brand is influenced by these differences in brand awareness, which we capture using parameter $a$. To simplify the calculation, we further assume that the brand awareness of the strong brand company is 1, and therefore that of the weak brand company is $1-a$. These assumptions are consistent with findings in the literature, where brand awareness influences the market share of brand companies [37,38]. The weak brand typically exhibits limited awareness, leading to lower familiarity and less purchase consideration by consumers. In contrast, strong brands such as Apple and Starbucks demonstrate that brand awareness drives market share growth [39].

The selling price for $F_i$ is $p_i$. The coefficient of price competition, $\theta$, represents the intensity of price competition between the two companies. A higher value of $\theta$ implies more aggressive competition, where the brands’ pricing decisions significantly influence each other’s market shares. When neither company adopts BCT, the market demand functions of the two companies are

$$D_1=1-a-p_1+\theta p_2;$$

$$D_2=1-p_2+\theta p_1.$$

Following the assumptions in the existing literature on BCT [11,36], we denote the level of BCT by $n_i^{S_i}$, where $i=1,2$, $S_1=\{BN,BB\}$, and $S_2=\{NB,BB\}$. The cost incurred by companies in adopting BCT is $k{n}_i^{S_i^2}$. The demand sensitivity to BCT, denoted by $\alpha$, reflects the fact that the more a company invests in BCT, the more information it can store, allowing it to achieve a larger impact on the market. While BCT can enhance product transparency, consumers also have concerns about information and data leakage during its application [24]. Therefore, the decrease in demand due to privacy concerns is denoted by $\epsilon$, representing the level of consumer privacy concerns regarding the adoption of BCT. The market demand for the two companies when BCT is adopted are

$$D_1=1-a-\epsilon -p_1+\theta p_2+\alpha n_1^{S_1};$$

$$D_2=1-\epsilon -p_2+\theta p_1+\alpha n_2^{S_2}.$$

Figure 1 illustrates the decision sequence: first, $F_1$ and $F_2$ choose whether to adopt BCT, and if BCT is used, $F_1$ decides the level of BCT to be $n_1^{S_1}$ ($S_1=\{BN,BB\}$) and $F_2$ decides the level of BCT to be $n_2^{S_2}$ ($S_2=\{NB,BB\}$); secondly, $F_1$ and $F_2$ simultaneously decide the selling price to be $p_1^S$ and $p_2^S$, respectively, where $S=\{NN,BN,NB,BB\}$. The meaning of all parameters is listed in

Table 1. Parameter symbols and their description in the model.

Symbol	Description
$F_1$	The weak brand company
$F_2$	The strong brand company
$a$	the difference in brand awareness
$n_i^{S_i}$	The level of BCT, $i=1,2$, $S_1=\{BN,BB\}$, $S_2=\{NB,BB\}$
$\alpha$	The demand sensitivity to BCT, $\alpha >0$
$k$	BCT cost coefficient, $k>0$
$\epsilon$	The decrease in demand due to consumer privacy concerns about applying BCT, $\epsilon >0$
$\theta$	The coefficient of price competition
$p_i^S$	The selling price for $F_i$, $i=1,2$, $S=\{NN,BN,NB,BB\}$
$D_i^S$	The demand for $F_i$, $i=1,2$, $S=\{NN,BN,NB,BB\}$
${\pi }_i^S$	Total profits for $F_i$, $i=1,2$, $S=\{NN,BN,NB,BB\}$

.

4. The Model and Equilibrium Results

The problem of two asymmetrically competing companies applying BCT and pricing is solved through inverse induction: First, the pricing problem of the two companies is solved. Second, the level of BCT is calculated. In order to calculate the level of BCT, if neither company adopts BCT (Case NN), we only need to solve the pricing problem; if only $F_1$ adopts BCT (Case BN), we also need to calculate its BCT level $n_1^{BN}$; if only $F_2$ adopts BCT (Case NB), we also need to calculate the level of BCT $n_2^{NB}$; if both companies adopt BCT (Case BB), then we need to calculate the level of BCT $n_1^{BB}$ and $n_2^{BB}$.

4.1. Case NN

In this case, neither of the two companies uses BCT. The demand functions for $F_1$ and $F_2$ are

$$D_1^{NN}=1-a-p_1^{NN}+\theta p_2^{NN};$$

$$D_2^{NN}=1-p_2^{NN}+\theta p_1^{NN}.$$

Further, the profit functions for weak and strong brand companies are

$${\pi }_1^{NN}=p_1^{NN}{D}_1^{NN};$$

$${\pi }_2^{NN}=p_2^{NN}{D}_2^{NN}.$$

Corollary 1.

For scenario NN, we have $p_1^{NN}<p_2^{NN}$ and ${\pi }_1^{NN}<{\pi }_2^{NN}$.

Proof of Corollary 1.

See Appendix B. □

Corollary 1 shows that the weak brand company’s selling price and profit are lower than those of the strong brand company. This means that when neither company adopts BCT, the strong brand maintains an advantage in pricing and profitability. This is because strong brands typically enjoy higher brand awareness and reputation, which leads to consumers preferring their products. Such brand preference grants the strong brand a larger market share and greater pricing power, enabling it to set higher prices and earn greater profits. In contrast, the weak brand suffers from lower brand influence and struggles to attract sufficient consumer attention and purchases. To compete for market share, the weak brand may have to reduce prices, resulting in relatively lower product prices and profits. These results underscore the competitive disadvantage faced by the weak brand in traditional market settings without technological intervention. The weak brand is often forced into a low-price strategy to capture market share, which erodes profit margins. This highlights the importance for the weak brand of considering innovative strategies—such as adopting blockchain technology—to enhance product credibility, differentiate from competitors, and potentially command higher prices.

4.2. Case BN

In this scenario, only $F_1$ adopts BCT to track products. The demand functions for $F_1$ and $F_2$ are

$$D_1^{BN}=1-a-\epsilon -p_1^{BN}+\theta p_2^{BN}+\alpha n_1^{BN};$$

$$D_2^{BN}=1-p_2^{BN}+\theta p_1^{BN}.$$

Further, the profit functions for $F_1$ and $F_2$ are

$${\pi }_1^{BN}=p_1^{BN}{D}_1^{BN}-k{n}_1^{B{N}^2};$$

$${\pi }_2^{BN}=p_2^{BN}{D}_2^{BN}.$$

Corollary 2.

For scenario BN, if $a\le a_1$, then $p_1^{BN}\ge p_2^{BN}$; if $a_1<a<1$, then $p_1^{BN}<p_2^{BN}$; if $a\le a_2$, then ${\pi }_1^{BN}\ge {\pi }_2^{BN}$; and if $a_2<a<1$, then ${\pi }_1^{BN}<{\pi }_2^{BN}$, where $a_1$ and $a_2$ are shown in Appendix C.

Proof of Corollary 2.

See Appendix B. □

Corollary 2 and Figure 2 show that when both $a$ and $k$ are small, the weak brand company’s selling price and profit are higher than those of the strong brand company. As the BCT cost coefficient increases, the relative advantages in selling price and profit for both the weak and strong brand companies change. When $a$ is high, the weak brand company’s selling price and profit can still surpass those of the strong brand company, which differs from the conclusion under the NN scenario.

When the brand awareness difference is small and the BCT cost coefficient is low, the weak brand can quickly enhance consumer purchase intention, establish product differentiation, and improve competitiveness through BCT. However, as the difference in brand awareness and BCT cost increases, a scale advantage emerges for the strong brand, even without adopting BCT. The marginal benefit of BCT diminishes, making it harder for the weak brand to counteract the strong brand’s market influence through technological differentiation. When the difference in brand awareness is large and the cost of BCT is high, the strong brand’s market dominance is strengthened and consumer decisions are heavily influenced by brand perception. In this case, even if the weak brand adopts BCT, it struggles to overcome the market awareness barrier, and the additional cost of adopting BCT may further reduce its profitability.

This analysis suggests that the adoption of BCT can be a strategic equalizer for the weak brand, but only under certain conditions. When the costs of BCT implementation are low and the difference in brand awareness is narrow, the weak brand can leverage transparency and traceability to build consumer confidence and command higher prices. However, as costs rise or the difference in brand awareness widens, the inherent advantages of strong brands will prevail. Therefore, the weak brand must carefully assess the cost–benefit trade-off of BCT adoption based on their competitive environment. On the other hand, strong brands should closely monitor competitors’ adoption of technology, as first movers may gain a temporary advantage in perceived product quality and trust.

4.3. Case NB

In this scenario, only $F_2$ adopts BCT to track products. The demand functions for $F_1$ and $F_2$ are

$$D_1^{NB}=1-a-p_1^{NB}+\theta p_2^{NB};$$

$$D_2^{NB}=1-\epsilon -p_2^{NB}+\theta p_1^{NB}+\alpha n_2^{NB}.$$

Further, the profit functions for weak and strong companies are

$${\pi }_1^{NB}=p_1^{NB}{D}_1^{NB};$$

$${\pi }_2^{NB}=p_2^{NB}{D}_2^{NB}-k{n}_2^{N{B}^2}.$$

Corollary 3.

For the scenario NB, if $a\le a_3$, then $p_2^{NB}\ge p_1^{NB}$; if $a_3<a<1$, then $p_2^{NB}<p_1^{NB}$; if $a\le a_4$, then ${\pi }_2^{NB}\ge {\pi }_1^{NB}$; and if $a_4<a<1$, then ${\pi }_2^{NB}<{\pi }_1^{NB}$, where $a_3$ and $a_4$ are shown in Appendix C.

Proof of Corollary 3.

See Appendix B. □

Corollary 3 and Figure 3 show that when only the strong brand company adopts BCT, and t$k$ is small, its product selling price and profit are consistently higher than those of the weak brand company. As the BCT cost coefficient increases, the relative advantages in selling price and profit between the two companies begin to shift. When $k$ is moderately high and $a$ is moderate, the strong brand company’s product selling price remains higher, but its profit becomes lower than that of the weak brand company. When $k$ is large and $a$ is small, both the selling price and profit of the strong brand company fall below those of the weak brand company.

When $k$ is low, the strong brand can further enhance consumer trust and brand premium through BCT. In this case, the strong brand’s market dominance and the synergetic effects of BCT form a positive cycle, resulting in significantly higher prices and profits compared to the weak brand that has not adopted BCT. For example, Starbucks has partnered with IBM Food Trust to utilize BCT in tracing the origin of its coffee beans and ensuring product quality transparency [40]. This technology strengthens consumer trust in the brand, allowing Starbucks to sustain its premium pricing strategy in the coffee market and consolidate its profit advantage.

As $k$ increases to a moderate level, the technology investment begins to erode the strong brand’s profit. Although strong brand awareness enables it to maintain a higher price, the weak brand may surpass it in profitability. When the BCT cost coefficient becomes relatively large and the difference in brand awareness is small, the strong brand’s technology investment turns into a cost burden, while the weak brand gains a cost advantage. In this scenario, consumers are less sensitive to brand differences and more inclined to choose better value products.

For the strong brand, adopting BCT can enhance consumer trust and justify premium pricing, but only if the technology costs are manageable. High implementation costs can offset profit gains, allowing the weaker brand to effectively compete on price. Therefore, the strong brand should conduct a cost–benefit analysis before investing in BCT. The weak brand, on the other hand, can capitalize on the high BCT costs faced by the strong brand by highlighting their cost-effectiveness and value proposition to attract price-sensitive consumers.

4.4. Case BB

In this scenario, both companies adopt BCT to track products. The demand functions for $F_1$ and $F_2$ are

$$D_1^{BB}=1-a-\epsilon -p_1^{BB}+\theta p_2^{BB}+\alpha n_1^{BB};$$

$$D_2^{BB}=1-\epsilon -p_2^{BB}+\theta p_1^{BB}+\alpha n_2^{BB}.$$

Further, the profit functions for weak and strong companies are

$${\pi }_1^{BB}=p_1^{BB}{D}_1^{BB}-k{n}_1^{B{B}^2};$$

$${\pi }_2^{BB}=p_2^{BB}{D}_2^{BB}-k{n}_2^{B{B}^2}.$$

Corollary 4.

For scenario BB, we have $p_1^{BB}<p_2^{BB}$, $n_1^{BB}<n_2^{BB}$; ${\pi }_2^{BB}<{\pi }_1^{BB}$.

Proof of Corollary 4.

See Appendix B. □

Corollary 4 shows that when both weak and strong brand companies adopt BCT, the selling price, the level of BCT investment, and the profit of the strong brand company are all higher than those of the weak brand company. The strong brand company can more effectively translate BCT adoption into consumer trust premiums by leveraging its existing market recognition, thereby further extending its brand value. In contrast, the weak brand is often constrained by limited financial resources when it comes to technology investment. For example, the FISCO BCOS blockchain platform developed by Microbank has supported over 500 application cases due to its financial-grade performance and mature ecosystem. However, small-and medium-sized enterprises (SMEs) typically need to rely on external partnerships to access such platforms [41]. By contrast, strong brands such as Walmart and Tencent are capable of independently building or leading consortium blockchain networks, enabling them to achieve higher marginal returns from their technology investments.

When two competitors adopt blockchain technology, the stronger brand, with its superior resources and market position, is able to reap greater benefits from the technology, widening the performance gap. The weaker brand should seek partnership opportunities, such as joining industry blockchain alliances, to reduce implementation costs and gain access to advanced technology. This creates a level playing field to some extent, but it does not eliminate the inherent advantages of stronger brands. Therefore, the weaker brand should focus on niche markets to enhance its competitiveness, even if there is a technological gap.

5. Analysis

So far, we have analyzed the optimal pricing and BCT adoption decisions of the two companies under four different cases. However, these represent only potential strategic choices. In this section, we investigate the conditions under which the weak and strong brand companies choose to adopt BCT, identify their equilibrium strategies, and examine the impact of brand awareness asymmetry on BCT adoption. In other words, we conduct a comparative analysis to determine which cases are more favorable for the two asymmetric companies, thereby helping them make more informed operational decisions.

5.1. Blockchain Investment

First, a company typically seeks to understand whether its competitor’s adoption of BCT should influence its own motivation to invest in the technology. We therefore analyze the investment decisions of both the weak and strong brand companies under various scenarios.

To gain more comprehensive insights, we adopt a numerical analysis approach. According to “Appendix A.2. The results of model BN”, “Appendix A.3. The results of model NB”, and “Appendix A.4. The results of model BB”, we only consider the scenario where $k>{\displaystyle \frac{4{\alpha }^2}{{\theta }^4-8{\theta }^2+16}}$. We set the sensitivity coefficient $\alpha =0.25$ to reflect that the demand is moderately sensitive to BCT adoption. In addition, similar sensitivity coefficients were set when Wang et al. [11] studied blockchain application issues. We establish the variables $k=0.1$ and $\theta =0.5$, following the parameter settings proposed by Pun, Swaminathan, and Hou [24] regarding BCT.

Proposition 1.

(1) The weak brand company invests more in BCT in Case BB than in Case BN; (2) the strong brand company invests more in BCT in Case BB than in Case NB; (3) the levels of BCT investment by weak and strong brands decrease as the difference in brand awareness increases.

Proof of Proposition 1.

See Appendix B. □

According to Proposition 1 (1) and Figure 4, the adoption of BCT by the strong brand company will increase the incentive for the weak brand company to invest in BCT. In practice, the strong brand company usually has higher market influence, and its BCT adoption can be regarded as a technological trend. For example, Walmart, a prominent retail brand, partnered with IBM to develop a BCT solution to track the journey of food from farm to table. This initiative by a strong brand encouraged the weak brand within the supply chain to adopt BCT for improved traceability and transparency, thereby enhancing their market competitiveness. Therefore, the weak brand company will follow the lead of the strong brand company to improve its market competitiveness. According to Proposition 1 (2) and Figure 4, BCT adoption by the weak brand will also increase the incentives for the strong brand to invest in BCT. When the weak brand starts to apply BCT, the strong brand may feel competitive pressure to maintain its market leading position, and thus has an incentive to increase its investment. According to Proposition 1 (3) and Figure 4, As the difference in brand awareness between weak and strong brands widens, the investment level of both parties in BCT shows a downward trend. The core reason for this is that the intensified market differentiation leads to a decrease in the marginal benefits of technology investment.

5.2. Equilibrium Analysis

Comparing the four cases, we analyze the optimal strategies for the weak and strong brand companies, their equilibrium strategies, and how the difference in brand awareness between weak and strong brand companies affects BCT adoption decisions.

Proposition 2.

When $0<\epsilon \le {\epsilon }_{11}$, BB is the optimal strategy for $F_1$; when ${\epsilon }_{11}<\epsilon \le {\epsilon }_{12}$, NB is the optimal strategy for $F_1$; and when $\epsilon >{\epsilon }_{12}$, NN is the optimal strategy for $F_1$. When $a$ increases, the area of BB decreases, and the areas of NB and NN increase. The expressions for ${\epsilon }_{11}$ and ${\epsilon }_{12}$ are shown in Appendix C.

Proof of Proposition 2.

See Appendix B. □

From Proposition 2, we know that there are three optimal strategies for the weak brand company: (1) When consumers are less concerned about BCT privacy issues (the bottom part of Figure 5), i.e., $0<\epsilon \le {\epsilon }_{11}$, the weak brand company obtains the highest profits under scenario BB. This is because when consumers are less concerned about privacy issues in BCT, they are more likely to view it as a technology that enhances transparency and security. As a result, the weak brand company is able to enhance the transparency of its products by adopting BCT, thereby attracting consumers who might otherwise be hesitant due to lower brand awareness. (2) When consumers’ concerns about BCT privacy issues are at a medium level (the middle part of Figure 5), i.e., ${\epsilon }_{11}<\epsilon \le {\epsilon }_{12}$, the weak brand company obtains the highest profit in scenario NB. This is because at a medium level of concern, consumers are cautious about the privacy issues of BCT. The strong brand company can more easily convince consumers that it can properly manage the data privacy issues brought by BCT due to its high brand awareness and market size accumulated over a long period of time. In contrast, if the weak brand company adopts BCT, it may find it difficult to fully enjoy the benefits of the technology due to its lower brand awareness. (3) The weak brand company obtains the highest profit under scenario NN when there are high levels of concern about privacy issues related to BCT (the upper part of Figure 5), i.e., $\epsilon >{\epsilon }_{12}$. At a high level of concern, the weak brand company may find it difficult to significantly improve its market competitiveness through BCT in this environment, as privacy concerns become an important factor that hinders consumer acceptance.

Interestingly, BN will not be an optimal strategy for the weak brand company, i.e., when the strong brand company does not adopt BCT, the weak brand company will not be willing to adopt BCT either. This is in line with the actual context. For one, BCT implementation requires a large amount of capital, and the weak brand company is weak in market competitiveness and limited in capital; therefore, it may not think that it is cost-effective to invest in BCT when the strong brand company has not yet applied it. Furthermore, the strong brand company usually dominates the market, while the weak brand company has limited awareness in the market and relatively low consumer trust. In this case, even if the weak brand company adopts BCT, it is difficult to gain wide recognition and acceptance in the market.

Proposition 3.

When $\left\{\begin{array}{l} 0<\epsilon \le {\epsilon }_{21} if 0<a\le a_{21}\\ 0<\epsilon \le {\epsilon }_{22} if a_{21}<a\le a_{22}\\ 0<\epsilon \le {\epsilon }_{23} if a_{22}<a\le 1\end{array}\right.$, BB is the optimal strategy for $F_2$; when ${\epsilon }_{21}<\epsilon \le {\epsilon }_{24}$ and $0<a\le a_{21}$, BN is the optimal strategy for $F_2$; when ${\epsilon }_{23}<\epsilon \le {\epsilon }_{25}$ and $a_{22}<a\le 1$, NB is the optimal strategy for $F_2$; and when $\left\{\begin{array}{l} \epsilon >{\epsilon }_{24} if 0<a\le a_{21}\\ \epsilon >{\epsilon }_{22} if a_{21}<a\le a_{22}\\ \epsilon >{\epsilon }_{25} if a_{22}<a\le 1\end{array}\right.$, NN is the optimal strategy for $F_2$. When $a$ increases, the areas of BB and BN decrease, while those of NB and NN increase. The expressions for ${\epsilon }_{21}$, ${\epsilon }_{22}$, ${\epsilon }_{23}$, ${\epsilon }_{24}$, ${\epsilon }_{25}$, $a_{21}$, and $a_{22}$ are shown in Appendix C.

Proof of Proposition 3.

See Appendix B. □

From Proposition 3, we know that, unlike the weak brand company, there are four cases of optimal strategies for the strong brand company. In addition, the optimal strategy choices for the strong brand company are not only related to consumers’ concerns about BCT privacy issues, but are also related to brand awareness. (1) As shown in Figure 5, in the bottom region, i.e., $\left\{\begin{array}{l} 0<\epsilon \le {\epsilon }_{21} if 0<a\le a_{21}\\ 0<\epsilon \le {\epsilon }_{22} if a_{21}<a\le a_{22}\\ 0<\epsilon \le {\epsilon }_{23} if a_{22}<a\le 1\end{array}\right.$, the strong brand company obtains the highest profit in Case BB. The positive effect of BCT in improving the transparency of product information is more significant in the case of low consumer privacy concerns, while in the case where privacy concerns are not a major barrier, both companies can jointly improve the transparency of the entire market when they both adopt BCT. (2) When $\left\{\begin{array}{l} \epsilon >{\epsilon }_{24} if 0<a\le a_{21}\\ \epsilon >{\epsilon }_{22} if a_{21}<a\le a_{22}\\ \epsilon >{\epsilon }_{25} if a_{22}<a\le 1\end{array}\right.$, the strong brand company earns the highest profit in Case NN. When consumer privacy concerns are high, the negative effects of BCT can significantly affect consumer demand, and therefore the strong brand company does not adopt BCT either. (3) When ${\epsilon }_{21}<\epsilon \le {\epsilon }_{24}$ and $0<a\le a_{21}$, the strong brand company obtains the highest profit under Case BN. If the application of BCT by the weak brand company does not pose a significant threat to the strong brand company, for example, due to the fact that it still has deficiencies in brand image and other aspects, then the strong brand may choose not to adopt BCT. (4) When ${\epsilon }_{23}<\epsilon \le {\epsilon }_{25}$ and $a_{22}<a\le 1$, the strong brand obtains the highest profit under Case NB. The strong brand expands its established market through BCT, a process that is difficult for the weak brand to replicate. In the case of moderate privacy concerns, the strong brand can balance privacy with traceability.

Proposition 4.

When $0<\epsilon \le {\epsilon }_{11}$, then BB is an equilibrium strategy; when ${\epsilon }_{23}<\epsilon \le {\epsilon }_{25}$ and $a_{22}<a\le 1$, then NB is an equilibrium strategy; when $\epsilon \ge {\epsilon }_{12}$, then NN is an equilibrium strategy; otherwise, there is no equilibrium. The expressions for ${\epsilon }_{11}$, ${\epsilon }_{12}$, ${\epsilon }_{23}$, ${\epsilon }_{25}$, and $a_{22}$ are shown in Appendix C.

Proof of Proposition 4.

See Appendix B. □

Proposition 4 shows that there are three equilibrium strategies for the weak brand company and the strong brand company. As shown in Figure 6, when $0<\epsilon \le {\epsilon }_{11}$, BB is an equilibrium strategy; when $\epsilon \ge {\epsilon }_{12}$, NN is an equilibrium strategy. That is to say, when consumers’ concerns about BCT-related privacy issues are low, both the weak brand company and the strong brand company will apply BCT. When consumers are less concerned about privacy issues associated with BCT, privacy breaches do not cause much dissatisfaction, and they are more likely to view BCT as a technology that enhances transparency and security, both the weak brand company and the strong brand company will adopt BCT. This is because it is an important way for the weak brand company to improve its competitiveness; while for the strong brand company, it can further consolidate its market position. On the other hand, consumers are very sensitive to the privacy leakages brought by BCT when they are more concerned about privacy issues. Although BCT can make product information more transparent, this advantage is not enough to cover consumers’ concerns about privacy leakage issues, so both weak and strong brand companies will not adopt BCT. However, NB will achieve an equilibrium when the level of consumer privacy concerns is moderate and the degree of difference in brand awareness for the weak brand company is low. Weak brand companies typically have relatively limited resources and capabilities due to their small market share and lower brand awareness, which often restricts their ability to invest in new technologies. This is consistent with prior research that suggests that companies with smaller market shares tend to face higher financial constraints when making investments in technology adoption [42,43]. In a market environment where privacy concerns are more sensitive, the weak brand company does not have sufficient resources and capabilities to invest in and address the privacy protection issues arising from the adoption of blockchain technology (BCT). Consequently, the weak brand company prefers not to adopt BCT, thus avoiding unnecessary cost investment and competitive pressure by maintaining the status quo. In contrast, the strong brand company is better equipped financially and technologically to invest in BCT, leveraging the technology to enhance consumer trust and transparency.

In addition, from Figure 5, Figure 6 and Figure 7 we know that as the BCT cost coefficient increases, only the range of NN increases, and the ranges of both NB and BB decrease. In other words, the difficulty of applying BCT for both weak and strong brand companies will increase with the increase in the BCT cost coefficient.

5.3. The Effect of the Coefficient of Price Competition on the Equilibrium Strategies

Market competition is complex and variable, and factors such as price competition and brand differences have an important impact on the BCT application strategies for weak and strong brand companies. Therefore, we discuss in a comprehensive way how price competition and brand differences affect the equilibrium strategies for weak and strong brand companies.

Proposition 5.

When $0<\epsilon \le {\epsilon }_{11}$, BB is an equilibrium strategy; when ${\epsilon }_{23}<\epsilon <{\epsilon }_{25}$ and $0<\theta \le {\theta }_1$, NB is an equilibrium strategy; when $\epsilon \ge {\epsilon }_{12}$, NN is an equilibrium strategy; otherwise, there is no equilibrium. The expressions for ${\epsilon }_{11}$, ${\epsilon }_{12}$, ${\epsilon }_{23}$, ${\epsilon }_{25}$, and ${\theta }_1$ are shown in Appendix C.

Proof of Proposition 5.

See Appendix B. □

From Proposition 5, we obtain two or three cases of equilibrium strategies. (1) As shown in Figure 8, when $a$ is small, there are still three equilibrium strategy cases: when $\epsilon$ is lower than a certain threshold, BB is the equilibrium strategy, and the range of the strategy BB is wider with $\theta$; when $\theta$ is low and $\epsilon$ is at the medium level, NB is the equilibrium strategy; and when $\theta$ is low and $\epsilon$ is higher than a certain threshold, NN is the equilibrium strategy. With low consumer privacy concerns, the positive impact of BCT on increasing the transparency of product information outweighs the negative impact due to privacy concerns. Therefore, regardless of whether a brand is a weak or strong, the application of BCT leads to an increase in demand and thus becomes an equilibrium strategy for companies. As the coefficient of price competition increases, the strategy interval for both weak and strong brand companies to adopt BCT is wider. This suggests that BCT is more attractive to both weak and strong brand companies as a means of increasing product differentiation and transparency in a market with high price competition. With a small price competition coefficient and high consumer privacy concerns, the negative impact of privacy concerns from BCT outweighs the positive impact of increased transparency. Therefore, in order to avoid losing consumers due to privacy concerns, weak and strong brand companies choose not to adopt BCT. With a small price competition coefficient and moderate consumer privacy concerns, the strong brand company is able to demonstrate the uniqueness and high quality of its products by adopting BCT to attract consumers due to its strong brand awareness and market position. However, for weak brand companies, it is difficult for the positive impact of BCT on increasing transparency to outweigh the negative impact of privacy concerns, and therefore they choose not to adopt BCT.

(2) As $a$ increases, the interval in which NB is the equilibrium strategy narrows. That is, when the degree of difference in brand awareness between the two brand companies shrinks, the NB strategy may provide an equilibrium strategy when the price competition coefficient is smaller. In addition, when $a$ exceeds a certain threshold, NB will no longer be an equilibrium strategy. This is because the competition between weak and strong brand companies becomes more intense as the difference in brand awareness shrinks, and at this time the weak brand company may lose its market share due to information opacity if it still does not adopt BCT.

6. Extension: Decision Sequence for Blockchain

In this section, we refine and adjust specific assumptions in the model to demonstrate the robustness of the findings in order to analyze the research questions more fully. We discuss the inconsistent sequence for applying BCT between weak and strong brand companies. In market environments with weak and strong brand companies, the strong brand company is usually in a stronger market position with higher brand awareness, consumer trust, and market share than the weak brand company. In general, the strong brand company has more influence and initiative in BCT application strategies. Therefore, we assume that the strong brand decides to use BCT first, and the weak brand decides to adopt BCT later. This assumption is realistic: For example, Walmart, a leading global retailer, developed a food traceability system using BCT in collaboration with IBM in 2016 [44]. After the successful implementation of BCT by the strong brand (Walmart), some weak brands started to follow suit.

Proposition 6.

When $\left\{\begin{array}{l} 0<\epsilon \le {\overline{\epsilon }}_1 if 0<a\le {\overline{a}}_1\\ 0<\epsilon \le {\overline{\epsilon }}_2 if {\overline{a}}_1<a<1\end{array}\right.$, BB is an equilibrium strategy; when ${\overline{\epsilon }}_3\le \epsilon \le {\overline{\epsilon }}_4$ and $0<a\le {\overline{a}}_2$, NB is an equilibrium strategy; when $\epsilon \ge {\overline{\epsilon }}_5$, NN is an equilibrium strategy; otherwise, there is no equilibrium. The expressions for ${\overline{a}}_1$, ${\overline{a}}_2$, ${\overline{\epsilon }}_1$, ${\overline{\epsilon }}_2$, ${\overline{\epsilon }}_3$, ${\overline{\epsilon }}_4$, and ${\overline{\epsilon }}_5$ are shown in Appendix C.

Proof of Proposition 6.

See Appendix B. □

Proposition 6 explores the equilibrium strategies for the weak and strong brand companies under different market power configurations, which can be seen in Figure 9. After comparing the previous findings, we find that the equilibrium strategy results between the two companies remain largely stable, but with some localized differences. Compared with the case of $k=0.05$ in Figure 9, the region of BB is significantly smaller. Specifically, the BB strategy becomes an equilibrium strategy when the specific condition $\left\{\begin{array}{l} 0<\epsilon \le {\overline{\epsilon }}_1 if 0<a\le {\overline{a}}_1\\ 0<\epsilon \le {\overline{\epsilon }}_2 if {\overline{a}}_1<a<1\end{array}\right.$ is satisfied; however, the optimal region for the BB strategy shrinks under this condition. This is due to the increased complexity and uncertainty of weak and strong brand companies sequentially deciding whether to adopt BCT or not. For one, the strong brand company takes the lead in adopting BCT and holds its market advantage; furthermore, the weak brand company faces greater market competition pressure and pressure to follow suit. The first-mover advantage of the strong brand company and the subsequent pressure on the weak brand company lead to a reduction in the optimal interval when both companies adopt BCT.

7. Conclusions

In e-commerce operation, BCT shows significant application potential; it can achieve product information transparency, enhance consumer trust, and bring new opportunities for market expansion. However, e-commerce enterprises face many challenges such as asymmetric competition and privacy protection when introducing BCT. In the present study, we construct an analytical model that includes a weak brand company and a strong brand company, which have different positions and advantages in the market. In the model, they all have the decision-making power to choose whether to apply BCT independently. Based on this model, the following research results and management insights can be summarized:

First, we compare the optimal price, BCT level, and profit of the two companies under different cases. We find that when neither the weak brand company nor the strong brand company adopts BCT or both of them adopt BCT, the strong brand has higher prices and profits. When both adopt BCT, the strong brand company has a higher BCT level. However, when only the weak brand company adopts BCT, the prices and profits of the weak brand company will be higher than those of the strong brand company if both the difference in brand awareness and the BCT cost coefficient are low; conversely, the prices and profits of the weak brand company will be lower than those of the strong brand company if both the difference in brand awareness and the BCT cost coefficient are high. This suggests that BCT should only be adopted if the costs and the difference in brand awareness are low; it should be avoided if both are high. Managers in strong brands should maintain BCT investment to maintain price and profit advantages when both types of company adopt the technology.

Second, in competition, when both weak and strong brand companies adopt BCT, the level of investment in BCT by both is higher than the level of investment when only one party adopts the technology. This phenomenon suggests that competitors’ decision on BCT drive resource investment; widespread adoption of BCT brings both competition pressure and collaboration opportunities.

Third, there are three scenarios for the equilibrium strategies of weak and strong brand companies: when the level of consumer privacy concerns is low, they both adopt BCT; when the degree of difference in brand awareness is high and the level of consumer privacy concerns is medium, only the strong brand company adopts BCT; and when the level of consumer privacy concerns is high, neither of them adopts BCT. Interestingly, the adoption of BCT by only the weak brand company is by no means a balanced strategy in a competitive scenario. Based on these results, weak brands should not adopt BCT unilaterally if the gap with their stronger rivals is large or the costs of BCT are high. Instead, they should partner with the strong brand to share BCT resources (e.g., joint traceability systems) and reduce costs. BCT should only be adopted if the costs and the difference in brand awareness are low in order to surpass the strong brand in terms of price/profit. Furthermore, strong brands should use BCT to widen their advantages. Higher BCT levels should be adopted compared to weak brands in scenarios where concerns surrounding privacy are low/medium, and investment in BCT should be increased if weak brands adopt it to maintain competitiveness.

In addition, we analyze the effect of the price competition coefficient on the equilibrium results. It is found that when the difference in brand awareness in the e-commerce market is more significant and the price competition coefficient is relatively low, companies have a wider choice of BCT application strategies; meanwhile, when the difference in brand awareness is low and the price competition coefficient is high, companies are more limited in their strategic choices and often can only choose to either apply BCT or not. This suggests that in the extremely competitive e-commerce market, companies need to weigh the delicate relationship between price competition and market share more carefully. Finally, the model extension in our study allowed a sequential order of BCT decisions to be determined for different companies. After the analysis, we find that the equilibrium strategies of weak and strong brand companies still exist in three different scenarios, but the area of the optimal intervals shrinks when both of them apply BCT at the same time. This result suggests that e-commerce companies should carefully evaluate the order in which they make BCT application decisions as a way to maximize their own benefits.

This model has several limitations that could be addressed in future research. First, while we currently examine inter-firm asymmetry based on e-commerce brand awareness, future research could explore this from the perspective of information asymmetry. Second, this model uses a linear, deterministic demand function, but future research could examine the impact of adopting BCT under uncertain market demand scenarios. Third, we focus on the fundamental properties of BCT, such as enhancing consumer purchase intention and protecting privacy. Future research could explore other BCT features, such as data immutability and decentralization, to more fully understand their impact in e-commerce.