Using blockchain to combat multiple types of counterfeits: Adoption strategy and value analysis

Abstract

This study examines manufacturers’ (i.e., brand-name firms’) incentives to adopt blockchain technology-supported (BTS) platforms in markets where deceptive and non-deceptive counterfeits coexist. It is among the first to analyze the adoption of BTS platforms in the context of a complex counterfeit market. First, the study integrates consumers’ perceived risk concerning product authenticity (CPRPA) into a signaling game model to examine the impacts of quality information asymmetry on market dynamics. The findings reveal that even though heightened CPRPA incentivizes deceptive counterfeiters to engage in non-deceptive sales (disclosing the true quality of their products), quality information asymmetry still exerts adverse impacts on both manufacturers and consumers, while simultaneously benefiting all counterfeiters. Second, the study evaluates manufacturers’ optimal strategies for adopting BTS platforms and the value derived from such adoption. The results suggest that CPRPA exerts a non-monotonic impact on manufacturers’ optimal decisions to adopt BTS platforms, and manufacturers may never benefit from such adoption in the presence of deceptive sales. Although higher adoption costs diminish the effectiveness of BTS platforms, such adoption can reduce illegal profits and improve consumer surplus. However, the adoption of BTS platforms may inadvertently increase demand for deceptive counterfeits. Consequently, BTS platforms are found to exert a more marked effect in combating non-deceptive counterfeits than deceptive ones. Additionally, BTS platform adoption contributes more to improving consumer surplus than to combating counterfeits.

Keywords

Deceptive Counterfeits Non-Deceptive Counterfeits Blockchain Technology-Supported Platform Quality Information Asymmetry Signaling Game Consumers’ Perceived Risk Concerning Product Authenticity

1. Introduction

Under weak intellectual property rights (IPR) protection and accelerating economic globalization, the counterfeit trade has flourished, inflicting considerable losses on the global economy (Zhao, 2006). The Organization for Economic Co-operation and Development (OECD) reported that counterfeit trade accounted for 2.5% of global trade in 2013, rising to 3.3% by 2016, and that global economic losses caused by counterfeiting were estimated to be as high as $1.82 trillion in 2020.¹ Counterfeiting now infiltrates nearly all industries. In the wine industry, fake versions of brands like DOC Bolgheri Sassicaia are produced for high profits.² Similarly, counterfeit golf equipment bearing brand names such as Titleist and PXG is rampant,³ as are the counterfeit Nike and Fendi in the fashion industry.⁴

Counterfeits are broadly divided into two categories: non-deceptive and deceptive counterfeits (Grossman and Shapiro, 1988). Non-deceptive counterfeits are visibly distinct from authentic products in appearance, allowing consumers to identify them before making a purchase. In contrast, deceptive counterfeits are meticulously crafted to replicate the look, packaging, logos, and even minute details of authentic products, making them indistinguishable from the authentic ones. Take the Cartier Love ring as an example (as illustrated in Figure 1), non-deceptive fake rings differ significantly in color from authentic rings, while deceptive fake rings are indistinguishable from authentic rings in appearance. In general, counterfeits are sold in grey markets at prices lower than those of authentic products (Wilcox et al., 2009). However, due to their high physical similarity to authentic products, deceptive counterfeits may infiltrate legitimate sales channels via fraudulent authorization and be sold at the same price as authentic products (Cho et al., 2015). For example, Louis Vuitton (LV) specialty stores were once accused of selling counterfeit handbags.⁵ The strong indistinguishability of deceptive counterfeits gives rise to quality information asymmetry, thereby posing a severe threat to the interests of brand-name firms and consumers.

Figure 1.

Three versions of the cartier love ring: authentic one (left), deceptive counterfeit (top right), non-deceptive counterfeit (bottom right).

To eliminate quality information asymmetry, brand-name firms employ full-time investigators and legal teams, redesign products to make counterfeiting harder (Green and Smith, 2002), and use technologies like RFID for product identification (Choi et al., 2019a). However, despite the investment of copious energy and money, deceptive counterfeits remain prevalent, especially in online markets. For example, Apple complained that 90% of Apple chargers sold on Amazon were counterfeit.⁶

Blockchain technology, known for its decentralization, transparency, traceability and anti-tampering features (Chod et al., 2020; Wang et al., 2021; Whitaker and Kraussl, 2020), offers a promising solution in anti-counterfeiting technologies (Babich and Hilary, 2020; Guha and Kumar, 2018; Hastig and Sodhi, 2020). These inherent advantages have stimulated the formation and commercialization of many blockchain technology-supported (BTS) platforms. As a blockchain technology provider, the BTS platform connects all authorized firms to a transparent shared database. Authorized firms can securely upload product datasets to the blockchain ecosystem, where records cannot be unilaterally altered. When brand-name firms adopt BTS platforms, each authentic product will be assigned an exclusive ID. Through QR codes, consumers can access detailed information about an authentic product. As counterfeiters are unable to provide such verified details, the adoption of BTS platforms achieves the quality disclosure effect (Shen et al., 2021b). The adoption of BTS platforms has gained traction in recent years. For example, in 2018, IBM developed the Food Trust and TrustChain platforms using the Hyperledger Fabric framework. These platforms have been employed by organizations such as the Richline Group to trace the origins of diamonds and precious metals,⁷ and by food manufacturers such as Nestlé and Unilever to improve food safety.⁸ Additionally, e-retailers have developed proprietary BTS platforms to collaborate with brand-name firms. For example, JD.COM's JD Chain, introduced in 2018, has been adopted by over 1900 brands, including Wyeth, Moutai, and Yili, to ensure product information traceability across diverse sectors, such as luxury goods, wine, and pharmaceuticals.

While the adoption of BTS platforms can achieve the quality disclosure effect, many brand-name firms remain hesitant to embrace such platforms. In April 2021, luxury brands Prada and Cartier launched the BTS platform AURA to improve the transparency and traceability of luxury products, inviting the entire industry to join AURA, which they termed “a new luxury era” enabled by blockchain technology.⁹ Despite these efforts, only a few brands, such as fashion group OTB and Hublot, have joined the platform. The reluctance stems primarily from two key factors. On the one hand, high operational costs pose a significant obstacle to brand-name firms’ adoption of BTS platforms for anti-counterfeiting purposes (Biswas and Gupta, 2019). On the other hand, deceptive counterfeiters’ behaviors significantly affect brand-name firms’ decisions. Specifically, as consumers’ willingness to purchase authentic products may decrease due to concerns about being deceived, deceptive counterfeiters may choose to sell their products in grey markets at a price lower than that of authentic products. Such a non-deceptive sales strategy renders the adoption of BTS platforms unable to achieve the quality disclosure effect, thereby potentially reducing the willingness of brand-name firms to adopt such platforms.

For many brand-name products, the coexistence of deceptive and non-deceptive counterfeits is a common phenomenon. Despite this prevalence, however, existing anti-counterfeiting research has largely neglected this practical scenario. Notably, the costs associated with adopting BTS platforms may put brand-name firms at a distinct disadvantage in price competition, inadvertently benefiting non-deceptive counterfeiters. Consequently, although the primary goal of adopting BTS platforms is to deter deceptive counterfeiters, its implications for non-deceptive counterfeiters also merit in-depth exploration. Additionally, analyzing the impact of quality information asymmetry between deceptive counterfeiters and consumers is crucial for brand-name firms to formulate strategic decisions. Therefore, this study seeks to address the following research questions:

RQ1: How does quality information asymmetry alter pricing, demand, and profits when deceptive and non-deceptive counterfeits coexist?

RQ2: Under what conditions does a manufacturer adopt a BTS platform to combat both types of counterfeits, and how does such adoption affect prices, profits, and consumer surplus?

This research employs game-theoretic analytical models to answer these questions. We model a market comprising three players: a manufacturer, a deceptive counterfeiter, and a non-deceptive counterfeiter. Consumers can reliably identify non-deceptive counterfeits based on appearance, but they may mistake deceptive counterfeits for authentic products. This insufficiency of consumer identification ability leads to quality information asymmetry between the deceptive counterfeiter and consumers, thereby incentivizing the former to engage in deceptive sales by sending a false price signal (setting the same price as the authentic product). Consumers’ perceived risk concerning product authenticity (CPRPA) triggered by deceptive sales will reduce the profit of the deceptive counterfeiter. Consequently, the deceptive counterfeiter may engage in non-deceptive sales under quality information asymmetry.

First, we develop a signaling game model to analyze the impacts of quality information asymmetry. Our results indicate that even though the manufacturer can employ a high-price strategy to induce the deceptive counterfeiter to engage in non-deceptive sales, quality information asymmetry still exerts adverse impacts on the market. On the one hand, quality information asymmetry drives consumers toward counterfeit purchases, thereby reducing demand for authentic products and potentially undermining the manufacturer's profit. On the other hand, quality information asymmetry drives up the prices of two types of counterfeits, resulting in higher profits for both counterfeiters but diminishing consumer surplus.

Then, we develop a Stackelberg game model to analyze the manufacturer's optimal strategy for adopting the BTS platform and examine the value of such adoption. The results show that because a rise in CPRPA under quality information asymmetry does not always have a detrimental effect on the manufacturer's profit, CPRPA exerts a non-monotonic impact on the manufacturer's optimal strategy for adopting the BTS platform. Since the manufacturer may benefit from quality information asymmetry, adopting the BTS platform is not always an optimal strategy. We demonstrate that higher adoption costs incentivize the manufacturer to raise prices, which deteriorates the effectiveness of BTS platform adoption. Nevertheless, the adoption of the BTS platform compels counterfeiters to lower their prices, improving consumer surplus and reducing counterfeiters’ profits. When the true quality is disclosed, deceptive counterfeits will cannibalize demand for non-deceptive counterfeits by virtue of their relative quality advantages. Hence, the adoption of the BTS platform may lead to an increase in the demand for deceptive counterfeits. Our theoretical findings highlight that the potential downside of adopting the BTS platform lies in stimulating the demand for deceptive counterfeits and reveal that such adoption contributes more to improving consumer surplus than combating counterfeits. The results also show that adopting the BTS platform is more effective in combating non-deceptive counterfeits than deceptive ones. Finally, we check the robustness of our key findings by exploring extensions.

This paper makes four contributions. First, we advance the counterfeiting and information disclosure literature by distinguishing between deceptive and non-deceptive counterfeits within a unified analytical framework. While prior research typically treats counterfeit quality uncertainty as homogeneous, we show that heterogeneity in deception fundamentally alters equilibrium pricing, demand allocation, and profit outcomes. This distinction generates market responses that cannot be obtained from models with a single counterfeit type. Second, our results demonstrate that when deceptive counterfeiters engage in deceptive sales, manufacturers may derive benefits from mitigating quality-based price competition. Consequently, we propose that in the presence of deceptive sales, brand-name firms do not necessarily need to adopt BTS platforms even if the adoption costs are quite low. Third, our results indicate that, in the absence of deceptive sales, quality information asymmetry still negatively impacts manufacturers’ profits. Consequently, we propose that brand-name firms should not limit their adoption of BTS platforms solely to scenarios with deceptive sales. Fourth, our results indicate that CPRPA exerts a non-monotonic impact on manufacturers’ optimal decisions to adopt BTS platforms. Consequently, we propose that brand-name firms should not only investigate the sales strategies of deceptive counterfeiters but also consider consumers’ attitudes toward counterfeiting when deciding whether to adopt BTS platforms. From a theoretical perspective, this study highlights the importance of distinguishing among counterfeit types when analyzing information disclosure and authentication technologies. Our results show that deception heterogeneity, rather than counterfeit presence per se, drives adoption incentives and welfare outcomes. This insight suggests that models assuming homogeneous counterfeit quality may systematically mischaracterize the effects of transparency-enhancing technologies.

The remainder of this paper is organized into six sections. Section 2 provides a review of the related literature. Section 3 outlines the notations and research assumptions. Section 4 analyzes the impacts of quality information asymmetry. Section 5 discusses the manufacturer's optimal strategy for adopting the BTS platform and analyzes the value of such adoption. Section 6 examines the robustness of our key findings under extended models. Section 7 summarizes the key findings, suggests the managerial implications, and provides future research directions. All equilibrium outcomes and proofs are provided in the Appendix.

2. Literature review

Earlier studies on counterfeits are predominantly empirical and descriptive. Bloch et al. (1993) conduct a field experiment to examine consumers’ willingness to knowingly purchase counterfeits. Clifford et al. (1996) outline ten actions that brand-name firms could take to combat counterfeiting, including advertising, legislation, and surveillance. Tom et al. (1998) identify that consumers’ motivations for purchasing counterfeits are driven by their shrewdness or economic concerns. Qian (2008, 2014a) empirically investigates the impact of counterfeits on brand-name firms in the shoe market.

In recent years, a growing body of literature has employed analytical approaches to examine the effects of counterfeiting on brand-name firms and their corresponding strategies. One stream of the literature focuses on non-deceptive counterfeits. Zhang et al. (2012) find that the presence of non-deceptive counterfeits reduces the price of authentic products and the brand-name firm's profit, but leads to increased sales of authentic products. Lahiri and Dey (2013) investigate how non-deceptive counterfeits affect manufacturers’ investment decisions in product quality. They show that the presence of non-deceptive counterfeits increases manufacturers’ motivation to improve product quality. Pun and Deyong (2017) develop a two-period game model to examine the competition between a manufacturer and a non-deceptive counterfeiter. They show that the manufacturer may benefit more by offering a lower-quality product. Gao et al. (2017a) develop a model that incorporates two consumer utilities and product attributes, and discuss the blocking strategy against non-deceptive counterfeiters. Gao et al. (2017b) find that the entry of non-deceptive counterfeits improves consumer surplus and social welfare. Yi et al. (2020) find that manufacturers are more inclined to incentivize retailers to undertake anti-counterfeiting measures than to implement such actions themselves. Hou et al. (2020) investigate how luxury manufacturers launch a fighter brand to combat non-deceptive counterfeits.

Another stream of the literature focuses on deceptive counterfeits. Qian (2014b) demonstrates that manufacturers investing in self-enforcement can incentivize deceptive counterfeiters to adopt non-deceptive sales strategies. Qian et al. (2015) develop a model in which product quality has both searchable and experiential attributes, and discuss an authentic firm's quality improvement strategy for dealing with deceptive counterfeits. They reveal that authentic firms should invest more in improving the searchable quality of authentic products. Cho et al. (2015) compare the effectiveness of anti-counterfeiting strategies to different types of counterfeits (deceptive and non-deceptive). They find that effective anti-counterfeiting strategies for dealing with non-deceptive counterfeits may not work well against deceptive counterfeits. Zhang and Zhang (2015) discuss the optimal channel choice of a brand-name firm in a market with deceptive counterfeits. Gao (2018) finds that the adoption of overt anti-counterfeiting technologies may lead to more counterfeit drug sales. Sun et al. (2020) find that the platform does not always exert the maximum effort to combat deceptive counterfeiters.

While the aforementioned studies offer valuable insights, a significant research gap remains in understanding the variability in sales strategies of deceptive counterfeiters. Much of the literature, excluding Qian (2014b) and Qian et al. (2015), assumes that deceptive counterfeiters always set their prices equal to those of authentic products, overlooking the complexity of their pricing strategies. Under the influence of consumers’ perceived risk concerning product authenticity, sending false price signals to deceive consumers may not maximize profits, which makes deceptive counterfeiters willing to choose low-price strategies. Following Qian (2014b) and Qian et al. (2015), we examine how deceptive counterfeiters develop sales strategies under the price signal effect (Milgrom and Roberts, 1986; Spence, 1978). However, our paper differs from theirs in three key perspectives. First, they consider the market setting in which there is only one type of counterfeit (deceptive counterfeit), while we consider a more complex market setting in which two types of counterfeits coexist. Second, we incorporate consumers’ perceived risk concerning product authenticity into a signaling game model to examine the deceptive counterfeiter's sales strategy. Third, they focus on adopting quality innovations to combat deceptive counterfeits, while we focus on adopting the BTS platform.

Blockchain technology has emerged as a promising solution to information asymmetry, gaining widespread attention within academia (Choi et al., 2020). Similar to information disclosure and reputation mechanisms that help market participants infer product and seller quality under conditions of uncertainty (Deng et al., 2023; Tripathi et al., 2022), blockchain enables consumers to verify product provenance and authenticity. Studies have explored its application across industries such as logistics (Choi et al., 2019b; Zhong et al., 2021), food (Wu et al., 2021), secondhand products (Shen et al., 2020), and medicine (Niu et al., 2021). In the context of counterfeiting, Shen et al. (2021a) demonstrate that BTS platforms are effective in combating deceptive counterfeits when there exists a low number of expert consumers. Pun et al. (2021) indicate that a brand-name firm should adopt blockchain technology to combat deceptive counterfeits when either counterfeit quality or consumers’ trust in market products is moderate. Unlike Shen et al. (2021a) and Pun et al. (2021), we examine the adoption of the BTS platform in a more complex market setting characterized by the coexistence of deceptive and non-deceptive counterfeits. Furthermore, we incorporate consumers’ perceived risk concerning product authenticity into the signaling game model to analyze the sales strategies of deceptive counterfeiters and the impacts of quality information asymmetry. Regarding research content, we discuss the value of adopting the BTS platform, addressing a gap in the literature. A comparison of analytical studies on counterfeits is presented in Table 1.

Table 1.
A comparison of analytical studies on counterfeits.

Paper Deceptive counterfeit Non-deceptive counterfeit Price signal Blockchain

Zhang et al. (2012); Lahiri and Dey (2013); Gao et al. (2017a); Gao et al. (2017b); Yi et al. (2020); Hou et al. (2020). √

Zhang and Zhang. (2015); Pun and Deyong (2017); Gao (2018); Sun et al. (2020). √

Cho et al. (2015). √ √

Qian (2014b); Qian et al. (2015). √ √ √

Shen et al. (2021b). √ √

Pun et al. (2021). √ √ √

Our paper √ √ √ √

Paper	Deceptive counterfeit	Non-deceptive counterfeit	Price signal	Blockchain
Zhang et al. (2012); Lahiri and Dey (2013); Gao et al. (2017a); Gao et al. (2017b); Yi et al. (2020); Hou et al. (2020).		√
Zhang and Zhang. (2015); Pun and Deyong (2017); Gao (2018); Sun et al. (2020).	√
Cho et al. (2015).	√	√
Qian (2014b); Qian et al. (2015).	√	√	√
Shen et al. (2021b).	√			√
Pun et al. (2021).	√		√	√
Our paper	√	√	√	√

3. Problem description

We consider a market consisting of a manufacturer (firm A), a deceptive counterfeiter (firm D), and a non-deceptive counterfeiter (firm N). Both counterfeiters sell counterfeits with lower quality than authentic products (counterfeit D and counterfeit N). Based on appearance, consumers can identify counterfeit N but may mistake counterfeit D for the authentic one. Hence, the insufficiency of consumer identification ability leads to quality information asymmetry between firm D and consumers, and incentivizes firm D to deceive consumers. Due to its non-deceptive nature, consumers can ascertain the true quality of counterfeit N. However, consumers may be unable to ascertain the true quality of counterfeit D, depending on whether firm D engages in non-deceptive or deceptive sales. Specifically, if firm D sets a price below that of firm A and explicitly informs consumers that its products are fake (i.e., engaging in non-deceptive sales), consumers can ascertain its product quality; if firm D sets the same price as firm A and claims that its products are genuine (i.e., engaging in deceptive sales), consumers cannot ascertain its product quality, and may purchase counterfeit D as an authentic one. Following the extant literature (Qian, 2014b; Sun et al., 2020), we assume that the quality of both types of counterfeits is exogenously given to ensure mathematical tractability.

We adopt the vertical differentiation model to describe consumers’ utility towards the product. Let θ denote the consumers’ pnce for product quality, which is uniformly distributed over [0,1], and $p_{i}$ denote the price of firm i's product, $i \in {A, D, N}$ . We normalize the true quality of the authentic product $q_{A}$ to 1, and use $q_{D}$ and $q_{N}$ to denote the true quality of counterfeit D and counterfeit N, where $0 < q_{N} < q_{D} < 1$ captures the difference in deception ability between the two types of counterfeits (i.e., counterfeit N is not as capable of deceiving consumers as counterfeit D). When firm D engages in non-deceptive sales, consumers can ascertain the true quality of all types of products. Correspondingly, the utility of a type-θ consumer from purchasing firm i's product at price $p_{i}$ is expressed as $U_{i} (θ) = q_{i} θ - p_{i}$ . When firm D engages in deceptive sales, consumers cannot ascertain the true quality of counterfeit D and can only know the existence of deceptive behavior in the market. Considering that consumers may worry about being cheated (Rachbini, 2018), our study models consumers’ perceived risk concerning product authenticity (CPRPA) $ε$ to capture consumer's utility loss under deception risk. We assume $ε$ is homogeneous across all consumers to capture general market-level perceptions of deception risk. Note that $ε$ is determined by consumers’ subjective perception of the likelihood of deception and is thus independent of the actual probability of deception. Correspondingly, the utility of a type-θ consumer from purchasing either the authentic product or counterfeit D is expressed as $U_{i} (θ) = θ - p_{A} - ε$ , $i \in {A, D}$ , where $ε < \bar{ε} = \frac{{q_{D}}^{2} {q_{N}}^{2} - 4 q_{D} q_{N} + 2 q_{D} + {q_{N}}^{2}}{2 (2 - q_{N}) (2 q_{D} - q_{N} - {q_{D}}^{2})}$ ensures that firm D has an incentive to engage in deceptive sales; the utility of a type-θ consumer from purchasing counterfeit N is expressed as $U_{N} (θ) = q_{N} θ - p_{N}$ .

To eliminate quality information asymmetry, firm A can choose to adopt the BTS platform. Due to the inability to provide traceable product information, firm D can only engage in non-deceptive sales when the BTS platform is adopted. In practice, the adoption cost for the BTS platform is customized based on the firm's needs. For example, Food Trust charges firms based on the number of traceability codes.¹⁰ Following the industry practice above, we set the adoption cost for the BTS platform c as marginal, where $c < \bar{c} = \frac{(1 - q_{D}) (2 q_{D} - q_{N} - q_{N} q_{D})}{2 q_{D} - q_{N} - {q_{D}}^{2}}$ ensures that the demand for the authentic product is positive. For mathematical tractability, we assume that the marginal production cost of all types of products is zero (we relax this assumption in Section 6 by incorporating differences in marginal production costs).

Table 2 shows the major notation used in our paper.

Table 2.
Major notation used in our paper.

Notation Definition

i Manufacturer (A), Deceptive counterfeiter (D), Non-deceptive counterfeiter (N)

$q_{i}$ True quality of the firm i's product, where $0 < q_{N} < q_{D} < q_{A} = 1$

$p_{i}$ Price of the firm i's product, where $i \in {A, D, N}$

θ Consumers’ preference for product quality, where $\sim U [0, 1]$

$ε$ Consumers’ perceived risk concerning product authenticity (CPRPA), where $ε \in [0, \bar{ε})$

$c$ Marginal adoption cost for the BTS platform, where $c \in (0, \bar{c})$

$D_{i}$ Demand for the firm i's product, where $i \in {A, D, N}$

$π_{i}$ Profit of the firm i, where $i \in {A, D, N}$

$C S$ Consumer surplus

Notation	Definition
i	Manufacturer (A), Deceptive counterfeiter (D), Non-deceptive counterfeiter (N)
$q_{i}$	True quality of the firm i's product, where $0 < q_{N} < q_{D} < q_{A} = 1$
$p_{i}$	Price of the firm i's product, where $i \in {A, D, N}$
θ	Consumers’ preference for product quality, where $\sim U [0, 1]$
$ε$	Consumers’ perceived risk concerning product authenticity (CPRPA), where $ε \in [0, \bar{ε})$
$c$	Marginal adoption cost for the BTS platform, where $c \in (0, \bar{c})$
$D_{i}$	Demand for the firm i's product, where $i \in {A, D, N}$
$π_{i}$	Profit of the firm i, where $i \in {A, D, N}$
$C S$	Consumer surplus

As shown in Figure 2, the sequence of events is as follows.

Stage 1: Considering the potential sales strategy of firm D, firm A decides whether to adopt the BTS platform to eliminate quality information asymmetry, and sets the price of the authentic product (i.e., $p_{A}$ ).

Stage 2: The two counterfeiters set prices for their respective products (i.e., $p_{D}$ and $p_{N}$ ). Unlike firm N, firm D can choose to engage in deceptive sales if firm A does not adopt the BTS platform. The popular press (e.g., finance.china.com.cn) has reported that many indistinguishable counterfeits stem from OEMs (original equipment manufacturers). Considering that these OEMs learn about the design and materials (technical know-how) used earlier than ordinary counterfeiters, our paper assumes that firm D enters the market earlier than firm N, and sets its price before firm N does (we examine an extension where two counterfeiters enter the market simultaneously in Section 6).

Stage 3: After observing the prices of all types of products, each consumer chooses to purchase either an authentic product, a counterfeit, or nothing. If firm A does not adopt the BTS platform and firm D chooses to engage in deceptive sales, consumers may unknowingly purchase counterfeit D as the authentic one.

Figure 2.

The sequence of events.

4. Without the BTS platform

In this section, we first derive equilibrium outcomes for the scenario without the BTS platform, and then analyze the impacts of quality information asymmetry.

4.1. Equilibrium outcomes under the scenario without the BTS platform

Under quality information asymmetry, firm D may send a false price signal to deceive consumers. Since price plays a role in conveying quality information, there is a signaling game between firm A and firm D. As with many signaling models (Wang et al., 2019; Zhen et al., 2019), two types of equilibrium are possible. Specifically, if firm D chooses to engage in non-deceptive (deceptive) sales, there exists a separating (pooling) equilibrium. We use superscripts “SE” and “PE” to denote the separating and pooling equilibria, respectively.

The consumer's choice behavior is driven by individual rationality (IR) and incentive compatibility (IC) constraints. Specifically, the IR constraint ensures that a consumer obtains non-negative utility from purchasing, and the IC constraint ensures that the consumer's product choice is optimal given the observed product qualities and prices. When firm D engages in non-deceptive sales, consumers can get to know the true quality of all types of products. The quality preference of the consumer who is indifferent between purchasing counterfeit N and nothing satisfies $q_{N} θ - p_{N} = 0 \Leftrightarrow θ = θ_{1}^{S E} = \frac{p_{N}}{q_{N}}$ . Therefore, the IR constraint requires that the quality preference of the consumer who purchases a product satisfies $θ > θ_{1}^{S E}$ . The quality preference of the consumer who is indifferent between purchasing counterfeit D and counterfeit N (between purchasing the authentic product and counterfeit D) satisfies $q_{D} θ - p_{D} = q_{N} θ - p_{N} \Leftrightarrow θ = θ_{2}^{S E} = \frac{p_{D} - p_{N}}{q_{D} - q_{N}}$ ( $θ - p_{A} = q_{D} θ - p_{D} \Leftrightarrow θ = θ_{3}^{S E} = \frac{p_{A} - p_{D}}{1 - q_{D}}$ ). Therefore, the IC constraint requires that the quality preference of the consumer who prefers to buy the authentic product (counterfeit N) satisfies $θ \geq θ_{3}^{S E} (θ < θ_{2}^{S E})$ , and that consumers with quality preferences in the interval $[θ_{2}^{S E}, θ_{3}^{S E})$ prefer to buy counterfeit D. Based on the IR and IC constraints above, we can derive consumers’ purchase decisions based on quality preferences in the separating equilibrium, as illustrated in Figure 3.

Figure 3.

Consumers’ purchase decisions based on quality preferences in the separating equilibrium.

Based on Figure 3, we can derive that the demand for three types of products are $D_{A}^{S E} = \int_{θ_{3}^{S E}}^{1} 1 d θ$ , $D_{D}^{S E} = \int_{θ_{2}^{S E}}^{θ_{3}^{S E}} 1 d θ$ , and $D_{N}^{S E} = \int_{θ_{1}^{S E}}^{θ_{2}^{S E}} 1 d θ$ , respectively. The profit functions of three firms in the separating equilibrium can be expressed as follows:

\begin{aligned} π_{A}^{S E} & = p_{A} D_{A}^{S E} = p_{A} (1 - \frac{p_{A} - p_{D}}{1 - q_{D}}) \end{aligned}

(1)

\begin{aligned} π_{D}^{S E} & = p_{D} D_{D}^{S E} = p_{D} (\frac{p_{A} - p_{D}}{1 - q_{D}} - \frac{p_{D} - p_{N}}{q_{D} - q_{N}}) \end{aligned}

(2)

\begin{aligned} π_{N}^{S E} & = p_{N} D_{N}^{S E} = p_{N} (\frac{p_{D} - p_{N}}{q_{D} - q_{N}} - \frac{p_{N}}{q_{N}}) \end{aligned}

(3)

When firm D engages in deceptive sales, consumers cannot ascertain the true quality of counterfeit D and may unknowingly purchase it as the authentic product. Given that real-world consumers commonly scrutinize product details (e.g., testing the flexibility of handbags), our paper assumes that firm D may fail to deceive consumers. Thus, the likelihood of consumers being deceived depends on whether they encounter firm D and fail to identify counterfeit D (the two events are independent because consumers’ ability to identify deceptive counterfeits is irrelevant to product distribution). Given the market structure constructed in this paper (comprising a manufacturer and a deceptive counterfeiter), we assume that consumers will randomly choose between the products of firm A and firm D with equal probability when intending to purchase authentic products. This assumption has also been adopted in Qian (2014b) and Sun et al. (2020) (we relax this assumption in Section 6 by incorporating the number of sales points). We set the probability of consumers mistaking counterfeit D for the authentic one as $q_{D}$ , which captures the fact that the likelihood of consumers being deceived decreases as the true quality of deceptive counterfeits decreases. Based on the basic assumptions above, the probability of consumers unknowingly purchasing counterfeit D can be expressed as $φ = P$ (encountering firm D) $\times P$ (failing to identify the counterfeit) $= \frac{1}{2} \times q_{D} = \frac{q_{D}}{2}$ . The quality preference of the consumer who is indifferent between purchasing counterfeit N and nothing (between purchasing the authentic product and counterfeit N) satisfies $q_{N} θ - p_{N} = 0 \Leftrightarrow θ = θ_{1}^{P E} = p_{N} / q_{N} (θ - p_{A} - ε = q_{N} θ - p_{N} \Leftrightarrow θ = θ_{2}^{P E} = ((p_{A} - p_{N} + ε) / (1 - q_{N})))$ . Therefore, the IR constraint requires that the quality preference of the consumer who purchases a product satisfies $θ > θ_{1}^{P E}$ ; the IC constraint requires that the quality preference of the consumer who prefers to buy counterfeit N (buy the authentic product and counterfeit D) satisfies $θ < θ_{2}^{P E} (θ \geq θ_{2}^{P E})$ . Based on the IR and IC constraints above, we can derive consumers’ purchase decisions based on quality preferences in the pooling equilibrium, as illustrated in Figure 4.

Based on Figure 4, we can derive that the demand for three types of products are $D_{A}^{P E} = (1 - φ) \int_{θ_{2}^{P E}}^{1} 1 d θ$ , $D_{D}^{P E} = φ \int_{θ_{2}^{P E}}^{1} 1 d θ,$ and $D_{N}^{P E} = \int_{θ_{1}^{P E}}^{θ_{2}^{P E}} 1 d θ$ , respectively. The profit functions of three firms in the pooling equilibrium can be expressed as follows:

\begin{aligned} π_{A}^{P E} & = p_{A} D_{A}^{P E} = p_{A} (1 - \frac{p_{A} - p_{N} + ε}{1 - q_{N}}) \frac{2 - q_{D}}{2} \end{aligned}

(4)

\begin{aligned} π_{D}^{P E} & = p_{D} D_{D}^{P E} = p_{A} (1 - \frac{p_{A} - p_{N} + ε}{1 - q_{N}}) \frac{q_{D}}{2} \end{aligned}

(5)

\begin{aligned} π_{N}^{P E} & = p_{N} D_{N}^{P E} = p_{N} (\frac{p_{A} - p_{N} + ε}{1 - q_{N}} - \frac{p_{N}}{q_{N}}) \end{aligned}

(6)

Under the scenario without the BTS platform, firm D engages in non-deceptive or deceptive sales depending on which sales strategy would yield greater profits. Firm D will choose to engage in non-deceptive sales only if the resulting profit is higher than that from engaging in deceptive sales. Therefore, a separating equilibrium must satisfy:

\begin{matrix} max_{p_{D}} π_{D}^{S E} (p_{D} \neq p_{A}) \geq π_{D}^{P E} (p_{D} = p_{A}) \end{matrix}

The constraint above can be converted to $p_{A} \geq \bar{p_{A}}$ (the derivation process and the expression of $\bar{p_{A}}$ are provided in the Appendix). Bearing in mind the response above, firm A can choose to employ a high-price strategy to induce firm D to engage in non-deceptive sales. If firm A can benefit from doing so, then there exists a separating equilibrium; otherwise, a pooling equilibrium occurs. To sum up, the existence condition for the separating equilibrium can be expressed as:

\begin{matrix} (({C1})) & \begin{matrix} max_{p_{A} : p_{A} \geq \bar{p_{A}}} π_{A}^{S E} \geq max_{p_{A} : p_{A} \leq \bar{p_{A}}} π_{A}^{P E} \end{matrix} \end{matrix}

Our paper adopts the intuitive criteria (Cho and Kreps, 1987) to check the stability of separating and pooling equilibria. Specifically, suppose that firm D benefits from a certain off-equilibrium path under a set of best responses associated with given off-equilibrium beliefs, intuitive criteria require that the equilibrium cannot survive. The checking process for separating and pooling equilibria is provided in Appendix B.

Our signaling game yields two distinct pooling equilibria. To avoid confusion, we denote them by superscripts “PE1” and “PE2,” respectively. Lemma 1 shows the equilibrium prices under the scenario without the BTS platform (the expressions of $ε^{P E}$ and $q_{N}^{P E}$ are provided in Appendix A).

Lemma 1.

Under the scenario without the BTS platform, the equilibrium prices of three types of products are as follows: (i)

When $ε \in [ε^{P E}, \bar{ε})$ , there exists a separating equilibrium, and the separating prices of three types of products are

\begin{aligned} p_{A}^{S E} & = \frac{(1 - q_{D}) (2 q_{D} - q_{N} - q_{D} q_{N}) (2 + q_{N ε} - 2_{ε} - 2 q_{N})}{- {q_{D}}^{2} {q_{N}}^{2} + 4 {q_{D}}^{2} q_{N} - 4 {q_{D}}^{2} - 4 q_{D} q_{N} + 6 q_{D} + 3 {q_{N}}^{2} - 4 q_{N}} \\ p_{D}^{S E} & = \frac{q_{D} (1 - q_{D}) (q_{D} - q_{N}) (2 + q_{N ε} - 2_{ε} - 2 q_{N})}{- {q_{D}}^{2} {q_{N}}^{2} + 4 {q_{D}}^{2} q_{N} - 4 {q_{D}}^{2} - 4 q_{D} q_{N} + 6 q_{D} + 3 {q_{N}}^{2} - 4 q_{N}} \\ p_{N}^{S E} & = \frac{q_{N} (1 - q_{D}) (q_{D} - q_{N}) (2 + q_{N ε} - 2_{ε} - 2 q_{N})}{2 (- {q_{D}}^{2} {q_{N}}^{2} + 4 {q_{D}}^{2} q_{N} - 4 {q_{D}}^{2} - 4 q_{D} q_{N} + 6 q_{D} + 3 {q_{N}}^{2} - 4 q_{N})} . \end{aligned}

(ii) When $ε \in [0, ε^{P E})$ , there exist pooling equilibria, and the pooling prices of three types of products are $(p_{A}^{P E}, p_{D}^{P E}, p_{N}^{P E}) = {\begin{array}{l} (p_{A}^{P E 1}, p_{D}^{P E 1}, p_{N}^{P E 1}), i f q_{N} \in (q_{N}^{P E}, q_{D}) \\ (p_{A}^{P E 2}, p_{D}^{P E 2}, p_{N}^{P E 2}), i f q_{N} \in (0, q_{N}^{P E}] \end{array}$ , where

\begin{aligned} p_{A}^{P E 1} & = p_{D}^{P E 1} = \frac{2 + q_{N ε} - 2_{ε} - 2 q_{N}}{2 (2 - q_{N})}, p_{N}^{P E 1} = \frac{q_{N} (2 + 2_{ε} - q_{N ε} - 2 q_{N})}{4 (2 - q_{N})} \end{aligned}

\begin{aligned} p_{A}^{P E 2} & = p_{D}^{P E 2} = \frac{(1 - q_{D}) (2 q_{D} - q_{N} - q_{D} q_{N}) (2 + q_{N ε} - 2_{ε} - 2 q_{N})}{- {q_{D}}^{2} {q_{N}}^{2} + 4 {q_{D}}^{2} q_{N} - 4 {q_{D}}^{2} - 4 q_{D} q_{N} + 6 q_{D} + 3 {q_{N}}^{2} - 4 q_{N}} \end{aligned}

\begin{aligned} p_{N}^{P E 2} & = \frac{q_{N} (1 - q_{N}) (2 q_{D} - q_{N} + q_{D ε} - q_{N ε} + {q_{D}}^{2} q_{N} - 2 {q_{D}}^{2})}{- {q_{D}}^{2} {q_{N}}^{2} + 4 {q_{D}}^{2} q_{N} - 4 {q_{D}}^{2} - 4 q_{D} q_{N} + 6 q_{D} + 3 {q_{N}}^{2} - 4 q_{N}} . \end{aligned}

Figure 4.

Consumers’ purchase decisions based on quality preferences in the pooling equilibrium.

Lemma 1 shows that firm A will employ a high-price strategy when CPRPA is higher than a certain threshold (i.e., $ε \in [ε^{P E}, \bar{ε})$ ). Correspondingly, firm D will choose to engage in non-deceptive sales in the context of quality information asymmetry. This result aligns with the practical observation that hard-to-distinguish counterfeits have not always been marketed in a deceptive manner. For example, some indistinguishable counterfeit Nike shoes are sold at just 10% of the price of their authentic counterparts.¹¹

4.2. The impacts of quality information asymmetry

To analyze the impacts of quality information asymmetry on the market, we construct a benchmark model with symmetric quality information (marked by the superscript “S”). Under quality information symmetry, consumers can get to know the true quality of counterfeit D. Thus, the profit functions of the three firms in the benchmark are the same as those in the separating equilibrium (i.e., $π_{i}^{S} = π_{i}^{S E}, i \in {A, D, N}$ ). Using backward induction, we can derive that the prices of the three types of products in the benchmark are $p_{A}^{S} = \frac{(1 - q_{D}) (2 q_{D} - q_{N} - q_{N} q_{D})}{2 (2 q_{D} - q_{N} - {q_{D}}^{2})}$ , $p_{D}^{S} = \frac{q_{D} (1 - q_{D}) (q_{D} - q_{N})}{2 (2 q_{D} - q_{N} - {q_{D}}^{2})}$ , and $p_{N}^{S} = \frac{q_{N} (1 - q_{D}) (q_{D} - q_{N})}{4 (2 q_{D} - q_{N} - {q_{D}}^{2})}$ , respectively.

By comparing the equilibrium outcomes of the signaling game and the benchmark model, we can derive the impacts of quality information asymmetry on the market. Proposition 1 shows the impacts of quality information asymmetry on product prices (the expression of $ε_{p_{A}}^{P E}$ is provided in Appendix A).

Proposition 1.
Compared with those under quality information symmetry, the product prices under quality information asymmetry exhibit the following changes. (i)
The price of the authentic product rises in the absence of deceptive sales, or in the presence of deceptive sales with CPRPA below a certain threshold ( $p_{A}^{S E} > p_{A}^{S}$ ; $p_{A}^{P E} > p_{A}^{S}$ if $ε \in (0, min {ε_{p_{A}}^{P E}, ε^{P E}})$ ).
(ii)
The prices of both types of counterfeits always rise ( $p_{i}^{j} > p_{i}^{S}, i \in {D, N}, j \in {S E, P E}$ ).

Proposition 1(ii) shows that regardless of the presence of deceptive sales, quality information asymmetry always leads to an increase in the prices of two types of counterfeits. This result is attributed to the following mechanism. For a given price of the authentic product, the optimal differential pricing responses of the two counterfeiters under quality information asymmetry align with those under quality information symmetry. Under quality information asymmetry, firm A can induce firm D to engage in non-deceptive sales only by employing a high-price strategy. Hence, the prices of two types of counterfeits in the absence of deceptive sales are higher than those under quality information symmetry. When firm D engages in deceptive sales, the quality-based price competition among the three firms is alleviated. Consequently, the prices of two types of counterfeits in the presence of deceptive sales are also higher than those under quality information symmetry.

One may intuit that under quality information asymmetry, the presence of deceptive sales will always lead to a decrease in the price of the authentic product, because consumers’ utility associated with authentic items becomes lowered. However, the mitigation of quality-based price competition incentivizes firm A to raise its product price. Consequently, as shown in Proposition 1(i), the price of the authentic product does not always decrease. As illustrated in Figure 5, the presence of deceptive sales may lead to a decrease in the price of the authentic product only if $q_{D} < 0.4978$ . This result aligns with practical observations that the market prices of products from well-known footwear brands (e.g., Nike) do not necessarily show a downward trend despite the proliferation of indistinguishable counterfeits.¹²

Figure 5.
The impact of quality information asymmetry on the price of the authentic product $(q_{N} = 0.45)$ .

Then, we analyze the impacts of quality information asymmetry on the manufacturer and consumers, with the corresponding findings presented in Proposition 2 (the expression of $ε_{π_{A}}^{P E}$ is provided in Appendix A).
Proposition 2.
Compared with those under quality information symmetry, the demand for the authentic product, the profit of firm A, and consumer surplus under quality information asymmetry exhibit the following changes. (i)
The demand for the authentic product always decreases ( $D_{A}^{j} < D_{A}^{S}, j \in {S E, P E}$ ).
(ii)
The profit of firm A decreases in the absence of deceptive sales, or in the presence of deceptive sales with CPRPA above a certain threshold ( $π_{A}^{S E} < π_{A}^{S}$ ; $π_{A}^{P E} < π_{A}^{S}$ if $ε \in (min {ε_{π_{A}}^{P E}, 0}, ε^{P E})$ ).
(iii)
Consumer surplus always decreases ( $C S^{j} > C S^{S}, j \in {S E, P E}$ ).

Regardless of the presence of deceptive sales, quality information asymmetry always drives up the prices of two types of counterfeits. Thus, as shown in Proposition 2(iii), quality information asymmetry always leads to a decrease in consumer surplus.

Interestingly, Proposition 2(i) and (ii) show that the presence of deceptive sales reduces the demand for the authentic product but may increase the profit of firm A. This result is attributed to the following mechanism. Although firm D diverts a share of the market demand originally targeted at authentic products, the mitigation of quality-based price competition incentivizes firm A to raise its product price, which may offset the profit losses incurred from diminished product demand. Thus, the presence of deceptive sales may increase the profit of firm A. However, when CPRPA is higher than a certain threshold, the positive effects stemming from the mitigation of quality-based price competition will be dominated by the negative effects arising from consumers’ distrust of product authenticity. Hence, under the above-stated condition, the presence of deceptive sales will result in a decrease in the profit of firm A. Figure 6 illustrates this result and shows that the profit of firm A will decrease due to the presence of deceptive sales if $ε \in (min {ε_{π_{A}}^{P E}, 0}, ε^{P E})$ .

Figure 6.
The impact of quality information asymmetry on the profit of firm A $(q_{N} = 0.45)$ .

Proposition 2(i) and (ii) also show that in the absence of deceptive sales, quality information asymmetry leads to a decrease in both demand and profit for the authentic product. This is driven by the fact that although firm A's high-price strategy drives up the prices of all products, the increase in the price of authentic products is greater than that of counterfeits $(p_{A}^{S E} - p_{A}^{S} > p_{i}^{S E} - p_{i}^{S}, i \in {D, N})$ , leading some consumers to opt for counterfeits. Hence, quality information asymmetry still exerts an adverse impact on firm A even if firm D does not engage in deceptive sales.

By comparing the impacts of quality information asymmetry on the manufacturer in the presence and absence of deceptive sales, we can find that the low-price dumping of deceptive counterfeits may be more detrimental to brand-name firms. The result corresponds to a real-world case: Masai Barefoot Technology (MBT) shoes, a prestigious Swiss brand, were forced to withdraw from the market amid the rampant proliferation of low-price deceptive counterfeits.¹³

We finally analyze the impacts of quality information asymmetry on the counterfeiters, with the corresponding findings presented in Proposition 3.
Proposition 3.
Compared with those under quality information symmetry, the demand for two types of counterfeits and the profit of firm N under quality information asymmetry exhibit the following changes. (i)
The demand for counterfeit D increases only in the absence of deceptive sales $(D_{D}^{S E} > D_{D}^{S}, D_{D}^{P E} < D_{D}^{S})$ .
(ii)
Both demand for counterfeit N and profit of firm N always increase ( $D_{N}^{j} > D_{N}^{S}, π_{N}^{j} > π_{N}^{S}, j \in {S E, P E}$ ).

Proposition 3(i) suggests that quality information asymmetry does not always lead to an increase in the demand for counterfeit D. The rationale lies in the fact that firm D only diverts part of the demand for authentic products when engaging in deceptive sales. This result reveals that the motivation for deceptive counterfeiters to engage in deceptive sales lies in obtaining higher unit profits rather than demand. However, both firm A's high-price strategy in the absence of deceptive sales and consumers’ perceived risk concerning product authenticity in the presence of deceptive sales drive consumers to purchase counterfeit N. Hence, as shown in Proposition 3(ii), quality information asymmetry always leads to an increase in the demand for counterfeit N and the profit of firm N.

Summarizing Propositions 1–3, our results reveal that regardless of the presence of deceptive sales, quality information asymmetry always exerts adverse impacts on both manufacturers and consumers, while benefiting all counterfeiters. The theoretical findings highlight the importance of eliminating quality information asymmetry and can explain why many brand-name firms invest considerable effort in combating indistinguishable counterfeits. The results also offer a managerial insight that only by helping consumers identify counterfeits can brand-name firms fundamentally mitigate the negative impact of quality information asymmetry.
5. With the BTS platform

In this section, we first derive equilibrium outcomes for the scenario with the BTS platform. Then, we analyze the manufacturer's optimal strategy for adopting the BTS platform. Finally, we discuss the value derived from BTS platform adoption under the manufacturer's optimal adoption strategy.

5.1. Equilibrium outcomes under the scenario with the BTS platform

Under the scenario with the BTS platform, firm D can only engage in non-deceptive sales. In this situation, a Stackelberg game exists among three firms. We use superscript “B” to denote the equilibrium outcomes under the scenario with the BTS platform. Following a similar derivation to the profit functions under separating equilibrium, we can obtain the profit functions of the three firms under the scenario with the BTS platform as follows:

\begin{aligned} π_{A}^{B} & = (p_{A} - c) D_{A} = (p_{A} - c) (1 - \frac{p_{A} - p_{D}}{1 - q_{D}}) \end{aligned}

(7)

\begin{aligned} π_{D}^{B} & = p_{D} D_{D} = p_{D} (\frac{p_{A} - p_{D}}{1 - q_{D}} - \frac{p_{D} - p_{N}}{q_{D} - q_{N}}) \end{aligned}

(8)

\begin{aligned} π_{N}^{B} 7 & = p_{N} D_{N} = p_{N} (\frac{p_{D} - p_{N}}{q_{D} - q_{N}} - \frac{p_{N}}{q_{N}}) \end{aligned}

(9)

By using backward induction, we can derive the equilibrium prices under the scenario with the BTS platform, as shown in Lemma 2.

Lemma 2.

Under the scenario with the BTS platform, the prices of three types of products are $p_{A}^{B} = \frac{2 q_{D} - q_{N} + 2 c q_{D} - c q_{N} - c {q_{D}}^{2} + {q_{D}}^{2} q_{N} - 2 {q_{D}}^{2}}{2 (2 q_{D} - q_{N} - {q_{D}}^{2})}$ , $p_{D}^{B} = \frac{q_{D} (q_{D} - q_{N}) (2 q_{D} - q_{N} + 2 c q_{D} - c q_{N} - c {q_{D}}^{2} + {q_{D}}^{2} q_{N} - 2 {q_{D}}^{2})}{2 (2 q_{D} - q_{N} - q_{D} q_{N}) (2 q_{D} - q_{N} - {q_{D}}^{2})}$ and $p_{N}^{B} = \frac{q_{N} (q_{D} - q_{N}) (2 q_{D} - q_{N} + 2 c q_{D} - c q_{N} - c {q_{D}}^{2} + {q_{D}}^{2} q_{N} - 2 {q_{D}}^{2})}{4 (2 q_{D} - q_{N} - q_{D} q_{N}) (2 q_{D} - q_{N} - {q_{D}}^{2})}$ , respectively.

Proposition 4 summarizes the impacts of the adoption cost for the BTS platform on product prices, product demand, firms’ profits, and consumer surplus.

Proposition 4.

As the adoption cost for the BTS platform rises: (i)

The prices of all types of products rise $(\partial p_{i}^{B} / \partial c > 0, i \in {A, D, N})$ .

(ii)

The demand for the authentic product decreases $(\partial D_{A}^{B} / \partial c < 0)$ ; the demand for both types of counterfeits increases $(\partial D_{i}^{B} / \partial c > 0, i \in {D, N})$ .

(iii)

The profit of firm A decreases $(\partial π_{A}^{B} / \partial c < 0)$ ; the profits of both firm D and firm N increase $(\partial π_{i}^{B} / \partial c > 0, i \in {D, N})$ .

(iv)

Consumer surplus decreases $(\partial C S^{B} / \partial c < 0)$ .

Under the scenario with the BTS platform, the optimal responses of the two counterfeiters to the price of the authentic product are independent of the adoption cost for the BTS platform. As the adoption cost rises, firm A has to set a higher price to offset the expenses. Consequently, a rise in the adoption cost will drive up the prices of all types of products (Proposition 4(i)), and these price adjustments will lead to a decrease in consumer surplus (Proposition 4(iv)). Notably, the price of the authentic product is more sensitive to changes in the adoption cost $(\partial p_{A}^{B} / \partial c > \partial p_{i}^{B} / \partial c, i \in {D, N})$ . Consequently, a rise in the adoption cost will lead to a decrease in both the demand for the authentic product and the profit of firm A, but benefit firm D and firm N (Proposition 4(ii) and (iii)).

To sum up, Proposition 4 reveals that a rise in the adoption cost for the BTS platform will diminish the effectiveness of BTS platform adoption.

5.2. The optimal strategy for adopting the BTS platform

By comparing the equilibrium outcomes under the scenarios with and without the BTS platform, we can derive the manufacturer's optimal strategy for adopting the BTS platform, as shown in Proposition 5 (the expressions of $c^{S E}$ and $c^{P E}$ are provided in Appendix A).

Proposition 5.
The optimal strategy for adopting the BTS platform is as follows: (i)
In the absence of deceptive sales $(ε \in [ε^{P E}, \bar{ε}))$ , firm A adopts the BTS platform if the adoption cost is lower than a certain threshold ( $π_{A}^{B} > π_{A}^{S E}$ if $c < c^{S E}$ ); a rise in CPRPA reduces firm A's willingness to adopt the BTS platform $(\frac{\partial c^{S E}}{\partial ε} < 0)$ .
(ii)
In the presence of deceptive sales $(ε \in [0, ε^{P E}))$ , firm A adopts the BTS platform if CPRPA is higher than a certain threshold and the adoption cost is lower than a certain threshold ( $π_{A}^{B} > π_{A}^{P E}$ if $ε \in (min {ε_{π_{A}}^{P E}, 0}, ε^{P E})$ and $c < c^{P E}$ ); a rise in CPRPA incentivizes firm A to adopt the BTS platform $(\frac{\partial c^{P E}}{\partial ε} > 0)$ .

Proposition 5 shows that firm A may never derive benefits from the adoption of the BTS platform even if the adoption cost is quite low. This is driven by the fact that firm A may benefit from the presence of deceptive sales (see Proposition 2(ii)). Figure 7 illustrates this result and shows that when consumers are indifferent to product authenticity and the adoption cost is quite low (i.e., $ε = 0, c = 0$ ), firm A adopts the BTS platform only if the quality of counterfeit N is higher than a certain level.

Figure 7.
Firm a's strategy for adopting the BTS platform $(ε = 0, c = 0)$ _.

One may intuit that a rise in CPRPA will incentivize firm A to adopt the BTS platform. However, Proposition 5 shows that this intuition does not hold in the absence of deceptive sales. This is driven by the fact that under quality information asymmetry, a rise in CPRPA diminishes firm D's willingness to engage in deceptive sales, thereby reducing firm A's loss incurred in employing a high-price strategy $(\frac{\partial π_{A}^{S E}}{\partial ε} > 0)$ . Hence, a rise in CPRPA will reduce firm A's willingness to adopt the BTS platform in the absence of deceptive sales. Figure 8 intuitively illustrates the differential impacts of CPRPA on manufacturers’ willingness to adopt the BTS platform between cases with and without deceptive sales.

Figure 8.
Firm a's strategy for adopting the BTS platform $(q_{D} = 0.5, q_{N} = 0.45)$ .

To sum up, Proposition 5 reveals that the manufacturer may never benefit from adopting the BTS platform in the presence of deceptive sales, and CPRPA exerts a non-monotonic impact on the manufacturer's optimal adoption decision. The theoretical findings offer a managerial insight that when deciding whether to adopt BTS platforms, brand-name firms should not only investigate the sales strategies of deceptive counterfeiters, but also consider consumers’ attitudes towards counterfeiting.
5.3. The value of adopting the BTS platform

By comparing the equilibrium outcomes under the scenarios with and without the BTS platform based on firm A's optimal adoption strategy, we examine the value of adopting the BTS platform. Proposition 6 shows the impacts of BTS platform adoption on product prices (the expression of $c_{p_{A}}^{P E}$ is provided in Appendix A).

Proposition 6.
Under firm A's optimal strategy, the adoption of the BTS platform gives rise to the following changes in product prices: (i)
The price of the authentic product decreases when the BTS platform is adopted in the absence of deceptive sales, or when the BTS platform is adopted in the presence of deceptive sales and the adoption cost is lower than a certain threshold ( $p_{A}^{B} < p_{A}^{S E}$ ; $p_{A}^{B} < p_{A}^{P E}$ if $c \in (0, min {c^{P E}, c_{p_{A}}^{P E}}$ )).
(ii)
The prices of both counterfeit D and counterfeit N always decrease $(p_{i}^{B} < p_{i}^{j}, i \in {D, N}, j \in {S E, P E})$ .

Summarizing (i) and (ii), Proposition 6 states the following results. First, it demonstrates that in the absence of deceptive sales, the adoption of the BTS platform results in a decrease in the prices of all product types. This result is attributed to the following mechanism. Under quality information asymmetry, firm A can induce firm D to engage in non-deceptive sales only by employing a high-price strategy. By leveraging the quality disclosure effect stemming from the adoption of the BTS platform, firm A no longer needs to resort to this tactic. Hence, in the absence of deceptive sales, the adoption of the BTS platform leads to a decrease in the price of the authentic product. This also forces both counterfeiters to lower their prices.

Second, Proposition 6 shows that in the presence of deceptive sales, the adoption of the BTS platform leads to a decrease in the prices of two types of counterfeits. This result can be explained as follows. Since the adoption of the BTS platform achieves the quality disclosure effect, firm D has to set its price based on the true quality of its product. Thus, the price of counterfeit D will decrease as a result of BTS platform adoption, which also forces firm N to lower its price. Unlike the results of Pun et al. (2021), we find that in the presence of deceptive sales, the adoption of the BTS platform may drive up the price of authentic products. The underlying reason for this result is as follows. As CPRPA rises, firm A can benefit more from the quality disclosure effect, which makes it willing to invest more in adopting the BTS platform. However, a rise in the adoption cost will lead to an increase in the price of the authentic product. Hence, when the BTS platform is adopted in the presence of deceptive sales, the price of the authentic product may rise. As illustrated in Figure 9, the price of the authentic product may rise as a result of BTS platform adoption if $ε \in [0.093, ε^{P E})$ .

Figure 9.
The impact of the BTS platform adoption on the price of the authentic product $(q_{D} = 0.5, q_{N} = 0.45)$ .

To sum up, Proposition 6 reveals that the adoption of the BTS platform can effectively suppress the prices of counterfeits, while offering a managerial insight that in the presence of deceptive sales, brand-name firms may need to raise prices to offset the costs incurred by such adoption.

Proposition 7 shows the impacts of BTS platform adoption on product demand, counterfeiters’ profits, and consumer surplus.
Proposition 7.
Under firm A's optimal strategy, the adoption of the BTS platform gives rise to the following changes in product demand, counterfeiters’ profits, and consumer surplus: (i)
The demand for the authentic product always increases $(D_{A}^{B} > D_{A}^{j}, j \in {S E, P E})$ ; the demand for counterfeit N always decreases $(D_{N}^{B} < D_{N}^{j}, j \in {S E, P E})$ ; the demand for counterfeit D decreases only when the BTS platform is adopted in the absence of deceptive sales $(D_{D}^{B} ⟨ D_{D}^{S E}, D_{D}^{B} ⟩ D_{D}^{P E})$ .
(ii)
The profits of both firm D and firm N always decrease $(π_{i}^{B} < π_{i}^{j}, i \in {D, N}, j \in {S E, P E})$ .
(iii)
Consumer surplus always increases $(C S^{B} > C S^{j}, i \in {D, N}, j \in {S E, P E})$ .

Summarizing (i) (ii), and (iii), Proposition 7 states the following results. First, it demonstrates that in the absence of deceptive sales, the adoption of the BTS platform can reduce both the demand for and profits of the two types of counterfeits, while increasing both the demand for authentic products and consumer surplus. This result is attributed to the following mechanism. Although the adoption of the BTS platform leads to a decrease in the prices of all types of products, the decrease in the price of authentic products is greater than that of counterfeits $(p_{A}^{S E} - p_{A}^{B} > p_{i}^{S E} - p_{i}^{B}, i \in {D, N})$ , attracting consumers to buy authentic products. Hence, the adoption of the BTS platform will increase the demand for authentic products, while reducing both the demand for and profits of the two types of counterfeits. Due to price declines across all product types, the adoption of the BTS platform will lead to an increase in consumer surplus.

Second, Proposition 7 shows that in the presence of deceptive sales, the only drawback of BTS platform adoption is that it increases the demand for deceptive counterfeits. The underlying reason for this result is as follows. The quality disclosure effect eliminates consumers’ concerns over product authenticity and depresses the prices of the two types of counterfeits, thereby increasing the demand for the authentic product, enhancing consumer surplus, and reducing the profits of the two counterfeiters. However, counterfeit D can cannibalize the demand for counterfeit N by virtue of its relative quality advantage, so the adoption of the BTS platform decreases the demand for counterfeit N, but increases the demand for counterfeit D.

Figure 10 illustrates the results stated in Proposition 7 (we use $Delta x$ to denote the change of the equilibrium outcome x when the BTS platform is adopted¹⁴). On the one hand, Figure 10(b) shows that the adoption of the BTS platform always reduces the profits of both firm D and firm N, while improving consumer surplus. On the other hand, Figure 10(a) shows that in the presence of deceptive sales, the adoption of the BTS platform will increase the demand for counterfeit D.

Figure 10.
The impact of the BTS platform adoption on product demand, counterfeiters’ profits, and consumer surplus $(q_{D} = 0.5, q_{N} = 0.45, c = 0.02)$ .

Extending the results of Pun et al. (2021), our paper reveals the following key conclusions. First, we indicate that in a market with both non-deceptive and deceptive counterfeits, the adoption of the BTS platform will decrease the demand for non-deceptive counterfeits but may increase the demand for deceptive counterfeits. The results above reveal that the potential downside of adopting the BTS platform lies in stimulating the demand for deceptive counterfeits, and further show that adopting the BTS platform is more effective in combating non-deceptive counterfeits than deceptive ones. Second, we indicate that in such a market, the adoption of the BTS platform always improves consumer surplus. Given that such adoption may increase the demand for deceptive counterfeits, this theoretical finding reveals that BTS platform adoption contributes more to improving consumer surplus than to combating counterfeits.
6. Extensions

In this section, we develop four extensions to the base model by relaxing some assumptions. Due to mathematical intractability, we numerically examine the robustness of our key findings. The settings of the main parameters are consistent with those in Figure 10 $(q_{D} = 0.5, q_{N} = 0.45, c = 0.02)$ .

6.1. Marginal production cost difference

In the base model, we assume that the marginal production cost of all products is zero. In this subsection, we relax this assumption by incorporating differences in marginal production costs of the three types of products. Specifically, we normalize the marginal production cost of counterfeit N to zero, and use $c_{D}$ and $c_{A}$ to denote the marginal production costs of counterfeit D and the authentic product, where $c_{A} > c_{D} > 0$ .

Figure 11 illustrates a comparison of the effects of BTS platform adoption between the base model and the extended model incorporating marginal production cost differences, with results for the latter denoted by the subscript “E1”. The numerical results show that our conclusions remain robust when differential marginal production costs are incorporated into the analysis (e.g., the adoption of the BTS platform always decreases the profits of both counterfeiters and improves consumer surplus, but may increase the demand for deceptive counterfeits).

Figure 11.

Comparison of the effects of BTS platform adoption between the base model and the extended model incorporating marginal production costs differences $(c_{A} = 0.05, c_{D} = 0.01)$ .

Figure 12.

Comparison of the effects of BTS platform adoption across different market-entering sequences of counterfeiters.

6.2. Alternative to the market-entering sequence of counterfeiters

In the base model, we assume that firm D enters the market earlier than firm N. In this subsection, we extend the base model by considering that two counterfeiters enter the market simultaneously; that is, the two counterfeiters set their prices simultaneously after observing the price of the authentic product and the manufacturer's decision on adopting the BTS platform.

Figure 12 illustrates a comparison of the effects of BTS platform adoption across different market-entering sequences of counterfeiters, with the results for the simultaneous entry case denoted by the subscript “E2”. The numerical results show that our core findings regarding the effects of BTS platform adoption remain consistent when two counterfeiters enter the market simultaneously. Additionally, Figure 12 shows that when the two counterfeiters enter the market simultaneously, firm D has a stronger incentive to engage in deceptive sales under quality information asymmetry $(ε_{E 2}^{P E} > ε^{P E})$ . This is driven by the fact that the absence of a first-mover advantage incentivizes firm D to evade quality-based price competition with firm N.

6.3. The number of sales points

In the base model, we assume that in the presence of deceptive sales, consumers will randomly choose between the products of firm A and firm D with equal probability when intending to purchase authentic products. In this subsection, we relax this assumption by incorporating the number of sales points. Specifically, we assume that both firm A and firm D operate multiple sales points, and the ratio of their sales points affects the probability that consumers unknowingly purchase counterfeit D (as firm N can only engage in non-deceptive sales, we need not consider its number of sales points). Let $α$ denote the ratio of the number of sales points operated by firm D to that of firm A. We set $α \geq 1$ to capture the real-world dynamic that counterfeiters face nearly no restrictions in locating their outlets, whereas the layout of authentic stores is strictly constrained by brand strategies. Based on this setting, the probability of consumers unknowingly purchasing counterfeit D can be expressed as $φ = P$ (encountering firm D) $\times P$ (failing to identify the counterfeit) = $(α / (α + 1)) \times q_{D} = (α q_{D} / (α + 1))$ . Correspondingly, the profit functions of firm A and firm D in the pooling equilibrium can be expressed as $π_{A}^{P E} = p_{A} (1 - \frac{p_{A} - p_{N} + ε}{1 - q_{N}}) (1 - \frac{α q_{D}}{α + 1})$ and $π_{D}^{P E} = p_{A} (1 - \frac{p_{A} - p_{N} + ε}{1 - q_{N}}) \frac{α q_{D}}{α + 1}$ , respectively.

Figure 13.

Comparison of the impacts of quality information asymmetry between the base model and the extended model incorporating the number of sales points $(α = 3)$ _.

Figure 14.

Comparison of the effects of BTS platform adoption between the base model and the extended model incorporating the number of sales points $(α = 3)$ _.

Figure 13 illustrates a comparison of the impacts of quality information asymmetry between the base model and the extended model incorporating the number of sales points, with results for the latter denoted by the subscript “E3” (we use $\nabla x$ to denote the impact of quality information asymmetry on the equilibrium outcome x¹⁵). The numerical results show that our conclusions regarding the impacts of quality information asymmetry remain robust when the number of sales points is incorporated into the analysis (i.e., regardless of the presence of deceptive sales, quality information asymmetry always exerts adverse impacts on both manufacturers and consumers, while benefiting all counterfeiters). Figure 14 illustrates a comparison of the effects of BTS platform adoption between the base model and the extended model. The numerical results demonstrate the robustness of our findings regarding the effects of BTS platform adoption in this extended analytical framework.

6.4. Privacy concerns regarding blockchain technology

Some blockchain-based transaction platforms (e.g., Everledger) require consumers to register their digital identities and upload personal information when utilising verification functions. Since consumers are unaware of how this private information will be used by platforms, the adoption of the BTS platform may lead to consumer privacy concerns (Pun et al., 2021). In this subsection, we consider that while adopting the BTS platform can eliminate quality information asymmetry, it may cause consumer privacy concerns and decrease their utility of purchasing authentic products. We use $r (r \geq 0)$ to capture the impact of consumer privacy concerns on the utility. Correspondingly, under the scenario with the BTS platform, the utilities of the type-θ consumer purchasing the authentic product can be expressed as $U_{A} (θ) = θ - p_{A} - r$ . Based on this utility function, the profit functions of firm A and firm D under the scenario with the BTS platform are $π_{A}^{B} = (p_{A} - c) (1 - ((p_{A} - p_{D} + r) / (1 - q_{D})))$ and $π_{D}^{B} = p_{D} (((p_{A} - p_{D} + r) / (1 - q_{D})) - ((p_{D} - p_{N}) / (q_{D} - q_{N})))$ , respectively.

Figure 15 illustrates a comparison of the effects of BTS platform adoption between the base model and the extended model incorporating consumers’ privacy concerns, with the results for the latter denoted by the subscript “E4”. The numerical results demonstrate the robustness of our findings.

Figure 15.

Comparison of the effects of BTS platform adoption between the base model and the extended model incorporating consumers’ privacy concerns $(r = 0.01)$ .

7. Conclusion

This study highlights how deception heterogeneity fundamentally alters the effects of transparency-enhancing technologies in counterfeit markets. By distinguishing between deceptive and non-deceptive counterfeits, our analysis shows that the economic consequences of quality information asymmetry and blockchain-based authentication depend not only on the presence of counterfeits, but also on how deception is strategically employed.

Using a signaling game framework that incorporates consumers’ perceived risk concerning product authenticity (CPRPA), we show that quality information asymmetry reshapes pricing, demand, and profits in nontrivial ways. In particular, deceptive counterfeiters may strategically engage in non-deceptive sales when perceived authenticity risk is sufficiently high. While quality information asymmetry consistently harms manufacturers and consumers, it benefits counterfeiters, underscoring the strategic importance of eliminating such asymmetry. Importantly, when deceptive sales are present, blockchain adoption may reduce quality-based price competition, leading manufacturers to optimally forgo adoption even at relatively low adoption costs. This result explains why brand-name firms hesitate to adopt BTS platforms to combat counterfeits. Moreover, the impact of CPRPA on adoption incentives is non-monotonic, challenging the intuition that higher perceived risk always strengthens firms’ incentives to adopt authentication technologies.

From a broader perspective, this study assesses the value of adopting BTS platforms in markets with deceptive and non-deceptive counterfeits. Extending existing research on anti-counterfeiting, we find that brand-name firms may need to set a higher price to offset the costs incurred by such adoption in markets with two types of counterfeits. We reveal that although higher adoption costs undermine the effectiveness of BTS platform adoption, such adoption will increase the demand for authentic products and consumer surplus, while reducing the profits of both types of counterfeiters. However, the adoption of BTS platforms may increase the demand for deceptive counterfeits. These results emphasize that BTS platform adoption contributes more to improving consumer surplus than to combating counterfeits, and is more effective in combating non-deceptive counterfeits than deceptive ones.

A key theoretical implication of our findings is that treating counterfeits as homogeneous can lead to systematically misleading conclusions about the value of transparency. In models that abstract from deception heterogeneity, blockchain-based authentication appears uniformly beneficial and adoption incentives are monotonic in adoption costs and consumer risk perceptions. Our analysis demonstrates that once deceptive behavior is explicitly modeled, these conclusions no longer hold. As a result, policy and managerial prescriptions derived from homogeneous-counterfeit frameworks may overstate the effectiveness of authentication technologies in combating counterfeits.

From a managerial perspective, our findings suggest that brand-name firms should evaluate blockchain adoption strategies in light of both counterfeiters’ sales tactics and consumers’ perceptions of authenticity risk. We offer several key managerial implications for brand-name firms. First, it is commonly held that the presence of deceptive sales practices will undermine the profitability of brand-name firms. However, we reveal that brand-name firms may benefit from the mitigation of quality-based price competition resulting from deceptive sales. Consequently, we propose that in the presence of deceptive sales, brand-name firms may not have an imperative to adopt BTS platforms, even though the associated adoption costs are quite low.

Second, while brand-name firms can attempt to influence the sales strategies of deceptive counterfeiters by adjusting their own prices, the detrimental effects of quality information asymmetry on their profits still persist. As such, we argue that brand-name firms ought not to restrict their adoption of BTS platforms exclusively to scenarios involving deceptive sales.

Third, it may be a common judgment that a rise in consumers’ perceived risk concerning product authenticity will incentivize brand-name firms to adopt BTS platforms. However, we find that the impact of such perceived risk on brand-name firms’ optimal adoption decisions is non-monotonic. Hence, we argue that brand-name firms ought to consider both the sales strategies of deceptive counterfeiters and consumers’ attitudes toward counterfeiting when deciding whether to adopt BTS platforms.

Our analysis deliberately abstracts from several institutional features in order to isolate the strategic roles of deception and information asymmetry, which suggests several directions for future research. First, under our setting, all consumers cannot distinguish between deceptive counterfeits and authentic products when deceptive counterfeiters implement deceptive sales. However, in reality, expert consumers who have a deep understanding of the authentic product and a rich shopping experience may not be deceived. Our results indicate that brand-name firms may set a lower price when adopting BTS platforms. However, in scenarios where expert consumers are present, reducing price may not be an optimal decision. Thus, the presence of expert consumers will influence brand-name firms’ pricing decisions, which in turn affect their adoption strategies for BTS platforms. Future research can examine how the size of the expert consumer segment affects brand-name firms’ strategies for adopting BTS platforms. Second, in our analysis, we assume that brand-name firms sell only a single product type. However, in practice, brand-name firms may offer a range of products. In such cases, counterfeiters may target one or more product types for imitation, which exerts a notable influence on the firms’ strategic decisions regarding BTS platform adoption. Consequently, exploring BTS platform adoption strategies among brand-name firms with multiple product types also constitutes a worthwhile research topic. More broadly, our study suggests that the benefits of transparency-enhancing technologies cannot be evaluated independently of the strategic behaviors they are intended to discipline.

Supplemental Material

sj-docx-1-pao-10.1177_10591478261465974 - Supplemental material for Using blockchain to combat multiple types of counterfeits: Adoption strategy and value analysis

Supplemental material, sj-docx-1-pao-10.1177_10591478261465974 for Using blockchain to combat multiple types of counterfeits: Adoption strategy and value analysis by Chao He, Chunqiao Tan, Yangyan Shi and Arvind K Tripathi in Production and Operations Management

Footnotes

Acknowledgments

This research was supported by the National Natural Science Foundation of China (No. 72371132), the Jiangsu Provincial Key Discipline Construction Project (Statistics), and open project of the Joint Lab for Statistics and Finance (2026JLSF203).

ORCID iDs

Chao He

Chunqiao Tan

Yangyan Shi

Arvind K Tripathi

Funding

The authors received no financial support for the research, authorship, and/or publication of this article.

Declaration of conflicting interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Supplemental material

Supplemental material for this article is available online (doi: ).

Notes

How to cite this article

He C, Tan C, Shi Y and Tripathi AK (2026) Using blockchain to combat multiple types of counterfeits: Adoption strategy and value analysis. Production and Operations Management XX(XX): 1–20.

References

Babich

Hilary

(2020) Distributed ledgers and operations: What operations management researchers should know about blockchain technology. Manufacturing & Service Operations Management 22(2): 223–240.

Biswas

Gupta

(2019) Analysis of barriers to implement blockchain in industry and service sectors. Computers & Industrial Engineering 136: 225–241.

Bloch

Bush

Campbell

(1993) Consumer “accomplices” in product counterfeiting. Journal of Consumer Marketing 10(4): 27–36.

Cho

Kreps

(1987) Signaling games and stable equilibria. The Quarterly Journal of Economics 102(2): 179–221.

Cho

Fang

Tayur

(2015) Combating strategic counterfeiters in licit and illicit supply chains. Manufacturing & Service Operations Management 17(3): 273–289.

Chod

Trichakis

Tsoukalas

, et al. (2020) On the financing benefits of supply chain transparency and blockchain adoption. Management Science 66(10): 4378–4396.

Choi

Feng

(2019a) Information disclosure structure in supply chains with rental service platforms in the blockchain technology era. International Journal of Production Economics 221: 1–14.

Choi

Guo

Liu

, et al. (2020) Optimal pricing in on-demand-service-platform-operations with hired agents and risk-sensitive customers in the blockchain era. European Journal of Operational Research 284: 1031–1042.

Choi

Wen

Sun

, et al. (2019b) The mean-variance approach for global supply chain risk analysis with air logistics in the blockchain technology era. Transportation Research Part E: Logistics and Transportation Review 127: 178–191.

10.

Clifford

Shultz

Saporito

(1996) Protecting intellectual property: Strategies and recommendations to deter counterfeiting and brand piracy in international markets. Columbia Journal of World Business 31: 18–28.

11.

Deng

Ghasemkhani

Tan

, et al. (2023) Actions speak louder than words: Imputing users’ reputation from transaction history. Production and Operations Management 32(12): 1096–1111.

12.

Gao

Lim

Tang

(2017a) Entry of copycats of luxury brands. Marketing Science 36(2): 272–289.

13.

Gao

Lim

Tang

(2017b) The impact of the potential entry of copycats: Entry conditions, consumer welfare, and social welfare. Decision Science 48(4): 594–624.

14.

Gao

(2018) On the use of overt anti-counterfeiting technologies. Marketing Science 37(3): 403–424.

15.

Green

Smith

(2002) Executive insights: Countering brand counterfeiters. Journal of Internation Marketing 10(4): 89–106.

16.

Grossman

Shapiro

(1988) Foreign counterfeiting of status goods. Quarterly Journal of Economics 103(1): 79–100.

17.

Guha

Kumar

(2018) Emergence of big data research in operations management, information systems, and healthcare: Past contributions and future roadmap. Production and Operations Management 25(11): 1866–1884.

18.

Hastig

Sodhi

(2020) Blockchain for supply chain traceability: Business requirements and critical success factors. Production and Operations Management 29(4): 935–954.

19.

Hou

Zhen

Pun

(2020) Combating copycatting in the luxury market with fighter brands. Transportation Research Part E: Logistics and Transportation Review 140: 1–14.

20.

Lahiri

Dey

(2013) Effects of piracy on quality of information goods. Management Science 59(1): 245–264.

21.

Milgrom

Roberts

(1986) Price and advertising signals of product quality. Journal of Political Economy 94(4): 796–821.

22.

Niu

Dong

Liu

(2021) Incentive alignment for blockchain adoption in medicine supply chains. Transportation Research Part E: Logistics and Transportation Review 152: 1–32.

23.

Pun

Deyong

(2017) Competing with copycats when customers are strategic. Manufacturing & Service Operations Management 19(3): 403–418.

24.

Pun

Swaminathan

Hou

(2021) Blockchain adoption for combating deceptive counterfeits. Production and Operations Management 30(7): 1965–1985.

25.

Qian

(2008) Impacts of entry by counterfeiters. Quarterly Journal of Economics 123(4): 1577–1609.

26.

Qian

(2014a) Counterfeiters: Foes or friends? How counterfeits affect sales by product quality tier. Management Science 60(10): 2381–2400.

27.

Qian

(2014b) Brand management and strategies against counterfeits. Journal of Economics & Management Strategy 23(2): 317–343.

28.

Qian

Gong

Chen

(2015) Untangling searchable and experiential quality responses to counterfeits. Marketing Science 34: 522–538.

29.

Rachbini

(2018) The impact of consumer trust, perceive risk, perceived benefit on purchase intention and decision. International Journal of Advanced Research 6(1): 1036–1044.

30.

Shen

Yuan

(2020) Selling secondhand products through an online platform with blockchain. Transportation Research Part E: Logistics and Transportation Review 142: 1–18.

31.

Shen

Cheng

Dong

, et al. (2021a) Battling counterfeit masks during the COVID-19 outbreak: Quality inspection vs. Blockchain adoption. International Journal of Production Research 61(11): 3634–3650.

32.

Shen

Dong

Minner

(2021b) Combating copycats in the supply chain with permissioned blockchain technology. Production and Operations Management 31(1): 138–154.

33.

Spence

(1978) Job market signaling. Uncertainty in Economics 281: 283–306.

34.

Sun

Zhang

Zhu

(2020) Counterfeiters in online marketplaces: Stealing your sales or sharing your costs. Journal of Retailing 96(2): 189–202.

35.

Tom

Garibaldi

Zeng

, et al. (1998) Consumer demand for counterfeit goods. Psychology and Marketing 15(5): 405–421.

36.

Tripathi

Lee

Basu

(2022) Analyzing the impact of public buyer–seller engagement during online auctions. Information Systems Research 33(4): 1264–1286.

37.

Wang

Liu

Zhao

(2019) On the interaction between asymmetric demand signal and forecast accuracy information. European Journal of Operational Research 277(3): 857–874.

38.

Wang

Zheng

Jiang

, et al. (2021) Blockchain-enabled data sharing in supply chains: Model, operationalization, and tutorial. Production and Operations Management 30(7): 1965–1985.

39.

Whitaker

Kraussl

(2020) Fractional equity, blockchain, and the future of creative work. Management Science 66(10): 4594–4611.

40.

Wilcox

Hyeong

Sankar

(2009) Why do consumers buy copycat luxury brand? Journal of Marketing Research 46(2): 247–259.

41.

Fan

Cao

(2021) An analysis of strategies for adopting blockchain technology in the fresh product supply chain. International Journal of Production Research 61(11): 3717–3734.

42.

Cheung

(2020) Impacts of counterfeiting on a global supply chain. Manufacturing & Service Operations Management 24(1): 159–178.

43.

Zhao

(2006) Conducting R&D in countries with weak intellectual property rights protection. Management Science 52(8): 1185–1199.

44.

Zhang

Hong

Zhang

(2012) Fighting strategies in a market with counterfeits. Annals of Operations Research 192(1): 49–66.

45.

Zhang

(2015) Supply chain structure in a market with deceptive counterfeits. European Journal of Operational Research 240(1): 84–97.

46.

Zhen

Cai

Song

, et al. (2019) The effect of herding and word of mouth in a two-period advertising signaling model. European Journal of Operational Research 275(1): 361–373.

47.

Zhong

Zhang

(2021) A stackelberg game based two-stage framework to make decisions of freight rate for container shipping lines in the emerging blockchain-based market. Transportation Research Part E: Logistics and Transportation Review 149: 1–24.

Supplementary Material

Please find the following supplemental material available below.

For Open Access articles published under a Creative Commons License, all supplemental material carries the same license as the article it is associated with.

For non-Open Access articles published, all supplemental material carries a non-exclusive license, and permission requests for re-use of supplemental material or any part of supplemental material shall be sent directly to the copyright owner as specified in the copyright notice associated with the article.

0.58 MB