2.1 Live streaming selling

Live-streaming selling, as an emerging sales model, can be deeply explained through several theoretical frameworks: signaling theory, information asymmetry theory, and consumer engagement theory. According to signaling theory, live-streaming serves as a credible signal of product quality. In markets characterized by information asymmetry, high-quality manufacturers can make substantial investments in live-streaming—such as hiring professional hosts, offering multi-angle real-time demonstrations, and providing unconditional return policies—to convey confidence in their products [26,7]. These signals are credible because they are difficult for low-quality manufacturers to imitate. Second, live-streaming significantly reduces uncertainty resulting from information asymmetry. By enabling real-time video displays, interactive Q&A with hosts, and public discussions in comment sections, implicit product attributes—such as actual size, texture, and user experience—are transformed into explicit, verifiable information [8]. This process enriches consumers’ information sets, lowers uncertainty in utility evaluation, and thus enhances purchase intention. Finally, consumer engagement theory offers a framework for understanding how live-streaming builds interaction and trust. Unlike one-way communication, live-streaming creates a highly interactive and immersive environment [6]. Hosts engage the audience through quizzes, giveaways, and limited-time promotions, stimulating cognitive and emotional involvement [27]. Viewers become active participants and co-creators of content rather than passive recipients. This deep level of engagement not fulfills social and entertainment needs—as explained by the uses and gratifications theory [28]—but also fosters emotional connection and trust between consumers and brands or hosts [11]. Consequently, it strengthens brand loyalty and facilitates long-term customer relationships.

Existing research on live streaming selling can be categorized into three main themes: First, consumer behavior and participation mechanisms. Scholars have primarily explained how live streaming stimulates purchase intention through trust-building [16,17], cognitive-affective dual pathways [15], and social presence [8]. For example, Sun et al. [10], from a technological affordance perspective, argued that live streaming enhances consumer participation intention by improving interactivity and authenticity. Gu et al. [22] identified an inverted U-shaped relationship between entertainment intensity and participation, suggesting that content strategies should be moderated appropriately. While these studies emphasize user experience, they often fail to fully incorporate consumer engagement theory or systematically analyze how live streaming serves as a signaling mechanism to reduce quality uncertainty. Second, manufacturers’ live streaming adoption strategies (also related to platform or channel mechanisms). Research indicates that manufacturers’ decisions to adopt live streaming depend on factors such as sales capabilities, commission structures, operational costs, and product attributes [18,29,30,31]. Li et al. [19] further noted that live streaming is more suitable for products with low price sensitivity, as it more effectively communicates non-price value. However, these studies predominantly focus on operational efficiency and cost-effectiveness, lacking discussion on quality disclosure strategies within an information asymmetry framework. They also do not treat the choice of live streaming as a strategic signaling game in competitive contexts. Third, the influencer effect (including platform governance and host contract design). Recent studies have begun examining contractual structures between platforms and hosts [32,23] and how platform governance influences host behavior [20]. This body of work offers an institutional perspective on the live streaming ecosystem, yet it does not extend to critical questions regarding how manufacturers can use live streaming to signal product suitability.

2.2 Consumer preference

According to consumer decision-making theory, preferences refer to consumers’ relatively stable evaluations and rankings of different product attributes—such as quality, brand, functionality, and appearance [33]. These preferences form a mental benchmark for an “ideal product.” Among these, quality preference is particularly crucial, as it determines consumers’ willingness to pay a premium for higher quality [34]. For example, some consumers prioritize high performance (reflecting a high-quality preference), while others prefer basic functionality (reflecting a lower quality preference). Research on consumer preferences often focuses on how quality preferences influence firms’ strategies [35,5 21,36,37]. For instance, examined a differentiated duopoly game and explored how varying degrees of consumer preference dispersion affect decision-making. Zhang and Li [37] demonstrated that firms can implement behavior-based price discrimination by strategically disclosing quality information to different customer segments. However, most studies in this stream of literature assume that consumers are already informed about product quality before purchase, overlooking contexts in which actual product fit constitutes private information.

In online environments, the actual suitability of products—such as how well clothes fit, whether a mobile phone’s performance meets expectations, or if a skincare product matches one’s skin type—constitutes hidden information for consumers, leading to significant information asymmetry [38]. This uncertainty generates perceived risk [39], wherein consumers fear that products may not meet their needs, potentially resulting in financial or psychological losses. The volume and quality of product information serve as key mechanisms to mitigate this uncertainty. Drawing on information processing theory, consumers actively seek information to bridge “knowledge gaps” and make more accurate judgments [40]. Live streaming addresses this by delivering a rich array of multi-dimensional information—such as dynamic demonstrations, real-time Q&A, and user comments—going far beyond traditional text and images. Although several studies have begun to examine how information volume influences suitability judgments [41, 42] and have explored corporate adaptability disclosure strategies [43,44,45,46,47], these issues remain underexplored in the context of live streaming. For instance, while Jia [48] and Xie et al. [49] emphasized the role of balanced disclosure and virtual try-ons in building trust and reducing return rates, their studies did not fully theorize the underlying mechanisms nor investigate strategic interactions within a competitive multi-manufacturer environment.

In summary, the current research exhibits the following core gaps: First, studies on live-streaming sales lack deep integration with foundational theories such as signaling theory, information economics, and consumer engagement theory. As a result, they fail to systematically explain how live-streaming mitigates quality uncertainty and adaptation risks through real-time interaction and multi-dimensional information disclosure. Second, although consumer preference research is well-established, its intersection with live-streaming remains underexplored. There is a particular scarcity of analysis from a competitive game-theoretic perspective regarding how manufacturers can utilize live-streaming to disclose information about both product quality and fit.

To address the aforementioned research gaps, this study constructs a duopoly model involving two manufacturers with differentiated product quality, where information about product fit is private. Grounded in information asymmetry and signaling theory [26], we conceptualize live streaming as an effective mechanism for manufacturers to convey credible signals. Drawing on consumer utility theory, the model incorporates a utility function that accounts for product quality, perceived fit, and the informational value provided through live streaming. Furthermore, by integrating insights from the Hotelling model and Bertrand competition logic, we analyze firms’ disclosure strategies and investment in live-streaming capabilities under quality differentiation. This study not only bridges the theoretical disconnect between live streaming and adaptation preference but also offers practical decision support for manufacturers aiming to devise disclosure and competitive strategies in live-streaming environments based on their product quality. Table 1 presents a comparative summary of existing literature and this study to further elucidate its research positioning and theoretical contributions (LS indicates live-streaming).

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Table 1. Summary of related literature.

https://doi.org/10.1371/journal.pone.0339997.t001

4.1 No one adopts live-streaming (Case NN)

In the scenario where neither manufacturer opens a live-streaming channel, consumers cannot ascertain whether a product fits their preferences, resulting in decision-making under imperfect information. This reflects a typical setting of information asymmetry [38], where consumers form expected utility based on probabilistic assessments of product suitability. According to the theory of consumer utility, the utility consumers acquired for and are and , respectively. is the valuation consumers perceive from the product and follows a uniform distribution over [0,1]. Consumers buy products depending on max{U₁, U₂, 0}. Then we have when . Accordingly, the market demands for the two products are as follows:

(1)

(2)

The two manufacturers engage in Bertrand competition with differentiated products, each aiming to maximize profit:

(3)

(4)

Driving the equilibrium solutions allows us to have the following results.

Property 1. When no one adopts live-streaming,

1. (1). is a concave function with respect to , indicating there is maximum value for .
2. (2). is a concave function with respect to , indicating there is maximum value for .

Property 1 shows that there exist optimal pricing strategies and such that both manufacturers receive maximum profit in product competition.

Proposition 1. In Case NN, the equilibrium prices, market demands, and profits can be acquired with the conditions , , and

. The equilibrium solutions are

4.2 The manufacturer adopts live-streaming (Case DN)

In the case where only adopts live streaming, the market is segmented according to consumer type, reflecting how information asymmetry is partially mitigated through signaling [26]. ’s decision to live-stream serves as a credible signal of its product attributes, reducing uncertainty for a segment of consumers while others remain under imperfect information.

1. (1). The consumers

Since adopts live-streaming and discloses product information, then those consumers who are well-fit with the product of gain an additional utility x₁. Moreover, consumers who purchase from gain a precise valuation rather than an expected one . Those consumers select from the live-streaming channel of and the direct channel of , depending on their utilities. In this situation, the utilities obtained by consumers are and , respectively. Thus we have if .

1. (2). The consumers

Since ’s product is unfit for those consumers even though discloses the product information, consumers will not buy the product of . However, the product information of is unknown. Then those consumers buy ’s product when they receive a positive utility. The utility for the product of is . In this situation, the market demands for the two products are as follows.

(5)

(6)

The two manufacturers engage in Bertrand competition with differentiated products. Their profit functions are:

(7)

(8)

Property 2. When the manufacturer adopts live-streaming,

1. (1). is a concave function with respect to , indicating there is maximum value for .
2. (2). is a concave function with respect to , indicating there is maximum value for .

Accordingly, we drive the equilibrium solutions and propose Proposition 2.

Proposition 2. In Case DN, the equilibrium prices, market demands, and profits can be acquired with the conditions and . The equilibrium solutions are

4.3 The manufacturer adopts live-streaming (Case ND)

In the case where only adopts live streaming, the market is segmented based on consumer fit, illustrating how partial information disclosure through live streaming serves as a signaling mechanism [26] and creates an asymmetric information structure across consumer groups.

1. (1). The consumers

Since adopts live-streaming and discloses product information, then those consumers who are well-fit with ’s product gain an additional utility x₂. Those consumers select from the live-streaming channel of and the direct channel of , depending on their utilities and . Then we have if .

1. (2). The consumers

Since ’s product is unfit for those consumers even though discloses product information, they will not buy the product of . However, the product information of is unknown. Then those consumers buy the product of when they receive a positive utility, where the utility for product of is . In this situation, the market demands for the two products are as follows.

(9)

(10)

The manufacturers engage in Bertrand competition with differentiated products. Their profit-maximization problems are:

(11)

(12)

Property 3. When the manufacturer adopts live-streaming,

1. (1). is a concave function with respect to , indicating there is maximum value for .
2. (2). is a concave function with respect to , indicating there is maximum value for .

Accordingly, we drive the equilibrium solutions and propose Proposition 3.

Proposition 3. In Case DN, the equilibrium prices, market demands, and profits can be acquired with the conditions , , and

. The equilibrium solutions are

4.4 Both manufacturers adopt live-streaming (Case DD)

In the case where both manufacturers adopt live streaming, the market is fully informed due to comprehensive product disclosure by both firms. This represents a scenario where information asymmetry is substantially reduced through dual signaling [26], allowing consumers to make decisions based on actual rather than expected utility. The consumer population is partitioned into four distinct segments based on product fit:

1. (1). The consumers

Since both and adopt live-streaming and disclose product information, then those consumers who are well-fit with the product of and gain an additional utility x₁ and x₂, respectively. Those consumers select from the and depending on their utilities, where the utilities are and . Then we have if .

1. (2). The consumers

The type consumers represent those who are only well-fit with the product of but not simultaneously well-fit with the product of and . Then those consumers will not buy the product of since has disclosed product information but does not fit. However, they will buy the product of since has disclosed product information and is well-fit with them. The utility is .

1. (3). The consumers

Similarly, the type consumers represent those who are only well-fit with the product of but not simultaneously well-fit with the product of and . Then those consumers will not buy the product of since has disclosed product information but does not fit. While they will buy the product of since has disclosed product information and is well-fit with them. The utility is .

1. (4). The consumers

Both the products of and do not fit consumers, as such, they will buy neither nor .

In this situation, the market demands for the two products are as follows.

(13)

(14)

The manufacturers engage in Bertrand competition. Their profit-maximization problems are:

(15)

(16)

Property 4. When both manufacturers adopt live-streaming,

1. (1). is a concave function with respect to , indicating there is maximum value for .
2. (2). is a concave function with respect to , indicating there is maximum value for .

Accordingly, we drive the equilibrium solutions and propose Proposition 4.

Proposition 4. In Case DD, the equilibrium prices, market demands, and profits can be acquired with the conditions ,

. The equilibrium solutions are

5.1 The operational strategy of competitive manufacturers

Property 5. The impact of the additional utility x₁ provided by on equilibrium outcomes under all four scenarios (j = NN, DN, ND, DD) is as follows:

1. (1). Pricing strategy: , .
2. (2). Market demand: , .
3. (3). Profit: , 0.

Property 5 indicates that when consumers derive higher additional utility (x₁) from using product (due to better fit, superior experience, or enhanced trust), can command a higher price while still attracting more demand. This happens because increased perceived value enhances consumers’ willingness to pay. In response, is forced to lower its price to remain competitive, yet still loses market share and profit.

Property 6. The impact of the additional utility x₂ provided by on equilibrium outcomes under all four scenarios (j = NN, DN, ND, DD) is as follows:

1. (1). Pricing strategy: , .
2. (2). Market demand: , .
3. (3). Profit: , .

Property 6 reveals a symmetric effect to that observed in Property 5: when consumers gain higher additional utility (x₂) from product, can raise its price while still expanding market demand and profit. This advantage forces to lower its price in response, yet still ends up with reduced market share and profitability.

Furthermore, from Properties 5 and 6, for competing manufacturers, enhancing the perceived additional utility that consumers receive from a product (such as through better product-match communication, service quality, or experiential value) directly strengthens pricing power, market share, and profit margins. In a competitive context, when one manufacturer significantly improves consumer utility, rivals cannot rely solely on price reductions to remain competitive. Instead, they should focus on elevating their own product value or live-streaming presentation quality. Besides, increasing the fit-based utility (x₁ or x₂) is a powerful competitive lever. Manufacturers should prioritize strategies that enhance perceived suitability and consumer trust (such as personalized demonstrations, trial use campaigns, or stronger after-sales support) to build sustainable advantage in live-streaming markets. These findings underscore that in live-streaming commerce, non-price advantages rooted in product suitability and consumer experience play a decisive role in shaping competitive outcomes.

Property 7. The impact of the consumer fit proportion on equilibrium pricing strategies is as follows:

1. (1). pricing: , , , .
2. (2). pricing: , , , .

Property 7(1) reveals that the effect of (the proportion of consumers suited to product) on optimal price depends critically on whether uses live streaming. In Case NN and ND ( does not live-stream), a higher allows to raise its price. Without detailed product information, consumers who naturally prefer are more likely to purchase directly, giving market power. In Case DN and DD ( does live-stream), a higher leads to lower its price. Despite high innate preference, full information disclosure may reveal misfits, prompting to use price cuts to retain potentially hesitant customers. By contrast, Property 7(2) shows that always lowers its price as increases. A stronger innate preference for forces to compete more aggressively on price to attract any remaining customers.

Property 8. The impact of consumer fit proportion (suitability for M₂‘s product) on equilibrium pricing is as follows:

1. (1). pricing: , , , .
2. (2). pricing: , , , .

Property 8 reveals how the proportion of consumers suited to product () affects both firms’ pricing strategies. consistently lowers its price as increases across all four scenarios. This reflects need to compete more aggressively when more consumers are inherently suited to product. response depends on its information disclosure strategy. When does not live-stream (Cases NN and DN), it can raise prices as increases, exploiting its natural advantage without full information disclosure. When does live-stream (Cases ND and DD), it lowers prices despite high , because detailed product information may reveal misfits even to naturally suited consumers.

Properties 7 and 8 together offer key insights into pricing and disclosure strategies in competitive markets. When consumer fit is high but product information is limited, manufacturers gain pricing power since information asymmetry enables them to capitalize on innate preference without reducing prices. In contrast, facing a competitor with strong consumer fit may necessitate price cuts, particularly for manufacturers with weaker inherent appeal. Live streaming, however, changes this dynamic: even with high fit, full information disclosure can weaken a manufacturer’s ability to maintain high prices, as greater transparency may make consumers more cautious unless prices are lowered. Therefore, optimal pricing depends not only on consumer preferences but also on the level of product information revealed. Manufacturers should anticipate pricing adjustments in response to rivals’ fit advantages and disclosure strategies. Emphasizing unique product attributes—rather than competing on price alone—can help maintain profit margins and enhance market positioning.

The impact of δ on equilibrium solutions cannot be proved analytically, we thus turn to numerical studies. We fix x₁ = x₂ = 0.2, c₁ = c₂ = 0.1, α = 0.7, and vary δ, α, β to study the mechanism, then we plot Figs 1 and 2.

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Fig 1. The impact of δ on the M₁’s pricing strategy in different cases.

https://doi.org/10.1371/journal.pone.0339997.g001

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Fig 2. The impact of δ on the M₂’s pricing strategy in different cases.

https://doi.org/10.1371/journal.pone.0339997.g002

Fig 1 shows that the higher the δ, the lower the price set by M₁. A high δ indicates the quality of the M₂’s product becomes higher. In this situation, M₁ should decrease its price to compete with M₂. However, Fig 2 shows that the changing rule of the M₂’s price is quite different from that of the M₁. More precisely, when δ is low, the M₂’s price increases with δ; while the M₂’s price decreases with δ when δ belongs to a high value. In a low δ, the M₁’s price is at a high level and thus M₂ could design a high price under a channel competitive environment. Nevertheless, when δ belongs to a high value, M₁ designs a low price even though its product has a quality advantage, namely, z₁(=1)>z₂(=δ). In this context, M₂ should also reduce its price to maintain the competitiveness of its product, leading to the result that M₂’s price decreases with δ when δ is in a high value. Obviously, a high quality of M₂’s product does not necessarily allow M₂ to raise the price. Rather, M₂ should refer to the pricing strategy of the manufacturer who has a quality advantage (i.e., M₁) in determining the price.

5.2 The opening strategy of live-streaming channel

In this section, we compare the equilibrium price and profit of M₁ and M₂ to study the impact of adopting a live-streaming channel.

Proposition 5. The equilibrium prices under the four scenarios satisfy the following relationships:

1. (1). The price differential for M₁: .
2. (2). The price differential for M₂: .

Proposition 5 indicates that, first, when a manufacturer opens a live streaming channel, it can charge higher prices, namely, and for M₁; and for M₂. This occurs because live streaming provides authentic product information, allowing consumers to better understand product value. With reduced uncertainty, manufacturers can command price premiums. Second, a case of unilateral opening live-streaming channel leads to the highest price for the manufacturer, namely, and . This represents a “first-mover advantage” in information disclosure—the streaming manufacturer gains significant pricing power while competitors lack equivalent means to demonstrate value. Thus, a manufacturer facing weak competition should consider unilateral live streaming to maximize margins before competitors adopt similar strategies. Third, when both manufacturers stream, prices decrease compared to unilateral streaming, namely, and for M₁; and for M₂. This reflects intensified competition—when both sides provide full information, consumers can make direct comparisons, forcing manufacturers to compete more aggressively on price.

Proposition 5 further indicates that in mature markets where live streaming is widely used by competitors, manufacturers should focus on product differentiation rather than relying solely on information disclosure to maintain prices. To use live streaming effectively, firms should adopt it early to gain a pricing advantage, anticipate price erosion as rivals follow suit, combine live streaming with distinctive product features to sustain premiums, and monitor competitors’ live streaming activities given their direct impact on pricing flexibility. Overall, the value of live streaming depends critically on competitive dynamics—it offers the greatest advantage when implemented before competitors.

Proposition 6. The comparison of ’s equilibrium profits across scenarios reveals:

1. (1). Case NN vs Case DN

if , where .

1. (2). Case ND vs Case DD

if , where

Proposition 6 shows that the opening of the live-streaming channel does not necessarily increase the profit of , and it happens only if the operational cost for the live-streaming channel is relatively low. Otherwise, may be worse off if the operational cost for the live-streaming channel is relatively high. Generally, when the operational cost is high enough to cover the benefit generated from the live-streaming channel, the opening of the live-streaming channel becomes uneconomic.

Proposition 7. The comparison of ’s equilibrium profits across scenarios reveals:

1. (1). Case NN vs Case ND

if , where

1. (2). Case DN vs Case DD

if , where

Proposition 7 confirms that the profitability of live streaming depends critically on cost management, only benefits from live-streaming adoption when operational costs remain below specific threshold levels. This mirrors the finding for in Proposition 6, highlighting a universal principle in live-streaming strategy.

Therefore, prior to committing to live-streaming investments, manufacturers should perform a systematic cost-benefit analysis to verify whether anticipated revenues justify operational expenditures and generate positive net value. A successful live-streaming strategy depends not only on content quality but on establishing a sustainable cost-benefit advantage. Through strategic cost management, manufacturers can achieve enduring competitive differentiation; conversely, excessive investment can erode profit margins despite driving sales growth.

Note that there are some factors relevant to consumer features that influence the live-streaming strategy. Given the complexity of deriving closed-form equilibrium solutions, we employ numerical simulation to analyze manufacturer profitability. Using the baseline values x₁ = x₂ = 0.5, C_d1 = C_d2 = 0.05, c₁ = 0.1, c₂ = 0.05, δ = 0.8, we systematically vary the consumer fit parameters α and β. The resulting profit patterns are presented in Figs 3 and 4.

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Fig 3. The opening strategy of live-streaming channel for with varying α and β.

https://doi.org/10.1371/journal.pone.0339997.g003

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Fig 4. The opening strategy of live-streaming channel for with varying α and β.

https://doi.org/10.1371/journal.pone.0339997.g004

Fig 3 reveals that the profitability of ’s live-streaming strategy depends critically on consumer fit parameters α and β, a finding that can be effectively interpreted through established psychological frameworks. In the absence of ’s live-streaming strategy (i.e., Fig 3(a)), benefits from the live-streaming when α and β belong to a median value. From the perspective of social presence theory, this enables live streaming to establish meaningful social presence while avoiding excessive cognitive load on consumers. As a result, it can retain consumers who access detailed product information through live-streaming while still capturing those with inherent preference for ’s products. The observed negative impact at high α levels aligns with principles of parasocial interaction theory. When consumers already maintain strong product affinity (high α), detailed exposure through live streaming may disrupt rather than strengthen existing psychological bonds, as they gain awareness of potential mismatches that were previously overlooked. This phenomenon reflects an “uncanny valley” effect in consumer engagement, where excessive transparency can undermine carefully constructed brand perceptions, potentially causing decision paralysis among previously loyal customers. Therefore, a high enough α may hurt .

When employs live streaming (i.e., Fig 3(b)), benefits from live-streaming only when α is moderate and β is sufficiently low. This pattern aligns with social comparison theory: in dual-streaming environments, consumers naturally compare offerings, making ’s live-streaming most effective against clearly inferior alternatives (low β). Conversely, suffers losses from live-streaming when α is high and β is low. Unlike scenarios without ’s live-streaming, the intensified channel competition demands even lower β values for to profit. This occurs because only when ’s product demonstrates substantially weaker consumer appeal can capture sufficient market share through live-streaming to offset competitive pressures. This analysis reveals that under competitive live-streaming conditions, success depends not only on a manufacturer’s own product appeal but crucially on the relative disadvantage of competing products.

Concerning the strategy of , Fig 4 shows that in the absence of ’s live-streaming strategy (i.e., Fig 4(a)), derives a higher profit from live-streaming when α is large and β belongs to a median value. This pattern aligns with social presence theory, in the absence of competing live-streaming, ’s live-streaming creates a distinctive social environment that attracts consumers who are naturally inclined toward ’s products but remain open to alternatives. The live-streaming experience provides the human warmth and immediacy that helps overcome initial preference barriers. While will be worse off by opening the live-streaming if α is large enough and β is low. This scenario reflects principles of customer engagement theory, when ’s product has low inherent appeal (low β), live-streaming exposure may actually reinforce negative perceptions rather than building engagement. Consumers comparing ’s clearly inferior product against ’s well-suited offering through live-streaming demonstrations experience cognitive dissonance that further diminishes purchase intention.

In the presence of ’s live-streaming strategy (i.e., Fig 4(b)), benefits from live-streaming when α and β belong to a high value, while is hurt by the live-streaming when α is high and β is low. From the perspective of social comparison theory, the dual-streaming context creates a natural comparison environment where consumers simultaneously evaluate both products. A high β enables to withstand this direct comparison, as its product demonstrates sufficient inherent appeal to compete effectively against ’s offering. The scenario where suffers despite high α and low β can be understood through decision avoidance theory. When consumers recognize ’s product as clearly inferior (low β) compared to ’s well-suited alternative (high α), the additional information provided through live-streaming may actually trigger decision avoidance rather than facilitating choice. The live-streaming exposure makes the quality disparity more apparent, leading consumers to defer purchase decisions altogether.

Totally, the opening of the live-streaming channel does not definitely increase the profit of manufacturers. Rather, a situation with a median preference for the manufacturer’s product is suitable to introduce the live-streaming. In this sense, if the competitor adopts live-streaming strategy, a weaker product preference for the competitor’s product is needed to allow the current manufacturer who has a quality advantage to derive higher profit; while a stronger product preference for the competitor’s product is needed to satisfy to allow the current manufacturer who has quality disadvantage to derive higher profit.