1. Introduction

This study synthesizes recent literature on imitation entry and self-preferencing in dual-role platforms, elucidating the interplay between these phenomena.

An increasing number of e-commerce platforms such as Amazon and JD function as dual-role intermediaries, providing sales channels for third-party sellers while operating proprietary brands or direct-retail stores. In this model, the platform plays a dual role, acting as both a market operator and a trader. Against the backdrop of increasingly fierce market competition that emphasizes fairness and innovation-driven development, the issue of leading platforms imitating and entering by relying on their "dual role advantages" has drawn much attention.

Typical imitation entry behaviors include platforms abusing the transaction data of third-party merchants they own (eg. product features, sales volume, etc.), copying popular products, and then using self-preferred operations, such as manipulating search result rankings and allocating traffic recommendations to gain an advantage for their imitation products, significantly squeezing the exposure rate and market space of third-party merchants. People are increasingly worried that this kind of imitation entry behavior will have adverse effects on both consumers and businesses.

This paper will sort out the motivations of imitation entry and the process of its interaction with self-preferencing, aiming to provide decision-making references for competition regulatory authorities and policymakers. Meanwhile, we plan to propose valuable research directions through the content of theoretical literature and inject new vitality into related discussions.

1.1. Dual roles of e-commerce platforms

E-commerce significantly embraces the dual-role platforms as a standard business model, with integrating third-party and hosting with proprietary retail activities. Among them, Amazon and JD, owing to their market power and representative characteristics, serve as essential research subjects for investigating dual-role dynamics. The following will specifically elaborate on the business models of these two major platforms under the dual identities of "platform operator" and "self-operated seller".

Amazon operates the Amazon Store. It first builds a transaction ecosystem that connects global third-party sellers and consumers, providing basic services such as store construction and payment settlement for third-party sellers, and through these services, helps sellers reach a large number of consumer groups. Meanwhile, Amazon has also built its own product system through the platform, covering all categories of its self-operated business, ranging from popular consumer electronics and fashionable clothing to niche handcrafted cultural and creative products and outdoor equipment. This operation has led to direct commercial competition between self-operated merchants and third-party sellers.

As a comprehensive e-commerce platform, JD integrates third-party marketplace facilitation with direct retail operations, selling proprietary products and managing collaborative procurement through its self-operated model. In the key categories (eg.3C digital products, home appliances, JD's self-operated products), they are relying on their mature supply chain advantages, naturally form a direct competitive pattern with the products of third-party sellers on the platform.

To sum up, through the explanation of the business structure of the two major e-commerce platforms, Amazon and JD, we can clearly understand the dual identity characteristics of dual-role e-commerce platforms as "service providers and direct competitors".

1.2. Cases of e-commerce platform imitation entry and self-preferencing

1.2.1. Self-preferencing: rule-making and resource allocation

The core of self-preferencing behavior lies in the platform taking advantage of its dual identity as the "rule-maker + business participant" to tilt traffic, data and rules, thereby enhancing the competitiveness of its own business and squeezing the survival space of third parties. The following takes Amazon's "Buy Box" as a typical case for illustration:

"Buy Box" (Golden shopping cart) is one of the vital factors influencing product sales on the Amazon platform. Its algorithm rules should be based on neutral dimensions such as "price, logistics, and service", and the exposure rate should be fairly distributed to all sellers (including self-operated and third-party sellers). However, in the actual operation of the platform, there is an obvious self-preferencing design that tilts towards self-operated merchants: When self-operated products (such as the Amazon Basics series) compete with the products of third-party sellers, even if the self-operated products do not have advantages in terms of price, service response speed, etc., they can still be preferentially locked in the "Buy Box" display position; Conversely, the third-party sellers need to meet strict conditions, such as a much higher logistics timeliness than self-operated standards with a lower return rate, to be eligible to enter the traffic competition pool.

In addition, there is a view that Amazon holds a notable advantage in terms of product ranking [1]. After controlling for product features, it was observed that the average search ranking of Amazon's self-operated products was 24 positions higher than that of other products; this finding suggests that Amazon does exhibit self-preferencing behavior. When compared with similar products offered by other sellers, the ranking advantage of Amazon's self-operated products is considerably greater than that of products from other comparable brands.

Here, Amazon leverages its control over "Buy Box" rules to favor its own products—even when they lag in price or service. Third-party sellers face steeper hurdles just to compete, showing how the platform's dual roles let it rig the game. Waldfogel's finding of a 24-place ranking edge drives home that this isn't random: it's a deliberate tilt that shapes who succeeds on the platform. Notably, this ranking advantage may create a "feedback loop of disadvantage" for third-party sellers: lower visibility reduces sales data, which in turn further weakens their algorithmic performance, exacerbating the imbalance beyond mere rule manipulation [2].

Although Waldfogel [1] has advanced our understanding of self-prioritization behaviors on e-commerce platforms through valuable empirical explorations, their work also presents opportunities for further extension when viewed from a broader research perspective. For instance, the study primarily drew inferences about self-prioritization based on changes in search rankings, with its analytical framework not yet incorporating unobservable product attributes—such as nuanced consumer preference variations or dynamic shifts in product quality. This focus, while methodologically consistent, leaves room for considering alternative explanations that could enrich the analysis, such as the potential influence of inherent popularity differences among Amazon’s own products.

Additionally, the research offers insightful observations on short-term price and quantity effects, yet the exploration of long-term dynamics remains an area where deeper investigation could be beneficial. Specifically, it has not fully addressed causal relationships related to long-term outcomes, including how platform behaviors might shape third-party sellers’ innovation incentives or drive evolution in market structure. As the existing research noted in related work, understanding phenomena like changes in third-party innovation following platform entry often requires integrating additional contextual factors to build a more comprehensive analytical framework—an angle that could complement Waldfogel’s findings [3].

1.2.2. Imitation entry: data collection and product replication

The imitation entry of dual-role e-commerce platforms essentially relies on the data advantages of the platforms to directly copy and squeeze the innovative businesses of third-party sellers on the platforms. In the Amazon platform ecosystem, the competition between third-party brand innovation and self-operated business has become a typical sample for understanding this behavioral pattern.

According to a report, the third-party brand Peak Design has opened up the photography equipment market with its innovative camera bag product "EverydaySling", attracting a large number of photography enthusiasts and professional users with its compact design, convenient access experience and functions that adapt to the shooting needs of multiple scenarios. As a platform operator, Amazon, through its ecosystem data system, deeply captures data such as product sales popularity, user reviews, and geographical distribution of purchases. It promptly launched the "Amazon Basics Camera Bag", replicating the features of Peak Design's popular camera bags in terms of appearance, storage structure, and core functions. It has achieved "rapid follow-up" on the innovative achievements of third parties [4].

This dynamic highlights a key imbalance: platforms leverage their data access to bypass the risks and costs third-party innovators incur. By repurposing seller-generated insights for direct competition, it creates a disincentive for original product development, potentially stifling the very innovation that enriches marketplace ecosystems.

2. Advantages of dual-role platforms

The advantages of dual-role platforms (i.e., platforms that both operate the market and sell their own products) are mainly reflected in improving market efficiency, enhancing competition, and optimizing consumer experience, and relevant studies have verified them from multiple dimensions from both theoretical and empirical perspectives.

2.1. Solving market coordination difficulties and expanding user networks

The research of Hagiu and Spulber pointed out that dual-role platforms can effectively solve the coordination problem where both buyers and sellers are reluctant to participate in the platform due to uncertain expectations [5]. Taking Amazon as an example, it not only provides a trading place for many small and medium-sized merchants but also launches its own businesses with platform endorsement such as self-operated brands, attracting a large number of consumers with high requirements for quality. This makes more small and medium-sized merchants see the potential of reaching a large number of consumers on Amazon, so they have settled on the platform one after another, successfully building a huge e-commerce ecological network, and breaking the low-efficiency equilibrium where both buyers and sellers were reluctant to participate in the platform due to uncertain expectations in the early stage.

2.2. Enhancing market competition intensity and promoting consumer welfare

The own products of dual-role platforms directly compete with third-party products, which may drive down prices. Analysis of multi-product enterprises' competitor-introduction strategies reveals that platform transformation mitigates direct competition through third-party seller integration, while demand aggregation through shopping to enhance consumer acquisition [6]. At the same time, the dual-role model is always better than the pure market model for the platform. Even if the commission set by the platform in the dual-role model is the same as that in the market model, the competitive pressure from the platform's own products will lower the price of innovative sellers (S), ultimately benefiting consumers and increasing consumer surplus and social welfare. The study also showed that if a ban is imposed on the dual-role model, the platform will switch to a pure seller model or a market model according to its own advantages, which may damage consumer welfare. When the platform is more efficient than marginal sellers in a certain type of product, the ban will force the platform to switch to the seller model and make consumers that unable to combine the high-quality products of innovative sellers with the convenient services of the platform, reducing the number of transactions and lowering total welfare. Even if the platform switches to the market model, consumer surplus will decline due to weakened competition.

Yet this "consumer welfare" narrative overlooks distributional inequalities: platform-exclusive services (e.g., Prime) may concentrate benefits among privileged users, while non-members face higher implicit costs, creating a two-tiered consumer landscape [7].

2.3. Optimizing resource allocation and utilizing management advantages.

Hagiu and Wright compared the market operator model and the dealer model and found that the platform can choose the model based on information advantages: when sellers have more accurate demand information, they tend to adopt the market model; when coordination problems are prominent, the resale model is more effective. The dual-role model can flexibly balance the advantages of the two models and improve the overall operational efficiency [8].

2.4. Promoting market expansion and innovation

A study based on Amazon's data showed that the platform's entry into new markets (such as the German home and kitchen category) can promote market expansion in the short term. Although it may inhibit the innovation of new products by third-party sellers in the long term, the platform's scale effect still has a positive effect on market expansion [3].

3. Imitation entry and self-preferencing in dual-role platforms

3.1. The motivation of imitation entry

In the research on enterprises' imitation behavior when entering new markets, the exploration of imitation motivations is one of the essence issues. Existing studies have shown that efficiency and legitimacy are the two momentous motivations driving enterprise imitation [9].

The efficiency motivation emphasizes that enterprises reduce the trial-and-error costs of their own brands by observing other successful or popular products and evaluating factors such as market demand, operation modes, and appearance designs. From the theoretical origin, this motivation is related to organizational learning theory. Enterprises obtain information from the behaviors of other enterprises through vicarious learning, evaluate the costs and benefits of innovation, and then decide whether to imitate [10-11]. As the number of enterprises adopting a certain innovation increases, the marginal benefit of information will decrease. Therefore, imitation behaviors driven by efficiency usually show the characteristic of "slowing down as the number of adopters increases". For example, the online channel entry behaviors of de novo retailers and small - sized retailers are mainly driven by efficiency motivation, and their decisions pay more attention to whether imitation can improve operational efficiency and profitability [9].

The legitimacy motivation refers to the situation where when a certain brand model or category becomes an industry hotspot, enterprises imitate to gain social recognition and meet the expectations of stakeholders (such as investors and consumers) . This motivation is rooted in the concept of imitation isomorphism in the new institutional theory, that is, under environmental uncertainty, enterprises obtain legitimacy by imitating the common behaviors in the industry to avoid being regarded as "outsiders" for not following industry norms [12-13]. imitation behaviors driven by legitimacy usually show the characteristic of "accelerating as the number of adopters increases". This is because the more enterprises adopt a certain behavior, the stronger its legitimacy as an industry standard, and the greater the pressure on non - adopters [14]. Studies have found that the online market entry decisions of large retailers and public companies are more inclined to be dominated by legitimacy motivation, and their imitation behaviors are more to respond to external pressures to maintain their social recognition [9].

It is worth noting that efficiency and legitimacy motivations are not mutually exclusive, but have different impacts on different types of enterprises in different contexts. Characteristics (eg. scale, ownership nature, and business scope of enterprises) will lead to significant differences in the priority of their imitation motivations [9].

3.2. The effects of imitation entry

Within the research framework of firms ' imitation entry behavior, its impacts on different market participants exhibit diverse and complex characteristics. By integrating the logic of efficiency - and legitimacy - driven imitation motives, we can conduct an in - depth analysis of the influence mechanism of imitation entry from the perspectives of consumers and third - party merchants [9].

3.2.1. Impact on consumers: short-term gains and long-term losses

From the perspective of efficiency-driven imitation behavior, enterprises quickly launch similar products by observing and learning from peers’ successful models. In the short term, this intensifies product competition, subsequently leading to lower prices. However, in the long run, it restrains consumer welfare. This is because imitation behavior undermines innovation outcomes, and enterprises’ R&D investments—dependent on expected innovation returns—consistently decline. Consequently, the market’s overall innovation level drops, and the range of differentiated products available to consumers shrinks, ultimately resulting in long-term welfare losses.

Similarly, in the short term, platforms often imitate and integrate third-party services; for example, Google replicates vertical search functions or Amazon copies niche products. They then leverage economies of scale to offer lower prices or even free services. Likewise, hybrid platforms such as WeChat reduce user switching costs by imitating and consolidating multiple third-party functions. However, in the long run, once original innovators are displaced, the market becomes dominated by platform-led homogeneous services. This dynamic reduces consumers’ access to differentiated options and weakens incentives for disruptive innovation, as seen in Facebook’s replication of Snapchat’s “Stories” feature instead of developing novel interaction models. Scholars argue that such imitation-driven strategies not only slow the pace of service iteration but also allow platforms to expand into new domains, thereby broadening their data collection scope. These comprehensive user profiles are then used for targeted advertising and promoting the platform’s own services, further constraining consumers’ autonomy in choice.

3.2.2. Impact on third-party merchants: weakening of innovation incentives and profit margins

Imitation entry will expose third-party merchants to a market environment where competition among similar products intensifies, and it is inevitable that sales diversion will occur. The "collective imitation" triggered by the motivation of legitimacy further dilutes the benefits of innovation: the imitation behavior strengthens the public nature of the innovation achievements, and it is difficult for third-party merchants to achieve value compensation for the monopolized innovation benefits. Against this backdrop, the innovation incentives for third-party merchants have been continuously weakening due to the imbalance between input and output, and the shift in customer sources has also compressed the profit margins of third-party merchants.

In conclusion, the impact of imitation entry behavior on consumers, third-party merchants, and the market ecosystem essentially reflects the contradiction between short-term market equilibrium and long-term innovative development under the influence of efficiency motives and legitimacy motives.

4. The combined effect of imitation entry and self-preferencing

In today's digital business environment, the coexistence of imitation entry and self-preferencing behaviors has become increasingly common, and the simultaneous adoption of these two behaviors has had a more far-reaching impact on all parties.

4.1. Short-term gains and long-term harm to consumer welfare

In the short term, imitation entry may enable consumers to obtain short-term welfare through increased product supply and price competition (such as lower prices and more choices), while self-preferencing may prompt platforms to improve service quality (such as faster delivery and better after-sales service) to consolidate user stickiness [2]. For example, Amazon expanded its product range through imitation entry and improved user experience through Prime's logistics preferential treatment, which enhanced consumer satisfaction in the short term [7].

However, in the long run, the synergistic effect of the two will lead to damage to consumer welfare. On the one hand, the suppression of third-party merchants' innovation reduces product diversity and technological iteration, and consumers will eventually face homogeneous choices [15]; on the other hand, after platforms build market barriers through self-preferencing, they may extract consumer surplus through dynamic pricing, personalized premiums, and other methods. Khan pointed out that Amazon implements "personalized pricing" through data analysis, charging different fees to different users, and the exclusivity of self-preferencing (such as only Prime members can enjoy discounts) further limits consumers' choices [7]. In addition, Google's mandatory requirement for applications to use its payment system in India has made it impossible for consumers to choose lower-cost payment methods, indirectly increasing transaction costs [16].

This dynamic is compounded by the opacity of algorithmic pricing: consumers cannot easily detect how imitation entry data informs price discrimination, making it harder to challenge or avoid exploitative practices [15].

4.2. Squeezing third-party merchants and suppressing innovation

The combination of imitation entry and self-preferencing significantly weakens the market space and innovation motivation of third-party merchants. Platforms, through imitation entry (such as copying third-party hot-selling products or services), and at the same time using self-preferencing (such as traffic tilt, priority in search ranking, and exclusive clauses) to put their own products in an advantageous position, forming a closed-loop squeeze of "imitation-preference" [7]. For example, after Amazon obtained third-party sales data through its Marketplace, it launched its own brands (such as Amazon Basics) to imitate best-selling products, and prioritized the promotion of its own products through Prime member services, resulting in a decline in sales of third-party merchants [7]. This behavior not only directly diverts customers but also further compresses their profit margins by increasing the operating costs of third parties (such as platform commissions and distribution fees) [2].

From the perspective of competitive impact, with the dual identity of "rule-maker + participant", the platform identifies the successful models of third-party sellers by virtue of data hegemony, and directly occupy the market space of innovative third-party sellers through the replication of self-operated businesses. Khan pointed out in "Amazon's Antitrust Paradox" that Amazon, by leveraging the synergistic advantages of "platform data + self-operated business", makes the innovation investment of third-party sellers face the risk of "input-return" imbalance: when innovative products created by third-party sellers with huge resources (such as Peak Design's characteristic camera bags) are quickly imitated by the platform's self-operated business and achieve wider reach with the help of platform resources (such as traffic tilt and cost advantages), the diversity and vitality of market innovation will continue to decline, eventually falling into an inefficient cycle of "innovation-imitation-re-innovation", which inhibits the continuous innovation motivation of third-party sellers on the platform [7].

From a theoretical perspective, this synergistic effect undermines the virtuous cycle of "innovation-return". Third-party merchants, because their innovative achievements are easily imitated and it is difficult to obtain fair competition opportunities, gradually reduce R&D investment, falling into a vicious circle of "diminished innovation motivation and intensified homogeneous competition" [9]. An investigation by Indonesia's KPPU on Google showed that Google forced app developers to use its payment system (Google Pay Billing) and charged a 15%-30% commission, while at the same time prioritizing the promotion of its own apps through algorithms, resulting in a significant reduction in the innovation willingness of third-party developers [16].

4.3. Distortion of market competition order and strengthening of monopoly

The combination of imitation entry and self-preferencing distorts the competition order through multiple mechanisms and increases market concentration. The specific manifestations are as follows:

4.3.1. Raised entry barriers

Platforms use data advantages (such as user preferences and sales trends) to accurately imitate potential products, and quickly occupy the market through self-preferencing, making it difficult for potential competitors to break through the existing pattern. The EU's antitrust ruling against Google pointed out that Google prevented competitors from entering the search market through exclusive agreements on the Android system (such as pre-installing Google apps), and at the same time imitated vertical services (such as maps and payments), forming an "ecological monopoly" [15].

4.3.2. Failure of fair competition mechanisms

Self-preferencing endows platforms with the dual role of "referee and player", enabling them to suppress opponents through non-price means (such as algorithm discrimination and data blocking). For example, Shopee gives priority to delivering its own products through its own logistics (SPX Express) and restricts third-party merchants from using other logistics, resulting in differences in delivery efficiency that further consolidate the platform's advantages [15].

4.3.3. Cross-domain transmission of market power

The platform extends its advantages from core business to related fields through imitation entry, and then consolidates cross-market monopolies through its own preferences. For instance, Amazon has extended from e-commerce to cloud computing (AWS), logistics and other fields. It optimizes AWS services by leveraging e-commerce data and forms a "cross-market collaborative monopoly " by prioritizing the recommendation and binding of customers [7].

4.3.4. Summary

In summary, imitation entry combined with self-preferencing distorts the market competition order and strengthens market concentration through interrelated mechanisms. These two strategies collectively raise entry barriers for potential competitors, impair the operation of fair competition mechanisms, and allow platforms to achieve cross-domain transmission of market power—eventually consolidating monopolistic structures in the platform economy. Such outcomes not only restrict market vitality and innovation but also pose threats to consumers’ long-term interests.​

5. Implication for regulations and policies

The synergy between imitation entry and self-preferencing has exposed the limitations of the existing anti-monopoly framework. Traditional anti-monopoly approaches focus on short-term price effects (such as "consumer welfare standards "), making it difficult to capture the long-term damage to innovation and market structure caused by imitation entry and self-preferencing . For instance, Amazon 's low-price strategy was in line with consumers ' interests in the short term, but its combination of "loss for growth " + "imitation + preference " eventually led to a monopoly. However, as it did not directly raise prices, it avoided regulation [7].

In response to this, many countries have explored targeted regulations: the EU 's Digital Markets Act (DMA) prohibits "gatekeeper platforms " from having self-preferencing (such as prioritizing the promotion of their own services). This policy effectively targets the core of self-preferencing behavior, but its cross-border enforcement may face challenges due to differences in legal systems and regulatory standards among countries. Indonesia has restricted platforms from mandating the use of specific payment systems by amending the Telecommunications Commercial Act [16]. Additionally,The US Department of Justice has filed a lawsuit against Google for its practice of giving preferential treatment to its own applications in the application store [15]. This judicial intervention sends a clear signal to the industry that self-preferencing will not be tolerated, but the lengthy litigation process may delay the time for rectifying the problematic behavior, affecting the timeliness of regulation.

These practices indicate that regulation needs to start with supervision and prohibition of imitation entry and self-preferred behaviors, strengthen the protection of intellectual property rights, and require platforms to prohibit the abuse of data and information.

From a practical perspective, this regulatory direction is in line with the current needs of curbing monopoly and promoting innovation, but it is necessary to formulate more detailed implementation rules to avoid overly vague provisions that make it difficult for platforms to implement and regulators to enforce—and such enforcement challenges are already evident in specific scenarios. For instance, defining "abusive data use" in imitation entry remains vague, and gatekeeper platforms may even circumvent existing rules through subtle algorithmic adjustments, which further underscores the need for not only detailed implementation rules but also more granular monitoring frameworks to address these loopholes and ambiguities.

6. Gaps in existing research

While existing studies on e-commerce dual-role platforms’ imitation entry and self-preferencing have yielded insightful findings—such as dissecting Amazon’s "Buy Box" traffic tilt, JD’s self-operated-third-party competition dynamics, and the short-term consumer welfare gains versus long-term third-party innovation suppression—key gaps persist within the e-commerce context.

First, there is insufficient analysis of how different data types affect imitation entry—an issue that also undermines the clarity of theoretical models and practical judgments. On the one hand, while the review confirms platforms use third-party transaction data (e.g., product features, sales volume) for imitation (e.g., Amazon copying Peak Design’s camera bags), it lacks key distinctions: it does not separate the role of structured data (e.g., sales figures) from unstructured data (e.g., user reviews), nor does it explore how combining these data types impacts imitation speed and precision. This gap directly leaves the core data-driven imitation mechanism unclear. On the other hand, current theoretical models—even with their incorporation of numerous scenarios and assumptions—fail to address this gap effectively, as they often overlook the complex, ever-evolving nature of digital platform competition and rely on overly restrictive premises. A typical example is the static single-path assumption: mainstream models rooted in static game theory assume platforms can optimize strategies (e.g., commission rates, ranking algorithms) in one go, ignoring two critical dynamic processes: platforms may gradually learn from third-party seller data before entering the market, and sellers, in turn, continuously adapt their strategies (e.g., search engine optimization, pricing adjustments) to counter competitive threats. This interconnected set of unresolved issues—from unclear data role distinctions to flawed theoretical premises—ultimately makes it difficult to distinguish whether ranking advantages stem from "self-preference" or "product superiority" [1].

Second, dynamic adjustments of self-preferencing in e-commerce scenarios are overlooked. The review covers static manifestations like Amazon’s "Buy Box" tilt and Waldfogel’s 24-place self-operated ranking advantage, but not how platforms adjust self-preferencing (e.g., traffic allocation) during e-commerce-specific events like promotions or market changes—failing to capture the flexibility of such behaviors.

Thirdly, there has been no in-depth research on the impact of emerging technologies (such as artificial intelligence) on imitation entry and self-preferencing. Researchers and regulatory authorities may consider how the AI that is widely used in e-commerce today is reshaping the imitation entry model. For instance, could AI-driven demand forecasting accelerate the replication process of products, or could algorithms hide traffic biases?

7. Conclusion

This paper examines how dual-role platforms such as Amazon are involved in imitation entry and self-preferencing, revealing their core mechanisms, motivations, and comprehensive influences. Based on existing theories and empirical research, it is obvious that these practices involve inherent trade-offs. In the short term, the dual-role model can benefit consumers by increasing product options, price competition and improving services. However, the interaction between imitation entry and self-preferencing brings long-term risks: the innovative drive of third-party sellers weakens, product diversity shrinks, market competition distorts, and monopolistic tendencies intensify.

Future research should delve into minute areas: the differences in these behaviors among various industries and regions, the effectiveness of emerging regulatory tools, and strategies for balancing platform efficiency with continuous third-party innovation. Ultimately, addressing these challenges requires a comprehensive approach to protect competition, promote innovation, and safeguard consumers ' interests in the evolving digital environment.