1. Introduction

In the evolving digital platform economy, local life service platforms are beginning to play a significant role in a comprehensive urban life services system that allows users to find what best suits their city. Platforms must manage the entire stack to stay competitive, from intricate systems for merchant management, rider incentive programs, and fulfillment control on the supply side to fine-grained operation management on the demand side to reduce delivery times.

Meituan and Uber Eats, the two most popular local life service platforms in China and the US, respectively, provide evidence. They represent two distinct operational frameworks: Meituan has worked hard to create a platform-governed collaborative model, which includes the Order Task Packages and Merchant Growth System. Uber Eats, on the other hand, has been using its market advantage, which is a decentralized structured commission mechanism and a crowdsourced delivery-rider model, to pursue a decentralized strategy. These two differences are not merely biases in business practices; rather, they are examples of the deeper institutional divide between China and the United States in the areas of data governance, labor laws, and market regulation. They show how different platform operational models, whether active or passive, can exist under institutional constraints.

Given the foregoing context, this study primarily compares the following three elements: data and merchant cooperation mechanism, the management and labor system of the riders, the operation mechanism, and the system feedback for the Users. The purpose of this paper is to show how these important institutional differences affect the firm-level platform strategy and how that affects the local organizing form of globalized platforms.

2. Comparative analysis

2.1. Cross-dimensional comparison

2.1.1. Data and merchant collaboration

In a platform-centered local lifestyle service network, merchants are no longer just supply chain nodes, but algorithm partners in a highly data-driven service network. This change of function demands that the platforms should not only aggregate and analyze a large volume of merchant data but should also frame a complete set of channels to influence the merchants' service responses, advertising investments, and collaborative fulfillment. Meituan and Uber Eats have constructed some typical ways of data governance and business partnering, which are intuitively related to the institutional differences of the two countries.

Meituan built a deeply integrated merchant growth system in China. The system computes per-user real-time rating scores by considering merchants' historical orders, reviews, CTRs, fulfillment timeliness, and translates those rating scores into ranking, traffic distribution, and operational support [1]. It shows merchants' data-based collaboration targets via task packages, which include performance targets, for instance, enhancement of order acceptance and meal preparation speed, increase of positive review rating, and commitment to better recommendation or fee reduction if these targets are reached [2]. The closed-loop logic of the platform constructs goals, feeds back data about the merchant, and adjusts algorithm behavior, and the platform has strong soft control over the data of the merchant.

Compare this to Uber Eats, which is much more market-oriented, decentralized in its operations. The weight of customer-local exposure of a merchant on an online retail platform is not mainly regulated by platform-set KPIs or scores. In fact, it is decided by a mix of factors, such as consumer feedback scores, order fulfillment rates, and payment conversion rates [3]. The platform does not create operational task packages or set rates to promote particular behavior, even though it provides a data dashboard that lets merchants monitor their operational data. Uber Eats more closely resembles a matchmaking-style data market than a winner-take-all platform ecosystem under this model, which maintains merchant autonomy while undermining the central platform's governance capabilities [4].

Additionally, the Meituan model is more stable in the short term, but it runs the risk of making the merchant stickiness issue worse. Small and micro merchants are burdened with too many tasks, which can easily lead to data anxiety and operating fatigue. For Uber Eats, retailers had greater flexibility and convenience; however, in an environment where incentives are dispersed, they would be susceptible to game algorithms and would not be motivated to continuously improve their services [5].

In conclusion, the differences in corporate governance logic are not the only reason why merchants in China and the U.S. have different cooperation mechanisms; the differences in institutional expectations and limiting circumstances are also major factors. Platform governance is more than just a technology for optimizing internal operations in the datafication era. It's an institutional adaptation process. When it comes to the platform, Meituan's cooperative governance and Uber Eats' commoditivist governance are similar to two different manifestations of the institutional boundaries of control.

2.1.2. Labor and rider management

Delivery riders are essential links between customers and retailers in the local lifestyle platform ecosystem. The platform's reputation for social responsibility and service quality is directly impacted by its working conditions and fulfillment efficiency. In light of it, how platforms balance efficiency-driven operations with labor protection has become a highly institutionalized issue. The management logic and institutional orientations of Meituan and Uber Eats on this issue reflect two distinctly different governance models.

Meituan adopts a quasi-employment system with strong control, where the platform highly intervenes in the delivery riders' work process through algorithmic scheduling, task assignment, and performance scoring. Although delivery riders are nominally freelancers, the platform effectively controls key variables such as working hours, order sequence, and service areas [6]. The platform sets multi-dimensional KPIs for riders, including order acceptance timeliness rate, on-time delivery rate, user ratings, and negative review rate. If ratings fall below a certain threshold, riders may be restricted from accepting orders or even have their accounts suspended. Such mechanisms reinforce the platform's compressive management of labor, referred to by scholars as the precise monitoring structure of algorithmic labor [7].

Furthermore, Meituan has introduced a dual-track system of crowdsourcing and dedicated delivery, in which dedicated delivery riders belong to cooperative outsourcing companies, wear uniform work attire, clock in at designated locations, and receive offline training. Crowdsourced riders, on the other hand, can log in freely and accept orders flexibly. Although the crowdsourcing mechanism superficially emphasizes flexible employment, it actually exerts constant pressure on labor behavior through platform algorithms to achieve the effect of the re-employment of de-employment [8]. This hybrid system allows the platform to maintain flexibility when facing labor inspections and compliance requirements while sustaining high operational efficiency.

In contrast, Uber Eats in the United States operates under a typical platform-matching employment model, emphasizing that riders are entirely independent contractors (independent contractor). The platform does not participate in arranging their working hours, routes, or service content, but merely provides the order-taking technical platform and settlement channels [9]. This model enables Uber Eats to avoid significant labor law-related liabilities and costs, such as minimum wage, workers' compensation insurance, and employee benefits. However, this light-touch labor relationship has also sparked widespread social criticism, with riders being referred to as atomized workers without platform protection.

Additionally, significant differences emerge between Chinese and U.S. platforms in terms of labor data governance. Meituan's performance algorithms are highly confidential and often operate as black-box mechanisms, making it difficult for riders to understand the logic behind order assignments and score changes, resulting in significant information asymmetry [6]. In contrast, Uber Eats discloses some algorithm rules in accordance with local laws and allows riders to view their historical scores and income distributions to mitigate the risk of being accused of algorithmic discrimination [4].

Overall, Meituan pursues the ultimate efficiency of delivery through highly controlled labor algorithm governance, resulting in systemic labor pressure, risk transfer, and digital oppression. While Uber Eats nominally safeguards riders' autonomy, the lack of institutional safeguards leads to a platform responsibility vacuum. The two platforms reflect different paths chosen by platforms under institutional differences regarding how labor is integrated into algorithmic systems.

From a global perspective, the governance logic of Chinese and American platforms toward riders not only reflects adaptation to local policy environments but also gradually influences the formulation of global platform rules. Riders, as the most vulnerable yet critical component of the platform economy, are caught between the triple forces of platform, market, and state, and have become a key touchstone for the social responsibility and institutional strength of today's platforms.

2.1.3. User operations and system feedback

Within the ecosystem of local lifestyle platforms, users are not only the initiators of orders but also the behavioral input and feedback debuggers for the algorithmic system. Every algorithmic adjustment and operational strategy change on the platform relies on the continuous collection and structured interpretation of user behavior data. In this regard, Meituan and Uber Eats exemplify high-frequency interactive and free preference-based user operation paths, respectively. The differences between them also reflect significant disparities between China and the US in terms of platform data ownership, privacy regulations, and consumer protection systems.

Meituan tends to guide user behavior through the construction of a high-density and high-frequency system feedback mechanism. For example, in the interface design after order completion, Meituan guides users through multiple rounds of feedback, including rating, tag selection, follow-up reviews, and tipping, ensuring that user behavior is continuously datafied and serves as a critical input for platform algorithm adjustments [1]. Additionally, Meituan has introduced a personalized recommendation engine based on user behavior, utilizing multi-dimensional data modeling such as LBS (a geolocation system), order history, and page dwell time to form a dynamic content distribution system. This model further supports the overall predict-satisfy-repredict cycle of positive feedback between the platform and user interests [2].

Conversely, Uber Eats takes a less personal approach on the user end. The platform focuses on zero friction, which reduces user operations and decisions to a minimum. Uber Eats adopts a more hands-off operational strategy on the user side. The platform emphasizes a frictionless experience, minimizing user operation steps and decision interventions. For example, post-order reviews are entirely voluntary, with no mandatory rating interface. Also, personalized recommendations are primarily based on product categories and historical orders, rather than comprehensive behavioral modeling, and in areas such as ad push notifications and interface layout, the platform avoids forced clicks [4].

In terms of user feedback handling mechanisms, the two platforms also exhibit differentiated logic. In handling negative reviews, Meituan's customer service typically intervenes and records the merchant's and user's appeals and compensation processes, with this data entering the merchant profiling system and influencing subsequent traffic distribution. Uber Eats, on the other hand, places greater emphasis on a self-organized user-merchant evaluation system, with the platform intervening less in the handling process. Manual judgments are only made in cases of major complaints, and users are explicitly informed of the confidentiality and irreversibility of their evaluations [10].

Additionally, differences in user data transparency also reflect the divergence between Chinese and US platforms. In China, platforms generally do not disclose the usage paths or algorithmic logic of user data. However, in the US, platforms often establish privacy centers that allow users to manage their personalized settings, view data usage summaries, and export data. While this transparency mechanism may limit the platform's ability to conduct precise operations, it enhances the platform's trustworthiness and legal boundaries [5].

In summary, Meituan reinforces the track-based management of user behavior through a highly integrated and responsive operational mechanism to achieve precise control over the platform ecosystem. Uber Eats, on the other hand, adopts a limited algorithmic intervention strategy under institutional regulations, placing greater emphasis on the spontaneous formation of user preferences and compliance with agreements.

2.2. Institutional factors driving divergence

The significant differences in operational mechanisms between the two Chinese-US local life service platforms described in the abovementioned text are not the result of corporate preferences or accidental market choices. Instead, they are structurally nested responses deeply rooted in the institutional environments of their respective countries. This section will systematically explore the core differences at the institutional level from three aspects to provide a theoretical analysis for understanding the formation of their platform strategies.

2.2.1. Data governance and platform responsibility

In China, platforms are widely regarded as "quasi-public governance entities", and they are required to assume an increasing number of institutional responsibilities in areas such as data management, merchant risk control, and consumer protection [11]. This requirement prompted Meituan to develop a closed-loop data architecture to support real-time monitoring and scheduling of merchants' behaviors. One example is that its SaaS service not only helps improve the operational efficiency of an operator but is also able to perform regulatory work, such as data collection, rating, and task tracking [1]. It relies on algorithms to exercise soft control over merchants, forming a closed-loop management from the collection of information to feedback on behavior.

In the U.S., Uber Eats is based on the light-touch regulatory model, which favors neutrality of platforms and non-intervention in data organization. Data privacy rights and antitrust laws form the key compliance boundaries for platform operations. To avoid policy risks such as data abuse or algorithm manipulation, Uber Eats mostly adopts the API open interface form, allowing merchants to selectively access the platform ecosystem [12]. It is entrusted with rights of control, yet institutional arrangement constrains the platform's ability for data coordination, but facilitate flexibility and legitimacy as a market matchmaker.

2.2.2. Labor laws and the boundaries of platform employment

From the perspective of the labor system, in recent years, the legislation on the opening of platform enterprises has shown a double tendency in policy guidance and responsibility expectation. Although Meituan positions its delivery riders as freelancers, it in practice exerts a high level of control over the riders by means of task programming and the establishment of KPI. The government has also increasingly treated the platform as a limited liability entity, which should bear a certain labor protection liability, encouraging it to establish the rider fund, implement high-temperature subsidies, and explore the transparency of algorithms and other compensation mechanisms [11]. While this is not yet a legal obligation, it already presents a high degree of institutional path dependency.

In contrast, in the United States, Uber Eats has taken advantage of the independent contractor legal status to avoid employment responsibilities such as minimum wage and social security benefits. Although attempts such as the California AB5 bill have tried to tighten the boundaries of platform employment, due to the inconsistency between the platform's policy lobbying and interstate legislation, the platform has so far maintained the employment model of algorithmic matching plus legal detachment [9]. Although labor unions and rider organizations have actively fought for their rights, they are still in a state of fragmented power and mainly protest. Such minimal institutionalization of platform responsibilities gives Uber Eats more strategic space, but labor protection heavily relies on market self-discipline.

2.2.3. User data and consumer protection system

On the user side, where data is concerned, users on Chinese platforms are relatively unrestricted. Local life service platforms not only take the data modeling of user behaviors as the breakthrough point, but also construct a full point system, task system, and negative review intervention system to improve user activity, user viscosity, and other aspects. The platforms own the data sovereignty [2]. Meituan is free to experiment and improve within the algorithm's gray area as long as there are no mandatory privacy disclosure requirements.

However, users in the United States are subject to an even stricter data protection system. Regulations such as the California Consumer Privacy Act (CCPA) grant users the rights to access, delete, and restrict the use of their personal data. Platforms must establish a data transparency center for users to access their data usage information [12]. Uber Eats has aimed to preserve the neutrality of the user experience under this regulatory framework, avoided forced feedback and rating systems, and implemented a more moderate incentive strategy. This has improved the platform's legitimacy and trustworthiness boundaries, despite somewhat reducing operational efficiency.

In conclusion, China and the United States differ significantly in the platform mode. The United States places more emphasis on the platform's market neutrality and data usage compliance, viewing it as a service aggregator that connects independent actors without deep coordination, whereas the Chinese system places more emphasis on the platform's social responsibility and data governance capabilities, viewing it as a social regulating extension tool. In terms of technical architecture, drive mechanism, and operational strategy, these institutional orientations have greatly influenced the overall design of platforms and are now crucial factors in the explanation of the relational diversity of local life platforms worldwide.

3. Global implications

In the context of local life services platforms, the operational distinction between Chinese and American platforms not only illustrates how the institutional environment influences platform strategy, but it also provides a crucial viewpoint for comprehending the development of global platform governance. The development paths of Meituan and Uber Eats, as representatives of two typical institutional architectures of these platforms, show the polarized approaches to platform governance, which are led by market rules, the other by state interventions. For other countries and transnational platform ventures, this path divergence under institutional nesting presents a threefold insight.

First, regarding the character of institutional adaptation. In institutions as sets of regulations or classroom procedures, they function both as legal norms and cultural constructs that are embodied in laws, customs, and codes of conduct. For example, when Uber Eats entered the regions of Taiwan, France, and Japan, it eventually borrowed some of the labor protection measures, such as establishing rider safety insurance and a rider rating system, which illustrates the platform's adaptive behavior to local institutions. Meituan, on the other hand, has ventured out very rarely outside its own borders, but if it crosses borders in the future, its directionally strong control and coordination mechanisms developed domestically may face stronger institutional friction in regions that are privacy-first and decentralized. This means that global platforms should be ready to adopt an institution-aware approach for updating their business and political structures in response to varying legal and socio-cultural frames.

Second, platform governance's legitimacy boundaries are transforming the evergreen tension of efficiency and responsibility. Although Chinese platforms are optimized for system efficiency by technological regulation, they have gradually entered the policy agenda with digital labor governance, algorithm transparency, work hour security, and platform liability clarification [6]. In both Europe and the U.S., the practice of platform tech neutrality in words, but also increasing labor organization, data rights, and algorithmic transparency in practice, is increasingly becoming an obstacle to innovation [9]. It is an interesting phenomenon, indicating globally how algorithms regulate human behavior is moving from a business problem to a legal and ethical one, and that if platforms want to develop sustainability, they need to build a sense of social responsibility into the business logic.

Third, there is an urgent institutional demand for coordination of multilateral mechanisms for global platform governance. As the cross-border platform operation becomes increasingly faster, the conventional national regulatory framework is hard to be suitable for the uncertainty zone of the readiness of the platform, which is characterized by weakness of labor protection, data misuse, and market monopoly problems typically [4]. For that reason, Large-scale regional or even global regulatory frameworks for digital labor and digital platforms should be promoted. One example of this is the new Digital Services Act (DSA) from the European Union, which proposes a replicable model of platform liability. Clear lines of operation for platforms, convergence of compliance standards, and increased institutional transparency can all be facilitated by these types of multilateral agreements.

In conclusion, a comparison between Meituan and Uber Eats provides institutional enlightenment on platform governance under the globalization process, as well as strategic references and a glimpse into the institutional diversity in local life platforms. The platform corporation must incorporate new aspects of globalization, such as institutional perception globalization, responsibility co-construction, and governance synergy, into the logic of efficiency if it hopes to accomplish the dual-wheel drive of local embeddedness and global expansion in today's renowned globally interconnected digital economy.

4. Conclusion

Three aspects of Meituan and Uber Eats' operations are compared and examined in this study: merchant cooperation, rider management, and user operation. It displays the local life service platforms' path choices in China and the US within the institutional embedding framework. The results indicate that, compared with the American platform, the user data processing and platform governance capabilities of the Chinese platform are stronger, while the market neutrality and algorithm transparency of the American platform are more highlighted. In addition to determining how the platform should be embedded locally, institutional differences also have an impact on technical architectural designs and strategic orientations. Furthermore, an increasingly significant factor in the internationalization of global platforms is the institutional context. Platform companies need to have better institutional sensing and local adaptation skills in a world where global platform governance is becoming more and more prevalent. To inform the development of a more inclusive and accountable framework for global platform governance, future research can expand to other nations or platform types.