This study examines the All-In-One (AIO) strategy, a digital platform strategy characterized by creating and executing an organized multiproduct ecosystem that integrates highly comprehensive and varied offerings. Using multi-case study methodology, we propose a framework that explains the definition and characteristics of the AIO strategy. By adopting a dual perspective that integrates both demand- and supply-side perspectives, our findings indicate that the AIO strategy facilitates supply-side resource bundling of complementary and exclusive resources and enhances demand-side value creation for users. Moreover, our study highlights the role of cross-product data capabilities and institutional factors in shaping digital platforms’ adoption of the AIO strategy. This research contributes to a deeper understanding of platform innovations on both the supply and demand sides in developing a multiproduct ecosystem.

Abstract Across the globe, spatial mismatches between work and residence reshape human mobility and influence regional air quality, yet their effects across urban–rural systems remain poorly quantified. China's ongoing urbanization and rural revitalization underscore the urgent need to understand these dynamics. Here, using multi‐source geospatial data, we conduct a nationwide analysis across China to assess how work–residence mismatches within and between urban and rural areas affect air pollution, accounting for seasonal variation. We find that overall, urban, and rural mismatch degrees follow an inverted‐U trend with city tier, with disparities between urban and rural degrees of mismatch intensifying within lower tiers. We show that increased mismatch aggravates pollution, particularly in rural regions, higher‐tier cities, and during winter. Furthermore, we identify six distinct urban–rural synergy patterns of spatial mismatch, each associated with varying pollution levels that eventually decline after peaking. Seasonal effects, especially in winter, exacerbate these disparities. Our results emphasize the need to incorporate rural planning into air quality strategies and to adopt seasonally and contextually tailored policies.

Technological breakthroughs often trigger market volatility, complicating capital allocation for investors. While prior work suggests that regulatory interventions chill innovation and venture activity, in some settings regulation might instead stabilize markets following technological shocks. We examine this stabilization dynamic through two concurrent shocks to China's entrepreneurial finance ecosystem: the rise of Generative AI and the subsequent implementation of AI regulation. Our findings reveal a counter-intuitive result: rather than constraining the market, AI regulation moderated the negative impact of the generative AI shock on venture capital investment. This suggests that in high-uncertainty environments, proactive regulation may act as a market-enabling force rather than a barrier.

While observational studies have long suggested a positive correlation between social relationships and online transactions, surprisingly little research demonstrates a causal link. Effects identified in observational data generally conflate the causal Information Effect with the non-causal Homophily/environment Effect. Against this background, we conducted a large-scale randomized field experiment on a major Chinese P2P platform, in which we manipulate buyer and seller's awareness of their pre-existing friendship ties. We provide the first empirical evidence that the effect of revealing friendship information between transaction parties is statistically insignificant. To explain this counterintuitive result, we develop a theoretical model of “relationship risk,” proposing that in high-friction settings, the fear of a transaction-related misunderstanding damaging a valuable social bond can counteract the benefits of trust. We also demonstrate that reliance on observational estimates of the “Total Effect” of friendship significantly overstates the benefits of providing friendship information. Our findings contribute to a better understanding of online anonymous P2P transactions, highlight the potential fallacy of relying on observational data in business studies, and introduces the “relationship risk” model to explain why social ties may fail to facilitate, or even hinder, commerce in high-friction settings.

Generative AI gives creative works a dual role as final goods and training inputs. This dual role creates an appropriation problem: copyrighted knowledge strengthens AI and supports content production through nonrival AI use, but infringing use of such knowledge erodes returns to original creation. We develop a general equilibrium model with an upstream AI sector, an original sector, and a derivative sector. In the decentralized equilibrium, labor is underallocated to original production and overallocated to AI production because private agents do not internalize the cross-sector effects of nonrival AI use and the knowledge spillovers embedded in AI training; the derivative sector's AI use creates no separate distortion. We examine two governance instruments: digital taxation and copyright litigation. Optimal taxation is asymmetric: the derivative sector is always taxed, while the original and AI sectors may be taxed or subsidized depending on the strength of appropriation and the value of AI as a nonrival input. Copyright litigation has an inverted-U welfare effect: moderate enforcement reallocates returns toward original creators through a beneficial compensation effect, whereas excessive enforcement contracts AI supply and downstream output through an adverse production effect. The optimal policy depends on the relative costs of tax administration and legal enforcement.

The carbon cap-and-trade mechanism is increasingly influencing the online selling channel selection and pricing strategy in an e-commerce platform supply chain with two different sales channels (agency selling and reselling channels). This paper examines how two carbon allowance allocation rules (grandfathering and benchmarking) influence channel selection, pricing and profitability in a platform supply chain consisting of one supplier and one e-commerce platform. Using game-theoretic models under four scenarios arising from two carbon allowance allocation rules and two sales channels, we find that the benchmarking rule consistently yields lower retail prices, higher demand, and greater consumer surplus compared to grandfathering. However, the preferred carbon allowance allocation rule differs across stakeholders. In agency channel, the platform’s profit is lower (higher) under benchmarking with the higher (lower) supplier’s carbon allowance compared to grandfathering. In reselling channel, the platform consistently benefits more from benchmarking. Under agency channel with a high (low) platform fee, the government prefers the grandfathering (benchmarking) when the grandfathering allowance is high (low). Under reselling channel, the government prefers the benchmarking (grandfathering) when the benchmarking allowance is high (low).

Data has become a key factor of production in the digital economy, yet its governance remains institutionally fragmented across borders. This paper asks why a resource with global quasi-public-good properties, whose value depends on cross-border reuse and recombination, has not generated a corresponding institutional supply for trusted international cooperation. Drawing on a public-good diagnosis and comparative institutional analysis, we argue that the central problem is not simply regulatory divergence, but the underprovision of the governance conditions required for credible, scalable, and sustainable cross-border data use. Existing arrangements make important but partial contributions, yet their mandates, scopes, and enforcement logics remain too fragmented to form a coherent governance architecture. In response, we propose a World Data Organization (WDO) as a complementary focal arrangement designed to address this institutional gap. The paper contributes by reframing cross-border data governance as a digital-economy problem of quasi-public-good governance, explaining why current arrangements remain structurally insufficient, and outlining a conceptual blueprint for a more integrated institutional response.

In an era where digital technology serves as a strategic emerging direction, this study aims to explore how industrial classification policy facilitates corporate digital innovation. Regarding China's Strategic Emerging Industries Classification (CSEIC) policy as a quasi-natural experiment, we use the difference-in-difference (DID) method and find that CSEIC has a significant and positive effect on digital innovation. We further find that CSEIC promotes exploratory digital innovation in state-owned enterprises (SOEs) and exploitative digital innovation in private-owned enterprises (POEs). The mechanism analysis suggests that in SOEs, the political arrangement and executives' political priority strengthen the positive relationship between CSEIC and exploratory digital innovation, while in POEs, the digital arrangement and executive priority can trigger the positive relationship between CSEIC and exploratory digital innovation. Based on the attention-based view (ABV), this paper provides a comprehensive understanding of how structural arrangement and selective focus match to leverage the influence of policy to innovation breakthrough in a specific attention situation.

Research Summary The trade-off between scale and scope has long posed a strategic dilemma, especially in digital settings, where specialization enables hyperscaling. Drawing on a longitudinal case study of ByteDance, we theorize how digital firms can overcome this constraint through the use of artificial intelligence (AI) combined with an adaptive organizational design. AI evolves and improves through self-learning and cross-fertilization across domains, becoming increasingly valuable as learning accumulates. This, however, is contingent on access to structurally related data that allow learning to transfer across domains. We show how AI reverses the conventional logic of the resource-based view: rather than valuable resources enabling diversification, diversification amplifies the value of resources. AI thus transforms the scale-scope nexus from being a trade-off into a source of strategic advantage.Managerial Summary The growing centrality of AI and digital platforms is reshaping how firms pursue and sustain growth. This study examines how ByteDance leveraged AI and adaptive organizational design not only to scale rapidly but also to diversify across industries and markets. Rather than incurring rising costs or coordination complexity, the firm's AI capabilities improved with each deployment through cross-fertilization across domains, enabling more efficient growth across multiple domains. For managers, the findings highlight how dynamic combinations of AI and organizational structure can help overcome traditional trade-offs between scale and scope, opening new pathways for scalable, cross-market expansion in increasingly competitive environments.

In the digital era, short videos have become a significant form of digital copyright, yet the debate over whether stronger copyright protection enhances their creation continues. To contribute to this discourse, we conducted an analysis based on a representative sample of short videos on a prominent Chinese short video platform, Douyin. Capitalizing on an external regulatory intervention, specifically the Campaign against Online Infringement and Piracy (COIP) implemented by the Chinese government, we employed the difference-in-differences (DID) method to assess the impact of reinforced copyright protection on the originality of short videos. Our findings reveal that strengthened copyright protection leads to a significant increase in the originality of short videos. Further research on creator heterogeneity shows that influencers exhibit a significantly more positive response to strengthened copyright protection than amateur creators. Finally, we present evidence explaining how external regulation works by enhancing intra-platform regulation. These results have rich implications for intellectual property protection, digital innovation management, and platform regulation.

The rise of platform-based multinational corporations (PMNCs) driven by the digital economy has increasingly provided new implications for international business (IB) theories. IB scholars have begun to explore alternative research streams that provide a fertile ground for studying PMNCs. Through in-depth investigations on the definition, categorization, and distinctive characteristics of PMNCs, we develop a research framework to study PMNCs and shed light on IB theories. We propose different types of PMNCs that influence international businesses from both the consumption side and production side of global value chains, including transaction PMNCs and industry PMNCs. We also provide a research agenda that guides future studies on PMNCs, aiming to make insightful contributions to IB literature and provide practical implications for IB managers and policymakers.

Following the end of the zero-COVID policy, China’s economic recovery elicited high hopes but turned out to be disappointing in 2023. This paper provides a novel explanation for the slow economic recovery based on city network disruption and restructuring after major shocks. The supply-demand connections, once disrupted by lockdowns, could not revert to their pre-lockdown state after reopening. Instead, they underwent further restructuring, incurring considerable transaction costs. Using a unique dataset of 1,004,818 heavy trucks with over 600 million inter-city origin-destination records, we find that although freight workload started to grow following the lifting of lockdown measures, it remained below pre-lockdown levels even seven months after reopening. The observed recovery pattern is primarily driven by a surge in short-distance travel, contrasting with a decline in long-distance travel. We show that this shift signifies a restructuring process of connections within the city network, which takes time to materialize and might lack logistics efficiency. Moreover, this restructuring has reshaped the landscape of China’s economic geography. It strengthened the economic connections within city clusters while weakening those between them. Conventional economic growth poles and coastal cities experienced a sluggish recovery, whereas certain inland cities emerged as primary beneficiaries of the network restructuring. Finally, we provide empirical evidence that the city network restructuring is associated with previous disruptions induced by lockdowns. Our analysis offers new insights into the recovery dynamics of economies post major economic shocks from the perspective of city networks.

Many jurisdictions have launched antitrust enforcement and brought in regulation of large tech platforms. The swift and strict implementation of China's Anti-Monopoly Guidelines for the Platform Economy (Platform Guidelines) provides a quasi-natural experiment to evaluate the impact of antitrust regulation on platform competition. We adopt a difference-in-differences approach to empirically explore the impact of China's Platform Guidelines on the number of investments and the entry of startups in platform markets. The results show that the Platform Guidelines did not increase entrepreneurship in these affected markets. Rather, entrepreneurship weakened in these markets, with less venture capital investment flowing into them and fewer startups entering these markets. Our study suggests that governments should consider more carefully the potential unintended consequences of antitrust platform regulation.

An increasing number of data policies and legislative models are being developed globally. These complex data laws and circulation models are often competing and have extra-territorial implications, thereby complicating global data management for multinational enterprises. Adopting an institutional logic perspective, this study focuses on the European Union, the United States, and China as key representative economies. It aims to outline the three prevailing regulatory patterns in cross-border data transfer, their evolutionary trajectories, and the institutional logic behind them. Furthermore, the research investigates the conflicts in data circulation across borders among these major economies, examining their effects on the global data governance of multinational enterprises (MNEs) and identifying the universal challenges in current global data transfers. In response, we propose a future research agenda, grounded in theoretical insights, for international business research to address these challenges through the efforts of MNEs and public policy.