Developing new quality productivity forces is an inherent requirement and important focus for solidly promoting high-quality development. This article is based on the basic principles and methodology of Marxist political economics and follows the research approach of “raising problems, analyzing problems, and solving problems”. It analyzes that insufficient effective investment is an important factor restricting the development of China’s new quality productivity forces, explains the theoretical connotation of effective investment and accelerator, and combines Marx’s capital circulation theory to analyze the accelerator effect of effective investment in promoting the development of new quality productivity forces. Based on the new development stage, it proposes a practical path to play the accelerator effect of effective investment, and finally puts forward safeguard measures from the aspects of market, capital, evaluation mechanism, and government to play the accelerator effect of effective investment. Research has found that: 
First, in terms of theoretical connotation, the effective investment is evaluated based on its purpose or function. Investment that can achieve expected goals well is considered effective investment, while investment that deviates significantly from expected goals is not considered effective investment. The core essence of accelerator is to accelerate the development speed or process of something by taking effective measures (i.e. accelerator) to achieve a certain goal, shorten the completion time of the goal, and thus achieve the expected goal faster. 
Second, in terms of internal mechanisms, it is necessary to grasp the objective development laws of new quality productivity forces from Marx’s capital circulation theory, and grasp the key elements such as optimizing and upgrading the industrial chain and supply chain system, enhancing technological innovation capabilities, cultivating high-quality labor force, and upgrading the built environment, and effective investment should be increased according to the principle of adapting measures to local conditions. The position and role of the government and the market in increasing effective investment should be properly handled. 
Third, in terms of practical path, it is necessary to accelerate the enhancement of its independent technological innovation capabilities by building high-level innovation consortia, leveraging the leading and driving role of chain leaders and chain owners in accelerating the optimization and upgrading of the industrial chain and supply chain system, absorb more capital into the strategic emerging industries and future industries through diversified policies, cultivate more high-quality talents through innovating joint training models, and better leverage the joint efforts of the government and the market in creating a high-quality built environment. 
Fourth, in terms of safeguard measures, further strengthen the market adjustment mechanism and enhance the effective investment growth momentum led by the market. Further improve the capital market system and promote more patient capital investment in new quality productive forces industries. Establish and improve a dynamic evaluation mechanism for effective investment, and promote sustained investment in effective investment. Further enhance the government’s macroeconomic governance capabilities, strengthen the organization, coordination, and stability of effective investment.

The high-quality development of the domestic service industry plays a critical role in revitalizing household consumption and constructing the micro-foundations of a domestic demand-driven economic paradigm. In the context of China’s transition toward dual circulation and consumption-led growth, understanding how service sector expansion reshapes household economic behavior is both theoretically significant and policy-relevant. Despite increasing attention to the role of services, the specific pathways through which domestic service development influences consumption dynamics remain underexplored in existing literature.
This study employs multi-wave micro-level survey data and a difference-in-differences (DID) strategy based on policy shocks and regional pilot programs to identify the causal effects of domestic service development on household consumption. Leveraging plausibly exogenous variation across time and space, we find that the expansion of the domestic service sector significantly increases total household consumption expenditure. More importantly, this expansion also unlocks latent consumption potential among previously underserved populations. These effects remain robust across alternative model specifications, placebo tests, and sample restrictions, confirming the reliability of the empirical strategy.
Mechanism analyses reveal a dual-channel transmission structure that operates through both supply- and demand-side mechanisms. On the supply side, the outsourcing of time-intensive domestic labor alleviates household time constraints, facilitates labor force participation—especially among women—and increases household disposable income, thereby supporting immediate consumption. On the demand side, domestic services strengthen familial care structures, reduce uncertainty in daily life management, and improve expectations for future living standards. These changes enhance consumer confidence and raise the marginal propensity to consume, particularly for durable and long-term expenditure categories, thus stimulating sustained consumption momentum. Heterogeneity analyses further uncover meaningful spatial and demographic asymmetries. The consumption-promoting effects of domestic services are most pronounced in eastern provinces with more mature service markets and stronger digital infrastructure. At the household level, women and low-income families benefit disproportionately, suggesting that domestic service development contributes to both economic inclusion and gender-equitable growth. These findings underline the redistributive potential of the service sector and its capacity to support inclusive domestic demand expansion.
Finally, the study identifies a threshold-type moderating role of digital infrastructure. At low levels of digital penetration, service access barriers—such as information asymmetry, platform exclusion, and regional mismatch—constrain the availability and reliability of domestic services, thereby dampening their consumption effects. Conversely, once a critical digital threshold is crossed, the integration of digital platforms significantly amplifies the efficiency, accessibility, and trust in service provision, thereby enhancing its ability to unlock household consumption potential. This non-linear moderating pattern points to the necessity of complementing service sector development with inclusive digitalization policies.
Together, these findings provide micro-level evidence for the strategic role of domestic services in shaping household consumption behavior, reinforcing the theoretical linkage between service economy expansion and endogenous demand growth. The study not only contributes to a deeper understanding of the household-service nexus but also offers actionable policy insights for optimizing service sector governance under the digital economy, especially in the context of China’s ongoing consumption transition.

China’s economy is currently in a critical period of transition from high-speed growth to high-quality development, with consumption playing an increasingly prominent role in economic growth. In recent years, however, the contribution of consumption to economic growth has declined. At the same time, tensions in the international landscape and trade frictions with countries such as the United States and Australia have adversely affected exports, and the structural imbalance caused by long-term high investment has also highlighted the limitations of China’s economic growth model. As such, shifting the growth paradigm and stimulating domestic demand have become important directions of the national economic development strategy. Nevertheless, China’s household savings rate has remained persistently high since the reform and opening-up period—rising from less than 28% in 2000 to 39% in 2010, and slightly declining to 37% in 2015—and is among the highest in the world. Such high saving rates have, to some extent, constrained the expansion of domestic demand, making it of great practical significance to study the factors influencing household savings. The existing literature mainly attributes China’s high saving rates to long-term factors such as uncertainties brought by market-oriented reforms, demographic changes, and institutional or cultural characteristics, which are however difficult to adjust in the short run. In contrast, the social security system offers a more policy-relevant perspective. Previous studies have shown that pension and health insurance schemes can reduce household saving rates, yet the role of the Housing Provident Fund (HPF) in shaping household saving decisions remains underexplored.
Using data from the Urban Household Survey (UHS) covering 2002-2014, this paper finds that a one-percentage-point increase in the HPF contribution rate reduces the household saving rate by 0.923 percentage points. Further mechanism analysis indicates that this effect is more pronounced among households with stronger precautionary saving motives, such as those with a higher share of employment in non-state-owned enterprises, lower per capita housing values, and lower education levels of the household head. Moreover, from a lifecycle perspective, the suppressing effect is stronger for households facing higher opportunity costs of interest loss, tighter liquidity constraints, or weaker tax benefits associated with HPF contributions. To address potential endogeneity concerns, we adopt housing reform allocation status and geographical distance to early pilot cities as instrumental variables, employ propensity score matching to re-balance the sample, and apply alternative measures of saving rates. Across all specifications, the negative impact of HPF contributions on household saving remains robust.
Based on the findings, this paper proposes the following policy recommendations: On the one hand, when adjusting the contribution ratio of the HPF, attention should be paid to the fact that it not only has the function of housing security but also exerts an impact on household consumption and savings decisions. Therefore, its dual attributes should be taken into account in evaluating its broader effects. On the other hand, raising the HPF contribution rate can serve as a feasible approach to reducing China’s persistently high household saving rate. Yet differentiated policies should be adopted for households with varying characteristics. For example, contribution rates may be appropriately lowered for households currently repaying housing loans or those not subject to liquidity constraints, while for residents nearing retirement—who face higher tax-shield costs and weaker policy effects—withdrawal conditions and limits may be relaxed to minimize welfare losses from contributions. This research confirms that the housing provident fund not only serves as a housing security mechanism but also significantly influences household savings behavior, offering a new perspective for understanding China’s high savings rate. Furthermore, it provides valuable insights for improving housing security policies, boosting consumption, and promoting high-quality economic development.

Against the backdrop of intensifying global climate change, persistent ecological degradation, and increasingly scarce resources, the concept of green development has become a universal consensus and shared goal among nations pursuing sustainable economic growth. As the world’s largest developing country, China has actively integrated into the global trade division of labor system by leveraging its low-cost labor advantage. While generating substantial economic gains, it has inevitably borne the environmental pressures transferred from developed countries, resulting in severe environmental pollution. Resolving the tension between economic transformation and ecological conservation has become an urgent requirement inherent to China’s pursuit of high-quality economic development. Green economic efficiency, as a key indicator balancing economic, social, and environmental performance, provides a scientific measure of China’s high-quality economic development. It not only demands finding a balance between resource utilization and environmental protection but also emphasizes enhancing the green attributes of economic activities through innovation-driven approaches and technological progress, thereby achieving synergistic development of economic growth and ecological conservation. In recent years, with the vigorous development of the digital economy, data elements have emerged as one of the core drivers of economic growth. They continuously empower socioeconomic expansion, industrial structure upgrading, and technological innovation, while also offering valuable insights for enhancing regional green economic efficiency.
This study uses the implementation of pilot policies for big data trading platforms as a quasi-natural experiment. Based on panel data from 284 prefecture-level cities and above in China from 2011 to 2023, it employs a combination of multi-period difference-in-differences models and super-efficiency SBM models to empirically examine the impact effects and mechanisms of data element market development on urban green total factor productivity. The study finds that the development of data element markets significantly enhances the efficiency of urban green economies, and this conclusion remains valid after undergoing a series of parallel trend tests, dual machine learning methods, and heterogeneity treatment effect tests. In terms of mechanisms, the development of data element markets primarily enhances government environmental regulation effectiveness, optimizes regional digital development environments, and stimulates regional green technological innovation vitality, thereby effectively driving a significant improvement in the green total factor productivity levels of Chinese cities. Further analysis shows that the development of data element markets also has significant spatial spillover effects, promoting the coordinated improvement of green total factor productivity in geographically adjacent regions and enhancing regional green development connectivity. In summary, this study provides important theoretical references and policy implications for deepening the understanding of the economic and environmental synergistic benefits of data element marketization reforms, releasing their technological dividends, and promoting the green and low-carbon sustainable development of regional economies.
Based on the findings, this paper proposes the following policy recommendations: first, strengthen the development of data resource trading platforms, improve the data trading platform system, establish a multi-tiered data trading market, and propel the transformation of data as an information technology factor into a new phase of deepened application and standardized development. Second, seize new opportunities presented by digital transformation, focus on key areas for establishing big data trading platforms, explore differentiated development paths, and comprehensively elevate the level of high-quality regional economic development. Third, narrowing development gaps between regions and building a regionally interconnected green digital economic development model are crucial measures for achieving green, high-quality development across the entire region. This research provides a scientific basis for governments to formulate more precise and effective digital economy policies, helping cities optimize and upgrade their industrial structures to achieve high-quality regional economic development. Establish a national-level data trading platform, build a multi-tiered data sharing and trading system, explore the cultivation of regional data trading platforms, and develop industry-specific data trading platforms.

The synergistic progression of digital transformation and environm-ental sustainability represents a critical pathway for industrial modernization, a process significantly shaped by supply chain networks where one firm’s digitalization can generate externalities for its partners’ environmental performance. Although digital technologies are recognized for their green potential, the direction and mechanisms of such spillovers along the supply chain remain theoretically and empirically unclear—specifically, whether digitalization influences upstream suppliers or downstream customers more strongly. This paper addresses this gap by developing a formal model and conducting rigorous empirical analysis to identify the direction, channels, and boundary conditions of these green spillovers.
Our theoretical model features a two-tier supply chain with a supplier, a client firm, and end consumers under incomplete contracting. It incorporates consumer utility from both physical and green attributes of final goods, where greenness accumulates from investments across production stages. As the demand-side leader and contract proposer, the client’s digitalization improves information flows and mitigates contractual incompleteness. This asymmetrically alters green investment incentives: it strengthens the client’s ability to monitor and signal demand for greener inputs, creating a strong incentive for upstream suppliers to improve green performance. In contrast, the model implies a weaker direct incentive for downstream customers, whose green efforts are more substitutable and less monitored upstream. Thus, we hypothesize that corporate digitalization generates a positive green spillover directed primarily upstream, with a negligible downstream effect.
Our empirical strategy is designed to test this proposition using micro-level data from China. We construct a novel and comprehensive dataset by meticulously matching the disclosed lists of major suppliers and customers of Chinese listed firms (sourced from CNRDS) with the granular establishment-level information from the National Tax Survey (NTS) spanning 2007 to 2016. This matching process is crucial as it allows us to incorporate the vast majority of non-listed firms that constitute supply chain links, mitigating a severe sample selection bias prevalent in studies relying solely on listed firm data. We derive two distinct analysis samples: a client-supplier panel (7,618 observations) and a client-customer panel (14,367 observations). Enterprise digitalization (DIG) is measured via text analysis of digital keywords in annual reports. Green development (GDE) is a composite index using entropy weighting, integrating NTS indicators on energy use, emissions, and pollution abatement costs.
Using a fixed-effects framework, we find robust evidence supporting the upstream spillover hypothesis. Client digitalization shows a significant negative association with supplier GDE (where lower GDE indicates better performance), while its effect on downstream customer GDE is negligible. This asymmetric finding holds across extensive robustness tests. We examine two underlying mechanisms. First, client digitalization reduces supplier transaction costs, proxied by business entertainment expense ratios, freeing resources for green investment. Second, it stimulates supplier innovation, raising expenditures on both external R&D services and internal R&D activities, thus enhancing technical capacity for green transformation.
Heterogeneity analysis shows the spillover is stronger in supply chains with higher transparency, supplier stability, and concentration. The effect is also more pronounced when clients and suppliers are geographically dispersed, suggesting digital tools can substitute for proximity and mitigate localized pollution agglomeration. We further find that digitally-induced supplier greening increases the supplier’s own total factor productivity (TFP) and contributes to overall supply chain TFP, indicating a “double dividend” aligning environmental and economic gains.
In conclusion, this study makes several contributions. It resolves a key ambiguity in the literature by providing theoretical reasoning and robust empirical evidence for a dominant upstream direction in the green spillover of corporate digitalization. It advances methodological practice by constructing a matched dataset that incorporates critical non-listed entities. It elucidates the dual operational channels of transaction cost reduction and innovation stimulation. Furthermore, it identifies critical moderating factors related to supply chain structure, geography, and ownership, offering actionable insights for managers and policymakers. To harness the full synergistic potential of digitalization for sustainability, strategies should focus on enhancing supply chain transparency and stability, leveraging digital tools for cross-regional green collaboration, and fostering governance structures that amplify market-driven environmental incentives.

Promoting the cross-regional flow of production factors and deepening the construction of a unified national market are foundational requirements for building a powerful domestic market in China. The core objective of this national strategy is to break down administrative boundaries, geographical barriers, and institutional transaction costs to achieve the efficient cross-regional allocation of resources. Firm cross-regional investment, which expands a firm’s business operating space, is a critical channel for facilitating this factor mobility.
Geographic distance, however, imposes significant cost burdens, information asymmetries, and management challenges, thereby hindering cross-regional investment and impeding the formation of an integrated market. While the economic impacts of physical infrastructure, such as high-speed rail, on mitigating these distances have been extensively studied, empirical research on the influence of digital infrastructure on corporate spatial location decisions remains scarce. This study examines whether the development of digital infrastructure can effectively stimulate cross-regional investment by firms into demonstration cities, thereby expanding their business operating space and providing new micro-level evidence on the construction of a unified national market.
To answer this question, this study employs China’s “Broadband China” strategy as a quasi-natural experiment. This top-down national policy was launched in 2013, with cities designated as demonstration zones in three phased batches during 2014, 2015 and 2016. Based on a comprehensive panel dataset constructed from 2007 to 2023, the study integrates firm-level financial data from all A-share listed companies, manually collected and geolocated parent-subsidiary relationship data, and the officially published list of “Broadband China” demonstration cities. A Difference-in-Differences model with multi-period shocks is employed to compare the differences in cross-regional investment attractiveness between demonstration and non-demonstration cities. 
The benchmark regression results indicate that the “Broadband China” policy significantly expands a firm’s business operating space by enhancing cities’ attractiveness for cross-regional investment. This result remains robust after a series of tests, including parallel trends analysis, placebo tests, PSM-DID, and entropy balancing. Mechanism analysis reveals this effect operates primarily through two channels. First, an information effect is identified. The study constructs a novel “average weighted geographic distance” metric to capture the economic spatial distance between parent companies and their subsidiaries. This finding provides evidence that optimized digital infrastructure facilitates the cross-regional flow of information, reduces the perceived risks and costs of remote management, and thus encourages firms to expand business operating space. Second is the cost effect, specifically reducing internal communication costs and lowering external transaction costs. Further analysis reveals a network synergistic effect. The study found that when both the starting point and destination of an investment possess high-quality digital infrastructure, it generates stronger network connectivity that surpasses the reduction of single-point information friction, thereby amplifying the effects on cross-regional investment activities. Regarding heterogeneity analysis, the impact of digital infrastructure varies across geographic distances and regional, urban, and corporate characteristics. Specifically, digital infrastructure significantly influences firms’ location choices for cross-regional investments in adjacent versus remote areas, eastern regions, large cities, non-state-owned enterprises, and high-tech firms. 
The contributions of this study are as follows. This paper makes contributions in three key areas. First, by examining firm’s business operating space, it reveals pathways for achieving the integration of efficient markets and effective government, providing micro-level evidence on how digital infrastructure drives the formation of a unified national market. Second, it uncovers the spatial effects and dual mechanisms through which digital infrastructure reshapes economic geography, enriching the existing research on digital infrastructure. Third, this study innovatively constructs the “average weighted geographic distance” metric between parent companies and subsidiaries. It validates the mechanism through which digital infrastructure drives spatial expansion of business operations from both information and cost perspectives. Furthermore, the heterogeneity analysis results provide policy references for optimizing digital infrastructure deployment and better advancing the construction of a unified national market.
Based on these findings, this study proposes the following policy recommendations. First, reinforce the “digital corridor” positioning of digital infrastructure to alleviate information asymmetry in cross-regional investment. Second, establish high-quality datasets complementary to digital infrastructure to strengthen data support for cross-regional allocation of factors. Third, optimize the spatial layout of digital infrastructure to leverage network synergies. Fourth, organically integrate digital infrastructure development plans with investment promotion policies.

Achieving the dual objectives of reducing corporate tax burdens and maintaining fiscal stability is crucial for advancing high-quality economic development. Artificial intelligence (AI), embedded with the connotations of new quality productive forces, presents a promising opportunity to address this dual challenge. Existing literature shows that AI significantly affects taxation-related factors such as labor structures, asset investment patterns, and R&D expenditure, thereby creating the possibility of corporate tax reductions. At the same time, AI adoption improves firms’ productivity and business performance, offering a pathway to tax base expansion. However, few studies directly examine the impact of AI on both corporate tax burden and government tax revenue, as well as the interconnection between them. Therefore, this paper integrates artificial intelligence, corporate tax burdens, and government tax revenue into a unified analytical framework to systematically examine the impact of AI technology on both corporate tax burdens and government tax revenue, as well as the intrinsic connection between these two effects, all through the analytical lens of new quality productive forces.
Compared with prior research, this paper makes four main contributions. First, departing from single-perspective assessments of technological effects, this study integrates AI technology, corporate effective tax burden, and government tax revenue into a unified analytical framework based on theories such as developing new quality productive forces. This approach extends the analytical boundaries of AI’s economic implications from both macro and micro perspectives. Second, through theoretical analysis and empirical evidence, this research reveals the mechanisms through which AI technology generates a “tax-reduction dividend” for firms and a “revenue-enhancement dividend” for the government. It further uncovers the intrinsic linkage between these two fiscal dividends, characterized by the pathway of “corporate tax reduction → output expansion → industry spillover → tax base broadening → government revenue increase.” Third, this study validates, in the domain of technology and taxation, the scientific rationale and practical relevance of fully implementing The Thought on Socialism with Chinese Characteristics for a New Era, vigorously developing new quality productive forces, and advancing the “AI+” initiative. Empowering enterprises with AI technology to drive innovation aligns with the essence of new quality productive forces, facilitates the dual objectives of reducing corporate tax burdens and increasing government revenue, and holds significant implications for high-quality economic development. Fourth, this research also provides verification and extension of the theoretical expectations derived from the supply-side economics perspective on tax reduction incentives. The “Laffer curve” posits an inverted U-shaped relationship between tax rates and revenue. This study demonstrates that AI technology enables firms to better adapt to tax incentive policies, thereby generating tax reduction effects, which in turn incentivize improvements in production efficiency and operational performance.
Based on data from Chinese A-share listed firms and city-level panels from 2008-2023, we employ fixed-effects models to empirically test the impact of AI adoption on corporate effective tax burdens and government tax revenue, and further explore the relationship between the corporate “tax-reducing dividend” and the government “revenue-enhancing dividend.”At the corporate level, the study finds that AI technology significantly reduces the effective tax burden of firms. This conclusion remains robust after accounting for endogeneity and undergoing rigorous robustness checks. The identified mechanisms for this effect are “Salary Tax Deduction,” “Intelligent Investment,” and “R&D Incentive.” Analysis of economic consequences reveals that the tax reduction induced by AI technology subsequently incentivizes improvements in firms’ operational performance and production efficiency. Furthermore, this tax reduction effect and its positive economic consequences exhibit significant spillover effects across industries. At the city level, the study finds that despite the tax reduction observed at the firm level, AI technology significantly increases overall government tax revenue. This finding also holds after controlling for endogeneity and passing robustness tests. The underlying reason is that the positive economic consequences stemming from AI’s tax reduction effect are also present at the city level. AI technology promotes the expansion of urban output and increases in aggregate profits, creating a “tax base broadening” effect that ultimately leads to higher government tax revenue. 
In conclusion, this study provides theoretical and empirical evidence that AI adoption enables the coexistence of lower corporate tax burdens and stable government revenue. Based on the research findings, we propose the following policy recommendations: i. promoting the targeted implementation of the “AI Plus” initiative to strengthen the foundation for developing new quality productive forces within enterprises, which includes supporting firms in intelligent transformation and talent cultivation; ii. fostering an industrial ecosystem conducive to new quality productive forces by enhancing cross-sector collaboration in the application of AI technologies, facilitating technology spillovers and coordinated development across sectors; and iii. optimizing fiscal policies related to AI technology to precisely cultivate high-quality tax sources, thereby providing sustainable fiscal support for the development of new quality productive forces, such as through tax incentives and innovation subsidies.

Under the policies background of innovation-driven and accelerated construction of manufacturing power, improving the technological innovation capability of enterprises is the key support for the transformation and upgrading of China’s manufacturing industry. With the intensification of global scientific and technological competition and the increase of research and development (R&D) costs, it is more difficult for enterprises to realize innovation only by relying on internal resources. It has become a realistic choice for many enterprises to actively obtain external innovation resources through cross-border mergers and acquisitions (M&A). With the continuous improvement of China’s outward foreign direct investment (OFDI) policies system, the overall scale of cross-border M&A of manufacturing enterprises has increased and become rational. In the context of the urgent need for the transformation and upgrading of the manufacturing industry and China’s commitment to promoting a high level of opening-up, it is of great practical significance to study the impact of cross-border M&A on enterprise innovation.
This paper focuses on the following questions: First, will cross-border M&A by Chinese manufacturing enterprises affect enterprise innovation? What kind of impact will it have? Second, are there any differences in the impact effects on different host country locations, enterprise entities, and M&A methods? Thirdly, through what mechanisms do cross-border M&A of Chinese manufacturing enterprises affect enterprise innovation? Does the influence effect of the mechanism vary depending on the location of the M&A?
To answer the above questions, the following research content is designed: First, construct a theoretical analysis framework for the impact of cross-border M&A on enterprise innovation, establish a mathematical model to reveal the mechanism of cross-border M&A and enterprise innovation, and explore the transmission mechanism of the impact of cross-border M&A on enterprise innovation. Second, based on the experimental data of cross-border M&A and innovative development of Chinese manufacturing enterprises, examine the direct impact of cross-border M&A on the quality and quantity of enterprise innovation. Third, examine the heterogeneous impacts of different M&A locations, enterprise entities, and M&A methods on the innovation effects of cross-border M&A enterprises. Fourth, examine the different intermediate path mechanisms by which cross-border M&A affect enterprise innovation, as well as the differences in the roles of individual mechanisms in different M&A locations.
In accordance with the corresponding paradigm of mutual confirmation of theories and empirical analysis, this paper takes China’s listed manufacturing enterprises as the research object, and based on the matching data of CSMAR listed companies database, the global M&A transaction database (Zephyr) and Chinese Research Data Services Platform (CNRDS) from 2009 to 2020. The fixed effect, propensity score matching and difference-in-differences (PSM-DID), parallel trend test, placebo test, mediating effect were comprehensively used in this study.
The following conclusions are drawn:
First, cross-border M&A have significantly promoted the improvement of both the quantity and quality of enterprise innovation, with the improvement effects on the quantity and quality of innovation being 3.3% and 2% respectively, and the promotion effect is sustainable. Second, cross-border M&A can enhance the level of innovation through the promotion effect of R&D efficiency and knowledge base. Third, the impact of cross-border M&A on enterprise innovation is heterogeneous. From the perspective of the location of M&A, M&A in non-OECD countries have a higher effect on enhancing the quantity and quality of innovation than those in OECD countries, and M&A in OECD countries are more conducive to promoting enterprise innovation by improving R&D efficiency. The innovative enhancement effect brought by M&A in countries with distant institutional distances is higher than that in countries with close institutional distances. The innovation enhancement effect brought by M&A in countries with a long cultural distance is higher than that in countries with a short cultural distance, especially reflected in the improvement of innovation quality. From the perspective of the enterprise entity, only those with a relatively high productivity level can effectively enhance the quantity and quality of their innovation through cross-border M&A. Both cross-border M&A of state-owned enterprises and non-state-owned enterprises can significantly increase the number of innovations of enterprises. Moreover, cross-border M&A of state-owned enterprises have a more significant effect on increasing the number of innovations, while non-state-owned enterprises can significantly improve the quality of innovation of enterprises. The effect of M&A of high-tech enterprises on improving the quantity and quality of innovation are more obvious than that of general technology enterprises. In terms of the effect of M&A on increasing the number of enterprise innovations, enterprises in the western region have the highest effect, followed by those in the central and eastern regions. Only M&A of enterprises in the western region can significantly promote the improvement of innovation quality. From the perspective of merger and acquisition methods, technology M&A can significantly enhance the quantity and quality of an enterprise’s innovation, while non-technology M&A have no obvious impact. The effect of controlling M&A on enhancing the quantity and quality of innovation is higher than that of non-controlling M&A. Vertical M&A have a higher effect on increasing the number of innovations. Horizontal M&A have a higher effect on improving the quality of innovation.
Based on this, this article puts forward the following countermeasures and suggestions. Manufacturing enterprises should actively carry out cross-border M&A and pay attention to the integration of resources and capabilities after the M&A. Enhance the R&D efficiency of manufacturing enterprises, encourage them to develop independent innovation capabilities and improve their knowledge base. Based on the actual situation and characteristics of the enterprise, flexibly formulate differentiated cross-border merger and acquisition decisions.
The possible marginal contribution of this paper lies in the following: First, it examines the specific effects of the amount and frequency of cross-border M&A on the quantity and quality of enterprise innovation, making up for the deficiency of previous studies that were not objective and comprehensive enough in measuring the indicators of enterprise cross-border M&A and innovation. Second, analyzing the heterogeneous impact of M&A in different locations, entities and methods on enterprise innovation has enriched the relevant research on cross-border M&A of heterogeneous enterprises. Third, revealing the mediating role of enterprise R&D efficiency and knowledge base in promoting enterprise innovation through cross-border M&A, as well as the differences in the mediating role of R&D efficiency in different merger and acquisition locations, enriches the relevant research on the mechanism of the impact of M&A on enterprise innovation.