The Impact of Market Integration Construction on the Innovation of Key Core Technologies of Enterprises: From the Perspective of Complex Adaptive System Theory

Abstract

Achieving breakthroughs in key core technologies is an inherent requirement for attaining a high level of scientific and technological self-reliance. The construction of a unified market (market integration construction) reshapes the rules of the innovation system and drives enterprises to tackle key core technologies. Based on the theory of complex adaptive systems, this paper uses the data of China’s A-share listed companies from 2008 to 2023 and the statistical yearbook to study the impact of market integration construction on the key core technological innovation of enterprises and its mechanism. The empirical research results show that: (1) Market integration construction reconstructs the rules governing resource flow, competitive incentives, and collaborative networks, guiding enterprises to achieve the emergence of key core technologies through nonlinear interactions. (2) Market integration construction exerts distinct effects on key core technological innovation by enhancing industrial investment and financial investment. (3) Agile responsiveness positively moderates the relationship between market integration construction and key core technological innovation. (4) The positive impact of market integration construction on key core technological innovation is more pronounced in non-state-owned, follower, and large enterprises. This study provides a theoretical basis and practical insights for advancing market integration construction and tackling key core technologies.

1. Introduction

Key core technologies are fundamental to national strength. Accelerating breakthroughs in these areas is an imperative choice for navigating technological competition and an inherent requirement for achieving a high level of scientific and technological self-reliance. The 2025 Government Work Report emphasizes the need to give full play to the advantages of the new system for mobilizing resources nationwide, strengthen efforts to tackle key core technologies and conduct R&D in frontier and disruptive technologies, and accelerate the organization, implementation, and advanced planning of major science and technology projects. Innovation in key core technologies exhibits systemic complexity, characterized by the integration of multidisciplinary knowledge, complex technologies, and long-term capital investment [1]. No single entity possesses all the necessary elements, making it essential to rely on efficient market mechanisms to identify, acquire, and synergistically integrate diverse innovation resources. However, market segmentation and local protectionism hinder the flow of factors, reduce the efficiency of resource allocation, and impede the realization of expected returns from technological innovation [2], thereby obstructing breakthroughs in key core technologies. The construction of a unified market helps leverage the government’s role in top-level design and guidance for promoting technological innovation and optimizing industrial layout. By promoting high-standard connectivity of market infrastructure, improving unified market regulation rules, and establishing unified factor and resource markets, market integration construction can stimulate the innovation vitality of market entities [3], thereby providing systemic institutional guarantees for tackling key core technologies. Therefore, exploring the impact of market integration construction on enterprises’ innovation in key core technologies is of significant importance.

Existing research on the relationship between markets and innovation primarily revolves around two perspectives. The first, based on single-factor markets, focuses on how specific segmented factor markets—such as capital, technology, and data—drive innovation. This strand explores the incentivizing effects of financial capital flow [4], technological knowledge recombination [5], and data information exchange [6] on innovative activities, constituting the foundational driving forces at the factor level. The second perspective, based on regional openness, examines the impact of market access regulation and regional integration on innovation. It investigates how the expansion of regional market demand enhances the expected returns on innovation [7] and the resulting regional resource agglomeration and industrial synergy [8], revealing how regional market openness reconstructs and amplifies innovation ecosystems through network effects. However, existing literature has not yet fully elucidated the critical role of an efficient, standardized, fair, competitive, and fully open integrated market in the complex system of key core technological innovation. Particularly against the backdrop of global counter-currents and intensified technological blockades, building an integrated market in response to the new development paradigm has become an indispensable strategic support and experimental platform for sustaining key core technological innovation. Based on this, focusing on the principal role of enterprises in technological innovation, an in-depth exploration of how market integration construction affects enterprises’ innovation in key core technologies has become an urgent issue to address.

Based on data from China’s A-share listed companies and statistical yearbooks from 2008 to 2023, and guided by Complex Adaptive Systems (CAS) theory, this paper empirically examines the effects, pathways, boundary conditions, and heterogeneous impacts of market integration construction on enterprises’ innovation in key core technologies. The marginal contributions of this paper are threefold. First, it constructs an “institution–innovation” analytical framework based on CAS theory. Placing the macro-institutional change in market integration construction within the CAS perspective, this paper systematically explains how it reconstructs rules for resource flow, competition incentives, and collaborative networks, guiding adaptive enterprises to adjust their organizational behavior and learning models [9], ultimately driving the dynamic evolution process of key core technology emergence. This theoretical framework transcends the limitations of existing studies that often focus on partial dimensions such as single-factor markets or regional openness, providing a new theoretical lens for understanding the systemic interaction among macro-institutions, unified markets, and micro-innovation in the Chinese context. Second, it reveals the transmission and moderating mechanisms of enterprise investment behavior and organizational capabilities within the “institution–innovation” relationship, opening the “black box.” This paper empirically identifies that industrial investment plays a mediating role, financial investment exhibits a suppression effect, and agile responsiveness significantly enhances the promoting effect of market institutions on innovation. This not only clarifies the key pathways through which market integration construction affects enterprises’ key core technological innovation but also echoes the complex interplay between “to the real” and “to the virtual” investments [10] and the critical role of enterprise adaptability in converting institutional dividends. Third, it reveals the heterogeneous characteristics of market integration’s impact on enterprises’ key core technological innovation, providing a basis for differentiated policies. From multiple dimensions such as ownership nature, market position, and firm size, this paper reveals how intrinsic enterprise characteristics—such as ownership constraints, capability gaps, and scale differences in China’s market transition—affect the utility of institutions, offering empirical evidence for promoting market integration construction, guiding differentiated enterprise innovation strategies, and tackling key core technologies.

The remainder of this paper is structured as follows: Section 2 lays the theoretical foundation and develops the research hypotheses, drawing on Complex Adaptive Systems theory to elucidate the mechanisms through which market integration construction influences enterprises’ key core technological innovation. Section 3 delineates the research design, including sample selection, variable definitions, measurement, and model specification. Section 4 presents the empirical results, encompassing baseline regressions, mediating and moderating effect tests, robustness checks, and endogeneity treatments, followed by extended heterogeneity analyses. Section 5 discusses the theoretical and practical implications of the findings, situating them within the broader literature. Finally, Section 6 concludes the study by summarizing the key insights, offering policy recommendations, and acknowledging limitations while suggesting directions for future research.

2. Theoretical Analysis and Research Hypotheses

2.1. The Impact of Market Integration Construction on Firms’ Innovation in Key Core Technologies

Innovation in key core technologies refers to the technological innovation that supports ecosystem members in the R&D of specific core technology modules. It exhibits systemic complexity characterized by demands for complex technological structures, knowledge composition, and the collaboration of innovation entities [1,11]. Under the constraints of traditional market segmentation and local protectionism, the innovation system has long been governed by rigid operational rules. This rigidity hinders interaction among entities and hampers the flow of factors, trapping the innovation system in a state of local equilibrium and inefficient lock-in, thereby making it difficult to achieve the breakthrough emergence of key core technologies. Market integration construction aims to break down geographical restrictions and construct an efficient, standardized, fair, and open market system [12]. According to Complex Adaptive Systems (CAS) theory, market integration construction essentially reshapes the rules of the innovation system, guiding firms—as adaptive agents—to adjust their behavioral strategies, thereby promoting the emergence of complex key core technologies.

First, market integration construction reconstructs the rules of resource flow, reducing firms’ adaptive costs. By unifying factor markets and promoting high-standard connectivity of market infrastructure, market integration construction significantly lowers the transaction costs and institutional frictions associated with the cross-regional and cross-entity flow of innovation resources within the system [13]. This enables firms to acquire and combine scarce resources from distant locations and across different domains at lower costs and with higher efficiency, thereby enhancing their capability for innovation in key core technologies. Second, market integration construction reconstructs the rules of competitive incentives, guiding the direction of firms’ adaptation. By unifying market regulation rules and dismantling administrative barriers, market integration construction shifts the system’s selection pressure from relationship-based competition under local protection to efficiency-based competition in technological innovation on a national scale [14]. This pressures firms to reallocate resources towards key core technology areas that can yield long-term competitive advantages [15]. Simultaneously, the ultra-large-scale market provides massive and diverse application scenarios for innovation outcomes, substantially increasing the expected scale returns from firms’ technological breakthroughs [8]. This partially internalizes the strong positive externalities of innovation, thereby systematically guiding firms to evolve towards high-risk, high-social-benefit key core technological innovation. Third, market integration construction reconstructs the rules of collaborative networks, shaping firms’ adaptive models. At the institutional level, market integration construction strengthens the systematic coordination capability of a “the whole country is working as one” [12]. This facilitates leveraging the advantages of the new nationwide system, selectively strengthening connections in specific directions within the system, investing in key generic technology platforms, organizing cross-departmental collaborative efforts, and consolidating strategic forces for scientific and technological innovation [16]. Through such adaptive governance mechanisms, it reduces the coordination costs and directional risks for firms in exploring cutting-edge, unknown technologies [17], accelerates the formation and consolidation of innovation networks aligned with national strategic needs, and provides sustainable systemic support for breakthroughs in key core technologies.

In summary, market integration construction reconstructs the rules of resource flow, competitive incentives, and collaborative networks, altering the constraints firms face within the innovation system. This drives the emergence of key core technological innovation outcomes through nonlinear interactions. Therefore, this study proposes the following hypothesis:

Hypothesis 1 (H1).

Market integration construction has a significant positive effect on firms’ innovation in key core technologies.

2.2. The Mediating Mechanism of “Shifting from Virtual to Real”

“Shifting from virtual to real” refers to firms reducing their holdings of financial assets while increasing the allocation of operating assets to balance the relationship between the real economy and financialization [18]. According to CAS theory, members within a system can interact adaptively with their environment and other agents, continuously learning and accumulating experience to make adaptive adjustments [19]. Faced with external environmental changes triggered by market integration construction, firms weigh the pros and cons of industrial investment versus financial investment, adaptively adjust their investment decisions, and thereby influence the emergence of their key core technological innovation.

(1) Industrial Investment: Market integration construction incentivizes firms to undertake industrial investment, laying a solid foundation for innovation activities and thereby promoting innovation in key core technologies.

Market integration construction promotes industrial investment through cost optimization and competitive incentives. First, by unifying factor markets and strengthening market regulation, market integration construction significantly weakens the institutional rents arising from local protectionism, reducing the adaptive costs for firms to acquire and integrate high-end factors from other regions [3]. This incentivizes firms to increase strategic industrial investment in core technologies, specialized equipment, and advanced processes to establish long-term competitive advantages [18]. Second, the creation of a unified, ultra-large-scale market through integration intensifies competition while also greatly enhancing the expected returns on successful innovation [7]. This pushes firms not only to expand internal capacity but also to extend industrial investment into areas that can strengthen their nodal positions within innovation networks, engaging in more forward-looking and exploratory industrial investment [8].

The adaptive adjustment of firms’ industrial investment triggers multiple feedback loops and interactions within the CAS, ultimately catalyzing innovation in key core technologies. First, industrial investment in purchasing advanced equipment and upgrading experimental facilities directly enhances a firm’s technology absorption and experimental capabilities, enabling it to more effectively identify and utilize external resource flows [20]. This, in turn, increases the success probability and expected returns of industrial investment, forming a self-reinforcing cycle of “investment–learning–reinvestment” and accumulating the material foundation for key core technological innovation. Second, a firm’s industrial investment sends clear signals of technological upgrading to the industrial chain, driving collaborative innovation upstream and downstream [16]. This facilitates the sedimentation and utilization of tacit knowledge within collaborative innovation networks, thereby enhancing the effectiveness of systematic technological breakthroughs and facilitating the research and development of key core technologies.

In summary, market integration construction guides firms towards industrial investment by lowering adaptive costs and amplifying innovation returns. This, in turn, triggers the self-reinforcing “investment–learning–reinvestment” cycle and drives industrial chain collaborative upgrading, ultimately catalyzing firms’ innovation in key core technologies. Therefore, we propose:

Hypothesis 2 (H2).

Industrial investment mediates the relationship between market integration construction and firms’ innovation in key core technologies.

(2) Financial Investment: Market integration construction may lead to an increase in firms’ financial investment, which can crowd out innovation resources and thereby negatively impact innovation in key core technologies.

Market integration construction leads firms to increase financial investment. First, the construction of market integration has promoted the integration of financial markets, significantly reducing the transaction costs and information barriers for enterprises’ participation in financial investments [21]. Not only does this enhance financial stability [22], but also this weakens firms’ subjective perception of financial risks, thereby guiding them to allocate more funds to financial assets [20]. Second, by breaking traditional financial development models and expanding the boundaries of financial services, market integration construction incentivizes financial institutions to innovate their business models [23], further enhancing the attractiveness of financial investment and inducing firms to engage in it. Third, the intensified nationwide competitive pressure resulting from market integration construction may reinforce managerial short-termism [24], making management more inclined to use financial investments to quickly meet performance evaluation requirements, thereby exacerbating firms’ financial investment.

Increased financial investment inhibits firms’ innovation in key core technologies. First, constrained by resource scarcity, the crowding-out of R&D funds by financial investment directly leads to insufficient innovation input [25], causing many nascent ideas for key core technologies to abort due to a lack of resources. Second, excessive managerial focus on financial investment reshapes their cognitive framework and decision-making priorities, shifting from a focus on long-term technological breakthroughs to an emphasis on capital operations [20]. This not only weakens the firm’s willingness to identify technological opportunities and tolerate innovation failure but also damages its commitment and credibility within the established innovation networks. Consequently, it reduces the stability of the collaborative relationship within the innovation system and the depth of the cooperation network, ultimately inhibiting breakthroughs in key core technologies.

In summary, market integration construction alters firms’ asset allocation preferences, enhances the attractiveness of financial investment, and reinforces short-term performance orientation, pushing firms to increase financial investment. This leads to the crowding out of innovation resources and the deterioration of the innovation environment, thereby inhibiting firms’ innovation in key core technologies. Therefore, we propose:

Hypothesis 3 (H3).

Financial investment has a masking effect in the relationship between market integration construction and firms’ innovation in key core technologies.

2.3. The Moderating Role of Agile Responsiveness

Agile responsiveness refers to a firm’s capability for rapid and innovative adaptation to continuously changing market environments to achieve growth [26]. Based on CAS theory, agile responsiveness can be viewed as the concentrated embodiment of a firm’s core adaptive capability when facing environmental changes. As adaptive agents within the innovation system, differences in firms’ agile responsiveness affect the adaptive translation of the macro-institutional change—market integration construction—into innovation in key core technologies.

First, agile responsiveness empowers firms to accurately identify the specific opportunities and potential challenges inherent in market integration construction and translate them into clear windows of technological opportunity and signals of competitive pressure. Firms with high agile responsiveness can keenly perceive and interpret the new rules brought by market integration construction and quickly translate them into a series of exploratory and strategic adaptive actions [27]. Through internal efficient positive feedback mechanisms, these actions accelerate the firm’s integration of scarce resources and occupation of core nodes within innovation networks. This makes it more likely for such firms to capture institutional dividends, take the lead in achieving breakthroughs in technological competition, and significantly increase the probability, speed, and quality of the emergence of key core technological innovation [28]. Conversely, firms with low agile responsiveness often make only weak and defensive behavioral adjustments in the face of market integration construction. They struggle to effectively utilize the resources and opportunities brought by institutional change and may even become more conservative due to intensified competition. This results in a weak, or even negligible, promoting effect of market integration construction on their key core technological innovation, leaving them at risk of marginalization. Second, agile responsiveness also helps firms leverage market integration construction to accelerate innovation and its commercialization. Firms with high agile responsiveness possess flexible and reconfigurable internal organizational models. They can utilize the resources and networks facilitated by market integration construction to engage in adaptive behaviors characterized by rapid trial-and-error, efficient learning, and dynamic adjustment [29], thereby significantly shortening the innovation cycle. Simultaneously, by leveraging the ultra-large-scale demand created by market integration construction, they can rapidly commercialize innovation outcomes. The resulting scale benefits help amortize innovation costs and support R&D investment [26], enabling the firm’s engagement in key core technological innovation. Conversely, firms with low agile responsiveness often miss the first-mover advantage in the face of market integration construction. They find it difficult to harness the supportive and amplifying effect of the ultra-large-scale market on their innovation activities, leading to delayed or even interrupted innovation processes, thereby inhibiting key core technological innovation.

In summary, agile responsiveness amplifies the promoting effect of market integration construction on firms’ innovation in key core technologies by enhancing the efficiency of identifying and translating institutional opportunities and by accelerating innovation iteration and market feedback. Therefore, we propose:

Hypothesis 4 (H4).

Agile responsiveness positively moderates the relationship between market integration construction and firms’ innovation in key core technologies.

3. Research Design

3.1. Sample Selection and Data Sources

This study utilizes A-share listed companies in China from 2008 to 2023 as the research sample. Data pertaining to market integration construction are sourced from the China Statistical Yearbook, China Population and Employment Statistics Yearbook, China Labour Statistical Yearbook, and China Financial Yearbook. Patent data are obtained from the China National Intellectual Property Administration, and financial data are sourced from the China Stock Market & Accounting Research (CSMAR) database. The following data processing steps were applied: companies labeled as ST or PT, as well as those in the financial and insurance industries, were excluded. Samples with missing values even after linear interpolation were also removed. To mitigate the influence of outliers and extreme values, continuous variables were winsorized at the 1st and 99th percentiles. The final dataset constitutes an unbalanced panel with 38122 firm-year observations.

3.2. Variable Definitions and Measurement

(1) Dependent Variable: Enterprise Key Core Technological Innovation (KCT). Drawing on the methodology of Wu and Yan [30], this study measures firms’ innovation in key core technologies. Based on the Industrial Foundation Innovation and Development Catalog (2021 Edition) issued by the National Manufacturing Power Construction Strategy Advisory Committee, which lists keywords for 1047 technologies across 21 key core technology areas, we matched these keywords with technical descriptions of five-level International Patent Classification (IPC) codes. The corresponding IPC codes for patents in these key core technology areas were identified. The firm’s annual patent count in these areas is aggregated, incremented by one, and then log-transformed to measure Key Core Technological Innovation (KCT).

(2) Core Independent Variable: Market Integration Construction (MIC). Following the approach of Zhang [31], an indicator system for assessing the level of market integration construction is developed across three dimensions: Market Development Quality Level, Market Integration Level, and Market Development Environment Level. The entropy method is then applied to assign weights to the core indicators within this system. A time variable is incorporated to enhance the rationality of the constructed index. The specific indicators are detailed in

Table 1. Market Integration Construction Level Index System.

Dimension	Factor	Index	Computational Method
Market development quality level	The quality of commodity market development	Distribution of commodity markets	Total retail sales of consumer goods in the region/Gross domestic product of the region
		Efficiency of commodity market	Sales revenue of goods of wholesale and retail enterprises/closing inventory value of goods
		Convenience of commodity market	Number of chain retail enterprise stores per 10,000 people
		Digitalization of commodity market	Online retail sales/Total retail sales of consumer goods
	Development quality of factor market	Labor market distribution	Number of employed persons/Year-end population
		Labor market efficiency	Gross regional domestic product/number of employed personnel
		Labor market resources	Average years of education for employed personnel
		Vitality of the labor market	Number of newly employed urban residents/Total number of urban residents
		Capital market distribution	Regional fixed assets investment/National fixed assets investment
		Capital market efficiency	Regional GDP/regional fixed assets investment
		Vitality of capital market	Growth rate of regional fixed assets investment
Level of market integration	Market connection	logistics	Total freight turnover
		flow of people	Total passenger transport turnover
		cash flow	Actual capital amount of fixed assets investment
		information flow	Mobile Internet access traffic
	market barriers	Barriers in commodity markets	Commodity market segmentation index
		Labor market barriers	Labor market segmentation index
		Capital market barriers	Capital market segmentation index
	Market division of labor	Specialized division of labor in the primary industry	Location entropy of primary industry
		Professional division of labor in the secondary industry	Location entropy of the secondary industry
		Specialized division of labor in the tertiary industry	Location entropy of the tertiary industry
Level of market development environment	Development environment of non-state-owned economy	Fixed assets investment of non-state-owned enterprises	Non-state-owned economy fixed assets investment/fixed assets investment
		Number of employees in non-state-owned enterprises	Number of employed people in urban non-state-owned units/Number of urban employed people
		Industrial output value of non-state-owned enterprises	Revenue from main business of non-state-owned industrial enterprises/Revenue from main business of industrial enterprises
		Number of newly established non-state-owned enterprises	Ratio of the number of registered non-state-owned enterprises in a region to the national average of the number of registered non-state-owned enterprises
	Market supervision environment	The severity of administrative penalties	(Administrative revenue + Confiscated revenue)/Fiscal revenue
		product quality	Product quality pass rate
	Innovative environment	R&D funding intensity	R&D expenditure/GDP
		Intensity of R&D personnel input	Full-time equivalent R&D personnel/Number of employed personnel
		innovation output	(Sales revenue of new products—Development expenditure of new products)/Development expenditure of new products
	Infrastructure environment	Transportation infrastructure	The sum of railway and highway mileage per square kilometer
		Network infrastructure	Number of Internet broadband access ports per square kilometer

.

(3) Mediating Variable: “Shifting from virtual to real”. Following Guo and Guo [20], this study employs Enterprise Industrial Investment (II) and Enterprise Financial Investment (FI) to represent this concept. Enterprise Industrial Investment (II) is measured by the ratio of the sum of cash paid for fixed assets, intangible assets, and other long-term assets to total assets. Enterprise Financial Investment (FI) is measured by the ratio of the sum of trading financial assets, net available-for-sale financial assets, net held-to-maturity investments, net long-term equity investments, and net investment property to total assets.

(4) Moderating Variable: Agile Responsiveness (AR). This study uses the number of board meetings held in the current year to measure a firm’s agile responsiveness. A higher value of this indicator signifies a faster response speed of the firm to changes in the external environment.

(5) Control Variables. In line with existing literature [32,33], this study controls for a series of variables that may influence a firm’s key core technological innovation. These mainly include: Firm Age (AGE), measured as the natural logarithm of the firm’s listing age plus one; Firm Size (SIZE), measured as the natural logarithm of total assets plus one; Return on Assets (ROA), calculated as the ratio of net profit to average total assets; Financial Leverage (LEV), calculated as the ratio of total liabilities to total assets; Ownership Concentration (TOP), measured by the shareholding ratio of the largest shareholder; CEO Duality (DUAL), a dummy variable that equals 1 if the chairman also serves as the CEO, and 0 otherwise; Management Shareholding Ratio (MSR), measured by the proportion of shares held by directors, supervisors, and senior executives to the total number of shares; and Industrial Structure (IS), measured by the ratio of the added value of the tertiary industry to that of the secondary industry.

3.3. Model Specification

To test the hypotheses proposed in this study, we construct the following econometric regression models (1), (2), (3), and (4). Model (1) is employed to examine the direct effect, specifically the impact of market integration construction on enterprises’ innovation in key core technologies (i.e., H1). Models (2) and (3) are used to test the mediating effects, investigating the mechanism through which enterprise industrial investment and financial investment operate in the relationship between market integration construction and innovation in key core technologies (i.e., H2 and H3). Model (4) is specified to test the moderating effect of agile responsiveness on the relationship between market integration construction and innovation in key core technologies (i.e., H4).

$$Y_{it}={\alpha }_0+{\alpha }_1{X}_{it}+\sum {Controls}_{it}+\sum Year+\sum ID+{\epsilon }_{it}$$

(1)

$$M_{it}={\beta }_0+{\beta }_1{X}_{it}+\sum {Controls}_{it}+\sum Year+\sum ID+{\epsilon }_{it}$$

(2)

$$Y_{it}={\delta }_0+{\delta }_1{X}_{it}+{\delta }_2{M}_{it}+\sum {Controls}_{it}+\sum Year+\sum ID+{\epsilon }_{it}$$

(3)

$$Y_{it}={\mu }_0+{\mu }_1{X}_{it}+{\mu }_2{W}_{it}+{\mu }_3{X}_{it}{W}_{it}+\sum {Controls}_{it}+\sum Year+\sum ID+{\epsilon }_{it}$$

(4)

Here, Y_it denotes the dependent variable, enterprise key core technological innovation. X_it represents the explanatory variable, the level of market integration construction. M_it signifies the mediating variables, enterprise industrial investment and enterprise financial investment. W_it indicates the moderating variable, agile responsiveness. ΣControls_it refers to the set of control variables. Year and ID represent the year fixed effects and entity (firm) fixed effects, respectively. ε_it stands for the random disturbance term. The subscripts i and t denote the individual firm and time period, respectively.

4. Empirical Results Analysis

4.1. Descriptive Statistics

Table 2. Descriptive Statistical Results.

Variable	Sample Size	Mean	Standard Deviation	Minimum	Median	Maximum
KCT	38,122	0.621	1.093	0.000	0.000	4.700
MIC	38,122	0.303	0.126	0.095	0.286	0.605
II	38,122	0.049	0.047	0.000	0.035	0.228
FI	38,122	0.072	0.101	0.000	0.032	0.522
AR	38,122	9.639	3.792	4.000	9.000	23.000
AGE	38,122	2.146	0.884	0.000	2.303	3.367
SIZE	38,122	22.209	1.335	19.714	22.023	26.292
ROA	38,122	0.034	0.065	−0.269	0.035	0.192
LEV	38,122	0.434	0.208	0.051	0.428	0.910
TOP	38,122	33.943	14.985	8.214	31.574	73.997
DUAL	38,122	0.277	0.448	0.000	0.000	1.000
MSR	38,122	12.389	19.079	0.000	0.287	68.183
IS	38,122	1.641	1.078	0.437	1.280	5.298

presents the descriptive statistics of the variables. The maximum value for enterprise key core technological innovation (KCT) is 4.7, while its minimum and median are 0, indicating a significantly skewed distribution. Its standard deviation (1.093) is greater than its mean (0.621), suggesting substantial variation in KCT across firms. The level of market integration construction (MIC) ranges from a minimum of 0.095 to a maximum of 0.605, reflecting considerable regional disparity and room for overall improvement. Its mean is 0.303 with a standard deviation of 0.126, indicating that firms operate under varying levels of market integration construction. The descriptive statistics for the remaining variables fall within reasonable ranges and are not elaborated further here.

4.2. Baseline Regression Analysis

The baseline regression results are presented in

Table 3. Baseline Regression Test.

Variable	(1)	(2)
	KCT	KCT
MIC	0.514 ***	0.351 ***
	(4.240)	(2.982)
Control variable	No	Yes
Individual fixation	Yes	Yes
Fixed time	Yes	Yes
Constant term	0.465 ***	−6.269 ***
	(12.680)	(−29.497)
Adjust the R2 value	0.598	0.614
Sample size	37,711	37,711

Note: * indicates p < 0.1, ** indicates p < 0.5, *** indicates p < 0.01; the values in parentheses are the corresponding T-values for the robust standard errors.

. Column (1) shows the results without control variables, considering only entity and time fixed effects. The regression coefficient for market integration construction on enterprise key core technological innovation is 0.514 and is significantly positive at the 1% level, providing preliminary evidence for a positive influence. After including a series of control variables in Column (2), the regression coefficient for market integration construction remains significantly positive at the 1% level, with a value of 0.351. This demonstrates that market integration construction has a significantly positive impact on enterprise key core technological innovation, thus supporting Hypothesis H1. As elaborated in the theoretical analysis, market integration construction drives enterprise key core technological innovation through nonlinear interactions by reconstructing the rules governing resource flow, competitive incentives, and collaborative networks [34].

4.3. Tests for Mediating Mechanisms

The results of the mediating mechanism tests are presented in

Table 4. Test of Mediating Mechanism and Moderating Effect.

Variable	(1)	(2)	(3)	(4)	(5)
	II	KCT	FI	KCT	KCT
MIC	0.024 ***	0.342 ***	0.040 ***	0.360 ***	0.311 ***
	(3.615)	(2.910)	(3.419)	(3.062)	(2.646)
II		0.359 ***
		(3.566)
FI				−0.233 ***
				(−4.240)
AR					0.003 *
					(1.893)
MIC_AR					0.050 ***
					(5.218)
Control variable	Yes	Yes	Yes	Yes	Yes
Individual fixation	Yes	Yes	Yes	Yes	Yes
Fixed time	Yes	Yes	Yes	Yes	Yes
Constant term	−0.060 ***	−6.247 ***	0.215 ***	−6.219 ***	−6.183 ***
	(−5.330)	(−29.417)	(10.220)	(−29.212)	(−28.794)
Adjust the R2 value	0.438	0.614	0.631	0.614	0.648
Sample size	37,711	37,711	37,711	37,711	37,711

Note: * indicates p < 0.1, ** indicates p < 0.5, *** indicates p < 0.01; the values in parentheses are the corresponding T-values for the robust standard errors.

. Column (1) shows that the regression coefficient of market integration construction on enterprise industrial investment (II) is 0.024 and significantly positive at the 1% level, indicating that market integration construction significantly increases enterprise industrial investment. Column (2) further reveals that the regression coefficients of market integration construction and II on key core technological innovation are 0.342 and 0.359, respectively, both significant at the 1% level. This suggests that enterprise industrial investment plays a partial mediating role in the relationship where market integration construction promotes key core technological innovation, supporting Hypothesis H2. Market integration construction can reduce adaptive costs and amplify innovation returns, thereby encouraging firms to increase industrial investment (i.e., “to the real”). This, in turn, triggers a self-reinforcing cycle of “investment–learning–reinvestment” and fosters collaborative upgrading within the industrial chain, ultimately assisting enterprises in achieving innovation in key core technologies.

Column (3) of Table 4 indicates that the regression coefficient of market integration construction on enterprise financial investment (FI) is 0.04 and significantly positive at the 1% level, suggesting that market integration construction prompts firms to engage in financial investment. Column (4) further shows that after controlling for FI, the regression coefficient of market integration construction on key core technological innovation remains significantly positive at the 1% level (0.36), while the coefficient for FI is −0.233 and significantly negative at the 1% level. This indicates that enterprise financial investment exerts a masking effect in the relationship where market integration construction promotes key core technological innovation, supporting Hypothesis H3. Market integration construction alters firms’ asset allocation preferences, enhances the attractiveness of financial investment, and reinforces short-term performance orientation, thereby driving firms towards financial investment (i.e., “to the virtual”). However, financial investment leads to the crowding out of innovation resources and the deterioration of the innovation environment [35], consequently inhibiting firms from pursuing innovation in key core technologies.

4.4. Test for the Moderating Effect

The results of the moderating effect test are shown in Column (5) of Table 4, where agile responsiveness (AR) and its interaction term with market integration construction were added to the baseline regression. The regression results indicate that the coefficient for the interaction term between market integration construction and agile responsiveness on key core technological innovation is 0.05 and statistically significant at the 1% level. This suggests that agile responsiveness plays a moderating role in the relationship between market integration construction and key core technological innovation, thereby supporting Hypothesis H4. As discussed in the theoretical analysis, agile responsiveness enhances a firm’s efficiency in identifying and capitalizing on institutional opportunities presented by the unified market, accelerates innovation iteration and market feedback [36], and thereby amplifies the positive effect of market integration construction on key core technological innovation.

4.5. Robustness Checks

4.5.1. Excluding Exogenous Shocks

We considered the impact of the 2008 financial crisis on the Chinese economy and its prolonged effects [37]. Consequently, samples from before 2012 were excluded, and the regression was re-run. The results, presented in Column (1) of

Table 5. Results of Robustness Test.

Variable	(1)	(2)	(3)	(4)
	Eliminate Exogenous Shocks	Handle Special Samples	Fix by Province/Time	Fix by Industry/Time
	KCT	KCT	KCT	KCT
MIC	0.506 ***	0.306 **	0.691 ***	0.411 ***
	(3.554)	(2.221)	(4.392)	(8.392)
Control variable	Yes	Yes	Yes	Yes
Individual fixation	Yes	Yes	Yes	Yes
Fixed time	Yes	Yes	Yes	Yes
Constant term	−6.789 ***	−6.565 ***	−6.160 ***	−7.038 ***
	(−25.908)	(−27.625)	(−44.343)	(−59.086)
Adjust the R2 value	0.649	0.599	0.170	0.305
Sample size	31,457	29,548	38,122	38,122

Note: * indicates p < 0.1, ** indicates p < 0.5, *** indicates p < 0.01; the values in parentheses are the corresponding T-values for the robust standard errors.

, show that the regression coefficient of market integration construction on key core technological innovation is 0.506 and remains significantly positive at the 1% level. The consistency in both significance and sign with the baseline regression results effectively verifies the robustness of the findings.

4.5.2. Handling Special Samples

We considered that municipalities directly under the central government possess unique advantages such as concentrated resources, significant demographic dividends, strong economic vitality, and higher levels of economic development. They often serve as pilot zones for important policies and measures, granting them a special status in the context of market integration construction. To ensure the generalizability and reliability of the research findings, samples from Beijing, Tianjin, Shanghai, and Chongqing were excluded, and the baseline regression test was re-conducted. The regression results, shown in Column (2) of Table 5, indicate that the coefficient for market integration construction on key core technological innovation remains significantly positive, largely consistent with the baseline regression results. This demonstrates the high robustness of the research findings.

4.5.3. Multidimensional Fixed Effects

After incorporating the fixed effects of entities and time, we further controlled for the potential influence of regional and industry factors on the results [38]. The regression results, presented in Column (3) of Table 5, show that the coefficient for market integration construction on key core technological innovation remains significantly positive after adding province fixed effects and time fixed effects to the model. Similarly, Column (4) of Table 5 shows that the coefficient remains significantly positive after adding industry fixed effects and time fixed effects. These results, consistent with the baseline regression, further validate the robustness of the core conclusion.

4.6. Addressing Endogeneity

4.6.1. Propensity Score Matching (PSM)

To mitigate potential biases from sample selection and other confounding factors, Propensity Score Matching (PSM) was employed to further validate the relationship between market integration construction and key core technological innovation. Following the variable handling method of Park and Chang [39], all sample firms were divided into a “high level of market integration construction” group and a “low level of market integration construction” group based on whether their market integration construction level exceeded the sample mean. Subsequently, 1:1 nearest-neighbor matching was performed using the control variables as covariates to find a control group with similar characteristics for the treatment group. Finally, regression analysis was conducted on the matched sample data. The regression result, presented in Column (1) of

Table 6. Results of Endogeneity Test.

Variable	(1)	(2)	(3)	(4)	(5)
	PSM	IV Method		Heckman Test
	KCT	MIC	KCT	T	KCT
MIC	0.241 *		0.633 ***		0.397 ***
	(1.703)		(3.112)		(3.132)
IV		0.056 ***		0.343 ***
		(67.002)		(57.195)
imr					−0.119 **
					(−2.442)
Control variable	Yes	Yes	Yes	Yes	Yes
Individual fixation	Yes	Yes	Yes	Yes	Yes
Fixed time	Yes	Yes	Yes	Yes	Yes
Constant term	−7.155 ***
	(−26.096)
Kleibergen–Paap rk LM		2765.453 ***
Kleibergen–Paap rk Wald F		4489.255
10% Stock–Yogo test		16.380
Adjust the R2 value	0.660		0.064		0.629
Sample size	32,963	37,711	37,711	35,475	35,072

Note: * indicates p < 0.1, ** indicates p < 0.5, *** indicates p < 0.01; the values in parentheses are the corresponding T-values for the robust standard errors.

, shows that the coefficient for market integration construction on key core technological innovation is significantly positive, indicating the robustness of the research findings.

4.6.2. Instrumental Variable (IV) Method

To address potential endogeneity issues, such as reverse causality and omitted variable bias, that might exist between market integration construction and key core technological innovation, this study employs the Instrumental Variable (IV) method for further testing [40]. We introduce the product of the standard deviation of geographic slope in each region and the corresponding regional and yearly level of market integration construction as the instrument variable (IV), and conduct a two-stage least squares (2SLS) estimation. The results are presented in Columns (2) and (3) of Table 6. The Kleibergen–Paap rk LM statistic is significant at the 1% level, and the Kleibergen–Paap rk Wald F statistic exceeds the 10% critical value (16.38), indicating that the instrument does not suffer from under-identification or weak identification problems. The first-stage results satisfy the relevance condition, and the instrument is assumed to satisfy the exclusion restriction. Furthermore, the second-stage estimation result shows that the regression coefficient for market integration construction remains significantly positive at the 1% level, which is largely consistent with the regression results reported earlier.

4.6.3. Heckman Two-Stage Model

To mitigate the potential sample selection bias arising from the non-random selection of firms for which data is unavailable (e.g., non-listed firms or those not disclosing annual reports timely), this study employs the Heckman two-stage model for further examination [41]. A dummy variable T is constructed based on whether a firm is registered in a region with a high level of market integration construction. T equals 1 if the firm’s registered location corresponds to a market integration construction level above the median, and 0 otherwise. In the first stage, a Probit regression is performed with T as the dependent variable, using the product of the geographic slope standard deviation and the regional and yearly market integration construction level (the same IV) as the exclusion restriction variable. The regression result, shown in Column (4) of Table 6, indicates that the coefficient for the exclusion restriction variable is 0.343 and significant at the 1% level, aligning with expectations. In the second stage, the Inverse Mills Ratio (IMR) calculated from the first stage is incorporated into the baseline regression model. The result, presented in Column (5) of Table 6, shows that the regression coefficient for market integration construction is 0.397 and remains significantly positive at the 1% level, again consistent with the prior regression results.

4.7. Extended Analyses

4.7.1. Heterogeneity Analysis by Ownership Nature

State-owned enterprises (SOEs), endowed with substantial capital, abundant political resources, and strong information acquisition capabilities, exhibit a relatively lower dependence on the market. Leveraging these advantages, SOEs can easily access external resources, possess higher bargaining power, enjoy well-secured economic benefits, and readily gain customer reliance. This positions SOEs in a relatively comfortable state within market competition, often lacking the urgency and motivation to innovate. In contrast, non-state-owned enterprises (non-SOEs), which are more market-dependent, often face resource constraints. Their market information acquisition capabilities are limited, and they are subject to intense competitive pressure [42]. While they may be keenly aware of new demands arising from changes in the external environment, their innovation confidence and capabilities are often insufficient due to a lack of resources and capabilities. However, the construction of market integration presents a turning point for non-SOEs. By breaking geographical restrictions, market integration construction provides non-SOEs with broader information channels, access to diversified funding, and the opportunity to build collaborative networks with external stakeholders to share resources such as technology, thereby powerfully driving their innovation in key core technologies. In comparison, SOEs can already easily access various resources by virtue of their inherent advantages, so the enhancement effect of market integration construction on their resource acquisition is limited. Consequently, for SOEs, the promoting effect of market integration construction on their innovation in key core technologies is not significant.

The sample is divided into SOEs and non-SOEs based on ownership nature. The regression results are shown in Columns (1) and (2) of

Table 7. Results of Heterogeneity Analysis.

Variable	(1)	(2)	(3)	(4)	(5)	(6)
	Non-SOEs	SOEs	Follower	Leading	Small and Medium-Sized	Large
	KCT	KCT	KCT	KCT	KCT	KCT
MIC	0.694 ***	0.167	0.249 **	−0.056	−0.049	0.594 ***
	(4.516)	(0.823)	(1.998)	(−0.189)	(−0.360)	(2.805)
Control variable	Yes	Yes	Yes	Yes	Yes	Yes
Individual fixation	Yes	Yes	Yes	Yes	Yes	Yes
Fixed time	Yes	Yes	Yes	Yes	Yes	Yes
Constant term	−6.810 ***	−6.170 ***	−4.448 ***	−7.313 ***	−3.082 ***	−7.582 ***
	(−25.436)	(−15.325)	(−18.553)	(−11.510)	(−11.745)	(−17.500)
Adjust the R2 value	0.572	0.673	0.480	0.707	0.431	0.680
Sample size	23,421	14,237	25,630	11,810	18,656	18,837

Note: * indicates p < 0.1, ** indicates p < 0.5, *** indicates p < 0.01; the values in parentheses are the corresponding T-values for the robust standard errors.

. Column (1) indicates that for non-SOEs, the regression coefficient of market integration construction on key core technological innovation is 0.694, which is significantly positive at the 1% level. Column (2) shows that for SOEs, the coefficient is positive but not statistically significant. These results confirm that the positive effect of market integration construction on key core technological innovation is significant for non-SOEs.

4.7.2. Heterogeneity Analysis by Market Position

Leading firms typically possess ample capital, advanced technology, sufficient talent, and stable market share. They face less competitive pressure and tend to focus more on the steady growth of existing businesses and the maintenance of market share, resulting in relatively weaker motivation for innovation. Their innovation investment primarily depends on their own resources and strategic planning; they show lower openness and proactiveness towards external collaboration and prefer to acquire technology through internal innovation or acquisitions to maintain market dominance. Therefore, the resource optimization facilitated by market integration construction has a limited impact on leading firms’ innovation in key core technologies. In contrast, follower firms operate with limited resources, smaller market shares and profit margins, insufficient capital investment capacity, and constrained R&D expenditure. To survive and develop, they must innovate to narrow the gap with leading firms and compete for more market share [43]. Market integration construction breaks geographical barriers, helping follower firms access resources such as technology, talent, and capital, and concentrate these on innovation. Simultaneously, market integration construction intensifies competition, lowers market risk and creates a safer environment for innovation, promoting accelerated innovation among follower firms and stimulating their motivation to undertake innovation in key core technologies.

Following the research of Adhikari and Agrawal [44], we measure market position using a firm’s revenue ranking within its two-digit industry. Firms ranked in the top 30% annually are defined as the leading firm subsample, and the remainder as the follower firm subsample. The regression results are shown in Columns (3) and (4) of Table 7. Column (3) indicates that for follower firms, the coefficient of market integration construction on key core technological innovation is significantly positive. Column (4) shows that for leading firms, the coefficient is negative but not statistically significant. These results confirm that the positive effect of market integration construction on key core technological innovation is significant for follower firms.

4.7.3. Heterogeneity Analysis by Firm Size

Large enterprises typically possess strong resources and capabilities, enabling them to more effectively seize the resource integration opportunities brought by market unification, such as attracting high-end talent and collaborating with top research institutions. Simultaneously, to maintain market competitiveness and industry standing, they have greater motivation to establish close cooperative relationships with upstream and downstream firms in the industrial chain as well as with research institutions, sharing resources, spreading risks, and accelerating the R&D of key core technologies. With strong risk tolerance and vast market potential, large enterprises can quickly transform their innovative achievements into economic benefits, which in turn nurtures key core technological innovations. In contrast, small and medium-sized enterprises (SMEs) have limited resources and face disadvantages in accessing high-end resources [45]. Although market unification provides more opportunities, constraints in funding, technology, and talent limit their innovation input and progress, making it difficult to significantly elevate their innovation level. Furthermore, SMEs often prioritize short-term survival, lack sufficient motivation for innovation, tend to select low-risk innovation projects with short-term payoffs, and lack the drive for long-term R&D in key core technologies.

The sample is divided into large enterprises and SMEs based on whether the number of employees exceeds the median. The regression results are presented in Columns (5) and (6) of Table 7. Column (5) shows that for SMEs, the coefficient of market integration construction on key core technological innovation is negative but not significant. Column (6) indicates that for large enterprises, the coefficient is 0.594 and significantly positive at the 1% level. These results confirm that market integration construction has a significant promoting effect on key core technological innovation for large enterprises, whereas its effect is not significant for SMEs.

5. Discussion

Based on the theory of Complex Adaptive Systems (CAS), this study constructs an “institution–innovation” analytical framework to empirically examine the impact of Market Integration Construction (MIC) on firms’ Key Core Technological Innovation (KCT), including its pathways, boundary conditions, and heterogeneous effects. In our model, ‘adaptive agents’ are represented by individual firms. The ‘rules of the system’—reconstructed by market integration—are operationalized through the Market Integration Construction (MIC) index. Firm-level adaptive behaviors, such as adjusting investment portfolios, are captured by Industrial Investment (II) and Financial Investment (FI). The ‘adaptive capability’ of agents is measured by Agile Responsiveness (AR). Finally, the ‘emergence’ phenomenon at the system level, driven by non-linear interactions among agents under new rules, is manifested in the firms’ output of Key Core Technological Innovation (KCT). The findings are as follows:

First, MIC reshapes the rules of resource flow, competitive incentives, and collaborative networks, guiding firms—as adaptive agents—to adjust their organizational behavior and learning models [46]. Furthermore, in the face of external disruptions, an integrated market enhances firm resilience by diversifying supply chain sources and providing access to a more stable and multi-layered capital pool [47], thereby cushioning the impact of localized economic shocks on core technology R&D. This triggers nonlinear interactions that catalyze the emergence of KCT. This aligns with the dynamic evolutionary logic of “rule restructuring–agent adaptation–system emergence” in CAS theory, and provides a new theoretical perspective for understanding the systemic interaction among macro-institutions, the unified market, and micro-level innovation in the Chinese context.

Second, firms’ investment behavior plays a significant transmitting and moderating role. The study finds that Industrial Investment (II) partially mediates the relationship between MIC and KCT, whereas Financial Investment (FI) exhibits a masking effect. This indicates that while MIC incentivizes firms to “shift from virtual to real,” it may also encourage a “shift from real to virtual” due to financial market integration and short-term performance pressures [48]. The opposing effects of these two investment behaviors reveal the dilemma firms face in choosing between “real” and “virtual” asset allocation during market integration, and provide mechanistic evidence for understanding how institutional dividends are translated into innovation performance through corporate investment behavior.

Third, organizational Agile Responsiveness (AR) significantly strengthens the positive effect of MIC on KCT. This suggests that in times of institutional change, a firm’s dynamic adaptive capability is the key micro-foundation for seizing institutional opportunities, accelerating innovation iteration, and achieving market feedback [49]. Firms lacking agile responsiveness may struggle to convert institutional advantages into innovation outcomes even under the same institutional environment [50], and may even become more conservative due to intensified competition.

Fourth, heterogeneity analyses reveal that the promoting effect of MIC on KCT is more pronounced among non-state-owned enterprises (non-SOEs), follower firms, and large enterprises, whereas it is limited for resource-abundant leading firms, state-owned enterprises (SOEs), and small and medium-sized enterprises (SMEs) with weak resource-acquisition capabilities [51]. This finding highlights how firms’ intrinsic characteristics—such as ownership constraints, capability gaps, and scale differences in China’s transitional market—moderate the effectiveness of institutions. It provides empirical support for guiding differentiated innovation strategies among firms and enhancing the overall innovation system.

6. Conclusions

6.1. Research Conclusions

Against the strategic backdrop of Market Integration Construction (MIC) and breakthroughs in Key Core Technological Innovation (KCT), this study develops a theoretical framework on how MIC affects firms’ KCT and empirically tests it using a sample of China’s A-share listed companies from 2008 to 2023. The main conclusions are as follows: (1) MIC significantly promotes firms’ KCT. (2) Industrial Investment (II) plays a partial mediating role in this relationship, while Financial Investment (FI) exhibits a masking effect. (3) Agile Responsiveness (AR) positively moderates the impact of MIC on KCT. (4) Heterogeneity analyses show that the promoting effect of MIC on KCT is more pronounced among non-SOEs, follower firms, and large enterprises, but limited among SOEs, leading firms, and SMEs.

This study provides theoretical support and empirical evidence for China to promote breakthroughs in key core technologies through building a unified national market. It also offers a referential practical paradigm for other emerging economies in coordinating market integration and innovation security.

6.2. Policy Implications

(1)

Targeting policies to guide innovation development precisely.

The state should accelerate the formulation of nationally unified rules for factor markets, establish cross-regional market regulation coordination mechanisms, and dismantle local protectionism. Implementing a “negative list” management model will ensure the free flow of resources. Fiscal and tax incentive policies for industrial investment should be introduced to channel funds precisely toward the real economy, especially in core technology fields. Financial regulation should be strengthened to curb short-term financial speculation by firms and steer financial resources toward long-term technological innovation. Strictly implementing national standards, establishing a big data-enabled market monitoring system and constructing a policy response mechanism capable of coordinating an “efficient market” with an “active government” are essential, ensuring both efficient resource flow and a dynamic balance between market uniformity and policy flexibility.

(2)

Using industry as the bow to steadily draw the direction of innovation.

Industry associations should take the lead in building industry-level sharing platforms—such as technology databases and supply chain networks—and form “regional innovation clusters” to break down information silos among firms and promote resource sharing and collaborative innovation. Policy preferences should be prioritized for non-SOEs and leading firms. Establishing “leading firm innovation demonstration zones” can focus on tackling key core technologies, with their exemplary effects radiating across the entire industry. Industry organizations should be encouraged to set up investment behavior monitoring mechanisms and conduct regular training sessions to guide firms in reducing financial speculative behavior and channeling funds toward R&D and capacity upgrading.

(3)

Leveraging enterprises as the arrow to powerfully unleash innovation breakthroughs.

Firms should formulate long-term capital allocation strategies based on their own development conditions, establish “innovation investment committees” to oversee financial activities, and ensure resources are directed toward core R&D. Digital technologies should be leveraged to build agile decision-making systems, enhance market intelligence collection and analytical capabilities, and promptly capture opportunities presented by MIC. Non-SOEs and large enterprises should capitalize on their dual advantages of scale and operational flexibility to create “innovation incubators.” By utilizing the resource agglomeration effects of the unified national market, they can attract external capital, technology, and talent, actively engage in cross-regional collaboration, jointly tackle key core technology challenges, and gradually cultivate unique competitive advantages.

6.3. Limitations and Future Recommendation

While this study provides valuable insights into the relationship between market integration construction and enterprises’ innovation in key core technologies, several limitations should be acknowledged, which also open avenues for future research.

First, the measurement of market integration construction in this study is based on provincial-level data, which may not fully capture the nuanced differences in policy implementation and market conditions at the city or county level. Future research could adopt more granular spatial units or employ micro-level indicators to enhance the precision and contextual relevance of the measurement. Future research could also explore the impact of digital infrastructure and blockchain technology on market integration in specific historical contexts, as well as delve into the differential effects of different network types within the market. Second, although this study identifies the mediating roles of industrial investment and financial investment, the underlying mechanisms may be more complex. Other potential mediators—such as organizational learning capability, innovation climate, or inter-firm collaboration intensity—were not examined. Future studies could explore these variables to provide a more comprehensive understanding of the transmission pathways. Moreover, while this study emphasizes the role of agile responsiveness as a firm-level adaptive capability, it does not delve into how such capability is developed. Future research could adopt a qualitative or case-based approach to uncover the micro-foundations of agile responsiveness and its cultivation in response to institutional change. Third, the sample is restricted to Chinese A-share listed companies, which may limit the generalizability of the findings to other institutional contexts or to non-listed firms. Comparative studies across different countries would help validate and extend the conclusions drawn in this study.