How Does Digital Transformation Drive Green Innovation? The Key Roles of Green Dynamic Capabilities and Environmental Munificence

Abstract

Against the backdrop of the global integration of green transformation and the digital economy, how manufacturing enterprises leverage digitalisation to drive green innovation has become a focal point for both academic and industrial sectors. Based on the Resource-Based View (RBV) and Dynamic Capabilities Theory (DCT), this study constructs a moderated mediation model to explore the internal mechanism through which digital transformation influences green innovation via green dynamic capabilities and examines the boundary role of environmental munificence. Questionnaire data, collected in two stages from 312 Chinese manufacturing enterprises using SPSS 27.0 and AMOS 24.0, was analysed, and the empirical results indicate that digital transformation not only directly promotes green innovation but also exerts an indirect influence through the three dimensions of green dynamic capabilities: insights into the capability of green opportunities, green resource integration, and green resource reconstruction. Furthermore, environmental munificence significantly and positively moderates the relationship between green dynamic capabilities and green innovation, suggesting that this relationship is strengthened in resource- and opportunity-rich environments. Path analysis of the three green dynamic capability dimensions reveals that back-end capabilities (resource integration and reconfiguration) have a more pronounced impact on green innovation than front-end capabilities (opportunity insights). From the dual perspectives of capability building and contextual fit, this study elucidates the mechanism and boundary conditions of digital transformation driving green innovation, enriches green innovation theory, and offers practical insights into the digital-green transformation of manufacturing enterprises.

1. Introduction

Promoting green innovation has become a core global strategy for addressing environmental challenges and achieving the United Nations’ 2030 Sustainable Development Goals [1]. The international community is committed to balancing economic growth and environmental protection, and extensive research has been conducted on this theme [2,3,4]. Among these efforts, constructing a green innovation system and shifting from resource-intensive to innovation-driven growth are key pathways to sustainable economic development. However, this transformation process faces numerous challenges, with most manufacturing enterprises constrained by high investment, high risks, and insufficient capabilities, among other bottlenecks [5]. These issues are widespread. Research from Europe and Southeast Asia indicates that resource constraints, environmental compliance pressures, and rising costs of green innovation are significant factors hindering green innovation in the manufacturing industry [6,7,8,9], and this challenge is particularly severe in China. As the world’s largest manufacturing hub, the Chinese government’s “Dual Carbon” goals (peak carbon emissions by 2030 and carbon neutrality by 2060) further underscore the urgency of domestic manufacturing enterprises in undertaking green innovation.

Digital transformation is crucial for addressing this challenge. Its core lies in the use of digital technologies, such as big data, artificial intelligence, and the Internet of Things (IoT), to fundamentally alter organisational operational models, value creation paths, and customer experiences [10]. By enhancing the efficiency of green transformation through data-driven approaches, enterprises can overcome resource limitations, adapt to policy requirements, and gain a sustained competitive advantage [9]. Simultaneously, digital transformation can maintain the sustainability of green innovation practices at lower costs and, through network and synergy effects, mitigate the issue of rising marginal costs associated with corporate green innovation [11], which has global applicability. For instance, in Germany, Industry 4.0 technologies have effectively promoted corporate green innovation by optimising information flow and knowledge accumulation [12]. Other studies based on samples from different countries also confirm a significant correlation between the application of digital technologies and improvement in corporate environmental performance [9,10,13].

However, although the existing literature highlights the importance of digitalisation for corporate green innovation from perspectives such as information networks, innovative resource elements, and resource allocation, most studies focus on industry leaders with rapid digital transformation or the energy sector [14,15]. In contrast, there is relatively less focus on manufacturing enterprises, which face greater urgency for green innovation, particularly small- and medium-sized manufacturing enterprises (SMEs) in a catch-up position. Furthermore, current research often emphasises factors such as management models and external pressures [16,17,18] but overlooks the role of organisational differential characteristics and internal capabilities. Existing research presents three theoretical gaps.

First, while the existing literature has yielded valuable insights into the green innovation of industry leaders and large firms [2,5,14], there is insufficient empirical research on the green innovation performance of catch-up manufacturing enterprises within industrial ecosystems, a gap that is particularly significant in China. Studies such as those by Lu et al. [2] and Liu et al. [14] typically focus on listed companies, leaving the mechanisms for resource-constrained latecomers underexplored. Catch-up manufacturers, which constitute a large proportion of the industry, are vital components of the ecosystem, and their strategic innovation has unique value [19,20,21]. The lack of research on these enterprises makes it difficult to analyse the new organisational capabilities and value arising from the utilisation of data resources from the perspective of external spillover effects of digital resources.

Second, while scholars have demonstrated that digital technologies can enhance green innovation performance in terms of access to green innovation elements [22,23,24], stakeholder pressure [25], and the integration of innovation resources [26], the discussion on how digital transformation cultivates organisational capabilities is not sufficiently deep. For instance, research by El-Kassar & Singh [11] highlights the role of big data, yet it does not adequately unpack the micro-foundational capabilities that act as the transmission mechanism. This study integrates scattered academic viewpoints from existing research [27] to demonstrate how the differential impact mechanisms of the three dimensions of green dynamic capabilities—green opportunity insight, green resource integration, and green resource reconstruction—enhance corporate green innovation.

Third, the exploration of the critical role of the external environment in green innovation is still incomplete. The green innovation performance of manufacturing enterprises is a multilateral mechanism that requires concerted internal and external efforts to generate, diffuse, and receive green knowledge [28]. Extant research often takes a bifurcated view: one stream, exemplified by Song et al. [29], emphasises only the supportive role of policy and technology, while another stream, represented by Hu et al. [30], focuses solely on corporate adaptability and flexibility. However, this body of work fails to integrate internal capabilities and external contingencies into a coherent framework, as manufacturing enterprises require not only internal capability support but also external resources such as green knowledge and technology [31,32,33]. This study introduces environmental munificence, exploring the extent to which enterprises obtain development opportunities and key resources from the external environment [34], and examines its impact on the mediating effect of innovation.

The theoretical innovation of this study is reflected in the following: First, by integrating DCT with existing fragmented academic viewpoints, it divides green dynamic capabilities into three dimensions—green opportunity perception, resource integration, and reconstruction—establishing a measurable empirical framework. Second, through the RBV, it innovatively introduces the variable of environmental munificence to explore the impact of internal and external dual factors on the green innovation of catch-up manufacturers. Finally, by designing three paths, we verified the differential impacts of the different dimensions of green dynamic capabilities. Empirically, based on data from 312 Chinese catch-up manufacturing enterprises, it validates the effective path of “digital transformation → green dynamic capabilities → green innovation” and the moderating role of environmental abundance. Focusing on the Chinese context, the conclusions drawn from the practices of the world’s largest manufacturing country have strong external validity. These findings provide practical insights for resource-constrained catch-up enterprises (especially those in emerging economies at similar transformation stages), helping them leverage digital technologies to identify green opportunities, integrate resources, and reconfigure processes to achieve sustainable development and enhance competitiveness.

The remainder of this study is organised as follows: Section 2 conducts a literature review and proposes the research model and hypotheses; Section 3 explains data collection and research methods; Section 4 presents the empirical analysis results; and Section 5 discusses and summarises the conclusions.

2. Theoretical Analysis and Research Hypotheses

2.1. Theoretical Framework Based on RBV and DCT

This study uses the RBV and its important extension, DCT, as its core to construct a theoretical framework of “foundational resources—dynamic conversion mechanism—sustainable competitive advantage—boundary conditions”. The definition and dimensional delineation of all core variables revolve around this logic.

RBV posits that the source of a firm’s sustainable competitive advantage lies in possessing strategic resources that are Valuable, Rare, Inimitable, and Non-substitutable (VRIN) [35]. Digital transformation is a foundational VRIN resource for manufacturing enterprises that drives green innovation. It is not merely the simple stacking of digital technologies but a strategic change that reconfigures organisational operational mechanisms, business processes, and value creation paths through digital technologies [36,37]. Its value is reflected in data-driven operational optimisation and resource synergy, providing demand insight and efficiency support for green innovation [38,39]. Its rarity stems from the unique differential value of contextual data arising from technological heterogeneity among firms [40]. Its inimitability stems from organisational adaptation experience and data governance capabilities developed through long-term digital practice [41]. Its non-substitutability is evident in the unique enhancement digital technologies provide for strategic planning efficiency [41]. It must be emphasised that digital transformation itself does not directly generate green innovation advantages but provides a resource foundation for subsequent capability building and performance transformation.

DCT further addresses the question of “how firms integrate and reconfigure resources to respond to environmental changes” [42], wherein higher-order dynamic capabilities are the core conversion mechanism linking foundational resources to competitive advantage [43]. In this study, green dynamic capabilities refer to the higher-order capability of enterprises to perceive, integrate, and reconfigure internal and external green resources in a dynamic environment, capture green opportunities, and break organisational rigidity to achieve sustainable development [27,44]. Based on the logic of resource conversion and drawing on existing research, this study divides green dynamic capabilities into three sub-dimensions: green opportunity insight capability [45,46] (resource sensing), green resource integration capability [47,48] (resource synergy), and green resource reconfiguration capability [44] (resource activation). These three dimensions together constitute a pathway for transforming digital transformation resources into green innovation.

Green innovation is the concrete manifestation of “sustainable competitive advantage” in RBV and the final performance outcome after transformation through green dynamic capabilities driven by digital transformation. It refers to sustainable innovation, where enterprises reduce environmental impact and create economic value for themselves and customers through product, process, or management model innovations [49]. Its competitive advantage lies in achieving “environmental–economic” dual-benefit synergy [50], avoiding environmental compliance risks while gaining market premiums through green differentiation, making it difficult for short-term profit-oriented firms to replicate the model.

RBV emphasises that the acquisition of competitive advantage depends on support from external environmental resources [35]. Environmental munificence embodies this situational dependency, referring to the abundance and accessibility of key resources in a firm’s external environment [31]. Its core role is to moderate the conversion efficiency of green dynamic capabilities into green innovation; that is, it acts as a “boundary condition” affecting the resource capability–performance transformation effect, thus becoming an indispensable boundary variable in the theoretical framework.

Based on the literature and theoretical analysis, this study proposes a framework (Figure 1) linking digital transformation (VRIN resources) to green innovation (sustainable advantage) through green dynamic capabilities (mediation), moderated by environmental munificence, providing an integrated basis for subsequent hypotheses.

2.2. Digital Transformation and Green Innovation

Digital transformation provides dual support—resources and efficiency—for green innovation. At the resource level, digital technologies broaden the channels through which enterprises can acquire green knowledge and information. Through digital platforms, enterprises can aggregate the innovative forces of stakeholders, such as consumers, suppliers, and research institutions, forming a “1 + 1 > 2” synergy for green innovation [51,52,53]. At the efficiency level, enterprises can use IoT to accelerate product iteration and increase the quantity of green innovations, optimise production processes through digital technologies to enhance the quality of green products [39,54], and simultaneously reduce trial-and-error costs and resource wastage in the process. Based on the RBV, the VRIN-type digital resources provided by digital transformation are a key prerequisite for building green innovation advantages. Based on the above analysis, this study proposes the following hypothesis:

Hypothesis 1. 

The digital transformation of manufacturing enterprises positively affects green innovation.

2.3. Digital Transformation and Green Dynamic Capabilities

Digital transformation influences the three sub-dimensions of green dynamic capabilities through technological empowerment: by leveraging the renewability and network effects of digital technologies, enterprises can perceive the external environment in real time through digital infrastructure and user data interchange [55], anticipate environmental market trends, and identify customer needs ahead of competitors [56], thereby enhancing green opportunity insight capabilities. Simultaneously, digital transformation accelerates internal data-driven effects, breaks down “information silos”, improves cross-departmental information transfer efficiency, and promotes the explicitness of tacit knowledge [56], thereby enhancing the green resource integration capability. Furthermore, enterprises with a high degree of digital transformation can support stakeholder collaboration through internal architectural changes and external digital ecosystem building, integrating consumers and suppliers into the green innovation system, breaking organisational inertia, and reorganising resources to adapt to innovation needs, thereby strengthening their green resource reconfiguration capability [53]. Based on the above discussion, this study proposes the following hypothesis:

Hypothesis 2. 

The digital transformation of manufacturing enterprises positively affects their green dynamic capabilities.

Hypothesis 2a. 

Digital transformation enhances the green opportunity insight capability of manufacturing enterprises.

Hypothesis 2b. 

Digital transformation enhances the green resource integration capability of manufacturing enterprises.

Hypothesis 2c. 

Digital transformation enhances the green resource reconfiguration capability of manufacturing enterprises.

2.4. Green Dynamic Capabilities and Green Innovation

Green dynamic capabilities drive green innovation at multiple levels: strong green opportunity insight capability can accurately capture innovation opportunities, anchor innovation direction through targeted research, reduce the risk of “innovation disconnect”, and accelerate innovation practices [57,58]. Strong green resource integration capability can break organisational inertia, coordinate innovation resources by leveraging network ties with upstream and downstream partners, promote the iteration and commercialisation of green knowledge, and compensate for the shortcomings of “poor knowledge absorption and slow value conversion” [47,57,59]. Strong green resource reconfiguration capability supports team and model optimisation through an open architecture, updates the innovation knowledge base to ensure product iteration, breaks path dependency to inject vitality into process innovation, and realises innovation value addition [49,57]. Based on the above elaboration, this study proposes the following hypotheses:

Hypothesis 3. 

The green dynamic capability of manufacturing enterprises positively affects green innovation.

Hypothesis 3a. 

Green opportunity insight capability enhances green innovation in manufacturing enterprises.

Hypothesis 3b. 

Green resource integration capability enhances green innovation in manufacturing enterprises.

Hypothesis 3c. 

Green resource reconfiguration capability enhances green innovation of manufacturing enterprises.

2.5. The Mediating Role of Green Dynamic Capabilities

The digital resources (data, technical tools) accumulated through digital transformation are merely the “raw materials” for green innovation; they need to be transformed into value through the mediation of green dynamic capabilities—this capability is the crucial “production line” that processes digital resources into “innovation outcomes” [60]. Through technological empowerment, it cultivates the three capabilities of green opportunity insight, resource integration, and resource reconfiguration [44]. Then, through these sub-dimensions, it translates the value of digital resources into actual green innovation actions and performance from the aspects of “direction control,” “resource synergy,” and “model innovation.” Without the mediating effect of green dynamic capabilities, the massive data and technological resources brought by digital transformation could lead to “information overload” problems [61], even trapping enterprises in an “overload” trap, preventing effective resource utilisation, and hindering the formation of substantive green innovation outcomes. Based on the above elaboration, this study proposes the following hypotheses:

Hypothesis 4. 

The green dynamic capabilities of manufacturing enterprises mediate the relationship between digital transformation and green innovation.

Hypothesis 4a. 

Green opportunity insight capability mediates the relationship between digital transformation and green innovation.

Hypothesis 4b. 

Green resource integration capability mediates the relationship between digital transformation and green innovation.

Hypothesis 4c. 

Green resource reconfiguration capability mediates the relationship between digital transformation and green innovation.

2.6. The Moderating Role of Environmental Munificence

Environmental munificence plays a significant moderating role in the relationship between green dynamic capabilities and green innovation: a higher level of environmental munificence enhances the effect of converting green dynamic capabilities into innovation performance by providing abundant external resources and a less competitive atmosphere. Specifically, a munificent environment helps enterprises use green opportunity insight capability more effectively to identify market and policy opportunities [62], strengthens the absorption and synergy of internal and external knowledge of green resource integration capability [55], and supports green resource reconfiguration capability in greening organisational processes and business models [63]. Therefore, the higher the environmental munificence, the stronger the promoting effect of the various dimensions of green dynamic capabilities on green innovation in the construction sector. Based on the above elaboration, this study proposes the following hypotheses:

Hypothesis 5. 

Environmental munificence positively moderates the relationship between green dynamic capabilities and green innovation.

Hypothesis 5a. 

The stronger the environmental munificence, the more significant the promoting effect of green opportunity insight on green innovation and vice versa.

Hypothesis 5b. 

The stronger the environmental munificence, the more significant the promotion effect of green resource integration on green innovation and vice versa.

Hypothesis 5c. 

The stronger the environmental munificence, the more significant the promotion effect of green resource reconfiguration on green innovation and vice versa.

3. Research Design

3.1. Sample Selection and Data Sources

Unlike large enterprises with substantial resources and clear innovation pathways, catch-up manufacturers (small and medium-sized enterprises, SMEs)—the bedrock of China’s manufacturing sector—face more severe survival pressures and green transition demands [64]. This renders it particularly urgent and crucial to explore how they can leverage digital transformation to build green dynamic capabilities and drive green innovation. Consequently, this study focuses on China’s small and medium-sized manufacturing enterprises as its research subjects. Drawing upon the Statistical Classification Method for Large, Medium, Small, and Micro Enterprises (2017) (latest edition), enterprises from key sectors, including electronics and information technology, equipment manufacturing, and new materials in eastern and central China, were selected. Data were collected through questionnaire surveys administered to senior and middle management personnel, alongside technical R&D staff. This demographic, directly involved in or overseeing critical innovation activities, ensures the relevance and reliability of the responses.

To mitigate common method bias, this study followed the methodology of Podsakoff et al. [65] and designed a two-stage paper-based questionnaire. Each stage contained distinct content, and participants were explicitly informed that there were no correct or incorrect answers. An informed consent statement was included at the outset, clarifying the survey’s purpose, the voluntary nature of participation, and the anonymity and confidentiality of data. Furthermore, all research scales were sourced from authoritative domestic and international journals and rigorously processed through a ‘translation-back-translation’ procedure.

Specifically, the questionnaire distribution period ran from February to May 2025 and was conducted in two phases, with a one-month interval between surveys. In the first round, the participants completed sections on digital transformation, environmental munificence, and basic demographic information. Approximately one month later, the same cohort was invited to participate in the second survey to assess their green dynamic capabilities and green innovation performance. Furthermore, to ensure questionnaire quality, this study incorporated quality control questions, such as “Please tick to indicate agreement”; questionnaires that were not correctly completed were deemed invalid.

A total of 386 questionnaires were distributed in both rounds. After excluding incomplete and invalid responses, 312 valid questionnaires from senior and middle-level corporate managers were collected, yielding a valid response rate of 80.8%. A Priori power analysis was conducted using G*Power 3.1 software, indicating a minimum required sample size of 123 (predictor variables = 11; power = 0.80; effect size f² = 0.15; α = 0.05). The effective sample size substantially exceeded the minimum requirement, and the sample characteristics are summarised in

Table 1. Basic characteristics of the sample enterprises and the respondents.

Feature	Type	Quantity	Proportion	Feature	Type	Quantity	Proportion
Company nature	Belong to the state	65	20.83%	Scale	Less than 20 people	10	3.21%
	Privately operated	194	62.18%		From 20–299 persons	63	20.19%
	Joint venture	39	12.50%		From 300–999 persons	136	43.59%
	Foreign capital	6	1.92%		More than 1000 persons	103	33.01%
	Other	8	2.564%	Average per year Sales volume	Less than $3 million yuan	18	5.7%
Company years	Under 3 years	37	11.86%		3–20 million yuan	104	33.3%
	3–5 Years	83	26.60%		20 million–400 million yuan	133	42.5%
	6–8 Years	79	25.32%		Over 400,000 million yuan	57	18.4%
	More than 8 years	113	36.22%

.

3.2. Variable Measurement

The independent variable was digital transformation. Drawing upon the research of Chi et al. [66], this study employed three items to measure this variable, with representative items such as “The enterprise is altering its business processes by integrating digital technologies”.

The dependent variable was green innovation. Following Li [67], green innovation was divided into two dimensions: “green product innovation” (4 items) and “green process innovation” (5 items). However, drawing on Chen et al. [68], this study measured it as a single, unified construct in empirical operations. The scale comprises nine items, with representative examples such as: “The enterprise adopts cleaner production technologies to conserve energy and prevent pollutant generation”.

The mediating variable was green dynamic capabilities. Drawing upon Qiu et al.’s [44] scale, this study measured it through three dimensions—“green opportunity insight capability”, “green resource integration capability”, and “green resource reorganisation capability”—comprising fifteen items in total. Representative items include: “The enterprise prioritises collaboration with environmental authorities, customers, and suppliers to minimise environmental impacts during production processes”.

The moderating variable was environmental munificence. Its measurement primarily draws upon the research by Li et al. [69], which employed four items such as: “The enterprise’s operating environment presents abundant profitable opportunities and facilitates access to resources required for its operations or expansion”.

Control variables. To mitigate potential confounding effects, this study incorporated firm characteristics, year of establishment, firm size, and average annual sales as control variables.

Data analysis was performed using SPSS 27.0 and AMOS 24.0. First, this software suite is widely adopted and technically mature in management research, fully meeting the analytical requirements of this study’s model and ensuring methodological mainstreaming and applicability. Second, this study adheres to the classical two-step approach: first, it utilises AMOS’ confirmatory factor analysis functionality to systematically examine the reliability and validity of the measurement model, thereby ensuring the reliability of subsequent analyses. Subsequently, following the validation of the measurement model, hypothesis testing was conducted using SPSS regression analysis and AMOS path analysis (incorporating Bootstrap procedures). This combined strategy not only examines specific path relationships but also assesses the overall fit of the theoretical model, thereby ensuring analytical rigour. Appendix A provides a full list of scale items, sources, and coding.

4. Empirical Analyses

4.1. Reliability and Validity Tests of the Questionnaire

Before carrying out the test step of the relationship between variables, a test of data reliability and validity was conducted to ensure that the conclusions drawn in this study have a high degree of reliability and validity. For the reliability test, we chose the internal consistency of Cronbach’s coefficient and composite reliability (CR).

Table 2. Results of reliability and validity measurement analysis.

Variable	Measure the Item	α Price	CR	AVE	Load	Overall α Values
Digital transformation	Are business processes based on digital technology	0.817	0.885	0.720	0.864	------
	Is digital technology used to integrate and change business processes				0.816
	Business operations are shifting to using digital technology				0.864
Green opportunity insight capability	Timely understanding and grasping of the support policies related to green development	0.886	0.889	0.667	0.831	0.945
	Timely grasping and responding to the green technology changes in the industry				0.831
	Timely understanding and grasping of the industry development trend				0.803
	Timely understanding of the green needs of customers to adapt to the market changes				0.800
Green resource integration capability	Environmental protection department, product design, manufacturing, marketing and other departments to cooperate with each other	0.890	0.890	0.618	0.774
	Customer requirements for environmental performance will be considered				0.792
	Supplier knowledge and capabilities will be incorporated into the environmental impact of raw materials and components				0.752
	Supplier knowledge and capabilities will be incorporated into the environmental impact of the production process				0.789
	Will work with wholesalers, retailers and other channel members to minimise environmental hazards to the product				0.823
Green resource reconstruction capability	Recruiting environmental experts in product lifecycle assessment and environmental design	0.903	0.904	0.611	0.761
	Training product development team members or developers by attending meetings, holding symposiums or other means to improve their environmental knowledge and competence				0.837
	Increase research and development efforts in product environmental protection (such as increasing investment)				0.814
	Restructuring by creating new divisions, adjusting product lines, or otherwise focusing on environmental sustainability				0.771
	Adjust the relationship with suppliers by conducting environmental audit or replacing suppliers to reduce the environmental pollution caused by their products				0.709
	Adjust the relationship with customers to mitigate the environmental impact of their products (such as lending products rather than selling them)				0.790
Green innovation	Use of environmentally friendly product materials in the product development, design, improvement, and production process	0.924	0.920	0.562	0.675	------
	Use of biodegradable packaging for existing products or new products				0.701
	The ease of recycling, reuse, and decomposition is evaluated during the product improvement and design processes				0.714
	Less resources are used in the product development, design, improvement, and production processes, and green product labels are used				0.744
	Reduce the use of water, electricity, coal, oil and other energy sources in the production process				0.804
	Use clean production technology to save energy and prevent pollutants				0.760
	Can be recycled, reused, and remanufactured materials				0.762
	Effectively reduce the discharge of harmful substances and waste in the production process				0.792
	Has the production process effectively reduced the use of raw materials				0.786
Environmental munificence	There is almost no possible threat to the survival and development of enterprises	0.864	0.901	0.694	0.842	------

Note: All load values are significant at the 0.001 level; ‘α value’ is the internal consistency reliability coefficient; ‘CR’ is the combined reliability coefficient; ‘AVE’ is the average variation extracted.

presents the analysis results. The internal consistency Cronbach’s coefficient values of the total scale and subscales of all variables were above 0.799, and the CR values of all subscales were above 0.8, which is greater than the reference value of 0.7, proving that this study has a high degree of reliability and validity for measuring the variables.

The validity test included content, convergent validity, and discriminant validity. Regarding content validity, the measurement tools in this study were all based on mature scales from authoritative journal literature at home and abroad. English-language scales underwent rigorous translation-back-translation procedures to ensure robust content validity. To assess convergent validity, a confirmatory factor analysis (CFA) was conducted. Multiple academically recognised indices were employed to evaluate model fit, primarily including the chi-square to degrees of freedom ratio (χ²/df), the comparative fit index (CFI), Tucker–Lewis Index (TLI), Incremental Fit Index (IFI), and Root Mean Square Error of Approximation (RMSEA). The results of the CFA showed that the fit of the 4-variable 6-factor model assumed in this study was relatively ideal (χ²/df = 2.086; CFI = 0.929; TLI = 0.922; IFI = 0.930; RMSEA = 0.059), as shown in Table 2. The vast majority of the question items loaded more than 0.7 on their corresponding factors. The AVE values were all greater than 0.5, indicating that the measures used in this study had excellent convergent validity. Regarding discriminant validity, this study conducted a CFA by constructing a competing model. The results are shown in Table 2, which shows that the 4-variable, 6-factor model (Model 1) assumed in this study fits best compared to the other two models. Furthermore, by taking the square root of the value of each latent variable and the correlation coefficient between each latent variable, the results are presented in

Table 3. Descriptive statistics and the results of the correlation analysis.

Variable	1	2	3	4	5	6	7	8	9	10
Nature	/
Years	0.102 *	/
Scale	0.061	0.4370 ***	/
annual sales volume	0.063	0.3710 ***	0.4150 ***	/
Digital transformation	0.041	0.2460 ***	0.1890 ***	0.018	0.848
Green opportunity insight capability	0.132 **	0.269 ***	0.064	0.081	0.2360 ***	0.816
Green resource integration capability	0.107	0.283 ***	0.198 ***	0.090	0.1730 ***	0.5240 ***	0.786
Green resource reconstruction capability	0.064	0.308 ***	0.173 ***	0.142 **	0.1630 ***	0.4770 ***	0.5920 ***	0.781
Green innovation	0.106 *	0.370 ***	0.293 ***	0.151 ***	0.220 ***	0.5380 ***	0.7310 ***	0.7310 ***	0.749
Environmental munificence	0.079	0.130 **	0.007	0.141 **	0.3120 ***	0.231 ***	0.1590 ***	0.1650 ***	0.1670 ***	0.833
Mean	2.030	2.860	3.060	3.070	4.970	4.761	4.915	4.678	4.759	4.321
Standard deviation	0.801	1.042	0.811	0.855	1.379	1.308	1.185	1.181	1.156	1.283

Note: ***, **, and * represent the significance levels of 0.01, 0.05, and 0.1, respectively; the diagonal bold value is the square root of the AVE value.

. The square root of the AVE value of each latent variable was greater than the correlation coefficient between each latent variable, indicating that the discriminant validity of the measurement instrument in this study was more satisfactory.

4.2. Common Method Bias

Although this study collected questionnaire data through the anonymous treatment of survey firms and the anonymity of the fillers, the fillers in the survey data may be the same or different, which can reduce the hazards of common method bias to some extent. However, because of the high similarity in the measurement environment and item context between the two data collections, even if the respondents of the two questionnaires were not the same, covariance was likely to be formed between the predictor and validity variables. This study used Harman’s one-factor test to measure the degree of common method bias in the data. The results of the unrotated principal component factor analysis showed that seven factors were extracted, explaining 70.570% of the total variance, with the first factor explaining 39.608% of the total variance (did not exceed the critical value of 40%), indicating that there was no single factor that explained most of the variance, which is preliminary evidence that the problem of common method bias in the data used in this study was better controlled.

Furthermore, this study compared the single-factor model with the 6-factor model by gradually combining all measurement question items and finally putting them into the same factor. By comparing the data in

Table 4. Confirmatory factor analysis: discriminatory validity test.

Model	Factor	χ2/df	CFI	TLI	IFI	RMSEA
Model 1	6 Factors: DT; GEIC; GRIC; GRRC; EM; GI	2.086	0.929	0.922	0.930	0.059
Model 2	4 Factor: DT; GEIC + GRIC + GRRC; EM; GI	3.873	0.809	0.792	0.810	0.096
Model 3	1 Factor: DT + GEIC + GRIC + GRRC + EM + GI	7.004	0.595	0.566	0.597	0.139

Note: ‘DT’ represents the variable ‘digital transformation’, ‘GEIC’ represents the variable ‘green opportunity insight’; ‘GRIC’ represents the variable ‘green resource integration capability’; ‘GRRC’ represents the variable ‘green resource reconstruction capability’; ‘EM’ represents the variable ‘environmental munificence’; ‘GI’ represents the variable ‘green innovation’; ‘+’ represents the combination of multiple factors into one factor.

, it can be seen that all the fitting indicators χ²/df, CFI, TLI, IFI, and RMSEA of the one-factor model were worse than those of the multi-factor model. This further proves that all measurement question items should not belong to the same factor and that the variables have better discriminant validity and no common method bias problem.

4.3. Descriptive Statistics and Correlation Analysis

The results of the descriptive statistics and correlation analysis in Table 3 show that the mean values of green opportunity insight capability, green resource integration capability, and green resource reconstruction capability are 4.761, 4.915, and 4.678, respectively, indicating that the surveyed enterprises’ digital transformation through building green resource integration capability is the most common and frequently used facilitation mechanism of green innovation strategy. Overall, the results of the descriptive statistical analysis were more in line with our perception of realistic observations. Correlation analysis used the Pearson correlation coefficient, which is commonly used in academia to measure the correlation between variables. The results shown in Table 3 indicate that the correlation coefficients between the main variables and their sub-dimensions studied in this paper are significant, proving that the hypotheses mentioned and argued in this study are reasonable. Hierarchical regression analysis was used to test the hypotheses and obtain robust results.

4.4. Test of the Mediating Effect of the Direct Effect of Digital Transformation and Green Dynamic Capabilities

Hierarchical regression analysis was used to test the research hypotheses. The resulting regression model is shown in

Table 5. Results of the stratified regression.

Variable	GI				GEIC				GRIC				GRRC				GI
	M1	M2	M3	M4	M5	M6	M7	M8	M9	M10	M11	M12	M13	M14	M15	M16	M17	M18	M19	M20
constant term	3.006 ***	2.606 ***	1.837 ***	0.636 *	0.7500 **	1.583 ***	0.387	0.503 *	3.551 ***	2.408 ***	0.6183 ***	2.979 ***	0.4123 ***	0.7652 ***	3.405 ***	3.425 ***	3.878 ***	3.938 ***	3.866 ***	3.935 ***
property	0.096	0.094	0.024	0.021	0.065	0.040	0.028	0.070	0.219 *	0.2290 **	0.115	0.121	0.047	0.053	0.023	0.030	0.018	0.012	0.061	0.036
age limit	0.3340 ***	0.3030 ***	0.2560 ***	0.1490 **	0.124 *	0.235 ***	0.1300 **	0.107 *	0.235 **	0.148 *	0.282 ***	0.2330 ***	0.3170 ***	0.2670 ***	0.2540 ***	0.2590 ***	0.1450 **	0.1530 **	0.120 *	0.1300 **
scale	0.2450 **	0.221 *	0.2490 **	0.144 *	0.2070 ***	0.2270 **	0.132 *	0.1910 **	−0.011	−0.066	0.155 *	0.124	0.059	0.028	0.2530 **	0.2550 ***	0.152 *	0.151 *	0.2140 ***	0.1960 **
Annual sales	−0.049	−0.028	−0.059	−0.003	−0.067	−0.037	0.012	−0.050	0.031	0.083	−0.070	−0.041	0.027 *	0.057	−0.064	−0.102	−0.011	−0.042	−0.075	−0.083
DT		0.276 *				0.117 **	0.095 ***	0.0930 ***		0.3000 ***		0.167 ***		0.170 ***
GEIC			0.3290 ***			0.275 ***									0.3220 ***	0.277 ***
GRIC				0.6550 ***			0.6240 ***										0.6490 ***	0.5930 ***
GRRC					0.6610 ***			0.6300 ***											0.6550 ***	0.6160 ***
EM															0.025	0.062	0.039	0.050	0.041	0.035
GEIC × EM																0.1210 ***
GRIC × EM																		0.1140 ***
GRRC × EM																				0.1350 ***
R2	0.163	0.177	0.295	0.571	0.575	0.317	0.588	0.590	0.060	0.191	0.095	0.145	0.098	0.150	0.295	0.330	0.573	0.596	0.577	0.608
ΔR2	0.163	0.014	0.131	0.408	0.412	0.154	0.424	0.427	0.060	0.131	0.095	0.050	0.098	0.052	0.132	0.035	0.410	0.023	0.414	0.031
F	14.970 ***	13.142 ***	25.553 ***	81.601 ***	82.805 ***	23.571 ***	72.412 ***	73.241 ***	4.907 ***	14.487 ***	8.034 ***	10.374 ***	8.326 ***	10.772 ***	21.291 ***	21.373 ***	68.263 ***	64.146 ***	69.326 ***	67.346 ***

Note: ***, **, and * represent the significance levels of 0.01, 0.05, and 0.1, respectively.

, which was further analysed using the data in the table. Model 1 is the regression model of the control variables: the nature of the company’s ownership, the number of years of establishment, the size of the company and the average annual sales on the green innovation. Model 2 was constructed by adding digital transformation based on the control variables. A regression model for the digital transformation of green innovation was constructed. Digital transformation and green innovation are positively correlated (β = 0.276, p < 0.05). This standardised coefficient indicates that, after controlling for other variables, a one-standard-deviation increase in a firm’s digital transformation level corresponds to a 0.276-standard-deviation increase in its green innovation level. This demonstrates that the effect is practically significant. Thus, Hypothesis 1 is supported. In terms of explanatory power, Model 2’s R² increased by 0.014 compared to Model 1, indicating that digital transformation independently accounts for 1.4% of the variance in green innovation, thereby providing a foundational explanatory power. Model 3, Model 4, and Model 5 are the regression models of the three dimensions of the green dynamic capabilities, namely green opportunity insight capability, green resource integration capability, and green resource reconfiguration capability on green innovation, and through the results of the data, it can be seen that the green opportunity insight capability (β = 0.329, p < 0.001), green resource integration capability (β = 0.655, p < 0.001), and green resource reconfiguration ability (β = 0.661, p < 0.001) all have a significant positive effect on green innovation. Thus, Hypothesis 3a, Hypothesis 3b, and Hypothesis 3c were all verified. In Table 5, Models 9, 11, and 13 are the regression models for the control variables of the nature of company ownership, year of establishment, company size, and average annual sales on green opportunity insight capability, green resource integration capability, and green resource reconstruction capability, respectively. Models 10, 12, and 14 were added based on the control variables to construct regression models for digital transformation of green opportunity insight capability, green resource integration capability, and green resource reconstruction capability, respectively. The data in the table show that digital transformation has a significant positive impact on green opportunity insight capability (β = 0.300, p < 0.001), green resource integration capability (β = 0.167, p < 0.001), and green resource reconstruction capability (β = 0.170, p < 0.001). Thus, Hypothesis 2a, Hypothesis 2b, and Hypothesis 2c are supported.

Concurrently, the magnitude of the regression coefficients revealed a pronounced stratification of the influence of each dimension. The effect sizes of green resource integration capability (β = 0.655) and reconstruction capability (β = 0.661) significantly exceeded those of green opportunity insight capability (β = 0.329). This indicates that, compared to front-end opportunity insight, back-end execution capabilities, such as resource integration and capability restructuring, are the dominant drivers of green innovation. This disparity in influence not only provides robust empirical support for the discriminant validity of the three dimensions but also deepens our understanding of the internal mechanisms underlying green dynamic capabilities.

The same methodology was used for the mediation test of the green dynamic capabilities. Models 6, 7, and 8 added the mediating variable sub-dimensions of green opportunity insight capability, green resource integration capability, and green resource reconstruction capability, respectively, based on Model 2 to construct the regression model. It is not challenging to see that the effect of digital transformation on green innovation is reduced from the original 0.276 to 0.117 (p < 0.001) by comparing the data in Model 6 with those in Model 2. Thus, the partial mediating effect of green opportunity insight capability has a partial mediating effect between digital transformation and green innovation, and Hypothesis 4a is valid. Similarly, comparing Models 7 and 8 with the data in Model 2, we find that the impact of digital transformation on green innovation is reduced from the original 0.276 to 0.095 (p < 0.001) and 0.093 (p < 0.001), respectively. This indicates that the green resource integration and reconfiguration capabilities in digital transformation and green innovation also have partial mediating effects between them, and Hypotheses 4b and 4c are supported. More crucially, the change in R² (ΔR²) highlights the pivotal role of green dynamic capabilities. For instance, in Model 7, incorporating green resource integration capability significantly elevated the model’s R² from 0.177 to 0.588, representing a net increase in explanatory power of 41.1% (ΔR² = 0.411). This demonstrates that green dynamic capabilities constitute the primary source of explanatory power in this study’s model, serving as a key mediating pathway through which digital transformation influences green innovation.

In summary, the hierarchical regression analysis provides robust support for our proposed mediation model. The results not only confirm that digital transformation fosters green innovation through the cultivation of green dynamic capabilities (Hypothesis 4a, Hypothesis 4b, and Hypothesis 4c) but also reveal a crucial nuance in the underlying mechanism. Notably, the effect sizes of back-end capabilities—green resource integration (β = 0.655) and green resource reconstruction (β = 0.661)—are substantially stronger than that of the front-end capability of green opportunity insight (β = 0.329). This disparity suggests that for the catch-up manufacturing enterprises in our sample, the ability to efficiently execute, integrate, and reconfigure resources internally may be a more critical driver of tangible green innovation outcomes than simply perceiving external opportunities.

4.5. Tests of the Moderating Effect of Environmental Munificence

This study constructs an interaction term by standardising the three subdimensions of environmental munificence and green dynamic capabilities to test the moderating effect of environmental munificence on the relationship between green dynamic capabilities and green innovation. Furthermore, it adopts a hierarchical regression analysis approach. The results are presented in Table 5. Models 15 and 16 in Table 5 were used to test the moderating effect of environmental munificence on the relationship between green opportunity insight capability and green innovation. Model 15 indicates that green opportunity insight capability has a significant facilitating effect on green innovation (β = 0.322, p < 0.001), and the interaction term between green opportunity insight capability and environmental munificence is added to Model 16. The data in Table 5 indicate that environmental munificence has a significant positive moderating effect (β = 0.121, p < 0.001) on the relationship between green opportunity insight capability and green innovation; thus, Hypothesis 5a is supported. Similarly, Models 17 and 18 were used to test the moderating effect of environmental munificence on the relationship between green resource integration capability and green innovation, and Models 19 and 20 were used to test the moderating effect of environmental munificence on the relationship between green resource reconstruction capability and green innovation. Models 17 and 19 indicate that both green resource integration capability (β = 0.649, p < 0.001) and green resource reconstruction capability (β = 0.655, p < 0.001) have a significant positive effect on green innovation. The interaction terms of green resource integration capability and green resource reconstruction capability with environmental munificence were added to Models 18 and 20. The results show that there is a significant positive moderating effect of environmental munificence on the relationship between green resource integration capability and green innovation (β = 0.114, p < 0.001) and the relationship between green resource reconstruction capability and green innovation (β = 0.135, p < 0.001). Hypotheses 5b and 5c are preliminarily verified.

Furthermore, to make the presentation of the moderating effect more intuitive, this study verified it by plotting the moderating effect (see Figure 2, Figure 3 and Figure 4). As shown in Figure 2, the slope under high environmental munificence is significantly larger than that under low environmental munificence, indicating that the influence of green opportunity insight capability on green innovation is stronger when environmental munificence is higher; that is, environmental munificence positively moderates the promotion of green opportunity insight capability on green innovation, further verifying Hypothesis 4a.

As shown in Figure 3, the slope under high environmental munificence is significantly larger than that under low environmental munificence, indicating that the green resource integration capacity has a stronger effect on green innovation when environmental munificence is at a higher level; that is, environmental munificence positively regulates the promotion of green innovation by green resource integration capacity, and Hypothesis 4b is further verified.

As shown in Figure 4, the slope under high environmental munificence is significantly larger than that under low environmental munificence, indicating that green resource reconstruction capacity has a stronger effect on green innovation when environmental munificence is at a higher level; that is, environmental munificence positively regulates the promotion of green innovation by green resource reconstruction capacity, and Hypothesis 4c is verified.

4.6. Tests for Moderated Mediation Effects

In this study, the bootstrap method was employed to examine the moderating effect of environmental munificence on the three sub-dimensions of green dynamic capabilities and the three mediating pathways of green innovation. PROCESS 4.1 was used to analyse the conditional indirect effects generated through green dynamic capabilities at both high environmental munificence (+1 standard deviation) and low environmental munificence (−1 standard deviation) levels. Although the validity of the bootstrap method is highly dependent on the representativeness of the original sample, and the method is primarily used to robustly estimate confidence intervals for indirect effects without correcting for potential errors inherent in questionnaire measurement itself, its advantage of not requiring strict normality of data distribution makes it widely regarded in academia as one of the most robust testing methods currently available for handling complex mediation models. From

Table 6. Results of the moderated mediation effects analysis.

Group of Regulatory Variables	Intermediary Effect Value	Standard Error	Confidence Interval		Significance p-Values
			Lower Limit	Superior Limit
Mediation effect 1: DTGEI GI
Low EM (−1 SD)	0.037	0.018	0.005	0.077	0.000
High EM (+1 SD)	0.114	0.028	0.064	0.173	0.000
Mediation effect 2: DTGRIC GI
Low EM (−1 SD)	0.074	0.020	0.039	0.116	0.000
High EM (+1 SD)	0.119	0.027	0.066	0.174	0.000
Mediation effect 3: DTGRRCEM
Low EM (−1 SD)	0.072	0.020	0.036	0.114	0.000
High EM (+1 SD)	0.129	0.032	0.068	0.193	0.000

mediation effect 1, it can be seen that when the level of environmental munificence is low, the mediation effect value of digital transformation affecting green innovation through green opportunity insight capability is 0.037, with a 95% confidence interval of [0.005, 0.077]; when the level of environmental munificence is high, the mediation effect value of digital transformation affecting green innovation through green opportunity insight capability is 0.114, with a 95% confidence interval of [0.064, 0.173], none of which is zero, indicating that environmental munificence moderates the mediating role of green opportunity insight capability between digital transformation and green innovation. Similarly, in Table 6, mediating effect 2 shows that at lower and higher levels of environmental munificence, the mediating effect values of digital transformation through green resource integration capability on green innovation are 0.074 and 0.119, respectively, with corresponding 95% confidence intervals of [0.039, 0.116] and [0.066, 0.174], which do not contain zero. This proves that environmental munificence moderates the mediating effect of green resource integration capacity and has a significant moderating effect on the mediating effect between digital transformation and green innovation. Mediating Effect 3 shows that when the level of environmental munificence is lower and higher, the mediating effect values of digital transformation on green innovation through green resource reconstruction capacity are 0.072 and 0.129, with corresponding 95% confidence intervals of [0.036, 0.114] and [0.068, 0.193], respectively, which do not contain zero. This finding proves that the mediating effect of environmental munificence on green innovation through green resource reconstruction capacity is significant.

4.7. Case Analysis from Other Countries

Case studies provide practical perspectives for exploring the applicability of research findings in international settings. In the preceding discussion, we empirically examined the mechanism through which digital transformation influences green innovation using data from Chinese enterprises and adopting a green dynamic capabilities framework. Although grounded in the context of China, this research remains globally pertinent. Against the backdrop of the dual transition towards digitalisation and greening, other major economies similarly face the imperative of leveraging digital transformation to empower corporate green innovation. Consequently, analysing representative international cases to provide insights for other nations is necessary.

Germany is a prime example of this. The practice of deeply integrating the “Industry 4.0” strategy with the “Energy Transition” demonstrates that digital transformation can enhance enterprises’ green dynamic capabilities, thereby restructuring their core business processes and production systems [70]. In practical terms, green dynamic capabilities extend beyond mere technology adoption to encompass the ability to perceive green opportunities, integrate innovation resources and achieve circular economy objectives through digital technologies [71]. In recent years, German enterprises have actively deployed technologies such as digital twins to achieve more efficient resource utilisation and circular economy objectives, thereby maintaining global leadership in green technology and sustainable manufacturing. This practice provides robust support for the research approach, demonstrating that this pathway is significantly relevant not only in the Chinese context but also in developed industrial nations.

Concurrently, driven by the demand for clean energy and sustainable development, both the European Union and the United States regard supportive industrial policies as pivotal measures for achieving these goals. The EU’s European Green Deal, by setting ambitious emission reduction targets and providing financial backing, has proven effective in guiding corporate green technology investments and strategic realignments, thereby accelerating the energy transition process [72]. Within this favourable external policy framework, corporate green innovation ceases to be a high-risk gamble and becomes a certainty-driven investment towards future market leadership, thereby tightly integrating sustainability objectives with core business strategy. Similarly, the US Inflation Reduction Act has significantly altered corporate investment feasibility calculations by offering historic clean energy subsidies and creating a highly loose market environment [73]. This macro-policy-shaped stability of expectations and generous incentives substantially reduces the market risk for businesses commercialising their green capabilities, thereby greatly amplifying the efficiency of converting internal capabilities into tangible green-innovation outcomes.

In summary, while the empirical testing in this study is grounded in Chinese enterprises, its core theoretical framework receives robust practical validation through an in-depth analysis of international cases from Germany, the EU, and the US, thereby extending its applicability. German industrial practice clearly demonstrates how digital transformation drives green innovation by enhancing green dynamic capabilities (including opportunity insight, resource integration, and reconfiguration). Simultaneously, the EU and US substantially enhanced firms’ efficiency and willingness to translate green dynamic capabilities into green innovation outcomes by creating highly favourable market environments. In essence, the case analysis not only corroborates the preceding theoretical and empirical findings but also demonstrates that the core transmission pathway established herein, “digital transformation → green dynamic capabilities → green innovation”, remains equally valid across other economic systems.

4.8. Discussion

As the core agents of green innovation, enterprises’ transformation pathways are pivotal in achieving regional and global sustainable development. Against the backdrop of dual digital and green transformations, leveraging digital transformation to empower corporate green innovation has become widely accepted. Numerous studies have confirmed the direct positive impact of digital transformation on green innovation among industry leaders and digital-native platform enterprises [9,11,12]. However, these investigations largely treat the relationship between the two as a “black box”. The digitalisation of relatively disadvantaged late-entrant manufacturing enterprises within ecosystems and the underlying mechanisms influencing green innovation practices remain unclear. Research deconstructing this process from the perspective of organisational green dynamic capabilities is particularly lacking. Existing research frequently neglects the contextual dependency of this influence process. The efficiency with which internal capabilities translate into innovation outcomes is inevitably shaped by external policy support, consumer demand incentives, and knowledge spillover from upstream and downstream partners. However, few studies have examined the interplay between internal digital empowerment processes and macro-level contextual factors, such as industrial policies and market environments, leaving the boundary conditions under which digital transformation positively impacts green innovation unclear. Consequently, this study examines the mediating role of green dynamic capabilities in the relationship between digital transformation and green innovation and further explores the moderating effect of environmental permissiveness on this mediating pathway, thereby constructing an integrated theoretical model.

The findings indicate that digital transformation does not directly influence green innovation outputs; instead, it exerts its impact by systematically building green dynamic capabilities. This finding aligns with the core tenets of Dangelico et al., who established the importance of green dynamic capabilities. However, our study provides a more nuanced understanding by deconstructing this capability into three distinct dimensions and empirically demonstrating their differential impacts, revealing that backend execution capabilities (integration and reconfiguration) have a more pronounced effect than front-end opportunity insight. This suggests a specific developmental logic for resource-constrained, catch-up manufacturing enterprises. Unlike industry leaders who may pioneer new green fields through radical opportunity-seeking, these firms may gain more traction by focusing on the efficient internal execution and adaptation of existing green practices. Digital transformation, in this context, is most valuable not just for identifying what could be done (opportunity insight), but for optimising how things are done (integration and reconstruction). This offers a finer-grained perspective on the internal workings of the “black box” that prior studies often treated monolithically.

Concurrently, the study reveals that the value realisation of green dynamic capabilities is not unconditional but significantly contingent upon environmental munificence. This finding challenges prior perspectives that overly emphasise the determinism of internal capabilities, underscoring that internal capability development must align with external environmental opportunities to maximise corporate value. This also resonates with and extends the work of Hartmann and Vachon [62], who highlighted the role of industry context. Our findings specifically demonstrate that for resource-constrained catch-up manufacturers, a munificent environment acts as a critical catalyst, significantly amplifying the translation of internal capabilities into innovation performance. This study offers a fresh perspective on understanding corporate heterogeneity: competitive advantage stems not only from possessing unique capabilities but also from the strategic alignment of these capabilities with external environmental opportunities.

Moreover, by drawing comparisons to the case studies of Germany, the European Union, and the United States, this study highlights the international applicability of its findings. The analysis reveals that while the theoretical framework constructed in this study possesses considerable universality, it also highlights differing national emphases in implementation pathways. This underscores that the process of digital transformation enabling green innovation should not be viewed as a homogeneous process. Future research should further examine how different types of digital technologies differentially influence the three specific dimensions of green dynamic capabilities and how various types of industrial policies generate more complex interactive effects with internal corporate capabilities.

5. Conclusions

5.1. Conclusions of the Research

To explore the intrinsic mechanism of manufacturing enterprises driving green innovation through digital transformation in the context of the prosperous development of the digital economy, this study explores the process of digital transformation, green dynamic capabilities, and green innovation based on the resource-based view and dynamic capability theory. This study deeply analyzes the model framework of digital transformation affecting the green innovation of manufacturing enterprises through three different dimensions of green dynamic capabilities and further extends the boundary conditions of the mechanism, confirming the moderating role of environmental munificence. The results of this study are summarised as follows.

First, the digital transformation of manufacturing enterprises improves their green innovation performance. Green innovation in manufacturing enterprises involves a wider range of fields and challenges than general enterprise innovation. In the context of increasingly stringent external environmental regulations and policy requirements, enterprises’ green innovation passively caters to the requirements of the external environment, which brings economic burden and capacity deficiencies to the enterprises themselves. However, suppose that enterprises carry out green innovation only by increasing their cost inputs to obtain basic legitimacy. In this case, it is contrary to the original purpose of green innovation in pursuit of sustainable development, that is, to enhance the long-term competitive advantage of the enterprises. This study demonstrates that the digital transformation of manufacturing enterprises can reduce the marginal cost of green innovation, realise the win-win goal of economic and ecological performance at a lower cost, and help manufacturing enterprises with relatively low competitiveness obtain and maintain long-term competitive advantages.

Second, green dynamic capabilities mediate the relationship between digital transformation and green innovation. There is a microscopic path mechanism of digital transformation → green dynamic capabilities → green innovation. The three green dynamic capabilities subdimensions play an important role in bridging the gap between digital transformation and green innovation. The digital transformation of catch-up manufacturing enterprises fosters their green opportunity insight, green resource integration, and green resource reconfiguration capabilities, thus realising an overall improvement in green innovation performance. Furthermore, the analysis reveals a distinction in the relative strengths of these three factors: compared with front-end opportunity insights, the back-end capabilities of green resource integration and reconstruction play a more dominant role in driving green innovation. Additionally, on the one hand, the results of related experiments prove that enterprises’ digital transformation can effectively capture external market and policy opportunities with the help of digital technology, provide the basis for the precise conversion of green innovation inputs, and thus reduce the marginal cost of green innovation. However, digital transformation can eliminate information silos inside and outside the organisation and between departments through green resource integration and reconstruction to overcome strategic inertia and update the knowledge base of green innovation to realise the recombination of internal capabilities and external heterogeneous resources.

Third, environmental munificence reinforces the relationship between green dynamic capabilities and green innovation, strengthening the mediating role of the three dimensions of green dynamic capabilities. This suggests that late-stage manufacturing firms have the disadvantage of a relative lack of resources, whereas the support of stakeholders in the external environment and the leniency of the market environment can compensate for their shortcomings to a certain extent, accelerating the transformation of capabilities into performance. This conclusion expands the research perspective beyond the organisation, emphasises the characteristics of bilateral or even multilateral participation in enterprises’ green innovation, considers the public and external attributes of digital technology, and ultimately provides a complete strategy for improving the mechanism of green innovation.

5.2. Theoretical and Managerial Implications

(1)

Theoretical implications

This study constructs and tests a moderated mediation model to elucidate how digital transformation influences corporate green innovation through green dynamic capabilities while examining the boundary role of environmental munificence. The exploration of the mediating effect of green dynamic capabilities and the moderating role of environmental munificence represents a novel contribution to this field. Specifically, the theoretical significance of this study manifests in three aspects.

First, this study provides a micro-mechanism framework grounded in a resource-based view and dynamic capability theory to analyse the relationship between digital transformation and corporate green innovation. Hypothesis 1 confirms that digital transformation exerts a significant positive influence on green innovation, supporting the macro-level assertion of existing research that digitalisation empowers green development [74,75]. This study further elucidates the underlying pathways of this relationship. Unlike existing research that treats digitalisation as a holistic input, we find that digital transformation exerts its influence by systematically cultivating three core capabilities: green opportunity insight, green resource integration, and green resource reconstruction capability (Hypotheses 2–4). Specifically, digital technologies, such as big data analytics and the Internet of Things, enable enterprises to discern overlooked green market demands and policy opportunities with heightened acuity. Industrial Internet platforms and cloud technologies substantially enhance the efficiency of cross-departmental and cross-boundary integration of green knowledge and technological resources. This aligns with scholars such as Wang et al., who posit that knowledge management is a core driver of green transformation [76]. Consequently, this study transforms the role of digital transformation from a “black box” to a transparent, manageable capability-building process. It provides more practical micro-level evidence for applying the resource-based view and dynamic capability theory in digital contexts, particularly demonstrating how digital transformation becomes a key enabler for resource-constrained, late-entrant manufacturing enterprises to build green competitive advantages.

Second, it validates and highlights the mediating role of green dynamic capabilities between digital transformation and green innovation. The results of testing the indirect effect indicate that the influence of digital transformation on green innovation is fully mediated by the green dynamic capabilities variable. This clarifies that if digital investments fail to effectively translate into an organisation’s intrinsic capabilities, their promotional effect on green innovation will be significantly diminished or even eliminated. This finding makes a significant contribution to the literature by clearly demonstrating that the key driver of corporate green innovation is not technology itself but the organisational capabilities enabled by technology. This deepens existing research, such as Yi [77] and Nguyen [78], which validated the importance of green innovation across different contexts but failed to elucidate the formation mechanisms of its upstream capabilities. By synthesising three dynamic capability dimensions (insight, integration, and reconstruction) into a unified concept and confirming its full mediating effect, this study emphasises that enterprises must shift their strategic focus from mere technology procurement to organisational capability cultivation when driving green innovation.

Finally, the findings of this study indicate that environmental munificence effectively mediates the relationship between green dynamic capabilities and green innovation, thereby revealing the boundary conditions of this mediating pathway. The results of Hypothesis 5 demonstrate that in contexts of high environmental munificence, such as where government subsidies are ample and market acceptance of green products is high, the promotional effect of green dynamic capabilities on green innovation becomes more pronounced. This finding aligns with the conclusions drawn by Farooq [79] and Langinier [80], collectively supporting the view that external environments play a pivotal role in corporate green innovation activities. It specifically addresses the call from scholars like Chowdhury & Endres [34] for more research on environmental munificence, providing new empirical evidence for understanding the interactive mechanisms between dynamic capabilities and the environment in the specific context of green innovation for catch-up enterprises. Concurrently, this study contributes a significant contingency perspective to capability theory, confirming that the value realisation of dynamic capabilities is neither endogenous nor absolute; their efficacy significantly depends on strategic alignment with the external environment. Current academic discourse remains insufficient to explore the contingency factors for green innovation pathways within the context of digital transformation. This study empirically examines the moderating role of environmental munificence, providing new empirical evidence for understanding the interactive mechanisms between dynamic capabilities and the environment.

(2)

Managerial Implications

This study delves into the positive impact of corporate digital transformation on the green innovation performance of latecomer manufacturing enterprises in the digital economy era from both theoretical and empirical perspectives, offering significant management insights for enhancing green technology and process innovation performance among manufacturing enterprises globally. The core findings of this study underscore the pivotal role of digital transformation in fostering green innovation and reveal that enterprises should fully assess and leverage their resource endowments and potential during this transformation process to maximise the opportunities presented by the digital economy.

First, the notable catalytic effect of digital transformation on green innovation performance, validated not only in the Chinese context but also in providing invaluable experience for global manufacturing enterprises, highlights its importance. Amid the global economy’s increasing pursuit of sustainability and high-quality development, manufacturing enterprises that actively adopt digital technologies can not only enhance production efficiency but also effectively reduce the marginal costs of green innovation, facilitating their ascent to higher levels in the global value chain. This finding holds substantial referential significance for global enterprises seeking to overcome the predicament of diminishing marginal returns on production factors and cultivate new economic growth points.

Second, this study emphasises the bridging role of green dynamic capabilities between digital transformation and green innovation, providing guidance to global enterprises on how to build and manage such capabilities. Green dynamic capabilities, encompassing green opportunity insight, green resource integration, and green resource reconfiguration, are crucial for enterprises to achieve green innovation in a complex and dynamic market environment. Global enterprises should prioritise the cultivation of these capabilities and optimise their organisational structures and processes to facilitate the effective integration and utilisation of internal and external resources, thereby propelling the in-depth development of green innovation.

Furthermore, this study’s exploration of the moderating role of environmental leniency offers a new perspective for global enterprises in formulating green innovation strategies. When engaging in green innovation, enterprises should consider external environmental factors such as policy support, market demand, and resource acquisition. In contexts of high environmental leniency, enterprises should actively utilise external resources to accelerate the green innovation process. In contexts of low environmental leniency, they should focus more on cultivating and enhancing internal capabilities to strengthen their resilience against external challenges. This finding provides an important reference for global enterprises to flexibly adjust their innovation strategies in different market environments to achieve sustainable development.

In summary, by thoroughly investigating the relationship between digital transformation and green innovation in Chinese manufacturing enterprises, this study offers universally applicable management insights for manufacturing enterprises. In the context of global economic integration and sustainable development, these insights help guide enterprises to leverage digital technologies more efficiently, build dynamic green capabilities, and achieve continuous breakthroughs in green innovation in a complex and dynamic market environment.

5.3. Limitations and Future Research

This study has deficiencies that need to be further revised in future research. These are briefly summarised below.

First, this study exclusively selected small- and medium-sized manufacturing enterprises in China as its research samples without conducting more detailed industry segmentation. This limits the external validity of the research conclusions to specific industry contexts. Future research could build upon this study by undertaking cross-industry comparative analyses, thereby providing more targeted practical guidance for enterprises in specific sectors. Furthermore, although we have conducted case studies in other countries, nations should adapt their strategic planning according to their own circumstances when referencing the conclusions of this study.

Second, while the current model primarily validates the positive effects of linearity, digital transformation may simultaneously foster new path dependencies and core rigidities alongside developing new capabilities. Future research should examine whether an inverted U-shaped relationship exists between these factors and green innovation. Furthermore, subsequent studies may incorporate internal organisational context variables, such as executive environmental awareness and organisational learning orientation, to more comprehensively elucidate the complex process by which digitalisation enables green innovation.

Finally, all variable data were collected through questionnaire surveys, which inherently possess limitations in capturing the dynamic evolution of enterprises and inevitably influence common methodological bias. Future research may further validate the robustness of these findings through approaches such as multiperiod tracking, multisource data collection (e.g., core suppliers, customer feedback), or by combining subjective questionnaires with objective data (e.g., corporate environmental investments, and government environmental ratings).