Research on the Impact of Green Supply Chain Integration on Enterprises’ Green Innovation

Abstract

Purpose: The objective of this study is to examine the influences of green supply chain integration, digital transformation of the supply chain, and environmental uncertainty on corporate green innovation. Moreover, relevant policy suggestions are put forward to facilitate the sustainable development of corporate green innovation. Design/methodology/approach: This study adopts the literature research method to sort out variables to understand the current situation as the foundation for our study, uses the questionnaire survey method to create a questionnaire to collect data, and analyzes the data with the help of the empirical research method to verify our hypotheses to draw conclusions. Findings: Green supply chain integration positively promotes corporate green innovation and also positively affects supply chain digital transformation, which in turn positively promotes corporate green innovation. Moreover, supply chain digital transformation acts as a mediator, while environmental uncertainty plays a moderating role and affects green supply chain integration and green innovation. Originality/value: This study delves into how green supply chain integration, with green internal integration, green supplier integration, and green customer integration as its independent variables, affects the green innovation of domestic manufacturing enterprises. By doing so, it breaks new ground for empirical research in this area and offers theoretical directions for the green innovation efforts of enterprises. Meanwhile, in the digital era, from the overall supply chain perspective, we break through the limitations of previously studied intermediary variables. We construct a theoretical model by integrating supply chain digital transformation and environmental uncertainty variables and explore the variable influence paths. This can fill the research gaps, lay a solid theoretical groundwork for enhancing enterprise sustainable development, and open a new path for enterprise growth.

1. Introduction

With the increasingly severe global environmental problems, enterprises are facing increasingly fierce market competition and increasingly strict environmental regulations. People are paying more and more attention to environmental issues, and society’s demand for corporate environmental responsibility is growing higher and higher. In the “14th Five-Year Plan”, the State Council emphasized the need to promote green, intelligent, and ecological civilization comprehensively. It is necessary to facilitate the synergistic development of digitalization and greening [1]. Digitalization will lead to greening, and greening will drive digitalization [2]. Therefore, enterprises, as an essential economic participant and source of pollution, shoulder the responsibility of achieving green transformation, actively taking measures to reduce resource consumption, achieving sustainable development, and taking the path of green and low-carbon development, which will be the keys to future development of the global economy [3].

However, two critical challenges remain underexplored: First, while green supply chain integration (GSCI) is recognized as a strategic approach to sustainability, its specific mechanisms for driving corporate green innovation are unclear. Second, in an era where digital transformation reshapes supply chains, how environmental uncertainty interacts with GSCI and digital initiatives to influence green innovation lacks systematic evidence. This study addresses these gaps by investigating the synergistic effects of GSCI, supply chain digital transformation, and environmental uncertainty, aiming to provide actionable insights for enterprises navigating the dual demands of economic growth and ecological responsibility.

Enterprises need to take measures in order to promote resource utilization, reduce costs, increase efficiency and competitiveness at the economic level, fulfill responsibilities, and promote sustainable development at the social level. Green supply chain integration is an important and key direction for the development of modern enterprises. Green supply chain integration, corporate green innovation, and digital transformation are closely intertwined [4]. Green integration and innovation in supply chains can achieve sustainability with the help of digital technologies [5,6], and the promotion of green supply chain integration and digital transformation can achieve a harmonious symbiosis between the economy and the environment. In the current academic field, there is a lack of research papers on green supply chain integration, but its research significance is far-reaching. By exploring the relationship between green supply chain integration and green innovation, this study provides a key guideline for the sustainable development of enterprises, helping them to stand in the tide of the new era and achieve long-term, high-quality development.

In the digital economy era, digital transformation has emerged as a crucial element for reshaping enterprises to reach sustainable development. For enterprises, digital transformation is an urgent necessity. Digitizing the supply chain can boost green innovation among nodal enterprises. It does this by enhancing upstream–downstream integration and increasing the internal efficiency of supply chain management [7]. Digitization of the supply chain has a marked effect on enhancing both the quantity and quality of enterprise green innovation, with the influence on quality being more pronounced [8]. Firms are able to enhance the innovation capacity of upstream and downstream SMEs through digital transformation, and the digital transformation of firms has a positive effect on green innovation and value creation, mediating the effects of green and digital innovation in the impact on firm performance. Digital transformation has a substantial effect on the advancement of green innovation among manufacturing firms [9]. Therefore, it is significant for firms to proactively embrace digital transformation. However, the current literature lacks empirical studies on how supply chain digital transformation impacts green innovation. There is a need to deeply explore the specific mechanisms through which supply chain digital transformation affects enterprises’ green innovation capabilities. This exploration will offer stronger theoretical support and practical guidance for enterprises to attain sustainable development in the digital age.

In enterprise development, environmental uncertainty is a prominent risk factor. On the one hand, it may interact with client power in green client integration governance relationships. High environmental uncertainty increases the likelihood that intermediary power leads to opportunistic behavior [10]. On the other hand, economic policy uncertainty moderates digital transformation’s direct impact on green STI. Greater uncertainty makes digital transformation more effective in enabling green STI and easing corporate financing constraints [1]. Demand uncertainty positively regulates various aspects of green supply chain integration and green innovation [11]. Demand uncertainty strengthens the weakening effect of intermediary power on the relationship between contract control and green customer integration [12]. Overall, within the realm of the green supply chain, environmental uncertainty serves as a connecting factor among green supply chain integration, digital transformation, and the green innovation capabilities of firms. The former affects the innovation capability. This uncertainty indirectly affects the latter two by influencing supply chain links, investments, resources, etc., through complex mechanisms, and there are limited studies on this topic. Therefore, this paper explores the relationships among green supply chain integration, digital transformation, and green innovation. It examines how green supply chain integration affects innovation capability in this context, answers relevant questions, fills the research void, and offers references for the development of this field.

This paper measures green supply chain integration comprehensively from three dimensions: green internal integration, green supplier integration, and green customer integration. Subsequently, it delves into how this integration affects the green innovation of Chinese manufacturing enterprises, offering crucial theoretical support for enterprises to realize green innovation. From the perspective of the whole supply chain, this research incorporates supply chain digital transformation as a mediating variable and environmental uncertainty as a moderating variable. It constructs a theoretical model to further explore the influence path and provide policy support for enterprises to enhance their sustainable development capability.

The rest of our study is structured as follows. In the following section, a literature review is conducted. Section 3 presents the theoretical model and formulates research hypotheses. Section 4 details the methodology, covering the data collection and analysis steps. Section 5 discusses the study results, and Section 6 concludes with contributions, limitations, and a future research agenda.

2. Literature Review

At present, numerous studies indicate that supply chain integration is crucial for enhancing the green innovation capabilities of enterprises. Zhang et al. [13] explored the link between green supply chain integration and green innovation performance along with its underlying mechanism. The three aspects of green supply chain integration—green internal integration, green supplier integration, and green customer integration—all have a positive effect on supply chain agility. However, this study mainly focused on a specific set of industries, which might limit the generalizability of its findings. Gao et al. [14] found that the government setting the green level helps to make the products greener and consumers more willing to pay higher prices, thus driving the market demand for green products. Nevertheless, their research did not fully consider the long-term dynamic changes in market demand and the corresponding adjustments of supply chain integration strategies. It is important for the internal integration of the supply chain [15].

Implementing suitable green initiatives, optimizing operational procedures, and formulating proper business strategies can counteract the short-term drawbacks of green supply chain management strategies. In the long run, this approach enables the achievement of both environmental and financial success [16]. Green innovation efforts yield beneficial effects on a company’s competitiveness and financial standing. Due to internal and external stimuli, firms that engage in sustainable activities acquire enhanced market influence and a more favorable market position [17]. Organizations with sustainable practices can gain better market leverage and positions due to external and internal factors. However, the existing research has not clearly defined the specific thresholds and conditions for different levels of sustainable practices to achieve these benefits. Customer orientation and supplier consolidation, moderated by green information systems, can improve firm responsiveness and innovation [18]. Research shows that the green innovation strategy positively influences both developmental and exploratory green innovation. Green supply chain integration plays a partial mediating role between the green innovation strategy and dual (developmental and exploratory) green innovation. To carry out green innovation, a company needs to integrate not only internally across its various departments but also externally with supply chain partners like suppliers and customers. Abbas [4] found that IT capabilities promote green supply chain integration and overall organizational performance and that green process innovation and green product innovation mediate the relationship between different dimensions of green supply chain integration and performance.

In conclusion, green internal integration, green customer integration, and green supplier integration each contribute to enhancing enterprise capabilities in distinct ways. They boost information utilization and synergy, drive green innovation, cut costs, improve environmental performance, and expedite green production. It provides a practical basis for promoting the enhancement of enterprises’ green innovation capability.

In terms of supply chain digital transformation, it was found that with the rapid development of digitalization, enterprises need to integrate multiple digital technologies to promote green supply chains [19]. Digital transformation enables better communication and collaboration among alliance partners. This results in enhanced decision-making, quicker response times, and greater overall agility and adaptability within the alliance. Dubey [20] found that digital transformation indirectly affects innovation performance by reducing supplier concentration, and digital transformation is directly and positively related to innovation performance. Zimmermann, R [21] found that the higher the degree of adoption of a digital technology mix in supply chain management processes, the more significant the impact on innovation performance and value creation of their products and services [22]. In essence, leveraging digital technology empowers enterprises to more effectively surmount information silos. This enables them to amass and consolidate green knowledge, information, and technological resources, thereby augmenting their capacity for green innovation.

As far as environmental uncertainty is concerned, it is mainly an important factor affecting enterprises in supply chain management practices, especially in innovation practices related to green supply chain management. For example, operations managers are advised to use governance mechanisms to facilitate collaboration with customers and adopt green customer integration. However, manufacturers’ influence over customers and environmental uncertainty may affect customers’ willingness to comply with contractual rules and relationship norms when adopting GCI [23]. An analysis of the relationship among environmental uncertainty, environmental regulation, and green technology innovation reveals that both environmental uncertainty and environmental regulation drive green technology innovation. Specifically, higher environmental uncertainty, fierce market competition, and stricter environmental regulation encourage firms to engage in green technology innovation activities [24]. The perceived corporate environmental policy uncertainty has a significant negative effect on green innovation. Corporate environmental policy uncertainty affects green innovation by reducing information transparency and R&D investment [25]. Environmental policy uncertainty has a moderating effect on green innovation, and there is significant heterogeneity in the effect of environmental policy uncertainty on green innovation across geographic locations [26]. Under high-uncertainty conditions, green innovations are better able to improve profitability. In summary, environmental uncertainty can be introduced as a moderating variable to strengthen the explanatory power of the model, better reflect the role of green supply chain integration in the actual situation, and further confirm the importance of green supply chain integration.

After reviewing the literature on green supply chain integration, supply chain digital transformation, and environmental uncertainty, it is clear that the current research has gaps. Most studies focus on single aspects, not integrating key dimensions and variables. This limits the understanding of complex relationships and theoretical support. Regarding technology application, past research mainly looked at direct digital tech effects in the supply chain. It did not explore the mediating role of digital transformation in green supply chain integration and innovation well, restricting insights into green supply chain innovation. In the research context, differences in market environments are often ignored. China’s manufacturing industry, a key global part, has unique features. But, few studies test the moderating role of environmental uncertainty in its supply chain integration, leaving a lack of evidence for emerging markets.

This paper fills these gaps. Theoretically, it combines relevant dimensions and variables in one framework to analyze interactions, improving the theoretical basis. Mechanistically, it reveals the mediating path of digital transformation for practical guidance. Contextually, it validates the moderating role in China’s manufacturing, offering new evidence for emerging market green supply chain management.

3. Modelling

Green innovation has become a key driver of sustainable business development. The aim of green innovation is to mitigate the adverse effects on the natural environment during every stage of the product production cycle. However, for individual firms, they usually lack sufficient capacity and knowledge to cope with the environmental issues arising during the product life cycle. Therefore, resource-based theory suggests that individual firms need to cooperate with relevant stakeholders in the supply chain nodes. At the same time, in order to strengthen core sustainability competencies, organizational information processing theory suggests that firms are able to synchronize their internal management mechanisms through pollution prevention strategies and project-based product management and integrate partners in the firm’s supply chain nodes into an inter-organizational practice environment.

Through the integration of internal and external resources of enterprises, it can enable each node enterprise of the supply chain to obtain a large amount of data, achieve efficient interaction of information, improve supply chain agility, and optimize supply chain decision-making ability. The realization of supply chain digital transformation accelerates the maximum use of information, promotes the interaction of information, and helps to maximize resources. The use of various data sources also improves resource efficiency, accelerates the achievement of sustainable growth, reduces costs and associated risks, and further promotes enterprise green innovation.

At the same time, influenced by the theory of weights and measures, enterprises will always be in the midst of environmental uncertainty, considering the accelerated frequency of social change. Therefore, environmental uncertainty needs to be integrated into the research framework of this paper. By reviewing the relevant literature and theoretical bases, synthesizing scholars’ research on variable relationships, and integrating relevant theories, we deeply analyze the interaction among variables such as green supply chain integration, supply chain digital transformation, and green innovation. Meanwhile, environmental uncertainty is incorporated as a moderating variable to construct the theoretical analysis framework for green supply chain integration, supply chain digital transformation, and green innovation (depicted in Figure 1 below).

4. Research Hypotheses

4.1. The Impact of Green Supply Chain Integration on Green Innovation

Green internal integration can improve the information processing capacity of enterprises and ensure the consistency of enterprise activities, thereby enabling the creation of low-cost and eco-efficient products. Meanwhile, internal integration and external cooperation are key elements in implementing digital green supply chains and promoting eco-innovation, with the former optimizing the enterprise internally and the latter introducing external resources, which intertwine with each other to promote goal attainment [27]. Previous studies indicate that green customer integration and green supplier integration, along with their interactions, have a positive correlation with green innovation performance [28]. Internal integration can promote departmental coordination and information sharing, eliminate barriers and gaps, facilitate multi-departmental collaborative operations, and enhance the ability of companies to produce products according to environmental requirements. Moreover, the distinct information-handling capacities stemming from green internal integration and green customer integration can facilitate green process innovation and enhance both environmental and cost-effectiveness [29]. Green customer integration mediates the relationship between green operations and corporate sustainability of service and manufacturing companies and has different levels of impacts on the environmental, social, and economic performance of companies, so companies undertaking green operations management should actively promote green customer integration, and the government should also support it to help companies develop sustainably [30].

Moreover, the impact of the two facets of green customer integration on green product innovation is subject to moderation by the quality of information sharing [31]. Knowledge sharing plays a crucial role in driving green processes and technological innovation. Implementing integration enables resource sharing and enhances the enterprise’s skill level and innovation ability. Green customer integration acts as a crucial mediator in the connection between green operations and services and the manufacturing companies’ corporate sustainability [32]. In this study, by balancing and combining suppliers’ interests in green integration, the relationship between supplier development and upstream GSCI is significantly moderated by cost and customer drivers. From a management standpoint, green supplier integration spurs enterprises to engage in green innovation using advanced equipment and innovative technologies. Its upstream cooperation mechanism helps in setting goals and solving problems, enabling suppliers to offer enterprises new knowledge and eco-friendly raw materials, thus promoting the enterprises’ green innovation. In light of the above analysis, this paper puts forward the following hypotheses:

H1: 

Green supply chain integration positively affects enterprise green innovation;

H1a: 

Green internal integration in green supply chain integration positively affects enterprise green innovation;

H1b: 

Green customer integration in green supply chain integration positively affects corporate green innovation;

H1c: 

Green supplier integration in green supply chain integration positively affects corporate green innovation.

4.2. Impact of Green Supply Chain Integration on Supply Chain Digital Transformation

Green supply chain integration is based on supply chain integration, highlighting environmental protection and reorganizing supply chain links through green management tools. This not only strengthens internal and external information sharing and cooperation and improves supply chain flexibility and responsiveness but also allows enterprises to integrate upstream and downstream resources to obtain a large amount of information, enhance competitive advantage and supply chain agility, meet the demand for efficient information interaction, and also aggregate data for real-time decision-making and operation optimization, laying the foundation for digital transformation. The convergence of digital technology and green transformation creates new opportunities for new entrepreneurial initiatives that address environmental sustainability issues [33]. In green supply chain integration, green customer integration designs target products and services based on customer interaction data.

Green supplier integration, on the other hand, enables firms to obtain timely and accurate information about manufacturing resources. This information can be used to develop production plans, improve business processes and supply chain networks, and enhance management. Moreover, there is a positive correlation between a firm’s integration with suppliers and customers and its environmental cost performance, and digital transformation enhances this relationship. Despite the limitations in relevant research, these studies emphasize the influence of integration on performance and the moderating role of digital transformation. This indicates that digital transformation has a positive effect within the context of green supply chain integration [34]. In light of the foregoing analyses, the following hypotheses are put forward in this paper.

From the perspective of resource sharing, according to the resource-based theory, a firm’s competitive advantage stems from its unique resource combination. GSCI combines supplier and customer resources, creating a vast data pool. This pool serves as a bountiful basis for the utilization of digital technologies like blockchain and the Internet of Things.

Regarding the collaborative mechanism, the dynamic capabilities theory highlights a company’s capacity to combine, construct, and restructure both its internal and external capabilities in order to adjust to a fast-changing environment. GSCI promotes cross-departmental collaboration and optimizes business processes. In this process, communication and collaboration among departments become smoother, which can effectively reduce the coordination costs during digital transformation.

From the perspective of environmental adaptability, under the current pressure of green development, in order to meet environmental protection requirements, firms are more inclined to adopt digital tools. Based on the resource-based theory, firms will utilize their own resources to acquire digital capabilities to cope with environmental changes.

H2: 

Green supply chain integration will positively affect consumers’ supply chain digital transformation;

H2a: 

Green internal integration in green supply chain integration positively affects the digital transformation of the supply chain of consumers;

H2b: 

Green customer integration in green supply chain integration positively affects consumers’ supply chain digital transformation;

H2c: 

Green supplier integration in green supply chain integration is moving towards the digital transformation of supply chains affecting consumers.

4.3. Impact of Supply Chain Digital Transformation on Green Innovation

In the context of digital transformation, a series of positive changes have been observed in the supply chain sector [22]. On the one hand, the driving effect of collaborative green innovation in supply chains has been strengthened, while the side effects of impediments have been weakened, and the threshold of positive moderating effects has been expanded, which together, contribute to the development of collaborative green innovation. Therefore, companies should actively leverage digital technologies to capitalize on this opportunity. Moreover, supply chain digitization plays an important role in promoting green innovation [7]. It promotes green innovation through greater integration and internal efficiency and presents positive effects such as quality prioritization, crowding-in, and sustainability. However, it is important to note that this facilitating effect manifests itself differently in different segments of green innovation. In addition, digital transformation has a significant facilitating effect on green innovation in manufacturing firms [35], especially in terms of substantive green innovation. This facilitating effect is mainly achieved by improving the quality of internal control of enterprises and promoting cooperation between industry, academia, and research. At the same time, both equity-based and compensation-based incentives can enhance the positive link between digital transformation and green innovation even further.

Furthermore, this facilitating impact is more evident in state-owned manufacturing enterprises, non-highly polluting manufacturing enterprises, and enterprises in areas with robust intellectual property protection. It is also more conspicuous when enterprises carry out digital underpinning technology transformation. In addition, digital transformation contributes to the reduction in polluting emissions. From a broader business development perspective, digital transformation is effective in stimulating innovation by starting with business needs [35]. It motivates firms to find production solutions that meet customers’ expectations for green products, thus breaking their dependence on traditional models. In this process, the internal organizational governance structure, operational mechanisms, and production processes can be optimized to reduce management costs and meet diversified needs. More importantly, digital transformation can change the internal management mode of the enterprise. It can accelerate the optimization and integration of internal and external business processes. Digital transformation can also improve the flexibility of the enterprise. Moreover, it can promote the integration of the enterprise and the industry. Furthermore, it makes the exchange of information more frequent. Building on the analysis presented above, the following hypotheses are posited in this paper:

H3: 

Supply chain digital transformation positively affects enterprise green innovation.

4.4. The Mediating Role of Supply Chain Digital Transformation

Green supply chain integration is of great significance, as it can integrate the dispersed advantageous resources in the supply chain and open up effective ways for enterprises to obtain resources. Digital transformation plays a mediating role between GSCI and green innovation by optimizing green innovation decisions through real-time data sharing and reducing the costs of trial and error. In the current situation, the traditional supply chain model faces difficulties in guaranteeing the smooth implementation of green innovation, and enterprises urgently need to improve their supply chain management level. This requires enterprises to make rapid adjustments to internal and external resources to ensure that the business process can achieve effective transformation, enhance the ability to adapt to the environment, and thus effectively improve the level of green innovation.

Specifically, green supply chain integration can create a shared, participatory, and collaborative atmosphere, leading to the formation of in-depth cooperative relationships between different departments, suppliers, and customers. Such cooperation greatly enhances the level of information sharing among these three stakeholders. As pointed out in [36], active digital transformation can further strengthen the strong ties between supplier integration and customer integration. This close and continuous information-sharing pattern brought about by digital transformation is an important safeguard for firms to access high-quality green information and key complementary environmental knowledge resources. Based on this, the organization’s supply chain management capabilities are enhanced. When companies focus on digital transformation, they are able to enhance supply chain integration and eco-design. This process enables firms to rapidly spot green opportunities in the market and formulate strategies to capitalize on them ahead of competitors, thereby facilitating green innovation initiatives [37].

Simultaneously, the enhancement of supply chain management is also capable of boosting the environmental consciousness of enterprises. In a complex, changing, and unpredictable environment, this ability enables firms to remain alert to potential risks. In high-uncertainty scenarios, enterprises are more inclined to invest resources in digital transformation, such as intelligent supply chain systems, to enhance their adaptability to market fluctuations, thus amplifying the promoting effect of GSCI on green innovation. As a result, the risk of supply chain disruption will be effectively reduced, the supply chain system can operate stably, and the green innovation ability of enterprises will improve accordingly. In view of the preceding analysis, the following hypotheses are put forward in this paper:

H4: 

Supply chain digital transformation mediates the relationship between green supply chain integration and consumers’ green innovation;

H4a: 

Supply chain digital transformation mediates the relationship between green internal integration and consumers’ green innovation;

H4b: 

Supply chain digital transformation mediates the relationship between green customer integration and consumers’ green innovation;

H4c: 

Supply chain digital transformation mediates the relationship between green supplier integration and consumers’ green innovation.

4.5. Environmental Uncertainty’s Moderating Function

Environmental uncertainty serves as a moderator in the association between digital transformation and green supply chain management (GSCM), tempering the direct influence that digital transformation has on GSCM [38]. Firm uncertainty affects firm innovation-related relationships, with increased demand uncertainty increasing forecasting errors, although firms can improve agility and innovation by focusing on the market and strengthening internal and external linkages in a strongly uncertain demand environment. In green innovation activities, green competition is the most powerful impetus. Among other factors, formal institutions, informal institutions, and customer green demand follow, in that order. Moreover, top-management environmental awareness mediates the connection between external factors and green innovation actions [39].

Environmental uncertainty has a directional and moderating effect on risk transmission [40]. In green supply chain management, environmental uncertainty poses challenges for firms in predicting customer demand and gauging their inclination towards green products. When faced with high uncertainty, firms tend to closely monitor the market. They adjust the integration process to match demand, strengthen both internal and external collaboration, and work on enhancing their green innovation capabilities. The contingency theory posits that corporate behavior needs to be aligned with the external environment. When environmental uncertainty is high, enterprises need to respond rapidly to changes in market demands through closer supply chain integration, such as real-time information sharing. Digital transformation, like predictive analytics, can enhance the efficiency of this integration, thereby amplifying the positive effect of GSCI on green innovation. In light of the above-mentioned analyses, the following hypotheses are proposed in this paper:

H5a: 

Environmental uncertainty plays a positive moderating role between green internal integration and corporate green innovation;

H5b: 

Environmental uncertainty positively moderates the relationship between green customer integration and corporate green innovation;

H5c: 

Environmental uncertainty positively moderates the relationship between green supplier integration and corporate green innovation.

5. Methodology

This research centers on how green supply chain integration, supply chain digital transformation, and environmental uncertainty affect corporate green innovation. Choosing these variables is highly significant both theoretically and in practice.

Theoretically, green supply chain integration is a crucial path for enterprises to achieve green development. The resource-based theory emphasizes that enterprises obtain competitive advantages by integrating internal and external resources. Green supply chain integration helps enterprises acquire the resources necessary for green innovation. The organizational information processing theory indicates that digital technologies can optimize the information-processing process and enhance enterprises’ innovation capabilities. As an important external factor in enterprise operations, environmental uncertainty, according to the contingency theory, requires enterprises to adjust their strategies in response to environmental changes. In green supply chain management, environmental uncertainty affects enterprise decision-making and resource allocation and thus influences green innovation.

From a practical perspective, enterprises are currently facing increasingly strict environmental regulations and market demands for green products. Enterprises find that integrating the green supply chain and undergoing digital transformation are essential steps to boost competitiveness and reach the Sustainable Development Goals. Moreover, environmental uncertainty exacerbates the challenges and opportunities for enterprises during the green innovation process. Therefore, studying the relationships among these variables has important guiding value for enterprise practices.

5.1. Data Collection

Questionnaires are utilized to investigate the influence of green supply chain integration on corporate green innovation. The questionnaire was designed based on the questionnaire star, and the design process included (1) searching and sorting out the existing research-related measurement scales through an analysis and a comparison and modifying the selected scales appropriately according to the content of this paper’s research to come up with the specific questions used in this paper; (2) designing the questionnaire content, including the title, introduction, basic information, and variable items, with the questions in this study using a Likert five-level scale; and (3) before formal distribution of the questionnaire, distributing a small-scale pre-survey questionnaire to relevant MBA students and relevant staff engaged in the manufacturing industry by means of a pre-survey.

In this study, data were collected by posting questionnaires on relevant websites, and finally, 350 questionnaires were collected. To ensure data authenticity and accuracy, questionnaires completed in 1 min or less were removed from the data set. Secondly, questionnaires with all the same options chosen were excluded using Excel software (2019), and finally, data related to enterprises with a size below 100 were deleted. After screening, the remaining valid questionnaires amounted to 330. The 330 valid questionnaires were tested for reliability and validity, and the questionnaires were improved by combining the measurement situations and feedback from the investigators. Finally, the formal questionnaire used in this study was formulated, and the specific questionnaire content is shown in Appendix A. The demographic profile of valid respondents (N = 330) is summarized in

Table 1. Demographic profile of valid respondents.

Category	Classification	Proportion/Mean
Industry	Manufacturing	58%
	Retail	30%
	Third-party logistics	12%
Work Experience	Mean	7.5 years
Management Level	Supervisor or above	65%
Certification	CSCP/CPIM holders	28%

.

5.2. Variables and Data Description

We precisely measured the green supply chain integration dimension and partitioned it into three components: green internal integration, green supplier integration, and green customer integration. Green internal integration encompasses cross-departmental collaboration, mid-to-high-level involvement, knowledge dissemination, and a response system. Green customer integration consists of strategic customer cooperation, information exchange, and joint production. Green supplier integration involves forging partnerships, information sharing, and participation in management and auditing.

Meanwhile, for the purpose of this study, ten green supply chain integration question items were designed using a five-point Likert scale (1–5, the higher the number, the higher the agreement) with reference to the scale setting of scholars. This study designs the supply chain digital transformation scale mainly with reference to the research of Frank [41]. The scale focuses on enterprises’ digital capabilities. It encompasses aspects like information digital processing, multi-source big data collection, using digital technology to construct inter-business supply chain networks, improving customer interfaces, and achieving digital information exchange. These comprehensively illustrate the key traits of enterprises in this domain. The scale consists of five question items in total. Measuring green innovation through green products and green processes, the scale examines the extent to which new products are developed with raw materials that consume less energy and pollute less, and new technologies, equipment, processes, and organizational management methods are introduced into production.

The enterprise green innovation scale consists of six questions, which are based on the development or use of non-polluting, low-energy-consuming, and easy-to-recycle new products; the improvement of processes to reduce pollution; the introduction of new technologies and equipment to save energy; the adoption of new environmental management system methods; the implementation of green innovation activities; etc., to comprehensively show the practice of green innovation in enterprises. The current research mainly measures environmental uncertainty from the macro and micro perspectives, the complexity and dynamics of the environment itself, and the market and technology perspectives. In this paper, considering that uncertainty on the market side has the greatest impact on corporate green innovation, the scale of environmental uncertainty is mainly designed around the market level, modified, and set up. The scale of environmental uncertainty measurement is mainly reflected in the following aspects: firstly, the ease of access to the market by other green competitive products; secondly, the difficulty of enterprises in accurately assessing customers’ demand for environmentally friendly products; thirdly, firms’ difficulty in predicting customers’ future preferences for environmentally friendly products; and fourthly, the state of inaccuracy in firms’ forecasts of demand for environmentally friendly products. Taken together, these elements depict the environmental uncertainty faced by firms.

5.3. Descriptive Statistical Analysis

To identify outliers in the data, this study employs SPSS statistical software (27.0.1) to analyze the valid data from the survey.

Table 2. Descriptive statistical analysis.

Variables	Question Items	Sample Value	Mean	Variance	Standard Deviation	Kurtosis	Skewness
Green Supply Chain Integration	Q1	330	3.86	0.81	0.90	−0.33	−0.30
	Q2	330	3.72	0.74	0.86	0.17	−0.56
	Q3	330	3.81	0.66	0.81	−0.01	−0.33
	Q4	330	3.67	0.74	0.86	−0.41	−0.29
	Q5	330	3.45	0.99	0.98	−0.48	−0.21
	Q6	330	3.73	0.76	0.87	−0.38	−0.22
	Q7	330	3.79	0.68	0.83	−0.16	−0.25
	Q8	330	3.53	0.94	0.97	−0.38	−0.23
	Q9	330	3.30	0.98	0.99	−0.43	−0.13
	Q10	330	3.54	0.94	0.97	−0.39	−0.31
Digital Transformation of Supply Chain	Q11	330	3.92	0.56	0.75	1.17	−0.59
	Q12	330	3.41	0.99	0.99	−0.14	−0.49
	Q13	330	3.83	0.75	0.87	0.50	−0.59
	Q14	330	3.35	0.90	0.95	−0.07	0.02
	Q15	330	3.25	1.08	1.04	−0.53	−0.20
Green Innovation	Q16	330	3.46	0.98	0.99	−0.35	−0.30
	Q17	330	3.47	0.86	0.93	−0.28	−0.25
	Q18	330	3.25	1.25	1.12	−0.70	−0.20
	Q19	330	3.34	0.90	0.95	−0.34	−0.09
	Q20	330	3.51	0.95	0.98	−0.17	−0.32
	Q21	330	3.62	0.71	0.84	−0.11	−0.13
Environmental Uncertainty	Q22	330	3.12	0.57	0.75	−1.22	−0.19
	Q23	330	3.23	0.49	0.70	−0.94	−0.33
	Q24	330	3.16	0.58	0.76	−1.23	−0.26
	Q25	330	3.14	0.59	0.77	−1.26	−0.29

presents the descriptive statistical analysis of the variables. It can be seen that the overall sample value of each variable is 330, and there are no outliers or missing values; at the same time, the mean, variance, standard deviation, kurtosis, and skewness of each question item are also within a reasonable range, which is in line with the requirements for conducting analyses of reliability and validity, a correlation analysis, and hypothesis testing.

5.4. Reliability Analysis

The reliability and trustworthiness of the questionnaire data were assessed by using the Cronbach coefficient a (Cronbach’s a) value for the reliability test; usually, a Cronbach’s a value greater than 0.7 indicates that the reliability is good and a Cronbach’s a value less than 0.6 indicates insufficient reliability and needs to be revised.

The reliability analysis results achieved by using SPSS software in this paper are presented in

Table 3. Reliability analysis.

	Dimensions	Deleted Cronbach’s a Value	Dimensions Cronbach’s a Value	Overall Cronbach’s a Value
Green Supply Chain Integration	Green Internal Integration	0.826	0.850	0.896
	Green Internal Integration	0.788
	Green Internal Integration	0.808
	Green Internal Integration	0.815
	Green Customer Integration	0.683	0.794
	Green Customer Integration	0.741
	Green Customer Integration	0.736
	Green Supplier Integration	0.749	0.768
	Green Supplier Integration	0.673
	Green Supplier Integration	0.650
Digital Transformation of Supply Chain	Digital Transformation of Supply Chain	0.819	0.912	0.912
	Digital Transformation of Supply Chain	0.901
	Digital Transformation of Supply Chain	0.863
	Digital Transformation of Supply Chain	0.873
	Digital Transformation of Supply Chain	0.851
Green Innovation	Green Innovation	0.907	0.922	0.922
	Green Innovation	0.905
	Green Innovation	0.911
	Green Innovation	0.904
	Green Innovation	0.905
	Green Innovation	0.914
Environmental Uncertainty	Environmental Uncertainty	0.758	0.785	0.785
	Environmental Uncertainty	0.723
	Environmental Uncertainty	0.726
	Environmental Uncertainty	0.719

. For the three dimensions of the green supply chain integration scale, Cronbach’s α values are 0.850, 0.794, and 0.768 respectively, while the overall Cronbach’s α value is 0.896. This indicates that the research on the green supply chain integration scale has high reliability. The overall Cronbach’s a value of the supply chain digital transformation scale is 0.912, indicating that the supply chain digital transformation scale in this study has a high reliability. The overall Cronbach’s a value of 0.922 for the enterprise green innovation scale indicates that the enterprise green innovation scale in this study has high reliability. The overall Cronbach’s a value of 0.785 for the environmental uncertainty scale similarly indicates that the environmental uncertainty scale in this study has high reliability. In conclusion, the settings of the scales used in this study have high reliability.

5.5. Validity Analysis

The use of scales recognized by existing scholars in this study ensured the content validity of this study, but to further test the accuracy and validity of using the final questionnaire, this paper examined the validity of the two components through an exploratory factor analysis and a validation factor analysis. Exploratory factor analysis: the purpose of an exploratory factor analysis is to check whether the values of KMO and Bartlett’s test of sphericity in the SPSS output results meet the requirements and then to proceed to the next validation factor analysis after meeting the requirements. As shown in

Table 4. KMO and Bartlett’s test of sphericity.

Variable	KMO Value	Bartlett’s Test of Sphericity (Sig)
Green supply chain integration	0.904	0.000
Supply chain digital transformation	0.895	0.000
Enterprise green innovation	0.917	0.000
Environmental uncertainty	0.789	0.000

, the KMO values for green supply chain integration, supply chain digital transformation, green innovation, and environmental uncertainty are 0.904, 0.895, 0.917, and 0.789, respectively. Since all these values exceed 0.7, and the results of Bartlett’s test of sphericity for each variable are significant, which implies that the scales of each variable satisfy the requirements for the factor analysis.

A validation factor analysis was performed by using Amos software (24.0.0).

Table 5. Table of overall fit coefficients for each variable.

Parameter Indicators	χ2/df	RMSEA	CFI	RFI	TLI	IFI	NFI
Green Supply Chain Integration	2.407	0.065	0.968	0.924	0.954	0.968	0.946
Supply Chain Digital Transformation	2.632	0.073	0.953	0.97	0.969	0.98	0.969
Green Innovation	2.863	0.073	0.969	0.987	0.977	0.988	0.981
Environmental Uncertainty	0.271	0	0.996	1.001	1.013	1.004	0.998
Evaluation Criteria	<3	<0.08	>0.9	>0.9	>0.9	>0.9	>0.9

shows the overall fit coefficients for the integration of the variables: the RMSEA values were less than 0.08; the values of CFI, RFI, TLI, IFI and NFI were all greater than 0.9, which indicates that the overall model was well fitted.

Moreover, as presented in

Table 6. Table of factor loading coefficients for each variable.

	Dimensions	Question Items	Standardized Load Factor	Mean Variance Extraction AVE Value	Combined Reliability CR	AVE Square Root
Green Supply Chain Integration	Green Internal Integration	Q1	0.82	0.578	0.845	0.76
	Green Internal Integration	Q2	0.726
	Green Internal Integration	Q3	0.76
	Green Internal Integration	Q4	0.73
	Green Customer Integration	Q5	0.801	0.559	0.791	0.748
	Green Customer Integration	Q6	0.701
	Green Customer Integration	Q7	0.738
	Green Supplier Integration	Q8	0.748	0.57	0.799	0.755
	Green Supplier Integration	Q9	0.752
	Green Supplier Integration	Q10	0.764
Digital Transformation of Supply Chain	Digital Transformation of Supply Chain	Q11	0.869	0.735	0.933	0.857
		Q12	0.876
		Q13	0.846
		Q14	0.86
		Q15	0.836
Green Innovation	Green Innovation	Q16	0.77	0.669	0.924	0.818
		Q17	0.838
		Q18	0.839
		Q19	0.801
		Q20	0.835
		Q21	0.82
Environmental Uncertainty	Environmental Uncertainty	Q22	0.722	0.48	0.786	0.693
		Q23	0.705
		Q24	0.721

, the factor loadings of the question items for each variable dimension all exceeded 0.7. This implies that the question items for each dimension were reasonably configured. The combined reliability’s CR value is above 0.7, and the minimum AVE value is greater than 0.5. These figures suggest that the scales of each variable possess good convergent validity.

5.6. Correlation Analysis

Pearson’s correlation coefficient (r), in the range of (−1, 1), was applied to assess the correlation degree among the variables. When the value of r approaches 1 or −1, the correlation between variables gradually strengthens. Conversely, as r nears 0, the correlation between variables weakens. As shown in

Table 7. Correlation analysis of research variables.

	Green Internal Integration	Green Customer Integration	Green Supplier Integration	Digital Transformation of Supply Chain	Green Innovation	Environmental Uncertainty
Green Internal Integration	1
Green Customer Integration	0.611 **	1
Green Supplier Integration	0.585 **	0.555 **	1
Digital Transformation of Supply Chain	0.712 **	0.734 **	0.641 **	1
Green Innovation	0.646 **	0.709 **	0.566 **	0.801 **	1
Environmental Uncertainty	0.427 **	0.470 **	0.458 **	0.476 **	0.512 **	1
Green Internal Integration	1

Note: ** indicates significance at the 1% level.

, the correlation coefficient of r between the variables in this study is in the range of (−1, 1), and any two variables are significant at the level of 0.01, which indicates that there is a significant correlation between any two variables, and it is possible to carry out the regression analysis in the next section.

5.7. Hypothesis Testing

5.7.1. Regression Analysis of Green Supply Chain Integration on Green Innovation

In this paper, we select the variables of employee’s job type, business nature, years of establishment, and location as the control variables; construct the model with green supply chain integration as the independent variable and green innovation as the dependent variable; and carry out a regression analysis.

Regression analysis is an effective tool for exploring such causal relationships. It can clearly demonstrate the direction and degree of the impact of independent variables on the dependent variable. In this research, green supply chain integration, supply chain digital transformation, and environmental uncertainty serve as independent variables, with corporate green innovation being treated as the dependent variable. A regression analysis can help us accurately evaluate the impact of each independent variable, both individually and jointly, on corporate green innovation. Compared with other methods, for example, although a correlation analysis can reveal the degree of association between variables, it cannot determine the causal relationship. A regression analysis, on the other hand, can accurately determine the causal relationship among the research variables by controlling other factors that may affect corporate green innovation, such as enterprise size and industry type.

Table 8. Regression outcomes of green supply chain integration regarding green innovation.

Variables	Green Innovation
	Model 1	Model 2	Model 3	Model 4	Model 5
Type of Work	0.036	0.014	−0.032	0.066	0.04
Business Nature	0.049	0.042	0.054	0.032	0.043
Years of Establishment	−0.025	0.073	0.019	0.041	0.08
Location	0.14	0.009	0.036	0.034	0.071
Green Supply Chain Integration		0.762 ***
Green Internal Integration			0.642 ***
Green Supplier Integration				0.711 ***
Green Customer Integration					0.572 ***
R2	0.027	0.579	0.424	0.511	0.336
Adjusted R2	0.015	0.573	0.416	0.503	0.325
F-value	2.215	89.122	47.779	67.681	32.743

Note: *** indicates significance at the 1‰ level.

shows the regression results of green supply chain integration on green innovation. Model 1 is the benchmark model, which only includes control variables (employee work type, enterprise nature, years of establishment, and geographical location). Model 2 adds green supply chain integration (overall dimension) as an independent variable on the basis of Model 1 to verify Hypothesis H1. Models 3–5 separately incorporate the sub-dimensions of green supply chain integration (internal integration, supplier integration, and customer integration) to test Sub-hypotheses H1a, H1c, and H1b. By gradually adding variables, the independent effects of each dimension on green innovation can be distinguished.

The SPSS output shows that the regression coefficient of green supply chain integration with respect to corporate green innovation is 0.762. This value passes the 1% significance level test, demonstrating that green supply chain integration has a positive impact on enhancing corporate green innovation. Thus, Hypothesis H1 is validated. When the green internal integration variable is incorporated into the model, the R² value increases. The regression coefficient of green internal integration on enterprise green innovation is 0.642, significant at the 1% level. This shows that green internal integration has a significant positive impact on enterprise green innovation, validating Hypothesis H1a. Furthermore, upon separately adding the green customer integration and green supplier integration variables, the regression outcomes consistently show that both green customer integration and green supplier integration have a significant positive effect on enterprise green innovation. As a result, Hypotheses H1b and H1c are validated.

5.7.2. Regression Analysis of Green Supply Chain Integration on Supply Chain Digital Transformation

This paper constructs a model with green supply chain integration as the independent variable and supply chain digital transformation as the dependent variable and conducts a regression analysis; the regression results are shown in

Table 9. Regression results of green supply chain integration on supply chain digital transformation.

Variables	Digital Transformation of Supply Chain
	Model 1	Model 2	Model 3	Model 4
Type of work	−0.076	−0.128	−0.022	−0.048
Business Nature	0.030	0.043	0.020	0.030
Years of Establishment	−0.011	−0.067	−0.048	0.000
Location	0.007	0.032	0.040	0.072
Green Supply Chain Integration	0.814 ***
Green Internal integration		0.710 ***
Green Supplier Integration			0.718 ***
Green Customer Integration				0.626 ***
R2	0.681	0.538	0.546	0.422
Adjusted R2	0.677	0.531	0.539	0.413
F-value	138.616	75.384	77.822	47.327

Note: *** indicates significance at the 1‰ level.

. Model 1 is the benchmark model with control variables. In Models 2–4, the variables of green internal integration, green customer integration, and green supplier integration are introduced one by one to test Sub-hypotheses H2a, H2c, and H2b. Through the analysis of different dimensions, the differences in the contributions of various integration paths to digital transformation are revealed.

The SPSS output reveals that the regression coefficient of green supply chain integration is 0.814. This value passes the 1% significance level test, demonstrating that green supply chain integration has a significantly positive effect on supply chain digital transformation. Thus, Hypothesis H2 is validated. When the green internal integration variable is added to the model, the R² value rises. The regression coefficient of green internal integration for supply chain digital transformation is 0.710, significant at the 1% level. This implies that green internal integration significantly and positively affects supply chain digital transformation, thus validating Hypothesis H2a. Moreover, upon the individual addition of green customer integration and green supplier integration variables, the regression findings consistently show that both green customer integration and green supplier integration have a notable positive influence on supply chain digital transformation. As such, Hypotheses H2b and H2c are confirmed.

5.7.3. Regression Analysis of Supply Chain Digital Transformation on Enterprise Green Innovation

In this paper, a model was constructed with supply chain digital transformation as the independent variable and enterprise green innovation as the dependent variable. A regression analysis was then carried out, and the results are presented in

Table 10. Regression results of supply chain digital transformation on green innovation.

Variable	Enterprise Green Innovation
	Model 1	Model 2
Type of work	0.036	0.079
Business Nature	0.049	0.019
Years of Establishment	−0.250	0.069
Location	0.140	0.020
Digital Transformation of Supply Chain		0.813 ***
R2	0.027	0.654
Adjusted R2	0.015	0.649
F-value	2.215	122.494

Note: *** indicates significance at the 1‰ level.

. Model 1 contains only control variables, while Model 2 includes the supply chain digital transformation variable to test Hypothesis H3. The SPSS output reveals that the regression coefficient of supply chain digital transformation with respect to enterprise green innovation is 0.813. This coefficient passes the 1% significance level test, demonstrating that supply chain digital transformation exerts a significant positive impact on enhancing enterprise green innovation. Consequently, Hypothesis H3 is validated.

5.8. Mediating Effect Test

Based on the main effect test results, green supply chain integration significantly and positively impacts both supply chain digital transformation and enterprise green innovation. Also, supply chain digital transformation significantly and positively affects enterprise green innovation, enabling the conduction of a mediating effect test. The mediating effect of supply chain digital transformation in the process of green supply chain integration and its three dimensions influencing enterprise green innovation are presented in

Table 11. Tests of mediating effects of supply chain digital transformation.

Variables	Green Innovation
	Model 1	Model 2	Model 3	Model 4	Model 5	Model 6	Model 7	Model 8
Type of work	0.023	0.095	−0.053	0.066	0.109	0.132	0.066	0.126
Business Nature	0.072	0.043	0.093	0.024	0.055	0.034	0.074	0.035
Years of Establishment	0.068	0.073	0.017	0.06	0.038	0.066	0.075	0.074
Location	0.006	0.003	0.023	0.012	0.021	0.006	0.045	0.011
Green Supply Chain Integration	0.774 **	0.389 **
Green Internal integration			0.748 **	0.155 **
Green Customer Integration					0.709 **	0.264 **
Green Supplier Integration							0.672 **	0.139 **
Digital Transformation of Supply Chain		0.559 **		0.713 **		0.618 **		0.785 **
R2	0.579	0.682	0.424	0.662	0.511	0.686	0.336	0.66
AdjustedR2	0.573	0.677	0.416	0.656	0.503	0.68	0.325	0.654

Note: ** indicates significance at the 1% level.

. Models 1, 3, 5, and 7 examine the direct impact of green supply chain integration (both in the overall dimension and in the sub-dimensions) on green innovation. Models 2, 4, 6, and 8 incorporate the variable of digital transformation into the corresponding models to verify its partial mediating effect (Hypothesis H4 and Sub-hypotheses H4a–c). If the coefficient of the original independent variable significantly decreases but remains significant, then the mediating effect is established.

It can be seen that both green supply chain integration and supply chain digital transformation significantly affect enterprise green innovation in Model 2. When comparing Model 2 with Model 1, the regression coefficient of green supply chain integration on enterprise green innovation in Model 2 decreased. However, it remains significant at the 1% level. This shows that supply chain digital transformation serves as a partial mediator between green supply chain integration and enterprise green innovation, thus validating Hypothesis H4. Both green internal integration and supply chain digital transformation significantly affect enterprise green innovation in Model 4.

Compared with Model 3, the regression coefficient of green internal integration on enterprise green innovation in Model 4 is reduced but still significant at the 1% level. This shows that supply chain digital transformation acts as a partial mediator between green internal integration and enterprise green innovation, thereby validating Hypothesis H4a. Both green customer integration and supply chain digital transformation significantly affect corporate green innovation in Model 6.

When contrasted with Model 5, the regression coefficient of green customer integration for corporate green innovation in Model 6 decreased yet remained significant at the 1% level. This indicates that supply chain digital transformation plays a partial mediating role between green customer integration and enterprise green innovation, and Hypothesis H4b is verified. Both green supplier integration and supply chain digital transformation significantly affect corporate green innovation in Model 8.

Compared with Model 7, the regression coefficient of green supplier integration on corporate green innovation in Model 8 is reduced but still significant at the 1% level. This shows that supply chain digital transformation acts as a partial mediator between green supplier integration and enterprise green innovation, thus validating Hypothesis H4c.

5.9. Moderating Effect Test

Firstly, to examine the moderating effect of environmental uncertainty, we performed mean-centering on the independent variables (green internal integration, green customer integration, and green supplier integration) and the moderating variable (environmental uncertainty). Mean-centering involves subtracting the mean of a variable from each of its observed values. This treatment makes the interpretation of regression coefficients more meaningful in practice and facilitates the analysis of the effects of interaction terms. Once the mean-centering was performed, we multiplied each of the processed independent variables by the moderating variable. This resulted in the creation of the corresponding interaction terms: green internal integration × environmental uncertainty, green customer integration × environmental uncertainty, and green supplier integration × environmental uncertainty.

Subsequently, green internal integration (along with green customer integration and green supplier integration), corporate green innovation, environmental uncertainty, and their corresponding interaction terms were incorporated into the regression model for analysis. The regression outcomes are shown in

Table 12. Results of the moderating effect of environmental uncertainty on green internal integration affecting green innovation.

Dependent Variable	Enterprise Green Innovation
	Beta (β)	t	Beta (β)	t
Control Variables
Type of work	−0.012	−0.28	−0.016	−0.411
Business Nature	0.037	0.888	0.048	1.206
Years of Establishment	0.018	0.448	0.016	0.404
Location	0.013	0.315	0.025	0.612
Independent Variables
Green Internal Integration	0.524	11.760 **	0.479	11.178 **
Environmental Uncertainty	0.282	6.333 **	0.245	5.756 **
Green Internal Integration × Environmental Uncertainty			0.243	6.177 **
Equation Metrics	R2 = 0.488		R2 = 0.542
	Adj.R2 = 0.478		Adj.R2 = 0.532
	F = 51.305 **		F = 54.486 **
Green Customer Integration	0.608	14.227 **	0.521	12.127 **
Environmental Uncertainty	0.227	5.332 **	0.211	5.250 **
Green Customer Integration × Environmental Uncertainty			0.241	6.140 **
Equation Metrics	R2 = 0.550		R2 = 0.598
	Adj.R2 = 0.542		Adj.R2 = 0.589
	F = 65.912 **		F = 68.302 **
Green Supplier Integration	0.432	8.794 **	0.32	6.567 **
Environmental Uncertainty	0.31	6.378 **	0.292	6.407 **
Green Supplier Integration × Environmental Uncertainty			0.298	6.836 **
Equation Metrics	R2 = 0.410		R2 = 0.485
	Adj.R2 = 0.399		Adj.R2 = 0.474
	F = 37.407 **		F = 43.278 **

Note: ** indicates significance at the 1% level.

.

Otherwise, our study is in line with the findings of [42]. Encouraging firms to use digital technologies and adopting green supply chain management can reduce costs and improve management, and implementing green innovations can create selling points and promote development, where digital innovations play an intermediary role in connecting and synergizing the various elements to facilitate the realization of sustainable supply chain practices [43]. Digitalization significantly and positively affects the green transformation of the industry. Strengthening green technological innovation and speeding up technological transformation are crucial intermediary routes.

When environmental uncertainty is included in the regression model, the regression coefficients of the interaction term are significant at the 1% level. Moreover, they are in the same direction as the regression coefficients of how green internal integration, green customer integration, and green supplier integration impact corporate green innovation. This shows that environmental uncertainty positively moderates the link between green internal integration, green customer integration, green supplier integration, and corporate green innovation, thus validating Hypotheses H5a, H5b, and H5c.

6. Discussion

This research centers on the connections among green supply chain integration, supply chain digital transformation, and corporate green innovation. Meanwhile, the mediating role of supply chain digital transformation between green supply chain integration and enterprises and the moderating role of environmental uncertainty between green supply chain integration and enterprise green innovation are investigated.

Supply chain integration has become a key initiative for business development in the face of intensifying globalized competition, changing consumer demands driven by technological advances, and the urgent need for cost control and efficiency improvement. Supply chain agility serves as a mediator between the three dimensions of green supply chain integration and firms’ green product and process innovation. Additionally, supply chain digital transformation acts as a mediator between green supply chain integration and firms’ green innovation. From our findings, green supply chain integration makes enterprises in the supply chain work together to build green strategies, integrate resources and information, and equip enterprises with their own unique advantages, which is an important factor in driving enterprises to achieve green innovation.

In green supply chain integration, external integration mainly consists of supplier integration and customer integration. These two components jointly facilitate knowledge sharing, co-development, and environmental cooperation among supply chain participants. Meanwhile, internal integration emphasizes resource sharing and capacity building. It encourages departments to learn from one another and utilize the enterprise’s internal resources and capabilities, thus playing a crucial part in enhancing green innovation.

Amid the global economy’s green and sustainable development trends and rapid technological progress, digital transformation holds immense importance for enterprises. It enables them to effectively blend green supply chain management with innovation strategies, thereby achieving optimal outcomes. Ikea’s digital transformation has excelled in sustainable supply chain management, especially in logistics and material sourcing. Its use of IoT and machine learning to optimize warehousing and transportation processes to reduce energy consumption and carbon emissions, as well as its use of blockchain technology to ensure that timber purchases are transparent and environmentally compliant, demonstrates how digital tools can facilitate supply chain integration and green innovation [36,44].

However, for enterprises to effectively implement GSCI and digital transformation for green innovation, they can follow these steps. First, conduct a comprehensive assessment of the current supply chain, identifying areas with high environmental impact and potential for digital improvement. Second, set clear goals and timelines. If the goal is to reduce carbon emissions by 20% within two years, break it down into quarterly targets for different departments. In terms of digital technology prioritization, different industries have different needs. For the manufacturing industry, IoT technology should be a top priority. By installing IoT sensors on production equipment, enterprises can monitor energy consumption in real time and adjust production parameters accordingly.

This research centers on the connections among green supply chain integration (GSCI), supply chain digital transformation (SCDT), and green innovation (GI). It also delves into the mediating function of SCDT and the moderating impact of environmental uncertainty (EU). Although prior research has examined aspects of GSCI and digitalization in sustainability contexts [13,20], this study advances the literature in three distinct ways, addressing critical gaps in theoretical framing, mechanism exploration, and contextual specificity.

(1).

Theoretical Novelty: Integrating Mediating and Moderating Pathways

Existing studies predominantly analyze the direct effects between GSCI and GI [29] or focus on digital transformation as an independent driver of innovation. However, this paper uniquely positions SCDT as a mediator bridging GSCI and GI, thereby revealing a sequential mechanism where digital capabilities amplify the benefits of green integration. This contrasts with prior work that treats digitalization as a parallel or isolated factor [41]. Additionally, while environmental uncertainty has been examined in risk management contexts [40], its moderating role in strengthening the GSCI–GI relationship under dynamic market conditions provides new insights.

(2).

Contextual Specificity: Empirical Validation in China’s Manufacturing Sector

While research on GSCI and digitalization often centers on Western or multinational firms (e.g., Ikea’s case [36]), this study focuses on China’s manufacturing industry, which faces unique regulatory pressures and market dynamics under the “Dual Carbon” policy. For example, our results show that green supplier integration has a stronger mediating effect through SCDT compared to findings in Southeast Asian contexts [45]. This highlights how institutional and technological disparities shape implementation outcomes, offering actionable insights for emerging economies.

(3).

Methodological Rigor: Multi-Dimensional Analysis

Previous studies often treat GSCI as a monolithic construct, whereas this paper dissects it into internal, supplier, and customer integration. This granular approach reveals that green customer integration exerts the strongest indirect effect via SCDT (β = 0.618, p < 0.01), a finding that challenges those from [14], who prioritized supplier collaboration. Such divergence may stem from China’s consumer-driven green demand, emphasizing the need for context-sensitive strategies.

In the digital age, for companies, comprehending and incorporating diverse digital technology applications is crucial for attaining green supply chains. Digital transformation can effectively improve the decision-making efficiency of enterprises, enhance operations, and reduce operational risks [46]. Consequently, it enables the optimal distribution of enterprise resources and enhances the input–output efficiency of enterprise green technology innovation. Under conditions of high and low environmental uncertainty (EU), firms can positively impact the environmental and social dimensions by collaborating with customers to meet demand. Ghanaian pharmaceutical firms under institutional pressure [47], Huawei as a global ICT leader, and Patagonia in their respective developments have all demonstrated positive moderating effects by enhancing competitiveness through green supply chain integration and enhanced green innovation under environmental uncertainty.

Firms that are customer-demand-orientated will increase their agility, including their ability to innovate, in an unstable demand environment; demand uncertainty will lead to the inability of firms to accurately assess their customers’ demand preferences, and therefore, firms need to test the market in order to reduce prediction errors and then enhance their green supply chain integration in order to quickly satisfy the market demand. This will lead to further integration of external partners and linkage of internal departments, thus enhancing the green innovation capability of the enterprise.

7. Conclusions

This research explores the relationships between green supply chain integration, supply chain digital transformation, and corporate green innovation. It also takes environmental uncertainty into account as a moderating variable. The findings validate the theoretical frameworks—resource-based theory, organizational information processing theory, and contingency theory—while offering nuanced insights that extend existing research. The findings validate the resource-based theory as GSCI enhances CGI by integrating resources, with green internal integration being the top CGI driver. The organizational information processing theory holds true as SCDT’s mediating role uses digital tech to break info silos, boost decision-making, and amplify the GSCI–CGI link. The contingency theory is confirmed when EU’s moderating effect shows that firms ramp up GSCI under high EU to adapt and optimize results.

With supply chain research garnering increasing attention, improved integration has become essential for the sustainable growth of enterprises. However, research on green supply chain integration tends to focus on firm performance or competitiveness. There are fewer domestic studies on its relationship with green innovation, and foreign studies are mostly theoretical and lack empirical evidence. This paper enriches this research area. This study defines the dimensions of green supply chain integration within the supply chain framework. It then explores how digital transformation acts as a mediator in the process of its impact. Environmental uncertainty as a moderator enhances the explanatory power of the model. This reflects the actual role of green supply chain integration and its importance. This study has implications for policymakers and regulators. Managers should consider environmental attributes at the early stage of design and involve suppliers and customers in goal setting. Internal integration should focus on knowledge sharing. Collaboration between supply chain participants is key to digital transformation. Governments can improve quality and innovation by increasing business visibility, creating a collaborative environment, and investing in information technology.

The research limitations of this paper include the following: (1) In choosing the dimensions of green supply chain integration, this study only looked at green internal integration, green supplier integration, and green customer integration. It did not take into account other variable factors, nor did it conduct an in-depth exploration of the internal relationships and action mechanisms among these three dimensions. (2) The questionnaire survey method was mainly adopted, and although the autonomy and exclusivity of questionnaire responses were emphasized in the introduction, the interference of other objective factors in the survey results could not be completely ruled out, which might lead to some bias in the questionnaire results. (3) Environmental uncertainty is introduced as a moderating variable in the content framework, and its role in the relationship between green supply chain integration and corporate green innovation is investigated. However, this study did not explore other relationships in depth. For instance, the connections between green supply chain integration and supply chain digital transformation, as well as the roles of supply chain digital transformation in enterprise green innovation, remain unexamined and call for further investigation.

Future research directions: (1) Examining green supply chain integration from other dimensions will help to study its impact on enterprise green innovation in depth, and its specific mechanism can be explored in subsequent research. (2) This study primarily employs the questionnaire survey method to collect all research data. However, data from questionnaire surveys inherently possess objective uncertainties. To enhance the credibility of the research results, in future studies, we could consider using the experimental method. This would involve simultaneously establishing a control group and an experimental group for survey research to rule out the impact of other factors. An alternative approach would be to utilize secondary data to verify the authenticity and reliability of the findings. (3) In future research, if the researcher has the capacity, they can further investigate the moderating effect of environmental uncertainty across different dimensions of green supply chain integration and supply chain digital transformation. Additionally, the research model can be augmented by exploring other moderating variables like culture, trust, and behavior. For instance, one could examine the impact of cooperation among supply chain stakeholders and the application of digital technologies on behavioral and human aspects in attaining the Sustainable Development Goals (SDGs).