1. Introduction
As the environment deteriorates and resources deplete, stakeholders are increasingly concerned about environmental issues [1,2]. In this context, more and more firms are adopting green innovation to address stakeholders’ environmental demands. Nevertheless, manufacturing firms face significant challenges in implementing green innovation successfully due to the environmental chain responsibility caused by upstream suppliers and the extensive requirement for environmental knowledge and technological resources [3]. Hence, it is crucial to achieve collaborative green innovation in product, process, and logistics activities on the supply chain involving suppliers and customers [4]. Green supply chain innovation encompasses systematic green innovation activities across multiple dimensions, including technology, information systems, network design, business processes, and management practices [3,5]. The effective implementation of green supply chain innovation can leverage the knowledge base of supply chain members to help firms anticipate and seek innovative solutions to environmental issues [6,7]. Conversely, it can effectively address the problem of unsustainable competitive advantage arising from the imitation of individual green products or processes.
The Chinese government prioritizes implementing green supply chain innovation, integrating it into strategic initiatives such as supply-side reform. Considering the manufacturing industry’s significant contribution to China’s GDP and its environmental impact, the government actively promotes the development of green supply chain innovation to mitigate pollution. In 2019, the government launched the “Green Manufacturing Enterprise” program, leading to the emergence of exemplary firms such as Lenovo and Huawei. However, given the diverse sectors within the manufacturing industry, many demonstration firms are still in the early stages of exploring green supply chain innovation. Due to the diverse nature of the manufacturing industry and the variation in specific factors and practices related to green supply chain innovation among various sectors, it becomes challenging to achieve a broad demonstration effect. Therefore, studying how Chinese manufacturing firms can successfully implement green supply chain innovation is crucial for driving the industry’s overall transformation.
Several scholars have examined the effective implementation of green supply chain innovation in organizations. According to Ojha et al. [8], inter-organizational trust and innovation focus are essential prerequisites for the effective implementation of supply chain innovation. Solaimani and van der Veen [9] and Wu and Li [10] further emphasize the advantages of relationship-specific investments and close knowledge collaboration among supply chain members in achieving (green) supply chain innovation. However, existing research primarily focuses on investigating the impact of supply chain member relationships on (green) supply chain innovation. As a complex system, the creation of exceptional supply chain performance (supply chain innovation) requires the collaborative operation of both the social subsystem (relationships among supply chain members) and the technological subsystem [11,12]. However, research on the influence of the technological subsystem on green supply chain innovation has been relatively overlooked, limiting our understanding of this research question. As a technological subsystem, digital technology can enhance an organization’s ability to handle data and share information, facilitating efficient strategic collaboration and improving transparency among supply chain members, resulting in increased consistency in environmental awareness [2,13]. Many leading firms, such as Apple and Dell, are actively utilizing digital technology to enhance green supply chain performance. However, some scholars have pointed out that the application of digital technology may have negative effects on supply chain management. Building digital capabilities requires firms to allocate resources to redesign the operational processes and organizational structure of the supply chain, which may compete with resources allocated to supply chain management [14,15]. Furthermore, due to significant differences in resource endowments among supply chain members, an imbalance exists in the development of digital capabilities, resulting in asymmetry. This asymmetry may increase the risk of critical data leakage and elevate supply chain vulnerability.
Despite some exploration of the driving factors and impact of digital capabilities on green supply chain innovation, the influence of digital capabilities on this innovation remains under-researched. The existing literature suggests that digital capabilities enhance organizations’ ability in environmental innovation and supply chain management, potentially influencing green supply chain innovation. However, there are paradoxical findings regarding the impact of digital capabilities on supply chain performance, which introduces uncertainty when using digital capabilities to promote green supply chain innovation. Nevertheless, there is still a relatively limited amount of research on the relationship between digital capabilities and green supply chain innovation, and our understanding of how digital capabilities facilitate green supply chain innovation is still unclear. Therefore, to fill these research gaps, this study aims to address the following two research items:
RQ1: What is the impact of digital capability advantage on green supply chain innovation?
RQ2: How can digital capability advantage impact green supply chain innovation?
In order to address these research items, this study explores the issues from the perspective of inter-organizational learning. According to the theory of inter-organizational learning, successful implementation relies on two prerequisites: an open and sharing learning environment, and the absorptive capacity of the learning entities. Successful implementation of inter-organizational learning fosters shared understanding among members and enriches their knowledge bases [16,17]. The digital advantage of firms offers opportunities for effective inter-organizational learning activities, including green supply chain learning. On one hand, it enables firms and supply chain members to establish convenient, efficient, and transparent channels for communication. On the other hand, through data acquisition and analysis, it generates new insights, expands the knowledge base, and enhances absorptive capacity. The smooth implementation of green supply chain learning activities contributes to achieving green supply chain innovation in firms. The findings of this study not only enhance our understanding of the impact and pathways through which digital capability advantages affect green supply chain innovation, but also offer new managerial insights for manufacturing company managers to leverage digital technologies in empowering green supply chain innovation.
The rest of the paper is structured as follows: the second part provides a comprehensive review of the theoretical background and related concepts. The third part presents the conceptual framework and hypotheses. In the fourth part, we outline the research methodology, including scale design, data collection, and data quality testing. The fifth part of the article examines the hypotheses using structural equation modeling. In the last part, we discuss the results, draw conclusions, and highlight the main findings, theoretical contributions, management implications, limitations, and directions for future research.
2. Theoretical Background and Literature Review
2.1. Green Supply Chain Innovation
Scholars in the field of innovation have recognized that possessing innovative capabilities is crucial for organizations to introduce innovation and gain competitive advantages [18]. Green innovation (GI) also plays a crucial role in enhancing competitive advantages, and improving sustainable performance [19,20]. Green innovation emphasizes environmental protection and aims to provide environmentally friendly products [18]. Furthermore, Asiaei et al. [21] indicate that green innovation focuses on providing products, services, technologies, and management approaches that promote sustainable development and enhance sustainable performance by increasing an organization’s responsiveness to environmental risks. Organizations can implement green innovation to enhance environmental performance, meet stakeholders’ environmental requirements, ensure productivity, and empower competitive advantages sustainably [19].
However, competition among firms has shifted towards competition between supply chains [7]. Firms increasingly recognize that shaping a green supply chain to meet environmental requirements is crucial for sustained success. A supply chain that adds environmental value to customers is referred to as a green supply chain [18]. It purifies environmentally harmful logistics activities, reduces carbon emissions and other harmful gases, and enhances public safety in handling hazardous chemicals within the supply chain. Green supply chain innovation, an important activity to drive internal green initiatives in firms, has been proven to enhance sustainable performance by improving the efficiency of the supply chain [3]. Green supply chain innovation is an environmentally focused model that develops and manages innovative resources in supply chain operations [18]. It includes green product innovation, green process innovation, green marketing innovation, and green logistics innovation [10]. Such innovation goes beyond seeking radical changes within the supply chain (e.g., introducing novel and original products or services), and also emphasizes incremental improvements and extensions to existing products and services. Implementing green supply chain innovation can enhance companies’ proactive organizational capabilities to mitigate environmental risks and issues, reduce product life cycle burdens, and drive collaborative efforts to enhance sustainable performance.
2.2. Digital Capability Advantage
Disruptive digital technologies such as robotic process automation, big data, artificial intelligence, cloud computing, and blockchain are extensively utilized in supply chain strategy and operations [22,23]. Supply chains are transforming into tightly interconnected networks of digital technologies, fundamentally altering how data are collected, distributed, and processed [24]. This trend is referred to as supply chain digitization [25]. Previous studies have demonstrated the association between supply chain digitization and powerful information processing capabilities [26]. It revolutionizes the supply chain by enabling firms to flexibly and intelligently integrate more precise and granular data [25], including implementing consumer feedback [27], managing supply chain relationships [17], integrating the supply chain [28], and making data-based decisions to gain a competitive advantage [29,30]. Particularly in the context where green supply chain management has become a crucial source of sustainable competitive advantage for firms, the role of supply chain digitization is emerging. For instance, Liu et al. [13] demonstrated that big data analytics capabilities, which help organizations acquire and analyze data to generate new insights, can facilitate green supply chain integration by enhancing firms’ information processing capabilities. Supply chain digitization serves as a key driver of corporate performance in green supply chain management. The concept of digital capability advantage refers to the extent to which buyers have more advanced digital capabilities than suppliers [17]. The digital capability advantage of buyers represents a versatile ability to process information that can be applied in various domains, such as customer preference analysis and demand forecasting.
However, some scholars have proposed that digital capabilities, while helping supply chains acquire and create value, also have a dark side [31]. Differences in ecological position, resource endowment, and developmental stage among supply chain partners may lead to uneven development of digital technologies among members. Uneven development of digital capabilities between buyers and suppliers can result in the emergence of new supply chain structures and relationships. Asymmetric digital capabilities create winners between buyers and suppliers, with companies in advantageous positions often generating greater value [31]. Additionally, building digital capabilities requires companies to invest resources in redesigning operational processes and organizational structures, potentially encroaching upon resources used for supply chain management and increasing vulnerability and risks [14,15].
2.3. Digital Capability Advantage and Green Supply Chain Innovation
By reviewing existing research on the relationship between digital capability advantage and green supply chain innovation, we identified gaps in the current literature. The analysis results are presented in Table 1. We identified two gaps in the existing research on the relationship between digital capability advantage and green supply chain innovation. Firstly, the impact of digital capability advantage on green supply chain innovation remains unclear. Existing studies have yielded inconsistent conclusions regarding the role of digital capability advantage. Some scholars argue that digital capabilities, such as big data analytics, contribute to enhancing green supply chain integration [13], environmental innovation [2,14], and reducing supplier unethical behavior [17]. On the other hand, research has also found that the digital capability advantage of buying firms can amplify opportunistic behavior and increase the risk of relationship breakdown [31]. Furthermore, current research has primarily explored the antecedents of green supply chain innovation from the perspectives of relational-specific investments and knowledge transfer, while relatively neglecting the influence of digital capabilities [10]. The enhancement of digital capability advantage strengthens firms’ ability to engage in environmental innovation and supply chain management, potentially positively impacting green supply chain innovation. Therefore, it is necessary to clarify the impact of digital capability advantage on green supply chain innovation and address the gaps in the existing research. Secondly, the mechanism by which green supply chain management influences supplier environmental commitment remains unclear. Zhang et al. [17] identified the mediating role of relationship transparency in the association between digital capability advantage and supplier unethical behavior based on information processing theory. However, limited attention has been given to how digital capability advantage influences green supply chain innovation. This contributes to the ambiguity surrounding the role of digital capability advantage in green supply chain innovation and hampers the comprehensive understanding of effectively leveraging digital capabilities to drive successful green supply chain innovation. Therefore, elucidating the critical pathway through which digital capability advantage influences green supply chain innovation will help bridge this theoretical gap and enhance the body of research on green supply chain innovation.
2.4. Inter-organizational Learning
Learning is defined as the process of improving actions through enhanced knowledge and understanding [32]. Learning enables firms to not only develop core competencies and enhance control over operational processes but also to absorb new knowledge, thereby maintaining a broader knowledge base [33]. Organizational learning involves discovering, selecting, and adapting new practices and integrating them into new firm-specific processes. Essentially, organizational learning aims to address problems in order to bridge the gap between the current state and the desired state [16]. With the emergence of knowledge boundaries, learning is no longer confined to the internal organization but extends to the external organization. Inter-organizational learning refers to the acquisition of knowledge that occurs beyond the boundaries of individuals and organizations, specifically at the interorganizational level [34]. It encourages firms to obtain diverse knowledge from external sources, playing a crucial role in both knowledge creation and application. The supply chain serves as a vital source of knowledge resources, expanding the firm’s knowledge base through the provision of complementary knowledge. Supply chain learning is a cross-organizational learning behavior that describes how organizations coordinate their supply chain members to create collective knowledge [35].
Green supply chain learning, derived from supply chain learning, aims to enhance firms’ environmental sustainability strategies and practices through learning behaviors, emphasizing interaction with supply chain members, and supporting the transformative process of implementing circular economy initiatives [2,36]. Green supply chain learning facilitates the acquisition and mutual sharing of vital information between suppliers and customers on waste reduction, energy efficiency, and collaboration for building an eco-friendly supply chain [37]. Lisi et al. [36] classified green supply chain learning into two types: green supplier learning and customer learning. Green supplier learning involves collectively addressing environmental issues, acquiring environmental management capabilities, obtaining environmental protection information, and understanding suppliers’ green skills and knowledge [33]. Green customer learning centers on acquiring information, knowledge, and professional skills from suppliers to drive environmental sustainability initiatives [35].
3. Conceptual Model and Hypotheses Development
The knowledge base of a firm is fundamental for creating and sustaining competitive advantages, according to the theory of inter-organizational learning [38]. The acquisition and utilization of knowledge are crucial for learning activities in firms [33]. The existing knowledge base of firms influences their understanding and internalization of the acquired knowledge when they acquire knowledge through learning activities [16]. Moreover, when recipients trust a specific source of knowledge, the credibility of that source is high. Firms can select and evaluate the knowledge held by an information source based on its reputation [39]. Effective interorganizational learning requires two prerequisites: organizations need a solid knowledge base to filter valuable knowledge effectively, reducing redundant learning [40]. Additionally, organizations must accurately identify reliable knowledge sources and have effective channels for sharing and transferring knowledge. Learning activities occurring outside the organizational boundaries provide important pathways for organizations to acquire, absorb, integrate, and create new heterogeneous knowledge. Heterogeneous knowledge has a profound impact on firm performance and innovation [41].
This paper adopts the perspective of interorganizational learning theory and constructs a mechanistic model to investigate the influence of digital capability advantage on green supply chain innovation. Within the supply chain context, inter-organizational learning frequently occurs between buyers and suppliers [42]. Firms can acquire green knowledge and information by learning from suppliers and customers, who serve as diverse knowledge sources. The utilization of digital technologies creates favorable conditions for green supply chain learning. Digital technologies enhance firms’ ability to capture, integrate, and analyze large volumes of information, thereby significantly improving their information processing capabilities [17]. With improved information processing capabilities, firms can gain insights into the knowledge required for their own green supply chain innovation and identify relevant environmental knowledge sources among existing supply chain members. Simultaneously, firms can forecast industry trends in environmental technologies and customer environmental demands, providing guidance for the direction of learning. Through continuous green supply chain learning, firms can progressively attain a systematic transformation and exploration of their existing environmental systems within the organization, accumulating green knowledge along the way. Hence, the digital capability advantage of firms can foster green supply chain innovation by enhancing both green supplier and green customer learning. Based on this, we propose the research framework of this paper, as shown in Figure 1.
3.1. Digital Capability Advantage and Green Supply Chain Innovation
Buying firms can fully leverage the insights and predictive advantages of digital technologies to strengthen interorganizational learning and enhance the rate and effectiveness of green supply chain innovation. On one hand, digital technologies such as big data analytics, artificial intelligence, and the Internet of Things enable firms to collect and analyze vast amounts of data in supply chains, resulting in valuable insights. Through digital technologies, firms can monitor environmental indicators in supply chains, such as energy consumption, waste emissions, and carbon footprint. They can also gather and analyze data on environmental standards, design specifications, green product development, and clean production technologies from their supply chain partners, thereby gaining a deep understanding of innovation in green supply chains [17,25]. Based on this, firms can accurately select and acquire the knowledge, information, and skills required for the development of innovation in green supply chains from a wealth of data, thereby enhancing the rate of innovation in green supply chains [43]. On the other hand, leading digital technology advantages empower firms with stronger environmental scanning and demand forecasting capabilities. Through digital technologies, firms can stay updated on and learn about the cutting-edge developments in green technologies within the industry, as well as analyze and grasp customer environmental demand data in a timely manner, predicting the environmental needs of existing and potential customers [44]. Digital technologies also help firms anticipate environmental risks and opportunities in supply chains, enabling them to take proactive measures. By leveraging digital technologies, firms can proactively adjust existing green products and logistics transportation methods within supply chains, enhancing the effectiveness of innovation in green supply chains. Based on the above considerations, we propose the following hypotheses:
Digital capability advantage has a positive effect on green supply chain innovation.
3.2. Digital Capability Advantage and Green Supply Chain Learning
The successful implementation of inter-organizational learning relies on two essential factors: transparency and receptivity [45]. Transparent and open communication channels and atmosphere offer opportunities for cross-organizational boundary learning. However, due to differences in knowledge and experience, different organizations have varying levels of learning capability [16]. Therefore, the key to successfully implementing green supply chain learning activities is establishing fair and smooth communication bridges with suppliers (customers) and enhancing the acceptance of environmental knowledge acquired by buyers.
Digital technologies provide buying firms with opportunities to learn green knowledge and information from suppliers and customers, while also facilitating the continuous expansion of their knowledge base, enhancement of their green absorptive capacity, and improvement of learning effectiveness. Digital technologies allow supply chain members to easily share data and information. Supply chain members can assess and compare the overall green performance of the entire supply chain by sharing environmental data, energy consumption data, waste emissions data, and other relevant information. Increased transparency promotes mutual learning and benchmarking among members [46]. Thus, buying firms can identify crucial external knowledge sources necessary for green supply chain innovation from numerous suppliers and customers. Additionally, by utilizing the benefits of digital technologies, buying firms can establish data interchange with key suppliers (customers) who act as sources of green knowledge. This fosters a fair and transparent communication channel and enhances mutual trust. Moreover, digital technologies offer collaborative platforms to supply chain members, such as supply chain management systems, online collaboration tools, and social media platforms [33,47]. Supply chin members can communicate, collaborate, and share knowledge on these platforms. These platforms enable supply chain members to share best practices, lessons learned, and technological innovations, thereby fostering a culture of learning. Thus, by leveraging the benefits of digital technologies, buying firms can effectively integrate green knowledge resources from upstream suppliers, ensure efficient sharing of green knowledge, improve their relationship with suppliers, and facilitate communication with customers on environmental issues [47,48]. This increases the possibility of obtaining valuable and reliable environmental knowledge from suppliers and customers [43,49]. Based on these insights, we propose the following hypothesis:
Digital capability advantage has a positive effect on green supplier learning.
Digital capability advantage has a positive effect on green customer learning.
3.3. Green Supply Chain Learning and Green Supply Chain Innovation
The literature on inter-organizational learning suggests that the network in which an organization operates offers valuable knowledge sources and opportunities for partners within the network [50]. Through inter-organizational learning, organizations can mobilize these resources to enhance knowledge transfer across organizational boundaries, facilitating learning and the generation of new knowledge [34]. Inter-organizational learning supports organizations in navigating complex and dynamic external environments, contributing to their long-term stability and development [42]. Green supply chain innovation involves various complex and dynamic factors, including green technologies and diverse customer demands [35]. It encompasses systematic environmental innovation activities across multiple stages of supply chain management, such as procurement, production, marketing, and transportation [10,18]. Compared to other forms of innovation, green innovation requires a greater amount of knowledge and information that cannot be solely obtained from internal resources [35]. Suppliers’ knowledge of supply chain strategies, processes, products, and services aimed at achieving environmental sustainability goals, as well as information about customers’ current and future environmental needs and desires, is essential for driving green supply chain innovation [37]. Therefore, learning from various parties within the supply chain is an effective approach to acquiring relevant knowledge. Supply chain learning fosters collaboration among supply chain members and facilitates the dissemination, interpretation, and utilization of proprietary technical knowledge and information, thereby accelerating the realization of green supply chain innovation.
By learning from green suppliers, buying firms can achieve breakthroughs in three aspects of green supply chain innovation. Firstly, buyers gain access to more information about raw materials, enabling maximum reduction in the use of non-renewable resources. Moreover, buyers enhance their capabilities in material reuse, ensuring the recyclability, remanufacturing, and circular use of products [51]. Secondly, firms can obtain critical information about material selection, energy efficiency, waste management, and potential issues related to the green attributes of new products from green suppliers [52]. Consequently, they can enhance the effectiveness of green product innovation by modifying their existing environmental management systems and products. Thirdly, learning from green suppliers facilitates the establishment of shared norms and goals between buying firms and suppliers, enabling buyers to innovate their manufacturing processes, technologies, or equipment to address the mismatch between manufacturing materials and processes [52].
Learning from green customers facilitates the exchange of environmental information between buying firms and customers, enhancing their understanding of customer expectations regarding the environment [53]. Customer demands for environmental protection and corporate social responsibility serve as significant drivers for eco-innovation in products [54]. In order to create a lasting impression on customers and maintain their loyalty, firms must showcase their commitment to the environment and proactively embrace green technologies and production equipment during the manufacturing process [54,55]. Based on these insights, we propose the following hypothesis:
Green supplier learning has a positive effect on green supply chain innovation.
Green customer learning has a positive effect on green supply chain innovation.
3.4. The Mediating Roles of Green Supply Chain Learning
According to organizational learning theory, interacting with diverse entities in a transparent and open environment enhances the effectiveness of knowledge acquisition [56]. Furthermore, the theory suggests that frequent and in-depth interactions enhance the absorptive capacity of knowledge exchange partners, enabling them to systematically identify and transfer valuable knowledge across organizational boundaries [35]. Consequently, firms can generate new knowledge by integrating it with their existing knowledge resources. In the context of the green supply chain, firms can utilize digital capabilities to cultivate an open and collaborative learning environment while enhancing their absorptive capacity for green knowledge. This will enhance firms’ acquisition of environmental knowledge from supply chain members and ultimately drive systematic innovation in environmental aspects within the supply chain. Organizations with strong digital capabilities are better equipped to identify valuable sources of green knowledge (customers or supplier firms) within the supply chain network. By leveraging the insights and predictive advantages of digital technologies, buying firms can recognize and understand opportunities and challenges in green supply chain innovation, enabling them to pursue win-win scenarios for environmental and economic benefits. This involves formulating appropriate innovation strategies and action plans [23,57]. Additionally, digital technologies create a platform for strengthening interorganizational learning within buying firms [8]. By utilizing digital technologies, enterprises can share supply chain data and best practices, facilitating information exchange and collaboration with suppliers, partners, and stakeholders. Cross-organizational learning and knowledge sharing contribute to continuously enhancing their absorptive capacity for green knowledge, thereby fostering the collective development of green supply chain innovation. In summary, the advantage of digital capabilities enables buying firms to acquire the necessary knowledge, technologies, and information for green supply chain innovation from upstream supplier firms and downstream customer firms in a timely manner, in response to external environmental changes. This, in turn, promotes improvements and breakthroughs in green products, processes, marketing, and logistics throughout the supply chain. Therefore, we propose the following hypothesis:
Green supply chain learning mediates the impacts of digital capability advantage on green supply chain innovation.
Green supplier learning mediates the impacts of digital capability advantage on green supply chain innovation.
Green customer learning mediates the impacts of digital capability advantage on green supply chain innovation.
4. Research Design
4.1. Sampling and Data Collection
Our sample firms are primarily selected from the list of green manufacturing firms published by the Ministry of Industry and Information Technology of China between 2019 and 2022. This list is a compilation of benchmark green firms established by China to implement the “14th Five-Year Plan for Green Industrial Development” and the “Implementation Plan for Carbon Peaking in the Industrial Sector”. It aims to lead the green transformation of the manufacturing industry and includes multiple directories of firms, including green factories and companies implementing green supply chain management. The list covers various manufacturing industries, such as electronics, food, biotechnology, and metal processing, providing comprehensive industry coverage.
To ensure the representativeness of the sample, we sent inquiry letters to each firm prior to the survey, inviting their participation and confirming their implementation of digital technology and green supply chain innovation. The survey included a total of 267 firms that met the criteria and expressed willingness to participate. We placed specific restrictions on the respondents to ensure their expertise and knowledge in corporate strategy, environmental protection, and digitalization. Specifically, we restricted our respondents to senior or mid-level managers in the areas of digitalization, sustainability, environmental issues, operations, or strategic management. Additionally, the questionnaire included questions about the respondents’ years of experience in their respective firms and industries to ensure that they possessed substantial industry-specific expertise. By applying these selection criteria and gathering information about the qualifications of the respondents, our aim was to enhance the validity and reliability of our survey data.
To mitigate potential common method bias resulting from same-source respondents and its interference with the study, the questionnaire data were collected in two phases. The first data collection occurred from August to October 2022, while the second data collection took place from January to March 2023. Ultimately, 236 questionnaires were returned with complete responses. By filtering based on work experience (excluding those with less than 3 years of industry or company experience), detecting outliers, and analyzing missing data, 15 invalid questionnaires were eliminated. This resulted in 221 valid questionnaires, yielding a response rate of 93.64%. The main characteristics of the sample firms are presented in Table 2. Among the sample firms, 28.5% had fewer than 100 employees, 33.9% had 100–1000 employees, and over 37.6% had more than 1000 employees. Regarding the respondents, 34.4% had been working in the industry for 5–10 years, while 56.1% had over 10 years of industry experience. Only 9.5% of the respondents had less than 5 years of industry experience. Regarding their tenure in the current organization, 15.4% of the respondents had worked for less than 5 years, 31.2% had 5–10 years of tenure, and 53.4% had over 10 years of tenure. Table 2 presents some basic characteristics of the sample firms and respondents.
4.2. Measures
The measurement items used in this study were selected from established scales utilized in previous research. Certain scales were adjusted to more accurately measure corporate digital capabilities, green supply chain learning, and green supply chain innovation. Since the research focused on Chinese manufacturing firms, we invited three experts from different fields to translate the English questionnaire into Chinese to avoid potential interpretation biases caused by translation issues [58]. A professional translator unfamiliar with the research context and objectives then translated the Chinese questionnaire back into English. The translated version was compared with the original scale, and any inconsistencies were addressed by revising the Chinese translation. Multiple iterations of revisions were conducted to eliminate measurement errors resulting from cultural differences and interpretation biases [59]. Furthermore, we discussed the questionnaire items with several firm managers and sought their input on the respondents’ understanding of each item after completing the questionnaire. Based on these interactions and feedback, a formal questionnaire comprising 24 items was finalized (See Appendix A). All measurement items were assessed using a Likert five-point scale (1—Strongly Disagree, 5—Strongly Agree). The specific measurement approaches for each variable are outlined below:
Green supply chain innovation. Green supply chain innovation refers to the integration of environmentally friendly innovation practices by firms in their supply chain management. For this study, we utilized the green supply chain innovation scale developed by AL-Khatib [18] due to its strong alignment with our research context. Nevertheless, certain items were revised to ensure accurate understanding in relation to the practical context of businesses and the objectives of the study. The final scale consisted of 10 items designed to gather respondents’ evaluations of firms’ green innovation performance within their respective supply chains. The adoption of this scale ensures a robust and customized measurement of green supply chain innovation for our research objectives.
Digital capability advantage. This concept aims to evaluate a firm’s capacity to acquire and leverage digitalization. We utilized a four-item scale, as employed by Zhang et al. [17], to assess this construct. Respondents were requested to evaluate the degree to which a firm is capable of engaging in real-time monitoring of business operations and resources, utilizing artificial intelligence to enhance processes and explore new business opportunities, exchanging digital data with supply chain partners, and consistently following up on digital technologies and innovation cases. This scale enables us to assess the level of digital capability advantage within the context of our study.
Green supply chain learning. Agyabeng-Mensah et al. [11] employed a scale that assesses green supply chain learning as a unidimensional construct and investigates its holistic impact on green innovation. However, based on the literature and the specific context of firms, we identified distinctions between green supplier learning and green customer learning. To align with the purpose and context of our study, we contend that differentiating supply chain learning into two dimensions—green supplier learning and green customer learning—would yield a more comprehensive and nuanced comprehension of firms’ performance in green supply chain learning. To accomplish this, we employed the scale developed by Agyabeng-Mensah et al. [11] and modified it to encompass the unique facets of green supplier learning and green customer learning. Consequently, the revised scale consists of five items for green supplier learning and an additional five items for green customer learning.
Control variables. To ensure the robustness of the study findings, we controlled for other factors that influence the innovation performance of green supply chains. In this study, we selected firm size and relationship length as control variables to minimize interference with the research results. Larger firms possess unique characteristics that influence their approach to environmental practices. They demonstrate greater market stability, prioritize long-term development goals, and are more vulnerable to public attention, thus emphasizing their environmental reputation [10,60]. Additionally, larger firms tend to have a stronger environmental reputation compared to small and medium-sized enterprises (SMEs) due to their greater resources for environmental protection activities. Consequently, they are more inclined to actively collaborate with buying firms in implementing green supply management practices and participating in environmental initiatives. Therefore, in this study, we selected firm size as a control variable and operationalized it based on the number of employees, categorized into four groups: 1 = less than 100 employees, 2 = 100–1000 employees, 3 = 1000–10,000 employees, and 4 = greater than 10,000. Additionally, long-term buyer–supplier relationships promote improved working relationships and foster interorganizational trust, facilitating effective collaboration, the generation of new ideas, and value creation. Consequently, relationship length was controlled for in this study. Respondents were asked to indicate the number of years they have been working with their key customers to measure relationship length [61].
4.3. Non-Response and Common Method Bias
This study utilized cross-sectional survey data collected from multiple distinct buyer firms, which introduces potential sources of systematic errors, including non-response bias and common method bias. To examine the potential impact of non-response bias on the study, a t-test was conducted to compare data collected from two different data collection rounds [62]. The results indicated no significant difference between early and late respondents in terms of the variance of the demographic variables: Firm size (p = 0.284), Tenure in the industry (p = 0.188), and Tenure in the firm (p = 0.336). The chi-squared t-test for the means of each variable revealed p > 0.05, suggesting that early and post-period respondents did not significantly differ in terms of firm size, industry tenure, and firm tenure. Therefore, the study sample is considered representative.
To address the issue of common method bias, we implemented two types of initiatives: procedural control and statistical tests. Firstly, we ensured that each independent and dependent variable was presented on a separate page of the electronic questionnaire with distinct instructions. Secondly, to mitigate order effects, we performed two rounds of data collection and randomized the order of the question items in the questionnaire administered to the respondents [63]. Additionally, we employed two methods, Harman’s one-factor test and potential error variable control, to identify and address potential common method bias issues. We conducted the Harman one-factor test using exploratory factor analysis (EFA). The results indicated that the largest single factor accounted for 24.368% of the total variance across the seven factors, which was not sufficient to explain the majority of the variance. Subsequently, we incorporated a common method bias factor into the validated factor analysis model (CFA) and compared the loadings of the method factor with the original indicator structure. If the model fit indicators improved after the inclusion of the common method factor (comparative fit index and Tacker–Lewis index increased by more than 0.1), it would suggest a significant common method bias issue [64]. The two models, one including the method factors and the other excluding them, show no significant differences in terms of χ2/df, comparative fit index (CFI), Tacker–Lewis index (TLI), incremental fit index (IFI), and root mean square error of approximation (RMSEA) as reported in Table 3. The study does not show significant evidence of common method bias based on the comprehensive assessment of the two methods.
4.4. Reliability and Validity
Reliability was assessed using Cronbach’s alpha (CA) and validity was evaluated through convergent validity and discriminant validity. Table 4 shows that the Cronbach’s alpha (CA) and composite reliability (CR) values for the four variables (digital capability advantage, green supplier learning, green customer learning, and green supply chain innovation) all exceeded the threshold of 0.70, indicating high internal consistency within the scales. Additionally, convergent validity and discriminant validity were examined. Table 4 shows that the average variance extracted (AVE) values for all four variables exceeded the recommended threshold of 0.5, indicating good convergent validity for each scale.
Furthermore, discriminant validity was assessed by comparing the square root of the AVE for each variable with the inter-variable correlation coefficients. Furthermore, discriminant validity of the scales was assessed by comparing the square root of the average variance extracted (AVE) for each variable with the inter-variable correlation coefficients. The results (see Table 5) indicate that the square root of the AVE exceeds 0.8 for all variables. The correlation coefficients between pairs of variables range from 0.4 to 0.6, and none of them exceed the square root of the AVE. Therefore, the variables of digital capability advantage, green supplier learning, green customer learning, and green supply chain innovation demonstrate good discriminant validity [65]. Table 5 summarizes the means and standard deviations of the variables: digital capability advantage, green supplier learning, green customer learning, and green supply chain innovation.
5. Data Analysis and Results
5.1. Hypothesis Testing
Given its suitability for structural equation modeling (SEM), Mplus was selected as the software for testing both direct and mediated relationships in this study. The Bootstrap analysis command in Mplus 8.8 was employed to test the proposed hypothetical model (see Figure 2 and Table 6). The analysis revealed significant positive effects of digital capability advantage (DCA) on green supply chain innovation (GSCI), green supplier learning (GSL), and green customer learning (GCL) (β = 0.465, p < 0.001; β = 0.378, p < 0.001; β = 0.503, p < 0.001), providing support for hypotheses H1, H2a, and H2b. Moreover, GSL and GCL demonstrated significant positive impacts on GSCI, thus confirming hypotheses H3a and H3b. The paths of DCA → GSL → GSCI and DCA → GCL → GSCI exhibited non-zero values within the 95% confidence interval, indicating the presence of a significant mediating effect. Therefore, DCA indirectly impacts GSCL through both dimensions of GSL and GCL. These results provide support for hypotheses H4a and H4b (β = 0.135, p < 0.01; β = 0.187, p < 0.001). Furthermore, the control variables, namely firm size and relationship length, did not show a significant effect on GSCI (β = 0.095, p > 0.05; β = 0.103, p > 0.05). Furthermore, this study also examined the mediating role of green supply chain learning (GSCL) in the relationship between DCA and GSCI. The results, as shown in Table 6, indicate that the path from DCA → GSCL → GSCI does not include zero within the 95% confidence interval, indicating a significant mediating effect. This finding provides support for hypothesis H4.
5.2. Robustness Tests
Due to the cross-sectional nature of the data in this paper, the ability to infer causality is somewhat limited. To ensure the reliability of the study findings, we conducted robustness tests by replacing the dependent variable with a measure of green innovation (GI), which assesses the extent of innovation in terms of products and processes within firms. Green innovation, which shares similarities with the concept of green supply chain innovation investigated in this paper, was measured using the methodology proposed by Chiou et al. [1]. Substituting the dependent variable GI, our analysis confirms the positive and significant influence of DCA on GI, GSL, and GCL (β = 0.368, p < 0.001; β = 0.411, p < 0.001; β = 0.455, p < 0.001). Additionally, the significant positive effects of GSL and GCL on GI persist (β = 0.285, p < 0.001; β = 0.307, p < 0.001). The three mediated pathways of DCA on GI also remain valid, as none of the confidence intervals within the deviation-corrected 95% range include zero, and the pathway coefficients demonstrate significance (β = 0.173, p < 0.01; β = 0.189, p < 0.001; β = 0.186, p < 0.001).
5.3. Discussion
This paper explores the relationship between digital capability advantage, green supply chain learning, and green supply chain innovation. In the following sections, we will provide a detailed discussion of these findings.
Firstly, we identified a positive impact of digital capability advantage on green supply chain innovation, providing support for hypothesis H1. Firms’ digital capability advantage empowers them with strong insights, prediction, and analytical capabilities, contributing to the systematic implementation of green transformation in the supply chain. Our findings are consistent with Liu et al. [13] and Guo et al. [2], confirming the significant role of digital capability advantage in facilitating green management practices. In contrast to Son et al. [31], we did not find any negative effects of digital capability advantage on green supply chain management. Our findings provide positive evidence for the existing paradox regarding the effects of digital capability advantage in organizations.
Secondly, our findings support hypotheses H2a and H2b by demonstrating the positive and significant impact of digital capability advantage on both green supplier learning and green customer learning. Furthermore, the presence of digital capability advantage fosters favorable conditions for acquiring environmental knowledge and information from suppliers and customers, making a significant contribution to the current body of research on the factors that influence green supply chain learning. Previous studies have investigated the drivers of (green) supply chain learning from organizational and interorganizational perspectives. Organizational factors examined include intellectual capital [34], leadership styles [35,56], and strategic orientation [2], while inter-organizational factors encompass buyer—supplier relationships, knowledge sharing, and supply chain governance [34]. In contrast to existing research, our study expands the scope of existing studies on the antecedents of green supply chain learning (green supplier learning and green customer learning) by examining how firms enhance their supply chain learning activities through their own capabilities, with a particular emphasis on incorporating digital capability. Digital capabilities not only enhance firms’ ability to acquire green information but also improve their ability to integrate and utilize environmental knowledge within the supply chain. Strengthening these two capabilities contributes to the smooth implementation of green supply chain learning [56].
Thirdly, we confirmed that both green supplier learning and green customer learning contribute to the successful implementation of green supply chain innovation, supporting hypotheses H3a and H3b. Existing research emphasizes the significant role of supply chain learning in improving performance, such as operational and financial performance [66], innovation performance [39], and service performance [67]. However, previous studies have somewhat overlooked the impact of green supply chain learning on environmental performance [11]. In contrast, we found that green supply chain learning facilitates companies acquiring environmental knowledge from suppliers and customers, promoting the development of cross-organizational green innovation activities within the supply chain, thereby enhancing the performance of a circular supply chain. This finding partially addresses the proposition put forth by Agyabeng-Mensah, Baah, et al. [11] to explore the relationship between green supply chain learning and sustainable performance, thereby enhancing our understanding of the effects of green supply chain learning. This finding responds to the proposition put forth by Agyabeng-Mensah, Baah et al. [11] to explore the relationship between green supply chain learning and sustainable performance, thereby enhancing our understanding of the effects of green supply chain learning.
Fourthly, this study demonstrates the mediating role of green supply chain learning, including its dimensions of green supplier learning and green customer learning, in the relationship between digital capability advantage and green supply chain innovation. It provides support for hypotheses H4, H4a, and H4b. Digital capability advantage empowers firms to capture environmental information, technology, and raw material knowledge from suppliers, while also improving their understanding of customer-driven environmental demands. Consequently, it enables the effective integration and utilization of environmental knowledge within the supply chain, thereby facilitating innovation breakthroughs in product, process, and transportation. These findings corroborate Guo et al.’s [2] perspective on the positive reinforcing impact of digitalization on green innovation and provide additional insights into the influence of digital capability, specifically on supply chain green innovation activities.
6. Conclusions
This study establishes a research framework to explore the relationship between digital capability advantage and green supply chain innovation, drawing upon inter-organizational learning theory. The hypothesis model is validated with data collected from 221 manufacturing firms in China. The main findings are as follows: (1) Digital capability advantage positively and significantly influences green supply chain innovation, as well as green supplier and customer learning. (2) Green supplier learning and green customer learning exert a positive and significant influence on green supply chain innovation. (3) Green supplier learning and green customer learning serve as mediators in the relationship between digital capability advantage and green supply chain innovation. These findings enhance our comprehension of the role of digital capability advantage in fostering green supply chain innovation and offer theoretical guidance for the implementation of such innovation in manufacturing firms.
6.1. Theoretical Contributions
We provide new empirical evidence on the role of digital capability advantage in green supply chain innovation from a theoretical perspective. Previous studies have suggested a double-edged sword effect of digital technology. On one hand, the application of digital technology benefits companies in green innovation and supply chain management [2,68,69]. The results of our research clearly demonstrate the positive impact of digital capability advantage on green supply chain innovation. This not only addresses the paradox of digital capability advantage but also provides valuable insights into the role of digital capability in environmental management. Additionally, previous studies have given limited attention to the influence of digital capability on green innovation [46]. Guo et al. [2] are the only researchers who have explored the boundary conditions of the impact of digitalization on environmental innovation based on institutional theory. Adopting an interorganizational learning perspective, we further reveal the mediating role of green supply chain learning (specifically, green supplier learning and green customer learning) in the relationship between digital capability advantage and green supply chain innovation. Furthermore, this study contributes to the theoretical understanding of inter-organizational learning within the context of green supply chain management. We recognize the digital capability advantage as a critical antecedent variable for green supply chain learning. The learning atmosphere within the supply chain and the learning capacity of its members are essential factors influencing green supply chain learning. Strong digital capabilities not only foster a shared and transparent supply chain environment but also contribute to developing the green absorptive capacity of buyers.
6.2. Managerial Implications
Green supply chain innovation is an activity that involves collaborative efforts between upstream and downstream stakeholders in the supply chain. It requires effective adjustments to existing supply chain products, services, technologies, and management practices in order to meet stakeholders’ environmental requirements and create sustainable competitive advantages [5,70]. Our research indicates that the effective implementation of green supply chain learning activities can facilitate green supply chain innovation. Therefore, manufacturing firms should prioritize green supply chain learning, which includes learning from both upstream suppliers and downstream customers. Specifically, firms can regularly participate in well-known supplier exhibitions within industry to acquire timely information on environmentally friendly materials and technologies offered by suppliers. Firms can also regularly conduct on-site investigations and discussions with suppliers to enhance their understanding of raw material information and gather more parameters for adopting new environmentally friendly processes. Additionally, firms should maintain communication and exchange with customers through online platforms, webinars, virtual conferences, and other channels to stay updated on the latest changes in customers’ environmental product requirements and promptly adjust their innovation direction.
Our findings indicate that the digital capability advantage not only positively and significantly impacts green supply chain innovation but also indirectly improves the quality of its implementation by promoting green supply chain learning. Therefore, another crucial managerial insight is for manufacturing firms to continuously strengthen their digital capabilities to enhance green supply chain learning and drive innovation in green supply chains. Firstly, firms can establish an internally integrated cross-functional management information system that ensures a standardized data storage framework and facilitates real-time information sharing within the organization. The internal system can be extended to enable seamless connectivity with supply chain members, ensuring the use of consistent data standards. Secondly, manufacturing firms should prioritize the development of big data analysis capabilities. Managers should foster a data-driven decision-making mindset among employees, emphasizing the importance of using data in strategic decision-making. The organizational structure should be adjusted to include a dedicated data analysis department that supports decision-making in the daily operations of various departments. Considering the adoption of advanced data analysis tools, such as robust analytical frameworks and visualized data management systems, can facilitate efficient and effective data analysis.
6.3. Limitations and Future Directions
While this paper contributes to the existing literature, it is important to acknowledge certain limitations and encourage further research to address these gaps in understanding. Firstly, the paper emphasizes the importance of acquiring, integrating, and utilizing external knowledge resources for green supply chain innovation as an inter-organizational activity. However, it is important to recognize that the quality of relationships among supply chain members also influences green supply chain innovation, in addition to the quantity of knowledge resources acquired. Therefore, future research could adopt a relationship view and incorporate principal–agent theory to further explore the mechanism of the impact of digital capability advantage on green supply chain innovation. Secondly, while we provide a reasonable theoretical argument for how digital capability advantage enhances the development of green supply chain innovation activities by facilitating firms implementing green supply chain learning, our use of cross-sectional data limits our ability to empirically test for reverse causality. In other words, we cannot determine whether there is a reciprocal effect between green supply chain learning, green supply chain innovation, and the accumulation of digital capability advantages. To address this limitation, we encourage future researchers to utilize longitudinal data and compare these two causal models. Thirdly, it is worth noting that our sample primarily consists of firms from China. Therefore, our knowledge and understanding of the issue are somewhat limited. To expand our insights, future researchers are encouraged to address this issue by conducting comparative studies in various cultural and institutional contexts.
J.Q. is responsible for the theoretical model construction and paper writing; S.L. is responsible for the theoretical model construction and paper review; S.X. was responsible for data collection; N.L. is responsible for the revision of the paper. All authors have read and agreed to the published version of the manuscript.
Informed consent was obtained from all subjects involved in the study.
Data sharing is not applicable to this article.
The authors declare no conflict of interest.
Footnotes
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.
Enlarge this image.Figure 1. Conceptual model. Note: The solid line represents the direct effect and the dotted line represents the intermediate effect.
Enlarge this image.Figure 2. Structural equation model. Note: Hypothesis paths are marked with standardized path coefficient. *** p < 0.001.
Research review.
| Author | Topic | Sample Data | Research |
Research Conclusions |
|---|---|---|---|---|
| Son et al. (2021) [ |
What is the impact of buyer’s digital capability advantage on buyer opportunistic behavior? | 125 small and medium-sized firms in Republic of Korea | Empirical research | Buyers with superior digital capabilities compared to suppliers from small and medium-sized firms, making them more vulnerable to buyer opportunism. |
| Liu et al. (2022) [ |
How does big data analytics capability drive green supply chain integration? | 317 Chinese manufacturing firms | Empirical research | Big data analytics capability positively contributes to green internal integration, green customer integration, and green supplier integration. Additionally, green internal integration serves as a mediator in the relationship between big data analytics capability and green supplier (customer) integration. |
| Guo et al. (2022) [ |
What is the impact of green digitization on environmental innovation? | Panel data collected from Chinese listed companies and provincial information (excluding Tibet) spanning the years 2012 to 2018, a total of 19,752 sample observations were included. | Empirical research | Green digitization significantly promotes environmental innovation, and this effect can be achieved through the reinforcement of formal and informal institutional forces. |
| Xu et al. (2023) [ |
What is the impact of digital strategy and capabilities on ecological innovation? | 10 Chinese manufacturing firms. | Empirical research | Digital strategy and capability play a significant role in promoting in ecological processes innovation, ecological products innovation, and ecological management innovation. |
| Zhang et al. (2022) [ |
How does the digital capability advantage of buying firms reduce supplier unethical behavior? | 223 Chinese manufacturing firms | Empirical research | Buyers’ digital capability advantage indirectly decreases supplier unethical behavior by enhancing relationship transparency. |
| Wu and Li (2019) [ |
Which factors influence green supply chain innovation? | 187 Chinese high-tech firms | Empirical research | Relationship-specific investment and knowledge transfer have a significant positive impact on green supply chain innovation. |
| Al-Khatib (2022) [ |
What is the impact of big data analytics capabilities on dual green supply chain innovation? | 303 Jordanian manufacturing firms | Empirical research | Big data analytics capability has a significant positive impact on both green radical supply chain innovation and green incremental supply chain innovation. |
Summary of sample firms and respondents’ characteristics.
| Characteristics | Frequency | % | |
|---|---|---|---|
| Firm size | Less than 100 | 63 | 28.5% |
| 100–1000 | 75 | 33.9% | |
| 1000–10,000 | 46 | 20.8% | |
| More than 10,000 | 37 | 16.8% | |
| Tenure in the industry | Less than 5 years | 21 | 9.5% |
| 5–10 years | 76 | 34.4% | |
| more than 10 years | 124 | 56.1% | |
| Tenure in the firm | Less than 5 years | 34 | 15.4% |
| 5–10 years | 69 | 31.2% | |
| More than 10 years | 118 | 53.4% | |
Result of the unmeasured latent method factor test.
| Model | χ2 | df | χ2/df | CFI | TLI | IFI | RMESA |
|---|---|---|---|---|---|---|---|
| Five-factor model | 748.073 | 532 | 1.406 | 0.946 | 0.939 | 0.952 | 0.056 |
| Model including the five factors and the method factor | 765.948 | 531 | 1.442 | 0.947 | 0.941 | 0.952 | 0.057 |
Reliability and convergent validity.
| Variables | Measurement Items | Factor Loadings | Cronbach’s Alpha | CR | AVE |
|---|---|---|---|---|---|
| Digital capability advantage (DCA) | DCA1 | 0.857 | 0.913 | 0.916 | 0.728 |
| DCA2 | 0.868 | ||||
| DCA3 | 0.836 | ||||
| DCA4 | 0.865 | ||||
| Green supplier learning |
GSL1 | 0.810 | 0.915 | 0.920 | 0.713 |
| GSL2 | 0.880 | ||||
| GSL3 | 0.842 | ||||
| GSL4 | 0.834 | ||||
| GSL5 | 0.808 | ||||
| Green customer learning |
GCL1 | 0.800 | 0.911 | 0.917 | 0.668 |
| GCL2 | 0.835 | ||||
| GCL3 | 0.818 | ||||
| GCL4 | 0.826 | ||||
| GCL5 | 0.870 | ||||
| Green supply chain innovation (GSCI) | GSCI1 | 0.809 | 0.948 | 0.956 | 0.706 |
| GSCI2 | 0.834 | ||||
| GSCI3 | 0.876 | ||||
| GSCI4 | 0.863 | ||||
| GSCI5 | 0.794 | ||||
| GSCI6 | 0.785 | ||||
| GSCI7 | 0.811 | ||||
| GSCI8 | 0.827 | ||||
| GSCI9 | 0.834 | ||||
| GSCI10 | 0.848 |
Descriptive statistics and discriminant validity.
| Mean | S.D. | 1 | 2 | 3 | 4 | |
|---|---|---|---|---|---|---|
| 1. DCA | 3.887 | 0.795 | 0.853 | |||
| 2. GSL | 3.504 | 0.943 | 0.404 ** | 0.844 | ||
| 3. GCL | 3.532 | 0.896 | 0.501 ** | 0.631 ** | 0.817 | |
| 4. GSCI | 4.437 | 0.488 | 0.455 ** | 0.481 ** | 0.586 ** | 0.840 |
Notes: Diagonal entries (in bold) are the square root of the AVE (average variances extracted). Entries below the diagonal are correlations. ** p < 0.01.
Hypothesis test results.
| Path Relationships | Standardized Coefficient | Boot SE | Bias-Corrected 95% CI | ||
|---|---|---|---|---|---|
| Lower | Upper | p | |||
| DCA→GSCI | 0.465 | 0.022 | 0.011 | 0.048 | 0.000 |
| DCA→GSL | 0.378 | 0.018 | 0.009 | 0.034 | 0.000 |
| DCA→GCL | 0.503 | 0.043 | 0.041 | 0.153 | 0.000 |
| GSL→GSCI | 0.392 | 0.035 | 0.027 | 0.103 | 0.000 |
| GCL→GSCI | 0.416 | 0.029 | 0.112 | 0.227 | 0.000 |
| DCA→GSL→GSCI | 0.235 | 0.018 | 0.253 | 0.296 | 0.007 |
| DCA→GCL→GSCI | 0.277 | 0.027 | 0.035 | 0.118 | 0.000 |
| DCA→GSCL→GSCI | 0.242 | 0.035 | 0.107 | 0.366 | 0.004 |
Appendix A. Measurement Scales
| Digital capability advantage | |
| DCA1 | We have the capability to monitor business operations and resources in real time. |
| DCA2 | We have the capability to analyze big data with AI for process improvement and new business generation (e.g., intelligent defect detection, preventive machine maintenance, machine failure prevention). |
| DCA3 | We have the capability to exchange digitalized data with our supply chain partners in real time for effective sales and operations and inventory planning. |
| DCA4 | We constantly keep current with new digitalization technologies and innovative use cases. |
| Green supply chain learning | |
| Green supplier learning | |
| GSL1 | We have acquired important environmental protection information from our major supplier. |
| GSL2 | We have learnt new environmental management abilities from our major supplier. |
| GSL3 | The relationship with major suppliers enhances our capacities to maintain sustainable development. |
| GSL4 | We constantly learn better ways to work with our major suppliers jointly in dealing with environmental issues. |
| GSL5 | We have established a strong capability in understanding green knowledge and skills of our major suppliers. |
| Green customer learning | |
| GCL1 | We have acquired important environmental protection information from our major customer. |
| GCL2 | We have learnt new environmental management abilities from our major customer. |
| GCL3 | The relationship with major customers enhances our capacities to maintain sustainable development. |
| GCL4 | We constantly learn better ways to work with our major customers jointly in dealing with environmental issues. |
| GCL5 | We have established a strong capability in understanding green knowledge and skills of our major customer. |
| Green supply chain innovation | |
| GSCI1 | We adopt and encourage new green products in the supply chain. |
| GSCI2 | We exploit new green products and processes in the supply chain. |
| GSCI3 | We invest heavily in new technology and uses it to innovate green products in the supply chain. |
| GSCI4 | We encourage new employee ideas in the supply chain. |
| GSCI5 | We radically adjust its strategy to adopt green innovation in its activities in the supply chain. |
| GSCI6 | We improve the current green technology in the supply chain. |
| GSCI7 | We improve its existing green products and operations in the supply chain. |
| GSCI8 | We encourage employees to incorporate their environmental suggestions into its existing products and operations in the supply chain. |
| GSCI9 | We continuously maintain production lines to reduce pollution in the supply chain. |
| GSCI10 | We provide waste recycling systems in the supply chain. |
References
1. Chiou, T.-Y.; Chan, H.K.; Lettice, F.; Chung, S.H. The influence of greening the suppliers and green innovation on environmental performance and competitive advantage in Taiwan. Transp. Res. Part E Logist. Transp. Rev.; 2011; 47, pp. 822-836. [DOI: https://dx.doi.org/10.1016/j.tre.2011.05.016]
2. Guo, Q.; Geng, C.; Yao, N. How does green digitalization affect environmental innovation? The moderating role of institutional forces. Bus. Strategy Environ.; 2022; in print [DOI: https://dx.doi.org/10.1002/bse.3288]
3. Feng, Y.; Lai, K.-h.; Zhu, Q. Green supply chain innovation: Emergence, adoption, and challenges. Int. J. Prod. Econ.; 2022; 248, 108497. [DOI: https://dx.doi.org/10.1016/j.ijpe.2022.108497]
4. Melander, L.; Pazirandeh, A. Collaboration beyond the supply network for green innovation: Insight from 11 cases. Supply Chain Manag. Int. J.; 2019; 24, pp. 509-523. [DOI: https://dx.doi.org/10.1108/SCM-08-2018-0285]
5. Gao, D.; Xu, Z.; Ruan, Y.Z.; Lu, H. From a systematic literature review to integrated definition for sustainable supply chain innovation (SSCI). J. Clean. Prod.; 2017; 142, pp. 1518-1538. [DOI: https://dx.doi.org/10.1016/j.jclepro.2016.11.153]
6. Dai, J.; Cantor, D.E.; Montabon, F.L. How Environmental Management Competitive Pressure Affects a Focal Firm’s Environmental Innovation Activities: A Green Supply Chain Perspective. J. Bus. Logist.; 2015; 36, pp. 242-259. [DOI: https://dx.doi.org/10.1111/jbl.12094]
7. Li, S.; Qiao, J.; Cui, H.; Wang, S. Realizing the Environmental Benefits of Proactive Environmental Strategy: The Roles of Green Supply Chain Integration and Relational Capability. Sustainability; 2020; 12, 2907. [DOI: https://dx.doi.org/10.3390/su12072907]
8. Ojha, D.; Shockley, J.; Acharya, C. Supply chain organizational infrastructure for promoting entrepreneurial emphasis and innovativeness: The role of trust and learning. Int. J. Prod. Econ.; 2016; 179, pp. 212-227. [DOI: https://dx.doi.org/10.1016/j.ijpe.2016.06.011]
9. Solaimani, S.; van der Veen, J. Open supply chain innovation: An extended view on supply chain collaboration. Supply Chain Manag. Int. J.; 2022; 27, pp. 597-610. [DOI: https://dx.doi.org/10.1108/SCM-09-2020-0433]
10. Wu, A.; Li, T. Gaining sustainable development by green supply chain innovation: Perspectives of specific investments and stakeholder engagement. Bus. Strategy Environ.; 2019; 29, pp. 962-975. [DOI: https://dx.doi.org/10.1002/bse.2410]
11. Agyabeng-Mensah, Y.; Baah, C.; Afum, E. Do the roles of green supply chain learning, green employee creativity, and green organizational citizenship behavior really matter in circular supply chain performance?. J. Environ. Plan. Manag.; 2022; in print [DOI: https://dx.doi.org/10.1080/09640568.2022.2130036]
12. Liu, J.; Hu, H.; Tong, X.; Zhu, Q. Behavioral and technical perspectives of green supply chain management practices: Empirical evidence from an emerging market. Transp. Res. Part E Logist. Transp. Rev.; 2020; 140, 102013. [DOI: https://dx.doi.org/10.1016/j.tre.2020.102013]
13. Liu, Y.; Fang, W.; Feng, T.; Gao, N. Bolstering green supply chain integration via big data analytics capability: The moderating role of data-driven decision culture. Ind. Manag. Data Syst.; 2022; 122, pp. 2558-2582. [DOI: https://dx.doi.org/10.1108/IMDS-11-2021-0696]
14. Xu, J.; Yu, Y.; Zhang, M.; Zhang, J.Z. Impacts of digital transformation on eco-innovation and sustainable performance: Evidence from Chinese manufacturing companies. J. Clean. Prod.; 2023; 393, 136278. [DOI: https://dx.doi.org/10.1016/j.jclepro.2023.136278]
15. Yuan, S.; Pan, X. The effects of digital technology application and supply chain management on corporate circular economy: A dynamic capability view. J. Environ. Manag.; 2023; 341, 118082. [DOI: https://dx.doi.org/10.1016/j.jenvman.2023.118082]
16. Allenbacher, J.; Berg, N. How assessment and cooperation practices influence suppliers’ adoption of sustainable supply chain practices: An inter-organizational learning perspective. J. Clean. Prod.; 2023; 403, 136852. [DOI: https://dx.doi.org/10.1016/j.jclepro.2023.136852]
17. Zhang, Y.; Huo, B.; Haney, M.H.; Kang, M. The effect of buyer digital capability advantage on supplier unethical behavior: A moderated mediation model of relationship transparency and relational capital. Int. J. Prod. Econ.; 2022; 253, 108603. [DOI: https://dx.doi.org/10.1016/j.ijpe.2022.108603]
18. Al-Khatib, A.W. The impact of big data analytics capabilities on green supply chain performance: Is green supply chain innovation the missing link?. Bus. Process Manag. J.; 2022; 29, pp. 22-42. [DOI: https://dx.doi.org/10.1108/BPMJ-08-2022-0416]
19. Roh, T.; Noh, J.; Oh, Y.; Park, K.-S. Structural relationships of a firm’s green strategies for environmental performance: The roles of green supply chain management and green marketing innovation. J. Clean. Prod.; 2022; 356, 131877. [DOI: https://dx.doi.org/10.1016/j.jclepro.2022.131877]
20. Song, W.; Yu, H. Green Innovation Strategy and Green Innovation: The Roles of Green Creativity and Green Organizational Identity. Corp. Soc. Responsib. Environ. Manag.; 2018; 25, pp. 135-150. [DOI: https://dx.doi.org/10.1002/csr.1445]
21. Asiaei, K.; O’Connor, N.G.; Barani, O.; Joshi, M. Green intellectual capital and ambidextrous green innovation: The impact on environmental performance. Bus. Strategy Environ.; 2022; 32, pp. 369-386. [DOI: https://dx.doi.org/10.1002/bse.3136]
22. Rajesh, R. Predicting environmental sustainability performances of firms using trigonometric grey prediction model. Environ. Dev.; 2023; 45, 100830. [DOI: https://dx.doi.org/10.1016/j.envdev.2023.100830]
23. Saberi, S.; Kouhizadeh, M.; Sarkis, J.; Shen, L. Blockchain technology and its relationships to sustainable supply chain management. Int. J. Prod. Res.; 2018; 57, pp. 2117-2135. [DOI: https://dx.doi.org/10.1080/00207543.2018.1533261]
24. Chiappetta Jabbour, C.J.; Fiorini, P.D.C.; Ndubisi, N.O.; Queiroz, M.M.; Piato, É.L. Digitally-enabled sustainable supply chains in the 21st century: A review and a research agenda. Sci. Total Environ.; 2020; 725, 138177. [DOI: https://dx.doi.org/10.1016/j.scitotenv.2020.138177] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/32302825]
25. Mak, H.-Y.; Max Shen, Z.-J. When Triple-A Supply Chains Meet Digitalization: The Case of JD.com’s C2M Model. Prod. Oper. Manag.; 2021; 30, pp. 656-665. [DOI: https://dx.doi.org/10.1111/poms.13307]
26. Singh, G.; Rajesh, R.; Daultani, Y.; Misra, S.C. Resilience and sustainability enhancements in food supply chains using Digital Twin technology: A grey causal modelling (GCM) approach. Comput. Ind. Eng.; 2023; 179, 109172. [DOI: https://dx.doi.org/10.1016/j.cie.2023.109172]
27. Schmidt, C.G.; Wagner, S.M. Blockchain and supply chain relations: A transaction cost theory perspective. J. Purch. Supply Manag.; 2019; 25, 100552. [DOI: https://dx.doi.org/10.1016/j.pursup.2019.100552]
28. Yu, W.; Wong, C.Y.; Chavez, R.; Jacobs, M.A. Integrating big data analytics into supply chain finance: The roles of information processing and data-driven culture. Int. J. Prod. Econ.; 2021; 236, 108135. [DOI: https://dx.doi.org/10.1016/j.ijpe.2021.108135]
29. Rajak, S.; Parthiban, P.; Dhanalakshmi, R. Analysing barriers of sustainable transportation systems in India using Grey-DEMATEL approach: A supply chain perspective. Int. J. Sustain. Eng.; 2021; 14, pp. 419-432. [DOI: https://dx.doi.org/10.1080/19397038.2021.1929553]
30. Yadav, S.; Singh, S.P. Blockchain critical success factors for sustainable supply chain. Resour. Conserv. Recycl.; 2020; 152, 104505. [DOI: https://dx.doi.org/10.1016/j.resconrec.2019.104505]
31. Son, B.-G.; Kim, H.; Hur, D.; Subramanian, N. The dark side of supply chain digitalisation: Supplier-perceived digital capability asymmetry, buyer opportunism and governance. Int. J. Oper. Prod. Manag.; 2021; 41, pp. 1220-1247. [DOI: https://dx.doi.org/10.1108/IJOPM-10-2020-0711]
32. Gavronski, I.; Klassen, R.D.; Vachon, S.; Nascimento, L.F.M.d. A learning and knowledge approach to sustainable operations. Int. J. Prod. Econ.; 2012; 140, pp. 183-192. [DOI: https://dx.doi.org/10.1016/j.ijpe.2012.01.037]
33. Li, L.; Shan, S.; Dai, J.; Che, W.; Shou, Y. The impact of green supply chain management on green innovation: A meta-analysis from the inter-organizational learning perspective. Int. J. Prod. Econ.; 2022; 250, 108622. [DOI: https://dx.doi.org/10.1016/j.ijpe.2022.108622]
34. Yang, Y.; Jia, F.; Xu, Z. Towards an integrated conceptual model of supply chain learning: An extended resource-based view. Supply Chain Manag. Int. J.; 2019; 24, pp. 189-214. [DOI: https://dx.doi.org/10.1108/SCM-11-2017-0359]
35. Pham, H.T.; Pham, T.; Truong Quang, H.; Dang, C.N. Impact of transformational leadership on green learning and green innovation in construction supply chains. Eng. Constr. Archit. Manag.; 2022; 30, pp. 1883-1901. [DOI: https://dx.doi.org/10.1108/ECAM-05-2021-0379]
36. Lisi, W.; Zhu, R.; Yuan, C. Embracing green innovation via green supply chain learning: The moderating role of green technology turbulence. Sustain. Dev.; 2019; 28, pp. 155-168. [DOI: https://dx.doi.org/10.1002/sd.1979]
37. Agyabeng-Mensah, Y.; Afum, E.; Baah, C. Green corporate reputation and innovation: The role of non-supply chain learning and green supply chain knowledge. Int. J. Emerg. Mark.; 2022; in print [DOI: https://dx.doi.org/10.1108/IJOEM-08-2021-1277]
38. Santos, F.M.; Eisenhardt, K.M. Organizational Boundaries and Theories of Organization. Organ. Sci.; 2005; 16, pp. 491-508. [DOI: https://dx.doi.org/10.1287/orsc.1050.0152]
39. Haq, M.Z.U.; Gu, M.; Huo, B. Enhancing supply chain learning and innovation performance through human resource management. J. Bus. Ind. Mark.; 2020; 36, pp. 552-568. [DOI: https://dx.doi.org/10.1108/JBIM-12-2019-0534]
40. Santos, K.A.; Silva, M.E.; Pereira, S.C.F. (Un)Learning sustainability practices in a multi-tiered supply chain: An interpretive study. Int. J. Oper. Prod. Manag.; 2023; ahead-of-print [DOI: https://dx.doi.org/10.1108/IJOPM-08-2022-0504]
41. Hussinger, K.; Wastyn, A. In search for the not-invented-here syndrome: The role of knowledge sources and firm success. RD Manag.; 2016; 46, pp. 945-957. [DOI: https://dx.doi.org/10.1111/radm.12136]
42. Chen, L.; Jiang, M.; Li, T.; Jia, F.; Lim, M.K. Supply chain learning and performance: A meta-analysis. Int. J. Oper. Prod. Manag.; 2023; in print [DOI: https://dx.doi.org/10.1108/IJOPM-05-2022-0289]
43. Benzidia, S.; Makaoui, N.; Bentahar, O. The impact of big data analytics and artificial intelligence on green supply chain process integration and hospital environmental performance. Technol. Forecast. Soc. Change; 2021; 165, 120557. [DOI: https://dx.doi.org/10.1016/j.techfore.2020.120557]
44. Tseng, M.-L.; Wu, K.-J.; Lim, M.K.; Wong, W.-P. Data-driven sustainable supply chain management performance: A hierarchical structure assessment under uncertainties. J. Clean. Prod.; 2019; 227, pp. 760-771. [DOI: https://dx.doi.org/10.1016/j.jclepro.2019.04.201]
45. Hamel, G. Competition for competence and interpartner learning within international strategic alliances. Strateg. Manag. J.; 1991; 12, pp. 83-103. [DOI: https://dx.doi.org/10.1002/smj.4250120908]
46. Lai, K.-h.; Feng, Y.; Zhu, Q. Digital transformation for green supply chain innovation in manufacturing operations. Transp. Res. Part E: Logist. Transp. Rev.; 2023; 175, 103145. [DOI: https://dx.doi.org/10.1016/j.tre.2023.103145]
47. Gong, Y.; Jia, F.; Brown, S.; Koh, L. Supply chain learning of sustainability in multi-tier supply chains. Int. J. Oper. Prod. Manag.; 2018; 38, pp. 1061-1090. [DOI: https://dx.doi.org/10.1108/IJOPM-05-2017-0306]
48. Yang, Y.; Chen, J.; Lee, P.K.C.; Cheng, T.C.E. How to enhance the effects of the green supply chain management strategy in the organization: A diffusion process perspective. Transp. Res. Part E: Logist. Transp. Rev.; 2023; 175, 103148. [DOI: https://dx.doi.org/10.1016/j.tre.2023.103148]
49. Song, M.; Cen, L.; Zheng, Z.; Fisher, R.; Liang, X.; Wang, Y.; Huisingh, D. How would big data support societal development and environmental sustainability? Insights and practices. J. Clean. Prod.; 2017; 142, pp. 489-500. [DOI: https://dx.doi.org/10.1016/j.jclepro.2016.10.091]
50. Inemek, A.; Matthyssens, P. The impact of buyer–supplier relationships on supplier innovativeness: An empirical study in cross-border supply networks. Ind. Mark. Manag.; 2013; 42, pp. 580-594. [DOI: https://dx.doi.org/10.1016/j.indmarman.2012.10.011]
51. Agyabeng-Mensah, Y.; Afum, E.; Acquah, I.S.K.; Baah, C. How does supply chain knowledge enhance green innovation? The mediation mechanisms of corporate reputation and non-supply chain learning. J. Bus. Ind. Mark.; 2022; 38, pp. 852-868. [DOI: https://dx.doi.org/10.1108/JBIM-04-2021-0192]
52. Wu, A. How specific investments influence NPD performance: Exploring the roles of supplier involvement and IT implementation. Eur. J. Innov. Manag.; 2021; ahead-of-print [DOI: https://dx.doi.org/10.1108/EJIM-06-2021-0284]
53. Jean, R.-J.B.; Kim, D.; Chiou, J.-S.; Calantone, R. Strategic orientations, joint learning, and innovation generation in international customer-supplier relationships. Int. Bus. Rev.; 2018; 27, pp. 838-851. [DOI: https://dx.doi.org/10.1016/j.ibusrev.2018.01.007]
54. Wang, X.; Zhao, Y.; Hou, L. How does green innovation affect supplier-customer relationships? A study on customer and relationship contingencies. Ind. Mark. Manag.; 2020; 90, pp. 170-180. [DOI: https://dx.doi.org/10.1016/j.indmarman.2020.07.008]
55. Melander, L. Customer and Supplier Collaboration in Green Product Innovation: External and Internal Capabilities. Bus. Strategy Environ.; 2018; 27, pp. 677-693. [DOI: https://dx.doi.org/10.1002/bse.2024]
56. Gosling, J.; Jia, F.; Gong, Y.; Brown, S. The role of supply chain leadership in the learning of sustainable practice: Toward an integrated framework. J. Clean. Prod.; 2016; 137, pp. 1458-1469. [DOI: https://dx.doi.org/10.1016/j.jclepro.2014.10.029]
57. Gunasekaran, A.; Papadopoulos, T.; Dubey, R.; Wamba, S.F.; Childe, S.J.; Hazen, B.; Akter, S. Big data and predictive analytics for supply chain and organizational performance. J. Bus. Res.; 2017; 70, pp. 308-317. [DOI: https://dx.doi.org/10.1016/j.jbusres.2016.08.004]
58. Van de Vijver, F.; Leung, K. Methods and data analysis of comparative research. Handbook of Cross-Cultural Psychology: Theory and Method; 2nd ed. Allyn & Bacon: Needham Heights, MA, USA, 1997; Volume 1, pp. 257-300.
59. Brislin, R.W. Back-Translation for Cross-Cultural Research. J. Cross-Cult. Psychol.; 1970; 1, pp. 185-216. [DOI: https://dx.doi.org/10.1177/135910457000100301]
60. Qiao, J.; Li, S.; Capaldo, A. Green supply chain management, supplier environmental commitment, and the roles of supplier perceived relationship attractiveness and justice. A moderated moderation analysis. Bus. Strategy Environ.; 2022; 31, pp. 3523-3541. [DOI: https://dx.doi.org/10.1002/bse.3103]
61. Wagner, S.M.; Bode, C. Supplier relationship-specific investments and the role of safeguards for supplier innovation sharing. J. Oper. Manag.; 2014; 32, pp. 65-78. [DOI: https://dx.doi.org/10.1016/j.jom.2013.11.001]
62. Armstrong, J.S.; Overton, T.S. Estimating nonresponse bias in mail surveys. J. Mark. Res.; 1977; 14, pp. 396-402. [DOI: https://dx.doi.org/10.1177/002224377701400320]
63. Baker, W.E.; Grinstein, A.; Harmancioglu, N. Whose Innovation Performance Benefits More from External Networks: Entrepreneurial or Conservative Firms?. J. Prod. Innov. Manag.; 2016; 33, pp. 104-120. [DOI: https://dx.doi.org/10.1111/jpim.12263]
64. Spector, P.E.; Rosen, C.C.; Richardson, H.A.; Williams, L.J.; Johnson, R.E. A New Perspective on Method Variance: A Measure-Centric Approach. J. Manag.; 2017; 45, pp. 855-880. [DOI: https://dx.doi.org/10.1177/0149206316687295]
65. Hair, J.F., Jr.; Hult, G.T.M.; Ringle, C.M.; Sarstedt, M. A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM); Sage Publications: London, UK, 2021.
66. Haq, M.Z.U. Supply chain learning and organizational performance: Evidence from Chinese manufacturing firms. J. Knowl. Manag.; 2020; 25, pp. 943-972. [DOI: https://dx.doi.org/10.1108/JKM-05-2020-0335]
67. Huo, B.; Haq, M.Z.U.; Gu, M. The impact of IT application on supply chain learning and service performance. Ind. Manag. Data Syst.; 2019; 120, pp. 1-20. [DOI: https://dx.doi.org/10.1108/IMDS-10-2018-0476]
68. Le, T.T. Linking big data, sustainable supply chain management and corporate performance: The moderating role of circular economy thinking. Int. J. Logist. Manag.; 2022; 34, pp. 744-771. [DOI: https://dx.doi.org/10.1108/IJLM-01-2022-0011]
69. Sumarliah, E.; Al-hakeem, B. The effects of digital innovations and sustainable supply chain management on business competitive performance post-COVID-19. Kybernetes; 2023; ahead-of-print [DOI: https://dx.doi.org/10.1108/K-09-2022-1326]
70. Yang, Y.; Jiang, Y.; Chen, H.; Xu, Z. Linking relation-specific investments and sustainability performance: The mediating role of supply chain learning. Int. J. Oper. Prod. Manag.; 2023; in print [DOI: https://dx.doi.org/10.1108/IJOPM-07-2022-0463]
© 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.
Details
; Li, Suicheng 1 ; Xiong, Su 2 ; Li, Na 3 1 School of Economics and Management, Xi’an University of Technology, Xi’an 710048, China;
2 School of Economics, Guizhou University, Guiyang 550025, China
3 School of Economics and Management, Xi’an Shiyou University, Xi’an 710065, China